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Abstract Asthma is a chronic inflammatory disease that affects millions of people worldwide. Obesity, particularly visceral adipose tissue (VAT), is known to secrete adipokines and pro-inflammatory factors, which are closely associated with various metabolic and cardiovascular diseases. Research indicates that these metabolic disturbances can exacerbate inflammatory conditions, contributing to both cardiovascular and respiratory diseases, including asthma. Despite these associations, studies on the specific relationship between VAT and asthma remain limited and warrant further investigation. Utilizing the NHANES database from 2011 to 2018, we included a total of 11,137 participants. Multivariable regression analysis was performed, stratifying subjects based on VAT levels and adjusting for various confounders. Subgroup interaction analysis and nonlinear analysis were conducted to explore potential effect modifiers and nonlinear associations. In this study, 11,137 participants were included, with 49.74% being female. Among the 509 asthma patients, 69.35% were female. The number of asthma patients among Non-Hispanic Whites was 212, representing 41.65% of the total, the highest proportion among the studied groups. The VAT for asthma patients was 529 g, significantly higher than the 455 g in the non-asthma group (P

Abstract In recent years, multi‐view clustering (MVC) has had significant implications in the fields of cross‐modal representation learning and data‐driven decision‐making. Its main objective is to cluster samples into distinct groups by leveraging consistency and complementary information among multiple views. However, the field of computer vision has witnessed the evolution of contrastive learning, and self‐supervised learning has made substantial research progress. Consequently, self‐supervised learning is progressively becoming dominant in MVC methods. It involves designing proxy tasks to extract supervisory information from image and video data, thereby guiding the clustering process. Despite the rapid development of self‐supervised MVC, there is currently no comprehensive survey analysing and summarising the current state of research progress. Hence, the authors aim to explore the emergence of self‐supervised MVC by discussing the reasons and advantages behind it. Additionally, the internal connections and classifications of common datasets, data issues, representation learning methods, and self‐supervised learning methods are investigated. The authors not only introduce the mechanisms for each category of methods, but also provide illustrative examples of their applications. Finally, some open problems are identified for further investigation and development.

Abstract This study explores the use of machine learning algorithms and EEG data to predict consumer purchasing decisions in an online shopping context. In a field experiment with 66 participants, we observed 328 decision-making instances. Two key EEG features—power spectral density (PSD) and prefrontal asymmetry index (PAI)—were analyzed. We employed traditional classifiers (KNN, RF, SVM) and a shallow neural network to assess predictive performance. Various feature selection methods (F-Score, T-value, Correlation, PCA, recursive feature elimination) were utilized. The results showed the SVM with a Gaussian kernel achieving the highest accuracy of 87.1%. Results underscore the significant roles of frontal and occipital regions in purchasing decisions. The PAI indicated the prefrontal cortex’s critical role in processing cognitive and emotional dimensions, while occipital PSD features highlighted visual processing and attention management in an online shopping environment. These findings affirm the potential of EEG features in consumer behavior analysis and emphasize the importance of advanced machine learning for interpreting neural decision-making correlates. This study provides a foundation for enhancing neuromarketing strategies and creating more engaging customer experiences, suggesting a comprehensive framework for improving marketing effectiveness and understanding consumer decision-making.

Wireless cross-medium communication for underwater equipment may be possible by using millimeter-wave radar to detect the micro-wave vibration on the water surface excited by sound waves of underwater equipment. Studying the effect of water surface waves is of great value for achieving trans-medium communication based on micro-wave vibration detection by millimeter-wave radar. Therefore, experiments were carried out on the trans-medium communication of binary phase-shift keying(BPSK) and binary frequency-shift keying(BFSK) modulation signals in a wave tank. The influence of different water surface wave vibrations on trans-medium communication was tested and analyzed, and the communication performance based on spatial diversity technology was evaluated. The experimental results show that moderate water surface wave vibrations have the least impact on the trans-medium communication performance of millimeter-wave radar, and the spatial diversity technology based on multi-channel data merging can improve trans-medium communication performance on wavy water surfaces. The research results can provide a reference for practical applications of trans-medium communication technology based on micro-wave vibration detection by millimeter-wave radar on wavy water surfaces.

Abstract Background Air pollution poses a significant threat to global public health. While broad mitigation policies exist, an understanding of the economic consequences, both in terms of health benefits and mitigation costs, remains lacking. This study systematically reviewed the existing economic implications of air pollution control strategies worldwide. Methods A predefined search strategy, without limitations on region or study design, was employed to search the PubMed, Scopus, Cochrane Library, Embase, Web of Science, and CEA registry databases for studies from their inception to November 2023 using keywords such as “cost–benefit analyses”, “air pollution”, and “particulate matter”. Focus was placed on studies that specifically considered the health benefits of air pollution control strategies. The evidence was summarized by pollution control strategy and reported using principle economic evaluation measurements such as net benefits and benefit–cost ratios. Results The search yielded 104 studies that met the inclusion criteria. A total of 75, 21, and 8 studies assessed the costs and benefits of outdoor, indoor, and mixed control strategies, respectively, of which 54, 15, and 3 reported that the benefits of the control strategy exceeded the mitigation costs. Source reduction (n = 42) and end-of-pipe treatments (n = 15) were the most commonly employed pollution control methodologies. The association between particulate matter (PM) and mortality was the most widely assessed exposure-effect relationship and had the largest health gains (n = 42). A total of 32 studies employed a broader benefits framework, examining the impacts of air pollution control strategies on the environment, ecology, and society. Of these, 31 studies reported partially or entirely positive economic evidence. However, despite overwhelming evidence in support of these strategies, the studies also highlighted some policy flaws concerning equity, optimization, and uncertainty characterization. Conclusions Nearly 70% of the reviewed studies reported that the economic benefits of implementing air pollution control strategies outweighed the relative costs. This was primarily due to the improved mortality and morbidity rates associated with lowering PM levels. In addition to health benefits, air pollution control strategies were also associated with other environmental and social benefits, strengthening the economic case for implementation. However, future air pollution control strategy designs will need to address some of the existing policy limitations.

Abstract Red swamp crayfish, Procambarus clarkii, is the most cultured freshwater crayfish species. It attracts significant research attention due to its considerable economic importance. However, the limited availability of genome information has impeded further genetic studies and breeding programs. By utilizing Illumina, PacBio, and Hi-C sequencing technologies, we present a more comprehensive and continuous chromosome-level assembly for P. clarkii than the published one. The final genome size is 4.03 Gb, consisting of 2,358 scaffolds with a N50 of 42.87 Mb. Notably, 3.68 Gb, corresponding to 91.42% of the genome, was anchored to 94 chromosomes. The assembly comprises 70.64% repetitive sequences, including 5.21% tandem repeats and 65.40% transposable elements. Additionally, a total of 4,456 non-coding RNAs and 28,852 protein-coding genes were predicted in the P. clarkii genome, with 96.26% of the genes were annotated. This high-quality genome assembly not only represents a significant improvement for the genome of P. clarkii and provides insights into the unique genome evolution, but also offers valuable information for developing freshwater aquaculture and accelerating genetic breeding.

