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Abstract As global energy demand continues to rise and renewable energy sources develop rapidly, the operational efficiency and stability of power systems have emerged as primary challenges in energy management. Line loss within these systems is a critical factor for both energy efficiency and economic performance. This study leverages an electric energy data management platform that facilitates the sharing of archival information, the development of line loss calculation models, and the automated computation of electricity and line loss formulas. This ensures accurate and real-time calculations of line losses in the power grid, supporting multi-time scale analyses and providing timely, comprehensive data for effective line loss management. The platform utilizes theoretical line loss values to identify anomalies, which are categorized into five types: topological relationships, archival information, data collection, electricity metering, and consumption behavior. In response to the abnormal monthly power imbalance rate of 220 kV and 110 KV stations, and the − 3.684% exceeding the − 1% assessment limit, the designed line loss management system service layer does not need to go deep into the bottom layer of the power system. It hides the complexity of the power grid through middleware and provides data, application, and security services.

Abstract Nonlinear optical responses in two-dimensional (2D) materials can build free-space optical neuromorphic computing systems. Ensuring the high performance and the tunability of the system is essential to encode diverse functions. However, common strategies, including the integration of external electrode arrays or photonic structures with 2D materials, and barely patterned 2D materials, exhibit a contradiction between performance and tunability. Because the unique band dispersions of 2D materials can provide hidden paths to boost nonlinear responses independently, here we introduced a new free-space optical computing concept within a bare molybdenum disulfide array. This system can preserve high modulation performance with fast speed, low energy consumption, and high signal-to-noise ratio. Due to the freedom from the restrictions of fixed photonic structures, the tunability is also enhanced through the synergistic encodings of the 2D cells and the excitation pulses. The computing mechanism of transition from two-photon absorption to synergistic excited states absorption intrinsically improved the modulation capability of nonlinear optical responses, revealed from the relative transmittance modulated by a pump-probe-control strategy. Optical artificial neural network (ANN) and digital processing were demonstrated, revealing the feasibility of the free-space optical computing based on bare 2D materials toward neuromorphic applications.

Abstract Multivalent antigen display on nanoparticles can enhance the immunogenicity of nanovaccines targeting viral moieties, such as the receptor binding domain (RBD) of SARS-CoV-2. However, particle morphology and size of current nanovaccines are significantly different from those of SARS-CoV-2. Additionally, surface antigen patterns are not controllable to enable the optimization of B cell activation. Herein, we employ an icosahedral DNA origami (ICO) as a display particle for RBD nanovaccines, achieving morphology and diameter like the virus (91 ± 11 nm). The surface addressability of DNA origami permits facile modification of the ICO surface with numerous RBD antigen clusters (ICO-RBD) to form various antigen patterns. Using an in vitro screening system, we demonstrate that the antigen spacing, antigen copies within clusters and cluster number parameters of the surface antigen pattern all impact the ability of the nanovaccines to activate B cells. Importantly, the optimized ICO-RBD nanovaccines evoke stronger and more enduring humoral and T cell immune responses in female mouse models compared to soluble RBD antigens, and the multivalent display broaden the protection range of B cell responses to more mutant strains. Our vaccines activate similar humoral immunity, observable stronger cellular immunity and more memory immune cells compared to trimeric mRNA vaccines.

In order to evaluate the effect of Streptococcus thermophilus fermentation on green tea extract and its storage characteristics, this study used Streptococcus thermophilus FUA329 and S. thermophilus ST1.8748 (control group) to conduct experiments on green tea extracts. After fermentation and storage at 4 ℃ for 0, 30, 60, 120 and 180 d, pH, viable bacterial count, tea polyphenol content, ellagic acid content, and urolithin A content were used as the main evaluation indicators, combined with anti-oxidation activity to evaluate the storage quality of fermented green tea extract. The results showed that compared with the S. thermophilus ST1.8748 control group, the pH of S. thermophilus FUA329 fermented green tea extract during the storage period was between 5.39 and 5.71. The highest number of viable bacteria at 48 h was 9.97×108 CFU/mL. The content of tea polyphenols gradually decreased and the ellagic acid content tended to be stable. Urolithin A was produced at 48 h, and its concentration was 0.664 μmol/L. The antioxidant activity was always maintained at a high level. In summary, S. thermophilus FUA329 fermented green tea extract can significantly delay the quality decline and nutrient loss of green tea extract during storage.

Abstract Background The sect. Chrysantha Chang of plants with yellow flowers of Camellia species as the “Queen of the Tea Family”, most of these species are narrowly distributed endemics of China and are currently listed Grde-II in National Key Protected Wild Plant of China. They are commercially important plants with horticultural medicinal and scientific research value. However, the study of the sect. Chrysantha species genetics are still in its infancy, to date, the mitochondrial genome in sect. Chrysantha has been still unexplored. Results In this study, we provide a comprehensive assembly and annotation of the mitochondrial genomes for four species within the sect. Chrysantha. The results showed that the mitochondrial genomes were composed of closed-loop DNA molecules with sizes ranging from 850,836 bp (C. nitidissima) to 1,098,121 bp (C. tianeensis) with GC content of 45.71–45.78% and contained 48–58 genes, including 28–37 protein-coding genes, 17–20 tRNA genes and 2 rRNA genes. We also examined codon usage, sequence repeats, RNA editing and selective pressure in the four species. Then, we performed a comprehensive comparison of their basic structures, GC contents, codon preferences, repetitive sequences, RNA editing sites, Ka/Ks ratios, haplotypes, and RNA editing sites. The results showed that these plants differ little in gene type and number. C. nitidissima has the greatest variety of genes, while C. tianeensis has the greatest loss of genes. The Ka/Ks values of the atp6 gene in all four plants were greater than 1, indicating positive selection. And the codons ending in A and T were highly used. In addition, the RNA editing sites differed greatly in number, type, location, and efficiency. Twelve, six, five, and twelve horizontal gene transfer (HGT) fragments were found in C. tianeensis, Camellia huana, Camellia liberofilamenta, and C. nitidissima, respectively. The phylogenetic tree clusters the four species of sect. Chrysantha plants into one group, and C. huana and C. liberofilamenta have closer affinities. Conclusions In this study, the mitochondrial genomes of four sect. Chrysantha plants were assembled and annotated, and these results contribute to the development of new genetic markers, DNA barcode databases, genetic improvement and breeding, and provide important references for scientific research, population genetics, and kinship identification of sect. Chrysantha plants.

Abstract A large-scale, high-speed, long-runout landslide occurred in Xinmo Village, Maoxian, Sichuan Province, China, on June 24, 2017. It was characterized by fast sliding speed, rapid volume growth, and large impact area. The dynamic process of such landslides and the influence of erosion on the dynamic process are studied by field investigation, numerical inversion and simulation. The results showed that entrainment erosion was a major factor of landslide volumetric change and a salient feature of the landslide process. An exponential equation relating the erosion rate and the deposition volume was established. Moreover, the study found that a slight change of the erosion rate (1e-4) also had a significant impact on the lateral spreading, longitudinal runout, and vertical erosion. As the erosion rate increased, the lateral spreading, longitudinal runout, and vertical erosion of this type of landslide became more obvious. By using the coefficient of variation method, it was obtained that the erosion rate had a greater influence on the vertical erosion than on the lateral spreading and longitudinal runout. In the study of the velocity of the rock avalanche under different erosion conditions, it was found that the erosion amount and the landslide velocity were not strictly linearly related. This study has important significance for understanding the dynamic process and erosion effect of rock avalanche, and provides useful references and insights for future research in related fields.

