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Abstract Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4. Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC, on the same complex amplicons such as ecDNA. We characterized a MYC-ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC’s enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Objectives: Hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKp) poses a significant threat to public health. This study reports an infection related to hv-CRKp in a premature infant and reveals its colistin resistance and evolutionary mechanisms within the host. Methods: Three KPC-producing CRKp strains were isolated from a patient with sepsis and CRKp osteoarthritis who had been receiving colistin antimicrobial therapy. The minimum inhibitory concentrations (MICs) of ceftazidime, ceftazidime-avibactam (CAZ-AVI), meropenem, imipenem, tigecycline, amikacin, minocycline, sulfamethoxazole/trimethoprim, ciprofloxacin, levofloxacin, aztreonam, cefepime, cefoperazone/sulbactam, piperacillin/tazobactam, and colistin were determined using the microbroth dilution method. The whole-genome sequencing analysis was conducted to determine the sequence types (STs), virulence genes, and antibiotic resistance genes of the three CRKp strains. Results: Whole-genome sequencing revealed that all three CRKp strains belonged to the ST11 clone and carried a plasmid encoding blaKPC-2. The three strains all possessed the iucABCDiutA virulence cluster, peg-344 gene, and rmpA/rmpA2 genes, defining them as hv-CRKp. Further experiments and whole-genome analysis revealed that a strain of K. pneuomniae had developed resistance to colistin. The mechanism found to be responsible for colistin resistance was a deletion mutation of approximately 9000 bp including the mgrB gene. Conclusion: This study characterizes colistin resistance of the ST11 clone hv-CRKp during colistin treatment and its rapid evolution within the host.

Abstract Background Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions. Method In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models. Results The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models. Conclusion The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

Abstract China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This method uses a feature space of six integrals from the crops’ phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Osteochondral lesions are common pathological alterations in synovial joints. Different techniques have been designed to achieve osteochondral repair, and tissue-engineered osteochondral grafts have shown the most promise. Histological assessments and related scoring systems are crucial for evaluating the quality of regenerated tissue, and the interpretation and comparison of various repair techniques require the establishment of a reliable and widely accepted histological method. To date, there is still no consensus on the type of histological assessment and scoring system that should be used for osteochondral repair. In this review, we summarize common osteochondral staining methods, discuss the criteria regarding high-quality histological images, and assess the current histological scoring systems for osteochondral regeneration. Safranin O/Fast green is the most widely used staining method for the cartilage layer, whereas Gomori and Van Gieson staining detect new bone formation. We suggest including the graft–host interface and more sections together with the basic histological information for images. An ideal scoring system should analyze both the cartilage and bone regions, especially for the subchondral bone plate. Furthermore, histological assessments should be performed over a longer period of time to minimize discrepancies caused by defect size and animal species.

Abstract Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.

Abstract The t(X,17) chromosomal translocation, generating the ASPSCR1::TFE3 fusion oncoprotein, is the singular genetic driver of alveolar soft part sarcoma (ASPS) and some Xp11-rearranged renal cell carcinomas (RCCs), frustrating efforts to identify therapeutic targets for these rare cancers. Here, proteomic analysis identifies VCP/p97, an AAA+ ATPase with known segregase function, as strongly enriched in co-immunoprecipitated nuclear complexes with ASPSCR1::TFE3. We demonstrate that VCP is a likely obligate co-factor of ASPSCR1::TFE3, one of the only such fusion oncoprotein co-factors identified in cancer biology. Specifically, VCP co-distributes with ASPSCR1::TFE3 across chromatin in association with enhancers genome-wide. VCP presence, its hexameric assembly, and its enzymatic function orchestrate the oncogenic transcriptional signature of ASPSCR1::TFE3, by facilitating assembly of higher-order chromatin conformation structures demonstrated by HiChIP. Finally, ASPSCR1::TFE3 and VCP demonstrate co-dependence for cancer cell proliferation and tumorigenesis in vitro and in ASPS and RCC mouse models, underscoring VCP’s potential as a novel therapeutic target.

Timely and accurate crop yield estimation is crucial for managing crops, trade, and food security. The combination of remote sensing technology with machine learning methods is increasingly popular for global yield prediction. However, traditional machine learning methods rely on data correlation rather than causality, leading to poor interpretability. To address this issue, we propose a novel approach that combines a structural causal model (SCM) with deep learning to develop a causal graph attention network model (SCM-GAT) based on causal relationships for soybean yield prediction at the county level of 10 major soybean-producing states in the United States. The SCM-GAT model considers not only conventional vegetation indices and weather variables but also the causal relationships between variables as inputs. Using independent validation and five-fold cross-validation strategies, our results show that the predictive performance of the SCM-GAT model is not only superior to traditional prediction models, such as selection operator regression (LASSO), random forest, but also superior to the common deep learning models based on correlation relationships, such as long short-term memory network, and transformer. It has also proved to be more robust than other models under extreme weather events. In addition, we identify branching to blooming and blooming to setting pods as the key growth phases for soybean yield, and adding these phases as inputs to the model further improves prediction accuracy. Our findings suggest that incorporating causal relationships in deep learning models can improve prediction accuracy. This study provides a novel approach for remote sensing prediction of crop yield.

In recent years, change detection (CD) methods have faced challenges in being applied to various types of remote sensing datasets and related research fields, particularly in the domain of CD in remote sensing images. While convolutional neural networks (CNNs) have significantly advanced CD in remote sensing images, they struggle with modeling long-distance dependencies between image pairs, leading to poor recognition of semantically similar objects with different features. Meanwhile, transformer technology has gained widespread popularity for global applications, but it lacks in extracting local features effectively. Current approaches typically rely on single or dual-branch network structures for mining change-related features in remote sensing images, yet they still lack in extracting both local and global features comprehensively. To address these issues, this article proposes a triple-branch network combining transformer and CNN, comprising CNN, transformer, and channel feature-guided branch. These branches extract and fuse three types of change features from both global and local perspectives. Importantly, the channel feature-guided branch is introduced to capture continuous and detailed change relationship features, thus enhancing the model's change discrimination ability. Experimental results on three datasets (LEVIR-CD, WHU-CD, and GZ-CD) demonstrate the superior performance of the model over state-of-the-art methods.

