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Abstract Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data—a task at which the benchmarked single-omics methods proved inadequate.

Abstract Aquaporin-4 (AQP4) is essential for normal functioning of the brain’s glymphatic system. Impaired glymphatic function is associated with neuroinflammation. Recent clinical evidence suggests the involvement of glymphatic dysfunction in LRRK2-associated Parkinson’s disease (PD); however, the precise mechanism remains unclear. The pro-inflammatory cytokine interferon (IFN) γ interacts with LRRK2 to induce neuroinflammation. Therefore, we examined the AQP4-dependent glymphatic system’s role in IFNγ-mediated neuroinflammation in LRRK2-associated PD. We found that LRRK2 interacts with and phosphorylates AQP4 in vitro and in vivo. AQP4 phosphorylation by LRRK2 R1441G induced AQP4 depolarization and disrupted glymphatic IFNγ clearance. Exogeneous IFNγ significantly increased astrocyte expression of IFNγ receptor, amplified AQP4 depolarization, and exacerbated neuroinflammation in R1441G transgenic mice. Conversely, inhibiting LRRK2 restored AQP4 polarity, improved glymphatic function, and reduced IFNγ-mediated neuroinflammation and dopaminergic neurodegeneration. Our findings establish a link between LRRK2-mediated AQP4 phosphorylation and IFNγ-mediated neuroinflammation in LRRK2-associated PD, guiding the development of LRRK2 targeting therapy.

Rapid urbanization during the past decades have turned southeastern coastal China from traditional brick settlements into concrete forests. However, studies in Putian Plain reveal that apart from the physical appearances, local popular religion welcomed repercussions in these decades, even during the pandemic years. This paper looks at contemporary ritual practices and points out how social, cultural and ecological system of a unique ritual alliance system (Qijing) actives. Through practicing a ritualized everyday life, local communities managed to maintain certain indolence in different political and economic circumstances and achieve cultural resilience through various challenges brought by urbanization.

Summary: Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer’s disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.

Abstract Background Prader-Willi syndrome (PWS) is a genetic disorder involving multiple systems, with an incidence of about 1/10000–25000. Ovarian torsion (OT) is not commonly found in children. Ovarian cyst acts as one of the primary factors resulting in OT. While ovarian cyst torsion with Prader-Willi Syndrome has not been reported before. Case presentation A 12-years old female was admitted to Emergency Department of our hospital with the chief complaint of abdominal pain. The outcomes of physical examination revealed the height of 150 cm, weight of 103 kg, BMI of 45.77 kg/m2. The patient manifested the special facial features, an obese body, with the abdomen distended into a spherical shape. The fat accumulation in the abdomen significantly embarrassed the palpation. The abdominal CT scan indicated a huge cystic mass in the abdominal cavity, sized about 138 mm × 118 mm. According to medical history, the patient was born with low crying and hypotonia, who has developed the uncontrollable eating behavior since 3-years old. These abnormalities led to a speculation of PWS syndrome, so a genetic test was performed and finally confirmed it, concluding a torsion of ovarian cyst with PWS. With the multidisciplinary consultation, a careful treatment strategy containing the control of blood pressure and blood sugar, coenzyme Q10 was administrated to nourish the myocardium and the application of Growth Hormone was developed. All the above preoperative treatments have brought great benefits to patients. Thus promising the successful completion of operation. The postoperative follow-up till now indicated that the abdominal incision was well healed, without operative complications. Conclusions This may be the first case report. In the treatment of ovarian cyst torsion, PWS syndrome requires fully consideration, as the latter can lead to multisystem abnormalities, especially the relation to perioperative management, and even fatalities. Genetic testing should be conducted early when PWS was suspected, accompanied with adequate preparation for the perioperative period, the follow-ups of patients should be maintained for a long time after surgery.

Despite the significant impacts of biomass burning (BB) on global climate change and regional air pollution, there is a relative lack of research on the temporal trends and geographic patterns of BB in Northeast China (NEC). This study investigates the spatial–temporal distribution of BB and its impact on the atmospheric environment in the NEC region during 2004 to 2023 based on remote sensing satellite data and reanalyzed data, using the Siegel’s Repeated Median Estimator and Mann–Kendall test for trend analysis, HDBSCAN to identify significant BB change regions, and Moran’s Index to examine the spatial autocorrelation of BB. The obtained results indicate a fluctuating yet overall increasing BB trend, characterized by annual increases of 759 for fire point counts (FPC) and 12,000 MW for fire radiated power (FRP). BB predominantly occurs in the Songnen Plain (SNP), Sanjiang Plain (SJP), Liaohe Plain (LHP), and the transitional area between SNP and the adjacent Greater Khingan Mountains (GKM) and Lesser Khingan Mountains (LKM). Cropland and urban areas exhibit the highest growth in BB trends, each surpassing 60% (p < 0.05), with the most significant growth cluster spanning 68,634.9 km2. Seasonal analysis shows that BB peaks in spring and autumn, with spring experiencing the highest severity. The most critical periods for BB are March–April and October–November, during which FPC and FRP contribute to over 80% of the annual total. This trend correlates with spring planting and autumn harvesting, where cropland FPC constitutes 71% of all land-cover types involved in BB. Comparative analysis of the aerosol extinction coefficient (AEC) between areas with increasing and decreasing BB indicates higher AEC in BB increasing regions, especially in spring, with the vertical transport of BB reaching up to 1.5 km. County-level spatial autocorrelation analysis indicates high–high clustering in the SNP and SJP, with a notable resurgence of autocorrelation in the SNP, suggesting the need for coordinated provincial prevention and control efforts. Finally, our analysis of the impact of BB on atmospheric pollutants shows that there is a correlation between FRP and pollutants, with correlations for PM2.5, PM10, and CO of 0.4, 0.4, and 0.5, respectively. In addition, the impacts of BB vary by region and season, with the most significant impacts occurring in the spring, especially in the SNP, which requires more attention. In summary, considering the escalating BB trend in NEC and its significant effect on air quality, this study highlights the urgent necessity for improved monitoring and strategic interventions.

