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Societal Impact Statement Nitrogen, phosphorus, and potassium are essential elements for plant growth and are the primary nutrients in commercial fertilizers. However, the extent to which these nutrients individually limit plant growth is still unclear, as is the influence of their interactions. Using a meta‐analysis approach, plant growth was found to be co‐limited by nitrogen, phosphorus, and potassium. Furthermore, these findings demonstrate that multiple nutrient additions are primarily additive, with the exception of synergistic effects observed with NP additions on plant growth. These findings are important as they provide a timely and valuable reference for assessing the ecosystem‐scale consequences of increasing nutrient deposition caused by human activities. Summary Numerous field studies and a few meta‐analyses have confirmed that terrestrial primary production is individually or jointly limited by nitrogen (N) and phosphorus (P). Meanwhile, a recent synthesis emphasized that potassium (K) limitation in terrestrial ecosystems is more widespread than previously thought. However, it remains unclear whether N, P, and K co‐limit terrestrial production and how their interactions influence plant growth. Therefore, we conducted a globally comprehensive synthesis of a total 2877 observations to explore the individual and interactive effects of N, P, and K additions on plant growth. We found that N, P, K, NP, NK, PK, and NPK additions significantly increased aboveground biomass production (AGP) by 30.8%, 15.3%, 12.3%, 68.4%, 36.5%, 21.1%, and 78.7%, respectively. The combined addition of multiple nutrients resulted in a larger increase in AGP than a single addition of nutrients. These findings suggested that terrestrial production is widely co‐limited by N, P, and K. However, no significant responses of belowground biomass production (BGP) to nutrient additions were observed. Except for synergistic interactions between N and P additions, the responses of AGP to other pairs of nutrient additions were predominantly additive. This meta‐analysis highlights the importance of multiple‐nutrient limitation in regulating terrestrial primary production while pointing out that non‐additive effects (including synergistic and antagonistic) of multiple‐nutrient additions on terrestrial production are relatively rare.

Background: Teacher well-being, encompassing emotional, psychological, and social dimensions, significantly influences teaching effectiveness. Understanding the factors that enhance teacher well-being and effectiveness is crucial, particularly amidst global teacher shortages and high turnover rates. Method: The present study gathered data from 252 university-level foreign language teachers from eight provinces in China. Participants completed measures assessing well-being (using the PERMA model), teaching effectiveness, and life satisfaction scales through online surveys. Data were analyzed using JASP and SPSS for regression and mediation analyses. Results: The findings revealed that Positive emotions and relationships significantly predicted life satisfaction, while accomplishment significantly predicted teaching effectiveness. Both teaching effectiveness and overall well-being were significant predictors of life satisfaction. However, teaching effectiveness did not significantly mediate the relationship between overall well-being and life satisfaction, indicating that these factors independently contribute to life satisfaction. Conclusion: Our findings share the inclusion of PERMA principles in enhancing teaching effectiveness and life satisfaction among foreign language teachers. Constructive future directions are suggested for professional development for improving teacher well-being and effectiveness.

In order to develop functional rice flour products with anti-digestive and antioxidant activities, the effects of adding Wuyi rock tea (WRT) on the quality, in vitro digestive characteristics and antioxidant activity of rice noodles were studied. The texture and sensory quality of rice noodles were determined by texture analyzer and sensory evaluation method, respectively. The flavor components were characterized by GC-MS, and the antioxidant activity was assessed by FRAP, DPPH and ABTS+ assays. The digestive characteristics of rice noodles were detected by the in vitro simulated digestion method. The results showed that compared to the control rice noodles, the breaking rate of rice noodles added with 2.9%~7.0%. WRT was not significantly changed (P>0.05), while the breaking rate of rice noodles added with 8.3% WRT was significantly increased (P

ObjectivesThe emergency response to the COVID-19 pandemic may disrupt hospital management activities of antimicrobial resistance (AMR). This study aimed to determine the changing AMR trend over the period in China when stringent COVID-19 response measures were implemented.MethodsThis retrospective study was conducted in a designated hospital for COVID-19 patients in Guangzhou, China from April 2018 to September 2021. The prevalence of 13 antimicrobial-resistant bacteria was compared before and after the COVID-19 responses through Chi-square tests. Interrupted time series (ITS) models on the weekly prevalence of AMR were established to determine the changing trend. Controlled ITS models were performed to compare the differences between subgroups.ResultsA total of 10,134 isolates over 1,265 days were collected. And antimicrobial-resistant strains presented in 38.6% of the testing isolates. The weekly AMR prevalence decreased by 0.29 percentage point (95% CI [0.05–0.80]) after antimicrobial stewardship (AMS) policy, despite an increase in the prevalence of penicillin-resistant Streptococcus pneumoniae (from 0/43 to 15/43, p

