Identification of cancer cell-intrinsic biomarkers associated with tumor progression and characterization of SFTA3 as a tumor suppressor in lung adenocarcinomas.

Background: Recent advancements in contemporary therapeutic approaches have increased the survival rates of lung cancer patients; however, the long-term benefits remain constrained, underscoring the pressing need for novel biomarkers. Surfactant-associated 3 (SFTA3), a long non-coding RNA predominantly expressed in normal lung epithelial cells, plays a crucial role in lung development. Nevertheless, its function in lung adenocarcinoma (LUAD) remains inadequately understood.

Characterization of perceptual interactions among aroma compounds found in Rose damascena and Angelica dahurica root essential oil with threshold, S-curve, σ-τ plot and molecular docking.

The study investigated the perceptual interaction between two types of Rose damascena essential oil and two types of Angelica dahurica root essential oil. Using gas chromatography-olfactometer (GC-O) and gas chromatography-mass spectrometer (GC-MS), 24 and 25 aromatic compounds in Rose damascena essential oil and Angelica dahurica root essential oil were identified and quantified, respectively. Based on flavor dilution (FD) values and odor activity values (OAVs), 10 important aroma compounds in Rose damascena essential oil and 6 in Angelica dahurica root essential oil were identified.

An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.

Background: Accurate classification of patient complaints is crucial for enhancing patient satisfaction management in health care settings. Traditional manual methods for categorizing complaints often lack efficiency and precision. Thus, there is a growing demand for advanced and automated approaches to streamline the classification process.

Typicality analysis on statistical shape model-based average head and its head-related transfer functions.

In acoustics, an artificial head generally comprises two pinnae and occasionally a torso, which are useful for recording binaural signals and acquiring head-related transfer functions (HRTFs). Currently, most artificial heads are designed based on the anthropometric parameters of specific populations. However, anthropometric parameters do not accurately express head surface shapes, and thus, typical HRTFs are difficult to generate.

Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals.

Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation. However, understanding the underlying chemical mechanisms still faces challenges due to the limited interpretability of the ML models. In this study, interpretable ML models were established to predict the second-order rate constants between hydroxyl radicals (OH) and organics ().