Abstract Climate plays a critical role in altering soil carbon (C) turnover and long‐term soil C storage by regulating water availability and temperature, and in turn biological activity. However, a systematic analysis of how key climatic factors shape the global patterns of soil C turnover is still lacking. Using global observation‐based data sets and a transit time theory, here we show that—excluding croplands and cold regions—soil C turnover time (τTO) and its variability are strongly related to ecosystem aridity through a power law scaling. According to such a relation, soil C turnover is faster but also more variable in wetter regions, suggesting more complex C cycling processes. The observed scaling of τTO and its coefficient of variation with aridity underlines the fundamental controls of climate on soil C turnover and may help reconcile soil C models with empirical observations for improved projection of soil C dynamics under climate change.

Abstract Background The site of lymph node metastasis (LNM) may affect the prognosis of patients with esophageal squamous cell carcinoma (ESCC). To investigate the prognoses of pararespiratory and paradigestive LNM and to propose a novel N (nN) staging system that integrates both the LNM site and count. Methods This study was a multicenter, large-sample, retrospective cohort study that included ESCC patients with LNM between January 2014 and December 2019 from three Chinese institutes. Patients were set into training (two institutes) and external validation (one institute) cohorts. The primary outcomes were survival differences in LNM site and the development of novel nodal staging system. The overall survival (OS) of patients with pararespiratory LNM only (Group A), paradigestive LNM only (Group B), and both sites (Group C) was evaluated by Kaplan-Meier. Cox proportional hazards models were used to identify the independent prognostic factors. An nN staging system considering both the LNM site and count was developed and evaluated by the area under the receiver operating characteristic curve (AUC). Results In total, 1313 patients were included and split into training (n = 1033) and external validation (n = 280) cohorts. There were 342 (26.0%), 568 (43.3%) and 403 (30.7%) patients in groups A, B and C, respectively. The OS of patients with pararespiratory and patients with paradigestive LNM presented significant differences in the training and validation cohorts (P

Abstract Background Chronic lung allograft dysfunction (CLAD) directly causes an abysmal long-term prognosis after lung transplantation (LTx), but effective and safe drugs are not available. Metformin exhibits high therapeutic potential due to its antifibrotic and immunomodulatory effects; however, it is unclear whether metformin exerts a therapeutic effect in CLAD. We sought to investigate the effect of metformin on CLAD based on rat models. Methods Allogeneic LTx rats were treated with Cyclosporin A (CsA) in the first week, followed by metformin, CsA, or vehicle treatment. Syngeneic LTx rats received only vehicles. All rats were sacrificed on post-transplant week 4. Pathology of lung graft, spleen, and thymus, extent of lung fibrosis, activity of profibrotic cytokines and signaling pathway, adaptive immunity, and AMPK activity were then studied. Results Allogeneic recipients without maintenance CsA treatment manifested CLAD pathological characteristics, but these changes were not observed in rats treated with metformin. For the antifibrotic effect, metformin suppressed the fibrosis extent and profibrotic cytokine expression in lung grafts. Regarding immunomodulatory effect, metformin reduced T- and B-cell infiltration in lung grafts, spleen and thymus weights, the T- and B-cell zone areas in the spleen, and the thymic medullary area. In addition, metformin activated AMPK in lung allografts and in α-SMA+ cells and T cells in the lung grafts. Conclusions Metformin attenuates CLAD in rat models, which could be attributed to the antifibrotic and immunomodulatory effects. AMPK activation suggests the potential molecular mechanism. Our study provides an experimental rationale for further clinical trials.

IntroductionStratification of Alzheimer’s disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.MethodsUsing whole genome sequencing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.ResultsadORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.DiscussionCompared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Over the past decade, high-throughput sequencing and high-resolution single-cell transcriptome sequencing technologies have undergone rapid development, leading to significant breakthroughs. Traditional molecular biology methods are limited in their ability to unravel cellular-level heterogeneity within woody plant tissues. Consequently, techniques such as single-cell transcriptomics, single-cell epigenetics, and spatial transcriptomics are rapidly gaining popularity in the study of woody plants. In this review, we provide a comprehensive overview of the development of these technologies, with a focus on their applications and the challenges they present in single-cell transcriptome research in woody plants. In particular, we delve into the similarities and differences among the results of current studies and analyze the reasons behind these differences. Furthermore, we put forth potential solutions to overcome the challenges encountered in single-cell transcriptome applications in woody plants. Finally, we discuss the application directions of these techniques to address key challenges in woody plant research in the future.

