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Abstract Background Anterior cruciate ligament (ACL) injuries are common among physically active individuals, and obesity may increase the risk of such injuries due to factors like biomechanical stress on the knee. We aimed to determine if obesity affects postoperative outcomes after ACL reconstruction. Methods Data from adults aged 20 years and older with ACL injuries who underwent inpatient reconstruction from 2005 to 2018 were extracted from the United States (US) Nationwide Inpatient Sample (NIS) database. Patients were divided into two groups based on the presence of co-existing obesity, defined as a body mass index (BMI) ≥ 30 kg/m2. Propensity-score matching (PSM) was employed to balance between-group differences. Associations between obesity and concomitant meniscus injury, length of stay (LOS), post-procedural complications, and non-routine discharge were examined using univariate and multivariable logistic and linear regressions. Results After PSM, data from 1323 patients (representing 6396 individuals in the US) were analyzed. Of these, 441 (33%) were classified as obese, while 882 (67%) were not obese. After adjustment, obesity was significantly associated with a longer LOS (adjusted beta (aBeta) = 0.32, 95% confidence interval (CI) 0.31–0.321) and an increased likelihood of non-routine discharge (adjusted OR (aOR) = 2.18, 95% CI 1.47–3.22). There were no significant associations between obesity and concomitant meniscus injury (aOR = 1.04, 95% CI 0.81–1.32) or post-procedural complications (aOR = 0.97, 95% CI 0.74–1.27). Conclusions In patients undergoing ACL reconstruction in the US, obesity is independently associated with a longer LOS and a higher risk of non-routine discharge. Nevertheless, obesity does not appear to be associated with concomitant meniscus injury or post-procedural complications.

Abstract Background Although various anti-inflammatory medicines are widely recommended for osteoarthritis (OA) treatment, no significantly clinical effect has been observed. This study aims to examine the effects of vitamin B6, a component that has been reported to be capable of alleviating inflammation and cell death in various diseases, on cartilage degeneration in OA. Methods Collagen-induced arthritis (CIA) mice model were established and the severity of OA in cartilage was determined using the Osteoarthritis Research Society International (OARSI) scoring system. The mRNA and protein levels of indicators associated with extracellular matrix (ECM) metabolism, apoptosis and inflammation were detected. The effect of vitamin B6 (VB6) on the mice were assessed using HE staining and masson staining. The apoptosis rate of cells was assessed using TdT-mediated dUTP nick end labeling. Results Our results showed a trend of improved OARSI score in mice treated with VB6, which remarkably inhibited the hyaline cartilage thickness, chondrocyte disordering, and knees hypertrophy. Moreover, the VB6 supplementation reduced the protein expression of pro-apoptosis indicators, including Bax and cleaved caspase-3 and raised the expression level of anti-apoptosis marker Bcl-2. Importantly, VB6 improved ECM metabolism in both in vivo and in vitro experiments. Conclusions This study demonstrated that VB6 alleviates OA through regulating ECM metabolism, inflammation and apoptosis in chondrocytes and CIA mice. The findings in this study provide a theoretical basis for targeted therapy of OA, and further lay the theoretical foundation for studies of mechanisms of VB6 in treating OA.

Bifunctional photocatalysis for biomass conversion and fuel production not only utilizes abundant and renewable biomass resources, but also obtains high‐energy fuels while obtaining fine chemicals. It is a green, clean, efficient, low‐cost, and high‐value strategy, which is conducive to achieving carbon neutralization, carbon cycle, and solving energy and environmental problems. However, very few literatures have classified and analyzed the bifunctional photocatalysis of biomass. On the basis of latest research progress, the bifunctional photocatalysts based on the classification of biomass transformation coupled with fuel production (hydrogen production or CO2 reduction) by mainly using biomass conversion rate, biomass value‐added product yield, fuel (H2, CO) production rate, and so on as evaluation indicators are reviewed. The reaction mechanisms, development status, and prospects of bifunctional photocatalysts are analyzed, summarized and prospected. This review helps to better understand photocatalysts in a biomass transformation and fuel production bifunctional route.

Abstract Farmland transfer is a critical component in facilitating agricultural scale management and improving agricultural production efficiency. This study examines the spatial distribution of farmland transfer in China and identifies the factors influencing it, offering valuable guidance for advancing China’s farmland transfer practices. Through the application of mathematical statistics and GIS spatial analysis, the study investigates changes in spatial patterns related to the scale, rate, mode, and recipients of farmland transfer across China's 30 provinces from 2015 to 2020. Geographical detectors are also employed to identify the key factors influencing the extent and pace of farmland transfer. The study reveals that between 2015 and 2020, China's farmland transfer area increased from 29,789 to 37,638 million hectares. Provinces with abundant farmland resources generally experienced larger farmland transfers, while economically developed regions and major grain-producing areas saw higher rates of farmland transfers. The predominant mode of farmland transfer in China was leasing (subcontracting), accounting for over 80% of the total transferred area. Large-scale grain growers and family farms were significant participants in farmland transfers, acquiring approximately 60.1% of the transferred lands, followed by professional cooperatives (21.5%), enterprises (10.4%), and other entities (7.9%). Key factors influencing the farmland transfer area include the "regional farmland area", the "proportion of family farms supported by loans", and the "proportion of non-agricultural population", with explanatory powers of 0.663, 0.319, and 0.225, respectively. Notably, there is a substantial interaction between the "regional farmland area" and factors such as the "proportion of family farms supported by loans" and the "grain yield per unit area", with explanatory powers reaching 0.957 and 0.901, respectively. These findings offer valuable insights for promoting farmland transfer in agriculturally rich regions. Factors affecting the farmland transfer rate include "grain yield per unit area", "GDP per capita", and the "proportion of non-agricultural population", each with an explanatory power above 0.500. Moreover, their interactive explanatory powers with other indicators exceed 0.600, indicating that provinces with high agricultural productivity or economic development levels are more likely to undergo farmland transfer. The paper concludes by proposing strategies and recommendations to promote farmland transfer in both "large agricultural areas" and "metropolitan suburbs."

Abstract Background Ion channels play an important role in tumorigenesis and progression of cervical cancer. Multiple long non‐coding RNA genes are widely involved in ion channel‐related signaling regulation. However, the association and potential clinical application of lncRNAs in the prognosis of cervical cancer are still poorly explored. Methods Thirteen patients with cervical cancer were enrolled in current study. Whole transcriptome (involving both mRNAs and lncRNAs) sequencing was performed on fresh tumor and adjacent normal tissues that were surgically resected from patients. A comprehensive cervical cancer‐specific lncRNA landscape was obtained by our custom pipeline. Then, a prognostic scoring model of ion‐channel‐related lncRNAs was established by regression algorithms. The performance of the predictive model as well as its association with the clinical characteristics and tumor microenvironment (TME) status were further evaluated. Results To comprehensively identify cervical cancer‐specific lncRNAs, we sequenced 26 samples of cervical cancer patients and integrated the transcriptomic results. We built a custom analysis pipeline to improve the accuracy of lncRNA identification and functional annotation and obtained 18,482 novel lncRNAs in cervical cancer. Then, 159 ion channel‐ and tumorigenesis‐related (ICTR‐) lncRNAs were identified. Based on nine ICTR‐lncRNAs, we also established a prognostic scoring model and validated its accuracy and robustness in assessing the prognosis of patients with cervical cancer. Besides, the TME was characterized, and we found that B cells, activated CD8+ T, and tertiary lymphoid structures were significantly associated with ICTR‐lncRNAs signature scores. Conclusion We provided a thorough landscape of cervical cancer‐specific lncRNAs. Through integrative analyses, we identified ion‐channel‐related lncRNAs and established a predictive model for assessing the prognosis of patients with cervical cancer. Meanwhile, we characterized its association with TME status. This study improved our knowledge of the prominent roles of lncRNAs in regulating ion channel in cervical cancer.