Abstract Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.

Introduction: Previous studies have evaluated metagenomic next-generation sequencing (mNGS) of cell-free DNA (cfDNA) for pathogen detection in blood and body fluid samples. However, no study has assessed the diagnostic efficacy of mNGS using cellular DNA. Objectives: This is the first study to systematically evaluate the efficacy of cfDNA and cellular DNA mNGS for pathogen detection. Methods: A panel of seven microorganisms was used to compare cfDNA and cellular DNA mNGS assays concerning limits of detection (LoD), linearity, robustness to interference, and precision. In total, 248 specimens were collected between December 2020 and December 2021. The medical records of all the patients were reviewed. These specimens were analysed using cfDNA and cellular DNA mNGS assays, and the mNGS results were confirmed using viral qPCR, 16S rRNA, and internal transcribed spacer (ITS) amplicon next-generation sequencing. Results: The LoD of cfDNA and cellular DNA mNGS was 9.3 to 149 genome equivalents (GE)/mL and 27 to 466 colony-forming units (CFU)/mL, respectively. The intra- and inter-assay reproducibility of cfDNA and cellular DNA mNGS was 100%. Clinical evaluation revealed that cfDNA mNGS was good at detecting the virus in blood samples (receiver operating characteristic (ROC) area under the curve (AUC), 0.9814). In contrast, the performance of cellular DNA mNGS was better than that of cfDNA mNGS in high host background samples. Overall, the diagnostic efficacy of cfDNA combined with cellular DNA mNGS (ROC AUC, 0.8583) was higher than that of cfDNA (ROC AUC, 0.8041) or cellular DNA alone (ROC AUC, 0.7545). Conclusion: Overall, cfDNA mNGS is good for detecting viruses, and cellular DNA mNGS is suitable for high host background samples. The diagnostic efficacy was higher when cfDNA and cellular DNA mNGS were combined.

Chinese coal reservoirs are characterized by “Three High and One Low”, and the pore structure of coal is an important factor affecting the permeability, the degree of drainage difficulty and the subsequent drainage mode. In order to accurately characterize the full scale pore size structure of coal, three methods of high pressure mercury injection, low temperature liquid nitrogen and carbon dioxide adsorption are used. Based on the compressibility correction of the high pressure mercury injection experimental data, combined with the fractal theory, the dominant pore size characterization sections of different experimental methods are analyzed, and the full scale joint characterization of 10 groups of coal samples in Yunnan Laochang mining area is realized. The results show that: The compression coefficient of coal matrix ranges from 7.06179×10−11 to 1.23531×10−10 m2/N, and the cumulative mercury intake after correction is reduced by 15.89% ～ 27.82% compared with that before correction, with an average of 21.65%. The coal matrix begins to be compressed when the mercury injection pressure reaches 0.7 MPa. Irreversible damage occurs when the mercury injection pressure reaches 14.3 MPa. In the liquid nitrogen adsorption experiment, the pore volume increases gradually with the increase of the pore size on the whole, the pore area tends to be stable and the adsorption volume is small, and the pore adsorption rate is the fastest. The overall rate decreases with the increase of the pore size. The adsorption volume curve and differential curve of carbon dioxide adsorption experiment stage are basically consistent, and the pore volume decreases with the increase of pore size. The mercury injection fractal curve can be divided into three sections. When lgPi > 1.1, the fractal dimension reaches the maximum, and the coal matrix suffers irreversible compression failure. The low temperature liquid nitrogen adsorption fractal curve takes Pj/Ps =0.5 as the boundary line. The fractal dimension of 2−5 nm pore size is between 1.93−2.92, with an average value of 2.57. The fractal dimension of 5−100 nm pore diameter is 2.16−2.76, with an average value of 2.49, indicating strong heterogeneity. Based on the three experimental results, with 1.5nm and 100nm as the splicing points, the coal samples are dominated by micropores with pore sizes ranging from 0.41nm to 0.86nm, with the largest proportion of pore volume.

Abstract This work proposes a storage element (SE) design for in‐memory computing (IMC). Using the proposed SE design, an IMC array has been constructed to enable in‐situ updates of stored weights. Compared with some existing related works which employ 2n bit cells for storing an n‐bit value, the proposed structure in this work exhibits a O(n) complexity of area overhead, which means that only n bit cells are needed to store n‐bit values, offering a significant improvement in silicon area usage. Compared to the purely digitally‐controlled weight update schemes, the approach proposed in this work demonstrates an approximate 1.47× increase in power consumption. Furthermore, it exhibits superior robustness compared to existing work. Additionally, the proposed SE design can achieve the necessary shift functionality in the cutting‐edge floating‐point (FP)‐IMC architecture through simple control mechanisms.