Abstract Background Given the ever-increasing rate of failure related to proximal femoral nail antirotation (PFNA), it is expected that an increasing number of PFNA individuals will undergo conversion to total hip arthroplasty (THA). The long-term survivorship of conversion of the initial PFNA to cemented THA is still debated. The aim of this retrospective study was to assess the long-term revision-free survivorship of cemented THAs after initial failures of PFNA in geriatric individuals. Methods Consecutive geriatric individuals who underwent secondary cemented THA after initial PFNA fixation from July 2005 to July 2018, were retrospectively identified from three medical centres. The primary outcome was revision-free survivorship estimated using the Kaplan–Meier method and Cox proportional hazards regression with revision for any reason as the endpoint; secondary outcomes were functional outcomes and key THA-related complications. Follow-ups occurred at 3 months, 6 months, 12 months and then every 12 months after conversion. Results In total, 186 consecutive patients (186 hips) were available for study inclusion. The median follow-up was 120.7 months (60–180 months) in the cohort. Kaplan–Meier survivorship with revision for any reason as the end point showed that the 10-year revision-free survival rate was 0.852 (95% confidence interval [CI], 0.771–0.890). Good functional outcomes were seen, and the HHS decreased markedly over the 24th month to the final follow-up interval from 92.2 to 75.1 (each p

Leaf functional traits are increasingly used as proxies for plant functions. Here, the authors show that leaf water affects other leaf traits and is a better predictor of whole-leaf photosynthesis and leaf area than leaf nitrogen or phosphorus content.

Objective: Right lung transplantation in rats has been attempted occasionally, but the technical complexity makes it challenging to apply routinely. Additionally, basic research on inverted lobar lung transplantation is scarce because of the lack of a cost-effective experimental model. We first reported right lung transplantation in a rat model using left-to-right inverted anastomosis to imitate the principle of clinically inverted lung transplantation. Methods: Right lung transplantation was performed in 10 consecutive rats. By using a 3-cuff technique, the left lung of the donor rat was implanted into the right thoracic cavity of the recipient rat. The rat lung graft was rotated 180° along the vertical axis to achieve anatomic matching of right hilar structures. Another 10 consecutive rats had received orthotopic left lung transplantation as a control. Results: All lung transplantation procedures were technically successful without intraoperative failure. One rat (10%) died of full pulmonary atelectasis after right lung transplantation, whereas all rats survived after left lung transplantation. No significant difference was observed in heart-lung block retrieval (8.6 ± 0.8 vs 8.4 ± 0.9 minutes), cuff preparation (8.3 ± 0.9 vs 8.7 ± 0.9 minutes), or total procedure time (58.2 ± 2.6 vs 56.6 ± 2.1 minutes) between the right lung transplantation and standard left lung transplantation groups (P > .05), although the cold ischemia time (14.2 ± 0.9 vs 25.5 ± 1.7 minutes) and warm ischemia time (19.8 ± 1.5 vs 13.7 ± 1.8 minutes) were different (P

Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy.Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms.Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively.Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics.

Jasmine flower residue (JFR) is a by-product retained in the production process of jasmine tea and can be used as an unconventional feed due to its rich nutrient value. This study aimed to evaluate the effects of the addition of JFR to the diet of goats on their meat quality and flavor. Twenty-four castrated Nubian male goats were randomly divided into two groups and fed a mixed diet containing 10% JFR (JFR, n = 12) or a conventional diet (CON, n = 12) for 45 days. Meat quality and flavor were measured at the end of the treatment. The addition of JFR to the diet could reduce the shear force of the longissimus dorsi muscle, as well as, the cross-sectional area and diameter of muscle fibers, indicating that the addition of JFR improved meat quality. JFR also increased the content of glutamic acid and ω-3 polyunsaturated fatty acid (C18:3n3 and C20:5N3) and reduced the content of C24:1 and saturated fatty acid (C20:0 and C22:0). In addition, the use of JFR increased the content of acetaldehyde and hexanal in the meat. Furthermore, JFR introduced new volatile components in the meat. The umami, saltiness, and richness of the meat also improved. In conclusion, the addition of jasmine flower residue to the diet can improve the meat quality and flavor of goat.

There are obvious drawbacks for the traditional treatment methods of antibiotics, such as low efficiency and high cost. In this paper, FeOx catalysts, modified with the biochar (BC) of maple leaf (FeOx@BC), were successfully prepared by the hydrothermal method. Then, the FeOx@BC was investigated to activate peroxymonosulfate (PMS) under UVA-LED irradiation for the degradation of tetracycline hydrochloride (TC). Subsequently, the changes in valence states before and after the reaction of ions were investigated by XPS spectra, and the process mechanism was presented. The results demonstrated that the TC degradation efficiency reached 96% in the FeOx@BC + PMS + UVA-LED system within 40 min, which was higher than 57% efficiency for the α-Fe2O3 + PMS + UVA-LED system. The electron transfer was promoted in the FeOx@BC + PMS + UVA-LED system due to the doping of BC. The Fe(III) was transformed into Fe(II) under UVA-LED irradiation, and Fe(II) activated continuously PMS to generate active oxygen species. Furthermore, it had excellent reusable performance and structural stability, and the degradation efficiency was still as high as 80% after five cycles. It was proved that SO4−•, •OH, O2•− and h+ participated in the degradation process of TC to different degrees by quenching experiments and EPR tests. In summary, FeOx@BC is an inexpensive, reusable and efficient catalyst.