Abstract For pulsed power load (PPL) accommodation in a medium‐voltage DC (MVDC) shipboard power system (SPS), the charging control of energy storage systems (ESSs) and the generation control of distributed generators (DGs) need to be properly coordinated. Targeting the important but not well‐studied problem, an optimal output‐constrained control algorithm for the offline PPL accommodation strategy is presented. Three control objectives including realising the generation and charging control references, maintaining the DC bus and supercapacitor voltages within the safe operating ranges, and minimising the total generation cost of DGs, are fulfilled concurrently. First, an SPS model with multiple DGs, a supercapacitor ESS, and regular loads is developed. By restricting the DC bus and supercapacitor voltages within pre‐defined constraints, both the transient‐ and steady‐state performances of the SPS are guaranteed. Furthermore, by incorporating the cost minimisation objective into designed virtual control signals, the third control objective on energy efficiency is realised. The stability of the presented algorithm is rigorously proven based on the Lyapunov method. Finally, detailed case studies are conducted to validate the performance of the designed algorithm.

The interaction between woody plants and mycorrhizal fungi is an important biological interaction; however, the driving factors behind the diversity of mycorrhizal trees formed through the symbiosis of mycorrhizal fungi and woody plants remain unclear. In this study, we collected and compiled the woody plant data of 34 forest dynamic plots containing 3350 species from habitats around the world and divided them into AM and EcM trees. We tested the contribution of AM and EcM trees to tree diversity and its components in forest communities worldwide. Our results showed that AM trees rather than EcM trees affect the tree diversity of forest communities, and that the diversity of AM trees has a significant latitudinal gradient pattern. Climate variables, especially temperature, are strongly correlated with the diversity patterns for AM trees rather than EcM trees. Topography is the most significant factor affecting the diversity of EcM trees. Our findings highlight the importance of AM trees for the tree diversity of forest communities worldwide. Our findings have important implications for understanding the response of complex woody plant communities with different types of mycorrhizal symbiosis to climate change.

PurposeThe aim to assess treatment failure in patients with stage III colon cancer who underwent radical surgery and was analyzed using the nomogram.MethodsClinical factors and survival outcomes for stage III colon cancer patients registered in the SEER database from 2018 to 2019 were analyzed, with patients split into training and testing cohorts (7:3 ratio). A total of 360 patients from the First Affiliated Hospital of Longyan served as an external validation cohort. Independent predictors of treatment failure were identified using logistic regression analyses. The nomograms was evaluated by concordance index (C-index), calibration curves, and the area under the curve (AUC), decision curve analysis (DCA) and clinical impact curves (CIC) assessed the clinical utility of nomograms versus TNM staging.ResultsThe study included 4,115 patients with stage III colon cancer. Multivariate logistic analysis age, tumor site, pT stage, pN stage, chemotherapy, pretreatment CEA levels, number of harvested lymph nodes, perineural invasion and marital status were identified as independent risk factors for treatment failure. The C-indices for the training and testing sets were 0.853 and 0.841. Validation by ROC and calibration curves confirmed the stability and reliability of the model. DCA showed that the net clinical effect of the histogram was superior to that of the TNM staging system, while CIC highlighted the potentially large clinical impact of the model.ConclusionsThe developed Nomogram provides a powerful and accurate tool for clinicians to assess the risk of treatment failure after radical surgery in patients with stage III colon cancer.

Background In a context of long-term highly intensive grazing in grassland ecosystems, a better understanding of how quickly belowground biodiversity responds to grazing is required, especially for soil microbial diversity. Methods In this study, we conducted a grazing experiment which included the CK (no grazing with a fenced enclosure undisturbed by livestock), light and heavy grazing treatments in a desert steppe in Inner Mongolia, China. Microbial diversity and soil chemical properties (i.e., pH value, organic carbon, inorganic nitrogen (IN, ${\mathrm{NH}}_{4}^{+}$ NH 4 + -N and ${\mathrm{NO}}_{3}^{-}$ NO 3 − -N), total carbon, nitrogen, phosphorus, and available phosphorus content) both in rhizosphere and non-rhizosphere soils were analyzed to explore the responses of microbial diversity to grazing intensity and the underlying mechanisms. Results The results showed that heavy grazing only deceased bacterial diversity in the non-rhizosphere soil, but had no any significant effects on fungal diversity regardless of rhizosphere or non-rhizosphere soils. Bacterial diversity in the rhizosphere soil was higher than that of non-rhizosphere soil only in the heavy grazing treatment. Also, heavy grazing significantly increased soil pH value but deceased NH4+-N and available phosphorus in the non-rhizosphere soil. Spearman correlation analysis showed that soil pH value was significantly negatively correlated with the bacterial diversity in the non-rhizosphere soil. Combined, our results suggest that heavy grazing decreased soil bacterial diversity in the non-rhizosphere soil by increasing soil pH value, which may be due to the accumulation of dung and urine from livestock. Our results highlight that soil pH value may be the main factor driving soil microbial diversity in grazing ecosystems, and these results can provide scientific basis for grassland management and ecological restoration in arid and semi-arid area.

Abstract Background Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. Results In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely ‘virtual’ spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. Conclusions This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.

Abstract Lymphocyte homing is mediated by the interaction between L-selectin on lymphocytes and its glycoprotein ligands modified with 6-sulfo sialyl Lewis x (6-sulfo sLex) glycans on high endothelial venules (HEVs) in peripheral lymph nodes (PLNs). However, the lack of specific antibodies reactive with both human and mouse 6-sulfo sLex has limited our understanding of its function in vivo. Here, we generated a novel monoclonal antibody, termed SF1, that specifically reacts with 6-sulfo sLex expressed on HEVs in both species in a manner dependent on sulfate, fucose, and sialic acid modifications. Glycan array and biolayer interferometry analyses indicated that SF1 specifically bound to 6-sulfo sLex with a dissociation constant of 6.09 × 10–9 M. SF1 specifically bound to four glycoproteins from PLNs corresponding to the molecular sizes of L-selectin ligand glycoproteins. Consistently, SF1 inhibited L-selectin-dependent lymphocyte rolling on 6-sulfo sLex-expressing cells ex vivo and lymphocyte homing to PLNs and nasal-associated lymphoid tissues in vivo. Furthermore, SF1 significantly attenuated ovalbumin-induced allergic rhinitis in mice in association with significant suppression of Th2 immune responses. Collectively, these results suggest that SF1 can be useful for the functional analysis of 6-sulfo sLex and may potentially serve as a novel therapeutic agent against immune-related diseases.

Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption, elucidating the relationship between power consumption per unit mileage and these factors. Subsequently, we construct an ACO-SVR model utilizing an improved ant colony optimization algorithm, fitting the relationship between power consumption, speed, and temperature to derive the EV power consumption per unit mileage model. Finally, leveraging operational data from EVs in Guangdong, Hong Kong, and Macao as a case study, we validate the energy consumption model of EVs by considering factors such as ambient temperature and driving speed. The results demonstrate that the model proposed in this paper is both accurate and effective.

In this work, steam explosion (SE) was applied to prompt the rapid extraction of ergosterol and polysaccharides from Flammulina velutipes root (FVR) waste. Ultrasound-assisted saponification extraction (UASE) followed by water extraction was used to prepare ergosterol and polysaccharides. The results indicated that SE destroyed the complicated structure of FVR and increased its internal porosity and surface roughness. SE caused the thermal degradation of FVR’s structural components and increased the polysaccharide content 0.97-fold. As a result, the extraction yield and efficiency of ergosterol and polysaccharides were improved. The theoretical maximum extraction concentration (C∞) and diffusion coefficient (D) were increased by 34.10% and 78.04% (ergosterol) and 27.69% and 48.67% (polysaccharides), respectively. The extraction yields obtained within 20–30 min of extraction time exceeded those of untreated samples extracted after several hours. For polysaccharides, SE led to a significant reduction in the average molecular weight, increased the percentage of uronic acids and decreased the neutral sugar percentage. The monosaccharide composition was changed by SE, with an increase in the molar ratio of glucose of 64.06% and some reductions in those of other monosaccharides. This work provides an effective method for the processing of fungi waste and adds to its economic value, supporting its high-value utilization in healthcare products.

