Asymmetric winter warming reduces microbial carbon use efficiency and growth more than symmetric year-round warming in alpine soils.

Asymmetric seasonal warming trends are evident across terrestrial ecosystems, with winter temperatures rising more than summer ones. Yet, the impact of such asymmetric seasonal warming on soil microbial carbon metabolism and growth remains poorly understood. Using 18O isotope labeling, we examined the effects of a decade-long experimental seasonal warming on microbial carbon use efficiency (CUE) and growth in alpine grassland ecosystems. Moreover, the quantitative stable isotope probing with 18O-H2O was employed to evaluate taxon-specific bacterial growth in these ecosystems. Results show that symmetric year-round warming decreased microbial growth rate by 31% and CUE by 22%. Asymmetric winter warming resulted in a further decrease in microbial growth rate of 27% and microbial CUE of 59% compared to symmetric year-round warming. Long-term warming increased microbial carbon limitations, especially under asymmetric winter warming. Long-term warming suppressed the growth rates of most bacterial genera, with asymmetric winter warming having a stronger inhibition on the growth rates of specific genera (e.g., Gp10, Actinomarinicola, Bosea, Acidibacter, and Gemmata) compared to symmetric year-round warming. Bacterial growth was phylogenetically conserved, but this conservation diminished under warming conditions, primarily due to shifts in bacterial physiological states rather than the number of bacterial species and community composition. Overall, long-term warming escalated microbial carbon limitations, decreased microbial growth and CUE, with asymmetric winter warming having a more pronounced effect. Understanding these impacts is crucial for predicting soil carbon cycling as global warming progresses.

Evaluation and optimization of park cooling benefits based on cumulative impact and landscape pattern.

City parks can cool the surrounding environment and mitigate the urban heat island (UHI) effect, considerable improving the city's adaptability to climate. In this study, 20 city parks in Nanjing, China, were considered, and four indexes for quantifying the cooling benefits from a cumulative impact perspective were proposed. These indexes are park cooling area (PCA), park cooling efficiency (PCE), park cooling intensity (PCI), and park cooling gradient (PCG). The results reveal the following: first, city parks have a positive impact on the surrounding thermal environment. The factors park area (PA), park perimeter (PP), landscape shape index (LSI), and normalized difference vegetation index (NDVI) determine cooling benefits. Second, PA and PP are significantly positively correlated with PCA but are significantly negatively correlated with PCE. LSI is negatively correlated with PCE, while NDVI is positively correlated with PCI and PCG. No significant correlation exists between the four cooling indexes and modified normalized difference water index(MNDWI). Finally, different parks exhibit variations in their ability to provide cooling benefits. Special or community parks are more appropriately situated in areas with constrained urban land resources. In designing comprehensive parks, the intricate boundary features and vegetation conditions need to be considered to optimize their cooling effects. Moreover, a larger number of residents are allowed to enjoy cooling services. The findings of this project will aid in the construction and optimization of city parks in future to combat the UHI effect.

Osthole attenuates asthma-induced airway epithelial cell apoptosis and inflammation by suppressing TSLP/NF-κB-mediated inhibition of Th2 differentiation.

OBJECTIVE: The aim of this study was to investigate the influence of osthole (OS) on asthma-induced airway epithelial cell apoptosis and inflammation by restraining Th2 differentiation through suppressing TSLP/NF-κB. METHODS: An asthma mouse model and an inflammation cell model were constructed with ovalbumin (OVA) and lipopolysaccharide (LPS), respectively. CD4 + T cells were treated with IL-4 to induce Th2 differentiation. Model mice were treated with OS (15,40 mg/kg) for 7 days, and 10 µg/mL OS was added to cell treatment groups. The levels of relevant indices were detected by RT‒qPCR, HE and Masson staining, Western blotting, ELISA and flow cytometry. RESULTS: In a mouse asthma model, TSLP expression was elevated, and the NF-κB pathway was activated. Therefore, OS could restrain the apoptosis and inflammation of airway epithelial cells. Downstream mechanistic studies revealed that OS can suppress Th2 differentiation by restraining the level of TSLP and NF-κB nuclear translocation, thus facilitating the proliferation of airway epithelial cells, restraining their apoptosis and inflammation, and alleviating airway inflammation in asthmatic mice. CONCLUSION: OS can inhibit Th2 differentiation by inhibiting the TSLP and NF-κB pathways, which can reduce the apoptosis and inflammation of airway epithelial cells caused by asthma.

Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models.

OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models. MATERIALS AND METHODS: A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis. RESULTS: In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02). CONCLUSION: The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications. CLINICAL RELEVANCE STATEMENT: The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.

Unraveling soil geochemical, geophysical, and microbial determinants of the vertical distribution of organic phosphorus pesticide pollutants.

Pesticide contamination has emerged as a global threat to humans. Here, we investigate the soil distribution pattern of organic phosphorus pesticide contamination at a pesticide manufacturing site in northern China, exploring their relationships with soil properties and microbial communities. The concentrations of four organic phosphorus pesticides (i.e., phorate, terbuthion, fenitrothion, and parathion) decreased substantially with soil depths from the surface down to 2 m. However, terbuthion, fenitrothion, and parathion had second-peak concentrations at a depth of 8 m. The concentrations of those organic phosphorus pesticides were negatively correlated with soil water content, but positively correlated with sulfide, pH, and total phosphorus. The distribution patterns of organic phosphorus pesticides closely aligned with that of soil organic matter and clay minerals, especially in the presence of montmorillonite, kaolinite, and chlorite. Various bacterial genera known to degrade organic phosphorus pesticides, such as Flavobacterium, Bacillus, Acinetobacter, Lactobacillus, Pseudomonas, Sphingomonas, and Thiobacillus, were correlated with these pesticides. Since these genera were among the top 50 abundant genera in our samples, they might play a significant role in the degradation of organic phosphorus pesticides. Together, this study unveils previously unrecognized pesticide-soil-microbe interactions, thus providing an important knowledge basis for environmental remediation strategies.

More sensitive microbial responses to the interactive effects of warming and altered precipitation in subsoil than topsoil of an alpine grassland ecosystem.

Subsoil is a large organic carbon reservoir, storing more than half of the total soil organic carbon (SOC) globally. Conventionally, subsoil is assumed to not be susceptible to climate change, but recent studies document that climate change could significantly alter subsoil carbon cycling. However, little is known about subsoil microbial responses to the interactive effects of climate warming and altered precipitation. Here, we investigated carbon cycling and associated microbial responses in both subsoil (30-40 cm) and topsoil (0-10 cm) in a Tibetan alpine grassland over 4 years of warming and altered precipitation. Compared to the unchanged topsoil carbon (ß = .55, p = .587), subsoil carbon exhibited a stronger response to the interactive effect of warming and altered precipitation (ß = 2.04, p = .021), that is, warming decreased subsoil carbon content by 28.20% under decreased precipitation while warming increased subsoil carbon content by 18.02% under increased precipitation.Furthermore, 512 metagenome-assembled genomes (MAGs) were recovered, including representatives of phyla with poor genomic representation. Compared to only one changed topsoil MAG, 16 subsoil MAGs were significantly affected by altered precipitation, and 5 subsoil MAGs were significantly affected by the interactive effect of warming and precipitation. More than twice as many populations whose MAG abundances correlated significantly with the variations of carbon content, components and fluxes were observed in the subsoil than topsoil, suggesting their potential contribution in mediating subsoil carbon cycling. Collectively, our findings highlight the more sensitive responses of specific microbial lineages to the interactive effects of warming and altered precipitation in the subsoil than topsoil, and provide key information for predicting subsoil carbon cycling under future climate change scenarios.

Long term impact of electrical resistance heating on soil bacterial community based on a field test.