Abstract Background Rhododendron delavayi is a natural shrub that is distributed at different elevations in the karst region of Bijie, China, and that has an important role in preventing land degradation in this region. In this study, we determined the soil mineral element contents and soil enzyme activities. The composition of the soil bacterial community of R. delavayi at three elevations (1448 m, 1643 m, and 1821 m) was analyzed by high-throughput sequencing, and the interrelationships among the soil bacterial communities, mineral elements, and enzyme activities were determined. Results The Shannon index of the soil bacterial community increased and then decreased with increasing elevation and was highest at 1643 m. Elevations increased the number of total nodes and edges of the soil bacterial community network, and more positive correlations at 1821 m suggested stronger intraspecific cooperation. Acidobacteria, Actinobacteria and Proteobacteria were the dominant phyla at all three elevations. The Mantel test and correlation analysis showed that Fe and soil urease significantly affected bacterial communities at 1448 m; interestingly, Chloroflexi was positively related to soil urease at 1448 m, and Actinobacteria was positively correlated with Ni and Zn at 1821 m. Fe and soil urease significantly influenced the bacterial communities at lower elevations, and high elevation (1821 m) enhanced the positive interactions of the soil bacteria, which might be a strategy for R. delavayi to adapt to high elevation environments. Conclusion Elevation significantly influenced the composition of soil bacterial communities by affecting the content of soil mineral elements and soil enzyme activity.

Abstract Background The Synotis (C. B. Clarke) C. Jeffrey & Y. L. Chen is an ecologically important genus of the tribe Senecioneae, family Asteraceae. Because most species of the genus bear similar morphology, traditional morphological identification methods are very difficult to discriminate them. Therefore, it is essential to develop a reliable and effective identification method for Synotis species. In this study, the complete chloroplast (cp.) genomes of four Synotis species, S. cavaleriei (H.Lév.) C. Jeffrey & Y.L. Chen, S. duclouxii (Dunn) C. Jeffrey & Y.L. Chen, S. nagensium (C.B. Clarke) C. Jeffrey & Y.L. Chen and S. erythropappa (Bureau & Franch.) C. Jeffrey & Y. L. Chen had been sequenced using next-generation sequencing technology and reported here. Results These four cp. genomes exhibited a typical quadripartite structure and contained the large single-copy regions (LSC, 83,288 to 83,399 bp), the small single-copy regions (SSC, 18,262 to 18,287 bp), and the inverted repeat regions (IR, 24,837 to 24,842 bp). Each of the four cp. genomes encoded 134 genes, including 87 protein-coding genes, 37 tRNA genes, 8 rRNA genes, and 2 pseudogenes (ycf1 and rps19). The highly variable regions (trnC-GCA-petN, ccsA-psaC, trnE-UUC-rpoB, ycf1, ccsA and petN) may be used as potential molecular barcodes. The complete cp. genomes sequence of Synotis could be used as the potentially effective super-barcode to accurately identify Synotis species. Phylogenetic analysis demonstrated that the four Synotis species were clustered into a monophyletic group, and they were closed to the Senecio, Crassocephalum and Dendrosenecio in tribe Senecioneae. Conclusions This study will be useful for further species identification, evolution, genetic diversity and phylogenetic studies within this genus Synotis and the tribe Senecioneae.

Abstract Background The systematic status of sect. Tuberculata and its taxonomy have recently attracted considerable attention. However, the different bases for defining the characteristics of sect. Tuberculata has led to many disagreements among the plants in this group. Camellia neriifolia and Camellia ilicifolia have been the subject of taxonomic controversy and have been treated as different species or varieties of the same species. Therefore, it is important to use multiple methods, i.e., integrative taxonomy, to determine the taxonomic status of C. neriifolia and C. ilicifolia. This is the first study to systematically explore the taxonomic position of these two plants on the basis of Morphology, Anatomy, Palynology and Molecular Systematics. Results Extensive specimen reviews and field surveys showed that many differences exist in C. neriifolia and C. ilicifolia, such as the number of trunk (heavily debarked vs. slightly peeling), leaf type (smooth thin leathery, shiny vs. smooth leathery, obscure or slightly shiny), leaf margin (entire vs. serrate), flower type (subsessile vs. sessile), number of styles (3–4 vs. 3), and sepal (ovate vs. round). Moreover, C. neriifolia has a more distinctive faint yellow flower color, and trunk molting was more severe in C. neriifolia than that in C. ilicifolia. In addition, micromorphological analysis of the leaf epidermis showed that the two species differed in the anticlinal wall, stomatal apparatus, and stomatal cluster, and pollen morphology analyses based on pollen size, germination furrow, and polar and equatorial axes showed that they are both distinct from each other. The results of the phylogenetic tree constructed based on the whole chloroplast genome, protein-coding genes, and ITS2 showed that both C. ilicifolia and C. neriifolia were clustered in different branches and gained high support. Conclusions The results combine morphology, anatomy, palynology, and molecular systematics to treat both C. neriifolia and C. ilicifolia as separate species in the sect. Tuberculata, and the species names continue to be used as they were previously. In conclusion, clarifying the taxonomic status of C. neriifolia and C. ilicifolia deepens our understanding of the systematic classification of sect. Tuberculata.

Abstract The issue of academic procrastination is highly prevalent among university students. It not only has a deterimental effect on students' academic performance but also poses a risk to their physical and mental well-being. Anxiety, as a negative emotion, has attracted researchers’ attention in relation to academic procrastination. Research indicates a correlation between state anxiety and academic procrastination, but the underlying mechanisms that drive this association remain unclear. When individuals experience ego-depletion, it can lead to psychological exhaustion, subsequently leading to procrastination. Gender role conceptions, shaped by sociocultural and psychological mechanisms, have profound implications on individuals’ cognition, emotions, and behaviors. This study primarily aims to explore the relationship between state anxiety and academic procrastination among university students, with a particularly focus on the mediating role of ego-depletion and the moderating role of gender. A survey using the State Anxiety Scale, Ego-Depletion Scale, and Irrational Procrastination Scale was administered to 3370 undergraduates. State anxiety shows positive correlations with ego depletion and academic procrastination (r = 0.665, p

BackgroundPrevious research has linked systemic inflammatory markers and the Dietary Inflammatory Index (DII) with depression. However, the relationship between DII and these markers, and their impact on mortality risk among depressed adults, remains underexplored. This study aims to explore the association between DII and systemic inflammatory markers and their mediating effect on mortality risk in adults with depression.MethodsThis study analyzed data from 4,981 adults with depression in the National Health and Nutrition Examination Survey (NHANES). This study quantified dietary inflammatory potential with the DII and systemic inflammation with the Systemic Immune-Inflammation Index (SII) and Systemic Inflammation Response Index (SIRI). Cox proportional hazards regression and inverse probability weighting evaluated the impact of DII, SII, and SIRI on mortality risk in depressed adults, as well as their mediating effects. Multiple linear regression analyzed the associations between DII and SII/SIRI. Restricted cubic spline analysis explored the non-linear relationship between DII and mortality risk.ResultsIn adjusted regression models, DII, SII, and SIRI were significantly associated with all-cause mortality risk in depressed adults, with hazard ratios (HRs) (95% CIs) from 1.333 to 1.497 (1.051–1.233, 1.689–1.832). DII was linearly related to SII, with βs (95% CIs) from 0.001 to 0.121 (0.001–0.017, 0.001–0.224). SII significantly mediated the DII-mortality risk link, especially in males (8.07%). The DII-mortality relationship was linear (Pnon-linear = 0.174), with a beneficial threshold at 1.62.ConclusionDII and SII are associated with increased all-cause mortality risk in depressed adults. The DII-related mortality risk in depression can be partially mediated by SII, with a more pronounced effect in males.

