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Two-dimensional (2D) transition metal silicon nitrides (MSi2N4: M denotes Mo or W) are promising channel materials for nanoelectronics owing to their attractive structural and electronic properties. The integration of high-κ dielectrics into 2D semiconductors MSi2N4 is one of the vital steps for achieving high-performance electronic devices, which however remains challenging. In this study, we propose silicon nitride (Si3N4) as the native high-κ dielectric for 2D MSi2N4 and reveal their interfacial properties. Using first-principles calculations, we show that a high-performance interface can be formed, as supported by weak interface interaction, insignificant charge density redistribution, and nearly intact electronic properties of monolayer MSi2N4 with the integration of Si3N4. We further demonstrate that interfacial hydrogenation can effectively passivate the dangling bonds at the Si3N4 surface, leading to improved interface performance. Importantly, this interfacial hydrogenation does not bring a detrimental effect to both the high-κ dielectric and the 2D semiconductors, as it is thermodynamically and kinetically stable at the Si3N4 surface. These results provide a deep understanding for the integration of high-κ dielectrics on 2D semiconductors MSi2N4, design a viable interfacial engineering strategy to improve the interface performance, and therefore could be useful for the development of 2D MSi2N4 based high-performance electronics. [ABSTRACT FROM AUTHOR]

Silk fibroin (SF) has been widely used in biomedical applications for the hydrophilicity modification of high molecular polymer materials. However, the challenge remains to immobilize SF with high structure stability and strong adhesion strength between SF and the substrate. Here, we propose an effective two-step process for modifying polyethylene terephthalate (PET) with SF: dipping PET film in SF solution and subsequently carrying out plasma-assisted deposition in SF aerosol. The structure and property analysis revealed that the SF-modified PET (PET-SF) prepared using the two-step method exhibited superior structural stability and stronger adhesion strength compared to the dip-coating method and the plasma-assisted deposition method. In addition, PET-SF prepared using the two-step method resulted in a higher concentration of SF and an increased content of active groups on its surface, enhancing its hydrophilicity compared to the other two methods. Additionally, the influence of dipping time and deposition time in the two-step method was investigated. The results demonstrated that the dipping time for 6 h and the deposition time for 3 min resulted in maximum SF grafting amount with a highly stable structure. Furthermore, the PET-SF exhibited satisfactory hydrophilicity when the deposition time was more than 3 min and showed the most hydrophilicity surface at 8 min. [ABSTRACT FROM AUTHOR]

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in southern China over recent years. [ABSTRACT FROM AUTHOR]

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis–dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption. [ABSTRACT FROM AUTHOR]

Metal coatings with micron thickness, high conductivity, and a unique structure that can provide exceptional electromagnetic interference (EMI) shielding performance to insulation substrates are highly desirable, especially if the production method is inexpensive, pollution‐free, and efficient. In this study, the Ar/H2 mixed atmospheric pressure plasma jet (APPJ) was utilized to reduce AgNO3 to produce PC + ABS substrates coated with silver nanoparticles (AgNPs). It was found that the low roughness AgNPs coating has a multilayer network structure, which could exhibit high conductivity and excellent EMI shielding effectiveness (SE). In conclusion, this method is an excellent choice for producing metal EMI shielding coating. [ABSTRACT FROM AUTHOR]

Despite the considerable potential and significant promise of aluminum scandium nitride (AlScN) ferroelectric materials for neuromorphic computing applications, challenges related to device engineering, along with the considerable structural disorder in thin films grown on various substrates using different vapor synthesis methods, make it difficult to systematically study the structure–property relationship. In this work, we approach such issues from the crystal growth side by successfully growing high-quality single crystal AlScN nanowires through ultra-high vacuum reactive sputtering under high substrate bias and low atomic flux conditions, which leads to simultaneous growth and etching. Characterization of nanowire arrays using X-ray diffraction and transmission electron microscopy shows that the wires are epitaxial single crystals with significantly reduced mosaic spread and predominantly single ferroelectric domains. Moreover, ferroelectric and piezoelectric properties were evaluated using Piezoresponse Force Microscopy. The single crystal AlScN nanowires show an out-of-plane piezoelectric constant d33 that is greater than 20 pm V−1, which is higher than that of pure AlN by a factor of ∼4. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea syndrome (OSAS), characterized by chronic intermittent hypoxia (CIH), is an independent risk factor for aggravating non-alcoholic steatohepatitis (NASH). The prevailing mouse model employed in CIH research is inadequate for the comprehensive exploration of the impact of CIH on NASH development due to reduced food intake observed in CIH-exposed mice, which deviates from human responses. To address this issue, a pair-feeding investigation with CIH-exposed and normoxia-exposed mice is conducted. It is revealed that CIH exposure aggravates DNA damage, leading to hepatic fibrosis and inflammation. The analysis of genome-wide association study (GWAS) data also discloses the association between Eepd1, a DNA repair enzyme, and OSAS. Furthermore, it is revealed that CIH triggered selective autophagy, leading to the autophagic degradation of Eepd1, thereby exacerbating DNA damage in hepatocytes. Notably, Eepd1 liver-specific knockout mice exhibit aggravated hepatic DNA damage and further progression of NASH. To identify a therapeutic approach for CIH-induced NASH, a drug screening is conducted and it is found that Retigabine dihydrochloride suppresses CIH-mediated Eepd1 degradation, leading to alleviated DNA damage in hepatocytes. These findings imply that targeting CIH-mediated Eepd1 degradation can be an adjunctive approach in the treatment of NASH exacerbated by OSAS., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

