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Abstract Acute lung injury (ALI) is life-threatening. MicroRNAs (miRNAs) are often abnormally expressed in inflammatory diseases and are closely associated with ALI. This study investigates whether miRNA-206-3p attenuates pyroptosis in ALI and elucidates the underlying molecular mechanisms. ALI mouse and cell models were established through lipopolysaccharide (LPS) treatment for 24 h. Subsequently, the models were evaluated based on ultrasonography, the lung tissue wet/dry (W/D) ratio, pathological section assessment, electron microscopy, and western blotting. Pyroptosis in RAW264.7 cells was then assessed via electron microscopy, immunofluorescence, and western blotting. Additionally, the regulatory relationship between miRNA-206-3p and the Toll-like receptor (TLR)4/nuclear factor (NF)-κB/Nod-like receptor protein-3 (NLRP3) pathway was verified. Finally, luciferase reporter gene and RNA pull-down assays were used to verify the targeting relationship between miRNA-206-3p and TLR4. miRNA206-3p levels are significantly decreased in the LPS-induced ALI model. Overexpression of miRNA-206-3p improves ALI, manifested as improved lung ultrasound, improved pathological changes of lung tissue, reduced W/D ratio of lung tissue, release of inflammatory factors in lung tissue, and reduced pyroptosis. Furthermore, overexpression of miRNA-206-3p contributed to reversing the ALI-promoting effect of LPS by hindering TLR4, myeloid differentiation primary response 88 (MyD88), NF-κB, and NLRP3 expression. In fact, miRNA-206-3p binds directly to TLR4. In conclusion, miRNA-206-3p alleviates LPS-induced ALI by inhibiting inflammation and pyroptosis via TLR4/NF-κB/NLRP3 pathway modulation.

Abstract Background With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm (PheIndex) using electronic medical records to identify children aged 0–3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. Results Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. Conclusions The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.

Abstract The prevalence of Crohn’s disease (CD), a subtype of inflammatory bowel disease (IBD), is increasing worldwide. The pathogenesis of CD is hypothesized to be related to environmental, genetic, immunological, and bacterial factors. Current studies have indicated that intestinal epithelial cells, including columnar, Paneth, M, tuft, and goblet cells dysfunctions, are strongly associated with these pathogenic factors. In particular, goblet cells dysfunctions have been shown to be related to CD pathogenesis by direct or indirect ways, according to the emerging studies. The mucus barrier was established with the help of mucins secreted by goblet cells. Not only do the mucins mediate the mucus barrier permeability and bacterium selection, but also, they are closely linked with the endothelial reticulum stress during the synthesis process. Goblet cells also play a vital role in immune response. It was indicated that goblet cells take part in the antigen presentation and cytokines secretion process. Disrupted goblet cells related immune process were widely discovered in CD patients. Meanwhile, dysbiosis of commensal and pathogenic microbiota can induce myriad immune responses through mucus and goblet cell-associated antigen passage. Microbiome dysbiosis lead to inflammatory reaction against pathogenic bacteria and abnormal tolerogenic response. All these three pathways, including the loss of mucus barrier function, abnormal immune reaction, and microbiome dysbiosis, may have independent or cooperative effect on the CD pathogenesis. However, many of the specific mechanisms underlying these pathways remain unclear. Based on the current understandings of goblet cell’s role in CD pathogenesis, substances including butyrate, PPARγagonist, Farnesoid X receptor agonist, nuclear factor-Kappa B, nitrate, cytokines mediators, dietary and nutrient therapies were all found to have potential therapeutic effects on CD by regulating the goblet cells mediated pathways. Several monoclonal antibodies already in use for the treatment of CD in the clinical settings were also found to have some goblet cells related therapeutic targets. In this review, we introduce the disease-related functions of goblet cells, their relationship with CD, their possible mechanisms, and current CD treatments targeting goblet cells.

Abstract The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10−17 J).

Abstract The management of surgical instruments is related to the safety and efficiency of surgical operations, and a surgical instruments information management system (SIIMS) has been developed. The aim of the current study is to explore the application value of the SIIMS in sports medicine specialty. A set of self-developed SIIMS for sports medicine surgeries was applied to the study. The application value of the SIIMS was verified by comparing the safety and efficiency of instrument manipulation before and after its application, with instrument accidents, instrument repair rate, instrument scrap rate and instrument use efficiency as indicators. Through the application of the SIIMS, the incidence of surgical instrument accidents decreased from 3.7 times to 1.8 times (P = 0.02), the number of instrument repair decreased from 7.7 times to 2.9 times (P = 0.00), and the number of scrapped instruments decreased from 5.1 to 2.3 (P = 0.03), when referred to per thousand operations. Before and after the application of the SIIMS, the average instrument use efficiency was 74.0% ± 3.3% and 88.2% ± 4.4%, respectively, with statistically significant difference (P = 0.00). The application of the SIIMS in sports medicine specialty is helpful to the fine management of surgical instruments, improve surgical safety and instrument use efficiency.

ABSTRACT: Infectious laryngotracheitis (ILT) and Newcastle disease (ND) are 2 highly infectious avian respiratory diseases that have caused significant economic losses in the poultry industry worldwide. In ovo vaccination is administered during the late stage of incubation and is an attractive immunization method for poultry industry. However, most of the avian live vaccine strains that are safe for use after hatching are highly pathogenic to chicken embryos and therefore unsafe for in ovo vaccination. Previously, a recombinant Newcastle disease virus (NDV) strain, rTS-gB, expressing the gB protein of Infectious laryngotracheitis virus (ILTV), was demonstrated to be safe and immunogenic as a bivalent vaccine for hatched birds. In this study, we evaluated the safety and protective efficacy of rTS-gB as an in ovo vaccine. This vaccine strain was found to be safe for in ovo vaccination, with a hatchability and survival rate of 93.3% in chickens vaccinated in ovo with rTS-gB. In ovo vaccination with rTS-gB induced effective ILTV- and NDV-specific antibody responses in birds and conferred complete protection against virulent NDV and ILTV challenges. Furthermore, shedding of the challenged ILTV in cloacal and tracheal samples from in ovo vaccinated chickens was greatly reduced. These results indicate that the rNDV strain rTS-gB is a safe and highly immunogenic in ovo vaccine candidate against both ND and ILT.