Study region: Beijing-Tianjin-Hebei urban agglomeration in China Study focus: The prediction of water resources carrying capacity (WRCC) can provide an effective reference for the rational allocation and efficient utilization of water resources. Traditional prediction methods obtained a definite WRCC value but fail to reflect the uncertainty of WRCC changes and limit reference for the optimal allocation of water resources. To ensure the accuracy, availability and comprehensiveness of prediction, this paper adopts the improved principal component analysis (PCA) to screen indicators, and predicts the WRCC through the coupled model of Monte Carlo and Grey Wolf Optimization-Support Vector Machine(GWO-SVM), addressing single result issues and computational complexity. At the same time, various regulation schemes for sensitive indicators are designed to provide an effective guidance for the optimal allocation and sustainable use of water resources. New hydrological insights for the region: In 2025, the probability of WRCC in Tianjin, Handan, Xingtai, Hengshui, Cangzhou, Langfang to maintain grade III is more than 80 %, and that in Beijing, Baoding, Tangshan, Qinhuangdao, Zhangjiakou, Chengde to reach grade IV is more than 50 %. The sensitivity analysis shows that the sensitive indicators mainly focus on water supply and consumption, water use efficiency and pollutant gas emissions. The WRCC can be further improved under different schemes. The results can provide effective guidance for the optimal allocation of water resources and maintain sustainable economic and social development in Beijing-Tianjin-Hebei urban agglomeration.

BackgroundToward the close of the 20th century, Chinese scholars introduced a novel pedagogical approach to education in China, distinguished by its divergence from conventional teaching methods. This instructional strategy assumes a pivotal role in imparting indispensable medical knowledge to students within a meticulously structured and all-encompassing framework.ObjectiveThe objective of this study is to assess the effectiveness of a novel teaching approach that integrates the three action teaching model with learning plan guidance within a preventive medicine course. Through this investigation, empirical evidence will be provided regarding the impact of utilizing learning guided by the three action teaching model with learning plan guidance as an innovative instructional method, thereby shedding light on its potential to enhance students’ autonomous learning in the field of preventive medicine.MethodsThe control group consisted of 48 students from Class 2 of clinical medicine in grade 2021, who were taught using the traditional classroom teaching mode. Meanwhile, Class 1 served as the experimental group comprising 47 individuals, who received instruction through the three-action teaching model with learning plan guidance. Evaluation was conducted using course tests and questionnaires, and data analysis was performed utilizing t-tests, analysis of variance, and rank sum tests in SPSS software.ResultsThe average total score of the test group (79.44 ± 10.13) was significantly higher than that of the control group (70.00 ± 13.57) (t = 3.943, p

IntroductionHeterostyly is a genetically controlled style polymorphism, that plays an important role in promoting outcrossing and improving reproductive fitness. Although distyly is often studied in plants of the Rubiaceae family, little attention has been paid to the reproductive strategies of distylous species in fragmented habitats. Here, We report for the first time the growth of Ophiorrhiza alatiflora, a type distylous species, in karst areas and evaluate its reciprocity between long styled morph and short one. We analyze the two distyly morph differences in the ancillary polymorphic of flowers and explore their reproductive strategy in fragmented habitats.MethodsIn this study, we measured the floral characteristics of different morphs and performed differential secondary metabolite analysis on different morphs and tissue organs; Different pollination treatments were carried out to observe the fruit set, pollen germination, and pollen tube elongation of O. alatiflora.Results and discussionOur research indicates that O. alatiflora is a typical distylous plant for the distyly has high reciprocity. Both morphs exhibit the highest fruit set of intermorph outcrossing; The pollen germination and pollen tube elongation experiments have also demonstrated that the affinity of pollen from intermorph outcrossing is highest, regardless of whether it is the long or short morph as the maternal parent; Meanwhile, O. alatiflora is an incompletely self-incompatible plant that exhibits a certain degree of self-pollination and intramorph outcrossing, which may be one of the important means to ensure sustainable reproduction in severely disturbed habitats. In the ancillary polymorphic of flowers, L-morphs flowers produce more pollen, and S-morph flowers produce more ovules to improve their male-female fitness and compensate for the asymmetry of pollen flow; Compared with S-morphs, L-morphs contain significantly higher levels of several kinds of terpenoids. S-morphs produce more flavonoids than L-morphs. The differences in secondary metabolites between L-morphs and S-morphs are mainly reflected in the different nutritional organs (including stems and leaves). Overall, our work has revealed the unique reproductive strategy of O. alatiflora in fragmented habitats based on the characteristics of distyly, verifying the hypothesis that the distyly of O. alatiflora promotes outcrossing and avoids male-female interference, improving male-female fitness and this is the first time in the Ophiorrhiza genus.

IntroductionImproving the rationality and accuracy of classroom quality analysis is crucial in modern education. Traditional methods, such as questionnaires and manual recordings, are resource-intensive and subjective, leading to inconsistent results. As a solution, computer vision (CV) technologies have emerged as powerful tools for real-time classroom monitoring. This study proposes a novel Dense 3D Convolutional Residual Network (D3DCNN_ResNet) to recognize students’ expressions and behaviors in English classrooms.MethodsThe proposed method combines Single Shot Multibox Detector (SSD) for target detection with an improved D3DCNN_ResNet model. The network applies 3D convolution in both spatial and temporal domains, with shortcut connections from residual blocks to increase network depth. Dense connections are introduced to enhance the flow of high- and low-level features. The model was tested on two datasets: the CK+ dataset for expression recognition and the KTH dataset for behavior recognition.Results and DiscussionThe experiments show that the proposed method is highly efficient in optimizing model training and improving recognition accuracy. On the CK+ dataset, the model achieved an expression recognition accuracy of 97.94%, while on the KTH dataset, the behavior recognition accuracy reached 98.86%. The combination of residual blocks and dense connections reduced feature redundancy and improved gradient flow, leading to better model performance. The results demonstrate that the D3DCNN_ResNet is well-suited for classroom quality analysis and has the potential to enhance teaching strategies by providing real-time feedback on student engagement.

Eucalyptus has become one of the most widely planted species in tropical and subtropical regions in China, with important economic, ecological, and social values. However, it is currently unclear how climate change will affect different Eucalyptus species. Therefore, it is urgent to investigate the potential distribution and dynamics of Eucalyptus under current and future climate scenarios. In this study, we analyzed the potential distribution patterns of the three main Eucalyptus species (Eucalyptus grandis, Eucalyptus urophylla, and Eucalyptus tereticornis) under current and future climatic conditions (2041-2060 and 2061-2080) using the optimized MaxEnt model, which integrates a variety of environmental data including climate, topography, soil, and human influence. We also identified the main factors affecting the potential distributions of the three main Eucalyptus species. The model indicated that E. grandis exhibited heightened sensitivity to the mean temperature of the coldest quarter (7.0-20.0 °C) and annual mean temperature (11.9-24.2 °C), whereas E. urophylla displayed heightened sensitivity to precipitation of the warmest quarter (272-1694 mm) and annual precipitation (812-2624 mm). Conversely, E. tereticornis demonstrated heightened sensitivity to annual mean temperature (12.7-24.5 °C) and temperature seasonality (63.8-598.9). Under the current climate, E. tereticornis had the widest suitable distribution area (124.91 × 104 km²), followed by E. grandis (124.89 × 104 km²) and E. urophylla (119.81 × 104 km²). Under future climate change scenarios, the suitable ranges of E. grandis, E. urophylla and E. tereticornis will continue to expand. This study highlights the importance of climate change in Eucalyptus distribution and provides quantified potential distribution maps for three Eucalyptus species under current and future climate conditions in China. This research offers valuable scientific insights pertinent to the management and rational site selection for Eucalyptus plantations.

