Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents

Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field

Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia

Brain abscess caused by in an immunocompetent patient: a case report and literature review

Brain abscesses caused by Mycobacterium tuberculosis are rarely reported and are typically found in immunocompromised patients. The challenges in identifying the pathogen and the frequent delays in initiating appropriate therapy often result in a poor prognosis for patients. We herein report a rare case of a brain abscess caused by M. tuberculosis in a 63-year-old patient with a history of hypertension but no history of pulmonary tuberculosis. The patient was hospitalized twice because of worsening right upper limb weakness and speech difficulties. Both a pus culture and cerebrospinal fluid culture were negative. Ultimately, the diagnosis of M. tuberculosis infection was confirmed through next-generation sequencing and the tuberculosis-specific T-cell test (T-SPOT). The patient’s symptoms improved following anti-tuberculosis treatment. This case highlights the importance of considering M. tuberculosis as a potential cause of brain abscesses, even in immunocompetent individuals, and underscores the need for early diagnosis for effective treatment

TRIBOLOGICAL BEHAVIOR OF Al–Cr COATING OBTAINED BY DGPSM AND IIP COMPOSITE TECHNOLOGY

An Al–Cr composite alloyed layer composed of an Al enriched layer, a Cr enriched layer and a transition layer from the surface to the bulk along the cross-section was deposited on a 45# steel substrate by composite technology, where Cr was deposited using double glow plasma surface metallurgy (DGPSM), and Al was then implanted by ion implantation (IIP) to achieve higher micro-hardness and excellent abrasive resistance. The composite alloyed layer is approximately 5[Formula: see text][Formula: see text]m, and as metallurgical adherence to the substrate. The phases are Al8Cr5, Fe2AlCr, Cr[Formula: see text]C6, Cr (Al) and Fe (Cr, Al) solid solution. The wear resistance tests were performed under various rotational speed (i.e. 280, 560 and 840[Formula: see text]r/min) with silicon nitride balls as the counterface material at ambient temperature. The Al–Cr composite alloyed layer exhibits excellent wear resistance when the speed is 280[Formula: see text]r/min with a friction coefficient as low as 0.3, which is attributed to Al8Cr5 in the Al implanted layer that withstands abrasive wear. Better wear resistance (friction coefficient: 0.254) at 560[Formula: see text]r/min is resulted from the formation of a high micro-hardness zone, and an oxidation layer with lubrication capacity. In addition, the composite alloyed layer suffers severe oxidative wear and adhesive wear at 840[Formula: see text]r/min due to the increment of the frictional heating. When compared to the 45# steel substrate, the enhanced wear resistance of the Al–Cr composite alloyed layer demonstrates the viable method developed in this work. </jats:p