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In this paper, the p-GaN gate AlGaN/GaN high electron mobility transistors (HEMTs) with varying combinations of gate metal work function and gate geometry are fabricated and investigate the influence of gate leakage current (IGS) on the threshold voltage (VTH) and subthreshold swing (SS). The unique dependence of VTH and SS on gate geometry for different metals is observed, which is different from traditional field-effect transistors. A novel hybrid physics model, consisting of the traditional capacitance divider model and hole injection model, is proposed to explain this phenomenon, and the results exhibit an excellent agreement with the experimental data. The holes traverse the gate/p-GaN Schottky barrier by thermal emission or tunneling and inject into the p-GaN layer, generating the IGS. Expanding upon the traditional capacitance divider model, a portion of the injected holes accumulate at the p-GaN/AlGaN interface and induce the corresponding electrons at the AlGaN/GaN heterojunction, which promotes channel conduction. Hence, the transfer curves display the correlation between IGS and VTH as well as SS. The results show that high IGS can alleviate the instability of VTH caused by the lithographic overlay error, and simultaneously optimize SS. This work offers a novel perspective for examining the turn-on mechanism of p-GaN HEMTs, thereby contributing to device design. [ABSTRACT FROM AUTHOR]

The singular value decomposition order determination method used in nonlinear subspace identification may encounter challenges due to the noise in signals, resulting in the omission of modes or the occurrence of spurious mode. Additionally, the lack of an iterative process in the nonlinear subspace identification, which primarily relies on matrix operations, will lead to suboptimal solutions. To address these challenges, an improved framework for nonlinear subspaces identification is proposed in this paper. False modes are eliminated through data preprocessing and modal stability criteria, followed by the clustering of stable modes using the Density-Based Spatial Clustering of Applications with Noise algorithm for automatic order determination; Simultaneously, an iterative optimization approach based on response prediction error minimization is introduced to enhance the accuracy of the state-space model estimation results. The effectiveness of proposed method is validated through two simulation cases and one experimental verification. The results show that the clustering algorithm effectively distinguishes real modes from false ones and achieves automatic system order determination across various SNR conditions. The iterative optimization process notably enhances state-space model estimation accuracy. Compared to original nonlinear subspace identification, proposed method significantly improves identification accuracy. [ABSTRACT FROM AUTHOR]

It is a common practice that business and economy compartments share the seating capacity of a short or medium-haul European flight, with the middle seats in the business compartment kept vacant to enhance comfort and privacy for business passengers. The two compartments are separated by a movable curtain whose position determines the capacity allocated to each compartment. By postponing the decision on curtain position until departure, airlines gain extra flexibility in their capacity allocation. We formulate an airline's problem of dynamically determining the curtain position and the subset of fare products to offer in each compartment to maximize the combined revenue from business and economy passengers. We show that the airline should offer a business (resp., economy) fare product if and only if its fare exceeds a business (resp., economy) threshold or bid price. We then establish monotonicity properties which are useful in expediting the computation of the optimal policy. An extended model where the airline has the option to offer a free upgrade (from economy to business class) to a new arriving economy passenger is presented, and a necessary condition on when to offer upgrades is provided. Using a large-scale and real-life case study based on a proprietary dataset, we demonstrate significant revenue improvements from postponing the decision on curtain position, compared to the current practice of fixing it at the beginning of the booking horizon. Furthermore, we provide guidance for the implementation of our policy, taking into account passengers' seat selection and substitution towards lower fare classes. We also discuss the implications of our work in practice. [ABSTRACT FROM AUTHOR]

Introduction: When switching from premixed insulin to insulin degludec/aspart (IDegAsp), IDegAsp usually starts at the same dose as the premixed insulin according to limited clinical experience or at a dose according to clinician discretion. The dose of insulin degludec used in the real world after switching has been poorly investigated. Methods: A retrospective analysis was conducted on patients with type 2 diabetes who switched from premixed insulin to IDegAsp from October 2016 to December 2023. Repeated measures analysis of variance was used to compare changes in insulin dose, glycated hemoglobin (HbA1c), fasting blood glucose (FBG), and postprandial blood glucose (PBG) before and after switching. Results: Sixty-six patients with prior low-ratio premixed insulin and 22 with prior mid-ratio premixed insulin were included. Among the low-ratio insulin users, the total daily dose of insulin degludec (IDeg) decreased by 21.43% and 19.05% at 3 and 6 months, respectively, after switching, compared with prior basal insulin dose (both p < 0.001). Conversely, among mid-ratio insulin users, the IDeg daily dose increased by 10.71% and 32.14% at 3 and 6 months, respectively, after switching, compared with prior basal insulin dose (both p < 0.001). In all patients, HbA1c levels decreased by 0.70%, FBG decreased by 1.00 mmol/l, and PBG decreased by 1.61 mmol/l after 6 months of switching (all p < 0.05); the total daily insulin dose and injection frequency significantly decreased after switching (both p < 0.05); age and disease duration did not affect IDegAsp effects on HbA1c reduction. Conclusions: In the setting of transition to IDegAsp from premixed insulin, the dose of basal insulin in the premixed formulation can be a valuable reference for adjusting insulin degludec dose. IDegAsp is superior to premixed insulin in blood glucose control with reduced total daily dose and injection frequency. IDegAsp could be the best choice for the management of diabetes in elderly patients. [ABSTRACT FROM AUTHOR]

Oocyte meiotic maturation failure and chromosome abnormality is one of the main causes of infertility, abortion, and diseases. The mono-orientation of sister chromatids during the first meiosis is important for ensuring accurate chromosome segregation in oocytes. MEIKIN is a germ cell-specific protein that can regulate the mono-orientation of sister chromatids and the protection of the centromeric cohesin complex during meiosis I. Here we found that MEIKIN is a maternal protein that was highly expressed in mouse oocytes before the metaphase I (MI) stage, but became degraded by the MII stage and dramatically reduced after fertilization. Strikingly, MEIKIN underwent phosphorylation modification after germinal vesicle breakdown (GVBD), indicating its possible function in subsequent cellular event regulation. We further showed that MEIKIN phosphorylation was mediated by PLK1 at its carboxyl terminal region and its C-terminus was its key functional domain. To clarify the biological significance of meikin degradation during later stages of oocyte maturation, exogenous expression of MEIKIN was employed, which showed that suppression of MEIKIN degradation resulted in chromosome misalignment, cyclin B1 and Securin degradation failure, and MI arrest through a spindle assembly checkpoint (SAC)-independent mechanism. Exogenous expression of MEIKIN also inhibited metaphase II (MII) exit and early embryo development. These results indicate that proper MEIKIN expression level and its C-terminal phosphorylation by PLK1 are critical for regulating the metaphase–anaphase transition in meiotic oocyte. The findings of this study are important for understanding the regulation of chromosome segregation and the prevention meiotic abnormality. [ABSTRACT FROM AUTHOR]

The Gulong Sag in the northern Songliao Basin, China, possesses abundant shale oil resources and represents a highly prospective area for shale oil exploration. However, the Qingshankou formation shale oil reservoir within this region exhibits characteristics such as thin longitudinal thickness, pronounced horizontal heterogeneity, limited frequency range, and significant anisotropy that pose difficulties in accurately predicting the "sweet spot" of shale oil within the target interval. The azimuthal anisotropy characteristics of the target layer in the Qingshankou formation are analyzed in this manuscript, utilizing wide-azimuth and small bin seismic data from the Y3 research area. Considering the limitations of existing methods for fitting elliptical velocities in azimuthal anisotropy correction, the influence of azimuthal anisotropy time difference on the non-in-phase superposition of seismic in-phase axis is eliminated by employing a non-rigid dynamic matching method, thereby enhancing the resolution and imaging accuracy of seismic data. The azimuth anisotropy correction effectively broadens the frequency range of the stack profile by 7 Hz, thereby enhancing the reliability of data for shale oil reservoir prediction in the study area. [ABSTRACT FROM AUTHOR]