Abstract The teaching objectives of traditional approaches in higher education emphasize mostly students’ mastery of knowledge and have insufficient directionality to social needs. In this study, we developed a BOPPPS (Bridge-in, Objectives, Pre-assessment, Participatory Learning, Post-assessment and Summary) teaching model combined with the OBE (Outcome Based Education) concept to enhance the teaching outcomes. Firstly, based on the graduation requirements and professional training objectives of students, we divided the course objectives into three dimensions (knowledge, ability and quality), and further specified into index points. Then, the teaching content of each chapter was set to correspond with the index points. Finally, the BOPPPS teaching model was used to meet each requirement. Clinical biochemistry testing course was used as a model to assess the effects of the teaching reform. After the class, the teaching effect was analyzed based on the questionnaire surveys from the students and their scores of both the chapter and final examinations. The results showed that compared with the traditional approach, the BOPPPS teaching model combined with the OBE concept has demonstrated a notable enhancement in student engagement, and significant improvement of their mastery of knowledge, application skills, and problem-solving abilities. The examination scores of the BOPPPS group were markedly higher than those of the traditional group. Moreover, the difference between the two groups diverse assessment scores was much bigger than that between the two group examination scores. Our study indicates that the BOPPPS teaching model combined with the OBE concept is a highly effective teaching model for enhancing the learning effectiveness of students.

ABSTRACT Background With the increasing use of lung cancer screening, the detection of ground glass nodules (GGNs) has risen. However, the natural course of GGNs and their relationship to pathologic features remains unclear. Differentiating between invasive and pre‐invasive lesions based on GGN growth may improve clinical intervention timing. Ki‐67, a proliferation marker, holds value in assessing tumor malignancy. This study analyzes the association between GGN growth, pathology, and Ki‐67 expression to provide new insights into early‐stage lung cancer management. Methods We retrospectively evaluated 183 GGNs with at least two preoperative CT scans. Nodule location, type, natural course, and volume doubling time (VDT) were compared between invasive adenocarcinoma (IAC) and pre‐IAC groups. We also assessed differences in Ki‐67 expression and correlated VDT with Ki‐67 levels. Results A total of 183 nodules were finally included; gender, nodule location, smoking history, and duration of follow‐up did not differ between the IAC group and the pre‐IAC group, whereas age was statistically different between the two groups. Of the 183 nodules, 52 showed growth and the predominant pathologic type was IAC, these IACs showed more PSN in nodule type, while the IAC group showed more significant differences in nodule type, nodules growth, and VDT than the pre‐IAC group. There were also differences in pathologic type and VDT between different Ki‐67 expression groups, and Ki‐67 expression gradually increased as VDT decreased. Conclusion Lung adenocarcinoma (LUAD) presenting as GGNs exhibit distinct natural courses among pathologic subtypes. VDT effectively distinguishes these growth characteristics, with IACs showing shorter VDT. The significant correlation between VDT and Ki‐67 expression suggests that combining these parameters may provide valuable insights into the biological behavior and invasiveness of LUAD.

Background: Heatwave warnings provide crucial information about the nature of the event and the steps that can be taken to mitigate its impact. It is well known that heat events disproportionately impact the health of older adults. Therefore, it’s critical that heatwave warnings reach this population. However, our current understanding of the effectiveness of heatwave warning messages among older Queenslanders is limited. Methods: A Queensland wide survey was conducted in 2022 among 547 older adults (≥65 years), aiming to collect information on their perception of heat-related health risk, their knowledge of the existing heatwave warnings, and if they had ever heard of a heatwave warning. Chi-square analysis followed by multinomial or binomial logistic regression was utilized to understand various socio-economic and personal factors that impact the heatwave warning reach to older Queenslanders. Results: Only 43% of the respondents had heard a heatwave warning and only 49% of those who heard a warning(s) changed their behavior as a result. The results showed 20% of respondents perceived themselves to be at heat-related health risk, and these individuals were 1.98 times more likely to have heard heatwave warnings. Further, individuals who perceived themselves to be at heat-related health risk were 3.62 times more likely to adopt adaptive measures in response to heatwave warnings. Implications: This study suggests that in older adults, higher knowledge and perception of heat-related health risk are associated with higher likelihoods of attention to heatwave warnings and adoption of cooling measures.

BackgroundVenous Leg Ulcers (VLUs) are one of the most serious and intractable complications of chronic venous insufficiency. This study aims to develop a nomogram based on a theoretical model to predict the probability of wound recurrence in older patients with VLUs.MethodsThe elderly patients with VLUs attending the five hospitals between September 2021 and October 2022 were enrolled in this research, and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Recurrent events were recorded during a six-month follow-up after the baseline data collection. The univariate analysis, the least absolute shrinkage and selection operator (LASSO) regression method were used to screen variables, and multiple logistic regression was used to establish a risk prediction model, which was presented by nomogram. Receiver operating curves (ROC), Hosmer–Lemeshow test, as well as calibration curves, were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined.ResultsA total of 608 elderly patients with VLUs were included in the study. They were randomly divided into the training cohort (N = 421) and the validation cohort (N = 187). In the training cohort, Lasso regression and multivariate logistic regression analysis indicated that previous recurrence number, last ulcer duration, lower extremity DVT history, and frailty were independent risk factors for wound recurrence in elderly patients with VLUs, while daily exercise time and self-efficacy were protective factors. A nomogram was established with a good discrimination capacity and predictive efficiency with and the area under the curve (AUC) of 0.869 (95%CI: 0.831–0.908) in the training set and 0.890 (95%CI: 0.841–0.938) in the validation set. The p values of the Hosmer-Lemeshow test for both sets were 0.887 and 0.772, respectively, both greater than 0.05. The calibration degree charts showed that the data point connection was similar to the diagonal, indicating that the model’s prediction probability of wound recurrence in elderly VLUs patients is close to the actual probability.ConclusionThis study constructed a new nomogram to predict the risk of wound recurrence in elderly patients with VLUs. The nomogram has excellent accuracy and reliability, which can help healthcare workers and patients actively monitor and follow up with patients to prevent the recurrence of ulcers and make clinical decisions.

