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This study used an improved CMS-seq method to profile 5hmC in ormalin-fixed and paraffin-embedded (FFPE) samples from HNC tumors and adjacent normal tissues, identifying three genes (PRKD2, HADHA, and AIPL1) with promising potential as biomarkers for Head and neck cancer (HNC) diagnosis.

The mountain chain of the Western Ghats on the Indian peninsula, a UNESCO World Heritage site, is home to about 200 frog species, 89 of which are endemic. Distinctive to each frog species, their vocalizations can be used for species recognition. Manually surveying frogs at night during the rain in elephant and big cat forests is difficult, so being able to autonomously record ambient soundscapes and identify species is essential. An effective machine learning (ML) species classifier requires substantial training data from this area. The goal of this study was to assess data augmentation techniques on a dataset of frog vocalizations from this region, which has a minimal number of audio recordings per species. Consequently, enhancing an ML model’s performance with limited data is necessary. We analyzed the effects of four data augmentation techniques (Time Shifting, Noise Injection, Spectral Augmentation, and Test-Time Augmentation) individually and their combined effect on the frog vocalization data and the public environmental sounds dataset (ESC-50). The effect of combined data augmentation techniques improved the model's relative accuracy as the size of the dataset decreased. The combination of all four techniques improved the ML model’s classification accuracy on the frog calls dataset by 94%. This study established a data augmentation approach to maximize the classification accuracy with sparse data of frog call recordings, thereby creating a possibility to build a real-world automated field frog species identifier system. Such a system can significantly help in the conservation of frog species in this vital biodiversity hotspot.

Black goji berries have attracted interest for their high levels of anthocyanin pigment, which believed to have health-boosting effects. Yu and Zhu research a method for measuring goji berry quality by detecting anthocyanin content under different conditions.

Investigating the green valley hypotheses of galaxy evolution

The authors looked at soil quality of former military sites where chemical disposal was known to have occurred. Along with testing for heavy metals, the authors also looked for the presence (and number) of earthworms present in topsoil samples as a marker of soil health.

The authors looked at the ability of Spirulina platensis to reduce contaminants in wastewater in order to develop a more accessible treatment option. They found that S platensis did reduce the concentration of pollutants present within simulated agricultural wastewater.

Here, the authors investigated the integration of large language models (LLMs) with drug target affinity predictors (DTAPs) to improve drug repurposing, demonstrating a significant increase in prediction accuracy, particularly with GPT-4, for psychotropic drugs and the sigma-1 receptor. This novel approach offers to potentially accelerate and reduce the cost of drug discovery by efficiently identifying new therapeutic uses for existing drugs.

Here, recognizing that brain aneurysms pose a severe threat, often misdiagnosed and leading to high mortality, particularly in younger individuals, the authors explored a novel computer-aided engineering approach. They used magnetic resonance angiography images and computational fluid dynamics, to improve aneurysm detection and risk assessment, aiming for more personalized treatment.

The luxury market has grown through digital marketing, with many brands benefiting from exclusivity, which limits product availability. While exclusivity boosts sales, it may negatively affect consumers' mental health. This study of 129 respondents explored the main motivation for purchasing luxury goods and connection to mental health.

Authors examine the effectiveness of Large Language Models (LLMs) like BERT, MathBERT, and OpenAI GPT-3.5 in assisting middle school students with math word problems, particularly following the decline in math performance post-COVID-19.