Fibrotic scarring and impaired myocardial calcium homeostasis serve as the two main factors in the pathology of heart failure following myocardial infarction (MI), leading to poor prognosis and death in patients. Serca2a is a target of interest in gene therapy for MI-induced heart failure via the regulation of intracellular calcium homeostasis and, subsequently, enhancing myocardial contractility. A recent study also reported that Serca2a ameliorates pulmonary fibrosis by blocking nuclear factor kB (NF-kB)/interleukin-6 (IL-6)-induced (SMAD)/TGF-β signaling activation, while the effect in MI-induced myocardial fibrosis remains to be addressed. Here, we loaded Serca2a plasmids into type 1 collagen-targeting nanoparticles to synthesize the GKWHCTTKFPHHYCLY-Serca2a-Liposome (GSL-NPs) for targeted treatment of myocardial infarction. We showed that GSL-NPs were effectively targeted in the scar area in MI-induced mice within tail-vein delivery for 48 h. Treatment with GSL-NPs improved cardiac functions and shrank fibrotic scars after MI in mice by up-regulating Serca2a. In cardiac fibroblasts, GSL-NPs alleviated hypoxia-induced fibrotic progression partly by inhibiting NF-kB activation. Furthermore, treatment with GSL-NPs protected cardiomyocyte calcium homeostasis and enhanced myocardial contractility during hypoxia. Together, we demonstrate that type I collagen-targeted liposome delivery of Serca2a may benefit patients with myocardial infarction by inhibiting fibrotic scarring as well as modulation of calcium homeostasis.

Background: Questioned document examination (QDE) is a crucial sub-discipline of forensic science, and over the years, this discipline has developed a vast amount of research results and literature. However, the current studies have only reviewed the development of QDE for a period of time or a particular research direction, lacking a comprehensive review of this discipline since its origins. Aim: The aim of this study is to utilize bibliometric methods to review the history and development of QDE since 1934 and also to predict its future research trends. Materials: This study collects the literature of questioned document examination (QDE) from the Web of Science Core Collection database and uses four bibliometric analysis tools: CiteSpace, VOSviewer, SciMAT, and Bibliometric, to process relevant literature data. Methods: Through the analysis methods of journal co-citation analysis, keyword co-occurrence network analysis, literature cocitation analysis, and theme evolutionary analysis, the bibliometric data information in the QDE field is obtained, and a visualized knowledge mapping is constructed. Results: The results show that four research directions have been formed, basically. As far as the development trend of QDE research is concerned, the research field shows the change in content from cursory to detailed and in analysis from qualitative to quantitative, while the research field maintains a close relationship with chemical technology and computer science. Conclusions: The changes in QDE can be attributed to advancements in science and technology, which have influenced research methods. QDE also has a legal aspect, with a focus on enhancing scientific accuracy and evidentiary value. These developments aim to strengthen QDE’s identification power and promote its scientific and standardized growth. This will gradually enhance the reliability and strength of QDE evidence, making it more valuable in judicial practice.

Abstract Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure (XANES) has been a decade-long pursuit to probe local chemical reactions at the nanoscale. However, the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis, especially when the element of interest is at a low concentration. In this work, we developed a post-imaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images. The proposed neural network model could be trained to adapt to new datasets by incorporating the physical features inherent in the latent space of the XANES images and self-supervised to detect new features in the images and achieve self-consistency. Two examples are presented in this work to illustrate the model’s robustness in determining the valence states of Ni and Co in the LiNixMnyCo1-x-yO2 systems with high confidence.

Abstract Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world.

Abstract Chronic wounds, resulting from persistent inflammation, can trigger a cascade of detrimental effects including exacerbating inflammatory cytokines, compromised blood circulation at the wound site, elevation of white blood cell count, increased reactive oxygen species, and the potential risk of bacterial infection. The interleukin-17 (IL-17) signaling pathway, which plays a crucial role in regulating immune responses, has been identified as a promising target for treating inflammatory skin diseases. This review aims to delve deeper into the potential pathological role and molecular mechanisms of the IL-17 family and its pathways in wound repair. The intricate interactions between IL-17 and other cytokines will be discussed in detail, along with the activation of various signaling pathways, to provide a comprehensive understanding of IL-17’s involvement in chronic wound inflammation and repair.

Nanocrystalline NiTi shape memory alloys (SMAs) generally possess outstanding biocompatibility, good corrosion resistance, high cyclic stability and superior fatigue resistance. Nanocrystalline NiTi SMAs are frequently made into the components by experiencing plastic deformation at moderate temperatures in order to prevent nanocrystalline grains from growing up at high temperatures. Therefore, it is of great significance to investigate plastic deformation of nanocrystalline NiTi SMAs at moderate temperature by combining crystal plasticity finite element method (CPFEM) with experiment. In the present work, a CPFEM is employed for studying strengthening mechanism of nanocrystalline Ni50·3Ti49.7 SMA during hot deformation at 400 °C, where statistically stored dislocation (SSD) and geometrically necessary dislocation (GND) densities are integrated into the crystal plasticity constitutive model to account for strain gradients based on dislocation slip. The results show that nanocrystalline Ni50·3Ti49.7 SMA exhibits a strong size effect during plastic deformation at 400 °C, where yield stress along with SSD and GND densities increases with the decrease of grain size. It is evident that grain boundary strengthening and dislocation strengthening play an important role in size effect of nanocrystalline Ni50·3Ti49.7 SMA. The occurrence of grain rotation is induced by significant plastic strain gradients and high dislocation densities. With the increase of plastic strain, grain rotation results in the decrease of average grain boundary misorientation and activates additional available slip systems to+ accommodate the deformation, thereby leading to the decrease of the GND density and the increase of SSD density.

Abstract Sect. tuberculata plant belongs to the Camellia genus and is named for the “tuberculiform protuberance on the surface of the ovary and fruit”. It is a species of great ornamental value and potential medicinal value. However, little has been reported on the metabolites of C. tuberculata seeds. Therefore, this study was conducted to investigate the metabolites of C. tuberculata seeds based on UPLC/ESI-Q TRAP-MS/MS with extensively targeted metabolomics. A total of 1611 metabolites were identified, including 107 alkaloids, 276 amino acids and derivatives, 283 flavonoids, 86 lignans and coumarins, 181 lipids, 68 nucleotides and derivatives, 101 organic acids, 190 phenolic acids, 10 quinones, 4 steroids, 17 tannins, 111 terpenoids, and 177 other metabolites. We compared the different metabolites in seeds between HKH, ZM, ZY, and LY. The 1311 identified different metabolites were classified into three categories. Sixty-three overlapping significant different metabolites were found, of which lignans and coumarins accounted for the largest proportion. The differentially accumulated metabolites were enriched in different metabolic pathways between HKH vs. LY, HKH vs. ZM, HKH vs. ZY, LY vs. ZY, ZM vs. LY and ZM vs. ZY, with the most abundant metabolic pathways being 4, 2, 4, 7, 7 and 5, respectively (p

In view of the difficulties in quantification of drought and its potential impact on drought disaster, as well as the challenges of applying scenario simulation technology in regional drought risk assessment, we proposes a fuzzy identification model of drought disaster coupling improved Monte-Carlo model and Grey Wolf Optimization-Back Propagation (GWO-BP) neural network based on the disaster-forming process, aiming to seek a quantitative relationship between drought and drought disasters through numerous drought scenario simulations. Meanwhile, we also constructed a joint probability distribution model of drought and drought disasters, which can calculate the conditional probability of different grades of drought leading to different grades of drought disasters, then, we can calculate the drought disaster risk measurement(DDRM) based on the level of drought propagation, by combining the achievements of the above model and the determination of inhibitory or promoting mechanism in the disaster-forming process. Finally, we conducted an empirical study of the new method using Anhui Province as the research object. The results show that: (1) The improved Monte-Carlo model incorporates the Copula function and rank correlation coefficient, which can generate indicator sequences with correlation and maintain a similar correlation level to the original samples. (2) The fuzzy identification model of drought disaster can not only elucidate the propagation pattern of drought factors to drought disaster through numerous drought scenario simulations, but also effectively address the limitations of traditional drought risk research that heavily relies on long sequences of statistical data. (3) The DDRM can absolutely quantify the magnitude of regional inhibiting or promoting effects during the disaster formation process, which can make the assessment results comparable among the drought risk in different regions. The resultant findings offer crucial insights for pertinent decision-makers to alleviate the adverse effects of drought disaster.