Background: Studies have shown that abnormal temperature at night is a risk factor for respiratory health. However, there is limited evidence on the impact of hot and cold nights on cause-specific diseases such as pneumonia, which is a leading cause of morbidity and mortality in children. Methods: We collected daily data on pneumonia hospitalisations in children under five years from 2011 to 2017 in three low-, middle- and high-income countries (Bangladesh, China, and Australia). The intensity of hot and cold nights was measured by excess temperature. A space–time-stratified case-crossover analysis was used to estimate the association between hot and cold nights and childhood pneumonia hospitalisations. We further estimated the fraction of childhood pneumonia hospitalisations attributable to hot and cold nights. Results: Both hot and cold nights were associated with an increased risk of hospitalisations for childhood pneumonia in low-, middle-, and high-income countries, with a greater disease burden from hot nights. Specifically, the fraction of childhood pneumonia attributable to hot nights was the largest in Australia [21.2%, 95% confidence interval (CI): 11.8%-28.1%], followed by Bangladesh (15.2%, 95% CI: 4.1%–23.8%) and China (2.7%, 95% CI: 0.4%-4.7%). Additionally, the fraction of childhood pneumonia attributable to cold nights was 1.3% (95% CI: 0.4%-2.0%) in Bangladesh and 0.4% (95% CI: 0.1%-0.7%) in China. Conclusion: This multi-country study suggests that hot and cold nights are not only associated with a higher risk of pneumonia hospitalisations in children but also responsible for substantial fraction of hospitalisations, with a greater impact from hot nights.

Abstract Background Hypertrophy ligamentum flavum is a prevalent chronic spinal condition that affects middle‐aged and older adults. However, the molecular pathways behind this disease are not well comprehended. Objective The objective of this work is to implement bioinformatics techniques in order to identify crucial biological markers and immune infiltration that are linked to hypertrophy ligamentum flavum. Further, the study aims to experimentally confirm the molecular mechanisms that underlie the hypertrophy ligamentum flavum. Methods The corresponding gene expression profiles (GSE113212) were selected from a comprehensive gene expression database. The gene dataset for hypertrophy ligamentum flavum was acquired from GeneCards. A network of interactions between proteins was created, and an analysis of functional enrichment was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. An study of hub genes was performed to evaluate the infiltration of immune cells in patient samples compared to tissues from the control group. Finally, samples of the ligamentum flavum were taken with the purpose of validating the expression of important genes in a clinical setting. Results Overall, 27 hub genes that were differently expressed were found through molecular biology. The hub genes were found to be enriched in immune response, chemokine‐mediated signaling pathways, inflammation, ossification, and fibrosis processes, as demonstrated by GO and KEGG studies. The main signaling pathways involved include the TNF signaling pathway, cytokine–cytokine receptor interaction, and TGF‐β signaling pathway. An examination of immunocell infiltration showed notable disparities in B cells (naïve and memory) and activated T cells (CD4 memory) between patients with hypertrophic ligamentum flavum and the control group of healthy individuals. The in vitro validation revealed markedly elevated levels of ossification and fibrosis‐related components in the hypertrophy ligamentum flavum group, as compared to the normal group. Conclusion The TGF‐β signaling pathway, TNF signaling pathway, and related hub genes play crucial roles in the progression of ligamentum flavum hypertrophic. Our study may guide future research on fibrosis of the ligamentum flavum.

Biological soil crusts (BSCs) play an essential role in soil stabilization and nutrient cycling in arid environments, being a hotspot of microbial activity including soil enzyme production. However, the changes in microbial communities of the BSCs from different age are poorly understood. In over a 60-year dune revegetation chronosequence (2021, 2016, 1973, and 1962) in the southeastern Tengger Desert, China, we compared the development of BSCs and underlying sands using metagenome sequencing and enzyme assays. In both BSCs and the underlying sand substrate, enzyme activity increased along the time series but was always greater for BSCs than the substrate, emphasizing the potential for nutrient cycling activity. A clear trend in community composition and co-occurrence network complexity was observed in the BSCs: the family-level taxonomic network of BSCs became more connected in the older age BSCs, while in the substrate, there was no such trend. Unexpectedly, considering their low relative abundance and diversity, archaea emerged as major keystones in establishing community networks, being key to network stability. In the underlying substrate, by contrast, archaea did not play this key role. During the time series of BSCs, the dominant archaeal groups were Nitrososphaeria, Methanomicrobia, Halobacteria, and an unclassified Thaumarchaeota, mainly linked to functional genes related to carbon and nitrogen, such as CHB2, xylA, amyA, amoB, nxrA, nxrB, and nirB. This suggests that in BSCs, the key role of archaea relates to their role in nitrogen cycling. This perspective adds to the emerging view that archaea play an important role in community structure and function in dryland environments.

Abstract In order to cope with the increasingly serious problem of energy shortage, supercapacitors have been developed as a clean and renewable energy source, and the supercapacitors with excellent energy density and long cycle life are imperative. Here, by employing a facile liquid–liquid (L‐L) interfacial method at room temperature (RT), a set of two‐dimensional (2D) metal complex nanosheets N1‐N3 have been synthesized by the facile coordination between Co2+ ion and 2,3,6,7,10,11‐hexaiminotriphenylene (HITP). Given the layered superstructure with well‐ordered nanopores, the N1‐N3 electrodes displayed excellent capacities of 4751.9, 5770.9 and 6075.2 F g−1 at 1 A g−1, and a good cyclic stability with 92.1% capacity retention after 1000 cycles for the N3 electrode. The asymmetric supercapacitor device with N3 as the positive electrode delivers a maximum energy density of 238.2 Wh kg−1 at a power density of 1610.1 W kg−1 and an excellent cycling stability with a capacitance retention of 109.1% after 5000 cycles. This is the best electroactive bottom‐up metal complex nanosheet reported so far for use in supercapacitor, which greatly expands the applicability of this 2D nanomaterial in energy device applications.

ObjectivesTo identify the long-term spatiotemporal trend of ozone-related chronic obstructive pulmonary disease (COPD) burden by sex and country and to explore potential drivers.MethodsWe retrieved data of ozone-related COPD death and disability adjusted life year (DALY) from the Global Burden of Disease 2019. We used a linear regression of natural logarithms of age-standardized rates (ASRs) with calendar year to examine the trends in ASRs and a panel regression to identify country-level factors associated with the trends.ResultsGlobal ozone-attributable COPD deaths increased from 117,114 to 208,342 among men and from 90,265 to 156,880 among women between 1990 and 2019. Although ASRs of ozone-related COPD death and DALY declined globally, they increased in low and low-middle Socio-demographic Index (SDI) regions, with faster rise in women. Elevated average maximum temperature was associated with higher ozone-attributable COPD burden, while more green space was associated with lower burden.ConclusionMore efforts are needed in low and low-middle SDI regions, particularly for women, to diminish inter-country inequality in ozone-attributable COPD. Global warming may exacerbate the burden. Expanding green space may mitigate the burden.

