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In this study, the authors determined whether tautomerization dynamics in protic and aprotic solvents displayed differences in reaction rates and in the proportion of the keto and enol tautomers present.

In this work the authors investigate new possible treatment methods for gastric and bladder cancers. They specifically targeted the transient receptor potential cation subfamily M member 7 (TRPM7), an ion channel that plays an important role in the survival of both of these cancers, and extracellular regulated kinases (ERKs),which contributes to the carcinogenesis of many cancers including gastric cancer. As a result, the authors consider the effects of Ginsenoside Rd, NS8593, curcumin, and icariin , known to inhibit TRPM7 and ERK. The authors found that these treatments decrease proliferation and induce apoptosis in studies of gastric and bladder cancer cells.

Digital compasses are essential in technology that we use in our everyday lives: phones, vehicles, and more. Li and Liu address the accuracy of these devices by presenting a new algorithm for accurately calibrating low-cost magnetometers.

This study utilizes machine learning models to predict missing and unclear signs from the Indus script, a writing system from an ancient civilization in the Indian subcontinent.

Kashyap Jha et al. look at the formulation of MAT20, a crude extract of the moringa, amla, and tulsi leaves, as a potential complementary and alternative medicine. Using HeLa cells, they find MAT20 up-regulates expression of inflammation and cell cytotoxicity markers. Their data is important for understanding the anti-cancer and anti-inflammatory properties of MAT20.

Buildings, which are responsible for the majority of electricity consumption in cities like Dubai, are often exclusively reliant on electrical lighting even in the presence of daylight to meet the illumination requirements of the building. This inefficient use of lighting creates potential to further optimize the energy efficiency of buildings by complementing natural light with electrical lighting. Prior research has mostly used ballasts (variable resistors) to regulate the brightness of bulbs. There has been limited research pertaining to the use of pulse width modulation (PWM) and the use of ‘triodes for alternating current’ (TRIACs). PWM and TRIACs rapidly stop and restart the flow of current to the bulb thus saving energy whilst maintaining a constant illumination level of a space. We conducted experiments to investigate the feasibility of using TRIACs and PWM in regulating the brightness of bulbs. We also established the relationship between power and brightness within the experimental setups. Our results indicate that lighting systems can be regulated through these alternate methods and that there is potential to save up to 16% of energy used without affecting the overall lighting of a given space. Since most energy used in buildings is still produced through fossil fuels, energy savings from lighting systems could contribute towards a lower carbon footprint. Our study provides an innovative solution to conserve light energy in buildings during daytime.

In their paper, Kreiman et al. examined what it takes for an artificial neural network to be able to perform well on a new task without forgetting its previous knowledge. By comparing methods that stop task forgetting, they found that longer training times and maintenance of the most important connections in a particular task while training on a new one helped the neural network maintain its performance on both tasks. The authors hope that this proof-of-principle research will someday contribute to artificial intelligence that better mimics natural human intelligence.

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.

The authors analyze political endorsement patterns and impacts from the 2018 and 2020 midterm elections and find that such endorsements may be predictable based on the ideological and demographic factors of the endorser.

Since cough syrup and mouthwash are commonly used items and often end up flushed down the drain or toilet, they can eventually find their way into into freshwater waterways which can be harmful to many marine organisms, such as planarians (aquatic flatworms). To investigate the effects of these substances on planarians, the authors considered different concentrations of Listerine mouthwash and Robitussin syrup along with their active ingredients. By using a behavioral assay, they identified that the active ingredients of cough syrup detrimentally affect planarian behavior. They suggest that these findings could be used to guide disposal methods to lessen detrimental effects on aquatic life.