Pulmonary hypertension (PH) was once a devastating and fatal disease entity, the outlook of which has been significantly improved by the continued progress of medical treatment algorithms. However, some patients still ultimately fail to achieve an adequate clinical response despite receiving maximal medical treatment. Historically, lung transplantation (LTx) has been the only effective therapeutic option that could lead to satisfactory outcomes and save these advanced patients’ lives. However, patients with PH tend to have the highest mortality rates on the transplant waiting list; especially after comprehensive medical treatment, they continue to deteriorate very rapidly, eventually missing optimal transplantation windows. Balancing optimized medical treatment with the appropriate timing of referral and listing has been highly controversial in LTx for patients with PH. The 2021 consensus document for the selection of lung transplant candidates from the International Society for Heart and Lung Transplantation (ISHLT) updated the specific recommendations for the LTx referral and listing time for patients with PH based on objective risk stratification. Herein, we review the evolving PH-related concepts and highlight the optimization of LTx referral and listing for patients with PH, as well as their management on the waiting list.

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is the key to the battery health management system. However, problems of unstable model output and extensive calculation limit the prediction accuracy. This article proposes a Particle Swarm Optimization Random Forest (PSO-RF) prediction method to improve the RUL prediction accuracy. First, the battery capacity extracted from the lithium-ion battery data set of the National Aeronautics and Space Administration (NASA) and the University of Maryland Center for Advanced Life Cycle Engineering (CALCE) is set as the battery life health factor. Then, a PSO-RF prediction model is established based on the optimal parameters for the number of trees and the number of random features to split by the PSO algorithm. Finally, the experiment is verified on the NASA and CALCE data sets. The experiment results indicate that the method predicts RUL with Mean Absolute Error (MAE) less than 2%, Root Mean Square Error (RMSE) less than 3%, and goodness of fit greater than 94%. This method solves the problem of parameter selection in the RF algorithm.

Biodiversity-ecosystem functioning relationships may vary with climate. Here, the authors study relationships of plant and soil microbial diversity with soil nutrient multifunctionality in 130 dryland sites in China, finding a shift towards greater importance of soil microbial diversity in arid conditions.

Introduction The aim of this retrospective study was to assess the clinical outcomes of cemented or uncemented total hip arthroplasty (CTHA or UTHA) following prior failed proximal femoral nail antirotation (PFNA) fixation in patients with intertrochanteric femur fractures (IFFs). Materials and methods Data from 244 patients with IFFs who experienced a conversion of PFNA to CTHA (n = 120) or to UTHA (n = 124) due to screw cut-out, mal/nonunion, or osteonecrosis during 2008-2018 were retrospectively analyzed. Follow-up occurred 1, 3, 6, and 12 months postoperatively and yearly thereafter. The primary outcome was the incidence of orthopedic complications; the secondary outcome was the Harris hip score (HHS). Results The median follow-up was 60 months (range, 50-67 months). The incidences of orthopedic complications were 10% in the PFNA to CTHA group and 19.3% in the PFNA to UTHA group (P = .040). Significant differences were also observed regarding the incidence of prosthesis revision (1.7% for PFNA to CTHA vs 7.2% for PFNA to UTHA, P = .036). From the three years after conversion surgery to the final follow-up, significant differences were detected in HHS between groups (each P < .05). At the final follow-up, a statistically significant difference was detected in the HHS (79.54±18.85 for PFNA to CTHA vs. 75.26±18.27 for PFNA to UTHA, P = .014). Conclusion The results of the study may demonstrate a significant statistical advantage with respect to the orthopedic complication rate and HHS in favor of CTHA compared to UTHA in patients with failed PFNA.

Introduction The impacts of fragmented reading have been felt on a huge scale during the new media age. An increasingly fast-paced society and a corresponding drop in theoretical reading have affected reading literacy and cognitive development across communities—and among university students in particular. This study sought to identify the components of fragmented reading and cognitive development and investigate the former’s effects on the latter among university students in the new media age. Methods Paper-based and electronic surveys were used to gather demographic and related-reading data from undergraduates at six Chinese universities in early 2021. After testing the data from 916 samples for reliability and validity, descriptive statistics were obtained and path analysis was undertaken using structural equation modeling. Results The students reported relatively high levels of fragmented reading, particularly in its temporal form. Significant associations were found between the constructs of fragmented reading and cognitive development. Specifically, the fragmentation of content, time, and attention in reading behavior had significant, positive effects on cognitive breadth. However, each of these components was somewhat negatively associated with cognitive depth. Conclusions The findings disclosed the dual effects of fragmented reading on the cognitive development of students, opening a new perspective on this debate. As fragmented reading in the new media age grows inexorably, the study highlights the need to utilize its positive effects on cognitive development by integrating and classifying fragmented information into the mental maps of learners.

