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Abstract This study investigates the low‐frequency oscillations (LFOs) in MMC‐MVDC systems with high photovoltaic (PV) penetration, considering the whole operating conditions of the grid‐connected MMC, including both the inverter and rectifier modes. First, the multi‐port impedance models of converters and networks are developed, upon which a stability criterion is defined. The mechanism of LFOs is then identified using bus participation analysis and parameter sensitivity analysis. In addition, the stable zones of the dominant parameters are identified, taking into account the whole operating conditions of the grid‐connected MMC. Interestingly, a phase‐locked loop may induce LFOs in two low‐frequency ranges. Finally, the experimental results confirm the correctness of the theoretical analysis.

Abstract The min–max multi-vehicle Chinese postman problem is an NP-hard problem, which is widely used in path planning problems based on road network graphs, such as urban road structure probing planning, urban road underground cavity detection planning, high-voltage line inspection planning, and so on. With the rapid increase in the number of nodes and connections of road network graph, the solution time and path equilibrium constraints pose new challenges to the problem solving. In this paper, we propose a critical-edge tabu search algorithm, CTA-kroutes, for solving the min–max multi-vehicle postman problem for large-scale road networks. First, the initial solution with balanced path lengths is obtained by segmenting the Eulerian paths; second, the critical edges are moved in the initial solution to construct the neighborhood solution, and the tabu search algorithm is used to find the optimal solution iteratively; and lastly, the solution optimization algorithm is used at the end of each iteration to de-duplicate and optimally reconstruct the current search result. Experiments show that the CTA-kroutes algorithm can effectively improve the equalization of multi-vehicle paths and its applicability to large-scale road networks.

Objective The aim of this study was to investigate the potential importance of complement system activation, with particular emphasis on the complement alternative pathway (AP), in the pathogenesis of hypertensive renal damage.Methods Serum complement C3, complement Factor H (CFH) and AP activation were assessed in 66 participants with established essential hypertension with renal damage (RD). Fifty-nine patients with age- and sex-matched essential hypertension without renal damage (NRD) and 58 healthy participants (normal) were selected.Results Our study revealed that C3 and AP50 continuously increased from normal to NRD to RD (p

Purpose To investigate the effects of canagliflozin (20 mg/kg) on Dahl salt-sensitive (DSS) rat gut microbiota and salt-sensitive hypertension-induced kidney injury and further explore its possible mechanism.Methods Rats were fed a high-salt diet to induce hypertension and kidney injury, and physical and physiological indicators were measured afterwards. This study employed 16S rRNA sequencing technology and liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based metabolic profiling combined with advanced differential and association analyses to investigate the correlation between the microbiome and the metabolome in male DSS rats.Results A high-salt diet disrupted the balance of the intestinal flora and increased toxic metabolites (methyhistidines, creatinine, homocitrulline, and indoxyl sulfate), resulting in severe kidney damage. Canagliflozin contributed to reconstructing the intestinal flora of DSS rats by significantly increasing the abundance of Corynebacterium spp., Bifidobacterium spp., Facklamia spp., Lactobacillus spp., Ruminococcus spp., Blautia spp., Coprococcus spp., and Allobaculum spp. Moreover, the reconstruction of the intestinal microbiota led to significant changes in host amino acid metabolite concentrations. The concentration of uremic toxins, such as methyhistidines, creatinine, and homocitrulline, in the serum of rats was decreased by canagliflozin, which resulted in oxidative stress and renal injury alleviation.Conclusion Canagliflozin may change the production of metabolites and reduce the level of uremic toxins in the blood circulation by reconstructing the intestinal flora of DSS rats fed a high-salt diet, ultimately alleviating oxidative stress and renal injury.

Background: Mesangial cell (MC)-mediated immune inflammatory injury is a basic pathological process in glomerulonephritis. However, due to the limited drug accumulation and serious adverse effects, it remains challenging to explore a rational delivery system integrating high efficiency and low toxicity to deliver anti-inflammatory drugs to the glomerular MC region. Results: Salvianolic acid B (SAB)-loaded hybrid membrane biomimetic nanovesicles (SAB@HMVs) were developed by fusing erythrocyte membrane nanovesicles with mesenchymal stem cells biomimetic membrane nanovesicles. SAB@HMVs had a spheroidal structure, low cytotoxicity, particle size of 143.83 ± 1.33 nm and exhibited a high drug loading capacity (7.02 %±0.30 %) along with a good sustained release function. The in vitro anti-inflammatory results revealed that HMVs were effectively taken up by MCs and showed significant anti-inflammatory activity. Furthermore, in vivo pharmacodynamic studies revealed that SAB@HMVs could deliver SAB to kidney tissue and elicit an effective anti-inflammatory response to improve the pathological changes in the glomerular mesangial region, significantly reducing the levels of cytokines and alleviating kidney inflammation. Especially, noteworthy was the observation that SAB@HMVs augmented the renal delivery of SAB, downregulated the gene expression of p38 MAPK and NF-κB, inhibited the levels of MMP9 and TNF-α proteins as well as elevated the level of iκB protein, indicating that the anti-inflammatory mechanism of SAB@HMVs should be based on regulating MAPK and NF-κB signaling pathways. Conclusion: We provided a novel strategy to increase nanovesicles accumulation in MCs with the potential to exert anti-inflammatory regulatory effects in glomerulonephritis. Furthermore, this biomimetic hybrid membrane loaded with pure drugs may offer a functional platform to address multiple clinical needs.