The dynamic properties of concrete structures after being exposed to high temperatures play a crucial role in post-disaster repair and accident prevention measures. This study examined three different mixture ratios of Steel Fiber Reinforced Concrete (SFRC) with matrix strength of C60 and steel fiber contents of 0%, 1%, and 2%. A Split Hopkinson Pressure Bar (SHPB) with a diameter of 75 mm, along with a heating furnace, was employed to comprehensively investigate the dynamic compression behavior of materials. The specimens were exposed to temperatures ranging from 200 °C to 600 °C. In addition to evaluating the extent of mechanical degradation in SFRC after cooling, auxiliary impact experiments were also carried out at ambient temperature. The experimental data revealed that the dynamic peak stress and peak strain initially increased but eventually decreased as the loading rates continued to rise. The degree of strengthening, toughening, and energy absorption capacity of concrete materials progressively decreased as the escalation of strain rate after the cooling process. Moreover, the inclusion of steel fibers proved effective in preventing cracking and damage to the concrete, preserving the integrity of specimens even at higher temperatures. Simultaneously, the residual stress and deformation capacity of SFRC showed considerable enhancement after exposure to elevated temperatures.

Summary: Biofilm formation plays a significant role in antibiotic resistance, necessitating the search for alternative therapies against biofilm-associated infections. This study demonstrates that 20 μg/mL tryptanthrin can hinder biofilm formation above 50% in various A. baumannii strains. Tryptanthrin impacts various stages of biofilm formation, including the inhibition of surface motility and eDNA release in A. baumannii, as well as an increase in its sensitivity to H202. RT-qPCR analysis reveals that tryptanthrin significantly decreases the expression of the following genes: abaI (19.07%), abaR (33.47%), bfmR (43.41%), csuA/B (64.16%), csuE (50.20%), ompA (67.93%), and katE (72.53%), which are related to biofilm formation and quorum sensing. Furthermore, tryptanthrin is relatively safe and can reduce the virulence of A. baumannii in a Galleria mellonella infection model. Overall, our study demonstrates the potential of tryptanthrin in controlling biofilm formation and virulence of A. baumannii by disrupting different stages of biofilm formation and intercellular signaling communication.

Abstract Background Delirium is prevalent in ischemic stroke patients, particularly those in the intensive care unit (ICU), and it poses a significant burden on patients and caregivers, leading to increased mortality rates, prolonged hospital stays, and impaired cognitive function. Dysphagia, a common symptom in critically ill patients with ischemic stroke, further complicates their condition. However, the association between dysphagia and delirium in this context remains unclear. The objective of this study was to investigate the correlation between dysphagia and delirium in ICU patients with ischemic stroke. Methods A retrospective analysis was conducted on adult patients diagnosed with ischemic stroke at a medical center in Boston. Ischemic stroke cases were identified using the ninth and tenth revisions of the International Classification of Diseases. Dysphagia was defined as a positive bedside swallowing screen performed by medical staff on the day of ICU admission, while delirium was assessed using the ICU Confusion Assessment Method and review of nursing notes. Logistic regression models were used to explore the association between dysphagia and delirium. Causal mediation analysis was employed to identify potential mediating variables. Results The study comprised 1838 participants, with a median age of approximately 70 years, and 50.5% were female. Among the total study population, the prevalence of delirium was 43.4%, with a higher prevalence observed in the dysphagia group (60.7% vs. 40.8%, p

Milk production rate (MPR) refers to the rate of milk secretion per hour (kg/h), calculated from the harvested milk yield and milking interval, and it is considered an appropriate measure to evaluate the production potential of cows. The objective of this study was to estimate the phenotypic and genetic parameters of milk production rate traits. In this study, the milking records of 4760 Holstein cows were collected, and four milk yield traits and six milk production rate traits were defined. The MIXED procedure was used to detect the impacts of non-genetic effects on milk yield and milk production rate traits, including parity, measured season and lactation stage. Variance and covariance components for milk yield and milk production rate traits were estimated using a univariate linear repeatability model. Parity, measurement season and lactation stage had significant effects (p < 0.01) on milk yield, milk production rate and its stability. Milk yield and milk production traits had high heritability, and ranged from 0.25 to 0.39. The stability of milk production rate had low heritability (0.04~0.05). Milk production rate is beneficial for the devolving novel trait in dairy breeding and provides new insights for herd management and genetic selection of production performance of dairy cattle.

Multi-output regression aims to utilize the correlation between outputs to achieve information transfer between dependent outputs, thus improving the accuracy of predictive models. Although the Bayesian support vector machine (BSVR) can provide both the mean and the predicted variance distribution of the data to be labeled, which has a large potential application value, its standard form is unable to handle multiple outputs at the same time. To solve this problem, this paper proposes a multi-output Bayesian support vector machine model (MBSVR), which uses a covariance matrix to describe the relationship between outputs and outputs and outputs and inputs simultaneously by introducing a semiparametric latent factor model (SLFM) in BSVR, realizing knowledge transfer between outputs and improving the accuracy of the model. MBSVR integrates and optimizes the parameters in BSVR and those in SLFM through Bayesian derivation to effectively deal with the multi-output problem on the basis of inheriting the advantages of BSVR. The effectiveness of the method is verified using two function cases and four high-dimensional real-world data with multi-output.

Abstract Background With the development and popularization of low-dose chest CT technology, the diagnosis and survival rates of patients with early lung cancer (LC) have significantly improved. The occurrence of colorectal cancer (CRC) as the second primary cancer (SPC) in primary lung cancer (PLC) survivors has become an essential factor affecting the prognosis of early LC. This study explored the potential association between PLC and CRC genetically, laying a foundation for developing SPC-CRC prevention strategies after primary early LC. Methods Based on a two-sample bidirectional Mendelian randomization (MR) design, this study systematically screened genetic instrumental variables (IVs) based on the genome-wide association studies (GWAS) of PLC and CRC, applied inverse variance weighted (IVW) as the main method to assess the incidence association between the two cancers, and used a variety of other MR methods for supplementary analysis. Finally, the Genetic Risk Scores (GRS) method was used for secondary analysis to verify the results robustness further. Results From LC to CRC forward MR analysis, 20 genetic IVs of overall LC, 15 genetic IVs of squamous cell lung carcinoma (LUSC), and 10 genetic IVs of adenocarcinoma of the lung (LUAD) were screened. In the reverse MR analysis from CRC to LC, 47 genetic IVs for overall CRC, 37 for colon cancer, and 25 for rectal cancer were screened. The IVW method and a variety of MR methods all found that overall LC and CRC were significantly associated at the genetic level. Subgroup analysis also showed that LUSC was associated with CRC. And the results of the GRS method were consistent with those of the main analysis, confirming the robustness of the study. Summary Our MR study found an association between LC and CRC, with an increased risk of SPC-CRC following PLC, especially LUSC. Our study provides an essential basis for the precise prevention of SPC-CRC after PLC, suggesting that we should pay more attention to the population with a history of PLC in clinical work, and pay close attention to the incidence of SPC-CRC, and carry out intervention and treatment as soon as possible.