Dental restorative resin composites are widely used to repair tooth decay owing to attractive esthetics, adequate mechanical properties and minimally invasive tooth structure preparations. Nevertheless, dental restorative resin composites still face challenges because of their relatively high failure rate and short lifespan caused by secondary caries and bulk fracture. Thus, attempts have been carried out to explore a new generation of dental restorative resin composites with antibacterial, remineralizing, and self-healing capabilities to inhibit bacteria and lengthen the lifetime of the restorations. Such novel restorative composites can inhibit bacterial activity, reduce acid production, promote mineral regeneration and present a renewable advantage to achieve a higher performance, which are inspiring and provide support for further basic and clinical research. In this review, antibacterial dental restorative resin composites are first introduced, followed by remineralizing, self-healing, and multifunctional dental resin composites with two or more of the functions mentioned above. Meanwhile, we explain the mechanism of the corresponding dental restorative resin composites and describe their characteristics. Finally, we conclude and put forward prospects. This review will attract both researchers and clinicians in this field and help to provide innovative ideas to design new restorative resin composites for biomedical applications. [ABSTRACT FROM AUTHOR]

Autophagy plays an important role in determining stem‐cell differentiation. During the osteogenic differentiation of mesenchymal stem cells (MSCs), autophagosome formation is upregulated but the reason is unknown. A long‐standing quest in the autophagy field is to find the membrane origin of autophagosomes. In this study, cytoplasmic coat protein complex II (COPII) vesicles, endoplasmic reticulum‐derived vesicles responsible for the transport of storage proteins to the Golgi, are demonstrated to be a critical source of osteoblastic autophagosomal membrane. A significant correlation between the number of COPII vesicle and the autophagy level is identified in the rat bone tissues. Disruption of COPII vesicles restrained osteogenesis and decreased the number and size of autophagosomes. SEC31a (an outer coat protein of COPII vesicle) is found to be vital to regulate COPII vesicle‐dependent autophagosome formation via interacting with ATG9a of autophagosomal seed vesicles. The interference of Sec31a inhibited autophagosome formation and osteogenesis in vitro and in vivo. These results identified a novel mechanism of autophagosome formation in osteogenic differentiation of stem cells and identified SEC31a as a critical protein that mediates the interplay between COPII and ATG9a vesicles. These findings broaden the understanding of the regulatory mechanism in the osteogenic differentiation of MSCs. [ABSTRACT FROM AUTHOR]

The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. However, existing image segmentation algorithms still encounter issues related to accuracy, speed, and robustness, as well as a tendency to misdetect or overlook small fractures when applied to logging image fracture segmentation tasks. To address these challenges comprehensively, this paper proposes an end-to-end fracture segmentation algorithm named SWSDS-Net. This algorithm is built upon the UNet architecture and incorporates the SimAM with slicing (SWS) attention mechanism along with the deformable strip convolution (DSCN) module. The SWS introduces a fully 3D attention mechanism that effectively learns the weights of each neuron in the feature map, enabling better capture of fracture features while ensuring fair attention and enhancement for both large and small objects. Additionally, the deformable properties of DSCN allow for adaptive sampling based on fracture shapes, effectively tackling challenges posed by varying fracture shapes and enhancing segmentation robustness. Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. Moreover, serving as a lightweight network solution enables SWSDS-Net's deployment on mobile devices at remote sites—an advancement that lays a solid foundation for interpreting logging data and promotes deep learning technology application within traditional industrial scenarios. [ABSTRACT FROM AUTHOR]

To address the need for automated sorting of synthetic diamonds based on quality in manufacturing enterprises, this study developed a dedicated dataset and an enhanced YOLOv8n model for synthetic diamonds detection and quality evaluation, named YOLOv8n-adamas. We redesigned the backbone network to improve feature extraction capabilities and introduced a dynamic detection head based on attention mechanisms to further enhance model performance. Experimental results show that on synthetic diamonds dataset, YOLOv8n-adamas achieved a 4.0% improvement in precision (P), a 2.7% increase in recall (R), and improvements of 1.5% and 1.3% in mean average precisions at 50% and 95% Intersection over Union (IoU) thresholds (mAP50 and mAP95) compared to YOLOv8. Furthermore, YOLOv8n-adamas also outperforms other commonly used, high-performing models in various metrics on this dataset, offering effective technical support for the automated quality-based sorting of synthetic diamonds. [ABSTRACT FROM AUTHOR]

Importance: Obstructive sleep apnea (OSA) in children is linked with alterations in the gut microbiome. The influence of adenotonsillectomy (AT), a primary intervention for OSA, on gut microbiota dynamics relative to disease severity remains to be elucidated. Objective: This study aimed to investigate the impact of OSA severity and AT on the gut microbiome in pediatric patients. Design: A prospective observational study. Setting: Tertiary referral center. Participants: A cohort of 55 pediatric patients treated with AT for OSA. Intervention: Total tonsillectomy and adenoidectomy procedures. Main Outcome Measures: Comprehensive evaluations included in-laboratory polysomnography and 16S rRNA gut microbiome profiling at baseline, and again at 3rd and 12thmonth following surgery. Results: Initial findings showed uniform α-diversity across different severities of OSA, while β-diversity was significantly elevated in the severe OSA subgroup. Certain gut microbiota taxa (Lachnospiraceae NK4A136 group, Ruminococcaceae UCG-002, Ruminococcaceae UCG-014, Alloprevotella, Christensenellaceae R-7 group, Ruminococcaceae UCG-005, Lactobacillus murinus, and Prevotella nigrescens) were found to inversely correlate with the apnea-hypopnea index (AHI). Significant post-AT improvements in AHI and other polysomnographic metrics were observed. Notably, AHI changes post-AT were positively associated with microbial α-diversity (species richness), β-diversity, and specific bacterial taxa (Enterobacter, Parasutterella, Akkermansia, Roseburia, and Bacteroides plebeius DSM 17135), but negatively with other taxa (Fusicatenibacter, Bifidobacterium, UBA1819, Ruminococcus gnavus group, Bifidobacterium longum subsp. Longum, and Parabacteroides distasonis) and specific metabolic pathways (purine metabolism, transcription factors, and type II diabetes mellitus). The postoperative patterns of α- and β-diversity mirrored baseline values. Conclusions and Relevance: This study documents significant changes in the gut microbiome of pediatric patients after AT, including variations in α- and β-diversities, bacterial communities, and inferred metabolic functions. These changes suggest a potential association between the surgical intervention and microbiome alterations, although further studies are necessary to discern the specific contributions of AT amidst possible confounding factors such as antibiotic use. [ABSTRACT FROM AUTHOR]