Objectives: To evaluate the impact of pay-for-performance on antimicrobial consumption and antimicrobial expenditure in a large teaching hospital in Guangzhou, China. Methods: We collected data from hospital information system from January 2018 through September 2022 in the inpatient wards. Antimicrobial consumption was evaluated using antibiotic use density (AUD) and antibiotic use rate (AUR). The economic impact of intervention was assessed by antimicrobial expenditure percentage. The data was analyzed using interrupted time series (ITS) analysis. Results: Following the implementation of the intervention, immediate decreases in the level of AUD were observed in Department of Hematology Unit 3 (β = −66.93 DDDs/100PD, P = 0.002), Urology (β = −32.80 DDDs/100PD, P

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4.

Simple development of an electrochemiluminescence (ECL) immunosensor for convenient detection of tumor biomarker is of great significance for early cancer diagnosis, treatment evaluation, and improving patient survival rates and quality of life. In this work, an immunosensor is demonstrated based on an enhanced ECL signal boosted by nanochannel-confined Au nanomaterial, which enables sensitive detection of the tumor biomarker—carcinoembryonic antigen (CEA). Vertically-ordered mesoporous silica film (VMSF) with a nanochannel array and amine groups was rapidly grown on a simple and low-cost indium tin oxide (ITO) electrode using the electrochemically assisted self-assembly (EASA) method. Au nanomaterials were confined in situ on the VMSF through electrodeposition, which catalyzed both the conversion of dissolved oxygen (O2) to reactive oxygen species (ROS) and the oxidation of a luminol emitter and improved the electrode active surface. The ECL signal was enhanced fivefold after Au nanomaterial deposition. The recognitive interface was fabricated by covalent immobilization of the CEA antibody on the outer surface of the VMSF, followed with the blocking of non-specific binding sites. In the presence of CEA, the formed immunocomplex reduced the diffusion of the luminol emitter, resulting in the reduction of the ECL signal. Based on this mechanism, the constructed immunosensor was able to provide sensitive detection of CEA ranging from 1 pg·mL−1 to 100 ng·mL−1 with a low limit of detection (LOD, 0.37 pg·mL−1, S/N = 3). The developed immunosensor exhibited high selectivity and good stability. ECL determination of CEA in fetal bovine serum was achieved.

Weizmannia coagulans is increasingly employed in food processing owing to its health benefits. Our previous research developed Oolong tea-fortified rice noodles with unique flavor and potent antioxidant activity; however, their texture still requires improvement. In this study, Oolong tea-fortified rice noodles were fermented using W. coagulans PR06 at inoculation amounts of 1%, 3%, and 5% (v/v), and assessed for cooking quality, texture, and starch digestibility. The results indicated that fermentation with 3% and 5% W. coagulans PR06 altered the amylopectin length distribution in the rice noodles and increased the degree of starch short-range order. Furthermore, the fermentation process increased the storage modulus (G′) and loss modulus (G″) values, decreased the tan δ value, and strengthened the interactions among tea polyphenols, proteins, and starch in the rice flour gel. Consequently, this process increased the hardness and chewiness of the rice noodles, decreased their broken strip rate and cooking loss, and significantly reduced their in vitro starch digestibility. Overall, fermentation with W. coagulans PR06 markedly improved the texture and cooking quality of Oolong tea-fortified rice noodles while effectively delaying starch digestion. This study highlights the potential application of W. coagulans PR06 in developing diverse and functional rice noodle products.