Abstract Background The relationship between sleep disturbances and lung cancer is complex and bidirectional. This meta-epidemiological study aimed to explore the potential association between sleep disruption and the risk of pulmonary cancer. Methods We conducted a comprehensive literature search of the PubMed, Embase, Cochrane Library, and Web of Science databases to retrieve relevant studies. We employed the Newcastle–Ottawa Scale to assess the quality of the observational studies. Stata 17.0 was used to synthesize and conduct a meta-analysis of odds ratios (ORs) and corresponding 95% confidence intervals (CIs). We used funnel plot analysis and Egger’s regression test to evaluate potential publication bias. Results A total of 11 studies were included with 469,691 participants. The methodological quality of the included studies ranged from moderate to high. Compared with 7–8 h of sleep time, short sleep duration was associated with a 13% higher lung cancer risk [OR, 1.13; 95%CI: 1.02–1.25; I2 = 67.6%; P = 0.018] and long sleep duration with a 22% higher risk [OR, 1.22; 95%CI: 1.12–1.33; I2 = 6.9%; P

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Lithium-ion batteries (LIBs) have an extremely diverse application nowadays as an environmentally friendly and renewable new energy storage technology. The porous structure of the separator, one essential component of LIBs, provides an ion transport channel for the migration of ions and directly affects the overall performance of the battery. In this work, we fabricated a composite separator (GOP-PH-ATP) via simply laminating an electrospun polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) nanofibrous membrane coated with attapulgite (ATP) nanoparticles onto a PP nonwoven microfibrous fabric, which exhibits a unique porous structure with a pore-size gradient along the thickness direction that ranges from tens of microns to hundreds of nanometers. As a result, besides the enhanced thermal stability given by the chosen materials, the GOP-PH-ATP separator was endowed with a superhigh porosity of ~95%, strong affinity with electrolyte, and great electrolyte uptake of ~760%, thus effectively enabling an ionic conductivity of 2.38 mS cm−1 and a lithium-ion transference number of 0.62. Furthermore, the cell with the GOP-PH-ATP separator shows an excellent cycling performance with a capacity retention of 91.2% after 150 cycles at 1 C, suggesting that the composite separator with a pore-size gradient structure has great potential to be applied in LIBs.

Abstract Association between alcohol intake and Coronavirus disease 2019 (COVID-19) risk has been explored in several observational studies, but the results are still controversial. These associations may be biased by reverse causation or confounded by other environmental exposures. To avoid potential biases, we used Mendelian randomization (MR) method to evaluate whether alcohol intake is the causal risk factor for COVID-19. Two-sample MR analyses were performed utilizing summary data from the UK Biobank with 38,984 COVID-19 patients and 1,644,784 control participants. Both inverse-variance weighted (IVW) and genetic risk score (GRS) methods were applied to estimate the relationship including COVID-19 vs. general population, hospitalized COVID-19 vs. not hospitalized COVID-19, hospitalized COVID-19 vs. general population, and severe COVID-19 vs. general population. Additionally, we conducted various sensitivity analyses to evaluate the impact of assumptions on the findings and ensure the robustness of the results. Using 80 single nucleotide polymorphisms as instrumental variables, we found that alcohol intake was not significantly associated with the occurrence of COVID-19 in both IVW and GRS methods (IVW: beta = 0.0372; 95% CI − 0.1817 to 0.2561; P = 0.74; GRS: beta = 0.0372, 95% CI − 0.1737 to 0.2481, P = 0.73). Furthermore, similar results were also observed in comparison hospitalized COVID-19 with not hospitalized COVID-19 (IVW: beta = − 0.3625; 95% CI − 1.4151 to 0.6900; P = 0.50; GRS: beta = − 0.3625, 95% CI − 1.3633 to 0.6383, P = 0.48), hospitalized COVID-19 with general population (IVW: beta = − 0.1203; 95% CI − 0.5997 to 0.3591; P = 0.62; GRS: beta = − 0.1203, 95% CI − 0.5352 to 0.2946, P = 0.57), and severe COVID-19 with general population (IVW: beta = 0.2963; 95% CI − 0.3682 to 0.9607; P = 0.38; GRS: beta = 0.2963, 95% CI − 0.3240 to 0.9166, P = 0.35). Besides, the heterogeneity and sensitivity tests suggested absence of bias due to pleiotropy. Our results highlight no evidence to support the causal role of alcohol consumption in COVID-19 risk. Further large-scale prospective studies are warranted to replicate our findings.

Abstract Objective To construct a survival prediction model for patients with TNM stage III hepatocellular carcinoma (HCC) to guide the clinical diagnosis and treatment of HCC patients and improve prognosis. Methods Based on data from patients with stage III (AJCC 7th TNM stage) recorded by the American Institute of Cancer Research from 2010 to 2013, risk factors affecting the prognosis were screened by Cox univariate and multivariate regression, line plots was constructed, and the credibility of the model was verified by Boostrap method. ROC operating curves, calibration curves and DCA clinical decision curves were used to evaluate the model, and Kaplan–Meier was used for survival analysis was used to evaluate the efficacy of the model. External survival data from patients newly diagnosed with stage III hepatocellular carcinoma during 2014–2015 were used to validate and fit the model and to optimize the model. Results Age > 75 years vs.18-53 years [HR = 1.502; 95%CI(1.134–1.990)], stage IIIC vs. Stage IIIA [HR = 1.930; 95%CI(1.509–2.470)], lobotomy vs. non-surgery [HR = 0.295; 95%CI(0.228–0.383)], radiotherapy vs. non-radiotherapy [HR = 0.481; 95%CI(0.373–0.619)], chemotherapy vs. Non-chemotherapy [HR = 0.443; 95%CI(0.381–0.515)], positive serum AFP before treatment vs. negative [HR = 1.667; 95%CI(1.356–2.049)], the above indicators are independent prognostic factors for patients with stage III hepatocellular carcinoma, and the P values for the above results were less than 0.05. A joint prediction model was constructed based on age, TNM stage, whether and how to operate, whether to receive radiotherapy, whether to receive chemotherapy, pre-treatment serum AFP status and liver fibrosis score. The consistency index of the improved prognosis model was 0.725. Conclusions The traditional TNM staging has limitations for clinical diagnosis and treatment, while the Nomogram model modified by TNM staging has good predictive efficacy and clinical significance.