Background: Plesiomonas shigelloides is a gram-negative opportunistic pathogen associated with gastrointestinal and extraintestinal diseases in humans. There have been reports of specific functional genes in the study of P. shigelloides, but there are also many unknown genes that may play a role in P. shigelloides pathogenesis as global regulatory proteins or virulence factors. Results: In this study, we found a transcriptional regulator of the PadR family in P. shigelloides and named it PstR (GenBank accession number: EON87311.1), which is present in various pathogenic bacteria but whose function has rarely been reported. RNA sequencing (RNA-Seq) was used to analyze the effects of PstR on P. shigelloides, and the results indicated that PstR regulates approximately 9.83% of the transcriptome, which includes impacts on motility, virulence, and physiological metabolism. RNA-seq results showed that PstR positively regulated the expression of the flagella gene cluster, which was also confirmed by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and Luminescence screening assay. Meanwhile, the ΔpstR mutant strains lacked flagella and were non-motile, as confirmed by motility assays and transmission electron microscopy (TEM). Additionally, PstR also positively regulates T3SS expression, which aids in P. shigelloides' capacity to infect Caco-2 cells. Meanwhile, we also revealed that PstR negatively regulates fatty acid degradation and metabolism, as well as the regulatory relationship between PsrA, a regulator of fatty acid degradation and metabolism, and its downstream genes in P. shigelloides. Conclusions: Overall, we revealed the effects of PstR on motility, virulence, and physiological metabolism in P. shigelloides, which will serve as a foundation for future research into the intricate regulatory network of PstR in bacteria. [ABSTRACT FROM AUTHOR]

To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed. Combining SPSA, portable positioning plate, and moving camera capture strategy, a photogrammetric experiment for small-scale rock samples in the field was conducted, collecting 48 datasets with different shooting parameters. Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. Finally, the applicability and limitations of the newly proposed method were further discussed. [ABSTRACT FROM AUTHOR]

Urbanization has significantly altered urban landscape patterns, leading to a continuous reduction in the proportion of green spaces. As critical carbon sinks in urban carbon cycles, urban green spaces play an indispensable role in mitigating climate change. This study aims to evaluate the carbon capture and storage potential of urban green spaces in Luohe, China, and identify the landscape factors influencing carbon sequestration. The research combines on-site data collection with high-resolution remote sensing, utilizing the i-Tree Eco model to estimate carbon sequestration rates across areas with varying levels of greenery. The study reveals that the carbon sequestration capacity of urban green spaces in Luohe City is 1.30 t·C·ha−1·yr−1. Among various vegetation indices, the Enhanced Vegetation Index (EVI) explains urban green space carbon sequestration most effectively through an exponential model (R2 = 0.65, AIC = 136.5). At the city-wide scale, areas with higher greening rates, better connectivity, and more complex edge morphology exhibit superior carbon sequestration efficiency. The explanatory power of key landscape indices on carbon sequestration is 78% across the study area, with variations of 71.5%, 62%, and 84.9% for low, medium, and high greening rate areas, respectively. Moreover, when greening rates reach a certain threshold, maintaining and optimizing the quality of existing green spaces becomes more critical than simply expanding the green area. These insights provide valuable guidance for urban planners and policymakers on enhancing the ecological functions of urban green spaces during urban development. [ABSTRACT FROM AUTHOR]

This study utilizes ionospheric total electron contents (TECs) data from China and its surrounding areas to compute the regional ionospheric semi‐variance using the Gaussian variogram. The regional ionospheric model is then constructed by introducing an additional stochastic term, named adjusted Spherical Harmonics Adding KrigING method (SHAKING), in which the stochastic Vertical Total Electron Content estimated using Kriging interpolation based on Ionospheric Variogram Model Parameters (IVMP) predict model. In the IVMP prediction model, two notable periodic patterns, that is, diurnal and annual signals with periods of 365.25/i days and 24/j hours, are recognized as Fourier series decompositions. These patterns capture the regular daily and yearly periodic variations in variogram parameters, and the IVMP prediction model is established using harmonic estimation methods. IVMP‐SHAKING is employed to generate the regional ionospheric map in China using multi‐GNSS data from 27 reference stations during days of the year 150–250 in 2022, with 14 rover stations used for evaluation. Results indicate that the proposed model exhibits a standard deviation of 4.85 TECu compared to the difference of slant TEC extracted from rover stations. Compared to existing models, including BDS‐3 broadcast ionospheric model (BDGIM), GPS broadcast model (Klobuchar), Galileo broadcast model (NeQuickG) and post‐processing Global Ionospheric Map released by IGS (IGSG), the accuracy of IVMP‐SHAKING improves by 6%–52%. Additionally, the Standard Point Positioning performance with different ionospheric corrections is analyzed on test stations. Results demonstrate that the horizontal and vertical positioning accuracy of the IVMP‐SHAKING model improves by 31%–55% and 16%–33%, respectively, compared to other three solutions. Plain Language Summary: This study looks at data on the total amount of electrons in the ionosphere (called Total Electron Content (TEC)) in China and nearby regions. We use this data to understand how TEC varies across the region using a statistical method called Gaussian variogram. Then, we create a model of the ionosphere called the Adjusted Spherical Harmonics Adding Kriging method (or SHAKING). This new model considers regular patterns in TEC variations throughout the day and year. We tested this model using data from 27 fixed stations and 14 mobile stations during a specific time period in 2022. The contribution of this paper is twofold. Firstly, we analyzed the spatial correlation of the ionosphere in China and developed a predictive model for this spatial correlation. Subsequently, based on this predictive model, we established a model for ionospheric delay and applied it to Global Navigation Satellite System positioning. Key Points: The study introduces a novel ionospheric approach, IVMP‐SHAKING, which based on Gaussian variogram model and Kriging interpolationIVMP‐SHAKING incorporates Fourier series decomposition to capture diurnal and annual periodic patterns in ionospheric variogram parametersEvaluation against existing models like BDGIM, Klobuchar, and IGSG demonstrates that IVMP‐SHAKING outperforms them by a significant margin [ABSTRACT FROM AUTHOR]

P‐type NiOx is employed for the fabrication of NiO/Ga2O3p–n diode. Addressing the challenge of low hole mobility in NiOx, an extensive investigation into the impact of oxygen stoichiometry engineering in NiOx is conducted. The meticulous optimization of the O2/Ar ratio to 30% during the sputtering process results in significant improvements, notably achieving enhanced hole mobility of 1.61 cm2 V−1 s. It leads to a low specific on‐resistance of 2.79 mΩ cm2 and a high rectification ratio of ≈1011, underscoring the efficacy of recombination transport mechanism driven by enhanced hole mobility. Detailed band alignment analysis between NiOx and Ga2O3 reveals a small band offset, with a valence band offset of 2.47 eV and a conduction band offset of 1.70 eV. It suggests a tailored modification of band alignment through the engineering the oxygen stoichiometry in NiOx, facilitating enhanced recombination conduction. The device exhibits a superior breakdown voltage (Vb) of 2780 V and a notable Baliga's figure of merit (BFOM) of 2.77 GW cm−2, surpassing the SiC unipolar figure of merit. The insights gained from this work are expected to inform future designs and optimizations of high‐performance Ga2O3 electronic devices. [ABSTRACT FROM AUTHOR]

With countries proposing the goal of carbon neutrality, the clean transformation of energy structure has become a hot and trendy issue internationally. Renewable energy generation will account for the main proportion, but it also leads to the problem of unstable electricity supply. At present, large-scale energy storage technology is not yet mature. Improving the flexibility of coal-fired power plants to suppress the instability of renewable energy generation is a feasible path. Thermal energy storage is a feasible technology to improve the flexibility of coal-fired power plants. This article provides a review of the research on the flexibility transformation of coal-fired power plants based on heat storage technology, mainly including medium to low-temperature heat storage based on hot water tanks and high-temperature heat storage based on molten salt. The current technical difficulties are summarized, and future development prospects are presented. The combination of the thermal energy storage system and coal-fired power generation system is the foundation, and the control of the inclined temperature layer and the selection and development of molten salt are key issues. The authors hope that the research in this article can provide a reference for the flexibility transformation research of coal-fired power plants, and promote the application of heat storage foundation in specific coal-fired power plant transformation projects. [ABSTRACT FROM AUTHOR]