Background: The challenge of emerging antimicrobial resistance and variation in antibiotic use across provinces in China call for knowledge on antibiotic utilization at the regional level. This study aims to evaluate the long-term trends and patterns of antibiotic usage in Xinjiang Province, the largest provincial-level division located in the northwest of China, aiming to provide evidence in enhancing provincial antimicrobial stewardship (AMS) and developing policy measures to optimize regional antimicrobial use. Methods: This was an ecological study with temporal trend analysis on inpatient antibiotic utilization, with antibiotic use data from 92 public hospitals covered by Xinjiang’s Center for Antibacterial Surveillance from 2012 to 2022. Antibiotic use was measured by the number of daily defined doses per 100 patient days (DDDs/100 pds). Patterns of antibiotic use were described by Anatomical Therapeutic Chemical (ATC) subgroups and the Access, Watch, Reserve (AWaRe) classification. The Average Annual Percent Change (AAPC) of antibiotic use and the corresponding 95% confidence intervals (CIs) were calculated to describe the trend of antibiotic use over time. Joinpoint regression was performed using the Weighted Bayesian Information Criteria (WBIC) model with a parametric method. A pairwise comparison between secondary and tertiary hospitals was conducted to explore disparities in antibiotic use across hospital levels. The most commonly used antibiotics were also analyzed. Results: The total inpatient antibiotic use in Xinjiang was 27.6 DDDs/100 patient days in 2022, with a significant decreasing trend during 2012–2022 (AAPC, −2.0%; 95% CI, −3.6% to −0.4%). The Watch group antibiotics were the most used AWaRe category, with the Access-to-Watch ratio decreasing significantly from 46.4% to 24.4% (AAPC, −6.8%; 95% CI, −8.4% to −5.1%). No significant difference was found in the trend of total antibiotic use between secondary and tertiary hospitals, but there were disparities across hospital levels in subgroups. Third-generation cephalosporins, second-generation cephalosporins, and fluoroquinolones remained the top three antibiotic class throughout the study period. The number of antibiotics accounting for 90% of the total antibiotic use decreased from 34 antibiotics in 2012 to 18 antibiotics in 2022. Conclusions: The decreasing trend of inpatient antibiotic use in Xinjiang’s public hospitals reflects the effects of continuous AMS implementation. Patterns of antibiotic use underscore the need for further efforts on evidence-based antibiotic selection and for analyses on the appropriateness of antibiotic use.

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies.

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

The frequent occurrence of global flood disasters leads to millions of people falling into poverty each year, which poses immense pressure on governments and hinders social development. Therefore, providing more data support for flood disaster detection is of paramount importance. To facilitate the development of water body detection algorithms, we create the DaliWS dataset for water segmentation, which contains abundant pixel-level annotations, and consists of high spatial resolution SAR images collected from the GaoFen-3 (GF-3) satellite. For comprehensive analysis, extensive experiments are conducted on the DaliWS dataset to explore the performance of the state-of-the-art segmentation models, including FCN, SegNeXt, U-Net, and DeeplabV3+, and investigate the impact of different polarization modes on water segmentation. Additionally, to probe the generalization of our dataset, we further evaluate the models trained with the DaliWS dataset, on publicly available water segmentation datasets. Through detailed analysis of the experimental results, we establish a valuable benchmark and provide usage guidelines for future researchers working with the DaliWS dataset. The experimental results demonstrate the F1 scores of FCN, SegNeXt, U-Net, and DeeplabV3+ on the dual-polarization data of DaliWS dataset reach to 90.361%, 90.192%, 92.110%, and 91.199%, respectively, and these four models trained using the DaliWS dataset exhibit excellent generalization performance on the public dataset, which further confirms the research value of our dataset.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset can enhance the water information while reducing the data dimensionality: specifically, the Cloud-Free Label provided in the dataset can effectively alleviate the problem of labeled sample scarcity. Since a single form or uniform kernel size cannot handle the variety of sizes and shapes of water bodies, we propose the Gated Multi-Filter Inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize an attention mechanism by introducing a Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-Inception to further improve model performance. Extensive experiments on three benchmarks, including the WIPI, Chengdu and GF2020 datasets, demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on the WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 scores of 0.9191, 0.7410 and 0.8421, respectively, with the average F1 score on the three datasets 0.0045 higher than that of the U-Net model; likewise, GFLOPS were reduced by 62% on average. The new WIPI dataset, the code and the trained models have been released on GitHub.

Background: New hemoglobin (Hb) variants are constantly being updated as assays are developed and the testing population expands. Here, we report a novel Hb variant, named Hb Guigang. Methods: Hemoglobin (Hb) analysis was analyzed by capillary electrophoresis (CE) and high-performance liquid chromatography (HPLC). Glycated hemoglobin was performed by CE and HPLC. Routine genetic analysis was done with Gap-PCR and PCR-reverse dot-blot hybridization. The hemoglobin variant was identified by Sanger sequencing. Results: CE of three cases showed the presence of Hb variants in Zone 5 and Zone 12, respectively. HPLC indicated an elevated P3 peak, suggesting the possible presence of the Hb variant. Hb A1c was measured by CE and HPLC, and the results were 6.7% and 4.76%, respectively. Sanger sequencing confirmed an AAG˃AAT mutation at codon 90 of the HBA1 gene. This mutation was reported for the first time, and we named it Hb Guigang based on the proband’s place of residence. Conclusions: Hb Guigang with normal hematological parameters was separated and quantified by CE, whereas HPLC suggested that Hb Guigang co-eluted with the P3 peaks and could not be quantified. [ABSTRACT FROM AUTHOR]