Rabies is a fatal zoonotic disease that poses a threat to public health. Rabies virus (RABV) is excreted in the saliva of infected animals, and is primarily transmitted by bite. The role of the salivary glands in virus propagation is significant, but has been less studied in the pathogenic mechanisms of RABV. To identify functionally important genes in the salivary glands, we used RNA sequencing (RNA-seq) to establish and analyze mRNA expression profiles in parotid tissue infected with two RABV strains, CVS-11 and PB4. The biological functions of differentially expressed genes (DEGs) were determined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, which revealed 3,764 DEGs (678 up-regulated and 3,086 down-regulated) in the CVS-11 infected group and 4,557 DEGs (874 up-regulated and 3,683 down-regulated) in the PB4 infected group. Various biological processes are involved, including the salivary secretion pathway and the phosphatidylinositol 3-kinase-Akt (PI3K-Akt) signaling pathway. This study provides the first mapping of the transcriptome changes in response to RABV infection in parotid tissue, offering new insights into the study of RABV-affected salivary gland function and RABV pathogenic mechanisms in parotid tissue. The salivary gland-enriched transcripts may be potential targets of interest for rabies disease control.

PurposeWith the advancement in early diagnosis and treatment, the prognosis for individuals diagnosed with breast cancer (BC) has improved significantly. The prognosis of primary breast cancer (PBC) survivors can be significantly influenced by the occurrence of colorectal cancer (CRC) as a secondary primary cancer (SPC). The objective of this study is to explore the possible genetic association between PBC and CRC, aiming to lay a groundwork for the development of preventive strategies against SPC-CRC following BC surgery.MethodsWe employed a bidirectional two-sample Mendelian randomization (MR) approach to thoroughly examine genetic instrumental variables (IVs) derived from genome-wide association studies (GWAS) conducted on PBC and CRC. And applied inverse variance weighted (IVW) and multiple other MR methods (weighted median, simple median, MR-PRESSO and MR-RAPS) to evaluate the association between the two cancers (PBC and CRC) at genetic level. Furthermore, the robustness of the findings was further confirmed through the utilization of the genetic risk score (GRS) method in a secondary analysis.ResultsForward MR analysis, a total of 179 BC genetic IVs, 25 estrogen receptor-negative (ER-) genetic IVs and 135 ER-positive (ER+) genetic IVs were screened. Reverse MR analysis, 179 genetic IVs of CRC, 25 genetic IVs of colon cancer, 135 genetic IVs of rectal cancer, 25 genetic IVs of left colon cancer and 135 genetic IVs of right colon cancer were screened. IVW and other MR methods found no significant genetic association between PBC and CRC (P > 0.05). Subgroup analysis also showed that ER- BC and ER+ BC were not correlated with the occurrence of CRC (P > 0.05). The findings of the secondary analysis using GRS were consistent with those obtained from the primary analysis, thereby confirming the robustness and reliability of this study.ConclusionsOur findings do not provide any evidence supporting the association between PBC and CRC at the genetic level. Further large-scale prospective studies are warranted to replicate our findings.

The radial flow field structure, which has the advantages of low pressure drop, good water removal and good mass transfer, is an emerging structure for proton exchange membrane fuel cell (PEMFC) flow fields. However, optimization work on the design the structure is scarce for a complex structure that is difficult to manufacture. A comprehensive three-dimensional, non-isothermal and single-phase mathematical model is developed to describe the flow and heat, mass and charge transfer processes in a PEMFC. The transport phenomena and cell performance of radial flow fields with different gradient channels and parallel flow fields are studied and compared using the commercial computational fluid dynamics (CFD) software Fluent®. The distribution of oxygen concentration, pressure drop and temperature with different radial lengths is obtained. The effects of gradient channels, gas supply modes and radial lengths on cell performance are investigated. The results show that radial flow fields could offer more uniform oxygen distributions and lower pressure drops compared with parallel flow fields. Larger gradient channel sizes contribute to larger transfer volumes and higher gas molar concentrations in the catalyst layers and lower pressures in channels. The counter-flow supply mode is superior to the co-flow supply mode because it can enable a higher oxygen velocity and hence a higher current density.

Abstract Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. On the backbone of ResNet-50, it uses Transformer to capture long-distance feature information, adopts convolutional neural networks and depth-wise convolution to obtain local features, reduce the computational cost and acceleration the detection process. The RMT-Net includes four stage blocks to realize the feature extraction of different receptive fields. In the first three stages, the global self-attention method is adopted to capture the important feature information and construct the relationship between tokens. In the fourth stage, the residual blocks are used to extract the details of feature. Finally, a global average pooling layer and a fully connected layer perform classification tasks. Training, verification and testing are carried out on self-built datasets. The RMT-Net model is compared with ResNet-50, VGGNet-16, i-CapsNet and MGMADS-3. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. The size of RMT-Net model is only 38.5 M, and the detection speed of X-ray image and CT image is 5.46 ms and 4.12 ms per image, respectively. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency.

Tendon-bone insertion injuries (TBI), such as anterior cruciate ligament (ACL) and rotator cuff injuries, are common degenerative or traumatic pathologies with a negative impact on the patient's daily life, and they cause huge economic losses every year. The healing process after an injury is complex and is dependent on the surrounding environment. Macrophages accumulate during the entire process of tendon and bone healing and their phenotypes progressively transform as they regenerate. As the “sensor and switch of the immune system”, mesenchymal stem cells (MSCs) respond to the inflammatory environment and exert immunomodulatory effects during the tendon-bone healing process. When exposed to appropriate stimuli, they can differentiate into different tissues, including chondrocytes, osteocytes, and epithelial cells, promoting reconstruction of the complex transitional structure of the enthesis. It is well known that MSCs and macrophages communicate with each other during tissue repair. In this review, we discuss the roles of macrophages and MSCs in TBI injury and healing. Reciprocal interactions between MSCs and macrophages and some biological processes utilizing their mutual relations in tendon-bone healing are also described. Additionally, we discuss the limitations in our understanding of tendon-bone healing and propose feasible ways to exploit MSC-macrophage interplay to develop an effective therapeutic strategy for TBI injuries. The Translational potential of this article: This paper reviewed the important functions of macrophages and mesenchymal stem cells in tendon-bone healing and described the reciprocal interactions between them during the healing process. By managing macrophage phenotypes, mesenchymal stem cells and the interactions between them, some possible novel therapies for tendon-bone injury may be proposed to promote tendon-bone healing after restoration surgery.

Dengue is prevalent in tropical and subtropical regions. As an arbovirus disease, it is mainly transmitted by Aedes aegypti and Aedes albopictus. According to the previous studies, temperature is closely related to the survival of Aedes mosquitoes, the proliferation of dengue virus (DENV) and the vector competence of Aedes to transmit DENV. This review describes the correlations between temperature and dengue epidemics, and explores the potential reasons including the distribution and development of Aedes mosquitoes, the structure of DENV, and the vector competence of Aedes mosquitoes. In addition, the immune and metabolic mechanism are discussed on how temperature affects the vector competence of Aedes mosquitoes to transmit DENV.