Thermal remediation is an effective technology for organic contaminant remediation. However, the application of thermal remediation may have negative effects on soil properties and ecological functions, which requires further investigation. Based on a pilot test of electrical resistance heating remediation (ERH), soil samples were collected at different locations after heating for 116 days. Most soil physicochemical properties were less affected by the heating temperature difference. Application of high temperature increased microbial abundance but inhibited alpha diversity of the bacterial community. More significant changes in microbial communities were observed at temperatures above 60 °C. The genera mainly affected by heating temperature included Flavobacteria, Brockia, and S085, while the increase in temperature also inhibited the abundance of nitrochlorobenzene functional genes. At 140 days after the end of the pilot test, the bacterial community affected by thermal remediation could recover effectively, and the recovery of the bacterial community was not affected by temperature difference during the heating period. This study provides valuable field evidence of the long term impact of soil ERH treatment on soil properties and microbial communities, and provides further references for optimization of remediation performance with coupled technologies.

Microneedle-Delivered PDA@Exo for Multifaceted Osteoarthritis Treatment via PI3K-Akt-mTOR Pathway.

Osteoarthritis (OA) is marked by cartilage deterioration, subchondral bone changes, and an inflammatory microenvironment. The study introduces the Microneedle-Delivered Polydopamine-Exosome (PDA@Exo MN), a therapeutic that not only preserves cartilage and promotes bone regeneration but also improves localized drug delivery through enhanced penetration capabilities. PDA@Exo MN shows strong reactive oxygen species (ROS) scavenging abilities and high biocompatibility, fostering osteogenesis and balancing anabolic and catabolic processes in cartilage. It directs macrophage polarization from M0 to the anti-inflammatory M2 phenotype. RNA sequencing of treated chondrocytes demonstrates restored cellular function and activated antioxidant responses, with modulated inflammatory pathways. The PI3K-AKT-mTOR pathway's activation, essential for PDA@Exo's effects, is confirmed via bioinformatics and Western blot. In vivo assessments robustly validate that PDA@Exo MN prevents cartilage degradation and OA progression, supported by histological assessments and micro-CT analysis, highlighting its disease-modifying impact. The excellent biocompatibility of PDA@Exo MN, verified through histological (H&E) and blood tests showing no organ damage, underscores its safety and efficacy for OA therapy, making it a novel and multifunctional nanomedical approach in orthopedics, characterized by organ-friendliness and biosecurity.

SRGN promotes macrophage recruitment through CCL3 in osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a degenerative disease that affects synovial joints and leads to significant pain and disability, particularly in older adults. Infiltration of macrophages plays a key role in the progression of OA. However, the mechanisms underlying macrophage recruitment in OA are not fully understood. METHODS: The Serglycin (SRGN) expression pattern was analyzed, along with its association with macrophage infiltration in OA, using bioinformatic methods. SRGN expression in chondrocytes was altered by small interfering RNA (siRNA) and plasmids. Conditioned media (CM) was obtained from transfected chondrocytes to establish a co-culture model of chondrocytes and THP-1 derived macrophages. The impact of SRGN on macrophage recruitment was evaluated using a transwell assay. Furthermore, the regulatory effect of SRGN on CCL3 was validated through qPCR, WB, and ELISA experiments. RESULTS: In OA patients, the upregulation of SRGN positively correlated with K-L grade and macrophage infiltration. It was found that SRGN expression and secretion were up-regulated in OA and that it can promote macrophage migration in vitro. Further investigation showed that SRGN affects macrophage migration by regulating the expression of CCL3. CONCLUSION: SRGN in chondrocytes plays a role in promoting the recruitment of THP-1 derived macrophages in vitro by regulating production of CCL3.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

Objectives: To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods: MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results: The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion: In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement: Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

Enhancing soil petrochemical contaminant remediation through nutrient addition and exogenous bacterial introduction.