Background Despite substantial progress in reducing the global burden of chronic obstructive pulmonary disease (COPD), traditional methods to promote understanding and management of COPD are insufficient. We developed an innovative model based on the internet of things (IoT) for screening and management of COPD in primary healthcare (PHC).Methods Electronic questionnaire and IoT-based spirometer were used to screen residents. We defined individuals with a questionnaire score of 16 or higher as high-risk population, COPD was diagnosed according to 2021 Global Initiative for COPD (Global Initiative for Chronic Obstructive Lung Disease) criteria. High-risk individuals and COPD identified through the screening were included in the COPD PHC cohort study, which is a prospective, longitudinal observational study. We provide an overall description of the study’s design framework and baseline data of participants.Results Between November 2021 and March 2023, 162 263 individuals aged over 18 from 18 cities in China were screened, of those 43 279 high-risk individuals and 6902 patients with COPD were enrolled in the cohort study. In the high-risk population, the proportion of smokers was higher than that in the screened population (57.6% vs 31.4%), the proportion of males was higher than females (71.1% vs 28.9%) and in people underweight than normal weight (57.1% vs 32.0%). The number of high-risk individuals increased with age, particularly after 50 years old (χ2=37 239.9, p

Background: Climate change has shifted and will continue to alter the patterns of daytime and nighttime temperatures. However, it remains unclear whether hot days and nights, as well as cold days and nights, have varying impacts on mental disorders. Methods: Daily hospital-based data on 1.2 million outpatient visits for mental disorders were obtained from 15 Chinese cities (2011-2015). The index of hot day excess, hot night excess, cold day excess, and cold night excess were used to measure the intensity of hot/cold days and nights. Firstly, a distributed lag non-linear model was used to estimate the risk of mental disorders associated with hot/cold days and nights for each city. Secondly, city-specific estimates were pooled by multivariate meta-analysis to obtain national effect estimates. Thirdly, the attributable fraction of mental disorders was estimated for the nation, genders, age groups, heating methods, and air conditioning penetration rates. Results: Our findings revealed that both hot and cold nights were associated with a higher risk of mental disorders compared to hot and cold days. The attributable fraction of mental disorders was higher for cold (night: 25.74%; day: 19.65%) in contrast to heat (night: 16.91%; day: 7.24%). Males, the elderly, cities using central heating, and cities with low air conditioning penetration rates were vulnerable to cold nights. Conclusions: This nationwide study highlights the adverse effects and significant burden of hot and cold nights on mental disorders. It underscores the potential for mitigating these impacts through the adoption of air conditioning, particularly in southern cities across China.

Abstract Background Short-term exposure to air pollution may trigger symptoms of drug-resistant tuberculosis (DR-TB) through stimulating lung tissue, damaging tracheobronchial mucosa, the key anti-mycobacterium T cell immune function, and production and release of inflammatory cytokines. Objective To investigate the association between acute exacerbations of DR-TB and short-term residential exposure to air pollutants (PM10, PM2.5, SO2, NO2, CO and O3) based on a large prospective cohort in Anhui Province, China. Method Patients were derived from a prospective cohort study of DR-TB in Anhui Province. All DR-TB patients underwent drug-susceptibility testing and prefecture-level reference laboratories confirmed their microbiologies. The case-crossover design was performed to evaluate the association between the risk of acute exacerbations of DR-TB and short-term residential exposure to air pollution. Results Short-term NO2 exposure was significantly related to an elevated risk of first-time outpatient visit due to acute exacerbations of DR-TB(relative risk:1.159, 95% confidence interval:1.011 ~ 1.329). Stratification analyses revealed that the relationship between the risk of acute exacerbations and NO2 exposure was stronger in the elderly (age ≥ 65) DR-TB patients, and in individuals with a history of TB treatment. Conclusions NO2 Exposure was significantly associated with an elevated risk of acute exacerbation of DR-TB in Anhui Province, China.

Abstract Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet’s two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model’s superior recognition precision and rapid convergence capabilities in complex fault environments.

The transformation of ecosystem structure leads to changes in ecosystem services (ESs) and their relationship. However, most research in this area has focused on particular scales and timeframes, often overlooking the significance of spatial and temporal variations. Therefore, we used the equivalent value method to evaluate seven typical ESs in the Yangtze River Delta (YRD) between 2000 and 2020: food production (FP), water supply (WS), climate regulation (CR), environmental purification (EP), soil conservation (SC), biodiversity maintenance (BM), and aesthetic landscape (AL). We further employed the Spearman correlation coefficient and bivariate Moran’s I to evaluate the relationship of ESs and their spatial heterogeneity at grid, township, county and city scales. Our results show that (1) All ESs except AL exhibited a fluctuating upward trend from 2000 to 2020, resulting in a total increase in ecosystem service (ES) value of RMB 650.63 billion. (2) Approximately 70% of the ES pairs demonstrated a synergistic relationship, with the exception of FP and other ESs, which mainly showed a trade-off. (3) With the scale increased from grid to city level, the degree of trade-off between FP and other ESs strengthened at different levels, while the synergy degree of among other ESs gradually decreased. (4) The relationship between ESs demonstrated strong spatial heterogeneity, with FP and other ESs exhibiting trade-offs primarily in the northern and southern YRD, while other ES pairs exhibited mostly synergy in these regions. This study provides scientific information for governments to optimize land use distribution and improve ESs.

BackgroundObservational studies have suggested associations between functional gastrointestinal disorders (FGIDs) and variations in the cerebral cortex. However, the causality of these relationships remains unclear, confounded by anxiety and depression. To clarify these causal relationships and explore the mediating roles of anxiety and depression, we applied univariate, multivariable, and mediation Mendelian randomization (MR) analyses.MethodWe utilized genome-wide association study (GWAS) summary data from the FinnGen database and the ENIGMA consortium, identifying genetic variants associated with irritable bowel syndrome (IBS), functional dyspepsia (FD), and cerebral cortex structures. Data on anxiety and depression came from FinnGen and a large meta-analysis. Utilizing a bidirectional univariate MR approach, we explored correlations between FD, IBS, and cortex variations. Then, independent effects were assessed through multivariable MR. A meta-analysis of these results, incorporating data from two cohorts, aimed to increase precision. We also explored the potential mediating roles of anxiety and depression.ResultsOur findings indicate a negative causal correlation between FD and the thickness of the rostral anterior cingulate cortex (rACC) across both global and regional adjustments (β = −0.142, 95% confidence interval (CI): −0.209 to-0.074, P.FDR = 0.004; β = −0.112, 95%CI: −0.163 to-0.006, P.FDR = 0.003) and a positive causal correlation with the globally adjusted thickness of the superior frontal gyrus (SFG) (β = 0.107, 95%CI: 0.062 to 0.153, P.FDR = 0.001). The causal correlation with the rACC persisted after multiple variable adjustments (β = −0.137, 95% CI: −0.187 to-0.087, P.FDR = 1.81 × 10−5; β = −0.109, 95%CI: −0.158 to-0.06, P.FDR = 0.002). A significant causal association was found between globally adjusted surface area of the caudal anterior cingulate cortex (cACC) and IBS (odds ratio = 1.267, 95%CI: 1.128 to 1.424, P.FDR = 0.02). The analysis showed that neither anxiety nor depression mediated the relationship between FGIDs and cerebral cortex structures.ConclusionOur research provides significant MR evidence of a bidirectional causal relationship between FGIDs and the cerebral cortex structures. This evidence not only confirms the two-way communication along the brain-gut axis but also illuminates the underlying pathophysiology, paving the way for identifying potential therapeutic approaches.