Inflammatory bowel disease is a relapsing inflammatory disease seriously endanger human health. Wedelolactone (WED) is a major active ingredient from Eclipta prostrata (L.) L. and has shown anti-inflammatory effects. However, the mechanism of WED in treating inflammatory colitis remains unknown. We aimed to investigate the mechanisms of WED in treating ulcerative colitis through lipidomic study. Sixty male C57BL/6 mice were exposed to DSS to induce acute colitis. Disease progression was judged by the disease activity index (DAI) and pathological changes of colon tissue. An ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) method was performed for colon and plasma lipidomics analyses. Differential metabolites in the three groups were distinguished by univariate and multivariate analysis. WED exerted anti-inflammatory effects representing by body weight and DAI score. Three metabolites were identified in plasma and 20 in colon. According to pathway analysis, the effects of WED on colitis were associated with seven pathways. The glycerophospholipid metabolism and ether lipid metabolism were the primary pathways. The findings provide important insight of the mechanism of WED in treating DSS induced colitis through lipidomic perspective.

This paper focuses on the adaptive reinforcement learning-based optimal control problem for standard nonstrict-feedback nonlinear systems with the actuator fault and an unknown dead zone. To simultaneously reduce the computational complexity and eliminate the local optimal problem, a novel neural network weight updated algorithm is presented to replace the classic gradient descent method. By utilizing the backstepping technique, the actor critic-based reinforcement learning control strategy is developed for high-order nonlinear nonstrict-feedback systems. In addition, two auxiliary parameters are presented to deal with the input dead zone and actuator fault respectively. All signals in the system are proven to be semi-globally uniformly ultimately bounded by Lyapunov theory analysis. At the end of the paper, some simulation results are shown to illustrate the remarkable effect of the proposed approach.

Abstract Background In 2016, the American College of Obstetricians and Gynecologists recommended antenatal corticosteroids in the late preterm period for women at risk for preterm delivery. Limited real-world evidence exists on neonatal outcomes, particularly for twin gestations, following the guideline change. The study objective is to determine the association of antenatal corticosteroids in late preterm singleton and twin pregnancies with respiratory complications and hypoglycemia in a real-world clinical setting. Methods This is a retrospective cohort study comprising late preterm deliveries (4,341 mother–child pairs) within the Mount Sinai Health System, 2012–2018. The exposure of interest is antenatal corticosteroid administration of betamethasone during pregnancy between 34 0/7 and 36 6/7 weeks. Our primary outcomes are neonatal respiratory complications and hypoglycemia. Multivariable logistic regression was used to estimate the association between antenatal corticosteroid exposure and these two outcomes. We stratified the study population by singleton gestations and twins to minimize the potential confounding from different obstetric management between the two groups. Results Among a total of 4,341 mother–child pairs (3,309 singleton and 1,032 twin mother–child pairs), 745 mothers received betamethasone, of which 40.94% (305/745) received the full course. Relative to no treatment, a full course of betamethasone was associated with reduced odds of respiratory complications (OR = 0.53, 95% CI:[0.31–0.85], p

Environmental DNA (eDNA) metabarcoding is a powerful tool for monitoring biodiversity in natural ecosystems. The accuracy of eDNA metabarcoding relies heavily on the PCR primers that amplify target sequences. For vertebrates, most available primers are designed for particular groups. There have been inadequate attempts to design “universal” primers applicable to all vertebrates. Here, we developed three new universal primer sets (V12S-U, V16S-U, and VCOI-U) targeting mitochondrial 12S, 16S, and COI genes, respectively. They work for all vertebrate groups, with amplification length of 200-250 bp, facilitating the use of short-read sequencing platforms. We evaluated and compared the species detection ability of our primer sets and seven previously published ones through both in-silico PCR and experiment tests with mock DNA and zoo eDNA. The species detection success of the new primer sets is over 90%, showing better performance than previously published primer sets. We also tested our new primers with water eDNA samples of natural environments, and detected a total of 895 vertebrate OTUs (BLAST identity ≥ 0.95), comprising 182 species, 195 genera, and 94 families. Our results indicated that utilizing multiple markers can achieve better species detection than using only a single marker, which is especially important for monitoring large biological communities like vertebrates. The new primer sets enable researchers to detect the presence of species of different vertebrate groups in one eDNA metabarcoding survey, which simplifies the workflow and reduces the cost. It has the potential to serve as an alternative/complementary tool for future eDNA metabarcoding studies targeting vertebrates.

This study reports on the effects of pretreated biogas slurry on degraded farm soil properties, microflora and the production of Capsicum spp. The responses of soil properties, microorganisms and Capsicum spp. production to biogas slurry pretreated soil were determined. The biogas slurry pretreatment of degraded soil increases the total nitrogen (0.15–0.32 g/kg), total phosphorus (0.13–0.75 g/kg), available phosphorus (102.62–190.68 mg/kg), available potassium (78.94–140.31 mg/kg), organic carbon content (0.67–3.32 g/kg) and pH value of the soil, while the population, diversity and distribution of soil bacteria and fungi were significantly affected. Interestingly, soil ammonium nitrogen, soil pH and soil nitrate nitrogen were highly correlated with the population of bacteria and fungi present in the pretreated soil. The soil with biogas slurry pretreatment of 495 m3/hm2 favored the seedling survival rate, flowering rate and fruit-bearing rate of Capsicum spp. and significantly reduced the rate of rigid seedlings. In this study, the application of 495 m3/hm2 biogas slurry to pretreat degraded soil has achieved the multiple goals of biogas slurry valorization, soil biofertilization and preventing and controlling plant diseases caused by soil-borne pathogenic microorganisms. These findings are of significant importance for the safe and environmentally friendly application of biogas slurry for soil pretreatment.