Background Hyperuricemia is a common complication of type 2 diabetes mellitus and can lead to serious consequences such as gout and kidney disease. Methods Patients with type 2 diabetes mellitus from six different communities in Fuzhou were recruited from June to December 2022. Questionnaires, physical examinations, and laboratory tests were conducted to collect data on various variables. Variable screening steps were performed using univariate and multivariate stepwise regression, least absolute shrinkage and selection operator (LASSO) regression, and Boruta feature selection. The dataset was divided into a training-testing set (80%) and an independent validation set (20%). Six machine learning models were built and validated. Results A total of 8243 patients with type 2 diabetes mellitus were included in this study. According to Occam's razor method, the LASSO regression algorithm was determined to be the optimal risk factors selection method, and nine variables were identified as parameters for the risk assessment model. The absence of diabetes medication and elevated fasting blood glucose levels exhibited a negative correlation with the risk of hyperuricemia. Conversely, seven other variables demonstrated a positive association with the risk of hyperuricemia among patients diagnosed with type 2 diabetes mellitus. Among the six machine learning models, the artificial neural network (ANN) model demonstrated the highest performance. It achieved an areas under curve of 0.736, accuracy of 68.3%, sensitivity of 65.0%, specificity of 72.2%, precision of 73.6% and F1-score of 69.0%. Conclusions We developed an ANN model to better evaluate the risk of hyperuricemia in the type 2 diabetes population. In the type 2 diabetes population, women should pay particular attention to their uric acid levels, and type 2 diabetics should not neglect their obesity level, blood pressure, kidney function and lipid profile during their regular medical check-ups, in order to do their best to avoid the risks associated with the combination of type 2 diabetes and hyperuricemia.

BackgroundIn recent years, an increasing number of observational studies have reported the impact of air pollution on autoimmune diseases (ADs). However, no Mendelian randomization (MR) studies have been conducted to investigate the causal relationships. To enhance our understanding of causality, we examined the causal relationships between particulate matter (PM) and nitrogen oxides (NOx) and ADs.MethodsWe utilized genome-wide association study (GWAS) data on PM and NOx from the UK Biobank in European and East Asian populations. We also extracted integrated GWAS data from the Finnish consortium and the Japanese Biobank for two-sample MR analysis. We employed inverse variance weighted (IVW) analysis to assess the causal relationship between PM and NOx exposure and ADs. Additionally, we conducted supplementary analyses using four methods, including IVW (fixed effects), weighted median, weighted mode, and simple mode, to further investigate this relationship.ResultsIn the European population, the results of MR analysis suggested a statistically significant association between PM2.5 and psoriasis only (OR = 3.86; 95% CI: 1.89–7.88; PIVW 0.025). There was no pleiotropy in the results.ConclusionOur results suggest a causal association between PM2.5 and psoriasis in European populations. With the help of air pollution prevention and control, the harmful progression of psoriasis may be slowed.

IntroductionThe APETALA2/ethylene response factor (AP2/ERF) superfamily plays a significant role in regulating plant gene expression in response to growth and development. To date, there have been no studies into whether the ramie AP2/ERF genes are involved in the regulation of flower development.MethodsHere, 84 BnAP2/ERF members were identified from the ramie genome database, and various bioinformatics data on the AP2/ERF gene family, structure, replication, promoters and regulatory networks were analysed. BnAP2-12 was transferred into Arabidopsis through the flower-dipping method.ResultsPhylogenetic analysis classified the 84 BnAP2/ERF members into four subfamilies: AP2 (18), RAV (3), ERF (42), and DREB (21). The functional domain analysis of genes revealed 10 conserved motifs. Genetic mapping localised the 84 members on 14 chromosomes, among which chromosomes 1, 3, 5, and 8 had more members. Collinearity analysis revealed that 43.37% possibly resulted from replication events during the evolution of the ramie genome. Promoter sequence analysis identified classified cis-acting elements associated with plant growth and development, and responses to stress, hormones, and light. Transcriptomic comparison identified 3,635 differentially expressed genes (DEGs) between male and female flowers (1,803 and 1,832 upregulated and downregulated genes, respectively). Kyoto Encyclopaedia of Genes and Genomes pathway analysis categorised DEGs involved in metabolic pathways and biosynthesis of secondary metabolites. Gene Ontology enrichment analysis further identified enriched genes associated with pollen and female gamete formations. Of the 84 BnAP2/ERFs genes, 22 and 8 upregulated and downregulated genes, respectively, were present in female flowers. Co-expression network analysis identified AP2/ERF members associated with flower development, including BnAP2-12. Subcellular localisation analysis showed that the BnAP2-12 protein is localised in the nucleus and cell membrane. Overexpression BnAP2-12 delayed the flowering time of Arabidopsis thaliana.ConclusionThese findings provide insights into the mechanism of ramie flower development.

ObjectiveTo identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods.MethodsPlasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models.ResultsIn the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study.ConclusionIn the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Abstract Land surface phenology (LSP) products are currently of large uncertainties due to cloud contaminations and other impacts in temporal satellite observations and they have been poorly validated because of the lack of spatially comparable ground measurements. This study provided a reference dataset of gap-free time series and phenological dates by fusing the Harmonized Landsat 8 and Sentinel-2 (HLS) observations with near-surface PhenoCam time series for 78 regions of 10 × 10 km2 across ecosystems in North America during 2019 and 2020. The HLS-PhenoCam LSP (HP-LSP) reference dataset at 30 m pixels is composed of: (1) 3-day synthetic gap-free EVI2 (two-band Enhanced Vegetation Index) time series that are physically meaningful to monitor the vegetation development across heterogeneous levels, train models (e.g., machine learning) for land surface mapping, and extract phenometrics from various methods; and (2) four key phenological dates (accuracy ≤5 days) that are spatially continuous and scalable, which are applicable to validate various satellite-based phenology products (e.g., global MODIS/VIIRS LSP), develop phenological models, and analyze climate impacts on terrestrial ecosystems.

The properties of high-entropy alloys (HEAs) depend primarily on the composition and content of elements. However, getting the optimal composition of alloying elements through the traditional ''trial and error'' method is challenging, especially for non-equiatomic HEAs with a wide range of composition space. In this study, based on the knowledge that stacking fault energy (SFE) is the most crucial intrinsic property to determine the deformation mechanism and to optimize the mechanical properties of FCC HEAs, classical machine learning classification models including support vector classification (SVC) and random forest (RF), and deep learning regression model (Back Propagation Neural Network) were established to predict the stacking fault energy of Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloys. These models can obtain the SFE data of any atomic ratio composition of the FCC structured Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloy quickly and accurately. The high accuracy of these models indicates that using the compositions as features to predict stacking fault energy is feasible. Meanwhile, the monotonic relationship between alloying elements and SFE makes it possible to change the SFE of high-entropy alloy by fine-tuning the composition to realize the control of material deformation mechanism and mechanical properties. Component-based machine learning models provide a new method for rapidly discovering high-entropy alloys with exceptional strength and flexibility.