Although the use of heterosis in maize breeding has increased crop productivity, the genetic causes underlying heterosis for nitrogen (N) use efficiency (NUE) have been insufficiently investigated. In this study, five N-response traits and five low-N-tolerance traits were investigated using two inbred line populations (ILs) consisting of recombinant inbred lines (RIL) and advanced backcross (ABL) populations, derived from crossing Ye478 with Wu312. Both populations were crossed with P178 to construct two testcross populations. IL populations, their testcross populations, and the midparent heterosis (MPH) for NUE were investigated. Kernel weight, kernel number, and kernel number per row were sensitive to N level and ILs showed higher N response than did the testcross populations. Based on a high-density linkage map, 138 quantitative trait loci (QTL) were mapped, each explaining 5.6%–38.8% of genetic variation. There were 52, 34 and 52 QTL for IL populations, MPH, and testcross populations, respectively. The finding that 7.6% of QTL were common to the ILs and their testcross populations and that 11.7% were common to the MPH and testcross population indicated that heterosis for NUE traits was regulated by non-additive and non-dominant loci. A QTL on chromosome 5 explained 27% of genetic variation in all of the traits and Gln1-3 was identified as a candidate gene for this QTL. Genome-wide prediction of NUE traits in the testcross populations showed 14%–51% accuracy. Our results may be useful for clarifying the genetic basis of heterosis for NUE traits and the candidate gene may be used for genetic improvement of maize NUE.

Abstract Terahertz (THz) technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing systems. One of the key challenges facing the field is the need for tools to enable agile engineering of THz wave fronts. Here, we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays architecture. This subwavelength-spaced array, under the control of a 1-bit digital coding sequence, can switch between an enormous range of possible configurations, providing facile access to nearly arbitrary wave front control for signals near 0.34 THz. We demonstrate wide-angle beam scanning with 1° of angular precision over 70 GHz of bandwidth, as well as the generation of multi-beam and diffuse wave fronts, with a switching speed up to 100 MHz. This device, offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene, will open new realms of possibilities in sensing, imaging, and networking.

Regional characterization of soil properties requires not only geostatistical models considering the spatial variability of soil properties but also methods that account for various sources of heterogeneous information. This paper presents a multi-conditional random field approach to characterize the regional characteristics of the average shear-wave velocity in the top 30 meters of subsoil (vs30) based on hybrid geotechnical and geological data. The workflow for integrating multiple sources of soil properties information in a random field model for regional vs30 mapping is developed. A detailed two-dimensional (2D) synthetic digital soil field is generated to assess and verify this workflow. With the generated synthetic field, parametric studies on the investigation plans, the value of Markov Bayes coefficient B, element size, and a predefined grid of secondary data are performed. Practice on whether to incorporate secondary data for vs30 mapping and the determination of coefficient B are provided.

Objective: Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach. Methods: We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia. Results: 1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found. Conclusion: Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.

BackgroundSingle-cell RNA sequencing (scRNA-Seq) provides new perspectives and ideas to investigate the interactions between different cell types and organisms. By integrating scRNA-seq with new computational frameworks or specific technologies, better Alzheimer’s disease (AD) treatments may be developed.MethodsThe single-cell sequencing dataset GSE158234 was obtained from the GEO database. Preprocessing, quality control, dimensionality-reducing clustering, and annotation to identify cell types were performed on it. RNA-seq profiling dataset GSE238013 was used to determine the components of specific cell subpopulations in diverse samples. A set of genes included in the OMIM, Genecards, CTD, and DisGeNET databases were selected as highly plausible AD-related genes. Then, ROC curves were created to predict the diagnostic value using the significantly expressed genes in the KO group as hub genes. The genes mentioned above were mapped to the Coremine Medical database to forecast prospective therapeutic Chinese medicines, and a “Chinese medicine-ingredient-target” network was constructed to screen for potential therapeutic targets. The last step was to undertake Mendelian randomization research to determine the causal link between the critical gene IL1B and AD in the genome-wide association study.ResultsUsing the scRNA-seq dataset, five unique cell clusters were discovered. These clusters were further subdivided into four distinct cell types using marker genes. The KO group showed a more substantial differential subgroup of macrophages than the WT group. By using the available datasets and PPI network analysis, 54 common genes were discovered. Four clusters were identified using the MCODE approach, and correlation analysis showed that seven genes in those four clusters had a significantly negative correlation with macrophages. Six genes in four sets had a significantly positive correlation. Five genes had different levels of expression in the WT and KO groups. The String database was used to identify the regulatory relationships between the four genes (IL10, CX3CR1, IL1B, and IL6) that were finally selected as AD hub genes. Screening identified potential traditional Chinese medicine to intervene in the transformation process of AD, including Radix Salviae, ginseng, Ganoderma, licorice, Coptidis Rhizoma, and Scutellariae Radix, in addition to promising therapeutic targets, such as PTGS1, PTGS2, and RXRA. Finally, it was shown that IL1B directly correlated with immune cell infiltration in AD. In inverse variance weighting, we found that IL1B was associated with a higher risk of AD, with an OR of 1.003 (95% CI = 1.001–1.006, p = 0.038).ConclusionOur research combined network pharmacology and the scRNA-seq computational framework to uncover pertinent hub genes and prospective traditional Chinese medicine potential therapeutic targets for AD. These discoveries may aid in understanding the molecular processes behind AD genes and the development of novel medications to treat the condition.

Abstract Metal oxide materials are widely adopted in UV sensing due to their advantages of reliability, stability, and high sensitivity. However, the fabrication of metal oxide films on flexible substrates using micro/nano printing techniques still poses challenges. Notably, inks containing oxide nanoparticles often lead to nozzle clogging during the printing process. In addition, methods relying on precursor solutions typically require high post‐processing temperatures that conventional flexible substrates cannot withstand. In this study, a novel precursor ink based on zinc amine hydroxide complexes is proposed, enabling post‐processing at a low temperature of 250 °C. Through a fully printed approach, a wearable UV sensor based on a zinc oxide nanofilm on a flexible substrate is successfully fabricated. The sensor exhibits outstanding sensitivity and rapid response characteristics. By incorporating silver nanoparticles into the ink, the baseline resistance of the sensitive material is adjusted. Furthermore, a smart patch utilizing the NFC communication protocol is developed, allowing for wireless, passive, and wearable UV detection. In this study, a process paradigm for fabricating metal oxide films on flexible substrates using micro/nano printing techniques is introduced, showcasing significant potential in the field of micro/nano sensor fabrication.

Soil bacteria are an important part of the forest ecosystem, and they play a crucial role in driving energy flow and material circulation. Currently, many uncertainties remain about how the composition and distribution patterns of bacterial communities change along altitude gradients, especially in forest ecosystems with strong altitude gradients in climate, vegetation, and soil properties. Based on dynamic site monitoring of the Baiyun Mountain Forest National Park (33°38′–33°42′ N, 111°47′–111°51′ E), this study used Illumina technology to sequence 120 soil samples at the site and explored the spatial distribution mechanisms and ecological processes of soil bacteria under different altitude gradients. Our results showed that the composition of soil bacterial communities varied significantly between different altitude gradients, affecting soil bacterial community building by influencing the balance between deterministic and stochastic processes; in addition, bacterial communities exhibited broader ecological niche widths and a greater degree of stochasticity under low-altitude conditions, implying that, at lower altitudes, community assembly is predominantly influenced by stochastic processes. Light was the dominant environmental factor that influenced variation in the entire bacterial community as well as other taxa across different altitude gradients. Moreover, changes in the altitude gradient could cause significant differences in the diversity and community composition of bacterial taxa. Our study revealed significant differences in bacterial community composition in the soil under different altitude gradients. The bacterial communities at low elevation gradients were mainly controlled by stochasticity processes, and bacterial community assembly was strongly influenced by deterministic processes at middle altitudes. Furthermore, light was an important environmental factor that affects differences. This study revealed that the change of altitude gradient had an important effect on the development of the soil bacterial community and provided a theoretical basis for the sustainable development and management of soil bacteria.

