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IntroductionReforestation has been widely considered to best solve this problem, but this requires an accurate estimation of carbon stocks in the forest aboveground biomass (AGB) at a large scale. AGB models based on traits and remote sensing indices (moisture vegetation index (MVI)) are the two good methods for this purpose. But limited studies have developed them to estimate carbon stock in AGB during restoration of degraded mining areas.MethodsHere, we have successfully addressed this challenge as we have developed trait-based and MVI-based AGB models to estimate carbon stock in the AGB after performing reforestation in a 0.2 km2 degraded tropical mining area in Hainan Island in China. During this reforestation, seven non-native fast-growing tree species were planted, which has successfully recovered soil processes (including soil microorganisms, nematodes and chemical and physical properties).Results and discussionsBy using these two models to evaluate carbon stock in AGB, we have found that an average of 78.18 Mg C hm-2 could be accumulated by our reforestation exercise. Moreover, wood density could predict AGB for this restored tropical mining site, and indicated that strategies of planting fast-growing species leads to fast-growing strategies (indicated by wood density) which in turn determined the largely accumulated carbon stocks in the AGB during restoration. This restoration technology (multiple-planting of several non-native fast-growing tree species) and the two accurate and effective AGB models (trait-based and MVI-based AGB models) developed by us could be applied to 1) restore other degraded tropical mining area in China, and 2) estimate carbon stock in forest AGB after performing restoration.

The influence of post-weld heat treatment (PWHT) and fatigue damage on the hydrogen embrittlement sensitivity of X80 steel welded joints, obtained using flux-cored arc welding method, was investigated in the study. Compared to the as-welded specimens, the sensitivity of hydrogen embrittlement after PWHT was decreased. However, the coupling effect of PWHT and fatigue damage improved significantly the HE sensitivity. Furthermore, the fracture site of the PWHT + pre-fatigue specimens with hydrogen-charged was transferred to the weld metal, and the fracture was characterized by intergranular fracture, quasi-cleavage (QC) and shallow dimples ductile fracture. The increase of the hydrogen content might be attributed to the evolutions of dislocation and the proportion of high-angle grain boundary caused by fatigue damage. Meanwhile, the size of QC for the hydrogen-charged welded joints was related to the degree of fatigue damage, hydrogen content and microstructure strength.

National parks are critical components of protected areas and are drawing increased global attention. Clear and rational boundaries may provide a scientific foundation for the sustainable protection and management of these areas. However, criteria for feasible national park delimitation and techniques for resolving conflicts between socioeconomic development and environmental protection are still poorly defined, particularly regarding integrity during park establishment. Consequently, a systematic analytical framework is essential for advancing research and practice. This study developed a systematic framework to determine the spatial boundaries, using the Kunlun Mountain region in China as a case for empirical analysis. Key issues in national park development—ecology and management—were systematically integrated into this framework. Most importantly, spatial connectivity, a critical concern in national park planning, was thoroughly considered for improving integrity optimization, an aspect often overlooked in previous delimitation studies. The results demonstrated that our framework effectively identified potential national park boundaries, achieving a balance between development and conservation. Furthermore, core protected and general control areas were delineated to support differentiated management approaches. This framework offers a scientific foundation for policymakers and managers to pursue sustainable protection and management of national parks.

Abstract Histone lysine crotonylation (Kcr), as a posttranslational modification, is widespread as acetylation (Kac); however, its roles are largely unknown in kidney fibrosis. In this study, we report that histone Kcr of tubular epithelial cells is abnormally elevated in fibrotic kidneys. By screening these crotonylated/acetylated factors, a crotonyl-CoA-producing enzyme ACSS2 (acyl-CoA synthetase short chain family member 2) is found to remarkably increase histone 3 lysine 9 crotonylation (H3K9cr) level without influencing H3K9ac in kidneys and tubular epithelial cells. The integrated analysis of ChIP-seq and RNA-seq of fibrotic kidneys reveal that the hub proinflammatory cytokine IL-1β, which is regulated by H3K9cr, play crucial roles in fibrogenesis. Furthermore, genetic and pharmacologic inhibition of ACSS2 both suppress H3K9cr-mediated IL-1β expression, which thereby alleviate IL-1β-dependent macrophage activation and tubular cell senescence to delay renal fibrosis. Collectively, our findings uncover that H3K9cr exerts a critical, previously unrecognized role in kidney fibrosis, where ACSS2 represents an attractive drug target to slow fibrotic kidney disease progression.