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height.

Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics.

ObjectivesStaphylococcal small-colony variants (SCVs) are common in cardiac implantable electronic device (CIED) infections. This is the first retrospective and multi-case study on CIED infections due to staphylococcal SCVs, aiming to provide a theoretical basis for the clinical management of CIED and device-related infections caused by staphylococcal SCVs.MethodsNinety patients with culture positive CIED infections were enrolled between 2021 and 2022. We compared the demographic and clinical characteristics of patients with and without SCVs and performed genomic studies on SCVs isolates.ResultsCompared to patients without SCVs, those with SCVs had a longer primary pacemaker implantation time and were more likely to have a history of device replacement and infection. They showed upregulated inflammatory indicators, especially higher NEUT% (52.6 vs. 26.8%, P = 0.032) and they had longer hospital stays (median 13 vs. 12 days, P = 0.012). Comparative genomics analysis was performed on Staphylococcus epidermidis wild-type and SCVs. Some genes were identified, including aap, genes encoding adhesin, CHAP domain-containing protein, LPXTG cell wall anchor domain-containing protein, and YSIRK-type signal peptide-containing protein.ConclusionStaphylococcal SCVs affect the clinical characteristics of CIED infections. The process of staphylococcal SCVs adherence, biofilm formation, and interaction with neutrophils play a vital role.

Abstract Obesity/overweight and lipid metabolism disorders have become increased risk factors for lung cancer. Fatty acid translocase CD36 promotes cellular uptake of fatty acids. Whether and how CD36 facilitates lung adenocarcinoma (LUAD) growth in high-fat environment is unknown. Here, we demonstrated that palmitic acid (PA) or high-fat diet (HFD) promoted LUAD cell proliferation and metastasis in a CD36-dependent manner. Mechanistically, CD36 translocated from cytoplasm to cell membrane and interacted with Src kinase upon PA stimulation in human LUAD cells. Akt and ERK, downstream of Src, were then activated to mediate LUAD cell proliferation and metastasis. Furthermore, PA treatment promoted CD36 sarcolemmal translocation, where it activated Rac1 and upregulated MMP-9 through Src-Akt/ERK pathway, resulting in redistribution of cortactin, N-WASP and Arp2/3, and finally led to occurrence of finger-like protrusions of actin on cell surface to enhance cell metastasis. Compared with normal-chew diet (NCD) mice, the HFD group exhibited higher level of blood free fatty acid (FFA) and cholesterol (TC), developed larger xenograft LUAD tumors and enhanced tumor cell metastatic potential, which were accompanied by obvious sarcolemmal actin remodeling and were blocked by simultaneous CD36 knockdown in LUAD cells. Consistently, xenografted and tail vein-injected scramble-RNA-A549 cells but not CD36-shRNA-A549 in HFD mice formed metastatic LUAD tumors on the lung. CD36 inhibitor SSO significantly inhibited LUAD cell metastasis to the lung. Collectively, CD36 initiates Src signaling to promote LUAD cell proliferation and actin remodeling-involved metastasis under high-fat environment. Our study provides the new insights that CD36 is a valid target for LUAD therapy.

Abstract Background Porcine epidemic diarrhea (PED) is a contagious intestinal disease caused by porcine epidemic diarrhea virus (PEDV) characterized by vomiting, diarrhea, anorexia, and dehydration, which has caused huge economic losses around the world. However, it is very hard to find completely valid approaches to control the transmission of PEDV. At present, vaccine immunity remains the most effective method. To better control the spread of PED and evaluate the validity of different immunization strategies, 240 PED outbreak cases from 577 swine breeding farms were collected and analyzed. The objective of the present study was to analyze the epidemic regularity of PEDV and evaluate two kinds of different immunization strategies for controlling PED. Results The results showed that the main reasons which led to the outbreak of PED were the movement of pig herds between different pig farms (41.7%) and delaying piglets from the normal production flow (15.8%). The prevalence of PEDV in the hot season (May to October) was obviously higher than that in the cold season (January to April, November to December). Results of different vaccine immunity cases showed that immunization with the highly virulent live vaccine (NH-TA2020 strain) and the commercial inactivated vaccine could significantly decrease the frequency of swine breeding farms (5.9%), the duration of PED epidemic (1.70 weeks), and the week batches of dead piglets (0.48 weeks weaned piglets), compared with immunization with commercial attenuated vaccines and inactivated vaccine of PED. Meanwhile, immunization with the highly virulent live vaccine and the commercial inactivated vaccine could bring us more cash flows of Y̶275,274 per year than immunization with commercial live attenuated vaccine and inactivated vaccine in one 3000 sow pig farm within one year. Conclusion Therefore, immunization with highly virulent live vaccine and inactivated vaccine of PED is more effective and economical in the prevention and control of PED in the large-scale swine farming system.