Heavy metal resistance mechanisms and heavy metal response genes are crucial for microbial utilization in heavy metal remediation. Here, Corynebacterium crenatum was proven to possess good tolerance in resistance to copper. Then, the transcriptomic responses to copper stress were investigated, and the vital pathways and genes involved in copper resistance of C. crenatum were determined. Based on transcriptome analysis results, a total of nine significantly upregulated DEGs related to metal ion transport were selected for further study. Among them, GY20_RS0100790 and GY20_RS0110535 belong to transcription factors, and GY20_RS0110270, GY20_RS0100790, and GY20_RS0110545 belong to copper-binding peptides. The two transcription factors were studied for the function of regulatory gene expression. The three copper-binding peptides were displayed on the C. crenatum surface for a copper adsorption test. Furthermore, the nine related metal ion transport genes were deleted to investigate the effect on growth in copper stress. This investigation provided the basis for utilizing C. crenatum in copper bioremediation.

When studying the coupling coordination relationship between agricultural eco-efficiency and urbanization, it is crucial to consider food security, especially in a populous country like China. This paper focuses on 31 provinces in China as the research units, covering the time period from 2000 to 2020. Based on the concept of agricultural eco-efficiency, an evaluation index system was developed to include undesirable outputs (carbon emissions), and agricultural eco-efficiency scores were calculated using the SBM–DEA model. An urbanization evaluation index system, covering six dimensions and twelve indexes, was constructed. A comprehensive index of urbanization is measured using the entropy method. On this basis, a coupling coordination model was applied to quantify the relationship between agricultural eco-efficiency and urbanization at the provincial scale in China. The results showed that the agricultural eco-efficiency of all provincial units in China exhibited an overall trend of improvement. Average efficiency followed a spatial pattern of majority grain-consuming areas > grain production–consumption balance areas > majority grain-producing areas. The level of coupling between agricultural eco-efficiency and urbanization is generally low. Currently, no regions have reached the stage of synergy or high-level coupling. Most regions are currently in an antagonistic stage with a coupling degree of 0.3 < C ≤ 0.5. The classification of coupling coordination levels changed from four levels of “severe imbalance”, “moderate imbalance”, “mild imbalance”, and “primary coordination” to “moderate imbalance”, “mild imbalance”, “primary coordination”, and “intermediate coordination”. The level of “severe imbalance” disappeared, the level of “intermediate coordination” appeared, and the level of “mild imbalance” became the largest scale level. From the perspective of food security, the proportion of grain production in the categories of “primary coordination” and “intermediate coordination” was less than 10%, and these provinces never achieved self-sufficiency in food production. The proportion of grain production at the “mild imbalance” level reached 62.4%, while the per capita grain production at the “moderate imbalance” level reached 846.7 kg. Provinces with lower levels of coupling coordination have stronger food security capabilities. It can be observed that the weaker the coupling coordination between agricultural eco-efficiency and urbanization, the higher the food self-sufficiency. Based on the research results above, we discussed strategies to enhance agricultural eco-efficiency in majority grain-producing regions by focusing on technological progress and technical efficiency. Additionally, we analyzed approaches to achieve grain self-sufficiency in regions characterized by a high level of coordination between agricultural eco-efficiency and urbanization, considering both production and trade dimensions.

Lushan Yunwu tea (LSYWT) is a famous green tea in China. However, the effects of intercropping tea with flowering cherry on the overall aroma of tea have not been well understood. In this study, headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS) was used for analysis. A total of 54 volatile compounds from eight chemical classes were identified in tea samples from both the intercropping and pure-tea-plantation groups. Principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), and odor activity value (OAV) methods combined with sensory evaluation identified cis-jasmone, nonanal, and linalool as the key aroma compounds in the intercropping group. Benzaldehyde, α-farnesene, and methyl benzene were identified as the main volatile compounds in the flowering cherry using headspace solid-phase microextraction/gas chromatography–mass spectrometry (HS-SPME/GC–MS). These findings will enrich the research on tea aroma chemistry and offer new insights into the product development and quality improvement of LSYWT.

IntroductionAdenoid hypertrophy (AH) is a common upper respiratory disorder in children. Disturbances of gut microbiota have been implicated in AH. However, the interplay of alteration of gut microbiome and enlarged adenoids remains elusive.Methods119 AH children and 100 healthy controls were recruited, and microbiome profiling of fecal samples in participants was performed using 16S rRNA gene sequencing. Fecal microbiome transplantation (FMT) was conducted to verify the effects of gut microbiota on immune response in mice.ResultsIn AH individuals, only a slight decrease of diversity in bacterial community was found, while significant changes of microbial composition were observed between these two groups. Compared with HCs, decreased abundances of Akkermansia, Oscillospiraceae and Eubacterium coprostanoligenes genera and increased abundances of Bacteroides, Faecalibacterium, Ruminococcus gnavus genera were revealed in AH patients. The abundance of Bacteroides remained stable with age in AH children. Notably, a microbial marker panel of 8 OTUs were identified, which discriminated AH from HC individuals with an area under the curve (AUC) of 0.9851 in the discovery set, and verified in the geographically different validation set, achieving an AUC of 0.9782. Furthermore, transfer of mice with fecal microbiota from AH patients dramatically reduced the proportion of Treg subsets within peripheral blood and nasal-associated lymphoid tissue (NALT) and promoted the expansion of Th2 cells in NALT.ConclusionThese findings highlight the effect of the altered gut microbiota in the AH pathogenesis.

Abstract Background The global Phase III IMpower132 study evaluating atezolizumab plus pemetrexed and carboplatin or cisplatin (APP) versus pemetrexed plus carboplatin or cisplatin (PP) for first‐line treatment of non‐squamous advanced non‐small cell lung cancer (NSCLC) met its co‐primary progression‐free survival (PFS) endpoint at the primary analysis in the intention‐to‐treat (ITT) population. Although the co‐primary overall survival (OS) endpoint was not met, numerical OS improvement favoring APP over PP was observed at the final analysis. We report primary results for Chinese patients in IMpower132. Methods Treatment‐naive Chinese patients with non‐squamous stage IV EGFR/ALK mutation‐negative NSCLC were randomized 1:1 to receive 4 or 6 cycles of APP or PP, followed by maintenance atezolizumab plus pemetrexed or pemetrexed. Co‐primary endpoints were investigator‐assessed PFS and OS. Results The ITT population included 163 Chinese patients (82 in the APP arm and 81 in the PP arm). At data cutoff (median follow‐up, 11.7 months), the median PFS in the APP and PP arms was 8.3 and 5.8 months, respectively; the unstratified hazard ratio (HR) was 0.73 (95% CI: 0.50, 1.08). At the interim OS analysis, median OS was not estimable in either arm; the unstratified HR was 0.70 (95% CI: 0.40, 1.24). No new safety signals were observed. Conclusion Among Chinese patients in IMpower132, PFS benefit was seen with APP versus PP. Though interim OS data were immature, there was a trend toward OS benefit favoring APP versus PP. The safety profile of the APP was consistent with the known risks of the individual treatment components. ClinicalTrials.gov: NCT02657434.

Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to reduce unintended harm from sudden seizures. The purpose of this study is to investigate the applicability of transfer learning (TL) technique and model inputs for different deep learning (DL) model structures, which may provide a reference for researchers to design algorithms. Moreover, we also attempt to provide a novel and precise Transformer-based algorithm. Methods: Two classical feature engineering methods and the proposed method which consists of various EEG rhythms are explored, then a hybrid Transformer model is designed to evaluate the advantages over pure convolutional neural networks (CNN)-based models. Finally, the performances of two model structures are analyzed utilizing patient-independent approach and two TL strategies. Results: We tested our method on the CHB-MIT scalp EEG database, the results showed that our feature engineering method gains a significant improvement in model performance and is more suitable for Transformer-based model. In addition, the performance improvement of Transformer-based model utilizing fine-tuning strategies is more robust than that of pure CNN-based model, and our model achieved an optimal sensitivity of 91.7% with false positive rate (FPR) of 0.00/h. Conclusion: Our epilepsy prediction method achieves excellent performance and demonstrates its advantage over pure CNN-based structure in TL. Moreover, we find that the information contained in the gamma ( $\gamma$ ) rhythm is helpful for epilepsy prediction. Significance: We propose a precise hybrid Transformer model for epilepsy prediction. The applicability of TL and model inputs is also explored for customizing personalized models in clinical application scenarios.

Considering the high cost of cobalt, cobalt‐free lithium nickel oxide (LiNiO2), which has an extremely high theoretical specific capacity, has become attractive. However, its application is hampered by continuous irreversible phase changes, unstable interfaces, and particle pulverization. Herein, an ≈35 nm‐thick selenium (Se) coating is applied to modify LiNiO2 using a combination of solid‐phase mixing and low‐temperature sintering. After 300 cycles, the selenium‐coated LiNiO2 (Se–LNO) cathode exhibits unusually high capacity retention of 82.09% at a charge cutoff voltage of 4.3 V. Furthermore, the coating modification improves the rate performance of the cathode materials, which exhibit considerable specific capacities of 168.7 and 149.6 mAh g−1 at current densities of 2 and 5 C, respectively. The significantly enhanced electrochemical performance can be attributed to the ability of the Se coating to enhance the structural stability of the cathode materials by suppressing phase transitions, stabilizing the interface, enhancing the kinetic behavior of the electrode, and reducing particle pulverization. According to the findings of this study, manipulating the Se coating in cathodes may provide a viable path for lower‐cost and higher‐energy‐density Co‐free lithium‐ion batteries.

Sulfur cathode materials in lithium–sulfur chemistry suffer from poor electronic conductivity and shuttle of lithium polysulfides during charging and discharging. Serious shuttle effects and the sluggish redox reaction kinetics of polysulfides severely limit the development of lithium–sulfur batteries with high sulfur loading, impeding the practical process of lithium–sulfur batteries. Herein, a honeycomb73x02010;structured CoF2@C is introduced as a functional layer adhered to the separator, achieving rapid lithium‐ion transport, high catalytic activity, and suppressed shuttle effect simultaneously. As a result, the cell with CoF2‐modified separator presents satisfactory cycle stability with a capacity decay of 0.076% per cycle within 300 cycles at 1 C rate with the sulfur loading of 2.0 mg cm−2. A low‐capacity decay of 0.088% per cycle for 200 cycles at 0.2 C is also achieved with sulfur loading of 3.0 mg cm−2. In addition, a high‐capacity retention of 697.5 mA g−1 is achieved with sulfur loading of 4.0 mg cm−2 and the electrolyte volume/sulfur mass (E/S) ratio of 8 μL mg−1.

Coke production is an important source of environmental polycyclic aromatic compounds (PACs), including parent polycyclic aromatic hydrocarbons (PAHs) and their derivatives. The focus near coking plants has primarily been on parent-PAH contamination, with less attention given to highly toxic derivatives. In this study, soil samples were collected from both within and outside of a coking plant. The concentrations of parent-PAHs and their derivatives, including methylated-PAHs, oxygenated-PAHs, and nitrated-PAHs, were examined. Spatial interpolation was employed to determine their spatial distribution patterns. Methods for identifying potential sources and conducting incremental lifetime cancer risk analysis were used. This could achieve a comprehensive understanding of the status of PAC pollution and the associated health risks caused by coke production. The concentrations of total PACs inside the plant ranged from 7.4 to 115.8 mg/kg, higher than those outside (in the range of 0.2 to 65.7 mg/kg). The spatial distribution of parent-PAH concentration and their derivatives consistently decreased with increasing distance from the plant. A significant positive correlation (p < 0.05) among parent-PAHs and their derivatives was observed, indicating relatively consistent sources. Based on diagnostic ratios, the potential emission sources of soil PACs could be attributed to coal combustion and vehicle emissions, while principal component analysis–multiple linear regression further indicated that primary emissions and secondary formation jointly influenced the PAC content, accounting for 60.4% and 39.6%, respectively. The exposure risk of soil PACs was dominated by 16 priority control PAHs; the non-priority PAHs’ contribution to the exposure risk was only 6.4%.

Purpose: Improving agricultural carbon-output efficiency is an important path to realize the “double carbon” goal in the Yellow River Basin. In the context of rapid urbanization development, it is significant to explore whether promoting urbanization will affect agricultural carbon-output efficiency. Methods: Based on panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses the super-DEA model, three-dimensional kernel density model, and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural carbon-output efficiency in the Yellow River Basin. The panel Tobit model is used on this basis to analyze the relationship between urbanization and carbon-output efficiency in agriculture. Results: The results show the following: (1) The level of agricultural carbon-output efficiency in the Yellow River Basin is low and has not reached an effective state, showing a slow downward trend in general where the agricultural carbon-output efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches has the lowest. (2) Agricultural carbon-output efficiency in the Yellow River Basin has a negative trend of transitioning to a low level overall and maintaining its original level, and it is difficult to realize the leapfrog transfer between states. Agricultural carbon-output efficiency has an obvious spatial spillover effect and “club convergence” phenomenon; the high-efficiency area has a positive driving effect on the neighborhood area, while the low-efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the carbon-output efficiency of agriculture in the upper, middle, and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture.

Psychiatric disorders are among the leading causes of global health burden, with depression and anxiety being the most disabling subtypes. The two common disorders, depression and anxiety, usually coexist and are pathologically polygenic with complicated etiologies. Current drug-based therapies include selective serotonin reuptake inhibitors, serotonin and norepinephrine reuptake inhibitors, and 5-hydroxytryptamine partial agonists. However, these modalities share common limitations, such as slow onset and low efficacy, which is why potential mechanistic insights for new drug targets are needed. In this review, we summarize recent advances in brain localization, pathology, and therapeutic mechanisms of the serotonergic system in depression and anxiety.

Plants face various biotic and abiotic stress factors during their growth and development, among which, drought is a serious adverse factor that affects yield and quality in agriculture and forestry. Several transcription factors are involved in regulating plant responses to drought stress. In this study, the B-box (BBX) transcription factor CoBBX24 was cloned from Camellia oleifera. This gene encodes a 241-amino-acid polypeptide containing two B-box domains at the N-terminus. A phylogenetic analysis revealed that CoBBX24 and CsBBX24 from Camellia sinensis are in the same branch, with their amino acid sequences being identical by 96.96%. CoBBX24 was localized to the nucleus and acted as a transcriptional activator. The overexpression of CoBBX24 in Arabidopsis heightened its drought tolerance along with a relatively high survival rate, and the rate of water loss in the OX-CoBBX24 lines was observably lower than that of the wild-type. Compared to the wild-type, the root lengths of the OX-CoBBX24 lines were significantly inhibited with abscisic acid. Leaf senescence was delayed in the OX-CoBBX24 lines treated with abscisic acid. The expression of genes related to leaf senescence and chlorophyll breakdown (e.g., SAG12, SAG29, NYC1, NYE1, and NYE2) was downregulated in the OX-CoBBX24 lines. This study indicated that CoBBX24 positively regulates the drought tolerance in Arabidopsis through delayed leaf senescence.