Fractures play a crucial role in tight sandstone gas reservoirs with low permeability and low effective porosity. If open, they not only significantly increase the permeability of the reservoir but also serve as channels connecting the storage space. Among numerous fracture identification methods, seismic data provide unique advantages for fracture identification owing to the provision of three-dimensional information between wells. How to accurately identify the development of fractures in geological bodies between wells using seismic data is a major challenge. In this study, a tight sandstone gas reservoir in the Kuqa Basin (China) was used as an example for identifying reservoir fractures using deep-learning-based method. First, a feasibility analysis is necessary. Intersection analysis between the fracture density and seismic attributes (the characteristics of frequency, amplitude, phase, and other aspects of seismic signals) indicates that there is a correlation between the two when the fracture density exceeds a certain degree. The development of fractures is closely related to the lithology and structure, indirectly affecting differences in seismic attributes. This indicates that the use of seismic attributes for fracture identification is feasible and reasonable. Subsequently, the effective fracture density data obtained from imaging logging were used as label data, and the optimized seismic attribute near the well data were used as feature data to construct a fracture identification sample dataset. Based on a feed-forward neural network algorithm combined with natural fracture density and effectiveness control factor constraints, a trained identification model was obtained. The identification model was applied to seismic multi-attribute data for the entire work area. Finally, the accuracy of the results from the training, testing, and validation datasets were used to determine the effectiveness of the method. The relationship between the fracture identification results and the location of the fractures in the target reservoir was used to determine the reasonableness of the results. The results indicate that there is a certain relationship between multiple seismic attributes and fracture development, which can be established using deep learning models. Furthermore, the deep-learning-based seismic data fracture identification method can effectively identify fractures in the three-dimensional space of reservoirs.

Background: Syndrome of inappropriate anti-diuretic hormone secretion (SIADH) is associated with strongyloidiasis. Herein, a rare case of severe SIADH secondary to disseminated strongyloidiasis in a kidney transplant recipient is reported. Case presentation: A case involving a 43-year-old male kidney transplant recipient with severe disseminated Strongyloides stercoralis infection is reported. The patient was a construction worker with a history of consuming undercooked yellow eel and sashimi. On admission, the patient presented with poor appetite, nausea, vomiting and diarrhea. Laboratory investigations revealed persistent significant hyponatremia and low serum osmolality, confirming the diagnosis of SIADH. S. stercoralis was detected in the stool and bronchoalveolar lavage fluid. He was treated with empirical albendazole because S. stercoralis was detected in the stool; however, his symptoms and hyponatremia did not improve until ivermectin was administered, after which SIADH resolved quickly. Conclusion: This case suggests that S. stercoralis infection should be included in the differential diagnosis when a kidney transplant recipient presents with gastrointestinal symptoms and SIADH. In such situations, pre- or post-transplant screening for S. stercoralis is needed, and early ivermectin treatment is very important.

The anomalies of cadmium (Cd) in karst region pose a severe threat to plant growth and development. In this study, the responses of Rhododendron decorum to Cd stress were investigated at physiological, molecular, and endophytic microbial levels, and the potential correlation among these responses was assessed. The Cd stress impeded R. decorum growth and led to an increase in malondialdehyde (MDA) and hydrogen peroxide (H2O2) levels, as well as enhanced superoxide dismutase (SOD) and catalase (CAT) activities. Meanwhile, Cd stress increased the Cd (up to 80 times compared to the control), sodium (Na), aluminum (Al), and zinc (Zn) contents, while decreased the magnesium (Mg) and manganese (Mn) contents in R. decorum leaves. Transcriptome suggested that Cd significantly regulated the pathways including “protein repair”, “hormone-mediated signaling pathway”, and “ATP-binding cassette (ABC) transporters”. Additionally, q-PCR analysis showed that Cd stress significantly up-regulated the expressions of ABCB19-like and pleiotropic drug resistance, while down-regulated the expressions of indole-3-acetic acid-amido synthetase and cytokinin dehydrogenase. The Cd stress influenced the composition of endophytic microbial communities in R. decorum leaves and enhanced the interspecific bacterial associations. Furthermore, the bacterial genera Achromobacter, Aureimonas and fungal genus Vishniacozyma exhibited a high degree of connectivity with other nodes in networks constructed by the metal element contents, differentially expressed genes (DEGs), and microbial communities, respectively. These findings provide a comprehensive insight into the response of R. decorum to Cd-induced stress, which might facilitate the breeding of the Cd-tolerant R. decorum.

Speckle patterns generated by the intermodal interference of multimode fibers enable accurate broadband wavelength measurements. However, the measurement speed is limited by the frame rate of the camera that captures the patterns. We propose a compact and cost-effective ultrafast wavemeter based on multimode and multicore fibers, which employs spectral–spatial–temporal mapping. The speckle patterns generated by multimode fibers enable spectral-to-spatial mapping, which is then sampled by a multicore fiber into a pulse sequence to implement spatial-to-temporal mapping. A high-speed single-pixel photodetector is employed to capture the pulse sequence, which is analysed using a multilayer perceptron to estimate the wavelength. The feasibility of the proposed wavelength estimation method is experimentally verified, achieving a measurement rate of 100 MHz with a resolution of 2.7 pm in a 1 nm operation bandwidth.

Abstract In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites.

The aim of this study is to address the issue of difficulty in evaluating the combined sewer overflow (CSO) pollution effectively, especially for the monitoring of overflow from non-standard thin-plate weirs (NTPWs). In order to construct a discharge calculation mathematical model (DCMM) of NTPWs in an urban combined sewer overflow system (UCSOS), which can be used to construct a real-time monitoring and early warning system, a physical model constructed firstly for obtaining hydraulic characteristic sets under three flow conditions (free, submerged, pressurized flow). Then, the coefficient function expressions are obtained under three flow conditions through genetic algorithm (GA) fitting, and a DCMM of UCSOS is established subsequently. The results indicate that DCMM exhibits high accuracy, with mean relative error (MRE) at 0.0326, root mean square error (RMSE) at 1.569, and Nash–Sutcliffe efficiency (NSE) coefficient at 0.9387. The DCMM can not only evaluate the discharge capacity under different flow conditions, but also be used to realize reliable and convenient discharge monitoring. Although the DCMM constructed in this case is not universal, the modeling method of combining physical models with GA is an effective new approach to evaluate the discharge capacity of NTPW, which also holds value for application in other cities. HIGHLIGHTS We obtained data by conducting physical model experiments and used GA algorithm to fit expressions of the coefficient function under three flow conditions respectively to constructed the DCMM.; The DCMM can not only evaluate the discharge capacity under different flow conditions, but also be used to realize reliable and convenient discharge monitoring for NTPWs.; A real-time monitoring and early warning system based on the DCMM is constructed to meets the issue of difficulty in evaluating the combined sewer overflow pollution.;