Abstract As a enrichment plant, ramie can be used for the phytoremediation of cadmium (Cd)-contaminated soil. However, it is worth exploring the role of plant growth regulators and foliar fertilizers in the process of plant growth and development and Cd adsorption. By measuring the agronomic traits, Cd content of aboveground and underground ramie, calculating the Cd transfer coefficient (TF) and Cd bioconcentration factors (BCF), and the correlation between various indicators. This study examined the effects of plant growth regulators and foliar fertilizers on ramie’s capacity for Cd accumulation and transportation. Plant growth regulators and foliar fertilizers increased the Cd content of the aboveground ramie, reduced the Cd content of the underground ramie, and increased the TF. Among them, GA-1 increased the Cd content of the aboveground ramie to 3 times more than that of the control and reduced the Cd content of the underground ramie by 54.76%. Salicylic acid (SA) increased the Cd content of the aboveground ramie to three times more than that of the control. The combination of GA and foliar fertilizer reduced the Cd content of the aboveground and underground ramie and the TF and BCF of the underground ramie. After the hormones were sprayed, the TF of ramie had a significant positive correlation with the Cd content of the aboveground ramie; the BCF of the aboveground ramie had a significant positive correlation with the Cd content and TF of the aboveground ramie. The results indicate that Brassinolide (BR), gibberellin (GA), ethephon (ETH), polyamines (PAs), and salicylic acid (SA) have different effects on the enrichment and transport of Cd in ramie. This study provided an effective method to improve the capacity for ramie to adsorb heavy metals during cultivation.

The lateral thrust device is a typical high-pressure sealed cavity structure with dual O-rings. Because the O-ring is easily damaged during the assembly process, the product quality is unqualified. To achieve high-precision assembly for this structure, this paper proposes a reinforcement learning assembly research method based on O-ring simulation. First, a simulation study of the damage mechanism during O-ring assembly is conducted using finite element software to obtain damage data under different deformation conditions. Secondly, deep reinforcement learning is used to plan the assembly path, resulting in high-precision assembly paths for the inner and outer cylinder under different initial poses. Experimental results demonstrate that the above method not only effectively solves the problem that the O-ring is easily damaged but also provides a novel, efficient, and practical assembly technique for similar high-precision assemblies.

Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R2 = 0.71, MAPE = 15.78%) than that for the model without turbidity (R2 = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05).

In this work, microstructural characteristics, dynamic recrystallization and mechanical properties of the hot-rolled AZ31B were investigated. The results indicated that the different mechanisms of recrystallization are responsible for the microstructural evolution during the rolling deformation. It was found, as for the specimens with the rolling reduction of 30%, large number of twins are formed within the deformed matrix and the recrystallized grains are usually formed nearby the twins. This indicated that the mechanism of the twin-induced nucleation mainly dominates the recrystallization process of the specimens with the 30% reduction. As for the specimens with the rolling reduction of 75%, the recrystallized grains are nucleated on the bulged boundaries or within the deformed grains. This indicates that the mechanisms of the stress-induced grain boundary migration (SIBM) and the sub-grain growth mainly dominate the recrystallization process of the specimens with the reduction of 75%. The recrystallization process contributes to the grain refinement and to the improvement of mechanical properties of alloys. Eventually, the specimens with the rolling reduction of 75% obtain an excellent strength-ductility balance. The results of this work not only elaborate underlying knowledge on the correlation relationship between the recrystallization mechanisms and the mechanical properties, but provide an effective approach to optimize the microstructure-mechanical properties of alloys.

The application of Ti(C,N)-based cermets remains limited due to their low toughness. The fracture toughness of cermets can be significantly improved by using advanced sintering methods and appropriate additives. Here, cermets were prepared by spark plasma sintering (SPS) technology with graphene nanosheets (GNSs) and nano-scale tungsten carbide (WC) as additives. The effects of GNSs and sintering conditions on the microstructure, mechanical properties, and magnetic properties of Ti(C,N)-based cermets were studied. The results show that the sample with 0.6 wt% GNSs fabricated at 1450 °C, 8 min, and 35 MPa has a denser microstructure and better mechanical properties. The cermet has a Vickers hardness of HV 2415.40 and fracture toughness of 10.6 MPa·m1/2. Compared with the specimen without GNSs, the hardness and fracture toughness are significantly improved by 44.58% and 92.73%, respectively. A certain amount of GNSs can increase the movement of hard phases in the liquid phase, inhibiting their solubility and grain growth, and greatly improving the fracture toughness of cermets. The addition of nano-WC is conducive to promoting the formation of a (Ti,W) (C,N) rim phase and improving properties of cermets. SPS can activate particle surfaces and promote the densification of cermets.

Objective: This study aimed to screen and identify chitinase-producing strains and optimize the fermentation conditions of the target bacteria, which were eventually applied to the degradation fermentation of shrimp shells. Methods: The chitin degradation bacteria were screened on chitin hydrolysis zone taking marine mud from beach in Yancheng as samples, identified via bioinformatics technology, and analyzed in the form of phylogenetic tree. Fermentation conditions for the target strains were modified depending on the single-factor experiment. Results: Photobacterium sp. LYM-1, Vibrio sp. WM-1, and Shewanella sp. ZXY-1 were isolated and identified via 16S rDNA sequence analysis. The optimal fermentation conditions were determined as follows: For Photobacterium sp. LYM-1, chitin (10 g/L) as the carbon source, NH4Cl (2.0 g/L) as the nitrogen source, the inoculated quantified of 3%, initial medium pH6.5, and 32 ℃ for 1 d, in which the highest chitinase activity in the supernatant reached 15.37±0.55 U/mL, achieving 4.37 times higher than that before optimization. For Vibrio sp. WM-1, chitin (10 g/L) as the carbon source, NH4Cl (2.0 g/L) as the nitrogen source, the inoculated quantified of 3%, initial medium pH7.5, and 22 ℃ for 2 d, in which the highest chitinase activity reached 40.82±6.03 U/mL, achieving 1.60 times higher than that before optimization. For Shewanella sp. ZXY-1, chitin (10 g/L) as the carbon source, (NH4)2SO4 (2.0 g/L) as the nitrogen source, the inoculated quantified of 3%, initial medium pH6.5, and 22 ℃ for 1 d, which contributed the highest chitinase activity to reaching 25.64±3.29 U/mL，achieving 2.47 times higher than that before optimization. All three strains in this study could realize chitinase production under optimized conditions using shrimp shell powder as the sole substrate. The chitinase activities of Photobacterium sp. LYM-1, Vibrio sp. WM-1, and Shewanella sp. ZXY-1 on shrimp shell powders were 10.25±0.95, 32.16±2.25 and 21.81±4.27 U/mL, respectively, which was lower than those on chitin powder. Conclusion: Three chitinase-producing strains were successfully screened from saline-alkali soil, of which the enzyme activity was elevated after optimization. All of them could produce chitinase with the shrimp shell utilized as the substrate, serving as potential strains for chitinase production from the fermentation of shrimp shell.

