https://www.teknos.org/home daily 0.75 2026-01-12 https://www.teknos.org/home/2025/8/25/bridging-the-gap-an-exploration-of-the-hidden-factors-behind-minority-health-inequalities monthly 0.5 2025-08-25 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/cb079dc4-6084-4a09-b9f9-3adcc4d5a03f/unnamed+%2814%29.png Home - Bridging the Gap: An Exploration of the Hidden Factors Behind Minority Health Inequalities - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/teknoscoverart2024-s5waz monthly 0.5 2025-02-05 https://www.teknos.org/home/2023/6/25/2023-teknos-writing-competition-xhnh7 monthly 0.5 2024-09-02 https://www.teknos.org/home/teknoscoverart2024 monthly 0.5 2025-02-05 https://www.teknos.org/home/2024/1/4/threshold-and-teknos-science-poetry-contest monthly 0.5 2024-01-04 https://www.teknos.org/home/2024-teknos-journal-submission-958ca monthly 0.5 2024-01-29 https://www.teknos.org/home/2023/10/30/tirobot-debuts-in-the-orthopedic-or monthly 0.5 2024-01-04 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/c4df141f-a0b7-41ea-b37e-d9b0bdeadda0/unnamed.jpg Home - TiRobot Debuts in the Orthopedic OR - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/9f9615c0-70e3-4233-a2a2-a7a63d6db23e/unnamed.jpg Home - TiRobot Debuts in the Orthopedic OR - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/2023/7/21/viability-of-ace-inhibitors-to-reduce-space-radiation-induced-cardiovascular-injury monthly 0.5 2024-01-04 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/b8ca01c3-14bf-4b3e-a67e-3089d37a7085/unnamed.png Home - Viability of ACE-Inhibitors To Reduce Space Radiation-Induced Cardiovascular Injury - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/2023/10/22/nanomedicine-unleashed-harnessing-metformin-and-nanoparticles-for-glioblastoma-therapy monthly 0.5 2024-01-04 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/68007af2-0568-46c2-b6ef-a4075f92c3ff/3rd+Place+Nanomedicine+Unleashed+Harnessing+Metformin+and+Nanoparticles+for+Glioblastoma+Therapy%281%29.jpg Home - Nanomedicine Unleashed: Harnessing Metformin and Nanoparticles for Glioblastoma Therapy - Make it stand out Fig 1. Schematic representation of lipid-polymer hybrid nanoparticles (LPNs) and their non-hybrid polymer nanoparticle counterparts. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/c87a5ff1-da86-476f-bf07-3770c78603f2/3rd+Place+Nanomedicine+Unleashed+Harnessing+Metformin+and+Nanoparticles+for+Glioblastoma+Therapy%282%29.jpg Home - Nanomedicine Unleashed: Harnessing Metformin and Nanoparticles for Glioblastoma Therapy - Make it stand out Fig 2. Simplified schematic of metformin’s inhibition of mTOR https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/65c81b66-3785-487a-b6e2-5481de422ade/3rd+Place+Nanomedicine+Unleashed+Harnessing+Metformin+and+Nanoparticles+for+Glioblastoma+Therapy%283%29.jpg Home - Nanomedicine Unleashed: Harnessing Metformin and Nanoparticles for Glioblastoma Therapy - Make it stand out Fig 3. Synthesis of polymer-lipid hybrid nanoparticles. [10] https://www.teknos.org/home/2023/6/25/2023-teknos-writing-competition monthly 0.5 2024-07-31 https://www.teknos.org/home/2023-teknos-cover-design monthly 0.5 2023-04-11 https://www.teknos.org/home/the-origins-and-implications-of-malware monthly 0.5 2023-04-11 https://www.teknos.org/home/a-shift-in-the-culture-context-and-concerns-of-lab-grown-meat monthly 0.5 2023-04-11 https://www.teknos.org/home/under-the-scalpel-ethics-in-machine-learning-research monthly 0.5 2023-04-11 https://www.teknos.org/home/give-lofi-a-try monthly 0.5 2023-04-11 https://www.teknos.org/home/2023-teknos-journal-submission monthly 0.5 2024-01-03 https://www.teknos.org/home/2022/10/10/exploring-potential-methods-of-male-contraception monthly 0.5 2023-04-11 https://www.teknos.org/home/2022/10/10/if-you-gave-a-man-a-new-head monthly 0.5 2022-10-11 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/4a181bdc-22c3-412f-a40c-772e186a4b8c/Benji+Figure+1.jpg Home - If You Gave a Man a New Head, Would Life Follow? - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/2022/10/10/nanomedicine-paving-the-path-towards-better-targeted-drug-delivery monthly 0.5 2022-10-11 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/feca64d9-8e2a-4aa5-9a14-7aa43347c4f2/passive.png Home - Nanomedicine: Paving the Path Towards Better Targeted Drug Delivery - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/2022/10/10/chronic-stress-and-its-contribution-to-the-metastatic-spread-of-cancer monthly 0.5 2022-10-11 https://www.teknos.org/home/2022/10/10/mommm-i-hate-broccoli monthly 0.5 2022-10-11 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/7355624e-845a-4c3f-a5c2-f5957d037c09/tongue.png Home - “Mommm! I Hate Broccoli!”: Dietary Preferences and the Development of Taste - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/2022/8/17/thomas-jefferson-scientist monthly 0.5 2022-10-28 https://www.teknos.org/home/2022/8/17/corruption-and-correction-how-data-can-change-the-world monthly 0.5 2022-10-28 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/898c52fb-d07a-48d1-b893-5c284c5ea477/mona+lisa.png Home - Corruption and Correction: How Data Can Change the World - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://www.teknos.org/home/2022/6/17/2022-teknos-writing-competition monthly 0.5 2023-04-09 https://www.teknos.org/home/2022/2/22/a-comprehensive-16s-rrna-gene-analysis-of-the-role-of-childhood-factors-from-birth-to-age-three-in-the-development-and-adult-state-of-the-gut-microbiota monthly 0.5 2023-04-09 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/520e484e-b01c-43a6-910d-1a1c04ffedbb/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 1. Alpha and beta diversity. A) Boxplot of Faith’s phylogenetic diversity between levels of pet ownership (*p < 0.05). B) Boxplot of Faith’s phylogenetic diversity between levels of infant feeding habits. C) Boxplot of Pielou’s evenness between levels of pet ownership (*p < 0.05). D) PCoA of unweighted UniFrac distances between levels of pet ownership. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/a5536b14-1eae-4b98-9f16-8c33a9f2ea2c/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 2. Antibiotic history relative abundance changes depicted in a heat-tree. Negative log2 ratios represent subjects who have not taken antibiotics in the past year and positive log2 ratios represent all other levels. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/4310c855-0e7e-437e-ad15-b82f447b1533/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 3. Probiotic use frequency relative abundance changes depicted in a heat-tree. Negative log2 ratios represent subjects that never take probiotics and positive log2 ratios represent all other levels. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/aba6125f-3fb2-4db1-b57c-aa2c23a02fa5/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 4. Infant feeding habits relative abundance changes depicted in a heat-tree. Negative log2 ratios represent subjects fed a mixture of breast milk and infant formula and positive log2 ratios represent all other levels. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/a57ec871-4c3e-4aa7-acce-fa4a1b3a14be/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 5. Antibiotic history differential expression of KEGG families (generated from PICRUSt). Families with adjusted p-values below α = 0.001 depicted in a clustered heatmap. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/ee06c7a9-a45a-4d9c-97d7-7a6a2787db16/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 6. Probiotic use frequency differential expression of KEGG families (generated from PICRUSt). Families with adjusted p-values below α = 0.001 depicted in a clustered heatmap. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/5502b949-81b9-45e6-b192-f98b4c9085bd/Capture.PNG Home - A Comprehensive 16S rRNA Gene Analysis of the Role of Childhood Factors from Birth to Age Three in the Development and Adult State of the Gut Microbiota - Make it stand out Figure 7. Pet ownership differential expression of KEGG families (generated from PICRUSt). Families with adjusted p-values below α = 0.001 depicted in a clustered heatmap. https://www.teknos.org/home/2022/2/20/a-novel-computer-vision-approach-to-kinematic-analysis-of-handwriting-for-accessible-assessment-of-neurodegenerative-diseases monthly 0.5 2022-10-28 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/08e024f8-7414-4be9-bd53-8c18e6205772/Nachum_Figure_1+-+Ron+Nachum.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Figure 1. Experimental setup to collect synchronized data from smartphone videos and digitizing tablet quantification, enabling statistical comparisons to assess accuracy of the vision-based system https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/6690a5ca-a061-4dbe-a8bd-188a08ee4091/Screenshot+%2895%29.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/fa1affd1-7313-48ff-b39f-f562b9e19d88/Screenshot+%2896%29.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/d71ad5ac-ff7c-4650-ace6-b8291493c3f0/Nachum_Figure_2+-+Ron+Nachum.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Figure 2. Computer vision system for data extraction from videos, consisting of three sections: (1) preprocessing, (2) data extraction, and (3) outputs for classification. This paper places particular emphasis on the preprocessing in section 1 and coordinate extraction with recurrent region of interest feature matching algorithm in section 2C. Sections 2A and 2B have been investigated in a preliminary manner and will be investigated for future work. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/5790232b-949b-4346-b8d5-1616f18312f4/MachineLearningFlowchart.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Figure 3. Machine learning system for providing patient diagnostic assessment based on kinematic features of handwriting movements. Consists of (1) a preprocessing step to calculate kinematic and derived features and choose those with the greatest significance for (2) training and testing an ensemble classifier to provide (3) diagnostic assessments. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/3bd5191f-c064-46c4-93bf-5ba334982df2/Nachum_Figure_3+-+Ron+Nachum.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Figure 4. Relative comparison of our computer vision-based data collection system to digitizing tablet control. Demonstrated by overlaid graphs of extracted kinematic features in an Archimedean spiral and word writing task: (A) Pen tip position (B) Pen tip speed (C) Pen tip acceleration (D) Pen tip jerk. Graphs B-D display narrow 95% confidence intervals. Note the slight position drift in the word writing task, which is due to the pen’s increasing distance from the camera. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/475174b9-6d5c-4d4a-a8d3-a2a83cf736e6/Screenshot+%2897%29.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Table 1. Accuracy of Vision-Based Kinematic Data - Spiral https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/944d27a8-5c86-4971-96c5-bd25ae9d727d/Screenshot+%2898%29.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Table 2. Accuracy of Vision-Based Kinematic Data - Writing https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/ca417be0-24db-4415-80ee-bdeda2f313b2/Screenshot+%2899%29.png Home - A Novel Computer Vision Approach to Kinematic Analysis of Handwriting for Accessible Assessment of Neurodegenerative Diseases - Make it stand out Table 3. Diagnostic Assessment Performance by Data Capture Rates https://www.teknos.org/home/2022/2/20/fighting-amyotrophic-lateral-sclerosis-in-silico-molecular-docking-study-of-sfpq-protein-to-identify-a-potential-therapeutic-compound monthly 0.5 2022-02-20 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/41e26d83-5383-475f-8a7d-bd34853f15d4/Kovvali_Figure_1+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 1. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/f1b45644-3730-4520-9720-b207acdf1b15/Screenshot+%2894%29.png Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Table 1. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/d6ef45c4-4222-4544-a5fb-ad8d04ce7b25/Kovvali_Figure_2+-+Omkar+Kovvali.png Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 2. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/0b54dd02-fabb-479a-88e4-f15a68e9ed2a/Kovvali_Figure_3+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 3. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/01fe3caa-f125-4465-b1f4-7a7eb902bde3/Kovvali_Figure_4+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 4. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/6d9e9c8d-5913-4bc5-9979-ce706e112506/Kovvali_Figure_5+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 5. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/3decc865-d8f1-4b45-8c19-e37e15109317/Screenshot+%2893%29.png Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Table 2. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/80c563e4-7dc8-44bd-ba8a-5a52b6bc0eb4/Kovvali_Figure_6+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 6. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/fd5d800d-a5e8-4fb7-acff-8c6f2e546247/Kovvali_Figure_7+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 7. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/a95930ec-25da-4240-9f87-65936a67cfe2/Kovvali_Figure_8+-+Omkar+Kovvali.PNG Home - Fighting Amyotrophic Lateral Sclerosis: In-Silico Molecular Docking Study of SFPQ Protein to Identify a Potential Therapeutic Compound - Make it stand out Figure 8. https://www.teknos.org/home/2022/2/20/using-machine-learning-and-multivariate-statistical-analyses-to-study-stream-health monthly 0.5 2022-02-20 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/864fcb19-97e1-4d9a-a945-44dfaeb8df9f/Screenshot+%2891%29.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Table 1. Numbered experimental streams (n = 21) in Fairfax County, VA. Health category data is from the Fairfax County Park Authority (FCPA). https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/95ccfb36-4e93-4d6b-9ef7-b26b1ab40174/Tao_Graph_1+-+Lynn+Tao.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Figure 1. Data Collection Points on a Satellite Image. Major cities were labeled in blue text. Each collection point’s numbering was labeled in red text. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/234e8254-1613-498e-b1d4-faecc39d5fcf/Tao_Graph_2+-+Lynn+Tao.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Figure 2. Flow Chart of KNN Supervised Machine Learning Classifier https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/f37d2d33-7236-43aa-881d-f4294217e7a3/Screenshot+%2889%29.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/e58991fa-3cde-4c10-b4e4-18249178bc9e/Screenshot+%2890%29.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/d549cbdc-c93b-42a9-898d-1527d41f81a6/Screenshot+%2892%29.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Table 2. Accuracy, precision, and recall of the KNN algorithm. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/0bbba809-1d9d-4238-a24e-00b89835adb2/Tao_Graph_3+-+Lynn+Tao.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Figure 3. Multidimensional scaling analysis of PoIS and biological water variables. Bray Curtis is the distance measure option. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/2e20beb7-9084-41b5-af89-9253ccf744a8/Tao_Graph_4+-+Lynn+Tao.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Figure 4. Multidimensional scaling analysis of PoIS and water physio-chemical variables. Bray Curtis is the distance measure option. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/57c9893f-f57e-4b24-81ba-a672caef3e48/Tao_Graph_5+-+Lynn+Tao.png Home - Using Machine Learning and Multivariate Statistical Analyses to Study Stream Health - Make it stand out Figure 5. Correlation of PoIS and stream ecological number. Data points represent PoIS (x-axis) and the stream ecological number (y-axis) (n=20). Similar linear regression conveyed a correlation of -70% (SE=1.41739, p=0.00062). https://www.teknos.org/home/2022/2/10/computer-aided-diagnosis-of-melanoma monthly 0.5 2022-08-17 https://www.teknos.org/home/2022/2/10/a-point-picking-probability-problem monthly 0.5 2022-02-10 https://www.teknos.org/home/2022/2/10/the-effect-of-listening-to-music-on-driving-fatigue monthly 0.5 2022-09-30 https://www.teknos.org/home/2022/2/9/vtol-aircraft-perfecting-the-art-of-flight monthly 0.5 2022-02-09 https://www.teknos.org/home/2022/2/9/exploring-mars-an-electric-powered-manned-rover monthly 0.5 2022-02-09 https://www.teknos.org/home/2022/2/9/modify-tradition-to-outlast-chess-computers monthly 0.5 2022-02-09 https://www.teknos.org/home/2022/1/23/nitrogen-doped-graphene-face-masks-for-covid-19 monthly 0.5 2022-01-23 https://www.teknos.org/home/2022/1/23/computational-fluid-dynamics-simulating-the-future monthly 0.5 2022-01-23 https://www.teknos.org/home/2022/1/23/godietary-treatments-for-parkinsons-disease monthly 0.5 2022-01-23 https://www.teknos.org/home/2022/1/23/identifying-discriminatory-attitudes-through-artificial-intelligence-based-lie-detection monthly 0.5 2022-01-23 https://www.teknos.org/home/2021/10/3/climate-change-and-modernizing-urban-living monthly 0.5 2021-12-03 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/d986ca6a-9721-45f3-aeb9-a18deab36cd5/image1.png Home - Climate Change and Modernizing Urban Living - Make it stand out Figure 1. A map showing urban populations at risk of rising sea levels of 0.5 meters by the 2050s https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/9effacd4-09e9-461d-8f72-555d8840fa23/image2.png Home - Climate Change and Modernizing Urban Living - Make it stand out Figure 2. A graphic description of the Urban Heat Island Effect [7] https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/01f462a5-d6b4-4e48-a3ed-ffa9304ef141/image3.png Home - Climate Change and Modernizing Urban Living - Make it stand out Figure 3. A green space designed in Canada https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/54c1dc17-3194-4248-a121-ea215841f3e9/image4.png Home - Climate Change and Modernizing Urban Living - Make it stand out Figure 4. A graphic provided by the Singaporean government summarizing Gardens by the Bay [11] https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/9877b7d0-0644-43d4-8d44-66d18243dd4b/image5.png Home - Climate Change and Modernizing Urban Living - Make it stand out Figure 5. Three views provided by Boeri Studios of Bosco Verticale’s green wall system [12] https://www.teknos.org/home/2021/10/3/personalized-medicine-the-force-of-the-future-in-the-fight-against-cancer monthly 0.5 2021-11-18 https://www.teknos.org/home/2021/10/3/aducanumab-the-modern-snake-oil monthly 0.5 2021-11-18 https://www.teknos.org/home/2021/7/1/2021-teknos-writing-competition monthly 0.5 2022-06-17 https://www.teknos.org/home/2021/4/26/space-race-ii-mining monthly 0.5 2021-04-26 https://www.teknos.org/home/2021/4/25/comparing-muscle-spindle-afferent-models monthly 0.5 2021-04-25 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619371909650-J1FG7S3SIJO9KO0ST0QW/figure1.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619371958187-3ZCH9YED0AEDMNYXI2XC/figure2.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372021548-K9X5NNIY5LA6SQN700D4/figure3.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372045374-VLLQOL86539BL8A6ZPM4/figure4.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372068040-KXBGYK3UYVJGOOVLVKVQ/figure5.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372102953-BCNO02C8I4V3P9MCXOSN/figure6.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372139199-B2PJ7KD3AV4N67JSH0O7/figure7.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372172397-4VUCBSVRV16Y2YE5JITI/figure8.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372202879-8LG8TCDM27JQZIOO4YRV/figure9.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372225456-DKRTVQZSMODX8Q2SNFE9/figure10.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372255152-822CY8YVY6SO2JCCHIY4/figure11.PNG Home - Comparing Muscle Spindle Afferent Models https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1619372283225-FKIDPNOKTJCXPISTQ8YE/figure12.PNG Home - Comparing Muscle Spindle Afferent Models https://www.teknos.org/home/2021/4/1/naps-good-or-bad monthly 0.5 2021-04-03 https://www.teknos.org/home/2021/3/31/electoral-fraud-blocked-amp-chained-design-of-a-novel-secure-cryptographic-amp-blockchain-based-voting-architecture monthly 0.5 2021-04-01 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1617236164153-8MN6UKKDHELXLOAIILXR/teknos1.jpg Home - Electoral Fraud — Blocked &amp; Chained: Design of a Novel, Secure, Cryptographic &amp; Blockchain-based Voting Architecture Figure 1. Novel system architecture design & sample workflow. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1617236222022-LBBKLUHHPBK8085RNRAE/teknos2.PNG Home - Electoral Fraud — Blocked &amp; Chained: Design of a Novel, Secure, Cryptographic &amp; Blockchain-based Voting Architecture Figure 2. Notable Advances Found in our Architecture. https://www.teknos.org/home/2021/3/31/sense-switchup-synesthesia monthly 0.5 2021-04-13 https://www.teknos.org/home/2021/1/21/investigating-the-role-of-thiamine-in-epileptic-activity-in-the-drosophila-melanogaster monthly 0.5 2021-03-27 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589579340-3XQOUC3XKQ32L5OVA08T/Figure+1.PNG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589753142-RRPZUJVTQQAID8XSOY2L/Table1.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589798860-17BOPY505RO397YGUTFT/Table2.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589829668-0YZMSQSEZJMG4PSUJ807/Table3.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589893968-SNZEU9R9X979MY137ZH7/Figure2.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589931930-HNGK3ZVKVJIB7NQG255A/Figure3.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589969546-6Y3CQ1DFCLGU5EKUCT7Z/Figure4.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611589998987-Y5TBAKID4IJGGUPIA4IC/Table4.JPG Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590044074-RXZDOIGY46SB3JYE1HN3/image-asset.jpeg Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590058749-9TIT03DWDVCYHC2R598F/image-asset.jpeg Home - Investigating the Role of Thiamine in Epileptic Activity in the Drosophila melanogaster https://www.teknos.org/home/2021/3/1/caught-red-handed-by-dna-profiling monthly 0.5 2021-03-02 https://www.teknos.org/home/2021/1/21/using-drosophila-melanogaster-to-elucidate-sleep-and-circadian-rhythm-disruptions-after-traumatic-brain-injury monthly 0.5 2021-03-02 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346566521-OHF0QGKWVCNI9AAOM9JC/Figure+1a.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 1a. Inflicting TBI on Flies Katzenberger, 2013 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346658234-ZK7V4U8QII82YBQBWP10/Figure+1b.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 1b. Lateef HIT device Lateef et al. 2019 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346707957-31S5MYRQHEH9IBDMHRIO/Figure+2.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 2. Drosophila Activity Monitor System https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346805075-2ZNW6831ZH9IP2K1B5DK/Figure+3a.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 3a. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346838610-E4HLGTXOV2QG22GP8G4U/Figure+3b.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 3b. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346859972-AWLRVKBOYZF5MGMMWIHA/Figure+3c.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 3c. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346900995-JRLVTKQM1GHO46O6R7IX/Figure+3d.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 3d. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611346971251-9PYAO743EFCJP8FF2I26/Figure+7a.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 7a. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611347001235-SJ4D22ZH528A89HR13GE/Figure+7b.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 7b. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611347032584-YRP8BYDEHA7T6EIQC767/Figure+7c.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 7c. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611347052339-OROZM56GZAO7IEXEY82J/Figure+7d.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 7d. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611347369116-YEZ87DXDHX1KE477Y73C/table1.JPG Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Table 1. Period and average rhythmicity index after TBI https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611347410818-JSQHZIH7MG95NDB1XNH8/Figure+8a.