A purified polysaccharide with a galactose backbone (SPR-1, Mw 3,622 Da) was isolated from processed Polygonati Rhizoma with black beans (PRWB) and characterized its chemical properties. The backbone of SPR-1 consisted of [(4)-β-D-Galp-(1]9 → 4,6)-β-D-Galp-(1 → 4)-α-D-GalpA-(1 → 4)-α-D-GalpA-(1 → 4)-α-D-Glcp-(1 → 4,6)-α-D-Glcp-(1 → 4)-α/β-D-Glcp, with a branch chain of R1: β-D-Galp-(1 → 3)-β-D-Galp-(1→ connected to the →4,6)-β-D-Galp-(1→ via O-6, and a branch chain of R2: α-D-Glcp-(1 → 6)-α-D-Glcp-(1→ connected to the →4,6)-α-D-Glcp-(1→ via O-6. Immunomodulatory assays showed that the SPR-1 significantly activated macrophages, and increased secretion of NO and cytokines (i.e., IL-1β and TNF-α), as well as promoted the phagocytic activities of cells. Furthermore, isothermal titration calorimetry (ITC) analysis and molecular docking results indicated high-affinity binding between SPR-1 and MD2 with the equilibrium dissociation constant (KD) of 18.8 μM. It was suggested that SPR-1 activated the immune response through Toll-like receptor 4 (TLR4) signaling and downstream responses. Our research demonstrated that the SPR-1 has a promising candidate from PRWB for the TLR4 agonist to induce immune response, and also provided an easily accessible way that can be used for PR deep processing.

Abstract Biochar application is widely recognized as an effective approach for increasing soil organic carbon (SOC) and mitigating climate change in agroecosystems. However, the effects of biochar application on net accumulations and relative contributions of different SOC sources remain unclear. Here, we explored the effects of biochar application on plant‐derived (PDC) and microbial necromass C (MNC) in a 10‐year experimental rice–wheat rotation field receiving four different intensities of biochar application (0, 2.25, 11.5, and 22.5 t ha−1 for each crop season), using phospholipid fatty acids (PLFAs), lignin phenols and amino sugars as biomarkers of microbial biomass, PDC and MNC, respectively. Our results showed that biochar application increased SOC content and stock by 32.6%–203% and 26.4%–145%, respectively. Higher biochar application (11.5 and 22.5 t ha−1) increased soil pH, total nitrogen (TN), total phosphorus (TP), SOC/TN, and root biomass. In addition, higher biochar application enhanced bacterial, fungal, and total microbial biomass. Plant lignin phenols and MNC contents significantly increased, whereas their contributions to SOC significantly decreased with the increase in biochar application rates due to the disproportionate increase in PDC and MNC, and SOC. Fungal necromass had a greater contribution to SOC than bacterial necromass. The fungal/bacterial necromass decreased from 2.56 to 2.26 with increasing biochar application rates, because of the higher abundances of bacteria than that of fungi as indicated by PLFAs under higher biochar application rates. Random forest analyses revealed that pH, TP, and SOC/TN were the main factors controlling plant lignin and MNC accumulation. Structural equation modeling revealed that biochar application increased lignin phenols by stimulating root biomass, whereas enhanced MNC accumulation was primarily from increased microbial biomass and lignin phenols. Overall, our findings suggest that biochar application increases the accumulation of the two SOC sources but decreases their contributions to SOC in paddy soils.

Frequency agile technology provides full play to the advantage of radars for adopting electronic countermeasures actively, which can effectively enhance the antinoise suppression jamming performance of radars. However, with the increasing complexity of the interference environment, developing an online decision-making method for frequency-agile radar with dynamic adaptability and without foresight of the nature of the environment is a demanding task. According to the features of the jamming strategy, suppression jamming scenarios are divided into three categories, and an online decision-making method for frequency-agile radar based on Multi-Armed Bandit (MAB) is developed to maximize the radar’s detection probability. This approach is an online learning algorithm that does not need to interfere with the foresight of the environment and offline training process and realizes remarkable learning performance from noninterference scenarios to adaptive interference scenarios. The simulation results and theoretical analysis demonstrate that compared with the classical algorithm and stochastic agile strategy, the proposed method has stronger flexibility and can effectively improve the antijamming and target detection performances of the frequency-agile radar for various jamming scenarios.

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis and prediction soon after birth are urgently needed for VPT infants. Previous studies have applied deep learning models to learn the brain structural connectome (SC) to predict neurodevelopmental deficits in the preterm population. However, none of these models are specifically designed for graph-structured data, and thus may potentially miss certain topological information conveyed in the brain SC. In this study, we aim to develop deep learning models to learn the SC acquired at term-equivalent age for early prediction of neurodevelopmental deficits at 2 years corrected age in VPT infants. We directly treated the brain SC as a graph, and applied graph convolutional network (GCN) models to capture complex topological information of the SC. In addition, we applied the supervised contrastive learning (SCL) technique to mitigate the effects of the data scarcity problem, and enable robust training of GCN models. We hypothesize that SCL will enhance GCN models for early prediction of neurodevelopmental deficits in VPT infants using the SC. We used a regional prospective cohort of ∼280 VPT infants who underwent MRI examinations at term-equivalent age from the Cincinnati Infant Neurodevelopment Early Prediction Study (CINEPS). These VPT infants completed neurodevelopmental assessment at 2 years corrected age to evaluate cognition, language, and motor skills. Using the SCL technique, the GCN model achieved mean areas under the receiver operating characteristic curve (AUCs) in the range of 0.72∼0.75 for predicting three neurodevelopmental deficits, outperforming several competing models. Our results support our hypothesis that the SCL technique is able to enhance the GCN model in our prediction tasks.

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, “white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation,” 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

Abstract Purpose To investigate the distribution of the incidence and genotypes of human papillomavirus (HPV) among women with cervical cancer (CC) and precancerous cervical lesions in Yueyang City, China, to develop prevention and control strategies for CC. Methods A total of 3674 patients with cervical lesions and cervical cancer who attended 7 hospitals in Yueyang City between September 2019 and September 2022 were included. They included 1910 cervical intraepithelial neoplasia (CIN) I, 718 CIN II, 576 CIN II and 470 CC, respectively. The HPV genotyping of the above patients was detected by Real time-PCR in the laboratory department of each hospital. Results The total HPV prevalence was 74.69% (95% CI 73.28–76.09%) in 3674 patients. The incidence of high- and low-risk HPV was 73.46% and 7.21%, respectively. The prevalence of HPV in CIN I, CIN II, CIN III, and invasive CC (ICC) groups was 66.65% (1273/1910, 95% CI 64.53–68.77%), 80.78% (580/718, 95% CI 77.89–83.67%), 83.88% (483/576, 95% CI 80.84–86.87%), and 86.81% (408/470, 95% CI 83.74–89.88%), respectively. The top three HPV subtypes in ICC are HPV16, HPV52, and HPV58. The prevalence of HPV 16 increased with increasing disease severity, with this genotype being present in 12.57%, 20.89%, 36.98%, and 50.85% of CIN I, CIN II, CIN III, and ICC cases, respectively (p