Background: Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete features from 2D images. Here we aim to predict euploidy in human blastocysts using quantitative morphological parameters obtained by 3D morphology measurement. Methods: Multi-view images of 226 blastocysts on Day 6 were captured by manually rotating blastocysts during the preparation stage of trophectoderm biopsy. Quantitative morphological parameters were obtained by 3D morphology measurement. Six machine learning models were trained using 3D morphological parameters as the input and PGT-A results as the ground truth outcome. Model performance, including sensitivity, specificity, precision, accuracy and AUC, was evaluated on an additional test dataset. Model interpretation was conducted on the best-performing model. Results: All the 3D morphological parameters were significantly different between euploid and non-euploid blastocysts. Multivariate analysis revealed that three of the five parameters including trophectoderm cell number, trophectoderm cell size variance and inner cell mass area maintained statistical significance (P < 0.001, aOR = 1.054, 95% CI 1.034–1.073; P = 0.003, aOR = 0.994, 95% CI 0.991–0.998; P = 0.010, aOR = 1.003, 95% CI 1.001–1.006). The accuracy of euploidy prediction by the six machine learning models ranged from 80 to 95.6%, and the AUCs ranged from 0.881 to 0.984. Particularly, the decision tree model achieved the highest accuracy of 95.6% (95% CI 84.9-99.5%) with the AUC of 0.978 (95% CI 0.882–0.999), and the extreme gradient boosting model achieved the highest AUC of 0.984 (95% CI 0.892-1.000) with the accuracy of 93.3% (95% CI 81.7-98.6%). No significant difference was found between different age groups using either decision tree or extreme gradient boosting to predict euploid blastocysts. The quantitative criteria extracted from the decision tree imply that euploid blastocysts have a higher number of trophectoderm cells, larger inner cell mass area, and smaller trophectoderm cell size variance compared to non-euploid blastocysts. Conclusions: Using quantitative morphological parameters obtained by 3D morphology measurement, the decision tree-based machine learning model achieved an accuracy of 95.6% and AUC of 0.978 for predicting euploidy in Day 6 human blastocysts. Trial registration: N/A. [ABSTRACT FROM AUTHOR]

The canonical G406R mutation that increases Ca2+ influx through the CACNA1C-encoded CaV1.2 Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by life-threatening arrhythmias. Severe episodic hypoglycemia is among the poorly characterized non-cardiac TS pathologies. While hypothesized from increased Ca2+ influx in pancreatic beta cells and consequent hyperinsulinism, this hypoglycemia mechanism is undemonstrated because of limited clinical data and lack of animal models. We generated a CaV1.2 G406R knockin mouse model that recapitulates key TS features, including hypoglycemia. Unexpectedly, these mice do not show hyperactive beta cells or hyperinsulinism in the setting of normal intrinsic beta cell function, suggesting dysregulated glucose homeostasis. Patient data confirm the absence of hyperinsulinism. We discover multiple alternative contributors, including perturbed counterregulatory hormone responses with defects in glucagon secretion and abnormal hypothalamic control of glucose homeostasis. These data provide new insights into contributions of CaV1.2 channels and reveal integrated consequences of the mutant channels driving life-threatening events in TS. Gain of function mutant CaV1.2 channels drive life-threatening hypoglycemia in the multisystem disorder Timothy syndrome, but the underlying mechanisms are unknown. Here the authors show the mutant channels have adverse effects on counterregulatory hormones and CNS control of glucose homeostasis. [ABSTRACT FROM AUTHOR]

Influenza A virus (IAV) is known for causing seasonal epidemics ranging from flu to more severe outcomes like pneumonia, cytokine storms, and acute respiratory distress syndrome. The innate immune response and inflammasome activation play pivotal roles in sensing, preventing, and clearing the infection, as well as in the potential exacerbation of disease progression. This study examines the complex relationships between donor-specific characteristics and cytokine responses during H3N2 IAV infection using an ex vivo model. At 24 h post infection in 31 human lung explant tissue samples, key cytokines such as interleukin (IL)-6, IL-10, tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ) were upregulated. Interestingly, a history of lung cancer did not impact the acute immune response. However, cigarette smoking and programmed death-ligand 1 (PD-L1) expression on macrophages significantly increased IL-2 levels. Conversely, age inversely affected IL-4 levels, and diabetes mellitus negatively influenced IL-6 levels. Additionally, both diabetes mellitus and programmed cell death protein 1 (PD-1) expression on CD3+/CD4+ T cells negatively impacted TNF-α levels, while body mass index was inversely associated with IFN-γ production. Toll-like receptor 2 (TLR2) expression emerged as crucial in mediating acute innate and adaptive immune responses. These findings highlight the intricate interplay between individual physiological traits and immune responses during influenza infection, underscoring the importance of tailored and personalized approaches in IAV treatment and prevention. [ABSTRACT FROM AUTHOR]

The widespread adoption of dielectric elastomer actuators (DEAs) as compliant artificial muscle is on the horizon after a wide range of prototype demonstrations. The next step is to accelerate the material optimization for specific design requirements. This work proposes a data‐driven framework to predict long‐term DEA energy consumption and efficiency using measurements of initial electrical properties. DEA datasets are generated from 242 pre‐stretched single‐layer actuators and 53 multi‐layer bending actuators actuated for 30–180min. Devices are made with different elastomer and electrode materials, and tested with a novel instrument that can measure multiple aspects of DEA actuation. First, experiments are conducted to develop an empirical understanding of the electro‐mechanical energy conversion mechanism and the impact of material choices during DEA actuation. Second, data‐driven models are applied to the datasets to predict energy consumption and efficiency. Third, the potential generalization of this approach is investigated by using transfer learning strategies to predict energy efficiency at 100 min with as little as 1 min of input data. Moreover, it is found that linear regression can extend the prediction up to 180 min. Additionally, transfer learning is applied to predict energy‐related properties of multi‐layer DEAs using a small dataset. The proposed data‐driven framework can evolve into an intelligent system for accelerating new material discovery by predicting performance and providing device optimization strategies. [ABSTRACT FROM AUTHOR]

Time reversal has enabled many fundamental breakthroughs through the exquisite control over the complex systems. More recently, time reversal is predicted to be capable of enhancing the quantum sensing in unprecedented ways. Here, we report the experimental demonstration of time reversal based on a unitary and orthogonal squeezing interaction in an optical domain. Two cascaded degenerate optical parametric amplifiers are used to judiciously achieve a time reversal protocol. Finally, the concept of high-fidelity time reversal is demonstrated in the optical domain, offering remarkable opportunities for investigating many-body simulation and achieving quantum-enhanced sensing. [ABSTRACT FROM AUTHOR]