Side deep placement of nitrogen plays an important role in improving rice yield and nitrogen use efficiency. Few studies have examined the effects of reducing the times of nitrogen (RTN) application and reducing the nitrogen rate (RNR) of application on rice yield and nitrogen use efficiency under side deep placement of nitrogen in paddy fields. Therefore, a field experiment of RNT and RNR treatments was conducted with nine fertilization modes during the 2018–2019 rice growing seasons in a rice–wheat cropping system of the lower reaches of the Yangtze River, China. Rice yield and nitrogen use efficiency were investigated under side deep placement of nitrogen. We found that under the same nitrogen application rate, the yield of RTN3 increased by 9.64 and 10.18% in rice varieties NJ9108 and NJ5718, respectively, compared with the farmers’ fertilizer practices (FFP). The nitrogen accumulation of RTN3 was the highest at heading stage, at 11.30 t ha−1 across 2018 and 2019. Under the same nitrogen application rate, the N agronomic use efficiency (NAE), N physiological efficiency (NPE) and N recovery efficiency (NRE) of RTN3 were 8.1–21.28%, 8.51–41.76% and 0.28–14.52% higher than those of the other fertilization modes, respectively. RNR led to decreases in SPAD value, leaf area index (LAI), dry matter accumulation, nitrogen accumulation, and nitrogen use efficiency. These results suggest that RTN3 increased rice yield and nitrogen use efficiency under the side deep placement of nitrogen, and RNR1 could achieve the goals of saving cost and increasing resource use efficiency. Two fertilization modes RTN3 and RNR1 both could achieve the dual goals of increasing grain yield and resource use efficiency and thus are worth further application and investigation.

Huang et al. use satellite imagery spanning over 40 years to investigate the spatial patterning of vegetation patches in a North American coastal ecosystem. They find that woody plant encroachment follows a power law when approaching critical transition points, which may inform future ecological monitoring of coastal systems.

The debris detection characteristics of the inductive wear monitoring are researched by the method of combining theoretical research and simulation analysis in this paper. The mathematical model of the change in inductance is established based on the change in the coil magnetic field by the abrasive particles. By the COMSOL simulation software, the physical model of the three-coil wear monitoring is established, and the influence of the coil structure parameters on the output induced electromotance is compared and analyzed, resulting in the optimization of the coil parameters. For metal particles with different properties and sizes, the changes in the induced electromotance during the process of passing through the coil are analyzed, obtaining the mapping relationship between each particle size and the output induced electromotance. The simulation results show that the output voltage corresponding to the particles is related to the coil structure parameters, and the larger the particle size, the larger the output voltage. Finally, through experiments, the designed sensor coil structure has been proved to have a better detection effect on metal particles, realizing the detection of ferromagnetic abrasive particles above 100 µm and non-ferromagnetic abrasive particles above 200 µm.

Brain networks have attracted increasing attention due to the potential to better characterize brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years have witnessed enormous successes in deep learning. Many AI algorithms, especially graph learning methods, have been proposed to analyze brain networks. An important issue for existing graph learning methods is that those models are not typically easy to interpret. In this study, we proposed an interpretable graph learning model for brain network regression analysis. We applied this new framework on the subjects from Human Connectome Project (HCP) for predicting multiple Adult Self-Report (ASR) scores. We also use one of the ASR scores as the example to demonstrate how to identify sex differences in the regression process using our model. In comparison with other state-of-the-art methods, our results clearly demonstrate the superiority of our new model in effectiveness, fairness, and transparency.

BackgroundRecurrent laryngeal nerve (RLN) lymph node metastasis (LNM) is not rare in patients with esophageal squamous cell carcinoma (ESCC). We aimed to develop and externally validate a preoperative nomogram using clinical characteristics to predict RLN LNM in patients with ESCC and evaluate its prognostic value.MethodsA total of 430 patients with ESCC who underwent esophagectomy with lymphadenectomy of RLN LNs at two centers between May 2015 and June 2019 were reviewed and divided into training (center 1, n = 283) and external validation cohorts (center 2, n = 147). Independent risk factors for RLN LNM were determined by multivariate logistic regression, and a nomogram was developed. The performance of the nomogram was assessed in terms of discrimination, calibration, clinical usefulness, and prognostic value. The nomogram was internally validated by the bootstrap method and externally validated by the external validation cohort.ResultsMultivariate analysis indicated that clinical T stage (P