Abstract Background Cervical cancer is frequently detected gynecological cancer all over the world. This study was designed to develop a prognostic signature for an effective prediction of cervical cancer prognosis. Methods Differentially expressed genes (DEGs) were identified based on copy number variation (CNV) data and expression profiles from different databases. A prognostic model was constructed and further optimized by stepwise Akaike information criterion (stepAIC). The model was then evaluated in three groups (training group, test group and validation group). Functional analysis and immune analysis were used to assess the difference between high-risk and low-risk groups. Results The study developed a 5-gene prognostic model that could accurately classify cervical cancer samples into high-risk and low-risk groups with distinctly different prognosis. Low-risk group exhibited more favorable prognosis and higher immune infiltration than high-risk group. Both univariate and multivariate Cox regression analysis showed that the risk score was an independent risk factor for cervical cancer. Conclusions The 5-gene prognostic signature could serve as a predictor for identifying high-risk cervical cancer patients, and provided potential direction for studying the mechanism or drug targets of cervical cancer. The integrated analysis of CNV and mRNA expanded a new perspective for exploring prognostic signatures in cervical cancer.

Improvements in hyperspectral image technology, diversification methods, and cost reductions have increased the convenience of hyperspectral data acquisitions. However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. The optimal classification model was obtained by comparing a stacked autoencoder (SAE) and a deep belief network (DBN). Finally, the SAE was further optimized by adding sparse representation constraints and GPU parallel computation to improve classification accuracy and speed. The research results show that the SAE enhanced by deep learning is superior to the traditional feature extraction algorithm. The optimal classification model based on deep learning, namely, the stacked sparse autoencoder, achieved 93.41% and 94.92% classification accuracy using two experimental datasets. The use of parallel computing increased the model’s training speed by more than seven times, solving the model’s lengthy training time limitation.

Southern corn rust (SCR), caused by Puccinia polysora Underw, is a destructive disease that can severely reduce grain yield in maize (Zea mays L.). Owing to P. polysora being multi-racial, it is very important to explore more resistance genes and develop more efficient selection approaches in maize breeding programs. Here, four Doubled Haploid (DH) populations with 384 accessions originated from selected parents and their 903 testcross hybrids were used to perform genome-wide association (GWAS). Three GWAS processes included the additive model in the DH panel, additive and dominant models in the hybrid panel. As a result, five loci were detected on chromosomes 1, 7, 8, 8, and 10, with P-values ranging from 4.83×10-7 to 2.46×10-41. In all association analyses, a highly significant locus on chromosome 10 was detected, which was tight chained with the known SCR resistance gene RPPC and RPPK. Genomic prediction (GP), has been proven to be effective in plant breeding. In our study, several models were performed to explore predictive ability in hybrid populations for SCR resistance, including extended GBLUP with different genetic matrices, maker based prediction models, and mixed models with QTL as fixed factors. For GBLUP models, the prediction accuracies ranged from 0.56-0.60. Compared with traditional prediction only with additive effect, prediction ability was significantly improved by adding additive-by-additive effect (P-value< 0.05). For maker based models, the accuracy of BayesA and BayesB was 0.65, 8% higher than other models (i.e., RRBLUP, BRR, BL, BayesC). Finally, by adding QTL into the mixed linear prediction model, the accuracy can be further improved to 0.67, especially for the G_A model, the prediction performance can be increased by 11.67%. The prediction accuracy of the BayesB model can be further improved significantly by adding QTL information (P-value< 0.05). This study will provide important valuable information for understanding the genetic architecture and the application of GP for SCR in maize breeding.

Beijing is an international metropolis, that is also an earthquake-prone city. The aims of this study are detailed quantifying and qualifying soil layer properties for an accurate seismic safety evaluation in the Beijing area. The time average shear-wave velocity in the first 30 m of subsoil, Vs30, is an important site parameter used in site response analysis, site classification, and seismic loss estimation. Mapping of Vs30 over a city-scaled region is commonly done through proxy-based methods by correlating Vs30 with geological or topographic information. In this paper, a geostatistical-based random field model is presented and applied to mapping Vs30 over extended areas. This random field model is then coupled with Monte Carlo simulations to obtain an averaged Vs30 map and its associated uncertainties. Unlike the traditional deterministic prediction model, this framework accounts for spatial variations of Vs30 values and uncertainties, which makes the prediction more reliable. A total of 388 shear wave velocity measurements in the Beijing area are used to calculate Vs30 values, from which the statistical and spatial properties for the random field realizations are inferred. New spatially correlated probabilistic Vs30 maps for the Beijing area are then represented, and the effect of the maximum number of previously generated elements to correlate to in estimating Vs30 maps is tested.

Hematological malignancies encompass a wide variety of severe diseases that pose a serious threat to human health. Given the fact that hematological malignancies are difficult to treat due to their unpredictable and rapid deterioration and high rates of recurrence, growing attention has been paid to their early screening and diagnosis. However, developing a rapid and effective diagnostic tool featuring a noninvasive sampling technique is still extremely challenging. In recent years, novel nanomaterials-based electrochemical biosensors have attracted great interest because of such advantages as simple operation, low cost, fast response, etc. As a kind of rising nanomaterials, two-dimensional materials have excellent electronic and chemical properties, which have been proven to improve the performance of electrochemical biosensors. This review summarizes the applications of different types of electrochemical biosensors (nucleic acid sensors, immunosensors, aptamer biosensors, and cytosensors) based on two-dimensional materials in the detection of biological molecules related to hematological malignancies. Two-dimensional materials-based electrochemical biosensors designed for the diagnosis of leukemia could rapidly detect the target biomolecules at a trace level and show great merits such as wide linear range, low detection limit, high sensitivity, excellent selectivity, and cost-effectiveness. In addition, these biosensors have also achieved satisfactory results in the diagnosis of lymphoma and multiple myeloma. Thus, two-dimensional materials-based electrochemical biosensors are attractive for the early diagnosis of hematological malignancies in clinical practice. Nevertheless, more efforts are still required to further improve the performance of electrochemical biosensors. In this review, we propose the possible main concerns in the design of future two-dimensional materials-based electrochemical biosensors, involving the development of sensors for synchronous detection of diverse target biomolecules, the exploration of other superior two-dimensional materials, the simplification of the sensors fabrication process, the construction of new hybrid structures and how to avoid possible environmental issues.

The importance of phosphorus (P) in agriculture contrasts with the negative environmental impact and the limited resources worldwide. Reducing P fertilizer application by utilizing more efficient genotypes is a promising way to address these issues. To approach this, a large panel of maize (Zea mays L.) comprising each 100 Flint and Dent elite lines and 199 doubled haploid lines from six landraces was assessed in multi-environment field trials with and without the application of P starter fertilizer. The treatment comparison showed that omitting the starter fertilizer can significantly affect traits in early plant development but had no effect on grain yield. Young maize plants provided with additional P showed an increased biomass, faster growth and superior vigor, which, however, was only the case under environmental conditions considered stressful for maize cultivation. Importantly, though the genotype-by-treatment interaction variance was comparably small, there is genotypic variation for this response that can be utilized in breeding. The comparison of elite and doubled haploid landrace lines revealed a superior agronomic performance of elite material but also potentially valuable variation for early traits in the landrace doubled haploid lines. In conclusion, our results illustrate that breeding for P efficient maize cultivars is possible towards a reduction of P fertilizer in a more sustainable agriculture.

Agricultural green production has been regarded as an effective way to solve the increasing level of agricultural environmental pollution and the frequent safety crises of agricultural products. As the main decision makers of agricultural production, farmers’ agricultural green production behavior directly determines the process of agricultural green development. However, few studies have explored farmers’ agricultural green production behavior from the perspective of environmental literacy, and the formation mechanism of farmers’ agricultural green production behavior is still unclear. This study aims to clarify the effect of environmental literacy on farmers’ agricultural green production behavior and its impact mechanism. Based on survey data from 830 farmers in China, this study constructs comprehensive index systems to evaluate farmers’ environmental literacy and agricultural green production behavior, and adopts multiple linear regression models and quantile regression model to explore the impact of environmental literacy on this behavior. Meanwhile, the mediation effect model is used to explore the mediation effect of agricultural green production cognition and agricultural green production willingness in the influence of environmental literacy on farmers’ agricultural green production behavior. Three conclusions arise. First, farmers’ environmental literacy and agricultural green production behavior are at the middle level, both of which should be strengthened. Second, environmental literacy has a significant positive impact on farmers’ agricultural green production behavior. Finally, environmental literacy influences farmers’ AGP behavior through the independent and chain mediation effects of AGP cognition and AGP willingness. Environmental literacy has heterogeneity impact on farmers’ agricultural green production behavior under different level of agricultural green production and external environment. This research not only provides theoretical support for the study of farmers’ agricultural green production behavior from the perspective of environmental literacy, it also provides a reference to the relevant government departments so that they can guide farmers to adopt more agricultural green production behavior.