Biostimulation (providing favorable environmental conditions for microbial growth) and bioaugmentation (introducing exogenous microorganisms) are effective approaches in the bioremediation of petroleum-contaminated soil. However, uncertainty remains in the effectiveness of these two approaches in practical application. In this study, we constructed mesocosms using petroleum hydrocarbon-contaminated soil. We compared the effects of adding nutrients, introducing exogenous bacterial degraders, and their combination on remediating petroleum contamination in the soil. Adding nutrients more effectively accelerated total petroleum hydrocarbon (TPH) degradation than other treatments in the initial 60 days' incubation. Despite both approaches stimulating bacterial richness, the community turnover caused by nutrient addition was gentler than bacterial degrader introduction. As TPH concentrations decreased, we observed a succession in microbial communities characterized by a decline in copiotrophic, fast-growing bacterial r-strategists with high rRNA operon (rrn) copy numbers. Ecological network analysis indicated that both nutrient addition and bacterial degrader introduction enhanced the complexity and stability of bacterial networks. Compared to the other treatment, the bacterial network with nutrient addition had more keystone species and a higher proportion of negative associations, factors that may enhance microbial community stability. Our study demonstrated that nutrient addition effectively regulates community succession and ecological interaction to accelerate the soil TPH degradation.

Regulating Planarized Intramolecular Charge Transfer for Efficient Single-Phase White-Light Emission in Undoped Metal-Organic Framework Nanocrystals.

The technology of combining multiple emission centers to exploit white-light-emitting (WLE) materials by taking advantage of porous metal-organic frameworks (MOFs) is mature, but preparing undoped WLE MOFs remains a challenge. Herein, a pressure-treated strategy is reported to achieve efficient white photoluminescence (PL) in undoped [Zn(Tdc)(py)]n nanocrystals (NCs) at ambient conditions, where the Commission International del'Eclairage coordinates and color temperature reach (0.31, 0.37) and 6560 K, respectively. The initial [Zn(Tdc)(py)]n NCs exhibit weak-blue PL consisting of localized excited (LE) and planarized intramolecular charge transfer (PLICT) states. After pressure treatment, the emission contributions of LE and PLICT states are balanced by increasing the planarization of subunits, thereby producing white PL. Meanwhile, the reduction of nonradiative decay triggered by the planarized structure results in 5-fold PL enhancement. Phosphor-converted light-emitting diodes based on pressure-treated samples show favorable white-light characteristics. The finding provides a new platform for the development of undoped WLE MOFs.

A case study showing highly traceable sources of bacteria on surfaces of university buildings.

University students predominantly spend their time indoors, where prolonged exposure raises the risk of contact with microorganisms of concern. However, our knowledge about the microbial community characteristics on university campus and their underpinnings is limited. To address it, we characterized bacterial communities from the surfaces of various built environments typical of a university campus, including cafeterias, classrooms, dormitories, offices, meeting rooms, and restrooms, in addition to human skin. The classrooms harbored the highest α-diversity, while the cafeterias had the lowest α-diversity. The bacterial community composition varied significantly across different building types. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were common phyla in university buildings, accounting for more than 90 % of total abundance. Staphylococcus aureus was the most abundant potential pathogen in classrooms, dormitories, offices, restrooms, and on human skin, indicating a potential risk for skin disease infections in these buildings. We further developed a new quantitative pathogenic risk assessment method according to the threat of pathogens to humans and found that classrooms exhibited the highest potential risk. The fast expectation-maximization algorithm identified 59 %-86 % of bacterial sources in buildings, with the human skin as the largest bacterial source for most buildings. As the sources of bacteria were highly traceable, we showed that homogeneous selection, dispersal limitation, and ecological drift were major ecological forces that drove community assembly. Our findings have important implications for predicting the distribution and sources of indoor dust bacterial communities on university campus.

Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study.

OBJECTIVE: The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage. METHODS: This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model. RESULTS: The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process. CONCLUSION: Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.

[Application of Allgöwer-Donati suture in internal fixation of Schatzker type â ¤and â ¥ tibial plateau closed fractures].