This is a paper in the field of mineral synthesis, covering the areas of mineral processing engineering and ceramics and composites. Tailings of a vanadium-titanium magnetite plant in Panzhihua were subjected to regrinding and re-election experiments, and an iron ore concentrate with a Fe grade of 58.59% was obtained by the "step grinding and step separation" process. The ilmenite concentrate with TiO2 grade of 46.63% was obtained by adopting the flotation flowsheet of "one roughing, one scavenging, three cleaning". The TFe and TiO2 of the reselection tailings of vanadium-titanium magnetite were 5.71% and 2.96%, respectively. The process was achieved efficient separation of effective components. The foamed ceramic material with a volume density of 482 kg/m3 and a water absorption rate of 2.35%, and a compressive strength of 3.03 MPa was obtained by using the reselection tailings of vanadium-titanium magnetite, feldspar tailings and kaolinite pyrite tailings as the base material, and silicon carbide as blowing agent at 1160℃ for 30 min. The comprehensive utilization of vanadium-titanium magnetite tailings is realized.

Abstract Cost aggregation is crucial to the accuracy of stereo matching. A reasonable cost aggregation algorithm should aggregate costs within homogeneous regions where pixels have the same or similar disparities. Otherwise, the estimated disparity map is prone to the well‐known edge‐fattening issue and the problem of losing fine structures. However, current state‐of‐the‐art convolutional neural networks (CNNs) mainly do cost aggregation with square‐kernel convolutional layers that learn to adjust their kernel elements to make the actual receptive fields of the aggregated costs adapt to homogeneous regions with various shapes. This is a mechanism that easily leads to the above issues due to the translation equivalence and content‐insensitivity properties of CNNs. To tackle these problems, a novel densely connected asymmetric convolution block (Dense‐ACB) based on asymmetric convolutions is proposed to explicitly construct receptive fields with various shapes, which effectively alleviates the issues caused by mismatching shapes of receptive fields and homogeneous regions. The proposed Dense‐ACB brings new insight to CNN‐based stereo matching networks. Based on the proposed cost aggregation method, an efficient and effective stereo matching network is built, which not only achieves competitive overall accuracy compared with state‐of‐the‐art models but also effectively alleviates the edge‐fattening problem and preserves fine structures.

Abstract In the process of bioethanol production, more stable and active cellulase in high temperature condition is required. In this study, syringic acid was applied in cellulase hydrolysis system. At 70°C, TvEG3 activity increased 201.36%, CtBglA activity decreased 72.79% by syringic acid. With syringic acid assisting, TvEG3 thermostability was improved, CtBglA thermostability was reduced. Syringic acid scarcely affected CtCBH. In hydrolysis system with the cellulases containing TvEG3, CtCBH, and CtBglA, the reducing sugar yield improved by 28.37% with syringic acid assisting. With the molecular dynamic simulation in syringic acid system, the backbone root‐mean‐square deviation (RMSD) and the residue root‐mean‐square fluctuation (RMSF) of TvEG3, CtCBH reduced, while the RMSD and RMSF of CtBglA increased. The reduction in the number of secondary structures, especially α‐helix, caused the structure of CtBglA in the presence of syringic acid to collapse at high temperature. More secondary structures in TvEG3 and more α‐helix in CtCBH in the presence of syringic acid make them more stable at high temperatures. These means syringic acid can stabilize TvEG3 and CtCBH structure, destabilize CtBglA structure at high temperature. In summary, this study not only provides insight into cellulase hydrolysis at high temperature with syringic acid assisting but also demonstrates the promoting mechanism of syringic acid.

Objectives: This study aimed to determine the occurrence rate of malalignment of tibial prosthesis and explore the influencing factors. Methods: 296 patients from Southern China who underwent total knee arthroplasty (TKA) were selected as the research objects. Their general demographic data were recorded. The tibial bowing angle (TBA), tibial length, medial proximal tibial angle (MPTA), tibial plateau shift angle (TPSA), tibial bone loss, lateral distal tibial angle, and overall width of tibial plateau and widths of medial and lateral tibial plateau were measured before TKA. The tibial component coronal alignment angle (TCCA) was measured after the operation. Malalignment of the tibial prosthesis was defined as TCCA 93°. Tibial bowing was indicated by TBA >2°, and lateral bowing was recorded as +. The correlations of TCCA with demographic data and pre-operation imaging measurement parameters were statistically analyzed. Results: Bivariate correlation analysis revealed negative correlations between TCCA and TBA (r = −0.602, P

Millettia speciosa Champ. (MSCP), which is distributed and used in Southern China, is often sulfur-fumigated during postharvest handling to prevent insects and molds. In the present study, a comprehensive strategy was proposed by using ultrahigh-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UHPLC–QTOF–MS/MS) with multivariate analysis to rapidly identify potential chemical sulfur-fumigated MSCP (SF-MSCP) samples, discover chemical transformation mechanisms and control the quality of SF-MSCP. Eighty-six compounds were tentatively assigned, and ten compounds were screened out as potential markers. Moreover, the chemical transformation mechanism was deduced by hydrolysis, esterification and dehydration reactions that occurred during the sulfur-fumigated process. The developed method was successfully applied to predict 16 commercial MSCP samples, and 9 samples were found to contain sulfur compounds, ranging in sulfur residues from 100 to 310 mg/kg. These research outcomes can not only be used to distinguish the nonfumigated MSCP from SF-MSCP by chemical markers but also provide a chemical basis for further comprehensive investigating in-depth safety and functional evaluations of SF-MSCP.