Aims: It is unclear whether activated partial thromboplastin time (aPTT) or anti‐Xa is more accurate for monitoring heparin anticoagulation in mechanical circulatory support (MCS) patients. This study investigates the relationship between aPTT and anti‐Xa in MCS patients and identifies predictors of discordance. Methods and results: aPTT and anti‐Xa were simultaneously measured in a prospective cohort of MCS patients receiving unfractionated heparin at a tertiary academic medical centre. Therapeutic aPTT and anti‐Xa levels were 60–100 s and 0.3–0.7 IU/mL, respectively, and concordance was defined as both levels being subtherapeutic, therapeutic, or supratherapeutic. To identify predictors of discordance, both a machine learning random forest model and a multivariate regression model were applied to patient demographics, device type, and 14 laboratory variables; 23 001 pairs of simultaneously measured aPTT/anti‐Xa were collected from 699 MCS patients. aPTT and anti‐Xa were concordant in 35.5% of paired observations and discordant in 64.5% (aPTT > antiXa 61.5%; aPTT < antiXa 3.0%). Discordance with a high aPTT relative to anti‐Xa (aPTT > antiXa) was associated with high INR, eGFR, and total bilirubin, as well as low platelets, haemoglobin, pre‐albumin, white blood cell count, and haptoglobin. Total artificial heart and durable ventricular assist devices were more likely to be associated with aPTT > anti‐Xa than temporary MCS devices. Conclusions: aPTT and anti‐Xa were frequently discordant in MCS patients receiving heparin anticoagulation. Clinical conditions common in MCS patients such as concurrent warfarin use, malnutrition, haemolysis, and thrombocytopenia, as well as durable type of MCS devices were associated with a high aPTT relative to anti‐Xa. [ABSTRACT FROM AUTHOR]

Despite the emergence of many therapies aimed at combating cancer, the disease provides an overwhelming global concern. Recently, the metabolism of cancer cells has been identified as an essential factor in cancer progression. The metabolism of cancer is regulated through specific dietary interventions. Further, oxidative stress plays a key role in the initiation and progression of cancer, and its impact can be influenced by tumor metabolism remodeling. Branched-chain amino acids (BCAAs), which have been shown to have functions beyond protein synthesis and energy production, are now recognized as critical regulators of oxidative stress in tumor cells. In this paper, we aim to discuss the role of BCAAs in regulating oxidative stress in tumor cells. Our study involved using prostate and renal cancer cell lines to assess the expression of anti-oxidative stress enzymes after supplementing different amino acids. Our study's results suggest that amino acids' effect on oxidative stress and signaling activation depends on the cell line and type of cancer. Our findings highlight the importance of using nutrient therapy and dietary interventions with a tailored approach and explicit reference to which kind of cancer is being treated. [ABSTRACT FROM AUTHOR]

Monoatomic-layered carbon materials, such as graphene1 and amorphous monolayer carbon2,3, have stimulated intense fundamental and applied research owing to their unprecedented physical properties and a wide range of promising applications4,5. So far, such materials have mainly been produced by chemical vapour deposition, which typically requires stringent reaction conditions compared to solution-phase synthesis. Herein, we demonstrate the solution preparation of free-standing nitrogen-doped amorphous monolayer carbon with mixed five-, six- and seven-membered (5-6-7-membered) rings through the polymerization of pyrrole within the confined interlayer cavity of a removable layered-double-hydroxide template. Structural characterizations and first-principles calculations suggest that the nitrogen-doped amorphous monolayer carbon was formed by radical polymerization of pyrrole at the α, β and N sites subjected to confinement of the reaction space, which enables bond rearrangements through the Stone–Wales transformation. The spatial confinement inhibits the C–C bond rotation and chain entanglement during polymerization, resulting in an atom-thick continuous amorphous layer with an in-plane π-conjugation electronic structure. The spatially confined radical polymerization using solid templates and ion exchange strategy demonstrates potential as a universal synthesis approach for obtaining two-dimensional covalent networks, as exemplified by the successful synthesis of monolayers of polythiophene and polycarbazole.Free-standing nitrogen-doped amorphous monolayer carbon consisting of mixed five-, six- and seven-membered rings was prepared through the polymerization of pyrrole within the confined interlayer cavity of a removable layered-double-hydroxide template. [ABSTRACT FROM AUTHOR]

The distribution of data often evolves over time, necessitating classifiers to adjust in order to maintain optimal classification accuracy. This phenomenon, termed "concept drift", poses a significant challenge. Detectors specifically designed for identifying concept drift are typically integrated to bolster classifier performance. In this study, we introduce two innovative methodologies designed to address the prevalent issues of high miss detection, excessive false alarms, and prolonged detection latencies encountered in many contemporary concept drift detection algorithms. The first approach, termed the Hybrid Weighting-based Concept Drift Detection Method (HW_DDM), incorporates both linear and exponential weighting for long and short windows, respectively, within a composite window model. Subsequently, concept drift is detected by calculating the weighted mean value within the window, leveraging the Hoeffding and McDirmid inequality thresholds. The second strategy, named the Dynamic Weighting-based Hoeffding Drift Detection Method (DW_HDDM), employs a mechanism that dynamically adjusts the Hoeffding threshold and dynamically weights classification prediction outcomes, thereby catering to the drift and augmenting detection efficacy. Comparative evaluations using both synthetic and real-world datasets against leading-edge algorithms are presented. The empirical results underscore that HW_DDM exhibits the lowest false detection rate with negligible miss detections on synthetic datasets. In contrast, DW_HDDM shines with minimal detection delay, reduced miss detection rates, and a diminished false detection rate, demonstrating superior classification accuracy on real-world datasets when pitted against benchmark algorithms. [ABSTRACT FROM AUTHOR]

Key message: Transcription factor PpMYB5 promotes lignin synthesis by directly binding to the Pp4CL1/Pp4CL2 promoter and affecting their expression, which may be related to nectarine russeting formation. Nectarine russeting is usually considered to be a non-invasive physiological disease that usually occurs on late-maturing cultivars and seriously affects their appearance quality and commercial value. The cause of nectarine fruit rust is currently unknown. In this study, we compared two flat nectarine cultivars, ‘zhongyoupanweidi’ (HD; russeting-free cultivar) and ‘zhongyoupanweihou’ (TH; russeting-prone cultivar), with respect to nectarine russeting by means of microscopy, transcriptomics, and hormone analysis. Compared to HD fruits, TH fruits had a broken cuticle, missing wax layer, and heavy lignin deposition. RNA sequencing (RNA-seq) revealed significant alternations in the expression of genes related to lignin synthesis. Moreover, structure genes Pp4CL1 and Pp4CL2, MYB transcription factor (TF) gene PpMYB5 were identified through weighted gene co-expression network analysis (WGCNA). Molecular experiments and transgenic evidence suggested that PpMYB5 regulates Pp4CL1/Pp4CL2 expression to promote lignin synthesis. Overall, in addition to providing new insights into the formation of mechanisms for nectarine russeting, our study also establishes a foundation for nectarine russeting prevention. [ABSTRACT FROM AUTHOR]