Objective: To investigate the effectiveness of Allgöwer-Donati suture in open reduction and internal fixation of Schatzker type Ⅴ and Ⅵ tibial plateau closed fractures. Methods: A clinical data of 60 patients with Schatzker type type Ⅴ and Ⅵ tibial plateau closed fractures, who met the selection criteria and admitted between May 2022 and May 2023, was retrospectively analyzed. After open reduction and internal fixation via double incisions, the incisions were closed with conventional mattress suture in 30 cases (control group) and Allgöwer-Donati suture in 30 cases (observation group). There was no significant difference in gender, age, fracture side and type, time from injury to operation, body mass index, and other baseline data between the two groups ( P>0.05). The incidence of incision-related complications after operation, visual analogue scale (VAS) score of incision at 3 days and 1 and 2 weeks after operation, and the short-form 36 health survey scale (SF-36) [physical functioning (PF), role physical (RP), bodily pain (BP), and general health (GH)] at 12 weeks after operation were compared between the two groups. Results: All operations of the two groups successfully completed. All patients were followed up 6-14 months (mean, 12 months). Incision fluid leakage occurred in 1 case of observation group and 7 cases of control group within 1 week after operation, and the incisions healed after symptomatic treatment. The incisions of other patients healed by first intention. The incidence of early incision complications in observation group was significantly lower than that in control group ( P<0.05). No late incision complications was found in the two groups. There was no significant difference in VAS scores at each time point between the two groups ( P>0.05). The VAS score significantly decreased with the increase of time in the two groups, showing significant differences between the different time points ( P<0.05). There was no significant difference in SF-36 scores (PF, RP, BP, and GH) between the two groups at 12 weeks after operation ( P>0.05). Conclusion: Compared with conventional mattress suture, Allgöwer-Donati suture is effective in open reduction and internal fixation via double incisions for Schatzker type Ⅴand Ⅵ tibial plateau closed fractures, which can reduce the incidence of early incision complications.

An examination from 1990 to 2019: investigating the burden of knee dislocation on a global scale.

Background: The literature on the disease burden of knee dislocation is lacking. The aim of the study is to systematically assess the global burden, trends, causes, and influencing factors of knee dislocation. Methods: The incidence and years lived with disability (YLDs) of knee dislocation were assessed globally, as well as at the regional and national levels from 1990 to 2019. Subsequent analyses focused on the age and gender distribution related to knee dislocation. An investigation into the main causes of knee dislocation followed. Finally, the Pearson correlation between age-standardized rates and social-demographic index (SDI) was calculated. Results: Although the age-standardized incidence and YLDs rate of knee dislocation decreased over the past 30 years, the incidence and YLDs number increased. The disease burden remained higher in males compared to females. Males and females showed different patterns of incidence rates in each age group, but their YLDs rates were similar. Over the past 30 years, the disease burden of knee dislocation increased in the older population while declining in the younger population. Falls had consistently emerged as the most important cause for both incidence and YLD rates. Additionally, a positive correlation between SDI and the disease burden of knee dislocation was found. Conclusion: The disease burden of knee dislocation remains heavy. It is essential to recognize the evolving epidemiology of knee dislocation. Utilizing data-driven assessments can assist in formulating public health policies and strategies to improve overall well-being.

Low Carbon Loss from Long-Term Manure-Applied Soil during Abrupt Warming Is Realized through Soil and Microbiome Interplay.

Manure application is a global approach for enhancing soil organic carbon (SOC) sequestration. However, the response of SOC decomposition in manure-applied soil to abrupt warming, often occurring during diurnal temperature fluctuations, remains poorly understood. We examined the effects of long-term (23 years) continuous application of manure on SOC chemical composition, soil respiration, and microbial communities under temperature shifts (15 vs 25 °C) in the presence of plant residues. Compared to soil without fertilizer, manure application reduced SOC recalcitrance indexes (i.e., aliphaticity and aromaticity) by 17.45 and 21.77%, and also reduced temperature sensitivity (Q10) of native SOC decomposition, plant residue decomposition, and priming effect by 12.98, 15.98, and 52.83%, respectively. The relative abundances of warm-stimulated chemoheterotrophic bacteria and fungi were lower in the manure-applied soil, whereas those of chemoautotrophic Thaumarchaeota were higher. In addition, the microbial network of the manure-applied soil was more interconnected, with more negative connections with the warm-stimulated taxa than soils without fertilizer or with chemical fertilizer applied. In conclusion, our study demonstrated that the reduced loss of SOC to abrupt warming by manure application arises from C chemistry modification, less warm-stimulated microorganisms, a more complex microbial community, and the higher CO2 intercepting capability by Thaumarchaeota.

