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Analysis of clinical features and SS-OCT findings in patients with focal choroidal excavation
Criegee Intermediates Significantly Reduce Atmospheric (CF<sub>3</sub>)<sub>2</sub>CFCN
Cell type-specific multi-omics analysis of cocaine use disorder in the human caudate nucleus
Abstract Structural and functional alterations in the brain’s reward circuitry are present in cocaine use disorder (CocUD), but their molecular underpinnings remain unclear. To investigate these mechanisms, we performed single-nuclei multiome profiling on postmortem caudate nucleus tissue from six individuals with CocUD and eight controls. We profiled 30,030 nuclei, identifying 13 cell types including D1- and D2-medium spiny neurons (MSNs) and glial cells. We observed 1485 differentially regulated genes and 10,342 differentially accessible peaks, with alterations in MSNs and astrocytes related to neurotransmitter activity and synapse organization. Gene regulatory network analysis identified transcription factors including ZEB1 as exhibiting distinct CocUD-specific subclusters, activating downstream expression of ion- and calcium-channels in MSNs. Further, PDE10A emerged as a potential drug target, showing conserved effects in a rat model. This study highlights cell type-specific molecular alterations in CocUD and provides targets for further investigation, demonstrating the value of multi-omics approaches in addiction research.
The potential of evaluating shape drawing using machine learning for predicting high autistic traits
Background Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product characteristics can be objectively evaluated. This study aimed to assess the potential of evaluating shape drawing using machine learning to predict high autistic traits. Method Seventy boys (5.03 ± 0.16) and 63 girls (5.06 ± 0.18) from the general population participated in the study. Participants were asked to draw shapes in the following order: equilateral triangle, inverted equilateral triangle, square, and the sun. A model for classifying participants as likely to have high autistic traits was developed using a support vector machine algorithm with a linear kernel utilizing 16 variables. A 16-inch liquid crystal display pen tablet was used to acquire data on hand-finger fine motor activity while the participants drew each shape. The X and Y coordinates of the pen tip, pen pressure, pen orientation, pen tilt, and eye movements were recorded to determine whether the participants had any problems with this skill. Eye movements were assessed using a webcam. These data and eye movements were used to identify the variables for the support vector machine model. Data and Results For each shape, a model support vector machine was created to classify the high and low autistic trait groups, with accuracy, sensitivity, and specificity all above 85%. The specificity values across all models were 100%. In the inverted equilateral triangle model, specificity, accuracy, and sensitivity values were 100%. Conclusions These results demonstrate the potential of assessing shape characteristics using machine learning to predict high levels of autistic traits. Future studies with a wider variety of shapes are warranted to establish further the potential efficacy of drawing skills for screening for autism spectrum conditions.
Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm
Redox Studies of the Scandium Metallocene (C<sub>5</sub>H<sub>2</sub><sup>t</sup>Bu<sub>3</sub>)<sub>2</sub>Sc<sup>II</sup> Lead to a Terminal Side-On (N═N)<sup>2–</sup> Complex: [(C<sub>5</sub>H<sub>2</sub><sup>t</sup>Bu<sub>3</sub>)<sub>2</sub>Sc<sup>III</sup>(η<sup>2</sup>-N<sub>2</sub>)]<sup>−</sup>
Long-term physical exercise facilitates putative glymphatic and meningeal lymphatic vessel flow in humans
Retraction: Deep learning in public health: Comparative predictive models for COVID-19 case forecasting
Safety of intensive care hyperbaric oxygen therapy sessions at a tertiary academic hospital
In situ n-doped nanocrystalline electron-injection-layer for general-lighting quantum-dot LEDs
Abstract Quantum-dot optoelectronics, pivotal for lighting, lasing and photovoltaics, rely on nanocrystalline oxide electron-injection layer. Here, we discover that the prevalent surface magnesium-modified zinc oxide electron-injection layer possesses poor n-type attributes, leading to the suboptimal and encapsulation-resin-sensitive performance of quantum-dot light-emitting diodes. A heavily n-doped nanocrystalline electron-injection layer—exhibiting ohmic transport with 1000 times higher electron conductivity and improved hole blockage—is developed via a simple reductive treatment. The resulting sub-bandgap-driven quantum-dot light-emitting diodes exhibit optimal efficiency and extraordinarily-high brightness, surpassing current benchmarks by at least 2.6-fold, and reaching levels suitable for quantum-dot laser diodes with only modest bias. This breakthrough further empowers white-lighting quantum-dot light-emitting diodes to exceed the 2035 U.S. Department of Energy’s targets for general lighting, which currently accounts for ~15% of global electricity consumption. Our work opens a door for understanding and optimizing carrier transport in nanocrystalline semiconductors shared by various types of solution-processed optoelectronic devices.