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 8a. Proportion of arrhythmicity in female fly sample https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611347432500-CC5X2GPS3HDJUXLEMNSC/Figure+8b.jpg Home - Using Drosophila melanogaster to Elucidate Sleep and Circadian Rhythm Disruptions After Traumatic Brain Injury Figure 8b. Proportion of arrhythmicity in male fly sample https://www.teknos.org/home/2021/2/15/influential-intermolecular-interactions-in-supramolecules monthly 0.5 2021-03-02 https://www.teknos.org/home/2021/1/21/assessing-glaucoma-progression-using-machine-learning-trained-on-longitudinal-visual-field-and-clinical-data monthly 0.5 2021-02-10 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611255648211-CWL4TQG4K20ICKCZN1QX/table1.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data Table 1. Number of eyes marked as stable and progressing by each baseline algorithm. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590534095-ZFG7YV9SVD5E0N2S2HPD/Figure1.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590719508-06IFZGJ54O87YKPVRG30/Figure2.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590735124-2DHRPYF0400FCYO2746T/Figure3.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611256001561-4ZS78RW0SEBYN4UWN6UT/figure1.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611256305082-450GRAYTTE0YAI7XYDHI/table2.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data Table 2. Performance (accuracy, area under the ROC curve) of a model trained on a given number of visual fields (timesteps) with respect to ground truth from traditional algorithms. MD = Mean Deviation, PLR = Pointwise Linear Regression, VFI = Visual Field Index. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611256422214-MDBKXRA1XYHYYX3VRT6V/table3.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data Table 3. Performance (accuracy, area under the ROC curve) of model trained on a given number of sequential visual fields and samples of clinical data (timesteps) with respect to ground truth from different traditional algorithms. The change in accuracy and area under the ROC curve from Table 2 is displayed in parentheses after each individual accuracy. MD = Mean Deviation, PLR = Pointwise Linear Regression, VFI = Visual Field Index. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611256567007-1M5XALA4J5FRVMQUSHUQ/table4.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data Table 4. Statistical comparison of the area under receiver operating characteristic curves with respect to different definitions of glaucoma progression for model 1 (visual field only) and model 2 (visual field and clinical data). MD = Mean Deviation, PLR = Pointwise Linear Regression, VFI = Visual Field Index. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590785871-SYCJP0PT0G6TYUPYXSXW/Figure4.JPG Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590805358-EMP6O881UP8NBOTO89OA/image-asset.jpeg Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590819249-AQGDRHT4G4RWC10GTJRJ/image-asset.jpeg Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590836973-DS1C9P4H6BF6W3BVRA9I/image-asset.jpeg Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590850649-QTAF8HZHISZH7OAET5NE/image-asset.jpeg Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1611590864473-W640NQSKJ7D65GGJC8XD/image-asset.jpeg Home - Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data https://www.teknos.org/home/2021/2/1/artificially-natural-linguistics-machine-learning-and-natural-language-processing monthly 0.5 2021-02-02 https://www.teknos.org/home/2021/1/30/reach-out-to-bleached-reefs monthly 0.5 2021-01-31 https://www.teknos.org/home/2021/1/8/the-effect-of-age-on-duchenne-muscular-dystrophy monthly 0.5 2021-01-25 https://www.teknos.org/home/2021/1/15/2020-2021-teknos-journal-cover-contest monthly 0.5 2023-02-05 https://www.teknos.org/home/2021/1/8/eclipses-and-the-n-body-problem-determining-coherence-from-chaos monthly 0.5 2021-01-21 https://www.teknos.org/home/2021/1/4/elucidating-lucid-dreams monthly 0.5 2021-01-05 https://www.teknos.org/home/2020/12/28/machine-learning-for-prediction-of-fetal-neurodevelopmental-abnormalities monthly 0.5 2024-02-07 https://www.teknos.org/home/2020/12/21/no-dose-of-lactose monthly 0.5 2021-10-14 https://www.teknos.org/home/2020/12/11/high-entropy-alloys-a-new-wave-in-metallurgy monthly 0.5 2020-12-28 https://www.teknos.org/home/2020/12/7/zero-point-energy-the-lowest-and-limitless-energy-state monthly 0.5 2020-12-28 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1607372834593-OWP078RZJZCKGNA0TICJ/wavies.JPG Home - Zero-Point Energy: The Lowest and Limitless Energy State Figure 1. Example of Differing Precisions in Particle Position. In Wave A, the particle may be found anywhere along Area x. In Wave B, the particle is most likely to be found within the more precise Area y due to its higher amplitudes. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1607372894258-IPNH81YDEQ251H0BZF87/addingwaves.JPG Home - Zero-Point Energy: The Lowest and Limitless Energy State Figure 2. Example of Adding Multiple Waves. Note that this is an example to convey the idea that adding uniform waves creates wave packets, but this figure may not be mathematically accurate. https://www.teknos.org/home/2020/11/29/modify-your-thoughts-on-genetically-modified-organisms monthly 0.5 2020-12-07 https://www.teknos.org/home/2020/11/13/solving-the-human-activity-recognition-problem-in-sports monthly 0.5 2020-11-30 https://www.teknos.org/home/2020/11/10/patterns-in-the-darkness-mapping-dark-matter monthly 0.5 2020-11-30 https://www.teknos.org/home/2020/11/6/clearing-nuclear-fears monthly 0.5 2020-11-30 https://www.teknos.org/home/2020/10/30/fronds-out-establishing-a-novel-transformation-system-in-lemna-minor monthly 0.5 2020-11-11 https://www.teknos.org/home/2020/10/20/a5t82x18pd1u972yx9h6jsqixh5geg monthly 0.5 2020-10-26 https://www.teknos.org/home/2020/10/16/staring-into-the-eye-of-the-storm-machine-learning-for-automated-hurricane-eye-detection monthly 0.5 2020-10-26 https://www.teknos.org/home/2020/10/9/first-steps-across-the-stars-interstellar-travel-using-solar-sails monthly 0.5 2020-10-16 https://www.teknos.org/home/2020/10/5/unmasking-the-hidden-pandemic-crisis monthly 0.5 2020-10-16 https://www.teknos.org/home/2020/9/25/pun-processing-in-the-brain-whats-hap-punning monthly 0.5 2020-10-16 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1601310517189-T12D0H5RFC956PJEYX2S/lobyparts.jpg Home - Pun Processing in the Brain: What’s Hap-punning? Figure 1. The lobes of the brain. https://www.teknos.org/home/2020/9/21/eyewitness-identifications-and-the-science-behind-remembering monthly 0.5 2020-10-16 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1600702418648-MSA7YU62Q169PAB5U4K4/temporallobeagain.jpeg Home - Eyewitness Identifications and the Science Behind Remembering Figure 1. Temporal lobe gyri. This diagram displays different regions of the temporal lobe, which is mainly involved in registering sensory elements and encoding memories. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1600702665566-U1US6LBIV0ER0W9FCWXN/memorypartsofbrain.jpg Home - Eyewitness Identifications and the Science Behind Remembering Figure 2. Memory-involved parts of the brain. Different components of the brain having to do with memory are scattered throughout the human brain, and each part plays its own role in memory encoding, formation, and storage. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1600702875516-OJ7JD9S94OK6YEB7EQKF/fusiform.jpeg Home - Eyewitness Identifications and the Science Behind Remembering Figure 3. Fusiform gyrus. The fusiform gyrus has been found to play a role in recognizing faces and identifying visual cues. https://www.teknos.org/home/2020/9/14/neuroevolution-a-pathway-to-general-intelligence monthly 0.5 2020-10-17 https://www.teknos.org/home/2020/7/12/2020-teknos-writing-competition monthly 0.5 2020-07-17 https://www.teknos.org/home/2020/6/16/help-from-kelp monthly 0.5 2020-06-28 https://www.teknos.org/home/2020/6/21/covid-conscious-schools monthly 0.5 2020-11-17 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1593095948369-DM40X64IK3J7CWIJ37IH/china1.jpg Home - What a COVID-Conscious School Should Look Like Ng Han Guan/AP, FILE https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1593209075811-GV5W5JOSHIQ1BKWFW7PE/brussels.jpg Home - What a COVID-Conscious School Should Look Like Students wearing protective face masks talk while practicing social distancing in the courtyard of a flemish secondary school during its reopening in Brussels, Belgium, May 15, 2020. REUTERS/Yves Herman https://www.teknos.org/home/2020/6/16/staying-inside-to-save-the-outside monthly 0.5 2020-06-24 https://www.teknos.org/home/2020/1/24/java-but-not-the-cs-kind monthly 0.5 2020-06-14 https://www.teknos.org/home/2020/1/17/facial-recognition monthly 0.5 2020-05-31 https://www.teknos.org/home/2020/1/26/investigating-mir-409-5p-regulation-of-lrp8-expression-in-breast-cancer-cell-lines monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580085844593-DR6XFCA4JLJH66IFB23P/Figure+1.jpg Home - Investigating Regulation of LRP8 Expression in Breast Cancer Figure 1. Target gene identification for miR-409-5p based on four tools. Genes labeled in blue were identified by TargetMiner. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580085958533-U27D5XRNB72BQKL9AJYJ/Figure+2.png Home - Investigating Regulation of LRP8 Expression in Breast Cancer Figure 2. Target gene confirmation using computer program to isolate commonly identified genes from various programs. DIANA tools, miRDB, TargetScan, and TargetMiner were entered. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580086224350-1HY931PP00RLZRYUIJKR/Figure+3.png Home - Investigating Regulation of LRP8 Expression in Breast Cancer Figure 3. Expression of miR-409-5p using a qRT-PCR. Expression is highest in HS578T, which is a triple-negative breast cancer. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580086305870-OTPFFTV6H80QVOW2ZTXN/Figure+4.png Home - Investigating Regulation of LRP8 Expression in Breast Cancer Figure 4. Expression of LRP8 target gene using quantitative real-time polymerase chain reaction. Expression is highest in MDA-MB-468 and MCF-10A. MDA-MB-468 is a triple-negative breast cancer, and MCF-10A is normal-like. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580086383839-7Z3O1LQ5CHARABN3JJSO/Figure+5.png Home - Investigating Regulation of LRP8 Expression in Breast Cancer Figure 5. MTT assay in MCF-7 cells transfected with miR-409-5p mimic-mock, mimic, inhibitor-mock, and inhibitor. Since the mimic has statistically significantly lower expression than the mimic-mock and the inhibitor has higher expression than the inhibitor-mock, we can conclude that miR-409-5p decreases proliferation of MCF7 cells. MCF7 is a luminal A breast cancer, meaning it is ER+ and PR+ but HER2-. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580086448899-J4WYH56KSKICZO7L6R6B/Figure+6.png Home - Investigating Regulation of LRP8 Expression in Breast Cancer Figure 6. MTT assay in HS578T cells transfected with miR-409-5p mimic-mock, mimic, inhibitor-mock, and inhibitor. Since the mimic is statistically significantly higher than its mock, and the inhibitor is lower than the inhibitor-mock, we can conclude that miR-409-5p increases proliferation of HS578T cells. HS578T is a triple-negative breast cancer, so it is ER-, PR-, and HER2-. https://www.teknos.org/home/2020/1/25/seizuresight monthly 0.5 2020-06-11 https://www.teknos.org/home/2020/1/26/memories-as-data monthly 0.5 2020-06-11 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580084734104-XDOHQD8HQ5KO7P8QRQLM/Formula+1.JPG Home - Memories as Data: Deep Neural Network Learning https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580084875043-19ZVARZFE4O9BTN5IIUN/Formula+2.JPG Home - Memories as Data: Deep Neural Network Learning https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1580084929390-UZ6YH0BXDYUJLW1R1789/Formula+3.JPG Home - Memories as Data: Deep Neural Network Learning https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1591835143560-I6SAR5OZVWIHGYVKQZ7R/Formula4.JPG Home - Memories as Data: Deep Neural Network Learning https://www.teknos.org/home/2020/1/26/strokesave-a-novel-high-performance-mobile-application-for-stroke-diagnosis-using-deep-learning-and-computer-vision monthly 0.5 2022-02-20 https://www.teknos.org/home/2019/11/4/time-to-tackle-traumatic-brain-injury monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573245415120-91SUFM4SV4LKFAMAP7Y6/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 1a. Inflicting TBI on Flies Katzenberger, 2013 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573245573369-80KWEXT2V37PXX396ZB0/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 1b. Lateef HIT device Lateef et al. 2019 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573246028776-QHFRSGBQTZ9I4HFU541I/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 2. Immunofluorescent staining to detect apoptosis in the Drosophila brain after TBI https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573246099769-EKIPKPQ9SOX81QMFY5DY/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 2. Drosophila brains co-stained with DAPI (A) which is a blue DNA-binding dye, ELAV (B) which is a green neuron-specific marker and anti-cleaved-Caspase 3 antibody (C) shown in red. Figure D in each panel shows the composite image with all 3 stains. TBI brains show more apoptotic foci (circled) and the TBI + hypothermia brains may show some attenuation of this apoptotic effect. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573246330751-KYSTJ7OYFRCR8CGW8SPC/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 3. Relative levels of gene expression after TBI and with and without hypothermia https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573246423058-APID7RJF6ZTK9GM26CBY/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 4. Intestinal Integrity following TBI https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1573246481072-EGB8JR377I0DIW6K8O79/%E6%8D%95%E8%8E%B7.PNG Home - Time to Tackle Traumatic Brain Injury Figure 5. Smurf fly after TBI https://www.teknos.org/home/2020/1/7/the-automation-of-large-scale-painting monthly 0.5 2020-06-09 https://www.teknos.org/home/2020/1/10/assessing-property-value-assessments monthly 0.5 2020-06-09 https://www.teknos.org/home/2020/1/10/optimal-materials-for-a-robotic-batbird-hybrid-wing monthly 0.5 2020-06-09 https://www.teknos.org/home/2019/10/25/ideonella-sakaiensis monthly 0.5 2020-06-09 https://www.teknos.org/home/2019/11/8/a-next-generation-solution monthly 0.5 2020-06-09 https://www.teknos.org/home/2019/1/18/identify-hiv-and-hcv monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547822877917-O1QS4YGMCT8GRJ9ZAB3P/Screen+Shot+2019-01-18+at+9.47.33+AM.png Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Scheme 1. Reaction mechanism of ODI-CL. X is high-energy intermediate. L is luminophore in the ground state. L* is luminophore in the excited state. R is H or CH3. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548273281514-F27K7I4YGPP1W7URKU54/image1.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 1 Design of portable device with two sample holder (bottom) and two holes (right side) capable of injecting ODI using a multi-pipette. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548273425194-DRYPG0NALTDR2BAOP6RD/image2.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 2 Operation of the portable device capable of sensing HIV-1 PR and HCV PR. (A) Before adding ODI with the multi-pipette in a dark room, (B) Two holes for injecting ODI solution using the multi-pipette, (C) and (D) After taking the images in the presence of HIV-1 PR and HCV PR. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548273899589-MRONL94QAP1QRIWONGON/image3.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 3 ODI-CL reaction in the absence (A) and presence (B) of protease such as HIV-1 PR and HCV PR. (C) relative CL intensities in the absence and presence of HCV PR. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548274053702-T7KSODTZ5RCBRH7W38QJ/image-asset.jpeg Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 4 Determination of buffer solution for the analyses of HIV-1 PR (A) and HCV PR (B). Incubation time for fluorogenic substrate and protease (e.g., HIV-1PR, HCV PR) was 10 min at 37 °C. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548288882942-XNRFZV9068PZNT2HH21Q/teknos1.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 5 Determination of fluorogenic substrate concentration for the quantification of HIV-1 PR (A) and HCV PR (B) using ODI-CL detection. The incubation time for fluorogenic substrate and protease (e.g., 100 nM HIV-1 PR, 40 nM HCV PR) was 5 min at 37 °C. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548289332816-ZHM53B9RCZ0V5HFNUSQZ/teknos2.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig 6. Kinetics of the hydrolysis reaction of HCV fluorogenic substrate and HCV PR in Tris-HCl buffer (pH 8.5) at 37 °C. [fluorogenic substrate] = 13 μM, [HCV PR] = 45 nM. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548289674382-N2RDZKCKWVO1U1FHGNQO/teknos3.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 7 Calibration curves for the quantification of HIV-1 PR (A) and HCV PR (B) using ODI-CL detection. The concentration of HIV-1 fluorogenic substrate was 108 μM. The concentration of HCV fluorogenic substrate was 13 μM. The incubation time at 37 °C was 3 min. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548289993010-R0WMIQKWOH1ERU5LJKVI/teknos4.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 9 Positive and negative detections of HCV PR (left) and HIV-1 PR (right) using the portable device with a Smartphone and ODI-CL detection. Exposure time was 10 sec. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548290543757-P9A99CW5R83A4NH26BPN/teknos5.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 10 Detection of coagulation factors IIa (A) and Xa (B) using the portable device with the LG V10 Smartphone capable of sensing ODI-CL emission. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548290776885-C2YF2W2W4DJFIHXZS918/teknos6.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 11 Detection of glucose in a sample using the portable device using LG V10 Smartphone with ODI-CL detection. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548290949082-5BQFO3XUJP77A0FKVTEW/teknos7.JPG Home - Identifying HIV and HCV with a Chemiluminescent Biosensor Fig. 12 ODI-CL immunoassay using the device with LG V10 Smartphone for the diagnosis of prostate cancer. https://www.teknos.org/home/2019/1/24/sugar-rush-machine-learning-to-estimate-insulin-dosages monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548350595754-ZFWNHZYTTDYLDXAT22IP/Screen+Shot+2019-01-24+at+12.23.02+PM.png Home - Sugar Rush! Machine Learning for Insulin Dosages https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548359099357-LXW4QUEZ7QA8PTIPHAJT/Screen+Shot+2019-01-24+at+2.43.34+PM.png Home - Sugar Rush! Machine Learning for Insulin Dosages Figure 1: Programmatic Flow-Chart of Single Meal Entry https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548359166868-AFOYNRG41PTBI1C13NBO/Screen+Shot+2019-01-24+at+2.45.38+PM.png Home - Sugar Rush! Machine Learning for Insulin Dosages Figure 2: Visual Flow of a Single Meal Entry https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548359196240-8AE0SLXHXA64ISO5ZGYH/image-asset.png Home - Sugar Rush! Machine Learning for Insulin Dosages Figure 3: Dosage Feedback Modes https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548350861000-KX89GNOLC6P6EK7Q8HBB/Screen+Shot+2019-01-24+at+12.27.26+PM.png Home - Sugar Rush! Machine Learning for Insulin Dosages Figure 4: Learning Curve showing accuracy as model gains access to more training examples. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1548350874103-L7J628O7QSH8E061I6WE/Screen+Shot+2019-01-24+at+12.27.14+PM.png Home - Sugar Rush! Machine Learning for Insulin Dosages Figure 5. Sample hidden and learned ratios, demonstrating how the model closely imitates the hidden parameters, despite having no direct access to them. https://www.teknos.org/home/2019/10/25/the-tj-plague monthly 0.5 2020-10-20 https://www.teknos.org/home/2019/8/2/predicting-muscle-spindle-afferent-output-in-human-forearm-muscles-during-wrist-flexionextension-and-radialulnar-deviation monthly 0.5 2020-09-10 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868578126-UDBQMEJCIJICVC49XIM2/fig1-page-001.jpg Home - Predicting Muscle Spindle Afferent Output Figure 1 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868599504-08U2VNAOBJC0IJKIZRRU/fig2-page-001.jpg Home - Predicting Muscle Spindle Afferent Output Figure 2 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868614466-LZHI4HQU6F2UV065C3B1/image-asset.jpeg Home - Predicting Muscle Spindle Afferent Output Figure 3 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868626916-GVNIGACQS5K4XFBR4RYA/fig4-page-001.jpg Home - Predicting Muscle Spindle Afferent Output Figure 4 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868650390-J61MTPMP9RUQC8L5CM29/image-asset.jpeg Home - Predicting Muscle Spindle Afferent Output Figure 5 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868808397-49L68V3XXL4XPQR699DC/fig6-page-0.jpg Home - Predicting Muscle Spindle Afferent Output Figure 6 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868832678-CN9X8M0C8QFNYPQD44GJ/fig7-page-0.jpg Home - Predicting Muscle Spindle Afferent Output Figure 7 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868876802-58UFP9SUI1611G4RPE15/fig8-page-0.jpg Home - Predicting Muscle Spindle Afferent Output Figure 8 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868895696-GNK7IC5GXKBLVLIIM0G7/fig9-page-0.jpg Home - Predicting Muscle Spindle Afferent Output Figure 9 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1564868915600-MAGQ4TG3HKQPDTZ7WL8S/fig10-page-0.jpg Home - Predicting Muscle Spindle Afferent Output Figure 10 https://www.teknos.org/home/2019/1/9/making-everyones-vote-count-computer-detection-of-gerrymandering monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237751056-PFRX2K3UMMHPAS91JO2W/Screen%2BShot%2B2019-01-03%2Bat%2B10.13.23%2BAM.jpg Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 1: A sample 5 × 5 territory, with two different gerrymandering approaches. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237648784-A1DQLE5JZAI5VFCUMHNG/image-asset.jpeg Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 2: A sample population distribution from my walker algorithm, where color intensity indicates population density. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547235861964-PE594SRQX4Q8MLQV5EL6/image-asset.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547236378880-YR1RUDSDI6ZIYQ9NEUWQ/image-asset.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547691199903-KMPRG7HOAS2P4U1STMDD/Screen%2BShot%2B2019-01-16%2Bat%2B4.20.48%2BPM.jpg Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237138343-GHCO04YUSRQJ7SNC2ASX/Screen+Shot+2019-01-11+at+3.04.50+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237685414-HHKXHXB4OU8GNDJGUPR5/Screen%2BShot%2B2019-01-11%2Bat%2B3.06.56%2BPM.jpg Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 3: Voter bias distributions with two source points. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237386384-47ATL174VF2U7VCPIIIP/Screen+Shot+2019-01-11+at+3.09.25+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237528341-GXTUV1LE4AVL7JJGT7RT/Screen+Shot+2019-01-11+at+3.11.54+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Table 1: Benchmark models with 2 or 4 sources for a 21×21 lattice territory. A dash indicates that a given source is not included in a given model. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547237595311-OBACDDLXDY9HZZNKF10W/Screen+Shot+2019-01-11+at+3.12.54+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 4: Visualization of voter bias distributions for models #1 and #2. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547238028068-UEFGHVZ86VHBXTVUOGF4/Screen+Shot+2019-01-11+at+3.20.15+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 5: Example of the method from Friedman & Holden (2008). The x-axis indicates voter extremity E for two parties, assuming that voter bias has a pseudo-normal distribution. The bell curve is partitioned into five districts so that the results favor the E > 0 party. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547655982006-XKRHVEPB5W7LT9BAI99C/image-asset.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547660115958-JQLSHV3YGJNAGYXSBTLO/Screen+Shot+2019-01-16+at+12.35.02+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Table 2: Average net vote per district following redistricting via the FH method. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547660493474-OIKVPK0UC64L1L5X0D71/Screen+Shot+2019-01-16+at+12.41.23+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547660528222-KAXZS84CAALK0RHUQGDZ/Screen+Shot+2019-01-16+at+12.41.53+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Table 3: Average net vote per district for idealized symmetric districts. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547660662515-5L2FUBWJ94GSY87AWRCF/Screen+Shot+2019-01-16+at+12.44.07+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Table 4: Number of connected components for districted created by FH method. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547664616178-WMQOXASWEBBWM7YE4W3K/Screen+Shot+2019-01-16+at+1.49.25+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 7: Left: a hypothetical example of the most extreme proponent and opponent units. Center: the shortest path through unassigned territory between the extreme endpoints. Right: the set of population units (orange) considered for addition to the district. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547665063662-QKVJKKSJM51765ZZ2M08/Screen+Shot+2019-01-16+at+1.57.15+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1547672061822-8X54KGOUGQG7OQG0LEN8/Screen+Shot+2019-01-16+at+3.54.01+PM.png Home - Making Everyone's Vote Count: Computer Detection of Gerrymandering Figure 8: Districting results for sample voter extremity distributions. The same Gaussian population distribution was used in both samples, and the vote was balanced. In both cases, the proponent party, corresponding to the color red, sufficiently wins the popular vote in three of the five districts. https://www.teknos.org/home/2019/5/1/aquaporin monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1556736497302-LKM8SIPJKV6ORV4N13NV/Screen+Shot+2019-05-01+at+2.47.59+PM.png Home - Modeling Aquaporin Targets Against Tumor Growth Table 1. Binding affinities and Ki values. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1556736568542-4YIT55PYA7KWDAO67PAT/1.png Home - Modeling Aquaporin Targets Against Tumor Growth Figure 1. LigPlot of AQP5 and SAHA. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1556736580337-48TOLFG5HMFYLZU22PJP/image-asset.png Home - Modeling Aquaporin Targets Against Tumor Growth Figure 2. LigPlot of AQP9 and HTS13286. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1556736770599-0X1LJIE0LS32XKJBUXA0/3.png Home - Modeling Aquaporin Targets Against Tumor Growth Figure 3. MATLAB Model of Tumor Growth (AQP5 and SAHA). https://www.teknos.org/home/2018/12/25/2m9ik8ribr04895apt3x9lc2qm84cf monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/25/training-partners-computer-chemistry monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/25/genetic-links-to-obesity-a-neurological-approach monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/25/diagnosing-alzheimers-with-machine-learning monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/25/monodromy-groups-of-indecomposable-rational-functions monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784066996-D5D902GV015NI9R9OHUA/Screen+Shot+2018-12-25+at+7.22.13+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784385519-BJ2DT3F2SAAXNKONL3XJ/Screen%2BShot%2B2018-12-25%2Bat%2B7.22.31%2BPM.jpg Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784116000-YGBXDNAJLSIH3SETWVN8/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784129388-LXOBHK4911KQPCB1LSVX/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784413550-62IB9B8T415564J3FTEH/edited.jpg Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784150119-3BU9INT4C2O09N6IXBAU/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784177980-8L2FZP0E3DO5XDKPL9FM/Screen+Shot+2018-12-25+at+7.23.40+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784579102-I60JY808B43OI1WGDVXN/Screen+Shot+2018-12-25+at+7.24.22+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785685623-YRN2ERRBNPD66SQDNZ6G/image-asset.jpeg Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785704053-JS8K1XPDY44QCEVV9KXS/image-asset.jpeg Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784228830-V0IC1Z8FLLQQ22EZ90PH/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784241624-XGN7C0X0EMF5ZR37TTE1/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785753471-SR78A0N1YNXUCI5AL63X/Screen+Shot+2018-12-25+at+7.25.33+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785770009-E9C98VFTH0IMCCT44493/edited.jpg Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784289992-B1BCG8YRUI4TNENITHKR/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784304290-IGO7U96CD33KZEORNZCL/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784317655-GZ7YW5FOHH3QS9EHKFFY/Screen+Shot+2018-12-25+at+7.26.24+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784330439-QXJD13KRNZEXCCNEYRZD/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545784634478-TL5BNT5XR6BFQSPAU3R9/Screen+Shot+2018-12-25+at+7.26.52+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785794630-MB9800MTNCLI1IJ03O1B/Screen+Shot+2018-12-25+at+7.37.41+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785206089-VPTCL8GM2AV1CKZDNXL2/Screen+Shot+2018-12-25+at+7.38.23+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785215937-YEYQG49ZO1OYQQS7SK0D/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785225996-SCK3HNM7TCN6CES3UQXF/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785235366-AY53AUY9BOTVGQI4MJ34/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785244606-SLUZHZTA7I6UR1BTZWGY/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785269065-V9H1N9XMK3PGSH9K5LS9/Screen+Shot+2018-12-25+at+7.39.24+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785286157-48E0HE463XNPV5QAXDFZ/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785300991-6PJLTC5E336RJHF7YGO8/Screen+Shot+2018-12-25+at+7.39.52+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785323489-JRV40X3GC0DI2X9SODFA/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785332506-RQU1XVYRBXL54OYLFVQQ/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785340025-O9OAHCM8GXWAJHQSXVV4/Screen+Shot+2018-12-25+at+7.41.03+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785347925-UO51P8R67JURK3402QP9/Screen+Shot+2018-12-25+at+7.41.17+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785354482-8BDOJPIY9LVJURMYSGF9/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785362069-K7W3FNE506G7OM8BHMEJ/Screen+Shot+2018-12-25+at+7.41.46+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785369978-KX73S2WT1TBEH6DH01OH/Screen+Shot+2018-12-25+at+7.42.01+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785392348-OTTH965HPWKG1HDMPTRS/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785399303-A1QW67CRJ3DVCG1OP6QW/Screen+Shot+2018-12-25+at+7.42.31+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785405563-FD9UIH0UT815V7RJI44Z/Screen+Shot+2018-12-25+at+7.42.45+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785412529-OCP0QZBZSRRID98IRH43/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785426336-84E23NZ52K2NC3RKWEEV/Screen+Shot+2018-12-25+at+7.43.12+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785465071-LD3157H0S2X9UA1CZC0M/Screen+Shot+2018-12-25+at+7.43.33+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785482696-EPG9H25GHRPAQ5T6IKB7/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785489070-6IRW0UJA5R5H1A1B3BF8/Screen+Shot+2018-12-25+at+7.43.59+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785495343-P86G1Z509LEAT1LA0PAF/Screen+Shot+2018-12-25+at+7.44.09+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785515946-CCTE3FCDXEJIYPFWF59K/Screen+Shot+2018-12-25+at+7.44.09+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785523136-QBD6XHJW2D1JPSXLMCA4/Screen+Shot+2018-12-25+at+7.45.26+PM.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785534297-RMZQ6IQC7EUY1PMMW6IB/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1545785541036-C19KXJV2RF9IWTWJ5CZM/image-asset.png Home - Monodromy Groups of Indecomposable Rational Functions https://www.teknos.org/home/2018/12/19/dive-in monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/12/swarm-robotics monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/8/in-other-words-text-style-transfer monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/12/2/the-ethics-of-human-enhancement-and-genetic-modification monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/10/3/obesity-and-dementia-the-role-of-adipocyte-derived-exosomes-in-the-development-of-alzheimers-disease monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538963459379-S2UEET2D7GF0HYE07LLY/OBESITY1.png Home - Obesity and Dementia Figure 1. Manifestation of Visceral Adipose Tissue https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538963309997-U9I1QKA9SZ1KL5RKXUDA/OBESITY+2.png Home - Obesity and Dementia Figure 2. Exosome Quantification through Flow Cytometry https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538963341609-RT2Y4787B3M432K2Y5NF/OBESITY3.png Home - Obesity and Dementia Figure 3. Summary of Experimental Methods including FABP4+ selection and tagging, exosome/microRNA isolation, and miRNA amplification and microchip array analysis https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538963374099-89FYLQPJLTJ6543XRBER/OBESITY4.png Home - Obesity and Dementia Figure 4. Mean Probe Intensity of miRNA array in Serum vs. CSF. Each point represents the mean expression for a single microRNA across all samples. Black dotted lines indicate a 95% prediction band. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538962096626-Q22S5R62JIY0V85ZCS9U/OBESITY5.png Home - Obesity and Dementia Figure 5. ACT Preliminary Affymetrix Subjects with Patient Demographics and possible confounding variables https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538962267546-NEN2LFSU0YX3KCUJZZM3/Screen+Shot+2018-10-07+at+9.30.36+PM.png Home - Obesity and Dementia Figure 6. Principal Component Analysis removing Batch effect between serum and CSF samples https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538962365078-LMSEZX9Y89MQANV6PGQI/OBESITY7.png Home - Obesity and Dementia Figure 7. CSF Hierarchical Clustering with heat map https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538962430932-9GC60MMZ7E83B1YMOU0V/OBESITY8.png Home - Obesity and Dementia Figure 8. Principal Component Analysis of CSF Samples with Sex, Age, +/- Lewy Body disease. There is no pattern between cases and controls with respect to demographics. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538962476321-1Y3JJ6VX0XDP1CDHBYHX/OBESITY9.png Home - Obesity and Dementia Figure 9. Sphingosine-1-phosphate Signaling- Cerebrospinal Fluid MAP on Neurodegeneration https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1538965968859-S75IQVV4ANAIPSTKKBH9/Screen+Shot+2018-10-07+at+10.32.03+PM.png Home - Obesity and Dementia Figure 10. Table of 8 significant miRNAs https://www.teknos.org/home/2018/12/25/cubesat monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/3/1/editors-letter-a-new-beginning monthly 0.5 2020-06-13 https://www.teknos.org/home/2018/2/15/the-war-against-drug-resistant-bacteria monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518750957398-WQ22YXU3N5QJ61W0542K/eq1.png Home - The War Against Drug-Resistant Bacteria https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751018441-KMSD2N0AKT0K3C9O348Q/image-asset.png Home - The War Against Drug-Resistant Bacteria https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751192611-J099PPL3G8GZGC0VRIV8/1.JPG Home - The War Against Drug-Resistant Bacteria Figure 1. Formation of a pore inside the membrane after 60 µs of the molecular dynamics simulation. Subfigure A is the starting position with peptides position parallel to the membrane as described in the methods and subfigure B is the predicted result after the model simulated atomic interactions of the peptides. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751311460-C1A32SZ1W5FCSZTZB3DP/2.jpg Home - The War Against Drug-Resistant Bacteria Figure 2. Characterized peptide structures from the molecular dynamics simulations. Six different peptide structures were discovered to exist in the bacterial membrane from the simulations. The hexamer and octamer structures were the most prevalent in the membranes. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751421171-UEKR9YW3PQDPJ16MTNKG/image-asset.png Home - The War Against Drug-Resistant Bacteria Figure 3. Molecular dynamics simulation setup for conductance analysis in the bacterial membrane environment. This was the orientation of the octamer peptide, and a similar setup followed for other structures. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751365104-07W3E3JAUAKAZYGAK3H8/chart1.png Home - The War Against Drug-Resistant Bacteria Table 1. Conductance analysis data on the higher-order structures. All of the discovered structures except trimer and dimer were subject to a conductance analysis to determine membrane-disruption activity. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751675591-Z31UOASRJOIXY4ZKXQAV/image-asset.jpeg Home - The War Against Drug-Resistant Bacteria Figure 4. High-atomic detail analysis on the peptide structures. The molecular dynamics simulation allows for an analysis of individual amino acids on the peptide, and an example is shown with valine, represented in green. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751467768-FM78RGQW52BC581QHW6D/5.jpg Home - The War Against Drug-Resistant Bacteria Figure 5. Helical wheel representation of an individual peptide and the location of specific amino acids. The amino acids were grouped together based on where they face in the peptide structure. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751563910-2XURYZNETJJEN11VNXYI/6.png Home - The War Against Drug-Resistant Bacteria Figure 6. Final peptide library used for synthesis based on the results of the AMP prediction algorithm. The random forest algorithm was used since it had the highest accuracy, precision and AUROC, as well as the lowest false positive rate. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518751584735-ICSZCXG4PWS8BNV9KW5V/image-asset.png Home - The War Against Drug-Resistant Bacteria Table 2. Data on the effectiveness of different AMP prediction algorithms. Four different algorithms and their results after training and validation are summarized in this data table. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518752642603-CL1JMWAT11CMVYY9FI4H/7.jpg Home - The War Against Drug-Resistant Bacteria Figure 7. High-performance Liquid Chromatography results to determine if there are any contaminants mixed with the peptides. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518752677796-MH5ME7XMMC9B68JKHGFP/8.JPG Home - The War Against Drug-Resistant Bacteria Figure 8. Mass Spectrometry results, which helps determine if the peptide synthesis was done correctly based on the expected range of masses. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518753593533-WM522V7Q1GI9FK3L18US/9.jpg Home - The War Against Drug-Resistant Bacteria Figure 9. This figure illustrates the leakage efficiency of the peptides on POPC vesicles. The POPC vesicles are models for eukaryotic mammalian cells, and from the efficiency of the samples, it is evident that most of the peptides do not harm mammalian cells. https://www.teknos.org/home/2018/2/15/big-data-identifying-protein-decoys-1 monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518727582645-EHFU2MD6W8GQ39NU43JE/Pandit_Formula1.PNG Home - Big Data: Identifying Protein Decoys https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518727637131-U252DZCY7J7MOW86XAO5/Pandit_Formula2.PNG Home - Big Data: Identifying Protein Decoys https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518727922896-176WL4RKXNMH37MEJPKN/image-asset.png Home - Big Data: Identifying Protein Decoys https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518728001201-KEVZI9B2P5MP0LUU3C96/Pandit_Formula3.PNG Home - Big Data: Identifying Protein Decoys https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518728041917-SY6BEHQHIMULXSBXDJUJ/image-asset.png Home - Big Data: Identifying Protein Decoys https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518738305128-9IJVCWR7W1KGH3ZO8HI0/1.jpg Home - Big Data: Identifying Protein Decoys Figure 1. Results presented here are the structural data for 1HHP, the aspartyl protease from HIV-1 isolate BRU, projected on the top two diffusion coordinates. The amount of variance captured by the coordinates is unknown due to the use of ARPACK. Points are color coded based on their lRMSD from the native, marked by the red ‘x’. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518738350909-53PMHTBA6KY8Z7CB27BZ/2.jpg Home - Big Data: Identifying Protein Decoys Figure 2. Results presented here are the structural data for 1HHP, projected on the top two principal components, which capture approximately 36% of the accumulated variance. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518738434919-Q784W8ATZ0AG4LQ104P5/3.jpg Home - Big Data: Identifying Protein Decoys Figure 3. Results presented here the cluster sizes of k-means applied to 1HHP projected on the top 3000 diffusion coordinates. The cluster highlighted in red contains the native structure. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518738455159-BP2DT0XM06HWSXU44WP3/image-asset.jpeg Home - Big Data: Identifying Protein Decoys Figure 4. Here the decoys of 1HHP from the previous figure are broken down into three classes: close, medium, and far lRMSD from the native structure. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518738600381-31AKNEDMQII8Q2HMLJIR/T1.jpg Home - Big Data: Identifying Protein Decoys Table 1. The results presented here summarize the quality of clusters obtained via the three clustering algorithms (columns) on various protein conformations generated via de novo structural prediction techniques. The clusters generated from the principal components are highlighted red; from diffusion coordinates, green; and from lRMSD, green. Ideally a low rank and a high NMI is desired. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518738625652-ZEFJ4V61E1NGQA51ONFM/T2.jpg Home - Big Data: Identifying Protein Decoys Table 2. The results presented are identical to table one, except rather than use NMI as a metric for the quality of a cluster, rank of the cluster with the native structure and rank of cluster with the most near-native structures are provided instead. For example, “6, 3 of 1” means the native structure is in the 6th most populous cluster and the cluster with the most nearnative, defined as structures with the lowest 25% of lRMSDs, is the 3rd most populous cluster out of 10 total clusters. https://www.teknos.org/home/2018/2/14/towards-the-green-synthesis-of-gold-nanoparticles monthly 0.5 2020-06-09 https://www.teknos.org/home/2018/2/14/mealworms-styrofoam-conservationists monthly 0.5 2020-06-13 https://www.teknos.org/home/2018/2/14/a-bundle-of-ibeacons monthly 0.5 2020-07-12 https://www.teknos.org/home/2018/2/14/off-with-their-lead-heavy-metal-removal monthly 0.5 2020-06-13 https://www.teknos.org/home/2018/2/14/cost-effective-chromatography-techniques monthly 0.5 2020-06-13 https://www.teknos.org/home/2018/2/14/the-intersection-of-art-and-technology monthly 0.5 2020-06-13 https://www.teknos.org/home/2018/2/14/a-new-earth-terraforming-mars monthly 0.5 2020-06-13 https://www.teknos.org/home/2018/2/14/generative-networks-for-deep-learning monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518616449350-SI3D8S2J03RL2A25TF9X/GAN_Architecture.png Home - Generative Networks for Deep Learning Figure 1: The full stack of the training architecture. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617242354-RG9JMRF8XYOGJI3JQD3N/Landon2.png Home - Generative Networks for Deep Learning (a) FGSM-perturbed examples for standard discriminator (b) FGSM-perturbed examples for GAN-trained discriminator Figure 2: FGSM-produced adversarial examples https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617336414-0MGHZMSKL8O39KUINO37/Screen+Shot+2018-02-14+at+9.08.37+AM.png Home - Generative Networks for Deep Learning (a) Generator-created perturbed examples before training. (b) Generator-created perturbed examples with training. Figure 3: Generator-produced adversarial examples https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617500963-G2S14Q7FF90Y3STGYUV8/Screen+Shot+2018-02-14+at+9.11.17+AM.png Home - Generative Networks for Deep Learning Table 1: Adversarial accuracy, 2:1 training ratio https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617596747-62D6KACHVOR43B1RBOV3/2to1_adv_accuracy_graph.png Home - Generative Networks for Deep Learning Figure 4: Accuracy of discriminator at 2 to 1 training ratio against FGSM- perturbed images. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617683098-EN9OW17PWQA2FAYOH37J/table2.PNG Home - Generative Networks for Deep Learning Table 2: Adversarial accuracy, 4:1 training ratio https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617733008-757SQSQ69JS1V48HNH94/4to1_adv_accuracy_graph.png Home - Generative Networks for Deep Learning Figure 5: Accuracy of discriminator at 4 to 1 training ratio against FGSM-perturbed images. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617821654-DDANC0LGHAHPXXRQY7ES/table3.PNG Home - Generative Networks for Deep Learning Table 3: Adversarial accuracy, 6-8-12 stepped training ratio https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617876555-IQ6RVBWIXACEJ8VOLMUI/step_6_8_12_adv_accuracy_graph.png Home - Generative Networks for Deep Learning Figure 6: Accuracy of discriminator at 6-8-12 step training ratio against FGSM-perturbed images. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518617943406-LPS0SF8ZWTH38KMUY217/adv_adv_perturb_stepped_loss_graph.png Home - Generative Networks for Deep Learning Figure 7: Discriminator and generator loss through with stepped training ratio. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518618043492-662R36WJERVDTXC1OFK6/table4.PNG Home - Generative Networks for Deep Learning Table 4: Adversarial accuracy, continuously adjusted training ratio. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518618108526-QY9JUOP7FAMP7OM01MQ9/continuous_adv_accuracy_graph.png Home - Generative Networks for Deep Learning Figure 8: Accuracy of discriminator in continuously adjusted training ratio against FGSM-perturbed images. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518618234536-GCWG917BWB4MBTFXQJG8/Screen+Shot+2018-02-14+at+9.23.38+AM.png Home - Generative Networks for Deep Learning (a) Training ratio at each training cycle. (b) Discriminator and generator losses. Figure 9: Continuously adjusted training ratio. https://www.teknos.org/home/2018/1/31/synthesis-of-novel-small-molecule-lta-4-h-enzyme-activators-for-copd monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518582434343-H913AQC432VH7OCIUFFA/Suzie1.png Home - Synthesis of LTA4H Enzyme Activators Figure 1. The Michaelis-Menton plot shows that with increasing concentrations of Ala-pNA, the initial velocity of the reaction increased. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518582552194-PMEYS7CGZZMHW6EZZMM6/Suzie2.png Home - Synthesis of LTA4H Enzyme Activators Chart 1. Vmax is the maximum rate of reaction, Km is the concentration of substrate needed to achieve half Vmax, and Kcat is the turnover number for the aminopeptidase activity. The Kcat/KM value is the catalytic efficiency. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518582681804-EJI36UAVDHS4QLW3YFC0/Suzie3.png Home - Synthesis of LTA4H Enzyme Activators Figure 2. The chemical reaction that produced our product, 4-AcDM. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518582723810-N13808F7JLRR159E2PK5/Suzie4.png Home - Synthesis of LTA4H Enzyme Activators Figure 3. The initial compound tested by Oliveira et al., 4-MDM. https://www.teknos.org/home/2018/2/15/3d-printed-scaffolds-and-stem-cells-study monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723061704-R62KX3LLGXSCJLEJZZ4Q/T1.png Home - 3D Printed Scaffolds and Stem Cells: Study Table 1. The primers used for RT-PCR. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723127306-39KJXQB87KIWJHRBZRA5/1.PNG Home - 3D Printed Scaffolds and Stem Cells: Study Figure 1. SEM images of 230P and 215P. A) The 230P pictures show the fused filaments of PLA with higher magnifications for the rough and smooth areas. B) The 215P pictures show the unfused filaments of PLA with higher magnification for the rough and smooth areas. These results indicate that compared to 230P, 215P create incomplete surfaces that most likely resulted from the difference in extrusion temperature. The 215P was not hot enough to properly fuse together the PLA filaments. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723271383-164RK9ON7N10APH8SF5R/image-asset.png Home - 3D Printed Scaffolds and Stem Cells: Study Figure 2. Fluorescent images of 230P and 215P scaffolds with DPSCs on day 1. A) The 230P picture shows the cell adhesion for the 230P scaffold 24 hours after the initial plating of the DPSCs. B) The 215P pict ure shows the cell adhesion for the 215P scaffold 24 hours after the initial plating of DPSCs. The initial plating concentration is relatively the same. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723325960-68U394LYQ45DEK634BOX/3.PNG Home - 3D Printed Scaffolds and Stem Cells: Study Figure 3. Fluorescent images of 230P and 215P. A) 230P has more nodule formation and number of cells. B) 215P has less prominent nodule formation and a fewer number of cells. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723375005-6439HY3U48UG2YU5FGMX/4.PNG Home - 3D Printed Scaffolds and Stem Cells: Study Figure 4. SEM and EDAX for 230P scaffolds. The upper-most picture shows an SEM picture of biomineralization on the 230P scaffold at 28 days at 10,000 times magnification. The EDAX confirms the presence of calcium phosphate. The confocal shows that the 230P scaffold had biomineralization. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723401242-GUYJGYXCP36IPJEYSO1K/5.PNG Home - 3D Printed Scaffolds and Stem Cells: Study Figure 5. SEM and EDAX for 215P scaffolds. The picture on the left shows an SEM picture of biomineralization on the 215P scaffold at 28 days. The EDAX confirms the presence of calcium phosphate. The confocal shows that there is decreased biomineralization as compared to that of the 230P scaffold. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723464758-FS069YUR7BL6CP7TSG92/image-asset.png Home - 3D Printed Scaffolds and Stem Cells: Study Figure 6. SEM images of 3-D prints and spuncast. A) shows the 3-D printed scaffold at 5000 times magnification. The picture shows the microscale and nanoscale patterns formed. B) shows the spuncast scaffold at 5000 times magnification. The spuncast scaffolds had almost flat surface whereas 3-D prints had roughness. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723497967-S2JUY7O6KQEN6CFGX9QM/image-asset.png Home - 3D Printed Scaffolds and Stem Cells: Study Figure 7. Fluorescent image of 24 hours after plating cells on 3-D prints and spuncast. The above pictures show the cell attachment 24 hours after initial plating on 3-D printed scaffold (A) and spuncast scaffold (B). This shows similar levels of cell attachment for both scaffolds. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723530772-MHBH3FJY7KL6EB5ZGTUZ/8.PNG Home - 3D Printed Scaffolds and Stem Cells: Study Figure 8. Fluorescent images of 3-D prints on day 7. A) It was shown that the nodule formation of the 3-D printed scaffold at day 7. B) The picture shows the nodule formation on the 3-D printed scaffold with dexamethasone at day 7. C) The picture shows the proliferation increase due to shaking stimulus at day 7. When induced with dexamethasone, there was more differentiation (thicker nodules) and shaking stimulus increased proliferation and the spread of the cells. There is a clear network formed between DPSCs. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723566292-RI7HQLPG9OO4P7EUJYP2/image-asset.png Home - 3D Printed Scaffolds and Stem Cells: Study Figure 9. Fluorescent images of the spuncast scaffold on day 7. A) This picture shows the nodule formation of the spuncast scaffold on day 7. B) This picture shows the nodule formation on the spuncast scaffold treated with dexamethasone on day 7. C) This picture shows the proliferation increase due to shaking stimulus on day 7. Once again, differentiation was greater in dexamethasone-induced media and proliferation was greater for shaken samples. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723596252-724L5UCDK2KH8EJEO2PO/10A.png Home - 3D Printed Scaffolds and Stem Cells: Study Figure 10. Confocal images of 3-D prints (flats) and spuncast. This figure shows confocal images of biomineralization for the spuncast and 3-D printed scaffolds. A) Confocal of 3-D printed scaffolds without dexamethasone shows biomineralization spread throughout the scaffold. B) These samples, which were treated with dexamethasone, showed higher amounts of biomineralization throughout the scaffold, as compared to 11A. C) Spuncast without dexamethasone had minimal biomineralization and D) spuncast with dexamethasone had very small amounts of biomineralization. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518723657592-XGI5H94XTJ7KRR149ANM/10B.png Home - 3D Printed Scaffolds and Stem Cells: Study E) 3-D prints treated with shaking stimulus had small amounts of dense biomineralization. F) 3-D printed scaffolds treated with shaking stimulus and dexamethasone showed the largest amount of calcification spread throughout the scaffold. G) Spuncast with shaking stimulus shows very minimal amounts of calcification and H) Spuncast with shaking stimulus and dexamethasone treatment showed a slight increase in calcification in comparison to G. Overall, shaking increased proliferation and differentiation levels. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518724224916-ROMKA30JWO8Y7J80CY6D/image-asset.png Home - 3D Printed Scaffolds and Stem Cells: Study Figure 11. ALP and RUNX2 RT-PCR graph of relative mRNA levels. This figure shows the early markers for osteogenic differentiation, alkaline phosphatase (ALP) and runt-related transcription factor 2 (RUNX2). Flat signifies the 3-D printed scaffold whereas SC signifies the spuncast scaffold. A) ALP mRNA data indicates that all 3-D printed scaffolds had significant increases with the exception of the scaffolds treated with both dexamethasone and shake incubation. B) RUNX2 mRNA levels show that all 3-D printed scaffolds had significant increases with the exception of the scaffolds treated with both dexamethasone and shake incubation. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518724269088-994A14L1XYJCAKZM1BTR/12.PNG Home - 3D Printed Scaffolds and Stem Cells: Study Figure 12. OCN and COL1 a1 RT-PCR graph on relative mRNA levels. This figure shows the late markers for osteogenic differentiation, osteocalcin (OCN) and collagen type 1 alpha 1 (COL1 a1). Flat signifies the 3-D printed scaffold whereas SC signifies the spuncast scaffold. A) It was shown that across the board mRNA levels for OCN are low. All 3-D printed scaffolds show significant increases over their spuncast scaffold counterparts with the one exception of the no treatment scaffolds. B) It was shown that for COL1 a1 the mRNA levels show significant increases for all 3-D printed scaffolds as compared to their spuncast counterparts with the exception of the scaffolds treated with both dexamethasone and shake incubation. https://www.teknos.org/home/2018/1/31/investigating-interfacial-crosslinking-to-combat-hard-foulants monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518032598933-9A05D6NB21CKVKZTG51T/image-asset.png Home - Crosslinking to Combat Foulants Figure 1. Conceptual drawing of the anatomy of the barnacle, Balanus amphitrite. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1592077543752-N9O88B5QRKDLAFDWJ158/image-asset.jpeg Home - Crosslinking to Combat Foulants Table 1. Colors of the different combinations of substrates. The ADA column is step 1, ADA + H2O2 is step 2, and ADA + H2O2 + tBC is the third step of Rescigno et al.s colorimetric assay. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518554172660-48NOR0J6D8ZHDO953NRR/Screen+Shot+2018-02-13+at+3.35.47+PM.png Home - Crosslinking to Combat Foulants Table 2. Overview of 96 well plate. X indicates no solution, and the term wash represents the solution that had reacted with the beads and given substrate. For instance, when ADA was added to A2, the solution was taken and placed in B2, all the while leaving the beads behind. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518554619444-82SYPUZS3KEW54YR3QBQ/Screen+Shot+2018-02-13+at+3.42.58+PM.png Home - Crosslinking to Combat Foulants Figure 2. Native PAGE gels. S1, S2, S3: Longitudinal Canal lysate sample. B1: Barnacle body, SM: Sub mantle tissue. L: Laccase, T: Tyrosinase, H: HRP. a) First Step - Laccase activity assay via ADA substrate b) Second Step - HRP activity assay via H2O2 substrate c) Third Step - Tyrosinase activity assay via tBC d) Fourth Step - Coomassie blue stain https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518554933381-ECX4VVOPKZF5EZDR7885/Christine3.png Home - Crosslinking to Combat Foulants Figure 3. Laccase assay, the first step of Rescigno et al.’s colorimetric assay on the 4% polyacrylamide gel. Bottom view of the gel, and the barnacle is shown as the pink circular object near the center of the picture. The four circles represent positive and negative control injections. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518555000204-OVNG7FLK2080FNWRRUQC/image-asset.png Home - Crosslinking to Combat Foulants Figure 4. Peroxidase assay, the second step of Rescigno et al.’s colorimetric assay on the 4% polyacrylamide gel. In the bottom view the barnacle is shown as the bright pink circular object. There are four positive and negative control injections. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518555111007-H3A8U4T9K4Z96340XO98/image-asset.png Home - Crosslinking to Combat Foulants Figure 5. Tyrosinase assay, the third step of Rescigno et al.’s colorimetric assay on the 4% polyacrylamide gel. Bottom view of the gel, and the barnacle is shown as the blue circular object at the left. There are four positive and negative control injections. B1 is the side view of the barnacle. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518555420601-1TNFRXHAA07FSHI2TUTT/Screen+Shot+2018-02-13+at+3.56.27+PM.png Home - Crosslinking to Combat Foulants Figure 6. Glass microspheres covered in fibrous cement proteins. (a) Cluster of multiple beads glued together by the cement. (b) Two beads connected by cement. Scale bars ~ 250 μm. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518555495602-SOKVOKVBC1JJS16ADXPA/image-asset.png Home - Crosslinking to Combat Foulants Figure 7. Microsphere assay. Table is a depiction of what was inserted into the wells. Row A is filled with the cemented microspheres. Each substrate solution, stated in the table, was injected in row A, than extracted and placed in row B. B2 and B5 showed laccase activity, and B6 showed peroxidase activity. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518555625277-8F5NRMFDG6CLBT0U2OYL/image-asset.png Home - Crosslinking to Combat Foulants Figure 8. Microsphere assay specifically for Tyrosinase activity. L-tyrosine was used as a substrate. A1 (control) shows Tyrosinase activity with tyrosine. A2 (sample) depicts no activity when microsphere balls covered with cement and L-tyrosine was mixed. https://www.teknos.org/home/2018/1/31/classification-and-prediction-of-antimicrobial-peptides-using-n-gram-representation-and-machine-learning monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517582715580-6XM3UYADYI8RI8VZZNJE/Screen+Shot+2018-02-02+at+9.44.56+AM.png Home - Peptide Classification with Machine Learning Table 1. Number of AMP sequences in each class-specific AMP set https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517581988980-598MWTUQ7ASQJ0CR20JH/Screen+Shot+2018-02-02+at+9.32.31+AM.png Home - Peptide Classification with Machine Learning https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517581998805-M7CYG4BQPDLSYNE08QZG/image-asset.png Home - Peptide Classification with Machine Learning https://www.teknos.org/home/2018/1/31/metformin-affects-mitochondrial-function-of-mesenchymal-stromal-cells monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580290716-C50G74U17T9S18AFR2N3/Screen+Shot+2018-02-01+at+10.30.25+PM.png Home - Metformin and Mitochondria: Analysis Figure 1. qPCR Data from concentrations 0.0mM, 0.25mM, and 1.0mM, normalized to 18s. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580352355-H0TITA765X4DKNPUUZEQ/Screen+Shot+2018-02-01+at+10.31.30+PM.png Home - Metformin and Mitochondria: Analysis Figure 2. qPCR expression data for Metformin Concentrations 0.0mM, 0.5mM, 2.5mM and 5.0mM, normalized to 18s. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580379827-TROYRPRG0K4TCXFZTUDB/Adarsh3%264.png Home - Metformin and Mitochondria: Analysis Figures 3-4. Seahorse Stress Test Generated graphs after 72HR Metformin exposure. Baseline for Figure 3 complete after 3 data points; Baseline for Figure 4 complete after 5 data points https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580435163-1TNOP80UPEA2ONJL9XS0/Arad5.png Home - Metformin and Mitochondria: Analysis Figure 5. Graphs generated highlighting various values from Seahorse Analysis Program for concentrations 0mM, 2.5mM, and 5.0mM after 72 hrs. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580462801-FEKTP22DAQVPOLHK0FP5/Screen+Shot+2018-02-01+at+10.35.59+PM.png Home - Metformin and Mitochondria: Analysis Figure 6. Graphs generated highlighting various values from Seahorse Analysis Program for concentrations 0mM, 0.25mM, and 1.0mM after 72 hrs. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580498951-OUZD9TXND00ECTDQ6CPT/Arad7.png Home - Metformin and Mitochondria: Analysis Figure 7. Seahorse Stress Test Generated graphs after 7 days. Baseline complete after 3 data points https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517580562127-QAEVGSSSUII71G81OO79/Arad8.png Home - Metformin and Mitochondria: Analysis Figure 8. Graphs generated highlighting various values from Seahorse Analysis Program for concentrations 0mM, 0.25mM, and 1.0mM, cultured in no metformin, NG-DMEM for 7 days. https://www.teknos.org/home/2018/1/31/investigating-insider-trading-big-data monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517408145801-UNV8QKIZ2UJ57F97BPTM/Adarsh1.png Home - Investigating Insider Trading: Big Data Figure 1. This table shows the basic statistics of our database; the number of transactions, companies and insiders we have mined. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517578356329-G0N65M6JHMELP7P6PRVJ/Screen+Shot+2018-01-31+at+9.24.27+AM.png Home - Investigating Insider Trading: Big Data Figure 2. This graph shows the number of insiders holding insider positions in a given number of companies. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517578427341-NMB8B4G7KEF10RSKD3YF/Adarsh3.png Home - Investigating Insider Trading: Big Data Figure 3. This graph shows the proportion of insiders who have executed a given magnitude of a certain trade: sale or purchase. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517578572219-IHW9KG8BC1KLZ10BRCFO/Screen+Shot+2018-02-02+at+8.35.53+AM.png Home - Investigating Insider Trading: Big Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517578696908-76SB98GQKI0S7FKWS13L/Adarsh4.png Home - Investigating Insider Trading: Big Data Figure 4. This visualization of a company's insiders was created in Gephi: each node represents an insider; edges are created when their trading behavior is considered similar. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579288310-CJ2UPIG81BLE1F2QUODJ/Adarsh5.png Home - Investigating Insider Trading: Big Data Figure 5. Another example of company insiders a visualization. Note the varying thicknesses of edges, which represents the level of similarity. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579402093-9H6WHT5R7F0M0MYUB0W6/Adarsh6.png Home - Investigating Insider Trading: Big Data Figure 6. This graph shows the number of pairs of insiders whose longest common subsequence is of a certain length. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579462193-ZTRFFA7E6YJ35NVPT2MN/Adarsh7.png Home - Investigating Insider Trading: Big Data Figure 7. This figure shows the percentage of connected components with a given number of nodes https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579498539-VR4QZ3IGN3XRF0A213PX/image-asset.png Home - Investigating Insider Trading: Big Data Figure 8. This is the visualization of an ego-net as created in Gephi. The main ego of interest is shown in red. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579580947-IDWDL1GL5EP61KYGDFPW/image-asset.png Home - Investigating Insider Trading: Big Data Figure 9.This is another ego-net visualization from Gephi, https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579607570-87FYIZ0CUUK2MVX1ESDT/Screen+Shot+2018-01-31+at+2.53.15+PM.png Home - Investigating Insider Trading: Big Data Figure 10. This graph shows the power law that is defined by the trading behavior of an example company's insiders. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579700640-TYF5CFGNQ51WA59H2L43/image-asset.png Home - Investigating Insider Trading: Big Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579793400-VF8SLTE31W2DA4YSGGYQ/Screen+Shot+2018-02-02+at+8.56.13+AM.png Home - Investigating Insider Trading: Big Data https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517579888741-1YGR7AHD52GI0R9F9E8O/Adarsh11.png Home - Investigating Insider Trading: Big Data Figure 11. This chart is an example of the plots depicting an insider’s R-values for all of his or her trades. https://www.teknos.org/home/2017/11/6/an-unexpected-role-in-cell-adhesion monthly 0.5 2020-06-13 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517360753647-ZPA7I77X79VYIZPEMKVV/Abhi1.png Home - An Unexpected Role In Cell Adhesion https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517360825245-TCW3QOXIHQR11V6E99BK/Abhi2.png Home - An Unexpected Role In Cell Adhesion https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517360932510-7QZYOGQKU3VBCQWBHKSM/Abhi3.png Home - An Unexpected Role In Cell Adhesion https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517361058042-UDA31LADTPNRAFY3PIL2/Abhi4.png Home - An Unexpected Role In Cell Adhesion https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517361197794-SZS41HLJEAAWUGCE075E/Abhi5.png Home - An Unexpected Role In Cell Adhesion https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1517361212689-U10DODSJGONC85TOMRU1/Abhi6.png Home - An Unexpected Role In Cell Adhesion https://www.teknos.org/home/2018/2/14/riparian-land-use-evaluation monthly 0.5 2020-06-14 https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518629484208-E0VWQTOEAYALGTPT3EF3/image-asset.png Home - Riparian Land Use: Evaluation https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518629517615-Q0K1XZU964AIOMIGKWI7/Screen+Shot+2018-02-14+at+12.29.55+PM.png Home - Riparian Land Use: Evaluation https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518629661800-XOF36DXCBOU8QYPS6IVD/Victoria1.png Home - Riparian Land Use: Evaluation Figure 1. Map of ProbMon Sample Sites included in Statistical Analysis. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518631329757-2NU30DVGVUQ2FSRM0G9X/Victoria6.png Home - Riparian Land Use: Evaluation Figure 2. Map of ProbMon Sample Sites and Fixed Virginia Water Quality Monitoring Sites. Orange dots indicate ProbMon sites while green dots indicate fixed monitoring sites. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518631348768-UUDD340I4U35SD0HTO2J/Victoria7.png Home - Riparian Land Use: Evaluation Figure 3. Map of ProbMon Sample Sites by Land Use Diversity. Larger dots indicate higher land use diversity. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518630918717-Q7GJM4U39O6Q4TQ8CITT/Screen+Shot+2018-02-14+at+12.53.32+PM.png Home - Riparian Land Use: Evaluation Table 1. Descriptive Statistics of Key Variables from ProbMon Data. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518630026588-GHCLMCGRYL0T23KW8XWC/image-asset.png Home - Riparian Land Use: Evaluation https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518630199885-JJ8HHBF3UKWC40MJ8VKV/Screen+Shot+2018-02-14+at+12.42.58+PM.png Home - Riparian Land Use: Evaluation https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518631038334-DWT1HWPSQLJSUD7A425H/Victoria2.png Home - Riparian Land Use: Evaluation Table 2. Correlation of Land Use Variables between Adjacent and 30 Meter Areas. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518631120372-HVHRKVRYYQGOT1DQF2EK/Victoria3.png Home - Riparian Land Use: Evaluation Table 3. Regression Results for Lead Sediments. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518631184541-XEHC84355UNSST0ZEG5Q/Victoria4.png Home - Riparian Land Use: Evaluation Table 4. Regression Results for Bacterial Pollution. https://images.squarespace-cdn.com/content/v1/59fe52b5c027d858c22c333b/1518631244881-BHQ6ANOUF0Y5F8J5KABG/Victoria5.png Home - Riparian Land Use: Evaluation Table 5. Regression Results for Nutrients. https://www.teknos.org/home/category/Competition+Winner monthly 0.5 https://www.teknos.org/home/category/Foreword monthly 0.5 https://www.teknos.org/home/category/Article monthly 0.5 https://www.teknos.org/home/category/Paper monthly 0.5 https://www.teknos.org/home/category/Staff+Writing monthly 0.5 https://www.teknos.org/home/tag/Gene+Analysis monthly 0.5 https://www.teknos.org/home/tag/Satellite monthly 0.5 https://www.teknos.org/home/tag/Mathematics monthly 0.5 https://www.teknos.org/home/tag/TJ+Plague monthly 0.5 https://www.teknos.org/home/tag/Plastic monthly 0.5 https://www.teknos.org/home/tag/Genetic+Modification monthly 0.5 https://www.teknos.org/home/tag/Machine+Learning monthly 0.5 https://www.teknos.org/home/tag/Robotics monthly 0.5 https://www.teknos.org/home/tag/Ethics monthly 0.5 https://www.teknos.org/home/tag/Engineering monthly 0.5 https://www.teknos.org/home/tag/Marine monthly 0.5 https://www.teknos.org/home/tag/school monthly 0.5 https://www.teknos.org/home/tag/Thomas+Jefferson monthly 0.5 https://www.teknos.org/home/tag/Computer+Science monthly 0.5 https://www.teknos.org/home/tag/2022+Summer+Writing+Contest monthly 0.5 https://www.teknos.org/home/tag/Rational+Functions monthly 0.5 https://www.teknos.org/home/tag/Genetics monthly 0.5 https://www.teknos.org/home/tag/Bacteria monthly 0.5 https://www.teknos.org/home/tag/3D+Modeling monthly 0.5 https://www.teknos.org/home/tag/Space monthly 0.5 https://www.teknos.org/home/tag/global+health monthly 0.5 https://www.teknos.org/home/tag/Obesity monthly 0.5 https://www.teknos.org/home/tag/Neurons monthly 0.5 https://www.teknos.org/home/tag/Scientist monthly 0.5 https://www.teknos.org/home/tag/covid-19 monthly 0.5 https://www.teknos.org/home/tag/Chemiluminescence monthly 0.5 https://www.teknos.org/home/tag/Data monthly 0.5 https://www.teknos.org/home/tag/TJ-REVERB monthly 0.5 https://www.teknos.org/home/tag/Algorithm monthly 0.5 https://www.teknos.org/home/tag/CubeSat monthly 0.5 https://www.teknos.org/home/tag/Statistics monthly 0.5 https://www.teknos.org/home/tag/Physics monthly 0.5 https://www.teknos.org/home/tag/HIV monthly 0.5 https://www.teknos.org/home/tag/Coral monthly 0.5 https://www.teknos.org/home/tag/Alzheimer%27s+Disease monthly 0.5 https://www.teknos.org/home/tag/march+2018 monthly 0.5 https://www.teknos.org/home/tag/Biosensor monthly 0.5 https://www.teknos.org/home/tag/editor%27s+letter monthly 0.5 https://www.teknos.org/home/tag/Environment monthly 0.5 https://www.teknos.org/home/tag/Computer+Vision monthly 0.5 https://www.teknos.org/home/tag/Text+Style+Transfer monthly 0.5 https://www.teknos.org/home/tag/Editorial monthly 0.5 https://www.teknos.org/home/tag/Cancer monthly 0.5 https://www.teknos.org/forewords daily 0.75 2022-01-23 https://www.teknos.org/forewords/2022/1/22/2021 monthly 0.5 2022-01-23 https://www.teknos.org/forewords/2020/6/14/2020 monthly 0.5 2020-08-14 https://www.teknos.org/forewords/2020/6/14/2019 monthly 0.5 2020-06-15 https://www.teknos.org/forewords/2019/2/16/2018 monthly 0.5 2020-05-31 https://www.teknos.org/forewords/2018/2/7/2017 monthly 0.5 2020-05-31 https://www.teknos.org/forewords/2018/2/7/2016 monthly 0.5 2019-02-17 https://www.teknos.org/forewords/2018/2/7/2015 monthly 0.5 2018-02-07 https://www.teknos.org/forewords/2018/2/7/2014 monthly 0.5 2018-02-07 https://www.teknos.org/forewords/2018/2/3/2013-1 monthly 0.5 2018-02-03 https://www.teknos.org/forewords/2018/2/2/2012 monthly 0.5 2018-02-02 https://www.teknos.org/forewords/2018/2/1/2011 monthly 0.5 2018-02-02 https://www.teknos.org/forewords/2018/2/1/2010 monthly 0.5 2018-02-03 https://www.teknos.org/forewords/2018/2/1/2009 monthly 0.5 2018-02-03 https://www.teknos.org/forewords/2018/2/1/2008 monthly 0.5 2018-02-03 https://www.teknos.org/forewords/2018/2/1/2007 monthly 0.5 2018-02-03 https://www.teknos.org/forewords/2018/2/1/2006 monthly 0.5 2018-02-01 https://www.teknos.org/forewords/2018/2/1/2005 monthly 0.5 2020-05-31 https://www.teknos.org/forewords/2018/2/1/2004 monthly 0.5 2020-05-31 https://www.teknos.org/forewords/2018/2/1/2003 monthly 0.5 2020-05-31 https://www.teknos.org/podcast-episodes daily 0.75 2026-03-07 https://www.teknos.org/podcast-episodes/2020/4/30/tektalk-episode-3-dr-osborne monthly 0.5 2020-06-15 https://www.teknos.org/podcast-episodes/2020/2/29/tektalk-episode-2 monthly 0.5 2020-06-15 https://www.teknos.org/podcast-episodes/tektalk-episode-1 monthly 0.5 2020-06-15 https://www.teknos.org/our-team daily 0.75 2026-02-23 https://www.teknos.org/contact daily 0.75 2022-09-26 https://www.teknos.org/about-us daily 0.75 2026-01-26 https://www.teknos.org/welcome daily 1.0 2026-03-07