Previous studies revealed that both Helicobacter pylori infection and some cytokine gene polymorphisms are risk factors for gastric diseases. The association between H. pylori and gene polymorphisms is worth exploring. Here we conducted a case–control study and systematically evaluate on association between H. pylori infection and IL4, TNFA, and IL10 polymorphisms in a Chinese Han population in northwestern China, where gastric cancer is the major burden on the health and the leading cause of death in local community. The Sequenom MassArray platform was used to screen the genotypes and 14C-urea breath test was employed to determine H. pylori infection status of 705 participants of the local rural residents. Significant association between H. pylori infection and the IL4 rs34142320 polymorphism was observed in the dominant model (OR = 0.57, 95% CI = 0.40–0.80, P = 0.030). No significant association was found between IL4 rs2243250, TNFA rs1800630, TNFA rs1800629, TNFA rs1799964, IL10 rs1800871, IL10 rs1800896 and H. pylori infection. Haplotype analysis showed that T-DEL and G-C haplotypes of the observed IL4 SNPs were significantly associated with H. pylori infection. Our analysis demonstrated that the IL4 rs34142320 polymorphism has a protective effect on H. pylori infection in Chinese Han population.

Abstract Background To explore the community composition and diversity of the endophytic fungi in Taxillus chinensis, samples of the parasites growing on seven different hosts, Morus alba, Prunus salicina, Phellodendron chinense, Bauhinia purpurea, Dalbergia odorifera, Diospyros kaki and Dimocarpus longan, were isolated. The strains were identified by their morphological characteristics and their internal transcribed spacer (ITS) sequences. Results 150 different endophytic fungi were isolated from the haustorial roots of the seven hosts with a total isolation rate of 61.24%. These endophytic fungi were found to belong to 1 phylum, 2 classes, 7 orders, 9 families, 11 genera and 8 species. Among of them, Pestalotiopsis, Neopestalotiopsis and Diaporthe were the dominant genera, accounting for 26.67, 17.33 and 31.33% of the total number of strains, respectively. Diversity and similarity analyses showed that the endophytic fungi isolated from D. longan (H’=1.60) had the highest diversity index. The highest richness indexes were found in M. alba and D. odorifera (both 2.23). The evenness index of D. longan was the highest (0.82). The similarity coefficient of D. odorifera was the most similar to D. longan and M. alba (33.33%), while the similarity coefficient of P. chinense was the lowest (7.69%) with M. alba and D. odorifera. Nine strains showed antimicrobial activities. Among them, Pestalotiopsis sp., N. parvum and H. investiens showed significant antifungal activity against three fungal phytopathogens of medicinal plants. At the same time, the crude extracts from the metabolites of the three endophytic fungi had strong inhibitory effects on the three pathogens. Pestalotiopsis sp., N. parvum and H. investiens had the strongest inhibitory effects of S. cucurbitacearum, with inhibitory rates of 100%, 100% and 81.51%, respectively. In addition, N. parvum had a strong inhibitory effect on D. glomerata and C. cassicola, with inhibitory rates of 82.35% and 72.80%, respectively. Conclusions These results indicate that the species composition and diversity of endophytic fungi in the branches of T. chinensis were varied in the different hosts and showed good antimicrobial potential in the control of plant pathogens.

Abstract ABCB10, a member of ABC transporter superfamily that locates in the inner membrane of mitochondria, plays crucial roles in hemoglobin synthesis, antioxidative stress and stabilization of the iron transporter mitoferrin-1. Recently, it was found that ABCB10 is a mitochondrial biliverdin exporter. However, the molecular mechanism of biliverdin export by ABCB10 remains elusive. Here we report the cryo-EM structures of ABCB10 in apo (ABCB10-apo) and biliverdin-bound form (ABCB10-BV) at 3.67 Å and 2.85 Å resolution, respectively. ABCB10-apo adopts a wide-open conformation and may thus represent the apo form structure. ABCB10-BV forms a closed conformation and biliverdin situates in a hydrophobic pocket in one protomer and bridges the interaction through hydrogen bonds with the opposing one. We also identify cholesterols sandwiched by BVs and discuss the export dynamics based on these structural and biochemical observations.

Objectives This study assessed the antifibrotic effects of canagliflozin, with or without irbesartan, on renal injury in Dahl salt-sensitive (SS) rats fed a high-salt (HS) diet. Methods After the preconditioning stage, Dahl SS rats (n = 47) were divided into five experimental groups as follows: low-salt (LS, n = 7), HS (n = 10), HS with canagliflozin (n = 10), HS with irbesartan (n = 10), and HS with canagliflozin and irbesartan (n = 10). Results The HS diet increased systolic blood pressure (SBP), renal fibrosis, fibrotic protein expression, and transforming growth factor-β1 (TGF-β1)/Smad2/3 pathway protein expression compared with the findings in the LS group. Irbesartan reduced SBP and slowed the loss of renal function. Canagliflozin significantly reduced body weight and renal fibrosis and suppressed the TGF-β1/Smad2/3 pathway. The combined therapy exerted better renoprotective effects on all outcome parameters. Conclusions These results indicate that canagliflozin and irbesartan exert different effects on renal injury in SS hypertensive rats, and the combined regimen could have stronger effects than either monotherapy.

This study aims to summarize the available data and determine if the presence of venous thromboembolism (VTE) immune-related adverse event (irAE) in patients with immune checkpoint inhibitor (ICI) therapy is associated with improved treatment efficacy and clinical outcomes, which in turn was used to help optimize patient selection for anticoagulation therapy and inform rational treatment strategies for overcoming the mechanisms of ICI resistance. PubMed, Embase, Web of Science, and Cochrane Library were searched up to March 18, 2023, for studies assessing the relationship between VTE irAE development during ICI therapy and cancer outcomes. Seven primary articles with a total of 4437 patients were included in the overall survival (OS) meta-analysis. Patients with VTE had a significant increase in overall mortality compared to patients without VTE in adjusted hazard ratios (HRs 1.36, 95% confidence interval [CI] 1.06-1.75, P = .02). In the studies where immortal time bias (ITB) was accounted for, patients with VTE irAE also had poor OS than those without. HR and the corresponding 95% CI values in the non-ITB group were 2.53 (1.75-3.66, P

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.