Summary: Phosphatidylglycerol (PG) is a mitochondrial phospholipid required for mitochondrial cristae structure and cardiolipin synthesis. PG must be remodeled to its mature form at the endoplasmic reticulum (ER) after mitochondrial biosynthesis to achieve its biological functions. Defective PG remodeling causes MEGDEL (non-alcohol fatty liver disease and 3-methylglutaconic aciduria with deafness, encephalopathy, and Leigh-like) syndrome through poorly defined mechanisms. Here, we identify LPGAT1, an acyltransferase that catalyzes PG remodeling, as a candidate gene for MEGDEL syndrome. We show that PG remodeling by LPGAT1 at the ER is closely coordinated with mitochondrial transport through interaction with the prohibitin/TIMM14 mitochondrial import motor. Accordingly, ablation of LPGAT1 or TIMM14 not only causes aberrant fatty acyl compositions but also ER retention of newly remodeled PG, leading to profound loss in mitochondrial crista structure and respiration. Consequently, genetic deletion of the LPGAT1 in mice leads to cardinal features of MEGDEL syndrome, including 3-methylglutaconic aciduria, deafness, dilated cardiomyopathy, and premature death, which are highly reminiscent of those caused by TIMM14 mutations in humans.

Summary: Cardiac fibrosis is a major type of adverse remodeling, predisposing the disease progression to ultimate heart failure. However, the complexity of pathogenesis has hampered the development of therapies. One of the key mechanisms of cardiac diseases has recently been identified as long non-coding RNA (lncRNA) dysregulation. Through in vitro and in vivo studies, we identified an lncRNA NONMMUT067673.2, which is named as a cardiac fibrosis related lncRNA (CFRL). CFRL was significantly increased in both mouse model and cell model of cardiac fibrosis. In vitro, CFRL was proved to promote the proliferation and migration of cardiac fibroblasts by competitively binding miR-3113-5p and miR-3473d and indirectly up-regulating both CTGF and FN1. In vivo, silencing CFRL significantly mitigated cardiac fibrosis and improved left ventricular function. In short, CFRL may exert an essential role in cardiac fibrosis and interfering with CFRL might be considered as a multitarget strategy for cardiac fibrosis and heart failure.

Objective To analyze the value of postoperative human leukocyte antigen-DR (HLA-DR) expression and high mobility group box 1 (HMGB1) level in predictive diagnosis of postoperative sepsis for patients with percutaneous nephrolithotomy (PCNL) surgery.Methods The present prospective observational study included 387 patients with renal calculus who received PCNL surgery from January 2017 to October 2020 in our hospital. After exclusion criteria, 33 patients with sepsis and 78 patients with no sepsis remained. All patients received PCNL surgery. Sepsis definition is according to the third international consensus definitions for sepsis and septic shock (Sepsis-3). The data of the HMGB1, c-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and HLA-DR expression were collected within admission and 24 h and 72 h after surgery. Postoperative HMGB1 levels and HLA-DR expression at 24 h and 72 h were respectively compared between the two groups using t test. ROC cure was used to analyze the value of postoperative HLA-DR expression and HMGB1 level in predictive diagnosis of sepsis.Results The positive rate of urine culture and the time of hospitalization time in patients with sepsis were significantly higher than those in patients with no sepsis. Sepsis group had higher levels of HMGB1 at post-24 h ((93.07 ± 11.37) ng/mL vs (75.41 ± 4.85) ng/mL), p

Summary: Smoking carcinogen nicotine-derived nitrosamine ketone (NNK) is the most potent contributor to lung adenocarcinoma (LUAD) development, but the mechanism has not been fully elucidated. Here, we reported that fatty acid translocase CD36 was significantly overexpressed in both human LUAD tissues and NNK-induced A/J mice LUAD tumors. The overexpressed CD36 was positively correlated with Src kinase activation, smoking status, metastasis, and worse overall survival of patients with smoking history. Upon NNK binding with α7 nicotinic acetylcholine receptor (α7nAChR), sarcolemmal CD36 was increased and it interacted with surface α7nAChR and cytosol Src simultaneously, which in turn activated Src and downstream pro-carcinogenic kinase ERK1/2 and Akt, and finally caused LUAD cells to form subcutaneous and pulmonary metastatic tumors. This process could be blocked by CD36 knockdown and CD36 irreversible inhibitor SSO. Furthermore, the effect of NNK was inhibited obviously in CD36−/− A/J mice. Thus, targeting CD36 may provide a breakthrough therapy of LUAD.