Abstract Background To explore the risk factors for postoperative abnormal coagulation (PAC) and establish a predictive model for patients with normal preoperative coagulation function who underwent hepatectomy. Materials and Methods A total of 661 patients with normal preoperative coagulation function who underwent hepatectomy between January 2015 and December 2021 at the First Affiliated Hospital of Sun Yat-sen University were divided into two groups: the postoperative abnormal coagulation group (PAC group, n = 362) and the normal coagulation group (non-PAC group, n = 299). Univariate and multivariate logistic analyses were used to identify the risk factors for PAC. Results The incidence of PAC in 661 patients who underwent hepatectomy was 54.8% (362/661). The least absolute shrinkage and selection operator (LASSO) method was used for multivariate logistic regression analysis. The preoperative international normalized ratio (INR), intraoperative succinyl gelatin infusion and major hepatectomy were found to be independent risk factors for PAC. A nomogram for predicting the PAC after hepatectomy was constructed. The model presented a receiver operating characteristic (ROC) curve of 0.742 (95% confidence interval (CI): 0.697–0.786) in the training cohort. The validation set demonstrated a promising ROC of 0.711 (95% CI: 0.639–0.783), and the calibration curve closely approximated the true incidence. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the predictive model. The risk of PAC increased when the preoperative international normalized ratio (INR) was greater than 1.025 and the volume of intraoperative succinyl gelatin infusion was greater than 1500 ml. Conclusion The PAC is closely related to the preoperative INR, intraoperative succinyl gelatin infusion and major hepatectomy. A three-factor prediction model was successfully established for predicting the PAC after hepatectomy.

The deck slabs narrow joints in concrete bridges filled with Ultra-high performance concrete (UHPC) have the potential for wide application and high-efficiency promotion, but there is limited research available on this topic. This paper conducted flexural tests on the new joints. Ten precast concrete deck panels were designed and manufactured for concrete bridges. These panels were connected using UHPC-filled narrow joints. Combined with stress analysis, this study investigates the influence and mechanism of various factors on the mechanical performance of UHPC joints. These factors include the geometric shape of the joint, the roughness of the joint interface, the filling material used in the joint, the width of the joint, and the configuration of joint reinforcement (including the shape and length of lap reinforcement). The test results show that the panel with the UHPC joint filled with steel fibers has excellent flexural and bonding capacity. The jointed panel with a lap length of 140 mm and 120 mm has better flexural performance than the one with a lap length of 90 mm. The straight-jointed panel has a higher flexural capacity compared to the complex triangular-jointed panel. Additionally, reducing the joint width can achieve similar mechanical properties as before. A method has been proposed for calculating the flexural capacity of a jointed panel, utilizing the strut-and-tie model (STM). This method can accurately predict experimental results.

Abstract The rise of touchless technology, driven by the recent pandemic, has transformed human-machine interaction (HMI). Projections indicate a substantial growth in the touchless technology market, nearly tripling from $13.6 billion in 2021 to an estimated $37.6 billion by 2026. In response to the pandemic-driven shift towards touchless technology, here we show an organic cage-based humidity sensor with remarkable humidity responsiveness, forming the basis for advanced touchless platforms in potential future HMI systems. This cage sensor boasts an ultrafast response/recovery time (1 s/3 s) and exceptional stability (over 800 cycles) across relative humidity (RH) changes from 11% to 95%. The crystal structure’s 3D pore network and luxuriant water-absorbing functional groups both inside and outside of the cage contribute synergistically to superior humidity sensing. Demonstrating versatility, we showcase this cage in smart touchless control screens and touchless password managers, presenting cost-effective and easily processable applications of molecularly porous materials in touchless HMI.