Summary: Background: Cardiovascular disease (CVD) has been found to be particularly vulnerable to climate change and temperature variability. This study aimed to assess the extent to which human-induced climate change contributes to future heat-related CVD burdens. Methods: Daily data on CVD mortality and temperature were collected in 161 Chinese communities from 2007 to 2013. The association between heat and CVD mortality was established using a two-stage time-series design. Under the natural forcing, human-induced, and combined scenarios, we then separately projected excess cause-/age-/region-/education-specific mortality from future high temperature in 2010–2100, assuming no adaptation and population changes. Findings: Under shared socioeconomic pathway with natural forcing scenario (SSP2–4.5–nat), heat-related attributable fraction of CVD deaths decreased slightly from 3.3% [95% empirical confidence interval (eCI): 0.3, 5.8] in the 2010s to 2.8% (95% eCI: 0.1, 5.2) in the 2090s, with relative change of −0.4% (95% eCI: −0.8, 0.0). However, for combined natural and human-induced forcings, this estimate would surge to 8.9% (95% eCI: 1.5, 15.7), 14.4% (95% eCI: 1.5, 25.3), 21.3% (95% eCI: −0.6, 39.4), and 28.7% (95% eCI: −3.3, 48.0) in the 2090s under SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 scenarios, respectively. When excluding the natural forcing, the number of human-induced heat-related CVD deaths would increase from approximately eight thousand (accounting for 31% of total heat-related CVD deaths) in the 2010s to 33,052 (68%), 63,283 (80%), 101,091 (87%), and 141,948 (90%) in the 2090s under SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 scenarios, respectively. Individuals with stroke, females, the elderly, people living in rural areas, and those with lower education level would exhibit heightened susceptibility to future high temperature. In addition, Southern and Eastern regions of China were expected to experience a faster increase in heat-related attributable fraction of CVD deaths. Interpretation: Human activities would significantly amplify the future burden of heat-related CVD. Our study findings suggested that active adaptation and mitigation measures towards future warming could yield substantial health benefits for the patients with CVD. Funding: National Natural Science Foundation of China.

Background: Studies have shown that short- and long-term exposure to particulate matter (PM) can increase the risk of asthma morbidity and mortality. However, the effect of medium-term exposure remains unknown. We aim to examine the effect of medium-term exposure to size-fractioned PM on asthma exacerbations among asthmatics with poor medication adherence. Methods: We conducted a longitudinal study in China based on the National Mobile Asthma Management System Project that specifically and routinely followed asthma exacerbations in asthmatics with poor medication adherence from April 2017 to May 2019. High-resolution satellite remote-sensing data were used to estimate each participant's medium-term exposure (on average 90 days) to size-fractioned PM (PM1, PM2.5, and PM10) based on the residential address and the date of the follow-up when asthma exacerbations (e.g., hospitalizations and emergency room visits) occurred or the end of the follow-up. The Cox proportional hazards model was employed to examine the hazard ratio of asthma exacerbations associated with each PM after controlling for sex, age, BMI, education level, geographic region, and temperature. Results: Modelling results revealed nonlinear exposure-response associations of asthma exacerbations with medium-term exposure to PM1, PM2.5, and PM10. Specifically, for emergency room visits, we found an increased hazard ratio for PM1 above 22.8 µg/m3 (1.060, 95 % CI: 1.025–1.096, per 1 µg/m3 increase), PM2.5 above 38.2 µg/m3 (1.032, 95 % CI: 1.010–1.054), and PM10 above 78.6 µg/m3 (1.019, 95 % CI: 1.006–1.032). For hospitalizations, we also found an increased hazard ratio for PM1 above 20.3 µg/m3 (1.055, 95 % CI: 1.001–1.111) and PM2.5 above 39.2 µg/m3 (1.038, 95 % CI: 1.003–1.074). Furthermore, the effects of PM were greater for a longer exposure window (90–180 days) and among participants with a high BMI. Conclusion: This study suggests that medium-term exposure to PM is associated with an increased risk of asthma exacerbations in asthmatics with poor medication adherence, with a higher risk from smaller PM.

ObjectiveTo analyze the clinical epidemiological characteristics including clinical features, disease prognosis of pneumococcal meningitis (PM), and drug sensitivity of S. pneumoniae isolates in Chinese children.MethodsA retrospective analysis was performed on the clinical, laboratory microbiological data of 160 hospitalized children less than 15 years of age with PM from January 2019 to December 2020 in 33 tertiary hospitals in China.ResultsA total of 160 PM patients were diagnosed, including 103 males and 57 females The onset age was 15 days to 15 years old, and the median age was 1 year and 3 months. There were 137 cases (85.6%) in the 3 months to

Metal magnetic memory testing (MMMT) is a prominent NDT method for early damage diagnosis of ferromagnetic structures due to its significant advantages in stress concentration state evaluation. While it is still challengeable to apply the existing criteria from the laboratory to practical application, and some of the main reasons are the microstructure transformation and geometric dimensions for the ferromagnetic material itself. To explore the influence of microstructure and geometric dimension on MMMT, static tensile tests of specimens made of Optim 900QC steel with different microstructures and geometric dimensions have been carried out, and the MMMT signals of the specimens during the tests were collected and analyzed. It is found that the microstructure not only change the strength of the steel, but also affect the MMMT signal of the steel, and the bigger the grain is, the stronger the magnetic field is. Geometric dimension would also affect the MMMT signal, and thickness affects the MMMT signal the most, width less, and length the least. The results of the present work indicate that the microstructure and the geometric dimensions should be the same to fix the gap of damage determination criteria between the laboratory and the engineering application. When it is impossible to fabricate specimens with the same dimension with the engineering structure, set the same thickness would improve the accuracy of the criteria.

Summary: Clarifying the accumulation and decomposition of soil organic carbon (SOC) is crucial for comprehending carbon cycling in terrestrial ecosystems. SOC enrichment and decomposition lead to the fractionation of stable carbon isotopes, a complex process influenced by various factors, including microbes. However, this fractionation process during early soil formation and the role of microorganisms remain poorly explored. This study investigated the relative composition of stable carbon isotopes (δ13C) of recently formed biological soil crusts (BSCs) on stabilized sand dunes in the Tengger Desert, Northern China. A notable increase in δ13C was observed during early BSC development, likely driven by cyanobacteria’s direct fixation of CO2. Yet, δ13C values of BSCs gradually declined, approaching those of soils under native vegetation, probably linked to microbial succession within the BSCs. This finding highlights the potential microbial influence on early soil carbon turnover and underscores the effectiveness of isotope tracers for studying this process.