CoHet4Rec: A recommendation for collaborative heterogeneous information networks
Recommender Systems (RS) aim to predict users’ latent interests in items by learning embeddings from user-item graphs. Graph Neural Networks (GNNs) have significantly advanced RS by enabling the embedding of graph-structured data. However, relying solely on user-item interactions has limitations, such as the cold-start problem. Social recommendation has gained attention for its potential to improve outcomes by incorporating social information among users. Yet, existing social-aware models need further exploration of interaction semantics and other collaborative relationships beyond social connections. This paper addresses these limitations by proposing CoHet4Rec, a recommendation model leveraging GNNs and a Collaborative Heterogeneous Information Network (CHIN) with latent collaborative heterogeneous relation factors. CoHet4Rec captures diverse connections between users and items through factorized representations, and has the flexibility to easily incorporate more knowledge beyond social networks to alleviate data sparsity and cold-start problem. Extensive experiments on three benchmark datasets demonstrate the superiority of CoHet4Rec over 15 state-of-the-art (SOTA) recommendation techniques. The highest average improvement is 31.88% for HR@5 and 38.39% for NDCG@5.
Response Surface Methodology using desirability functions for multiobjective optimization to minimize indoor overheating hours and maximize useful daylight illuminance
Interface Preconstruction Enables Robust Passivation of the Ah-Level Aqueous Li-ion Batteries
Impact of perioperative organ injury on morbidity and mortality in 28 million surgical patients
Abstract Perioperative organ injury contributes to morbidity and mortality of surgical patients. This cohort study included all elective and emergent surgeries in Germany over 4 years to address the impact of perioperative organ injuries on outcomes. We analyzed 28,350,953 cases. In-hospital mortality was 1.4% (n = 393,157), and 4.4% of cases (n = 1,245,898) experienced perioperative organ injury. Perioperative organ injury was associated with 9-fold higher odds of death and prolonged hospital stay by 11.2 days. Acute kidney injury had the highest incidence (2.0%) and was associated with 25.0% mortality. While delirium had the second highest incidence (1.5%), it was associated with the lowest mortality (10.8%). This was followed by acute myocardial infarction (incidence 0.6%, mortality 15.6%), stroke (incidence 0.6%, mortality 13.1%), pulmonary embolism (incidence 0.3%, mortality 20.0%), liver injury (incidence 0.1%, mortality 68.7%), and acute respiratory distress syndrome (incidence 0.1%, mortality 44.7%). These findings help prioritize interventions for preventing or treating individual types of perioperative organ injury.
Barriers and limitations to the development of a telemental health service for workers in Peru- A user-centered approach
Introduction Over the past decade, the surge in digital healthcare services has transformed traditional healthcare, requiring multidisciplinary engagement to adapt to the digital realm. The rise of telehealth services, particularly amid COVID-19, has been widely embraced globally, but its implementation in Peru faces unique challenges, including infrastructure issues and economic constraints. Therefore, this research aims to identify the barriers and limitations in developing a telemental health service for screening, evaluation, and timely referral of vulnerable occupational groups. Materials and methods A qualitative study was undertaken. We adopted a phenomenological approach, utilizing semi-structured interviews with vulnerable occupational groups and decision-makers. We conducted 23 interviews: 5 providers of telemental health services, 5 teachers users, 5 police officers users and 5 health professionals of telemental health services, and 3 decision-makers involved in telemental health regulation in Peru.; exploring experiences, barriers, and facilitators related to mental telemental health. The interviews were recorded and transcribed verbatim, furthermore, a thematic analysis was done to identify key themes. Results The research identified barriers and limitations to developing a telemental health services screening service based on the experiences of workers, some of them were related to user dissatisfaction, emphasizing the need for personalized solutions beyond technical aspects. Scheduling issues call for flexibility and improved communication. Healthcare professionals’ varied experiences highlight the necessity for targeted training, while successful telemental health services integration demands addressing resource limitations through a comprehensive approach. Conclusion The study advocates for a holistic, user-centred paradigm in telemental health services implementation, addressing both technological aspects and human and systemic elements. Multifaceted challenges inherent in telemental health, particularly in Peru, emphasize the need for strategic interventions by stakeholders. The study calls for a policy shift towards enhancing telemental health equity through system-level changes and eliminating structural barriers for marginalized populations.
Empirical research of urban land use eco-efficiency in the Pearl River Delta urban agglomeration
Synthesis and Superconductivity of Ternary A15-(Lu, Y)<sub>4</sub>H<sub>23</sub> at High Pressures
Unraveling overestimated exposure risks through hourly ozone retrievals from next-generation geostationary satellites
When all is unequal, the rich get dominant: Inequality leads to expectations of dominant leadership among those high in SES
People of higher SES have been found to behave more dominantly than people of lower SES. We tested the hypothesis that this difference is exacerbated under conditions of high economic inequality, when the income/wealth difference between those of low and high SES becomes greater. Across four studies (N = 2,739), using both experiments that manipulate perceived inequality (Studies 1a, 1b, and 3) and a correlational study that measures perceived inequality (Study 2), we find evidence that people expect others and themselves to become more dominant if they are of high as opposed to low SES, and this difference is most extreme when economic inequality is perceived to be high.