Adulteration of natural colorants with artificial ones compromises the authenticity, quality, and safety of food products. It is significant for food companies to check the raw colorant ingredients to avoid potential adulteration. Combining thin-layer chromatography (TLC) and surface-enhanced Raman spectroscopy (SERS) is a promising colorant separation and detection approach. Herein, we developed a novel and simple TLC-SERS approach based on silver mirror-stamping, which was achieved by pressing a layer of aggregated silver nanoparticles on top of the target analytes on the developed TLC chip. The performance of the stamping method was evaluated on the separation and signal enhancement of an adulterated saffron model matrix (saffron and red 40) as compared to the other two methods (mirror-first and mirror-last methods). The results demonstrated that the mirror-stamping method achieved optimal separation performance with strong SERS signals. This can be attributed to the reduced sample disruption and dilution. Therefore, the mirror-stamping approach demonstrates great potential for use in TLC-SERS analysis to rapidly and accurately determine natural colorant authenticity and quality.

Objectives This study is designed to analyze the potential influencing factors of hyperlipidemia, and to explore the relationship between liver function indicators such as gamma-glutamyltransferase (GGT) and hyperlipidemia. Methods Data were derived from 7599 outpatients who visited the Department of Endocrinology of the First Hospital of Jilin University (2017–2019). A multinomial regression model is used to identify related factors of hyperlipidemia and the decision tree method is used to explore the general rules in hyperlipidemia patients and non-hyperlipidemia patients on these factors. Results The average of age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure, aspartate aminotransferase, alanine aminotransferase (ALT), GGT and glycosylated hemoglobin (HbA1c) in the hyperlipidemia group are higher than those in the non-hyperlipidemia group. In multiple regression analysis, SBP, BMI, fasting plasma glucose, 2-h postprandial blood glucose, HbA1c, ALT, GGT are associated with triglyceride. For people with HbA1c less than 6.0%, controlling GGT within 30 IU/L reduces the prevalence of hypertriglyceridemia by 4%, and for people with metabolic syndrome with impaired glucose tolerance controlling GGT within 20 IU/L reduces the prevalence of hypertriglyceridemia by 11%. Conclusions Even when GGT is in the normal range, the prevalence of hypertriglyceridemia increases with its gradual increase. Controlling GGT in people with normoglycemia and impaired glucose tolerance can reduce the risk of hyperlipidemia.

Purpose: This study was aimed at investigating the effect of canagliflozin (Cana) on atherosclerosis and further exploring its potential mechanism. Methods: ApoE −/− mice were fed a Western diet (WD) and randomly divided into a WD group and WD+Cana group. After 15 weeks of canagliflozin treatment, serum levels of fasting insulin and inflammatory cytokines were determined with ELISA kits. HE, Oil Red O, and Masson staining were used to estimate the extent of atherosclerosis. Immunohistochemistry, immunofluorescence, ROS staining, and RT-PCR were used to further investigate Cana’s potential mechanism. Results: Histological analysis indicated that Cana restrained atherosclerotic plaque development. Furthermore, Cana decreased the percentage of F4/80 positive cells, and the areal density of ROS and relative fluorescence intensity of P62, but enhanced the relative fluorescence intensity of LC3 in the aortic root. Analysis of factors associated with the inflammatory response mediated by AP-1, oxidative stress mediated through the ROS/Nrf2 pathway, and autophagy in the aorta indicated elevated mRNA levels of F4/80, MCP-1, VCAM-1, AP-1, ROS, NOX4, P62, NLRP3, and IL-1β, but diminished mRNA levels of Nrf2, GST, eNOS, and LC3, in the WD+Cana group. Conclusion: Canagliflozin may attenuate atherosclerosis by decreasing the inflammatory response mediated by AP-1, alleviating oxidative stress through the ROS/Nrf2 pathway, and enhancing autophagy in WD-fed ApoE −/− mice.

The transient responses of distributed energy resources (DERs) in a microgrid are dynamically correlated in spatial and temporal dimensions. Hence, the transient stability prediction in microgrids would require an effective modeling of time-varying correlations and the mining of spatial–temporal features of electrical data. This paper proposes a refined DER-level transient stability prediction method for microgrids considering the time-varying spatial–temporal correlations of DERs. First, the spatial–temporal dynamic correlation of DERs was extracted and modeled by an attention-based mechanism. Then, a spatial–temporal graph convolution network was proposed to predict the dynamics of unstable DERs and the instability severity trend in a microgrid. The TSP model consisted of three parts: (1) several stacked spatial–temporal convolution modules to simultaneously mine the spatial–temporal dynamic features of microgrids, (2) an unstable DER identification module to predict the microgrid system stability and identify unstable DERs, and (3) an instability severity trend prediction module for DERs in a microgrid. The test results on a realistic 16-bus 10-DER microgrid demonstrated that the proposed prediction method possessed the desirable reliability and interpretability and outperformed the state-of-the-art baselines in unstable DER identifications and DER instability severity trend predictions.

PurposeRadiotherapy is one of the most important treatments for high-grade glioma (HGG), but the best way to delineate the target areas for radiotherapy remains controversial, so our aim was to compare the dosimetric differences in radiation treatment plans generated based on the European Organization for Research and Treatment of Cancer (EORTC) and National Research Group (NRG) consensus to provide evidence for optimal target delineation for HGG.MethodsWe prospectively enrolled 13 patients with a confirmed HGG from our hospital and assessed dosimetric differences in radiotherapy treatment plans generated according to the EORTC and NRG-2019 guidelines. For each patient, two treatment plans were generated. Dosimetric parameters were compared by dose–volume histograms for each plan.ResultsThe median volume for planning target volume (PTV) of EORTC plans, PTV1 of NRG-2019 plans, and PTV2 of NRG-2019 plans were 336.6 cm3 (range, 161.1–511.5 cm3), 365.3 cm3 (range, 123.4–535.0 cm3), and 263.2 cm3 (range, 116.8–497.7 cm3), respectively. Both treatment plans were found to have similar efficiency and evaluated as acceptable for patient treatment. Both treatment plans showed well conformal index and homogeneity index and were not statistically significantly different (P = 0.397 and P = 0.427, respectively). There was no significant difference in the volume percent of brain irradiated to 30, 46, and 60 Gy according to different target delineations (P = 0.397, P = 0.590, and P = 0.739, respectively). These two plans also showed no significant differences in the doses to the brain stem, optic chiasm, left and right optic nerves, left and right lens, left and right eyes, pituitary, and left and right temporal lobes (P = 0.858, P = 0.858, P = 0.701 and P = 0.794, P = 0.701 and P = 0.427, P = 0.489 and P = 0.898, P = 0.626, and P = 0.942 and P = 0.161, respectively).ConclusionThe NRG-2019 project did not increase the dose of organs at risk (OARs) radiation. This is a significant finding that further lays the groundwork for the application of the NRG-2019 consensus in the treatment of patients with HGGs.Clinical trial registrationThe effect of radiotherapy target area and glial fibrillary acidic protein (GFAP) on the prognosis of high-grade glioma and its mechanism, number ChiCTR2100046667. Registered 26 May 2021.