Abstract Objective This study endeavors to examine the feasibility of predicting the clinical outcomes of patients suffering from peripheral artery disease (PAD) who undergo endovascular intervention, by employing the Syngo iFlow technology. Methods Retrospectively enrolling 76 patients from December 2021 to May 2023, yielding a total of 77 affected limbs, this study employs clinical outcomes (improvement or otherwise) as the gold standard. Two physicians conducted visual assessments on both DSA and iFlow images to gauge patient improvement and assessed inter-observer consistency for each image modality. The Time to Peak (TTP) of regions of interest (ROI) at the femoral head, knee joint, and ankle joint was measured. Differences in pre- and post-procedure TTP were juxtaposed, and statistically significant parameter cutoff values were identified via ROC analysis. Employing these cutoffs for TTP classification, multivariate logistic regression and the C-statistic were utilized to assess the predictive value of distinct parameters for clinical success. Results Endovascular procedure exhibited technical and clinical success rates of 82.58 and 75.32%, respectively. Diagnostic performance of iFlow image visual assessment surpassed that of DSA images. Inter-observer agreement for iFlow and DSA image evaluations was equivalent (κ = 0.48 vs 0.50). Post-classification using cutoff values, multivariate logistic regression demonstrated the statistical significance of ankle joint TTP in post-procedure iFlow images of the endovascular procedure for clinical success evaluation (OR 7.21; 95% CI 1.68, 35.21; P = 0.010), with a C-statistic of 0.612. Conclusion Syngo iFlow color-encoded imagery holds practical value in assessing the technical success of post-endovascular procedures, offering comprehensive lower limb arterial perfusion visualization. Its quantifiable parameters exhibit promising potential for prognosticating clinical success.

Abstract Background Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Traditional manual power inspections are characterized by low efficiency, lengthy processes, and high costs. Existing research on UAV-based power inspections has often overlooked critical factors such as the risk levels of target tasks, the duration of tasks executed by UAVs, and the utility per unit task. To address these gaps, this paper proposes a task allocation method for UAV power inspections based on the Time Window Matrix Reversal Genetic Algorithm (TMGA). Firstly, the proposed cost model accounts for the risk levels of inspection tasks and the impact of low-altitude flight on energy consumption. Secondly, an inspection task allocation model is constructed with the goal of maximizing UAV inspection unit utility. The model is then optimized using two-point crossover and single-point reversal mutation operations, which enhance the UAV unit utility and generate an optimal allocation matrix. The performance of TMGA is evaluated through simulation experiments in three different scenarios, comparing it with existing algorithms. The results show that TMGA outperforms these algorithms in terms of average task time, task completion rate, and unit utility. Specifically, TMGA reduces the average task time by 37% compared to the Cluster Grouping Consensus-base Bundle Algorithm and improves task unit utility by 56.91% compared to the Genetic Algorithm.

Fixed-length segment (FLS) optimization method offers a way to realize the high-efficiency analog inverse design of nanophotonic devices. However, due to the limitation of the variable dimensions and restricted search space, this method can hard to simultaneously achieve large bandwidth, compact size, and efficient performance when dealing with high-dimension design. Here, we propose a highly efficient variable-length segment (VLS) based inverse design method, aiming to solve complex analog inverse design and fully demonstrate the targeted performance. It divides the optimized region into several tapered segments of unequal length and inserts a subwavelength transition waveguide between each tapered segment, which can expand the search space of the algorithm, thus making it easier to obtain a better locally optimal solution. As typical complex proof-of-concept examples, a 1 × 4 power splitter on a silicon-on-insulator (SOI) platform is chosen to demonstrate the validity of our design paradigm. The simulation results show that, compared with the conventional FLS, VLS has about 4–5 times higher efficiency and obtains better optimization performance. In our experiment, the fabricated device has a compact footprint of 9.8 μm × 4.9 μm and is complementary metal oxide semiconductor (CMOS) compatible. The measured insertion loss and the uniformity are less than 0.58 dB and 0.8 dB, respectively. In addition, the tolerances to fabrication errors are also investigated. Our work may find important applications in the advanced design of future nanoscale high-quality optical devices.

We propose and fabricate an unconventional photonic lantern that is not only able to achieve mode multiplexing and demultiplexing, but also has the functionality of mode-dependent loss equalization. In the process of fusion splicing between the tail fiber of photonic lantern and the few-mode fiber, by controlling the fusion splicing parameters, a refractive index (RI) modified waveguide is formed at the fusion splicing region, through which the fundamental mode will have higher attenuation than the higher-order modes, which enables us to control the attenuation of different modes, so that we can customize the unconventional photonic lantern with different differential mode attenuation (DMA). Simulation has been carried out to investigate the dependence of DMA on the structure of the RI modified waveguide, and experiments have been implemented to investigate the dependence of DMA on discharge parameters, which matches well with the simulated tendency. In addition, CCD measurements of all three output ports have been performed on the fabricated devices, well-defined mode field patterns and high mode purity at the C-band can be observed. The proposed novel unconventional photonic lanterns open a new way to obtain simultaneous mode multiplexing / demultiplexing and mode-dependent loss equalization function, which will be greatly beneficial in the few-mode fiber transmission system.

Abstract For 800 Gbps/λ and beyond optical transmission systems, frequency-dependent impairments (FDI) degrade coherent optical transceiver (CO-TRx) performance severely. Calibration and compensation of such FDI in factories includes amplitude/phase frequency response (AFR/PFR), skew, and ripple for both transmitters(Tx) and receivers (Rx). However, due to the polarization rotation and phase rotation effects in optical link, the separation and extraction of FDI from different polarization or I/Q tributaries is challenging. Here we report a FDI calibration method based on orthogonal separation scheme and frequency domain analysis. The proposal can simultaneously characterize and separate the Tx/Rx sides FDI including AFR, PFR, and time skew of four tributaries. Finally, the effectiveness is demonstrated by transmitting a 96 GBuad Nyquist-16QAM signal on a 64 GBaud-class CO-TRx.

Abstract Endometrial carcinoma (EC) is a prevalent gynecological tumor in women, and its treatment and prevention are significant global health concerns. The mutations in DNA polymerase ε (POLE) are recognized as key features of EC and may confer survival benefits in endometrial cancer patients undergoing anti-PD-1/PD-L1 therapy. However, the anti-tumor mechanism of POLE mutations remains largely elusive. This study demonstrates that the hot POLE P286R mutation impedes endometrial tumorigenesis by inducing DNA breakage and activating the cGAS-STING signaling pathway. The POLE mutations were found to inhibit the proliferation and stemness of primary human EC cells. Mechanistically, the POLE mutants enhance DNA damage and suppress its repair through the interaction with DNA repair proteins, leading to genomic instability and the upregulation of cytoplasmic DNA. Additionally, the POLE P286R mutant also increases cGAS level, promotes TBK1 phosphorylation, and stimulates inflammatory gene expression and anti-tumor immune response. Furthermore, the POLE P286R mutation inhibits tumor growth and facilitates the infiltration of cytotoxic T cells in human endometrial cancers. These findings uncover a novel mechanism of POLE mutations in antagonizing tumorigenesis and provide a promising direction for effective cancer therapy.