The tribological performances reinforcement is limited by insufficient dispersion and random orientation of nanofillers in composites. In light of above difficulties, a three-dimensional (3D) interconnected graphene architecture (3DGA) was designed, in which the reinforcement is pre-formed in three dimensions. In the 3DGA reinforced epoxy composite (3DGA/EP) fabricated by resin transfer molding combined with in-situ ultrasonic method, 3DGA assembling from graphene sheets was in good dispersion and distribution state, which resulted in exceptional thermal stability, mechanical and tribological properties for epoxy composites. Compared with graphene oxide (GO)/EP composites in direct solution blending, glass transition temperature and tensile strength of the 3DGA/EP composites increased by 27.1 °C and 41.1 %, and wear rate decreased by 38.1 %, respectively. In this work, 3DGA was utilized as the ordered reinforcement of composites instead of two-dimensional graphene sheets, providing a promising strategy to achieve high-performance polymer-based composites with superior mechanical and tribological properties.

Abstract Background This multi-center study aimed to identify factors affecting fever and delayed defervescence in bacterial meningitis (BM) patients under 3 years of age because of the variability of fever in this patient population. Methods Only BM patients under 3 years treated at 49 centers in China from November 2018 to end-April 2021 were included in the study. Univariate and multivariate logistic regression analyses were performed to determine factors associated with afebrile presentation and fever of delayed defervescence. Results A total of 863 BM patients under 3 years were included in the study. Coagulase negative staphylococcus was associated with afebrile presentation (OR = 1.176), while septicaemia and ear-nose-throat infections were associated with fever (P

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities.

Groundwater resources serve as the primary source of water in the agro-pastoral ecotone of northern China, where scarcity of water resources constrains the development of agriculture and animal husbandry. As a typical rainfed agricultural area, the agro-pastoral ecotone in Inner Mongolia is entirely dependent on groundwater for agricultural irrigation. Due to the substantial groundwater consumption of irrigated farmland, groundwater levels have been progressively declining. To obtain a sustainable irrigation pattern that significantly conserves water, this study faces the challenge of unclear water transport relationships among water, soil, and crops, undefined water cycle mechanism in typical irrigation units, and water use efficiency, which was not assessed. Therefore, this paper, based on in situ experimental observations and daily meteorological data in 2022–2023, utilized the DSSAT model to explore the growth processes of potato, oat, alfalfa, and sunflower, the soil water dynamics, the water balance, and water use efficiency, analyzed over a typical irrigation area. The results indicated that the simulation accuracy of the DSSAT model was ARE < 10%, nRMSE/% < 10%, and R2 ≥ 0.85. The consumption of the soil moisture during the rapid growth stage for the potatoes, oats, alfalfa, and sunflower was 7–13% more than that during the other periods, and the yield was 67,170, 3345, 6529, and 4020 kg/ha, respectively. The soil evaporation of oat, potato, alfalfa, and sunflower accounted for 18–22%, 78–82%; 57–68%, and 32–43%, and transpiration accounted for 40–44%, 56–60%, 45–47%, and 53–55% of ETa (333.8 mm–369.2 mm, 375.2 mm–414.2 mm, 415.7 mm–453.7 mm, and 355.0 mm–385.6 mm), respectively. It was advised that irrigation water could be appropriately reduced to decrease ineffective water consumption. The water use efficiency and irrigation water use efficiency for potatoes was at the maximum amount, ranging from 16.22 to 16.62 kg/m3 and 8.61 to 10.81 kg/m3, respectively, followed by alfalfa, sunflowers, and oats. For the perspective of water productivity, it was recommended that potatoes could be extensively cultivated, alfalfa planted appropriately, and oats and sunflowers planted less. The findings of this study provided a theoretical basis for efficient water resource use in the agro-pastoral ecotone of Northern China.

With the growing energy crisis and environmental problems, distributed photovoltaic (PV), as a clean and renewable form of energy, is receiving more and more attention. However, the large-scale access to distributed PV brings a series of challenges to the distribution network, such as voltage fluctuation, frequency deviation, protection coordination, and other problems. In order to solve these problems, this paper provides a research overview of distribution network consumption strategies containing distributed PV. Firstly, this paper introduces the characteristics of distributed PV and its impact on the distribution grid. Then, the difficulties and challenges of distributed PV consumption are analyzed from the technical and economic levels. On this basis, a comprehensive consumption strategy is proposed, including the following aspects: firstly, it is mentioned to optimize the access method of distributed PV and to reduce the impact on the distribution grid by reasonably choosing the access point and access capacity of distributed PV. Secondly, it summarizes the ways to improve the regulation capacity of distribution networks and improve the consumption capacity of distribution networks for distributed PV through the introduction of energy storage equipment, flexible loads, and other means. Finally, advanced control strategies are summarized to improve the stability and reliability of the distribution network by implementing measures such as power prediction, voltage control, and protection coordination for distributed PV. Overall, this paper synthesizes the research on distribution network consumption strategies containing distributed PV from various aspects, which provides certain theoretical guidance and practical reference for solving distributed PV consumption problems.

Introduction: The value of extended lymphadenectomy in pancreatoduodenectomy (PD) has been discussed by five randomized controlled studies. However, the limitations in the studies made their conclusions not sufficiently reliable. Objectives: This multi-center randomized controlled study was designed to clarify the efficacy of extended lymphadenectomy in PD for pancreatic ductal adenocarcinoma (PDAC). Methods: From December 2016 to October 2018, 170 consecutive patients undergoing PD were enrolled and randomized to standard or extended lymphadenectomy for the treatment of PDAC from three high-volume institutions in China. Demographic, pathological characteristics and survival data of these patients were collected and analyzed. No neoadjuvant treatment was performed. The primary endpoint was the 3-year overall survival. Results: For all patients, the 3-year survival rate was 25.88 %. There was no between-group difference in 3-year survival rate (27.16 % vs 24.72 % p = 0.717). The median survival time for the standard group was 18 months, while for the extended group it was 15 months. The demographic and pathological characteristics were similar between groups. More positive lymph nodes could be found in the extended group (2.34 ± 3.46 vs 1.41 ± 2.12, p = 0.035), which led to nodule stage migration. All patients received chemotherapy. But patients in extended group were more likely to fail in completion of all-cycles chemotherapy before recurrence (31.46 % vs 17.28 %, p = 0.032). Incomplete chemotherapy before recurrence, higher N status and abnormal CA125 were independent risk factors for 1-year survival (p