Purpose: We sought to develop an effective combined model for predicting the survival of patients with diffuse large B-cell lymphoma (DLBCL) based on the multimodal PET-CT deep features radiomics signature (DFR-signature). Methods: 369 DLBCL patients from two medical centers were included in this study. Their PET and CT images were fused to construct the multimodal PET-CT images using a deep learning fusion network. Then the deep features were extracted from those fused PET-CT images, and the DFR-signature was constructed through an Automated machine learning (AutoML) model. Combined with clinical indexes from the Cox regression analysis, we constructed a combined model to predict the progression-free survival (PFS) and the overall survival (OS) of patients. In addition, the combined model was evaluated in the concordance index (C-index) and the time-dependent area under the ROC curve (tdAUC). Results: A total of 1000 deep features were extracted to build a DFR-signature. Besides the DFR-signature, the combined model integrating metabolic and clinical factors performed best in terms of PFS and OS. For PFS, the C-indices are 0.784 and 0.739 in the training cohort and internal validation cohort, respectively. For OS, the C-indices are 0.831 and 0.782 in the training cohort and internal validation cohort. Conclusions: DFR-signature constructed from multimodal images improved the classification accuracy of prognosis for DLBCL patients. Moreover, the constructed DFR-signature combined with NCCN-IPI exhibited excellent potential for risk stratification of DLBCL patients. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.) can fix N naturally within soils, which makes alfalfa cultivation useful for enhancing soil fertility while minimizing environmental impacts from pesticides, fertilizers, and soil pollution. To assess the influence of alfalfa cropping on degraded black soil, we determined the nutrient stoichiometry of the soil and soil microbial biomass under four corn cultivation systems at the Harbin Corn Demonstration Base (Heilongjiang, China), which is located in Wujia (126°23′ E, 45°31′ N), Shuangcheng district, Harbin, Heilongjiang Province. The cultivation systems included continuous corn cultivation for more than 30 years (CK), 2 years of alfalfa–corn rotation (AC), three years of alfalfa cropping (TA), and four years of alfalfa cropping (FA). Overall, AC, TA, and FA treatment increased the soil pH, reduced the soil salinity, and increased the organic matter content of the 0–15 cm soil layer. TA and FA presented soil nutrient levels comparable to those of degraded cornfields that were fertilized annually. The TA and FA treatments increased the soil available N:P, soil N:P, and soil C:P ratios. Moreover, TA significantly increased the soil microbial biomass P (SMBP) in the 0–15 cm (surface) soil layer and reduced the soil microbial biomass C (SMBC):SMBP ratio. AC, TA, and FA increased the storage and mineralization rates of soil N and alleviated the microbial P limitations in degraded black soil farmland. Compared with FA, TA resulted in greater improvements in the quality of degraded black soil farmland. The ability of alfalfa to enhance soil fertility makes an important component of sustainable agricultural practices aimed at rehabilitating degraded soils. [ABSTRACT FROM AUTHOR]

Treatment options for patients with esophageal squamous cell carcinoma (ESCC) often result in poor prognosis and declining health-related quality of life. Screening FDA-approved drugs for cancer chemoprevention is a promising and cost-efficient strategy. Here, we found that dronedarone, an antiarrhythmic drug, could inhibit the proliferation of ESCC cells. Moreover, we conducted phosphorylomics analysis to investigate the mechanism of dronedarone-treated ESCC cells. Through computational docking models and pull-down assays, we demonstrated that dronedarone could directly bind to CDK4 and CDK6 kinases. We also proved that dronedarone effectively inhibited ESCC proliferation by targeting CDK4/CDK6 and blocking the G0/G1 phase through RB1 phosphorylation inhibition by in vitro kinase assays and cell cycle assays. Subsequently, we found that knocking out CDK4 and CDK6 decreased the susceptibility of ESCC cells to dronedarone. Furthermore, dronedarone suppressed the growth of ESCC in patient-derived tumor xenograft models in vivo. Thus, our study demonstrated that dronedarone could be repurposed as a CDK4/6 inhibitor for ESCC chemoprevention. [ABSTRACT FROM AUTHOR]

Simple Summary: This research investigates how personal and health-related factors impact the quality of life of head and neck cancer survivors treated with radiotherapy and surgery. By studying 150 patients—60 with nasopharyngeal cancer and 90 with oral cavity cancer—researchers utilized a specific survey to assess the effects of various elements, including cancer type, age, gender, relationship status, education, and employment. Findings revealed that cancer type, treatment history, and employment status significantly influenced quality of life. Patients with nasopharyngeal cancer reported better social and functional outcomes than those with oral cavity cancer. Additionally, unemployment was correlated with lower quality of life, while having a partner and undergoing certain surgeries positively impacted outcomes. These insights can aid research efforts to better support cancer survivors and customize treatment approaches for improved well-being. Objectives: This study aimed to examine the relationship between health-related quality of life (HRQoL) and sociodemographic and clinical variables in survivors of head and neck cancer (HNC) treated with radiotherapy, with or without surgery. Materials and Methods: HRQoL was measured using the functional assessment of cancer therapy—head and neck (FACT-H&N) in a cross-sectional survey involving 150 patients. Of these, 60 had nasopharyngeal cancer (NPC), treated exclusively with radiotherapy, while 90 had oral cavity squamous cell cancer (OSCC), undergoing radical surgery followed by adjuvant radiotherapy. Key variables included cancer type, age, gender, partnership status, education, and employment, with additional clinical variables assessed in patients with OSCC. Statistical analyses included multiple regression, ANOVA, and t-tests to explore relationships between variables and HRQoL. Results: Cancer type, surgical treatment, and employment status emerged as significant independent predictors of HRQoL in HNC patients. Patients with NPC reported better HRQoL on three FACT-H&N subscales—social/family well-being, functional well-being, and additional concerns—compared to patients with OSCC. Unemployed individuals exhibited lower HRQoL on four subscales. In patients with OSCC, partnership status and segmental mandibulectomy were found to predict HRQoL independently. Conclusions: This study concludes that cancer type, surgical intervention, and employment status notably influence HRQoL among HNC patients undergoing radiotherapy. In addition, partnership status is a key factor affecting HRQoL in patients with OSCC. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal distribution and transport of atmospheric water vapor in urban areas is crucial for improving mesoscale models and weather and climate predictions. This study employs Multi-Axis Differential Optical Absorption Spectroscopy to monitor the dynamic distribution and transport flux of water vapor in Beijing within the tropospheric layer (0–4 km) from June 2021 to May 2022. The seasonal peaks in precipitable water occur in August, reaching 39.13 mm, with noticeable declines in winter. Water vapor was primarily distributed below 2.0 km and generally decreases with increasing altitude. The largest water vapor transport flux occurs in the southeast–northwest direction, whereas the smallest occurs in the southwest–northeast direction. The maximum flux, observed at about 1.2 km in the southeast–northwest direction during summer, reaches 31.77 g/m2/s (transported towards the southeast). Before continuous rainfall events, water vapor transport, originating primarily from the southeast, concentrates below 1 km. Backward trajectory analysis indicates that during the rainy months, there was a higher proportion of southeasterly winds, especially at lower altitudes, with air masses from the southeast at 500 m accounting for 69.11%. This study shows the capabilities of MAX-DOAS for remote sensing water vapor and offers data support for enhancing weather forecasting and understanding urban climatic dynamics. [ABSTRACT FROM AUTHOR]

The charging load of electric vehicles (EV) is one of the most rapidly increasing loads in current power distribution systems. It may cause distribution transformer/feeder overload without proper coordination or control, especially in residential area where household load and EV charging load are sharing transformer capacity. Existing smart meter-based orderly charging control (OCC) approaches commonly require costly but unreliable communication schemes to control EV charging behavior. In this work, a smart meter-based distributed controller is designed to establish a meter-to-EV communication interface with low cost and enhanced reliability, based on the state-of-the-art charging standard. An event-driven OCC algorithm is developed, and then, deployed in the data hub (concentrator) of the AMI with an easy-to-implement optimization formulation. The effectiveness of the proposed approach is validated using a numerical case study and a practical field test in Hangzhou, China. Both results indicate promising advantages of the proposed OCC approach in reducing the peak load of emerging EV charging demand by more than 30 % . [ABSTRACT FROM AUTHOR]