Abstract Endometrial cancer (EC) stands as one of the most prevalent malignancies affecting the female genital tract, witnessing a rapid surge in incidence globally. Despite the well-established association of histone methyltransferase SMYD3 with the development and progression of various cancers, its specific oncogenic role in endometrial cancer remains unexplored. In the present study, we report that the expression level of SMYD3 is significantly upregulated in EC samples and associated with EC progression. Through meticulous in vivo and in vitro experiments, we reveal that depletion of SMYD3 curtails cell proliferation, migration, and invasion capabilities, leading to compromised non-homologous end joining repair (NHEJ) and heightened sensitivity of EC cells to radiation. Furthermore, our pathway enrichment analysis underscores the pivotal involvement of the DNA damage repair pathway in regulating EC progression. Mechanistically, in response to DNA damage, SMYD3 is recruited to these sites in a PARP1-dependent manner, specifically methylating LIG4. This methylation sets off a sequential assembly of the LIG4/XRCC4/XLF complex, actively participating in the NHEJ pathway and thereby fostering EC progression. Notably, our findings highlight the promise of SMYD3 as a crucial player in NHEJ repair and its direct correlation with EC progression. Intriguingly, pharmacological intervention targeting SMYD3 with its specific inhibitor, BCI-121, emerges as a potent strategy, markedly suppressing the tumorigenicity of EC cells and significantly enhancing the efficacy of radiotherapy. Collectively, our comprehensive data position SMYD3 as a central factor in NHEJ repair and underscore its potential as a promising pharmacological target for endometrial cancer therapy, validated through both in vitro and in vivo systems.

High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPGD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.

Automatic polarization controllers find broad applications in various fields, including optical communication, quantum optics, optical sensing, and biomedicine. Currently, the predominant integrated automatic polarization controllers employ either lithium niobate or silicon platforms. Devices based on lithium niobate platforms exhibit excellent performance; however, their fabrication complexity hinders widespread commercial deployment. In contrast, silicon-based integrated automatic polarization controllers benefit from complementary metal–oxide–semiconductor compatibility and reduced fabrication costs. Nevertheless, these silicon automatic polarization controllers suffer from low tracking speeds, peaking at merely 1.256 krad/s. In this study, we demonstrated a silicon high-speed automatic polarization controller, incorporating innovative thermal tuning units combined with a sophisticated control algorithm. The response time of these thermal tuning units has been markedly decreased to 3.2 µs. In addition, we have implemented a novel automatic polarization control algorithm, utilizing gradient descent techniques, on a field-programmable gate array control board. The synergy of the rapid thermal tuning unit and the advanced control algorithm has enabled us to attain an unprecedented polarization control speed of up to 20 krad/s, with this rate being solely limited by the capabilities of our characterization equipment. To our knowledge, this speed is the fastest yet reported for a silicon-based integrated automatic polarization control chip. The proposed device represents a significant breakthrough in the field of silicon-based automatic polarization controllers, paving the way for the future integration of additional polarization management devices. Such an advancement would mark a substantial leap in the realm of integrated photonics, bridging the gap between performance efficiency, cost-effectiveness, and technological integration.

Summary: Background: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia. Methods: We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively. Findings: By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients’ subgroups. Interpretation: An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries. Funding: German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

ObjectivePrior research underscores the significance of paraspinal muscles in maintaining spinal stability. This study aims to investigate the predictive value of paraspinal muscle parameters for the occurrence of new vertebral compression fractures (NVCF) following percutaneous vertebroplasty (PVP) or percutaneous kyphoplasty (PKP) in patients with osteoporotic vertebral compression fractures (OVCF).MethodsRetrospectively collected data from October 2019 to February 2021 (internal validation, n = 235) and March 2021 to November 2021 (external validation, n = 105) for patients with OVCF treated with PVP/PKP at our institution. They were randomly divided into training (188 cases) and validation groups (47 cases) at an 8:2 ratio. Lasso regression and multivariable logistic regression identified independent risk factors in the training set, and a Nomogram model was developed. Accuracy was assessed using receiver operating characteristic curves (ROC), calibration was evaluated with calibration curves and the Hosmer-Lemeshow test, and clinical utility was analyzed using decision curve analysis (DCA) and clinical impact curve (CIC).ResultsSurgical approach, spinal computed tomography (CT) values, and multifidus skeletal muscle index (SMI) are independent predictors of postoperative NVCF in OVCF patients. A Nomogram model, based on the identified predictors, was developed and uploaded online. Internal validation results showed area under the curve (AUC) values of 0.801, 0.664, and 0.832 for the training set, validation set, and external validation, respectively. Hosmer-Lemeshow goodness-of-fit tests (χ2 = 7.311–14.474, p = 0.070–0.504) and calibration curves indicated good consistency between observed and predicted values. DCA and CIC demonstrated clinical net benefit within risk thresholds of 0.06–0.84, 0.12–0.23, and 0.01–0.27. At specificity 1.00–0.80, the partial AUC (0.106) exceeded that at sensitivity 1.00–0.80 (0.062).ConclusionCompared to the spinal CT value, the multifidus SMI has certain potential in predicting the occurrence of NVCF. Additionally, the Nomogram model of this study has a greater negative predictive value.

Lead (Pb) is a hazardous heavy metal that accumulates in many environments. Phytoremediation of Pb polluted soil is an environmentally friendly method, and a better understanding of mycorrhizal symbiosis under Pb stress can promote its efficiency and application. This study aims to evaluate the impact of two ectomycorrhizal fungi (Suillus grevillei and Suillus luteus) on the performance of Pinus tabulaeformis under Pb stress, and the biomineralization of metallic Pb in vitro. A pot experiment using substrate with 0 and 1,000 mg/kg Pb2+ was conducted to evaluate the growth, photosynthetic pigments, oxidative damage, and Pb accumulation of P. tabulaeformis with or without ectomycorrhizal fungi. In vitro co-cultivation of ectomycorrhizal fungi and Pb shots was used to evaluate Pb biomineralization. The results showed that colonization by the two ectomycorrhizal fungi promoted plant growth, increased the content of photosynthetic pigments, reduced oxidative damage, and caused massive accumulation of Pb in plant roots. The structural characteristics of the Pb secondary minerals formed in the presence of fungi demonstrated significant differences from the minerals formed in the control plates and these minerals were identified as pyromorphite (Pb5(PO4)3Cl). Ectomycorrhizal fungi promoted the performance of P. tabulaeformis under Pb stress and suggested a potential role of mycorrhizal symbiosis in Pb phytoremediation. This observation also represents the first discovery of such Pb biomineralization induced by ectomycorrhizal fungi. Ectomycorrhizal fungi induced Pb biomineralization is also relevant to the phytostabilization and new approaches in the bioremediation of polluted environments.