ObjectiveTo assess the effectiveness of acupuncture for treating depression and anxiety in patients diagnosed with functional dyspepsia (FD).MethodsPubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Data, Sinomed, and VIP Database were searched until April 30, 2023 for Randomized Controlled Trials (RCTs) comparing acupuncture to placebo or drugs for symptom alleviation. Two independent reviewers conducted the study search, data extraction, and bias risk assessment using the Cochrane Risk of Bias tool. Mean difference (MD), risk ratio (RR), and corresponding 95% confidence intervals (CI) were computed. Subgroup and sensitivity analyses were also performed. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was employed to evaluate the evidence level.ResultsA total of 16 RCTs involving 1315 participants were included. Acupuncture demonstrated marked superiority over placebo (MD = -7.07, 95%CI: -11.03 to -3.10, very low quality evidence) in mitigating Self-Rating Anxiety Scale (SAS) scores and was found to be more effective in reducing Self-Rating Depression Scale (SDS) scores than either placebo (MD = -4.63, 95%CI: -6.28 to -2.98, low quality evidence) or first-line drugs (MD = -2.71, 95%CI: -5.19 to -0.23, very low quality evidence). In terms of attenuating Hamilton Anxiety Rating Scale (HAMA) and Hamilton Depression Rating Scale (HAMD) scores, acupuncture consistently outperformed both placebo (HAMA: MD = -2.58, 95%CI: -4.33 to -0.83, very low quality evidence; HAMD: MD = -1.89, 95%CI: -3.11 to -0.67, low quality evidence) and first-line drugs (HAMA: MD = -5.76, 95%CI: -10.18 to -1.35, very low quality evidence; HAMD: MD = -5.59, 95%CI: -7.59 to -3.59, very low quality evidence). However, no significant difference was observed between acupuncture and placebo in terms of improvement in Hospital Anxiety and Depression Scale (HADS) scores.ConclusionsBased on current clinical evidence, acupuncture might have a positive effect on depression and anxiety in patients with FD. Further large-sample, multi-center, high-quality RCTs validation are required, as the conclusion is limited by the quantity and quality of the included studies.

We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging from stochastic to regular. We adopt StyleGAN3 for synthesis and demonstrate that it produces diverse textures beyond those represented in the training data. For texture analysis, we propose GAN inversion using a novel latent domain reconstruction consistency criterion for synthesized textures, and iterative refinement with a sliced Wasserstein loss for real-world textures. We propose perceptual procedures for evaluating network capabilities, exploring the global and local behavior of latent space trajectories, and comparing with existing texture analysis-synthesis techniques.

Abstract Incomplete Multi‐View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: (1) It is difficult to learn latent representations that account for complementarity yet consistency without using label information; (2) and thus fails to take full advantage of the hidden information in incomplete data results in suboptimal clustering performance when complete data is scarce. In this study, Contrastive Incomplete Multi‐View Image Clustering with Generative Adversarial Networks (CIMIC‐GAN), which uses Generative Adversarial Network (GAN) to fill in incomplete data and uses double contrastive learning to learn consistency on complete and incomplete data is proposed. More specifically, considering diversity and complementary information among multiple modalities, we incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Integrating GANs into the autoencoding process can not only take full advantage of new features of incomplete data, but also better generalise the model in the presence of high data missing rates. Experiments conducted on four extensively used data sets show that CIMIC‐GAN outperforms state‐of‐the‐art incomplete multi‐View clustering methods.

The hydraulic oil of marine equipment contains a large number of abrasive contaminants that reflect the operating condition of the equipment. In order to realize the detection of particulate contaminants, this research first proposes a shape-based classification method for oil abrasive particles, designs an oil abrasive particle collection system, and constructs a new dataset. After that, the research introduces deep learning target detection technology in computer vision, and uses GhostNet to lighten the network structure, the CBAM (Convolutional Block Attention Module) attention mechanism to improve the generalization ability of the model, and the ASPP module to enhance the model sensory wildness, respectively. A lightweight target detection model, WDD, is created for the identification of abrasive particles. In this study, the WDD model is tested against other network models, and the mAP value of WDD reaches 91.2%, which is 4.8% higher than that of YOLOv5s; in addition, the detection speed of the WDD model reaches 55 FPS. Finally, this study uses real ship lubricating oils for validation, and the WDD model still maintains a high level of accuracy. Therefore, the WDD model effectively balances the accuracy and detection speed of marine oil abrasive particle detection, which is superior to other oil abrasive particle detection techniques.

This paper begins with a description of methods for estimating probability density functions for images that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space—not every pattern of pixels is an image. It is common to say that images lie on a lower-dimensional manifold in the high-dimensional space. However, although images may lie on such lower-dimensional manifolds, it is not the case that all points on the manifold have an equal probability of being images. Images are unevenly distributed on the manifold, and our task is to devise ways to model this distribution as a probability distribution. In pursuing this goal, we consider generative models that are popular in AI and computer vision community. For our purposes, generative/probabilistic models should have the properties of (1) sample generation: it should be possible to sample from this distribution according to the modeled density function, and (2) probability computation: given a previously unseen sample from the dataset of interest, one should be able to compute the probability of the sample, at least up to a normalizing constant. To this end, we investigate the use of methods such as normalizing flow and diffusion models. We then show how semantic interpretations are used to describe points on the manifold. To achieve this, we consider an emergent language framework that makes use of variational encoders to produce a disentangled representation of points that reside on a given manifold. Trajectories between points on a manifold can then be described in terms of evolving semantic descriptions. In addition to describing the manifold in terms of density and semantic disentanglement, we also show that such probabilistic descriptions (bounded) can be used to improve semantic consistency by constructing defenses against adversarial attacks. We evaluate our methods on CelebA and point samples for likelihood estimation with improved semantic robustness and out-of-distribution detection capability, MNIST and CelebA for semantic disentanglement with explainable and editable semantic interpolation, and CelebA and Fashion-MNIST to defend against patch attacks with significantly improved classification accuracy. We also discuss the limitations of applying our likelihood estimation to 2D images in diffusion models.

Background: Epidemiological studies show that outdoor artificial light at night (ALAN) is linked to metabolic hazards, but its association with metabolic syndrome (MetS) remains unclear. We aimed to investigate the association of outdoor ALAN with MetS in middle-aged and elderly Chinese. Methods: From 2017–2020, we conducted a cross-sectional study in a total of 109,452 participants living in ten cities of eastern China. MetS was defined by fasting blood glucose (FG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), blood pressure (BP), and waist circumference (WC). In 2021, we followed up 4395 participants without MetS at the baseline. Each participant's five-year average exposure to outdoor ALAN, as well as their exposure to green space type, were measured through matching to their address. Generalized linear models were used to assess the associations of outdoor ALAN with MetS. Stratified analyses were performed by sex, age, region, physical activity, and exposure to green space. Results: In the cross-sectional study, compared to the first quantile (Q1) of outdoor ALAN exposure, the odds ratios (ORs) of MetS were 1.156 [95 % confidence interval (CI): 1.111–1.203] and 1.073 (95 %CI: 1.021–1.128) respectively in the third and fourth quantiles (Q3, Q4) of outdoor ALAN exposure. The follow-up study found that, compared to the first quantile (Q1) of outdoor ALAN exposure, the OR of MetS in Q4 of ALAN exposure was 1.204 (95 %CI: 1.019–1.422). Adverse associations of ALAN with MetS components, including high FG, high TG, and obesity, were also found. Greater associations of ALAN with MetS were found in males, the elderly, urban residents, those with low frequency of physical activity, and those living in areas with low levels of grass cover and tree cover. Conclusions: Outdoor ALAN exposure is associated with an increased MetS risk, especially in males, the elderly, urban residents, those lacking physical activity, and those living in lower levels of grass cover and tree cover.

