![Post-Traumatic Stress Disorder (PTSD) biomarker identification using a deep learning model](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBczhMIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--5dc18d2a26a53e84e94f5947b270b49ddf9da907/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJYW5CbkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--a3b53ba1a0f83efef18f6e75a8d4ce784384bee2/JEI-21-161_Fig1.jpg)
In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.
Read More...Post-Traumatic Stress Disorder (PTSD) biomarker identification using a deep learning model
In this study, a deep learning model is used to classify post-traumatic stress disorder patients through novel markers to assist in finding candidate biomarkers for the disorder.
Read More...Transfer Learning with Convolutional Neural Network-Based Models for Skin Cancer Classification
Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.
Read More...Modeling the heart’s reaction to narrow blood vessels
Cardiovascular diseases are the largest cause of death globally, making it a critical area of focus. The circulatory system is required to make the heart function. One component of this system is blood vessels, which is the focus of our study. Our work aims to demonstrate the numeric relationship between a blood vessel's diameter and the number of pumps needed to transport blood.
Read More...Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures
In this study, the authors test whether providing a larger dataset of glass transition temperatures (Tg) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and Tg is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's Tg, providing valuable improvements to this useful platform.
Read More...A novel in vitro blood-brain barrier model using 3D bioprinter: A pilot study
The authors looked at how a 3D bioprinter could be used to model the blood brain barrier.
Read More...Transfer Learning for Small and Different Datasets: Fine-Tuning A Pre-Trained Model Affects Performance
In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.
Read More...Developing a neural network to model the mechanical properties of 13-8 PH stainless steel alloy
We systematically evaluated the effects of raw material composition, heat treatment, and mechanical properties on 13-8PH stainless steel alloy. The results of the neural network models were in agreement with experimental results and aided in the evaluation of the effects of aging temperature on double shear strength. The data suggests that this model can be used to determine the appropriate 13-8PH alloy aging temperature needed to achieve the desired mechanical properties, eliminating the need for many costly trials and errors through re-heat treatments.
Read More...Homology modeling of clinically-relevant rilpivirine-resistant HIV-RT variants identifies novel rilpivirine analogs with retained binding affinity against NNRTI-resistant HIV mutations
Human immunodeficiency virus (HIV), which affects tens of millions of individuals worldwide, can lead to acquired immunodeficiency syndrome (AIDS). While there is currently no cure for HIV, the development of small molecule antiretroviral agents has greatly improved the prognosis of infected individuals, especially in developed countries. Here, the authors employ homology modeling and molecular docking towards the identification of novel rilpivirine analogs that retain high binding affinity to clinically relevant rilpivirine-resistant mutations of the HIV reverse transcriptase enzyme.
Read More...Jet optimization using a hybrid multivariate regression model and statistical methods in dimuon collisions
Collisions of heavy ions, such as muons result in jets and noise. In high-energy particle physics, researchers use jets as crucial event-shaped observable objects to determine the properties of a collision. However, many ionic collisions result in large amounts of energy lost as noise, thus reducing the efficiency of collisions with heavy ions. The purpose of our study is to analyze the relationships between properties of muons in a dimuon collision to optimize conditions of dimuon collisions and minimize the noise lost. We used principles of Newtonian mechanics at the particle level, allowing us to further analyze different models. We used simple Python algorithms as well as linear regression models with tools such as sci-kit Learn, NumPy, and Pandas to help analyze our results. We hypothesized that since the invariant mass, the energy, and the resultant momentum vector are correlated with noise, if we constrain these inputs optimally, there will be scenarios in which the noise of the heavy-ion collision is minimized.
Read More...A Statistical Comparison of the Simultaneous Attack/ Persistent Pursuit Theory Against Current Methods in Counterterrorism Using a Stochastic Model
Though current strategies in counterterrorism are somewhat effective, the Simultaneous Attack/Persistent Pursuit (SAPP) Theory may be superior alternative to current methods. The authors simulated five attack strategies (1 SAPP and 4 non-SAPP), and concluded that the SAPP model was significantly more effective in reducing the final number of terrorist attacks. This demonstrates the comparative advantage of utilizing the SAPP model, which may prove to be critical in future efforts in counterterrorism.
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