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by lidar sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor collects data systematically over the Earth's surface from the International Space Station. Since GEDI became operational, it has collected billions of ∼25-m diameter footprints. This massive dataset has been used to create higher level gridded and non-grided products. However, GEDI's ∼25-m footprints can be subject to errors associated with effects of geolocation, terrain slope, and beam sensitivity, among others, which are likely transferred to the downstream products. This study aims to (1) evaluate the effect of beam sensitivity and terrain slope on the accuracy of relative heights (RH) of GEDI product L2A version 2 through comparison with discrete-return airborne lidar data collected over transects in the Brazilian Amazon Forest biome, (2) assess GEDI's geolocation uncertainty and investigate its combined effects with beam sensitivity and terrain slope, and (3) re-evaluate beam sensitivity and terrain slope effects on the GEDI L2A RHs using footprints that were geolocation-adjusted through a simple novel approach. The analysis separates GEDI footprints by acquisition time, i.e., daytime, nighttime, and combined (all-data). The discrete-return airborne lidar point clouds are used to derive terrain slope within the GEDI footprints and to simulate GEDI waveforms and derive RHs comparable to the GEDI L2A product. Results indicate that terrain slope only causes significant effects on GEDI data collected during daytime because solar radiation affects waveform signal-to-noise ratio. Beam sensitivity causes significant effects on nighttime and all-data GEDI L2A RHs. If geolocation uncertainty is considered, the effects of beam sensitivity and terrain slope have just minor changes. Geolocation-adjusted data continue showing significant effects on nighttime and all-data RH differences caused by beam sensitivity but produce unbiased results. This study improves the understanding of how beam sensitivity, terrain slope, and their combined effect with geolocation uncertainty may affect the GEDI L2A RH collected over the Brazilian Amazon forest during daytime and nighttime.

Background The impact of heat waves and atmospheric oxidising pollutants on residential mortality within the framework of global climate change has become increasingly important. Objective In this research, the interactive effects of heat waves and oxidising pollutants on the risk of residential mortality in Fuzhou were examined. Methods We collected environmental, meteorological, and residential mortality data in Fuzhou from 1 January 2016, to 31 December 2021. We then applied a generalised additive model, distributed lagged nonlinear model, and bivariate three-dimensional model to investigate the effects and interactions of various atmospheric oxidising pollutants and heat waves on the risk of residential mortality. Results Atmospheric oxidising pollutants increased the risk of residential mortality at lower concentrations, and O3 and Ox were positively associated with a maximum risk of 2.19% (95% CI: 0.74–3.66) and 1.29% (95% CI: 0.51–2.08). The risk of residential mortality increased with increasing temperature, with a strong and long-lasting effect and a maximum cumulative lagged effect of 1.11% (95% CI: 1.01, 1.23). Furthermore, an interaction between atmospheric oxidising pollutants and heat waves may have occurred: the larger effects in the longest cumulative lag time on residential mortality per 10 µg/m3 increase in O3, NO2 and Ox during heat waves compared to non-heat waves were [−3.81% (95% CI: −14.82, 8.63)]; [−0.45% (95% CI: −2.67, 1.81)]; [67.90% (95% CI: 11.55, 152.71)]; 16.37% (95% CI: 2.43, 32.20)]; [−3.00% (95% CI: −20.80, 18.79)]; [−0.30% (95% CI: −3.53, 3.04)]. The risk on heat wave days was significantly higher than that on non-heat wave days and higher than the separate effects of oxidising pollutants and heat waves. Conclusions Overall, we found some evidence suggesting that heat waves increase the impact of oxidising atmospheric pollutants on residential mortality to some extent.

Abstract Our previous studies showed that dysregulation of the long noncoding RNA (lncRNA) HOXA11-AS plays an important role in the development of glioma. However, the molecular mechanism of HOXA11-AS in glioma remains largely unknown. In this study, we explore the molecular mechanisms underlying abnormal expression and biological function of HOXA11-AS for identifying novel therapeutic targets in glioma. The expression of HOXA11-AS, and the relationship between HOXA11-AS and the prognosis of glioma patients were analyzed using databases and glioma samples. Transcriptomics, proteomics, RIP, ChIRP, luciferase, and ChIP assays were used to explore its upstream and downstream targets in glioma. The role of HOXA11-AS in regulating the sensitivity of glioma cells to reactive oxygen species (ROS) was also investigated in vitro and in vivo. We found that HOXA11-AS was significantly upregulated in glioma, and was correlated with the poor prognosis of glioma patients. Ectopic expression of HOXA11-AS promoted the proliferation, migration, and invasion of glioma cells in vitro and in vivo. Mechanistically, HOXA11-AS acted as a molecular sponge for let-7b-5p in the cytoplasm, antagonizing its ability to repress the expression of CTHRC1, which activates the β-catenin/c-Myc pathway. In addition, c-Myc was involved in HOXA11-AS dysregulation via binding to its promoter region to form a self-activating loop. HOXA11-AS, functioned as a scaffold in the nucleus, also recruited transcription factor c-Jun to the Tpl2 promoter, which activates the Tpl2-MEK1/2-ERK1/2 pathway to promote ROS resistance in glioma. Importantly, HOXA11-AS knockdown could sensitize glioma cells to ROS. Above, oncogenic HOXA11-AS upregulates CTHRC1 expression as a ceRNA by adsorbing let-7b-5p, which activates c-Myc to regulate itself transcription. HOXA11-AS knockdown promotes ROS sensitivity in glioma cells by regulating the Tpl2-MEK1/2-ERK1/2 axis, demonstrating that HOXA11-AS may be translated to increase ROS sensitivity therapeutically.

Metagenomic data from cerebrospinal fluid was used to genotype Neisseria meningitidis in patients with invasive meningococcal disease and trace the origin of the pathobiont, providing a phylogeographic analysis of the strain’s evolution in China and its global spread.

Abstract Non-coding RNAs (ncRNAs) account for the majority of the widespread transcripts of mammalian genomes. They rarely encode proteins and peptides, but their regulatory role is crucial in numerous physiological and pathological processes. The m6A (N6-methyladenosine) modification is one of the most common internal RNA modifications in eukaryotes and is associated with all aspects of RNA metabolism. Accumulating researches have indicated a close association between m6A modification and ncRNAs, and suggested m6A-modified ncRNAs played a crucial role in tumor progression. The correlation between m6A modification and ncRNAs offers a novel perspective for investigating the potential mechanisms of cancer pathological processes, which suggests that both m6A modification and ncRNAs are critical prognostic markers and therapeutic targets in numerous malignancies. In the present report, we summarized the interaction between m6A modification and ncRNA, emphasizing how their interaction regulates pathological processes in cancer.