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain’s structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8–9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students’ diverse academic needs.

Introduction Heatwave is a major global health concern. Many countries including China suffered a record-breaking heatwave during the summer of 2022, which may have a significant effect on population health or health information-seeking behaviours but is yet to be examined.Methods We derived health information-seeking data from the Baidu search engine (similar to Google search engine). The data included city-specific daily search queries (also referred to Baidu Search Index) for heat-sensitive diseases from 2021 to 2022, including heatstroke, hospital visits, cardiovascular diseases and diabetes, respiratory diseases, mental health and urological diseases. For each city, the record-breaking heatwave days in 2022 were matched to days in the same calendar month in 2021.Results The 2022 record-breaking heatwave hit most cities (83.64%) in Mainland China. The average heatwave duration was 13 days and the maximum temperature was 3.60°C higher than that in 2021 (p

How memories are modified by new experiences remain elusive. Here, authors show that specific or generalized modification of memories depends on whether synaptic changes associated with previous memories are segregated or co-exist in dendritic branches.

Abstract Background The coronavirus disease 2019 (COVID‐19) pandemic resulted in enormous medical and economic burden worldwide during the past 3 years. The vaccination was deemed the effective option to prevent the severe symptoms, and especially recommended among cancer patients. Shanghai experienced the first lockdown during the recent Omicron pandemic since 2019. How patients with pancreatic adenocarcinoma (PAC) suffered from the pandemic and how vaccination influenced their oncological outcomes were unexplored yet. Method The retrospective study was carried out in a high‐volume referral center including 1157 consecutively enrolled patients with PAC experiencing the COVID‐19 pandemic. The primary outcome was the overall survival (OS). Results Limited postoperative patients (9.21%) received the vaccination. The lockdown in Shanghai (April to May, 2022) was not observed impacting the survival prognoses of patients with PAC. Though vaccination was not significantly associated with OS itself (adjusted hazard ratio (aHR): 2.032 [0.940–4.391], p = 0.071), it was discovered to synergistically improve the chemotherapy effect in the multivariate analyses (interaction p = 0.023). Conclusion The vaccination itself did not influence the survival prognoses of patients with PAC. A potential positive interaction was observed between chemotherapy and vaccination despite the limited follow‐up time. The postoperative patients should consider the vaccination more. The patients with PAC did not suffer worse prognostic outcomes from the strict sanitary policy during the wave of COVID‐19 pandemic in Shanghai.

Background:. Robotic-assisted pancreatoduodenectomy (RPD) has been widely performed in the treatment of pancreatic tumors. The oncologic outcome in the early stage of pancreatic ductal adenocarcinomas (PDAC) operated by RPD has not been evaluated. Methods:. Clinical data of pathology confirmed stage I PDAC from January 2015 to December 2020 in Ruijin hospital, Shanghai Jiao Tong University School of Medicine was collected and retrospectively analyzed. Patients were divided into the open pancreaticoduodenectomy (OPD) group and the RPD group. Clinicopathological data and prognosis data were compared and analyzed. Results:. A total of 426 patients were enrolled in this study, including 342 patients in the OPD group and 84 patients in the RPD group. The baseline characteristics of both groups were equivalent. Incidence of R1 resection was significantly lower in RPD group (3 [3.6%] vs 40 [11.7%], P = .044). RPD group was associated with better disease-free survival (DFS) and overall survival (OS) (DFS: Haszard Ratio [HR]: 0.681, 95% confidence interval [CI]: 0.491–0.945, P = .022; OS: HR: 0.667, 95% CI: 0.478–0.932, P = .017). T2 stage was associated with poor DFS and OS (DFS: HR: 1.471, 95% CI: 1.092–1.981, P = .011; OS: HR: 1.616, 95% CI: 1.209–2.160, P = .035). R1 resection was associated with poor OS (HR: 1.483, 95% CI: 1.028–2.140, P = .035) while adjuvant therapy was associated with better OS (HR: 0.589, 95% CI: 0.452–0.766, P < .001). Kaplan–Meier estimate showed significantly longer DFS and OS in RPD group (DFS: P = .004; OS: P = .009). Conclusion:. For stage I PDAC patients, RPD surgery achieved higher R0 resection rates and better DFS and OS. T1 stage was associated with better DFS. R0 resection and adjuvant therapy were the accesses to better OS.

Shrubs, encompassing important ecosystem engineers in dryland environments, have experienced mass mortality due to long-term droughts across the northwestern Israeli Negev. This massive die-back, which has particularly affected the predominant shrub species Noaea mucronata, predominates edaphically homogeneous (low geodiversity) hillslopes, and is minor in edaphically heterogeneous (high geodiversity) hillslopes. While the homogeneous hillslopes encompass a thick and non-stony soil layer, the heterogeneous hillslopes are composed of a thin, stony layer that overlies weathered, chalky bedrock. The objective of this geophysical study was to assess the subsurface and bedrock characteristics, and its effect on shrub durability vs mass mortality in a dryland ecosystem. A combination of 2D and 3D Ground Penetrating Radar (GPR) imaging and Frequency Domain Electromagnetics (FDEM) was applied. For the GPR procedure, the 2D and 3D reflection imaging was dominated by different dielectric permittivities (dielectric constants) of layers and changes in soil-rock-water content. For the FDEM procedure, the soil-rock electrical properties, alongside the concentration of dissolved salts, determined the measured electrical conductivity (EC). The results show substantial differences in EC values between the homogeneous and heterogeneous hillslopes, which are attributed to differences in the soil-rock interface, soil-water content, and salts concentration. Overall, the much greater EC values of the soil-rock interface in the heterogeneous hillslopes are expected to adversely affect shrub vitality. However, the greater shrub vitality in these hillslopes is likely attributed to the fractured and weathered chalky underground, which retains moisture in pockets or micro-aquicludes that increase water availability for the shrubs. Under these better habitat conditions of the heterogeneous hillslopes, the high EC values do not limit shrub survival or growth. At the same time, the absence of such water pockets in the thick soil layer of the homogeneous hillslopes imposes severe stress under long-term drought conditions, resulting in shrub die-off. This study illustrates the vital role of subsurface geodiversity in determining the resilience of dryland ecosystems to droughts and climatic change.