In this paper, based on the multi-scale multi-domain (MSMD) battery modeling approach, the NTGK model was used to model the 18650 cylindrical lithium-ion single battery on the electrochemical sub-scale. The model was successful, as it was able to fit the experimental voltage and temperature of the battery at different temperatures. Lithium-ion battery discharge capacity and energy output can be improved during cold starting by preheating and insulation, as demonstrated by a comparison of the impacts of heat transfer coefficient and preheating duration at −20 °C ambient temperature. For the traditional heating method, the heating model of heating film (HF) and liquid-cooled plate (LCP) is constructed in this paper, and the heating performance of both is compared by Fluent. Analysis of the energy balance of Li-ion battery at low temperatures has been presented, showing that Li-ion battery requires a suitable start-up temperature to maximize energy output. Taking care of the problem of excessive temperature difference inside the battery due to excessive heating power, we investigated the effects of axial thermal conductivity, heating power, and heating area on the heating uniformity of the battery in this paper. Finally, a multi-stage stepped power (MSP) heating method was proposed to improve the temperature control accuracy of HF. A level orthogonal test L16(43) without interaction was designed to determine the degree of influence of each parameter on the temperature control performance and the optimal level combination, revealing that the optimized maximum temperature and temperature control rate were reduced by 4.09% and 40.53%, respectively, when compared to direct heating.

Sm2Fe17N3 compounds, having excellent intrinsic magnetic properties, are prone to decomposition at high temperatures; thus, a low melting point metal binder is the key to prepare high performance bulk magnets at low temperatures. In this paper, a new low melting point alloy Ce72Cu28-xAlx was used as the binders, and high-performance Ce-based alloy bonding Sm2Fe17N3 magnets were realized by the hot-pressing method. The experimental results demonstrated that the content of Al in the Ce-based alloys had an important influence on the performance of the magnets. High performance Sm-Fe-N bonded magnets with remanence of 10.19 KGs and maximum magnetic energy product of 21.06 MGOe were achieved by using 5 wt.% Ce72Cu22Al6 alloy as a binder. At the same time, it was found that the Ce72Cu28-xAlx alloy has a lower density and better bonding effect than the common Zn binder. Its bonding magnets still have higher performance even with extremely high oxygen content. Therefore, Ce72Cu28-xAlx alloy with low melting point is a promising new rare earth-based alloy binder. If the oxygen content of the alloy powders could be reduced, higher performance Sm2Fe17N3 bonded magnets can be prepared.

The coil structure of the inductive wear debris sensor plays a significant role in the effect of wear debris detection. According to the characteristics of LC parallel resonance, the capacitor and coil are connected in parallel to make sensor coils in the LC parallel resonance state, which is beneficial to improve the ability to detect wear particles. In this paper, the mathematical model of output-induced electromotance of the detection coil is established to analyze the influence of the structure on the detection sensitivity and enhance the sensor’s current rate of change to the disturbance magnetic field, which is essential to resist noise interference. Based on the coherent demodulation principle, the AD630 lock-in amplifier is applied to the test platform to amplify and identify weak signals. In addition, experiments are designed to test the output signals of debris under the condition of different original output voltages of the sensor with a parallel structure. Meanwhile, the near-resonance state of the detection coil with LC parallel circuit is tested by output signal information. Results show that the sensor detection sensitivity will be effectively improved with the LC parallel coil structure. For the sensor structure parameters designed in this paper, the optimal raw output amplification voltage for abrasive particle detection is 4.49 V. The detection performance of ferromagnetic particles and non-ferromagnetic particles is tested under this condition, realizing the detection ability of 103.33 μm ferromagnetic abrasive particles and 320.74 μm non-ferromagnetic abrasive particles.

Background: Spinal cord injury (SCI) is a central nervous system (CNS) trauma involving inflammation and oxidative stress, which play important roles in this trauma’s pathogenesis. Therefore, controlling inflammation is an effective strategy for SCI treatment. As a hormone, melatonin is capable of producing antioxidation and anti-inflammation effects. In the meantime, it also causes a neuroprotective effect in various neurological diseases. Nrf2/ARE/NLRP3 is a well-known pathway in anti-inflammation and antioxidation, and Nrf2 can be positively regulated by melatonin. However, how melatonin regulates inflammation during SCI is poorly explored. Therefore, it was investigated in this study whether melatonin can inhibit the NLRP3 inflammasome through the Nrf2/ARE signaling pathway in a mouse SCI model. Methods: A model of SCI was established in C57BL/6 mice and PC12 cells. The motor function of mice was detected by performing an open field test, and Nissl staining and terminal deoxynucleotidyl transferase dUTP nick end labeling were carried out to evaluate the survival of neurons. Mitochondrial dysfunction was detected by transmission electron microscopy (TEM) and by assessing the mitochondrial membrane potential. In addition, the expression of NLRP3 inflammasome and oxidative-stress-related proteins were detected through Western blot and immunofluorescence double staining. Results: By inhibiting neuroinflammation and reducing neuronal death, melatonin promotes the recovery of neuromotor function. Besides this, melatonin is able to reduce the damage that causes neuronal mitochondrial dysfunction, reduce the level of reactive oxygen species (ROS) and malondialdehyde, and enhance the activity of superoxide dismutase and the production of glutathione peroxidase. Mechanically, melatonin inhibits the activation of NLRP3 inflammasomes and reduces the secretion of pro-inflammatory factors through the Nrf2/ARE signaling. Conclusions: In conclusion, melatonin inhibits the NLRP3 inflammasome through stimulation of the Nrf2/ARE pathway, thereby suppressing neuroinflammation, reducing mitochondrial dysfunction, and improving the recovery of nerve function after SCI.