Objective: Colorectal cancer (CRC) is a common cancer that cannot be detected at an early stage and is a major challenge in oncology research. Studies have shown that vitamin D3 has some anti-cancer and preventive effects on colorectal cancer, but the exact anti-cancer mechanism is not clear. We applied the relevant research methods of network pharmacology to speculate and validate the possible potential pharmacological mechanisms of vitamin D3 for the prevention of colorectal cancer, and to provide more theoretical support for the clinical anticancer effects of vitamin D3.Methods: The relevant targets for vitamin D3 and CRC were obtained from the database of drug and disease targets, respectively. The target of vitamin D3 and the target of colorectal cancer were taken to intersect to obtain common targets. Then, the PPI network was constructed. In addition, the pathways of drug-disease interactions were predicted by GO and KEGG enrichment analysis. Finally, the obtained results were verified to ensure the reliability of the experiments.Results: 51 targets of vitamin D3 for the prevention of colorectal cancer were obtained. The 10 core targets were obtained from the PPI network. The 10 core targets include: ALB, SRC, MMP9, PPARG, HSP90AA1, IGF1, EGFR, MAPK1, MAP2K1 and IGF1R. The core targets were further validated by molecular docking and animal experiments. The results suggest that vitamin D3 plays a key role in the prevention of CRC through core targets, PI3K-Akt pathway, HIF-1 pathway, and FoxO pathway.Conclusion: This study will provide more theoretical support for vitamin D3 to reduce the incidence of CRC and is important to explore more pharmacological effects of vitamin D3.

STAG2 is a core subunit of the cohesin complex involved in DNA looping, but its transcriptional targets are largely unknown. Here the authors show STAG2 controls the 3D chromatin structure at the IRF9 locus to restrict IRF9 expression. Loss of STAG2 results in IRF9 activation, which in turn upregulates PD-L1 expression in cancer cells, suggesting a tumor suppressor function in immune evasion.

Eutrophication of lakes harms aquatic organisms, changes the function of lakes and causes water pollution, which has affected global public health. As an internationally important wetland reserve, Honghu Lake is polluted to varying degrees and the eutrophication trend has accelerated. The objective of this study is to quarterly monitor and assess the trophic status of Honghu Lake from 2000 to 2021 using the trophic level index (TLI). The TLI was retrieved by developing a semi-empirical model based on radial basis function neural network (RBFNN), air temperature and water level data were added to the model as input parameters, forming a control group with the model that did not include air temperature and water level data. Based on 178 Landsat images from 2000 to 2021, the eutrophication status of the Honghu Lake over the last 20 years was successfully determined. The main findings are as follows: 1) The accuracy of TLI retrieval model, with air temperature and water level data included (R2 = 0.723, RMSE = 4.971), is significantly higher than that without air temperature and water level data (R2 = 0.197, RMSE = 10.453); 2) The eutrophication level of the waters in the northwestern part of Honghu Lake was higher than those observed in other lake water; 3) There were significant seasonal fluctuations in the eutrophication of Honghu Lake. The highest TLI values were observed in summer and autumn, while the lowest values were observed in winter and spring; 4) The eutrophication trend of the Honghu Lake has significantly accelerated from 2013 onwards, showing a change in eutrophication status, from light eutrophic to mid-eutrophic. Our results indicate that the TLI retrieval method with air temperature and water level is more predictive. Moreover, the eutrophication status for the entire lake from 2000 to 2021 was determined, which may contribute to the protection of the water environment.

Tumor immune microenvironment plays an important role in tumorigenesis and metastasis. Polo-like kinases 4 (PLK4) is a crucial regulatory factor in the process of cell cycle, and its abnormal regulation often leads to a variety of diseases including tumorigenesis. We have previously explored the function of PLK4 in sensitizing chemotherapy in glioma, but there are few studies on the correlation between PLK4 and tumor immune microenvironment. PLK4 was found to be highly expressed in various types of cancers, including glioma and closely related to histological and genetic features in public databases. Kaplan-Meier survival analysis and Cox regression analysis revealed that higher PLK4 expression is associated with poorer prognosis. GO and KEGG functional enrichment analysis showed that PLK4 expression level was significantly correlated with regulation of immune microenvironment, cell cycle and genomic instability. Immune infiltration analysis showed that high expression of PLK4 resulted in reduced infiltration of macrophages. M1 macrophage infiltration assays showed that PLK4 knockdown GBM cell lines promoted the recruitment of M1-type macrophages via altering expression of chemokines. And in intracranial tumor mouse models, PLK4 inhibition increased tumor-infiltrating M1 macrophages. In summary, our results demonstrated the correlation between high PLK4 expression level and malignant progression of gliomas, and the possible involvement of PLK4 in regulation of cell cycle, cell proliferation and macrophages infiltration in gliomas.

Size irregularity gradient and cell wall gradient, combined as the density gradient in previous studies, affect the macroscopic mechanical properties of the gradient metal foam. More and more complex mesostructures are designed and applied in metal foams, and the density gradient becomes insufficient to describe the difference in mesostructures. To explore the effect of mesostructures carefully, this study focuses on the effect of the size irregularity gradient on the macroscopic compressive properties of metal foams. A series of metal foam models were developed using the 3D Voronoi technique. These models have the same average relative densities, the same average diameters and different size irregularity gradients. Simulation results indicated that the macroscopic mechanical properties of cell wall gradient metal foams are significantly different from those of size irregularity gradient metal foams, though these models have the same relative density gradient. To explore the effect of size irregularity gradient, a theoretical model was developed to characterize the compression process from the first cell-collapse to full condensation. Theoretical results showed a linear relationship between the nominal stress and the current relative density. These findings can provide efficient guidance for the design and applications of gradient metal foams.