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval aims to retrieve related items in one modality using a query from another modality. As the foundational and key challenge of it, image-text retrieval has garnered significant research interest from scholars. In recent years, hashing techniques have gained widespread interest for large-scale dataset retrieval due to their minimal storage requirements and rapid query processing capabilities. However, existing hashing approaches either learn unified representations for both modalities or specific representations within each modality. The former approach lacks modality-specific information, while the latter does not consider the relationships between image-text pairs across various modalities. Therefore, we propose an innovative supervised hashing method that leverages intra-modality and inter-modality matrix factorization. This method integrates semantic labels into the hash code learning process, aiming to understand both inter-modality and intra-modality relationships within a unified framework for diverse data types. The objective is to preserve inter-modal complementarity and intra-modal consistency in multimodal data. Our approach involves: (1) mapping data from various modalities into a shared latent semantic space through inter-modality matrix factorization to derive unified hash codes, and (2) mapping data from each modality into modality-specific latent semantic spaces via intra-modality matrix factorization to obtain modality-specific hash codes. These are subsequently merged to construct the final hash codes. Experimental results demonstrate that our approach surpasses several state-of-the-art cross-modal image-text retrieval hashing methods. Additionally, ablation studies further validate the effectiveness of each component within our model. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval aims to retrieve related items in one modality using a query from another modality. As the foundational and key challenge of it, image-text retrieval has garnered significant research interest from scholars. In recent years, hashing techniques have gained widespread interest for large-scale dataset retrieval due to their minimal storage requirements and rapid query processing capabilities. However, existing hashing approaches either learn unified representations for both modalities or specific representations within each modality. The former approach lacks modality-specific information, while the latter does not consider the relationships between image-text pairs across various modalities. Therefore, we propose an innovative supervised hashing method that leverages intra-modality and inter-modality matrix factorization. This method integrates semantic labels into the hash code learning process, aiming to understand both inter-modality and intra-modality relationships within a unified framework for diverse data types. The objective is to preserve inter-modal complementarity and intra-modal consistency in multimodal data. Our approach involves: (1) mapping data from various modalities into a shared latent semantic space through inter-modality matrix factorization to derive unified hash codes, and (2) mapping data from each modality into modality-specific latent semantic spaces via intra-modality matrix factorization to obtain modality-specific hash codes. These are subsequently merged to construct the final hash codes. Experimental results demonstrate that our approach surpasses several state-of-the-art cross-modal image-text retrieval hashing methods. Additionally, ablation studies further validate the effectiveness of each component within our model. [ABSTRACT FROM AUTHOR]

Fe-embedded N-doped graphene (Fe–N–C) stands out as a promising electrocatalyst for the oxygen reduction reaction (ORR), a critical process in fuel cell technologies. The role of spin multiplicity at the Fe center and its influence on ORR intermediates, particularly the spin crossover effect, is a pivotal yet underexplored aspect of spin-related electrocatalysis. Unraveling the nuances of spin multiplicity and its manipulation holds the key to the next generation of Fe-based spin electrocatalysts for renewable energy. Our study employs a first-principles density functional theory (DFT) approach to dissect the electronic configurations across various spin states of ORR intermediates. Our wavefunction and bond analysis unveil a critical mechanism: the spin electron transfer from the nonbonding dx2–y2 orbital to the antibonding orbital, which dominantly dictates the spin state transition in ORR intermediates on the Fe–N–C catalyst. This insight is further complemented by the discovery that the electronic occupation of d–p π bonds, formed by X–Fe–Y (X/Y: *OH, *O, and *OOH), is insensitive to the IS–HS transition of adsorbates. Another novel finding of this research is the strain-induced modulation of spin states within Fe–N–C. We demonstrate that increasing strain drives a transition from an intermediate spin (IS) to a high spin (HS) state in FeN4 and its ORR intermediates, effectively suppressing ligand-induced spin crossover. Notably, a unique behavior is observed in five-coordinate FeN4OH and FeN4OOH, where strain prompts a return to IS due to electron injection into the dxy orbital. Our simulation of polarization current density reveals a direct correlation between applied strain and ORR onset potential. Specifically, at +8% strain, the onset potential is remarkably enhanced to 0.98 V vs. SHE due to the dominant active moiety of FeN HS4 *OH, underscoring the role of spin state modulation in weakening the Fe–O bond and reducing the desorption energy of *OH. This work not only elucidates the origin of spin multiplicity in ORR intermediates on Fe–N–C but also establishes strain-controlled spin regulation as a viable strategy for tuning ORR activity. Our findings offer a significant insight into the design of spin-related electrocatalysts, paving the way for more efficient and sustainable energy technologies. [ABSTRACT FROM AUTHOR]

Chaohusaurus is an early ichthyosauriform represented by three species known from the Early Triassic of Chaohu, Anhui Province, China, with a fourth species—Chaohusaurus zhangjiawanensis—known from the Nanzhang-Yuan'an region of Hubei Province. In contrast to the Chaohusaurus species from Chaohu, Chaohusaurus zhangjiawanensis remains poorly known, hindering our understanding of early ichthyosauriform evolution. Here, we report a new specimen of Chaohusaurus zhangjiawanensis, which provides new information on its dentition. The new specimen confirms that Chaohusaurus zhangjiawanensis had heterodont dentition consisting of pointed anterior teeth and robust, rounded posterior teeth, indicating a generalist diet. The posterior teeth of Chaohusaurus zhangjiawanensis are more robust (broader and larger) than the posterior dentition of Chaohusaurus chaoxianensis and Chaohusaurus brevifemoralis from Chaohu. This suggests differences in hard-shelled prey preference between species of Chaohusaurus from Chaohu and Nanzhang-Yuan'an, with Chaohusaurus zhangjiawanensis likely capable of feeding on harder and larger prey than Chaohusaurus brevifemoralis and Chaohusaurus chaoxianensis. In turn, this probably reflects differences in durophagous prey availability between the shallow-marine palaeoecosystem of Nanzhang-Yuan'an and the deeper, slope-basin palaeoecosystem of Chaohu. The posterior dentition and forefin of Chaohusaurus zhangjiawanensis are strikingly similar to those of Chaohusaurus geishanensis, the rarest species of Chaohusaurus from the Chaohu fauna. The scarcity of Chaohusaurus geishanensis in the Chaohu fauna, and its morphological similarity to Chaohusaurus zhangjiawanensis, possibly indicate that Chaohusaurus geishanensis was closely related with Chaohusaurus zhangjiawanensis and that it was also a shallow-marine species that was not a typical component of the Chaohu fauna. It probably occasionally wandered out into the deeper waters of Chaohu from a nearby coastal environment. [ABSTRACT FROM AUTHOR]

Inorganic scintillators play a pivotal role in diverse fields like medical imaging, nondestructive detection, homeland security, and high‐energy physics. However, traditional inorganic scintillators encounter challenges such as high fabrication costs and low light yield. Recently, low‐dimensional metal halide scintillators (LDMHS) have witnessed rapid progress, owing to their distinctive crystal structure and superior radioluminescence performance. Herein, an overview of recent advancements and proposed instructive pathways for achieving high‐performance LDMHS is provided. First, the scintillation physical mechanism and emphasis on the essential requirements of scintillators for diverse applications are elucidated. Furthermore, LDMHS are classified according to B‐site cations, and their respective characteristics and recent advancements are introduced. This encompasses the understanding of structure‐property relationships and the routes and rules for optimizing scintillation performance. Finally, the persisting challenges in this burgeoning field and proposed potential research directions for future exploration are discussed. [ABSTRACT FROM AUTHOR]