Object: This study sought to elucidate the role of microRNA-210 (miR-210) in the occurrence and development of lung adenocarcinoma (LUAD). Methods: The levels of lncRNA miR-210HG and miR-210 in LUAD tissues and corresponding normal tissues were analyzed by real-time quantitative PCR. The expression of the anti-hypoxia factor hypoxia inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) were measured by qRT-PCR and Western blot. The target of miR-210 on HIF-1α was confirmed using TCGA, Western blot and luciferase reporter assay. The regulatory role of miR-210 on HIF-1α and VEGF in LUAD was investigated. The correlation of genes with clinical prognosis was analyzed using bioinformatics methods. The effect of miR-210 on LUAD cells was verified through apoptosis assays. Results: The expression of miR-210 and miR-210HG was significantly higher in LUAD tissues than in normal tissues. The expression of hypoxia-related indicators HIF-1α and VEGF was also significantly higher in LUAD tissues. MiR-210 suppressed HIF-1α expression by targeting site 113 of HIF-1α, thereby affecting VEGF expression. Overexpression of miR-210 inhibited HIF-1 expression by targeting the 113 site of HIF-1, thereby affecting VEGF expression. Conversely, inhibition of miR-210 resulted in a significant increase in HIF-1α and VEGF expression in LUAD cells. In TCGA-LUAD cohorts, the expression of VEGF-c and VEGF-d genes in LUAD tissues was significantly lower than in normal tissues, while overall survival was worse in LUAD patients with high expression of HIF-1α, VEGF-c and VEGF-d. Apoptosis was significantly lower in H1650 cells after miR-210 inhibition. Conclusion: This study reveals that miR-210 exerts an inhibitory effect on VEGF expression by down-regulating HIF-1α expression in LUAD. Conversely, inhibition of miR-210 significantly reduced H1650 apoptosis and led to worse patient survival by upregulating HIF-1α and VEGF. These results suggest that miR-210 could serve as a potential therapeutic target for the treatment of LUAD.

The aim of this single-centre 10-year retrospective observational study was to evaluate the profile of Chinese cancerous patients complicated with venous thromboembolism (VTE) based on demographic features, clinical characteristics, and medication exposure. Consecutive 1482 patients with solid tumor complicated with VTE at a tertiary center between 2012 and 2021 were retrospectively enrolled. Data were collected on demographics, comorbidities, discharge diagnoses, laboratory examination data, treatment details, and imaging description of the lesion. The overall incidence of clinical VTE was 1.35% in hospitalized patients with cancer in our center. Lung cancer was the most frequent tumor subtype for developing VTE events, accounting for 24.83% of all cases. Over half of the patients (66.60%) were observed to have an increased risk of VTE within the first 6 months of cancer diagnosis. Close to half of the patients (46.49%) had received chemotherapy within 6 months prior to the diagnosis of VTE. The frequency of massive ascites group (＞2000 mL) in gynecological patients with VTE was significantly larger than that of nonmassive ascites group (≤2000 mL) ( P

Preterm brains commonly exhibit elevated signal intensity in the white matter on T2-weighted MRI at term-equivalent age. This signal, known as diffuse excessive high signal intensity (DEHSI) or diffuse white matter abnormality (DWMA) when quantitatively assessed, is associated with abnormal microstructure on diffusion tensor imaging. However, postmortem data are largely lacking and difficult to obtain, and the pathological significance of DEHSI remains in question. In a cohort of 202 infants born preterm at ≤32 weeks gestational age, we leveraged two newer diffusion MRI models – Constrained Spherical Deconvolution (CSD) and neurite orientation dispersion and density index (NODDI) – to better characterize the macro and microstructural properties of DWMA and inform the ongoing debate around the clinical significance of DWMA. With increasing DWMA volume, fiber density broadly decreased throughout the white matter and fiber cross-section decreased in the major sensorimotor tracts. Neurite orientation dispersion decreased in the centrum semiovale, corona radiata, and temporal lobe. These findings provide insight into DWMA's biological underpinnings and demonstrate that it is a serious pathology.

BackgroundSeveral studies showed inconsistencies in the relationships between inflammatory rheumatic diseases (IRDs) and the risk of Parkinson's disease (PD). Therefore, we carried out a meta-analysis to investigate the associations between different IRDs and PD risk.MethodsA comprehensive search was undertaken on PubMed, Embase, Cochrane Library, and Web of Science databases up to June 2022. Studies reporting the relationships between IRDs and PD risk were included. Pooled relative risks (RRs) with 95% confidence intervals (CIs) were calculated by using random-effects models.ResultsTwenty-two publications covering seven IRDs containing data from 833,004 patients were identified for quantitative analysis. The pooled results indicated that ankylosing spondylitis (RR = 1.55, 95% CI: 1.31–1.83, I2 = 32.1%, P < 0.001), Sjögren's syndrome (RR = 1.34, 95% CI: 1.22–1.47, I2 = 58.5%, P < 0.001), and Behcet's disease (RR = 1.93, 95% CI: 1.07–3.49, I2 = 57.6%, P = 0.030) were associated with an increased PD risk. However, no significant associations were observed between gout, rheumatoid arthritis, systemic lupus erythematosus, as well as polymyalgia rheumatica and the subsequent development of PD.ConclusionAnkylosing spondylitis, Sjögren's syndrome, and Behcet's disease may increase PD risk.