Better understanding of how internal short circuit causes thermal runaway will benefit the engineering for safer lithium-ion batteries. In this study, three-dimensional (3D) numerical simulations of a 20Ah lithium battery under internal shorting condition are performed. The effects of internal short circuit area, resistance, penetration depth, convective heat transfer coefficient and internal short circuit position, on the thermal runaway are investigated with the simulations in this work. This study demonstrates that the average cell temperature is only weakly affected by the internal short circuit area, penetration depth, and position. On the other hand, the internal short circuit resistance and the convective heat transfer coefficient have large impacts on the thermal runaway propagation in the lithium-ion battery. A high convective heat transfer coefficient can effectively suppress the thermal runaway propagation. However, such a high convective heat transfer coefficient is hard to achieve at the cell surface.

Given the severe pollution caused by particulate matters in diesel engine exhaust, we discuss herein the problem of exhaust back-pressure and regeneration of commonly used diesel particulate filter treatment technology. This paper proposes a method based on water film adsorption to remove exhaust particles from exhaust gas. Particulates flowing through the purification device are adsorbed by the continuous isobaric water film formed on the wave adsorption plate. The formation process and characteristics of the equal pressure-differential water film are studied through combined theoretical analysis and experimental verification. The regeneration process and influencing factors of the purification system are also analyzed, and the treatment of the device is verified. Experiments show that the device offers a better purification of exhaust particulates with lower exhaust resistance and regeneration of the system during the treatment.

Nitrogen (N) and phosphorus (P) play important roles in many aspects of plant biology. The allocation of N and P in plant vegetative organs (i.e., leaves, stems, and fine roots) is critical to the regulation of plant growth and development. However, how these elements are allocated in seeds is unclear. The aim of this study was to explore the N and P allocation strategies of seeds in an alpine meadow along an altitudinal gradient. We measured the seed N and P contents of 253 herbaceous species in 37 families along an altitudinal gradient (2,000–4,200 m) in the east Tibetan alpine meadow. The geometric means of seed N and P concentrations and N:P ratios were 34.81 mg g–1, 5.06 mg g–1, and 6.88, respectively. Seed N and P concentrations varied across major taxonomic groups and among different altitude zones. N:P ratios showed no significant variations among different taxonomic groups with the exception of N-fixing species. The numerical value of the scaling exponent of seed N vs. P was 0.73, thus approaching 3/4, across the entire data set, but varied significantly across major taxonomic groups. In addition, the numerical value of the scaling exponent of N vs. P declined from 0.88 in the high altitude zone to 0.63 in the low altitude zone. These results indicate that the variations in the numerical value of the scaling exponent governing the seed N vs. P scaling relationship varies as a function of major taxonomic groups and among different altitude zones. We speculate that this variation reflects different adaptive strategies for survival and germination in an alpine meadow. If true, the data presented here advance our understanding of plant seed allocation strategies, and have important implications for modeling early plant growth and development.

Introduction The ideal treatment for idiopathic granulomatous mastitis (IGM) remains unclear. In a prospective, single-centre, pilot study, we reported that ductal lavage treatment for non-lactational mastitis patients had a 1-year clinical complete response (cCR) rate of >90%, without any significant adverse events. Thus, in this multicentre, randomised, open-label, non-inferiority trial, we will aim to compare the effectiveness and safety of ductal lavage vs oral corticosteroids as the first-line treatment for patients with IGM.Methods and analysis The trial will be conducted at the Breast Tumor Center of Sun Yat-sen Memorial Hospital in China and at least at one participating regional centre. We plan to recruit 140 eligible IGM patients who will be randomised into the ductal lavage group or oral corticosteroid group with a 1:1 ratio. The patients in the oral corticosteroid group will receive meprednisone or prednisone for 6 months. The patients in the ductal lavage group will receive ductal lavage and breast massage, as previously reported. All the participants will be followed up at the clinic for 1 year post randomisation. The primary endpoint of this trial will be the 1-year cCR rate, and the secondary endpoints will include the time to cCR, treatment failure rate, relapse rate and protocol compliance rate. The trial was designed to determine whether ductal lavage is non-inferior to oral corticosteroids (1-year cCR rate assumed to be 90%), with a non-inferiority margin of 15%.Ethics and dissemination The ethics committee of Sun Yat-sen Memorial Hospital at Sun Yat-sen University approved the study (2018-Lun-Shen-Yan-No. 30). The results of the trial will be communicated to the participating primary care practices, published in international journals and presented at international clinical and scientific conferences.Trial registration number ClinicalTrials.gov Registry (NCT03724903); Pre-results.