The differences in muscle development potential between male and female ducks lead to variations in body weight, significantly affecting the growth of the Muscovy duck meat industry. The aim of this study is to explore the regulatory mechanisms for the muscle development differences between genders. Muscovy ducks of both sexes were selected for measurements of body weight, growth traits, hormone levels, and muscle gene expression. The results show that male ducks compared to females had greater weight and growth traits (p < 0.05). Compared to male ducks, the level of serum testosterone in female ducks was decreased, and the estradiol levels were increased (p < 0.05). The RNA-seq analysis identified 102 upregulated and 49 downregulated differentially expressed genes. KEGG analysis revealed that among the top 10 differentially enriched pathways, the AMPK signaling pathway is closely related to muscle growth and development. Additionally, the mRNA and protein levels of CD36, CPT1A, LPL, and SREBP1 were increased and the P-AMPK protein level decreased in the female ducks compared to the male ducks (p < 0.05). In conclusion, muscle development potential difference between male and female ducks is regulated by sex hormones. This process is likely mediated through the activation of the AMPK pathway. [ABSTRACT FROM AUTHOR]

The vertical tube indirect evaporative cooler (VTIEC) could be preferred one to air-conditioning buildings if its water film quality could be improved. This paper proposed a new VTIEC with inner grooved tubes to improve the water film quality and obtained its performance by experimental method. The results indicated that inner grooved tubes have positive effects on the water film and performance of the new VTIEC. A low working air enthalpy at inlet can significantly improve the total cooling capacity from evaporation, and then VTIEC performance. In this paper conditions, the velocity ratio of 1.52 of the produced air to the working air is better one condition compared to 1.01 and 2.28. The produced air velocity is positive effect on the cooling capacity per unit area until the produced air velocity of about 4.05 m/s and is negative effect on COP. The maximum unit cooling capacity, wet-bulb efficiency and COP are 307.74W/m2, 81.7% and 44.97.The results will promote the application of the new VTIEC. [ABSTRACT FROM AUTHOR]

Background: The fat-to-muscle mass ratio (FMR), integrating the antagonistic effects of fat and muscle mass, has been suggested as a valuable indicator to assess cardiometabolic health independent of overall adiposity. However, the specific associations of total and regional FMR with cardiometabolic risk are poorly understood. We aimed to examine sex-specific associations of total and regional FMR with single and clustered cardiometabolic risk factors (CRFs). Methods: 13,505 participants aged 20 years and above were included in the cross-sectional study. Fat mass and muscle mass were assessed using a bioelectrical impedance analysis device. FMR was estimated as fat mass divided by muscle mass in corresponding body parts (whole body, arm, leg, and trunk). Clustered CRFs was defined as the presence of two or more risk factors, including hypertension, elevated blood glucose, dyslipidemia, insulin resistance (IR), and hyperuricemia. IR was assessed by the triglyceride glucose (TyG) index. Multivariable logistic regression models were applied to explore the associations of FMR in the whole body and body parts with single and clustered CRFs. Results: The odds ratios (ORs) increased significantly for all single and clustered CRFs with the per quartile increase of total and regional FMR in both sexes (P for trend < 0.001), following adjustment for confounders. Among the regional parts, FMRs of the legs presented the strongest associations for clustered CRFs in both men and women, with adjusted OR of 8.54 (95% confidence interval (CI): 7.12–10.24) and 4.92 (95% CI: 4.24–5.71), respectively. Significant interactions (P for interaction < 0.05) were identified between age and FMRs across different body parts, as well as between BMI status and FMRs in different regions for clustered CRFs. Restricted cubic splines revealed significant non-linear relationships between FMRs of different body parts and clustered CRFs in both sexes (P for nonlinear < 0.05). Conclusions: FMRs in the whole body and different regions were significantly associated with single and clustered CRFs in the general Chinese population. The association between FMR and clustered CRFs was more pronounced in youngers than in the elderly. [ABSTRACT FROM AUTHOR]

Using data from wave 20 of the Household, Income and Labour Dynamics in Australia Survey, we provide a descriptive examination of the relationship between housing affordability stress (HAS) and a multi‐item measure of financial wellbeing across tenure types and test whether good financial wellbeing is protective of the negative mental health effects of HAS. We find that HAS is associated with lower financial wellbeing and that this is differentially distributed by tenure, with renters who experience HAS reporting, on average, lower financial wellbeing than owners. This suggests that HAS, which focuses on income to define housing stress, is different to financial wellbeing. Being in control of finances and feeling financially secure are important components of the way in which financial hardship impacts mental health. Renters may be more likely to experience these issues than owners; hence, they fare worse in terms of their mental health when in HAS than owners do. [ABSTRACT FROM AUTHOR]

Multifunctional targeted drug delivery systems have been explored as a novel cancer treatment strategy to overcome limitations of traditional chemotherapy. The combination of photodynamic therapy and chemotherapy has been shown to enhance efficacy, but the phototoxicity of traditional photosensitizers is a challenge. In this study, we prepared a multi-sensitive composite hydrogel containing gold nanoclusters (Au NCs) and the temperature-sensitive antitumor drug 5-fluorourac il (5-FU) using carboxymethyl cellulose (Carr) as a dual-functional template. Au NCs were synthesized using sodium borohydride as a reducing agent and potassium as a promoter. The resulting Au NCs were embedded in the Carr hydrogel, which was then conjugated with lactobionic acid (LA) as a targeting ligand. The resulting Au NCs/5-FU@Carr-LA composite hydrogel was used for synergistic photodynamic therapy (PDT), photothermal therapy (PTT), and chemotherapy. Au NCs/5-FU@Carr-LA releases the drug faster at pH 5.0 due to the acid sensitivity of the Carr polymer chain. In addition, at 50 °C, the release rate of Au NCs/5-FU@Carr-LA is 78.2%, indicating that the higher temperature generated by the photothermal effect is conducive to the degradation of Carr polymer chains. The Carr hydrogel stabilized the Au NCs and acted as a matrix for drug loading, and the LA ligand facilitated targeted delivery to tumor cells. The composite hydrogel exhibited excellent biocompatibility and synergistic antitumor efficacy, as demonstrated by in vitro and in vivo experiments. In addition, the hydrogel had thermal imaging capabilities, making it a promising multifunctional platform for targeted cancer therapy. [ABSTRACT FROM AUTHOR]

Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR‐implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario‐based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed‐integer non‐linear programming (MINLP). A time‐series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario‐based planning method. [ABSTRACT FROM AUTHOR]

The facile reconfiguration of phases plays a pivotal role in enhancing the electrocatalytic production of H2 through heterostructure formation. While chemical methods have been explored extensively for this purpose, plasma-based techniques offer a promising avenue for achieving heterostructured nano-frameworks. However, the conventional plasma approach introduces complexities, leading to a multi-step fabrication process and challenges in precisely controlling partial surface structure modulation due to the intricate interaction environment. In our pursuit of heterostructures with optimized oxygen evolution reaction (OER) behavior, we have designed a facile auxiliary insulator-confined plasma system to directly attain a Ni3N–NiO heterostructure (hNiNO). By meticulously controlling the surface heating process during plasma processing, such approach allows for the streamlined fabrication of hNiNO nano-frameworks. The resulting nano-framework exhibits outstanding catalytic performance, as evidenced by its overpotential of 320 mV at a current density of 10 mA·cm−2, in an alkaline environment. This stands in stark contrast to the performance of NiO-covered Ni3N fabricated using the conventional plasma method (sNiNO). Operando plasma diagnostics, coupled with numerical simulations, further substantiates the influence of surface heating due to auxiliary insulator confinement of the substrate on typical plasma parameters and the formation of the Ni3N–NiO nanostructure, highlighting the pivotal role of controlled surface temperature in creating a high-performance heterostructured electrocatalyst. [ABSTRACT FROM AUTHOR]