Smilax glabra Roxb. (SG) is widely used as functional food with various beneficial effects. Fresh SG without processing has been eaten directly for anti-inflammation from ancient China, while the underlying mechanism remains underexplored. This study aims to investigate the anti-inflammatory activity of fresh SG by using metabolites profiles, affinity ultrafiltration mass spectrometry, PDE4 enzyme inhibition assay, and in silico analysis. Encouragingly, fresh SG showed promising anti-inflammatory effect with IC50 value (0.009 μg/μL) on PDE4 was about 12 times higher than that of processed SG (0.110 μg/μL). Astilbin was identified as the main bioactive compound of fresh SG responsible for PDE4 inhibitory activity. We found that heat processing strongly affected astilbin isomerization, leading to significant changes in contents and PDE4 inhibitory activities of four astilbin isomers, resulting in decreased anti-inflammatory activity of fresh SG. This finding will provide theoretical basis for systematic research and food/nutraceutical applications of fresh Smilax glabra in the future.

PurposeThis study aimed to develop and validate a specific risk-stratification nomogram model for the prediction of venous thromboembolism(VTE) in hospitalized patients with lung cancer using readily obtainable demographic, clinical and therapeutic characteristics, thus guiding the individualized decision-making on thromboprophylaxis on the basis of VTE risk levels.MethodsWe performed a retrospective case–control study among newly diagnosed lung cancer patients hospitalized between January 2016 and December 2021. Included in the cohort were 234 patients who developed PTE and 936 non-VTE patients. The patients were randomly divided into the derivation group (70%, 165 VTE patients and 654 non-VTE patients) and the validation group (30%, 69 VTE patients and 282 non-VTE patients). Cut off values were established using a Youden´s Index. Univariate and multivariate regression analyses were used to determine independent risk factors associated with VTE. Variance Inflation Factor(VIF) was used for collinearity diagnosis of the covariates in the model. The model was validated by the consistency index (C-index), receiver operating characteristic curves(ROC) and the calibration plot with the Hosmer-Lemeshow goodness-of-fit test. The clinical utility of the model was assessed through decision curve analysis(DCA). Further, the comparison of nomogram model with current models(Khorana, Caprini, Padua and COMPASS-CAT) was performed by comparing ROC curves using the DeLong’s test.ResultsThe predictive nomogram modle comprised eleven variables: overweight(24-28) defined by body mass index (BMI): [odds ratio (OR): 1.90, 95% confidence interval (CI): 1.19-3.07], adenocarcinoma(OR:3.00, 95% CI: 1.88-4.87), stageIII-IV(OR:2.75, 95%CI: 1.58-4.96), Central venous catheters(CVCs) (OR:4.64, 95%CI: 2.86-7.62), D-dimer levels≥2.06mg/L(OR:5.58, 95%CI:3.54-8.94), PT levels≥11.45sec(OR:2.15, 95% CI:1.32-3.54), Fbg levels≥3.33 g/L(OR:1.76, 95%CI:1.12-2.78), TG levels≥1.37mmol/L (OR:1.88, 95%CI:1.19-2.99), ROS1 rearrangement(OR:2.87, 95%CI:1.74-4.75), chemotherapy history(OR:1.66, 95%CI:1.01-2.70) and radiotherapy history(OR:1.96, 95%CI:1.17-3.29). Collinearity analysis with demonstrated no collinearity among the variables. The resulting model showed good predictive performance in the derivation group (AUC 0.865, 95% CI: 0.832-0.897) and in the validation group(AUC 0.904,95%CI:0.869-0.939). The calibration curve and DCA showed that the risk-stratification nomogram had good consistency and clinical utility. Futher, the area under the ROC curve for the specific VTE risk-stratification nomogram model (0.904; 95% CI:0.869-0.939) was significantly higher than those of the KRS, Caprini, Padua and COMPASS-CAT models(Z=12.087, 11.851, 9.442, 5.340, all P

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better-partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.

Background: Acute kidney injury (AKI) is a severe clinical syndrome, and ischemia–reperfusion injury is an important cause of acute kidney injury. The aim of the present study was to investigate the related genes and pathways in the mouse model of acute kidney injury induced by ischemia–reperfusion injury (IRI-AKI).Method: Two public datasets (GSE39548 and GSE131288) originating from the NCBI Gene Expression Omnibus (GEO) database were analyzed using the R software limma package, and differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) and gene set enrichment analysis (GSEA) were performed using the differentially expressed genes. Furthermore, a protein-protein interaction (PPI) network was constructed to investigate hub genes, and transcription factor (TF)–hub gene and miRNA–hub gene networks were constructed. Drugs and molecular compounds that could interact with hub genes were predicted using the DGIdb.Result: A total of 323 common differentially expressed genes were identified in the renal ischemia–reperfusion injury group compared with the control group. Among these, 260 differentially expressed genes were upregulated and 66 differentially expressed genes were downregulated. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes analysis results showed that these common differentially expressed genes were enriched in positive regulation of cytokine production, muscle tissue development, and other biological processes, indicating that they were involved in mitogen-activated protein kinase (MAPK), PI3K-Akt, TNF, apoptosis, and Epstein–Barr virus infection signaling pathways. Protein-protein interaction analysis showed 10 hub genes, namely, Jun, Stat3, MYC, Cdkn1a, Hif1a, FOS, Atf3, Mdm2, Egr1, and Ddit3. Using the STRUST database, starBase database, and DGIdb database, it was predicted that 34 transcription factors, 161 mi-RNAs, and 299 drugs or molecular compounds might interact with hub genes.Conclusion: Our findings may provide novel potential biomarkers and insights into the pathogenesis of ischemia–reperfusion injury–acute kidney injury through a comprehensive analysis of Gene Expression Omnibus data, which may provide a reliable basis for early diagnosis and treatment of ischemia–reperfusion injury–acute kidney injury.

Magnetoencephalography is a noninvasive neuromagnetic technology to record epileptic activities for the pre-operative localization of epileptogenic zones, which has received increasing attention in the diagnosis and surgery of epilepsy. As reported by recent studies, pathological high frequency oscillations (HFOs), when utilized as a biomarker to localize the epileptogenic zones, result in a significant reduction in seizure frequency, even seizure elimination in around 80% of cases. Thus, objective, rapid, and automatic detection and recommendation of HFOs are highly desirable for clinicians to alleviate the burden of reviewing a large amount of MEG data from a given patient. Despite the advantage, the performance of existing HFOs rarely satisfies the clinical requirement. Consequently, no HFOs have been successfully applied to real clinical applications so far. In this work, we propose a multi-head self-attention-based detector for recommendation, termed MSADR, to detect and recommend HFO signals. Taking advantage of the state-of-the-art multi-head self-attention mechanism in deep learning, the proposed MSADR achieves a more superior accuracy of 88.6% than peer machine learning models in both detection and recommendation tasks. In addition, the robustness of MSADR is also extensively assessed with various ablation tests, results of which further demonstrate the effectiveness and generalizability of the proposed approach.