Abstract The nasal cavity harbors diverse microbiota that contributes to human health and respiratory diseases. However, whether and to what extent the host genome shapes the nasal microbiome remains largely unknown. Here, by dissecting the human genome and nasal metagenome data from 1401 healthy individuals, we demonstrated that the top three host genetic principal components strongly correlated with the nasal microbiota diversity and composition. The genetic association analyses identified 63 genome-wide significant loci affecting the nasal microbial taxa and functions, of which 2 loci reached study-wide significance (p

The genetic male sterile line (GMS) of the silkworm Bombyx mori is a recessive mutant that is naturally mutated from the wild-type 898WB strain. One of the major characteristics of the GMS mutant is its small larvae. Through positional cloning, candidate genes for the GMS mutant were located in a region approximately 800.5 kb long on the 24th linkage group of the silkworm. One of the genes was Bombyx mori CCAAT/enhancer-binding protein zeta (BmC/EBPZ), which is a member of the basic region-leucine zipper transcription factor family. Compared with the wild-type 898WB strain, the GMS mutant features a 9 bp insertion in the 3′end of open reading frame sequence of BmC/EBPZ gene. Moreover, the high expression level of the BmC/EBPZ gene in the testis suggests that the gene is involved in the regulation of reproduction-related genes. Using the CRISPR/Cas9-mediated knockout system, we found that the BmC/EBPZ knockout strains had the same phenotypes as the GMS mutant, that is, the larvae were small. However, the larvae of BmC/EBPZ knockout strains died during the development of the third instar. Therefore, the BmC/EBPZ gene was identified as the major gene responsible for GMS mutation.

Pyrroline-5-carboxylate reductase (PYCR) is pivotal in converting pyrroline-5-carboxylate (P5C) to proline, the final step in proline synthesis. Three isoforms, PYCR1, PYCR2, and PYCR3, existed and played significant regulatory roles in tumor initiation and progression. In this study, we first assessed the molecular and immune characteristics of PYCRs by a pan-cancer analysis, especially focusing on their prognostic relevance. Then, a kidney renal clear cell carcinoma (KIRC)-specific prognostic model was established, incorporating pathomics features to enhance predictive capabilities. The biological functions and regulatory mechanisms of PYCR1 and PYCR2 were investigated by in vitro experiments in renal cancer cells. The PYCRs’ expressions were elevated in diverse tumors, correlating with unfavorable clinical outcomes. PYCRs were enriched in cancer signaling pathways, significantly correlating with immune cell infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). In KIRC, a prognostic model based on PYCR1 and PYCR2 was independently validated statistically. Leveraging features from H&E-stained images, a pathomics feature model reliably predicted patient prognosis. In vitro experiments demonstrated that PYCR1 and PYCR2 enhanced the proliferation and migration of renal carcinoma cells by activating the mTOR pathway, at least in part. This study underscores PYCRs’ pivotal role in various tumors, positioning them as potential prognostic biomarkers and therapeutic targets, particularly in malignancies like KIRC. The findings emphasize the need for a broader exploration of PYCRs’ implications in pan-cancer contexts.

Objective •To construct a nomogram model for predicting the occurrence of the laparoscopic appendectomy surgical site infection (LASSI) and explore prevention strategies. Methods • A total of 995 patients who underwent laparoscopic appendectomy in Shanxi Bethune Hospital from October 2017 to August 2022 were selected. According to whether there was incision infection within 30 days after operation, the patients were divided into the LASSI (97 cases) and nonLASSI (898 cases) group. The following clinicopathological data from these two groups of patients were collected: gender, age, body mass index, ect. The subjects were randomly divided into training group and verification group according to the 7:3 ratio. Univariate and multivariate analysis was used to screen the related influencing factors and construct a nomogram model to predict the occurrence of LASSI. Rreceiver operating characteristic (ROC) curves and the calibration curve were used to evaluate the predictive value of the model. For patients with LASSI, a more effective preventive measure was explored. Results • Multivariate logistic regression analysis showed that operation time >1h (OR: 1.891; 95% CI: 1.07 to 3.36; P = .029), perforated and gangrenous appendix (OR: 4.078; 95% CI: 1.84 to 9.86; P = .001), free intraperitoneal fluid (OR: 2.836; 95% CI: 1.57 to 5.35; P = .001), BMI>30 kg/m² (OR: 2.828; 95% CI:1.54 to 5.12; P = .001), diabetes mellitus (DM) (OR: 2.795; 95% CI: 1.54 to 5.28; P = .001) were the independent prognostic factors of LASSI. The prediction nomogram model showed satisfactory performance in predicting the occurrence of LASSI, ROC curve area value of the training and verification groups were respectively 0.753 (95 % CI: 0.688 ~ 0.818) and 0.772 (95 % CI: 0.691-0.852). In the event of LASSI, we took out appendix specimens in sections and sterilized surgical site, which effectively prevented it. Conclusion • This study evaluated the risk factors related to the occurrence of LASSI and established a prediction model for LASSI. The prediction model provides a convenient and fast risk assessment tool for clinicians to predict the occurrence of LASSI. Combined with the newly discovered prevention strategy of segmental removal of appendix and incision disinfection, it can effectively avoid the occurrence of LASSI and potentially reduce the hospitalization time and costs. [ABSTRACT FROM AUTHOR]

