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Here, the authors sought to develop a new metric to evaluate the efficacy of baseball pitchers using machine learning models. They found that the frequency of balls, was the most predictive feature for their walks/hits allowed per inning (WHIP) metric. While their machine learning models did not identify a defining trait, such as high velocity, spin rate, or types of pitches, they found that consistently pitching within the strike zone resulted in significantly lower WHIPs.

Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.

Here, recognizing that alcohol manufacturers may target ethnic minorities and youths with specific forms of advertisements based on previous studies, the authors considered how alcohol storefronts differ depending on the community they are located in. Specifically, they looked at differences between Metro-Dtroit suburban communities of high- and low-incomes. They found that alcohol stores in the low-income areas had more and larger alcohol and malt liquor advertisements per store along with being within 1,000 feet of a school.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

In this paper, we measured the privacy budgets and utilities of different differentially private mechanisms combined with different machine learning models that forecast traffic congestion at future timestamps. We expected the ANNs combined with the Staircase mechanism to perform the best with every value in the privacy budget range, especially with the medium high values of the privacy budget. In this study, we used the Autoregressive Integrated Moving Average (ARIMA) and neural network models to forecast and then added differentially private Laplacian, Gaussian, and Staircase noise to our datasets. We tested two real traffic congestion datasets, experimented with the different models, and examined their utility for different privacy budgets. We found that a favorable combination for this application was neural networks with the Staircase mechanism. Our findings identify the optimal models when dealing with tricky time series forecasting and can be used in non-traffic applications like disease tracking and population growth.

This unique research study evaluated the potential use of the flatworm, brown planaria (Dugesia tigrine), as an alternative model for teratogenicity testing. In this study, we exposed amputated planaria to varying concentrations of a known teratogen, vitamin A (retinol), for approximately 2 weeks, and evaluated multiple parameters including the formation of blastema and eyes. The results from this study demonstrated that high concentrations of retinol caused defects in head and eye formation in regenerating planaria, with similarities to vitamin A related teratogenicity findings in mammals. Based on these results, regenerating brown planaria are a promising alternative model for teratogenicity testing, which can potentially be paradigm shifting as it can reduce cost, time, and pregnant animal use in research.

Treatments inhibiting Notch signaling pathways have been explored by researchers as a new approach for the treatment of glioblastoma tumors, which is a fast-growing and aggressive brain tumor. Recently, retinoic acid (RA) therapy, which inhibits Notch signaling, has shown a promising effect on inhibiting glioblastoma progression. RA, which is a metabolite of vitamin A, is very important in embryonic cellular development, which includes the regulation of multiple developmental processes, such as brain neurogenesis. However, high doses of RA treatment caused many side effects such as headaches, nausea, redness around the injection site, or allergic reactions. Therefore, we hypothesized that a combination treatment of RA and siRNA targeting NOTCH1 (siNOTCH1), the essential gene that activates Notch signaling, would effectively inhibit brain cancer cell proliferation. The aim of the study was to determine whether inhibiting NOTCH1 would inhibit the growth of brain cancer cells by cell viability assay. We found that the combination treatment of siNOTCH1 and RA in low concentration effectively decreased the NOTCH1 expression level compared to the individual treatments. However, the combination treatment condition significantly decreased the number of live brain cancer cells only at a low concentration of RA. We anticipate that this novel combination treatment can provide a solution to the side effects of chemotherapy.

Here, based on identification of iron deficiencies of a majority of people around the world, the authors sought to understand how the two main forms of dietary iron, heme and non-heme, affect the bacteria found in the human gut. by using a cell plate study, they found that bacterial growth increased with increasing concentration os either form of iron, up until the point where the high iron content resulted in cytotoxicity. They suggest this evidence points to the potential dangers of overconsumption of iron.

In this study, the authors investigated the time-dependent cytokine secretion ability of phyto-hemagglutinin (PHA)-activated T cells derived from human peripheral (PB) and cord blood (CB). They hypothesized that the anti-inflammatory cytokine, IL-10, and pro-inflammatory cytokine, TNFα, levels would be higher in PHA-activated T cells obtained from PB as compared to the levels obtained from CB and would decrease over time. Upon PHA-activation, the IL-10 levels were relatively high while the TNFα levels decreased, making these findings applicable in therapeutic treatments e.g., rheumatoid arthritis, psoriasis, and organ transplantation.

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.