![Building a video classifier to improve the accuracy of depth-aware frame interpolation](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBbHdJIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--10601cbe0c4bb758b99da42e856f509b86ddccb1/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/Figure7.png)
In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!
Read More...Building a video classifier to improve the accuracy of depth-aware frame interpolation
In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!
Read More...Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes
In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.
Read More...Breast cancer mammographic screening by different guidelines among women of different races/ethnicities
Mammographic screening is a common diagnostic tool for breast cancer among average-risk women. The authors hypothesized that adherence rates for mammographic screening may be lower among minorities (non-Hispanic black (NHB) and Hispanic/Latino) than among non-Hispanic whites (NHW) regardless of the guideline applied. The findings support other studies’ results that different racial/ethnic and socio-demographic factors can affect screening adherence. Therefore, healthcare providers should promote breast cancer screening especially among NHW/Hispanic women and women lacking insurance coverage.
Read More...Using CRISPR technology to inhibit the replication of human cytomegalovirus by deletion of a gene promoter
Human cytomegalovirus (HCMV) causes serious infections in immunocompromised patients and therapies to inhibit latent HCMV are not developed. Using CRISPR/Cas9, the authors were able to delete an important promoter region in HCMV.
Read More...Heat impact to food’s shelf life - An example of milk
Food spoilage happens when food is not kept in a good storage condition. Qualitatively estimating the shortened shelf life of food could reduce food waste. In this study, we tested the impact of heat on milk shelf life. Our results showed that an exposure at room temperature (25°C) for 3.2 hours will decrease the shelf life of milk by one day.
Read More...A novel bioreactor system to purify contaminated runoff water
In this study, the authors engineer a cost-effective and bio-friendly water purification system using limestone, denitrifying bacteria, and sulfate-reducing bacteria. They evaluated its efficacy with samples from Eastern PA industrial sites.
Read More...Application of arbuscular mycorrhizal fungi to inhibit nitrogen uptake of weeds within crop fields
In this study, the ability of arbuscular mycorrhizal fungi to limit the growth of an agricultural weed Cirsium arvense is tested. This has important implications for developing natural herbicides.
Read More...Observing how the distance from the mouth of a Bahamian mangrove affects biodiversity
In this study, the biodiversity of marine animals was studied at different locations along a mangrove, which is a salt-tolerant shrub with elaborate root structures that is found on tropical coastlines.
Read More...A Retrospective Study of Research Data on End Stage Renal Disease
End Stage Renal Disease (ESRD) is a growing health concern in the United States. The authors of this study present a study of ESRD incidence over a 32-year period, providing an in-depth look at the contributions of age, race, gender, and underlying medical factors to this disease.
Read More...Using explainable artificial intelligence to identify patient-specific breast cancer subtypes
Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.
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