ObjectivesAlthough hypervirulent Klebsiella pneumoniae (hvKp) is an increasing public health problem, there remains limited epidemiological information regarding hvKp infections in children. Here, we conducted a clinical, molecular and phenotypic surveillance of hvKp strains in a pediatric population.MethodsNon-repetitive K. pneumoniae (Kp) strains consecutively collected during 2019-2020 were screened for hypervirulence genes (prmpA, prmpA2, iucA, iroB, and peg344) using PCR. Positive strains were further characterized by four phenotypic assays (string test, serum killing assay, siderophore production, Galleria mellonella lethality assay), followed by murine sepsis model to determine virulence in vitro and in vivo. Also, capsular types, sequence types, plasmid replicon types, antimicrobial resistance determinants and susceptibility were analyzed.ResultsA total of 352 isolates were collected, wherein 83 (23.6%) were hypervirulence genes-positive Kp (hgKp). A significant increase in KPC-2-producing KL47-ST11 among hgKp strains was observed, from 5.3% (1/19) in 2019 to 67.6% (25/37) in 2020 (P

This study investigated the combined effects of calcium lactate (CA-L, 3 g L−1) and shortwave ultraviolet (UV-C, 4.0 kJ m−2) irradiation on quality attributes and antioxidant defense capacity of fresh-cut kiwifruits at refrigerated storage for 7 d. The results indicated that CA-L and UV-C joint treatment, compared to either treatment alone, alleviated microbial load, showed higher quality on ascorbic acid (AsA), green color, total chlorophyll, flesh hardness, total sugar, total acid and malonaldehyde (MDA) content. Besides, it inhibited O2·- and ·OH generation, induced H2O2 production, improved the activity of antioxidant enzymes (SOD, CAT and APX), activated critical enzymes (PAL, C4H and 4CL) in phenylpropanoid metabolism pathway and further enhanced total phenolic and proanthocyanidin content. Above results demonstrated that UV-C together with CA-L treatment could synergistically maintain overall quality and improve antioxidant capacity of kiwifruit slices. Therefore, the combination of CA-L and UV-C treatment showed a potential practical application in fresh-cut kiwifruits.

Droplet digital PCR (ddPCR) recently has been shown to be a potential diagnostic tool for adults with bloodstream infections (BSIs); however, its application in children remains obscure. In this study, 76 blood samples of children with suspected BSIs were synchronously detected by traditional blood cultures (BCs) and ddPCRs. Our team validated the diagnostic performance of ddPCR including sensitivity, specificity, and positive and negative predictive values. The 76 pediatric patients from the hematology department (67.1%), the pediatric intensive care unit (PICU, 27.6%), and other departments (5.2%) were enrolled. The positive rate of ddPCR results was 47.9%, whereas that for BC was 6.6%. In addition, the time consumption of ddPCR was shorter, only for 4.7 ± 0.9 h, in comparison with the detection timing of BC (76.7 ± 10.4 h, p < 0.01). The levels of agreement and disagreement between BC and ddPCR were 96.1% and 4.2%, and the negative agreement reached 95.6%. The sensitivity of ddPCR was 100%, with corresponding specificities ranging from 95.3 to 100.0%. In addition, a total of nine viruses were identified by ddPCR. In China, the multiplexed ddPCR first could be a tool for the rapid and accurate diagnosis of children with suspected BSIs and can be an early indicator of the possibility of viraemia in children with immunosuppression.

The scientific evaluation of the development of rural industrial integration is of great significance to understanding the changes occurring in the industrial structure of China’s agricultural sector. Combined with the practical problems related to the development of China’s rural industrial integration on the basis of four dimensions, i.e., agricultural industrial chain extension, agricultural multifunctionality, agricultural service industry integration development and the economic effect of rural industrial integration, the level of China’s rural industrial integration development is calculated for the period from 2008 to 2020 by means of the entropy method, and its spatial–temporal evolution characteristics and the regional heterogeneity of its driving factors are discussed on the basis of kernel density estimation, hotspot analysis and a fixed effect panel data model. The results showed the following: (1) From 2008 to 2020, the level of rural industrial integration development in 31 provinces in China improved to varying degrees. The growth rate of agricultural service industry integration was the highest, while the economic effect of rural industrial integration was second; the growth rate of the agricultural industrial chain extension was the lowest, and agricultural multifunctionality exhibited a fluctuating upward trend, peaking in 2017. In terms of spatial distribution, the representative provinces and cities with high and low levels of each dimension were different. (2) Rural industrial integration development in China was characterized by a regional imbalance, with polarization in the central and western regions. In terms of spatial distribution, an “east–middle–west” ladder pattern was obvious. The hotspots were mainly concentrated in the southeast region, and they showed a gradually expanding trend, while the coldspots were mainly concentrated in the northwest region, and they showed a trend of gradually decreasing size. (3) In terms of driving factors, the urbanization level, rural human capital, rural transportation facilities, rural ecological environment, intensity of financial support for agriculture and rural digitalization had significantly positive effects, while the degree of industrial upgrade had a significantly negative effect. Each driving factor had different effects on the rural industrial integration development in the eastern, central and western regions.

Peach (Prunus persica (L.) Batsch) is a fruit tree of economic and nutritional importance, but it is very sensitive to drought stress, which affects its growth to a great extent. Lauric acid (LA) is a fatty acid produced in plants and associated with the response to abiotic stress, but the underlying mechanism remains unclear. In this study, physiological analysis showed that 50 ppm LA pretreatment under drought stress could alleviate the growth of peach seedlings. LA inhibits the degradation of photosynthetic pigments and the closing of pores under drought stress, increasing the photosynthetic rate. LA also reduces the content of O2−, H2O2, and MDA under drought stress; our results were confirmed by Evans Blue, nitroblue tetrazolium (NBT), and DAB(3,3-diaminobenzidine) staining experiments. It may be that, by directly removing reactive oxygen species (ROS) and improving enzyme activity, i.e., catalase (CAT) activity, peroxidase (POD) activity, superoxide dismutase (SOD) activity, and ascorbate peroxidase (APX) activity, the damage caused by reactive oxygen species to peach seedlings is reduced. Peach seedlings treated with LA showed a significant increase in osmoregulatory substances compared with those subjected to drought stress, thereby regulating osmoregulatory balance and reducing damage. RNA-Seq analysis identified 1876 DEGs (differentially expressed genes) in untreated and LA-pretreated plants under drought stress. In-depth analysis of these DEGs showed that, under drought stress, LA regulates the expression of genes related to plant–pathogen interaction, phenylpropanoid biosynthesis, the MAPK signaling pathway, cyanoamino acid metabolism, and sesquiterpenoid and triterpenoid biosynthesis. In addition, LA may activate the Ca2+ signaling pathway by increasing the expressions of CNGC, CAM/CML, and CPDK family genes, thereby improving the drought resistance of peaches. In summary, via physiological and transcriptome analyses, the mechanism of action of LA in drought resistance has been revealed. Our research results provide new insights into the molecular regulatory mechanism of the LA-mediated drought resistance of peach trees.