Abstract Background Lung ischemia-reperfusion (I/R) injury is a serious clinical problem without effective treatment. Enhancing branched-chain amino acids (BCAA) metabolism can protect against cardiac I/R injury, which may be related to bioactive molecules generated by BCAA metabolites. L-β-aminoisobutyric acid (L-BAIBA), a metabolite of BCAA, has multi-organ protective effects, but whether it protects against lung I/R injury is unclear. Methods To assess the protective effect of L-BAIBA against lung I/R injury, an animal model was generated by clamping the hilum of the left lung, followed by releasing the clamp in C57BL/6 mice. Mice with lung I/R injury were pre-treated or post-treated with L-BAIBA (150 mg/kg/day), given by gavage or intraperitoneal injection. Lung injury was assessed by measuring lung edema and analyzing blood gases. Inflammation was assessed by measuring proinflammatory cytokines in bronchoalveolar lavage fluid (BALF), and neutrophil infiltration of the lung was measured by myeloperoxidase activity. Molecular biological methods, including western blot and immunofluorescence, were used to detect potential signaling mechanisms in A549 and BEAS-2B cells. Results We found that L-BAIBA can protect the lung from I/R injury by inhibiting ferroptosis, which depends on the up-regulation of the expressions of GPX4 and SLC7A11 in C57BL/6 mice. Additionally, we demonstrated that the Nrf-2 signaling pathway is key to the inhibitory effect of L-BAIBA on ferroptosis in A549 and BEAS-2B cells. L-BAIBA can induce the nuclear translocation of Nrf-2. Interfering with the expression of Nrf-2 eliminated the protective effect of L-BAIBA on ferroptosis. A screening of potential signaling pathways revealed that L-BAIBA can increase the phosphorylation of AMPK, and compound C can block the Nrf-2 nuclear translocation induced by L-BAIBA. The presence of compound C also blocked the protective effects of L-BAIBA on lung I/R injury in C57BL/6 mice. Conclusions Our study showed that L-BAIBA protects against lung I/R injury via the AMPK/Nrf-2 signaling pathway, which could be a therapeutic target.

Background: Poor wound healing is a significant complication of diabetes, which is commonly caused by neuropathy, trauma, deformities, plantar hypertension and peripheral arterial disease. Diabetic foot ulcers (DFU) are difficult to heal, which makes patients susceptible to infections and can ultimately conduce to limb amputation or even death in severe cases. An increasing number of studies have found that epigenetic alterations are strongly associated with poor wound healing in diabetes.Aim of review: This work provides significant insights into the development of therapeutics for improving chronic diabetic wound healing, particularly by targeting and regulating DNA methylation and demethylation in DFU.Key scientific concepts of review: DNA methylation and demethylation play an important part in diabetic wound healing, via regulating corresponding signaling pathways in different breeds of cells, including macrophages, vascular endothelial cells and keratinocytes. In this review, we describe the four main phases of wound healing and their abnormality in diabetic patients. Furthermore, we provided an in-depth summary and discussion on how DNA methylation and demethylation regulate diabetic wound healing in different types of cells; and gave a brief summary on recent advances in applying cellular reprogramming techniques for improving diabetic wound healing.

Synotis jinpingensis (Asteraceae, Senecioneae), a new species from Jinping county in southeastern Yunnan province, China, is described and illustrated. This species is distinguished by having white ray florets in the genus Synotis, in which only species with yellow ray florets have been hitherto known. In habit and leaf shape S. jinpingensis is most closely similar to S. duclouxii, a species occurring in southwestern Guizhou, southern Sichuan and northeastern Yunnan, China, but differs, in addition to the color of ray florets, by having fewer lateral veins of leaves, obviously longer bracts of calyculus, and larger phyllaries. The membership of the new species within Synotis is strongly corroborated by evidence from floral micromorphology and phylogenetic analyses based on ITS sequence data. Color photographs of living plants, a distribution map, and provisional IUCN status of S. jinpingensis are provided.

Abstract Background Owing to the advances in diagnosis and therapy, survival or remission rates for lymphoma have improved prominently. Apart from the lymphoma- and chemotherapy-related somatic symptom burden, increasing attention has been drawn to the health-related quality of life. The application of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has been routinely recommended for the staging and response assessment of FDG-avid lymphoma. However, up till now, only a few researches have investigated the brain metabolic impairments in patients with pre-treatment lymphoma. The determination of the lymphoma-related metabolic brain pattern would facilitate exploring the tailored therapeutic regimen to alleviate not only the physiological, but also the psychological symptoms. In this retrospective study, we aimed to establish the diffuse large B-cell lymphoma-related pattern (DLBCLRP) of metabolic brain network and investigate the correlations between DLBCLRP and several indexes of the staging and response assessment. Results The established DLBCLRP was characterized by the increased metabolic activity in bilateral cerebellum, brainstem, thalamus, striatum, hippocampus, amygdala, parahippocampal gyrus and right middle temporal gyrus and by the decreased metabolic activity in bilateral occipital lobe, parietal lobe, anterior cingulate gyrus, midcingulate cortex and medial frontal gyrus. Significant difference in the baseline expression of DLBCLRP was found among complete metabolic response (CMR), partial metabolic response (PMR) and progressive metabolic disease (PMD) groups (P 0.05), the post-treatment declines of DLBCLRP expression were significantly positively correlated with Ann Arbor staging (r s = 0.284, P

The toxicity of cadmium (Cd) not only affects the growth and development of plants but also has an impact on human health. In this study, high-throughput sequencing and LC-MS were conducted to analyze the effect of CdCl2 treatment on the microbial community and soil metabolomics of rhizosphere soil in Rhododendron decorum subsp. diaprepes. The results showed that CdCl2 treatment reduced the quality of the rhizosphere soil by significantly decreasing the soil organic carbon (SOC) content, urease, and invertase activities, increasing the percentage of the exchangeable Cd fraction. CdCl2 treatment did not significantly change the Chao1 and Shannon indices of bacterial and fungal communities in the rhizosphere soil. R. decorum was more likely to recruit Cd-resistant bacteria (e.g., Proteobacteria, Chloroflexi) and increase the abundance of Cd-resistant fungi (e.g., Basidiomycota, Rozellomycota). Moreover, CdCl2 treatment decreased the content of secondary metabolites associated with plants’ resistance to Cd. Rhizosphere soil urease, invertase activities, alkaline phosphatase (ALP), SOC, total potassium (TK), Cd, and nitrate nitrogen (NN) were the main drivers of the composition of rhizosphere bacterial and fungal communities. CdCl2 treatment weakened the relationships among bacterial/fungi, differential metabolites, and physicochemical properties in rhizosphere soil.

Abstract In our modern time, travel has become one of the most significant factors contributing to global epidemic spreading. A deficiency in the literature is that travel has largely been treated as a Markovian process: it occurs instantaneously without any memory effect. To provide informed policies such as determining the mandatory quarantine time, the non-Markovian nature of real-world traveling must be taken into account. We address this fundamental problem by constructing a network model in which travel takes a finite time and infections can occur during the travel. We find that the epidemic threshold can be maximized by a proper level of travel, implying that travel infections do not necessarily promote spreading. More importantly, the epidemic threshold can exhibit a two-threshold phenomenon in that it can increase abruptly and significantly as the travel time exceeds a critical value. This may provide a quantitative estimation of the minimally required quarantine time in a pandemic.

Myocardial dysfunction is the most serious complication of sepsis. Sepsis-induced myocardial dysfunction (SMD) is often associated with gastrointestinal dysfunction, but its pathophysiological significance remains unclear. The present study found that patients with SMD had higher plasma gastrin concentrations than those without SMD. In mice, knockdown of the gastrin receptor, cholecystokinin B receptor (Cckbr), aggravated lipopolysaccharide (LPS)-induced cardiac dysfunction and increased inflammation in the heart, whereas the intravenous administration of gastrin ameliorated SMD and cardiac injury. Macrophage infiltration plays a significant role in SMD because depletion of macrophages by the intravenous injection of clodronate liposomes, 48 h prior to LPS administration, alleviated LPS-induced cardiac injury in Cckbr-deficient mice. The intravenous injection of bone marrow macrophages (BMMs) overexpressing Cckbr reduced LPS-induced myocardial dysfunction. Furthermore, gastrin treatment inhibited toll-like receptor 4 (TLR4) expression through the peroxisome proliferator-activated receptor α (PPAR-α) signaling pathway in BMMs. Thus, our findings provide insights into the mechanism of the protective role of gastrin/CCKBR in SMD, which could be used to develop new treatment modalities for SMD.