Summary: In many regions of the world pollinator populations are rapidly declining, a trend that is expected to disrupt major ecosystem functions and services. These changes in pollinator abundance may be prone to critical transitions with abrupt shifts to a state strongly depleted both in pollinator and vegetation abundance. Here we develop a process-based model to investigate the effect of a positive pollinator-vegetation feedback, whereby an initial decline in plant density increases selfing thereby reducing floral resources and negatively affecting pollinators. We show that a decline in resource availability and an increase in disturbance intensity can induce an abrupt shift in vegetation and pollinator dynamics and potentially lead to the collapse of plant-pollinator systems. Thus, endogenous feedbacks can induce strong non-linearities in plant-pollinator dynamics, making them vulnerable to critical transitions to a state depleted of both plants and pollinators in response to resource deficiency and natural or anthropogenic disturbance. : Environmental Science; Ecology; Mathematical Biosciences Subject Areas: Environmental Science, Ecology, Mathematical Biosciences

Alzheimer's disease (AD) is a severe type of neurodegeneration which worsens human memory, thinking and cognition along a temporal continuum. How to identify the informative phenotypic neuroimaging markers and accurately predict cognitive assessment are crucial for early detection and diagnosis Alzheimer's disease. Regression models are widely used to predict the relationship between imaging biomarkers and cognitive assessment, and identify discriminative neuroimaging markers. Most existing methods use different matrix norms as the similarity measures of the empirical loss or regularization to improve the prediction performance, but ignore the inherent geometry of the cognitive data. To tackle this issue, in this paper we propose a novel robust matrix regression model with imposing Wasserstein distances on both loss function and regularization. It successfully integrate Wasserstein distance into the regression model, which can excavate the latent geometry of cognitive data. We introduce an efficient algorithm to solve the proposed new model with convergence analysis. Empirical results on cognitive data of the ADNI cohort demonstrate the great effectiveness of the proposed method for clinical cognitive predication.

A hybrid momentum theory of flat-plate boundary layer was proposed, to elucidate drag reduction on a van body surface under air film. The air mass and momentum before and after the outside air mixed with film air were compared, which may lead to the change of viscous force. A mathematical model for flat-plate boundary layer under air film was established based on the law of mass and momentum conservation, with selecting a unit of laminar boundary layer as analysis object and simultaneous solution of the momentum integral equation and the velocity distribution equation. The mass of air flow, momentum flux, loss of momentum, boundary layer thickness and surface viscous force were discussed to take a comparative analysis of flat boundary layer’s status change with and without an air film.

Nutrient resorption plays an important role in ecology because it has a profound effect on subsequent plant growth. However, our current knowledge about patterns of nutrient resorption, particularly among herbaceous species, at a global scale is still inadequate. Here, we present a meta-analysis using a global dataset of nitrogen (N) and phosphorus (P) resorption efficiency encompassing 227 perennial herbaceous species. This analysis shows that the N and P resorption efficiency (NRE and PRE, respectively), and N:P resorption ratios (NRE:PRE) across all herbaceous plant groups are 59.4, 67.5, and 0.89%, respectively. Across all species, NRE, PRE, and NRE:PRE, exhibited different patterns along climatic and soil nutrient gradients, i.e., NRE decreases with increasing mean annual precipitation (MAP) and soil N, PRE increases with aridity index (AI) but decreases with MAP and soil P, and NRE:PRE decreases with increasing potential evapotranspiration (PET), AI, and soil N:P. NRE, PRE, and NRE:PRE also differed in functional species group (graminoids vs. forbs). Soil nutrient level was the largest contributor to the total variations in NRE, PRE, and NRE:PRE, while climate and herbaceous types had relatively smaller effects on NRE, PRE, and NRE:PRE. Collectively, these trends can inform attempts to model biogeochemical cycling at a global scale.

Nitrogen (N) and phosphorus (P) are associated with the life history of all organisms. Soil microbes play essential roles in nutrient cycling and ecosystem dynamics. As compared to plants, however, our understanding of the soil microbial N versus P scaling relationship remains limited. In this study, we used a comprehensive dataset comprising 2210 observations from 422 sites worldwide to examine microbial C, N, and P concentrations and C:N:P ratios, and to determine the scaling of microbial N versus P in different ecosystems and spatial scales (i.e. soil depths, latitudinal zones, and local sites). The global mean values of microbial C, N, and P concentrations were 721.5 mg kg ^−1 , 84.7 mg kg ^−1 , 37.6 mg kg ^−1 , respectively, whereas C:N, C:P, and N:P ratios were 9.5, 30, and 3.4, respectively. Microbial C, N, and P concentrations varied within and across different ecosystems and spatial scales, but C:N:P ratios varied surprisingly little. The numerical value of the scaling exponent (i.e. the slope of the log-log linear relationship) of microbial N versus P was 0.89 across the entire dataset and for different ecosystems. However, the numerical value of the exponent varied significantly across different spatial scales. Soil total P was the largest contributor to the variation observed in the scaling of microbial N versus P at different local sites. These findings have important implications for predicting soil microbial growth rates and improving our understanding of nutrient cycling in plant-soil-microbe systems.

To elucidate drag reduction mechanism on a van body surface under air film condition, a thickened boundary layer theory was proposed and a frictional resistance calculation model of the van body surface was established. The frictional resistance on the van body surface was calculated with different parameters of air film thickness. In addition, the frictional resistance of the van body surface under the air film condition was analyzed by computational fluid dynamics (CFD) simulation and different air film states that influenced the friction resistance on the van body surface were discussed. As supported by the CFD simulation results, the thickened boundary layer theory may provide reference for practical application of air film drag reduction on a van body surface.