Microbial diversity and metabolic pathways linked to benzene degradation in petrochemical-polluted groundwater.

The rapid advance in shotgun metagenome sequencing has enabled us to identify uncultivated functional microorganisms in polluted environments. While aerobic petrochemical-degrading pathways have been extensively studied, the anaerobic mechanisms remain less explored. Here, we conducted a study at a petrochemical-polluted groundwater site in Henan Province, Central China. A total of twelve groundwater monitoring wells were installed to collect groundwater samples. Benzene appeared to be the predominant pollutant, detected in 10 out of 12 samples, with concentrations ranging from 1.4 µg/L to 5,280 µg/L. Due to the low aquifer permeability, pollutant migration occurred slowly, resulting in relatively low benzene concentrations downstream within the heavily polluted area. Deep metagenome sequencing revealed Proteobacteria as the dominant phylum, accounting for over 63 % of total abundances. Microbial α-diversity was low in heavily polluted samples, with community compositions substantially differing from those in lightly polluted samples. dmpK encoding the phenol/toluene 2-monooxygenase was detected across all samples, while the dioxygenase bedC1 was not detected, suggesting that aerobic benzene degradation might occur through monooxygenation. Sequence assembly and binning yielded 350 high-quality metagenome-assembled genomes (MAGs), with 30 MAGs harboring functional genes associated with aerobic or anaerobic benzene degradation. About 80 % of MAGs harboring functional genes associated with anaerobic benzene degradation remained taxonomically unclassified at the genus level, suggesting that our current database coverage of anaerobic benzene-degrading microorganisms is very limited. Furthermore, two genes integral to anaerobic benzene metabolism, i.e, benzoyl-CoA reductase (bamB) and glutaryl-CoA dehydrogenase (acd), were not annotated by metagenome functional analyses but were identified within the MAGs, signifying the importance of integrating both contig-based and MAG-based approaches. Together, our efforts of functional annotation and metagenome binning generate a robust blueprint of microbial functional potentials in petrochemical-polluted groundwater, which is crucial for designing proficient bioremediation strategies.

Warming effects on grassland soil microbial communities are amplified in cool months.

Global warming modulates soil respiration (RS) via microbial decomposition, which is seasonally dependent. Yet, the magnitude and direction of this modulation remain unclear, partly owing to the lack of knowledge on how microorganisms respond to seasonal changes. Here, we investigated the temporal dynamics of soil microbial communities over 12 consecutive months under experimental warming in a tallgrass prairie ecosystem. The interplay between warming and time altered (P < 0.05) the taxonomic and functional compositions of microbial communities. During the cool months (January to February and October to December), warming induced a soil microbiome with a higher genomic potential for carbon decomposition, community-level ribosomal RNA operon (rrn) copy numbers, and microbial metabolic quotients, suggesting that warming stimulated fast-growing microorganisms that enhanced carbon decomposition. Modeling analyses further showed that warming reduced the temperature sensitivity of microbial carbon use efficiency (CUE) by 28.7% when monthly average temperature was low, resulting in lower microbial CUE and higher heterotrophic respiration (Rh) potentials. Structural equation modeling showed that warming modulated both Rh and RS directly by altering soil temperature and indirectly by influencing microbial community traits, soil moisture, nitrate content, soil pH, and gross primary productivity. The modulation of Rh by warming was more pronounced in cooler months compared to warmer ones. Together, our findings reveal distinct warming-induced effects on microbial functional traits in cool months, challenging the norm of soil sampling only in the peak growing season, and advancing our mechanistic understanding of the seasonal pattern of RS and Rh sensitivity to warming.