Abstract Background Sepsis still threatens the lives of more than 300 million patients annually and elderly patients with sepsis usually have a more complicated condition and a worse prognosis. Existing studies have shown that both Hematocrit (HCT) and albumin (ALB) can be used as potential predictors of sepsis, and their difference HCT-ALB has a significant capacity to diagnose infectious diseases. Currently, there is no relevant research on the relationship between HCT-ALB and the prognosis of elderly sepsis patients. Therefore, this study aims to explore the association between HCT-ALB and mortality in elderly patients with sepsis. Methods This study was a multi-center retrospective study based on the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) in elderly patients with sepsis. The optimal HCT-ALB cut-off point for ICU mortality was calculated by the Youden Index based on the eICU-CRD dataset, and multivariate logistic regressions were conducted to explore the association between HCT-ALB and ICU/hospital mortality in the two databases. Subgroup analyses were performed for different parameters and comorbidity status. Results The number of 16,127 and 3043 elderly sepsis patients were selected from two large intensive care databases (eICU-CRD and MIMIC-IV, respectively) in this study. Depending on the optimal cut-off point, patients in both eICU-CRD and MIMIC-IV were independently divided into low HCT-ALB (

Abstract Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. Dysregulation of this four-dimensional (4D: x,y,z,time) network is implicated in numerous diseases ranging from cancer to neurodegeneration. While lattice light-sheet microscopy has recently made it possible to image mitochondria in 4D, quantitative analysis methods for the resulting datasets have been lacking. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking assignment. To validate the accuracy of tracking, we created a reaction-diffusion simulation to model mitochondrial network motion and remodeling events. We found that our tracking is >90% accurate for ground-truth simulations and agrees well with published motility results for experimental data. We used MitoTNT to quantify 4D mitochondrial networks from human induced pluripotent stem cells. First, we characterized sub-fragment motility and analyzed network branch motion patterns. We revealed that the skeleton node motion is correlated along branch nodes and is uncorrelated in time. Second, we identified fission and fusion events with high spatiotemporal resolution. We found that mitochondrial skeleton nodes near the fission/fusion sites move nearly twice as fast as random skeleton nodes and that microtubules play a role in mediating selective fission/fusion. Finally, we developed graph-based transport simulations that model how material would distribute on experimentally measured mitochondrial temporal networks. We showed that pharmacological perturbations increase network reachability but decrease network resilience through a combination of altered mitochondrial fission/fusion dynamics and motility. MitoTNT's easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analyses aim at making temporal network tracking accessible to the wider mitochondria research community.

Introduction: This paper proposes a sigmoid-based adaptive inertia control strategy for grid-forming (GFM) inverter to enhance frequency stability.Methods: Firstly, the frequency response characteristics under different disturbances are analyzed theoretically. Then, to solve the problem that fixed inertia leads to a contradiction between the maximum frequency deviation and the setting time, a non-linear inertia regulator is investigated. This non-linear inertia regulator, which is based on an improved sigmoid function, is applied to achieve real-time inertia by the frequency deviation. Moreover, the full-order small-signal model of the system containing the nonlinear inertia regulator is established and thus the stability of the proposed strategy is analyzed.Result and Discussion: The proposed control strategy is implemented without derivative action, which may suffer from high-frequency noises. Finally, the hardware-inthe-loop (HIL) results verify the remarkable performance of the proposed control strategy.

Cell survival or death is critical for cardiac function. Myocardial pyroptosis, as a newly recognized programmed cell death, remains poorly understood in sepsis. In this study, we evaluated the effect of aldehyde dehydrogenase (ALDH2) on myocardial pyroptosis and revealed the underlying mechanisms in sepsis. We established a septic shock mice model by intraperitoneal injection of Lipopolysaccharide (LPS, 15 mg/kg) 12 h before sacrifice. It was found that aldehyde dehydrogenase significantly inhibited NOD-like receptor protein 3 (NLRP3) inflammasome activation and Caspase-1/GSDMD-dependent pyroptosis, which remarkably improved survival rate and septic shock-induced cardiac dysfunction, relative to the control group. While aldehyde dehydrogenase knockout or knockdown significantly aggravated these phenomena. Intriguingly, we found that aldehyde dehydrogenase inhibited LPS-induced deacetylation of Hydroxyacyl-CoA dehydrogenase trifunctional multienzyme complex α subunit (HADHA) by suppressing the translocation of Histone deacetylase 3 (HDAC3) from nuclei to mitochondria. Acetylated HADHA is essential for mitochondrial fatty acid β-oxidation, and its interruption can result in accumulation of toxic lipids, induce mROS and cause mtDNA and ox-mtDNA release. Our results confirmed the role of Histone deacetylase 3 and HADHA in NOD-like receptor protein 3 inflammasome activation. Hdac3 knockdown remarkedly suppressed NOD-like receptor protein 3 inflammasome and pyroptosis, but Hadha knockdown eliminated the effect. aldehyde dehydrogenase inhibited the translocation of Histone deacetylase 3, protected ac-HADHA from deacetylation, and significantly reduced the accumulation of toxic aldehyde, and inhibited mROS and ox-mtDNA, thereby avoided NOD-like receptor protein 3 inflammasome activation and pyroptosis. This study provided a novel mechanism of myocardial pyroptosis through mitochondrial Histone deacetylase 3/HADHA- NOD-like receptor protein 3 inflammasome pathway and demonstrated a significant role of aldehyde dehydrogenase as a therapeutic target for myocardial pyroptosis in sepsis.

Abstract Bound states in the continuum (BICs) have a superior ability to confine electromagnetic waves and enhance light–matter interactions. However, the quality‐factor of quasi‐BIC is extremely sensitive to structural perturbations, thus the BIC metasurfaces usually require a very‐high precision nanofabrication technique that greatly restricts their practical applications. Here, distinctive 2.5D out‐of‐plane architectures based plasmonic symmetry protected (SP)‐BIC metasurfaces are proposed, which could deliver robust quality factors even with large structural perturbations. The high‐throughput fabrication of such SP‐BIC metasurfaces is realized by using the binary‐pore anodic aluminum oxide template technique. Moreover, the deep neural network (DNN) is adapted to conduct multiparameter fittings, where the 2.5D hetero‐out‐of‐plane architectures with robust high quality‐factors and figures of merit are rapidly predicted and fabricated. Finally, owning to its large second‐order surface sensitivity, the desired 2.5D hetero‐out‐of‐plane architecture demonstrates a detection limit of endotoxin as low as 0.01 EU mL‐1, showing a good perspective of biosensors and others.

Abstract Aims This study aimed to develop and validate the psychometric properties of Information Needs Questionnaire for Differentiated Thyroid Cancer (INQ‐DTC) in DTC patients with radioactive iodine (RAI) therapy. Design Mixed methods. Methods Using qualitative methods, we developed the initial questionnaire from a personal perspective of information needs of 15 patients with DTC. We used a formal Delphi consensus process to help assess the initial questionnaire and provide recommendations for its application. Totally, 230 DTC patients with RAI therapy were selected for the process of validation. Results The final version of INQ‐DTC contains 33 items. The total Cronbach's alpha coefficient was 0.945, the total split‐half reliability was 0.822, and the test‐retest value was 0.984 for the overall score. Exploratory factor analysis extracted 5 factors, which could explain 61.86% of the total variance. The Scale‐level content validity index (S‐CVI) was 0.928, and 0.929 for the item‐level content validity index (I‐CVI).