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field. [ABSTRACT FROM AUTHOR]

Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares—discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2–10%, w/w) and natural plant adulterants (safflower and turmeric at 20–100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R2 and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.

The specific effect of long-term successive biochar application on soil fertility, α-diversity, and composition of bacterial community and their correlations remain unclear. A field experiment was conducted to investigate the effects of straw biochar application on soil physical and chemical properties and the diversity and composition of the bacterial communities in 12 consecutive crop seasons. Four treatments: BC1 (2.25 Mgha−1 biochar), BC5 (11.25 Mgha−1 biochar), RS (2.25 Mgha−1 rice straw), and blank control (CK, without biochar or rice straw) were set up. The results indicated that biochar and rice straw reduced the richness indexes of the soil bacterial community (Chao1 and ACE by 10.3%–27.4% and 12.2%–26.4%, respectively). High-throughput sequencing results showed that the relative abundance of Cyanobacteria, Bacteroidetes, and Actinobacteria increased as the amount of biochar increased, while those of norank_c__Acidobacteria and norank_f__Anaerolineaceae Proteobacteria, Acidobacteria and Nitrospirae decreased. Long-term successive biochar application significantly increased soil pH, available potassium, total organic carbon, total nitrogen, and cation exchange capacity by 5.7%–25.9%, 40.0%–680.0%, 48.0%–217.1%, 51.0%–109.5%, and 5.4%–24.0%, respectively. Soil dissolved organic carbon was slightly decreased by 1.4%–4.0%. Soil pH and C/N were the two major environmental factors affecting the composition of the soil bacterial community according to redundancy analysis. Furthermore, the phylogenetic investigation of communities by reconstruction of unobserved states showed that biochar significantly increased the relative abundance of information on the functions of the metabolism of other amino acids, metabolism of terpenoids and polyketides, and biosynthesis of other secondary metabolites (p < 0.05). Therefore, long-term successive biochar amendment in rice-wheat rotation systems improved soil fertility, altered the structure of the soil bacterial community, and increased the functions of soil bacteria, but decreased the α-diversity of the bacterial community. This study will provide technical and theoretical support for rice-straw carbonization and long-term soil remediation from the perspective of microorganisms.

Neuroblastoma is a common childhood malignancy. Nucleotide excision repair (NER) polymorphisms have been shown to influence cancer susceptibility by modifying DNA repair efficiency. To investigate the association of NER gene polymorphisms with neuroblastoma risk, we constructed a three-center case-control study. A total of 19 candidate single-nucleotide polymorphisms (SNPs) in NER genes were analyzed. Odds ratios (ORs) and 95% confidential intervals (CIs) were calculated to evaluate the associations. We identified five independent SNPs that were significantly associated with neuroblastoma risk, including XPA rs1800975 (dominant model: adjusted OR = 0.73, 95% CI = 0.55–0.98, p = 0.033), XPA rs3176752 (recessive model: adjusted OR = 2.78, 95% CI = 1.12–6.91, p = 0.028), XPD rs3810366 (dominant: adjusted OR = 1.44, 95% CI = 1.05–1.97, p = 0.022; recessive: adjusted OR = 1.58, 95% CI = 1.18–2.11, p = 0.002), XPD rs238406 (dominant: adjusted OR = 0.64, 95% CI = 0.48–0.84, p = 0.002; recessive: adjusted OR = 0.67, 95% CI = 0.48–0.94, p = 0.021), and XPG rs2094258 (recessive: adjusted OR = 1.44, 95% CI = 1.03–2.04, p = 0.036). Stratified analysis was carried out. Furthermore, these findings were strengthened by false-positive report probability (FPRP) analysis and expression quantitative trait loci (eQTL) analysis. In conclusion, our study indicates that five SNPs in NER genes are correlated with neuroblastoma susceptibility in the eastern Chinese population, providing novel insight into the genetic underpinnings of neuroblastoma. However, further large-scale studies are required to verify these findings.

The diazabicyclooctane (DBO) scaffold is the backbone of non-β-lactam-based second generation β-lactamase inhibitors. As part of our efforts, we have synthesized a series of DBO derivatives A1–23 containing amidine substituents at the C2 position of the bicyclic ring. These compounds, alone and in combination with meropenem, were tested against ten bacterial strains for their antibacterial activity in vitro. All compounds did not show antibacterial activity when tested alone (MIC >64 mg/L), however, they exhibited a moderate inhibition activity in the presence of meropenem by lowering its MIC values. The compound A12 proved most potent among the other counterparts against all bacterial species with MIC from

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.

Approximately 32–42% of very preterm infants develop minor motor abnormalities. Earlier diagnosis soon after birth is urgently needed because the first two years of life represent a critical window of opportunity for early neuroplasticity in infants. In this study, we developed a semi-supervised graph convolutional network (GCN) model that is able to simultaneously learn the neuroimaging features of subjects and consider the pairwise similarity between them. The semi-supervised GCN model also allows us to combine labeled data with additional unlabeled data to facilitate model training. We conducted our experiments on a multisite regional cohort of 224 preterm infants (119 labeled subjects and 105 unlabeled subjects) who were born at 32 weeks or earlier from the Cincinnati Infant Neurodevelopment Early Prediction Study. A weighted loss function was applied to mitigate the impact of an imbalanced positive:negative (~1:2) subject ratio in our cohort. With only labeled data, our GCN model achieved an accuracy of 66.4% and an AUC of 0.67 in the early prediction of motor abnormalities, outperforming prior supervised learning models. By taking advantage of additional unlabeled data, the GCN model had significantly better accuracy (68.0%, p = 0.016) and a higher AUC (0.69, p = 0.029). This pilot work suggests that the semi-supervised GCN model can be utilized to aid early prediction of neurodevelopmental deficits in preterm infants.