Reactive magnesia cement is considered an eco-efficient binder due to its low synthesis temperature and CO2 absorption properties. However, the hydration of pure MgO–H2O mixtures cannot produce strong Mg(OH)2 pastes. In this study, nesquehonite (Nes, MgCO3·3H2O) was added to the MgO–H2O system to improve its strength properties, and its hydration products and pore structure were analyzed. The experimental results showed that the hydration product changed from small plate-like Mg(OH)2 crystals to interlaced sheet-like crystals after the addition of a small amount of Nes. The porosity increased from 36.3% to 64.6%, and the total pore surface area increased from 4.6 to 118.5 m2/g. At the same time, most of the pores decreased in size from the micron scale to the nanometer scale, which indicated that Nes had a positive effect on improving the pore structure and enhancing the compressive strength. Combined with an X-ray diffractometer (XRD), a Fourier transform infrared spectrometer (FTIR), and a simultaneous thermal analyzer (TG/DSC), the hydration product of the sample after Nes addition could be described as xMgCO3·Mg(OH)2·yH2O. When Nes was added at 7.87 and 14.35 wt%, the x-values in the chemical formula of the hydration products were 0.025 and 0.048, respectively. These small x-values resulted in lattice and property parameters of the hydration products that were similar to those of Mg(OH)2.

Endometrial cancer (EC) is the gynecological tumor with the highest incidence. In recent years, it has been proved that necroptosis is a method of cell death related to EC. However, the expression of necroptosis-related miRNA in EC and its correlation with prognosis still ill-defined. Use the Cancer Genome Atlas (TCGA) cohort to obtain prognostic data and related clinical data for ECs and normal endometrium tissues. In this study, we identified three necroptotic regulatory miRNAs that are necroptosis-related and survival-related miRNAs (DENSMs) between normal endometrium tissues and EC from 13 necroptosis-related miRNAs. The three DENSMs signature was built to develop prognostic model and classified all EC patients into a high or low risk group. EC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (p = 0.0242), and the risk score was found to be an independent prognosis factor for predicting the OS of EC patients (p = 0.0254) in multivariate Cox regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed dephosphorylation, microtubule, protein serine/threonine kinase activity, PI3K-Akt signaling pathway and MAPK signaling pathway are closely related to it. In conclusion, the risk prediction model based on necroptosis-related miRNAs can effectively predict the prognosis of EC patients.

Genome-wide prediction is a promising approach to boost selection gain in hybrid breeding. Our main objective was to evaluate the potential and limits of genome-wide prediction to identify superior hybrid combinations adapted to Northwest China. A total of 490 hybrids derived from crosses among 119 inbred lines from the Shaan A and Shaan B heterotic pattern were used for genome-wide prediction of ten agronomic traits. We tested eight different statistical prediction models considering additive (A) effects and in addition evaluated the impact of dominance (D) and epistasis (E) on the prediction ability. Employing five-fold cross validation, we show that the average prediction ability ranged from 0.386 to 0.794 across traits and models. Six parametric methods, i.e. ridge regression, LASSO, Elastic Net, Bayes B, Bayes C and reproducing kernel Hilbert space (RKHS) approach, displayed a very similar prediction ability for each trait and two non-parametric methods (random forest and support vector machine) had a higher prediction performance for the trait rind penetrometer resistance of the third internode above ground (RPR_TIAG). The models of A + D RKHS and A + D + E RKHS were slightly better for predicting traits with a relatively high non-additive variance. Integrating trait-specific markers into the A + D RKHS model improved the prediction ability of grain yield by 3%, from 0.528 to 0.558. Of all 6328 potential hybrids, selection of the top 44 hybrids would lead to a 6% increase in grain yield compared with Zhengdan 958, a commercially successful hybrid variety. In conclusion, our results substantiate the value of genome-wide prediction for hybrid breeding and suggest dozens of promising single crosses for developing high-yielding hybrids for Northwest China.

Abstract Background Rituximab (R) plus cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) is the current standard therapy for diffuse large B cell lymphoma (DLBCL). Obinutuzumab (G), a glycoengineered, type II anti-CD20 monoclonal antibody, has shown activity and an acceptable safety profile when combined with CHOP (G-CHOP) in patients with advanced DLBCL. We present the final analysis results of the Phase III GOYA study (NCT01287741), which compared the efficacy and safety of G-CHOP versus R-CHOP in patients with previously untreated DLBCL. Methods Patients aged ≥ 18 years with previously untreated advanced DLBCL were randomly assigned to receive eight 21-day cycles of R or G, plus six or eight cycles of CHOP. The primary endpoint was investigator-assessed progression-free survival (PFS). Secondary endpoints included overall survival, other time-to-event endpoints, and safety; investigator-assessed PFS by cell of origin subgroup was an exploratory endpoint. Results A total of 1418 patients were randomized, with 1414 included in this final analysis (G-CHOP, N = 704; R-CHOP, N = 710). Five-year PFS rates were 63.8% and 62.6% for G-CHOP and R-CHOP, respectively (stratified hazard ratio 0.94, 95% CI 0.78–1.12; p = 0.48). The results of the secondary efficacy endpoints did not show a benefit of G-CHOP over R-CHOP. In the exploratory analysis, a trend towards benefit with G-CHOP over R-CHOP was apparent in the patients with germinal center B cell DLBCL. The safety profile of G-CHOP was as expected, and no new safety signals were observed. More grade 3–5 (75.1% vs 65.8%), serious (44.4% vs 38.4%), and fatal (6.1% vs 4.4%) adverse events (AEs) were observed in the G-CHOP arm compared with the R-CHOP arm, respectively, with the most common fatal AEs being infections. A higher incidence of late-onset neutropenia occurred in the G-CHOP arm (8.7%) versus the R-CHOP arm (4.9%). Conclusions The final analysis, similar to the primary analysis, did not show a PFS benefit of G-CHOP over R-CHOP in previously untreated patients with DLBCL. The results of the secondary endpoints were consistent with the primary endpoint. Further exploratory analyses and investigation of biomarkers are ongoing.

To further enhance transient characteristics of space range-gated imaging system and widen its application potential in astronomical and military domains, a self-triggered structure and complex detecting mechanism are proposed in this paper. There being delays restriction among multiple subsystem of range-gated imaging system and the robust performance of multiple optical axis adjusted are firstly discussed and analyzed. Based on these, by researching the work principle and coupling process of microchannel plates in series in image intensifier, taking advantages of the coupling loss electrons to realize self-triggered is studied and its prototype device structure is further proposed too. Then, the space optical transfer theory of new self-triggered component is deduced and its waveguide process is simulated in the software. Finally, the self-trigger performance and optical transfer effect of cutting edge invention is verified by vacuum test. And the test results basically match with theoretical model and simulation data.

The progression of agricultural production, ever-increasing industrialization, population boom, and more water-concentrated lifestyles has placed a severe burden on Yellow River Basin’s existing water resources, particularly in the current century. In the context of resource and environmental constraints, improving the green efficiency of agricultural water use (AWGE) is an important measure for alleviating the shortage of water resources as well as meeting the intrinsic requirement to promote the green transformation and upgrading of agriculture. This study used the Super Slack-Based Measure (Super-SBM) to measure the AWGE of 87 regions in the Yellow River Basin from 2000 to 2019. Based on spatial and temporal perspectives, it applied Exploratory Spatial Data Analysis (ESDA) to explore the dynamic evolution and regional differences in AWGE. Then, this study used a spatial econometric model to analyze the main factors that influence AWGE in the Yellow River Basin. The results show that, firstly, the AWGE of the Yellow River Basin shows a steady upward trend from 2000 to 2019, but the differences among regions were obvious. Secondly, the AWGE showed an obvious spatial autocorrelation in the Yellow River Basin and showed significant high–high and low–low agglomeration characteristics. Thirdly, rural per capita disposable income and effective irrigation have a positive influence on AWGE, while rural labor transfer, the input intensity of agricultural machinery and water structure have a negative influence. The spatial econometric model regression results show that the influence factors of AWGE in the Yellow River Basin showed significant spatial spillover effects and spatial heterogeneity in their effect. Finally, when improving AWGE in the Yellow River Basin, plans should be formulated according to local conditions. The results of this study can provide new ideas on the study of AWGE in the Yellow River Basin and provide references for the formulation of regional agricultural water resource utilization policies as well.