Floods are common and inevitable natural disasters. Achieve Sustainable Development Goal (SDG) 11.5 is a critical challenge for coastal cities, especially those in deltaic lowlands such as in the case of Guangzhou, China. Regarding the spatial planning and design of such urban regions, it is crucial to study the impacts of flooding in compact or decentralized spatial development pathways. This reinforces the understanding of the relationship between strategic decisions for spatial planning and flood mitigation. However, the lack of a computer model to assess spatial evolution paths is a significant limitation. The non-dominated Sorting Genetic Algorithm II (NSGA-II) explores the possibility of a compact built-up land layout in 2030. The results showed that, concerning the 2030 decentralized scenario, the 2030 compact scenario presents a large increase in the integrated fitness function value from 0.618 to 0.771 (the increase is equivalent to 0.153 or about 24.75%). In addition, different development scenarios were constructed by setting different target weights. Compared to the decentralized scenario results, the fitness function values of the optimization results of each scenario showed better results at different levels. They could also serve as a reference for other similar coastal areas to achieve SDG 11.5 by 2030.

Limited evidence exists regarding the causal effects of air pollution and metabolic syndrome (MetS), as well as the potential moderating effect of adherence to healthy dietary patterns. We recruited participants with accessible clinical characteristics, dietary patterns, and blood biomarkers data from the 2009 China Health and Nutrition Survey. Multi-biomarkers combined with physical examinations were used to determine the status of MetS. The fine particulate matter (PM _2.5 ) concentration was calculated by the near-real-time historical data at the resolution of 1 km. The control function (CF) combined with probit model (CF-Probit) was used to determine the effect of long-term PM _2.5 exposure on MetS risk, with wind speed serving as the instrument. We assessed the dietary patterns of each participant using the dietary balance index (DBI). The modification analyses were conducted to investigate the potential moderating role of dietary patterns. The study included 4,277 adult participants participated with a mean age of 50.18 years and an incidence of MetS of 22.38% (957/4277 cases). The mean score for the DBI was 40.23. The mean long-term PM _2.5 level was 65.79 μ g m ^−3 . The CF-Probit marginal effects analysis showed significant causal effects of chronic PM _2.5 exposure on MetS incidence, with a marginal effect of 0.013 (95% confidence interval (CI): 0.003–0.022), suggesting that the average partial effect of long-term PM _2.5 level on the risk of MetS in adults is 1.3 percentage points. The modification analysis indicated that the average partial effect of PM _2.5 level on the risk of MetS is higher for male compared to female (4.22 pencetage points, 95% CI: 2.12 percentage points, 6.35 percentage points) and greater associated with unhealthy dietary patterns (1 percentage point, 95% CI: 0.17 percentage points, 1.86 percentage points). This study found that long-term exposure to PM _2.5 increases the risk of MetS, while a healthy dietary pattern can modulate this effect. The findings can provide scientific basis for health protection guidelines for air pollution and provide dietary recommendations for populations.

It is challenging to probe ergodicity breaking trends of a quantum many-body system when dissipation inevitably damages quantum coherence originated from coherent coupling and dispersive two-body interactions. Rydberg atoms provide a test bed to detect emergent exotic many-body phases and nonergodic dynamics where the strong Rydberg atom interaction competes with and overtakes dissipative effects even at room temperature. Here, we report experimental evidence of a transition from ergodic toward ergodic breaking dynamics in driven-dissipative Rydberg atomic gases. The broken ergodicity is featured by the long-time phase oscillation, which is attributed to the formation of Rydberg excitation clusters in limit cycle phases. The broken symmetry in the limit cycle is a direct manifestation of many-body collective effects, which is verified experimentally by tuning atomic densities. The reported result reveals that Rydberg many-body systems are a promising candidate to probe ergodicity breaking dynamics, such as limit cycles, and enable the benchmark of nonequilibrium phase transition. [ABSTRACT FROM AUTHOR]