Abstract Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel generalizable deep learning framework, LM-GVP, composed of a protein Language Model (LM) and Graph Neural Network (GNN) to leverage information from both 1D amino acid sequences and 3D structures of proteins. Our approach outperformed the state-of-the-art protein LMs on a variety of property prediction tasks including fluorescence, protease stability, and protein functions from Gene Ontology (GO). We also illustrated insights into how a GNN prediction head can inform the fine-tuning of protein LMs to better leverage structural information. We envision that our deep learning framework will be generalizable to many protein property prediction problems to greatly accelerate protein engineering and drug development.

Abstract Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad quality of underwater acoustics channels, the sonar images collected by sonar technology equipment are easily affected by various kinds of distortions. To obtain high‐quality sonar images, the authors devise a novel dual‐path deep neural network (DPDNN) to measure the quality of sonar images. In these two paths, the authors use a batch normalization layer to reduce the training time and use the skip operation to speed up the feature extraction . Based on the above two operations, the authors extract the microscopic and macroscopic structures of sonar images, respectively. Finally, a global average pooling layer and a fully connection layer are used to connect the above two paths. Experiments show that the authors' DPDNN achieves significant improvements in prediction performance and efficiency. The source code will be published in the near future.

Biogas slurry (BS) is an attractive agricultural waste resource which can be used to regulate soil microbial communities, enhance nutrient absorption capacity of crops, promote plant–soil interactions, and consequently, increase crop productivity. Presently, BS discharge is not environmentally friendly. It is therefore necessary to explore alternative efficient utilization of BS. The use of BS as fertilizer meets the requirements for sustainable and eco-friendly development in agriculture, but this has not been fully actualized. Hence, this paper reviewed the advantages of using BS in farmland as soil fertilization for the improvement of crop production and quality. This review also highlighted the potential of BS for the prevention and control of soil acidification, salinization, as well as improve microbial structure and soil enzyme activity. Moreover, this review reports on the current techniques, application methods, relevant engineering measures, environmental benefits, challenges, and prospects associated with BS utilization. Lastly, additional research efforts require for optimal utilization of BS in farmlands were elucidated.

In light of the ongoing depletion of global fishery resources, there has been a growing trend towards increasing the scope of stock enhancement activities. The objective of these efforts is to replenish the diminishing fishery resources and restore the ecological balance within marine biological communities. Nevertheless, the efficacy of the stock enhancement project has been hindered by the differential growth and environmental adaptability of released species, which can be attributed to the influence of abundant food resources. As a consequence, the project has not yielded the anticipated outcomes. One useful strategy for enhancing the efficacy of stock enhancement is the deliberate allocation of appropriate habitats for diverse released organisms. Fatty acids were extracted by the Folch method and the composition and content of muscle fatty acids were determined by gas chromatography mass spectrometry. This study examines the fatty acid composition of four commercially important species, namely, little yellow croaker (Larimichthys polyactis), red sea bream (Pagrus major), swimming crab (Portunus trituberculatus), and ridgetail white prawn (Exopalaemon carinicauda). The sum of available docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) is employed as an indicator to assess the appropriateness of the marine environment for releasing these species (DE). The red sea bream exhibited the lowest DE value of 13.69% in the northern coastal water of the Bohai Sea, and the little yellow croaker displayed the lowest DE value of 10.91% in the southern coastal water of the Shandong Peninsula. Conversely, the DE values of other seas were comparable, averaging 20.16%. The range of the DE value of the swimming crab across various coastal waters was observed to be between 33.59% and 45.21%. The ridgetail white prawn had a DE value of 21.10% in the coastal water of Yancheng City, Jiangsu Province, as well as the southern coastal water of the Shandong Peninsula, and a DE value of 31.75% in the southern and central coastal waters of Zhejiang Province. The findings of the study indicate that the Bohai Sea and the northern region of the Yellow Sea are the appropriate stock enhancement areas for red sea bream. Similarly, the Yellow Sea and the northern part of the East China Sea are identified as suitable habitats for the little yellow croaker. The swimming crab, on the other hand, is adapted to be released in the Yellow Sea, the Bohai Sea, and the East China Sea. The north of the central part of the East China Sea is an appropriate release area for the ridgetail white prawn.

The quality of remote sensing images has been greatly improved by the rapid improvement of unmanned aerial vehicles (UAVs), which has made it possible to detect small objects in the most complex scenes. Recently, learning-based object detection has been introduced and has gained popularity in remote sensing image processing. To improve the detection accuracy of small, weak objects in complex scenes, this work proposes a novel hybrid backbone composed of a convolutional neural network and an adaptive multi-scaled transformer, referred to as HAM-Transformer Net. HAM-Transformer Net firstly extracts the details of feature maps using convolutional local feature extraction blocks. Secondly, hierarchical information is extracted, using multi-scale location coding. Finally, an adaptive multi-scale transformer block is used to extract further features in different receptive fields and to fuse them adaptively. We implemented comparison experiments on a self-constructed dataset. The experiments proved that the method is a significant improvement over the state-of-the-art object detection algorithms. We also conducted a large number of comparative experiments in this work to demonstrate the effectiveness of this method.

Abstract Background Sepsis has high incidence and fatality rates in intensive care units, often leading to renal failure. The effectiveness of continuous renal replacement therapy (CRRT) in sepsis-associated acute kidney injury (S-AKI) patients is currently uncertain. Aim Joint model was used to determine the association between CRRT and the lactate trajectory trend and how it correlated to 28-day mortality for S-AKI patient in ICU. Methods A retrospective study was applied to patients with sepsis and AKI, which were extracted from the MIMIC-III public database, with the endpoint being 28-day mortality. Every lactate level measurement within 28 days was observed and calculated using logarithms. Joint model combined the longitudinal analysis of the natural logarithm of the lactate level [log(lactate)] in longitudinal submodel and Cox regression by trajectory function, demonstrating the effects of CRRT on 28-day survival and log(lactate) changes, and its final relationship with the event status. Results Among the 717 S-AKI patients, 157 received CRRT. CRRT was not associated with 28-day mortality. After adjustments, the relationship between CRRT use and log(lactate) elevation was statistically significant. The parameter estimation of CRRT and log(lactate) indicated that using CRRT will increase log(lactate) by 0.041 in S-AKI patients. The joint model also instigated a fixed association between changes in the lactate level and the event result, revealing an exp value of (0.755) = 2.12, indicating that an increase of one unit in log(lactate) will increase the risk of 28-day mortality 2.12-times. Conclusion There was no significant association between CRRT use and 28-day survival in S-AKI patients, and JM showed that CRRT use might be associated with elevation of longitudinal lactate levels. Therefore, additional attention should be paid to other treatments to control lactate levels when providing renal support for patients with S-AKI.