In this study, we employed a method integrating optical coherence tomography (OCT) with the U-Net and Visual Geometry Group (VGG)-Net frameworks within a convolutional neural network for quantitative characterization of the three dimensional whole blood during the dynamic coagulation process. VGG-Net architecture for the identification of blood droplets across three distinct coagulation stages including drop, gelation, and coagulation achieves an accuracy of up to 99%. In addition, the U-Net architecture demonstrated proficiency in effectively segmenting uncoagulated and coagulated portions of whole blood, as well as the background. Notably, parameters such as volume of uncoagulated and coagulated segments of the whole blood were successfully employed for the precise quantification of the coagulation process, which indicates well for the potential of future clinical diagnostics and analyses., (© 2024 Wiley‐VCH GmbH.)

Lead-free halide perovskites possess excellent photoelectric properties, making them widely used in the photoelectric fields. Herein, lead-free double perovskite crystals (PCs) doped with manganese (Cs 2 NaInCl 6 :Mn 2+ ) are successfully prepared by the more energy-efficient crystallization method. The crystals emit bright orange-red light under the ultraviolet (UV) lamp, showing unique optical properties. They have the highest photoluminescence quantum yield of 42.91%. The white light-emitting diodes (LEDs) are fabricated using these perovskite crystals, which show a color rendering index of 92 and external quantum efficiency (EQE) as high as 16.3%. Furtherly, perovskite-modified fiber paper made of aramid chopped fibers (ACFs) and polyphenylene sulfide (PPS) exhibited fluorescent properties under different conditions. This paper combines fiber composite technology with PPS fiber filter bags, which are widely used in environmental protection, for the first time and demonstrates functional fiber filter bags with fluorescent characteristics. This filter bag provides an idea for the automatic detection of industrial filtration. Meanwhile, after being exposed to industrial waste gas for 60 h, the filter bag can maintain superior fluorescence performance. In this study, lead-free double perovskites are synthesized using an efficient method for preparing high-performance LEDs and high-stability fluorescent fibers. Concurrently, the application of perovskites in environmental protection is expanded., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks., Competing Interests: Competing Interests JEG is a co-founder of Biosens8, Inc.

Purpose: This study aimed to explore the pathways between family functioning and mental health in people with neuropathic pain, as well as to discuss the mediating role of pain intensity, self-perceived burden, pain catastrophizing, and functional status., Design: Cross-sectional design reported using the STROBE guidelines., Methods: A total of 277 people with neuropathic pain completed face-to-face questionnaires to evaluate family functioning, pain intensity, pain catastrophizing, self-perceived burden, functional status, and mental health. Structural equation modeling (SEM) was constructed to analyze the pathways between these variables., Results: The positive total effect between family functioning and mental health was significant and partially mediated by self-perceived burden, pain catastrophizing, and functional status. In addition, better family functioning was associated with higher pain intensity, which worsens self-perceived burden, pain catastrophizing, and functional status, masking 23.68% of the positive effects between family functioning and mental health., Conclusions: Better family functioning was associated with better mental health, as explained by reduced self-perceived burden, reduced pain catastrophizing, and improved functional status. However, this benefit may be partially masked by the relationship that better family functioning explains higher pain intensity., Clinical Implications: Nurses' comprehensive assessment and management of neuropathic pain from both the family and individual levels, such as family functioning, pain intensity, self-perceived burden, pain catastrophizing, and functional status, may be beneficial in promoting patients' mental health. In addition, it is necessary to identify why good family functioning is associated with higher pain intensity and intervene in this regard., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objective: Recently, large language models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering (QA) situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this article, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA., Materials and Methods: We adapt a general-purpose LLM toward the medical domain, involving data-centric knowledge injection through the integration of 4.8M biomedical academic papers and 30K medical textbooks, as well as comprehensive domain-specific instruction fine-tuning, encompassing medical QA, rationale for reasoning, and conversational dialogues with 202M tokens., Results: While evaluating various public medical QA benchmarks and manual rating, our lightweight PMC-LLaMA, which consists of only 13B parameters, exhibits superior performance, even surpassing ChatGPT. All models, codes, and datasets for instruction tuning will be released to the research community., Discussion: Our contributions are 3-fold: (1) we build up an open-source LLM toward the medical domain. We believe the proposed PMC-LLaMA model can promote further development of foundation models in medicine, serving as a medical trainable basic generative language backbone; (2) we conduct thorough ablation studies to demonstrate the effectiveness of each proposed component, demonstrating how different training data and model scales affect medical LLMs; (3) we contribute a large-scale, comprehensive dataset for instruction tuning., Conclusion: In this article, we systematically investigate the process of building up an open-source medical-specific LLM, PMC-LLaMA., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

This cross-sectional study aimed to investigate the relationship between family functioning, pain intensity, self-perceived burden, and pain catastrophizing. Moreover, we also wanted to explore the multiple mediating roles of pain intensity and self-perceived burden. From October 2022 to March 2023, 252 Chinese people with neuropathic pain completed face-to-face questionnaires to assess family functioning, pain intensity, self-perceived burden, and pain catastrophizing. Data analysis was done using descriptive statistics and a structural equation model. The results showed better family functioning was significantly associated with more intense pain, less self-perceived burden, and less pain catastrophizing. Mediation analysis showed that family functioning could indirectly affect pain catastrophizing through pain intensity and self-perceived burden in addition to a direct effect on pain catastrophizing. Moreover, the mediating variable of pain intensity played a masking role. These findings suggest that good family functioning can effectively reduce the self-perceived burden and pain catastrophizing in patients with neuropathic pain. However, family functioning cannot show its maximum effectiveness, and it may be necessary to construct a model of family functioning suitable for patients with neuropathic pain in the future., (© 2024 John Wiley & Sons Australia, Ltd.)

Background: The shift in national policies for epidemic prevention and control in the post-pandemic era is undoubtedly a challenge for health care professionals. Nurses, as an important part of the health care professional population, have an even greater impact on their mental health and occupational safety. This may expose nurses to post-traumatic stress disorder (PTSD) and presenteeism, and ultimately lead to their turnover., Objective: This study aimed to investigate the relationship between turnover intention and post-traumatic stress disorder among Chinese nurses during post-pandemic era, and the mediating role of social support and presenteeism., Methods: In this study, a multicentre cross-sectional survey was conducted in April 2023 among nursing staff in several tertiary general hospitals in northern China, with online data collection using the Turnover intention Scale (PTSD), the Impact of Events Scale (IES), the Social Support Scale (SSS), and the Stanford presenteeism Scale (STAS) and the relationship between variables was analyzed using hierarchical multivariate regression, and Structural Equation Modeling (SEM) was used to analyze the relationship between post-traumatic stress disorder and the Turnover intention from the pathway between., Results: Data were collected from 2,513 nurses who met the inclusion criteria, in which general information such as age, department, specific department, Professional title, history of alcohol consumption, form of employment, Years of working, and Average working hours per day were statistically significant with the difference in the turnover intention. The results of the study showed a 32% high turnover intention among nurses in the post-pandemic era, which was lower than the turnover intention during the pandemic. The results of hierarchical multiple regression analysis showed that post-traumatic stress disorder, social support, and presenteeism were significant predictors of turnover intention. The total effect of post-traumatic stress disorder on turnover intention to work was 0.472 [bias modified 95% confidence interval (0.415-0.483), p < 0.001]. Social support and attendance played a partially intermediate role in post-traumatic stress disorder and propensity to leave (an indirect effect of 26% of the total effect)., Conclusion: Turnover intention and post-traumatic stress disorder levels are high and social support plays an important role in the tendency to leave the job and post-traumatic stress disorder, healthcare institution can be achieved by strengthening social support for nurses in the post-epidemic era and preventing the occurrence of presenteeism., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Yang, Zhang, Liu, Liu, Fang, Liu and Wu.)