Abstract Background Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. Methods Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. Results The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. Conclusion Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

In this paper, an angles of arrival(AOA) localization algorithm based on A-optimality criterion is proposed to solve the problem of unmanned aerial vehicle(UAV) Swarm locating various motion states targets. Firstly, a multi-sites AOA positioning model and a model of error variance changing with the received signal-to-noise ratio are established. Then, A-optimality criterion under this model is derived theoretically, and the optimal spatial configuration of UAV Swarm is analyzed by FIM. Secondly, the target is located by ML algorithm, and the change of flight angle and flight distance of UAV Swarm is analyzed with the minimum value of CRLB matrix trace as the objective function. At last, real-time path optimization is carried out on the flight path of UAV Swarm at the next moment. Simulation results show that path optimization is able to effectively improve the positioning accuracy of UAVs in various motion states.

Summary: Knowledge of RNA molecules regulating testicular development and spermatogenesis in bulls is essential for elite bull selection and an ideal breeding program. Herein, we performed direct RNA sequencing (DRS) to explore the functional characterization of RNA molecules produced in the testicles of 9 healthy Simmental bulls at three testicular development stages (prepuberty, puberty, and postpuberty). We identified 5,043 differentially expressed genes associated with testicular weight. These genes exhibited more alternative splicing at sexual maturity, particularly alternative 3’ (A3) and 5’ (A5) splice sites usage and exon skipping (SE). The expression of hub genes in testicular developmental stages was also affected by both m6A and m5C RNA modifications. We found m5C-mediated splicing events significantly (p

Three Lactobacillus Plantarum (LP), namely LP28, LP226 and LPC2W, were employed to investigate the effect on the aroma profiles of pasteurized litchi juice using E-nose, GC-IMS, GC-MS, and sensory evaluation. The E-nose results showed that pasteurization weakened the flavor profile of litchi juice, while LP fermentation effectively promoted flavor formation. The GC-MS analysis demonstrated that pasteurization significantly reduced the content of alcohols (28.51%), especially geraniol and citronellol, which give litchi juices a fruity and floral aroma. Different LP fermentation enhances the characteristic aroma and produces some new compounds that give it a strong fruity and citrus-like aroma. Moreover, 37 aroma-active compounds (OAV>1) indicated that the linalool (OAV 7504) was the highest, followed by (Z)-rose oxide (OAV 4265), 1-octen-3-ol (OAV 1055) and geraniol (OAV 764), which jointly form the main characteristic flavor. More esters were identified by GC-IMS, indicating the advantage of the combined approach for a better understanding of the impact of pasteurization and fermentation on the litchi juice. The sensory evaluation confirmed that the aroma attributes of fruity, citrus-like, floral, sweet and litchi-like were stronger for the samples fermented by LP28 than those for the other samples. The combination strategy used in this study would facilitate the awareness of litchi juice aroma and broaden our insight into the deep processing of litchi.

Abstract The varies of ice floe thickness (IFH) is a very influential research content in the Earth system academic research, it is closely related to the distribution of underwater acoustic fields. However, the discontinuous physical parameters of ice floes make it difficult to clear this relationship with conventional acoustic propagation models or measured data. Unlike the conventional acoustic models that treat ice floes as one or more physical simplified acoustic reflection layers, this study employs machine learning and FEM (finite element method) modeling to incorporate ice floes' physical parameters into the acoustic field model. The physical parameters of ice floes associated with acoustic field modeling are screened by using the principal component analysis method. On this basis, these ice floes were spatially divided into three highly differentiated and interpretable typical ice clusters using the K‐Means method. We treated ice as a multi‐layer elastic medium, and then the FEM acoustic model was built under the fluid‐structure coupling condition. Measured data (UNDER‐ICE) and MIT acoustic models (OASES) were used to validate our FEM results. We demonstrated that the modeling improves the distribution characteristics of surface channel arrival amplitude under ice floes cover. From the perspective of the model application, the acoustic field response as IFH varies is closely related to the category and region of ice floes.

It is important to develop a catalyst that has high catalytic activity and can improve the degradation efficiency of refractory organic pollutants in the catalytic ozonation process. In this study, Fe-Mn-Cu-Ce/Al2O3 was synthesised via impregnation calcination for catalytic ozonation of bio-treated coking wastewater. The physical and chemical characteristics of the catalysts were analysed using X-ray diffraction (XRD), X-ray fluorescence spectrometry (XRF), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Brunauer–Emmett–Teller nitrogen adsorption–desorption methods. The effects of catalyst dosage, pH, and reflux ratio on the degradation efficiency of wastewater were examined in laboratory-scale experiments. The chemical oxygen demand (COD) removal rate of bio-treated coking wastewater was estimated to be 52.76 % under optimal conditions. The experiments on the catalytic mechanism demonstrated that the surface hydroxyl formed by the Lewis acid sites on the surface of the catalyst can react with ozone as the active site forming the active oxygen (·OH, ·O2–, and 1O2), thereby efficiently degrading the organic pollutants in coking wastewater. Furthermore, a pilot-scale experiment on the catalytic ozonation of bio-treated coking wastewater was carried out using an Fe-Mn-Cu-Ce/Al2O3 catalyst, while the effects of the initial pollutant concentration, ozone concentration, and gas flow on the COD removal rate were studied on a pilot scale. It was found that the COD removal rate of the wastewater was ∼ 60 % under optimal parameters. After the treatment, the wastewater steadily reached the coking wastewater discharge standard (COD

Summary: Sepsis-associated acute kidney injury (S-AKI) is very common and early prediction is beneficial. This study aiming to develop an accurate ensemble model to predict the risk of S-AKI based on easily available clinical information. Patients with sepsis from the United States (US) database Medical Information Mart for Intensive Care-IV were used as a modeling cohort to predict the occurrence of AKI by combining Support Vector Machine, Random Forest, Neural Network, and Extreme Gradient Boost as four first-level learners via stacking algorithm. The external validation databases were the eICU Collaborative Research Database from US and Critical Care Database comprising infection patients at Zigong Fourth People’s Hospital from China, whose AUROC values for the ensemble model 48–12 h before the onset of AKI were 0.774–0.788 and 0.756–0.813, respectively. In this study, an ensemble model for early prediction of S-AKI onset was developed and it demonstrated good performance in multicenter external datasets.