Carbohydrate-binding modules (CBMs) are essential virulence factors in phytopathogens, particularly the extensively studied members from the CBM50 gene family, which are known as lysin motif (LysM) effectors and which play crucial roles in plant–pathogen interactions. However, the function of CBM50 in Tilletia horrida has yet to be fully studied. In this study, we identified seven CBM50 genes from the T. horrida genome through complete sequence analysis and functional annotation. Their phylogenetic relationships, conserved motifs, promoter elements, and expression profile were further analyzed. The phylogenetic analysis indicated that these seven ThCBM50 genes were divided into three groups, and close associations were observed among proteins with similar protein motifs. The promoter cis-acting elements analysis revealed that these ThCBM50 proteins may be involved in the regulation of the phytohormones, stress response, and meristem expression of the host plant during T. horrida infection. The transcriptome data indicated that four ThCBM50 genes were upregulated during T. horrida infection. We further found that ThCBM50_1 caused cell death in the leaves of Nicotiana benthamiana, and its signal peptide (SP) had a secreting function. These results offer important clues that highlight the features of T. horrida CBM50 family proteins and set the stage for further investigation into their roles in the interactions between T. horrida and rice.

ObjectiveInsomnia disorder stands out as one of the prevalent clinical sleep and psychiatric disorders. Prior research has unequivocally demonstrated variations in the diversity and abundance of gut microbiota among individuals with insomnia disorder. These alterations may play a direct or indirect role in the onset and progression of insomnia disorder by compromising the integrity of the intestinal barrier. This study aims to evaluate the impairment of the intestinal barrier in individuals with insomnia disorder by scrutinizing the serum functionality of this barrier.Materials and methods45 patients with chronic insomnia disorder and 30 matched healthy volunteers were meticulously selected based on inclusion criteria. ELISA technology was employed to measure serum levels of diamine oxidase (DAO), D-lactic acid (D-LA), intestinal fatty acid binding protein (I-FABP), and endothelin (ET). Spearman correlation analysis was used to explore the relationship between intestinal mucosal markers and clinical characteristics. Data were analyzed using SPSS 26.0.ResultsCompared to the healthy control group, the insomnia disorder group exhibited significantly elevated scores on subjective mood and sleep scales (GAD-7, PHQ-9, HAMA, HAMD, PSQI, and ISI) (P < 0.05). Overnight PSG indicated a notable increase in bed time, total wake time, sleep onset latency, and wake after sleep onset in individuals with insomnia disorder. Additionally, there was a decrease in sleep efficiency and alterations in sleep structure (increased proportion of N1 and N3 stages, prolonged N1 stage) (P < 0.05). The chronic insomnia disorder group displayed significantly reduced concentrations of serum DAO, D-LA, I-FABP, and ET (P < 0.05). Furthermore, significant positive correlations were identified between intestinal epithelial barrier markers and sleep efficiency, while negative correlations were found with wake after sleep onset, total wake time, PSQI, HAMA, and HAMD. Additionally, D-LA levels were significantly positively correlated with ET concentrations.ConclusionIndividuals with chronic insomnia disorder manifest disruptions in sleep structure, heightened susceptibility to anxiety and depressive moods, and impaired intestinal barrier function. These findings suggest that the occurrence and development of insomnia disorder may be linked to the impairment of the intestinal barrier.

AimsPleural effusion is not an infrequent complication in patients undergoing continuous ambulatory peritoneal dialysis. However, there is not adequate data to evaluate pleural effusion and prognosis in clinical practice. In this study, we validated this potential association by a multicenter cohort.MethodsWe screened 1,162 patients who met the inclusion criteria with PD. According to the existence of pleural effusion on stable dialysis (4-8 weeks after dialysis initiation), the participants were divided into pleural effusion and non-pleural effusion groups. The hazard ratios (HRs) of all-cause and cause-specific death were estimated with adjustment for demographic characteristics and multiple potential clinical confounders. Subgroup analysis and propensity score matching (PSM) were used to further verify the robustness of the correlation between hydrothorax and prognosis.ResultsPleural effusion was found in 8.9% (104/1162) of PD individuals. After adjusting for the confounding factors, patients with pleural effusion had significantly increased HRs for all-cause death was 3.06 (2.36-3.96) and cardiovascular death was 3.78 (2.67-5.35) compared to those without pleural effusion. However, it was not associated with infectious and other causes of death. After PSM, the HR of all-cause mortality was 3.56 (2.28-5.56). The association trends were consistent in the subgroup sensitivity analysis.ConclusionPleural effusion is not rare in PD, and is significantly associated with overall and cardiovascular mortality, which is independent of underlying diseases and clinically relevant indicators.

BackgroundMicrobial colonization represents one of the main threats to the conservation of subterranean cultural heritage sites. Recently, the microbial colonization on murals in tombs has gradually attracted attention.MethodsIn this study, a total of 33 samples, including 27 aerosol samples and 6 mural painting samples, were collected from different sites of Xu Xianxiu’s Tomb and analyzed using culture-dependent methods. We compared the diversities of culturable bacteria and fungi isolated from the air and murals and explored the potential impacts of microorganisms on the biodeterioration of the murals.ResultsPhylogenetic analyses revealed that the culturable bacteria belonged to Bacillus, Microbacterium, Lysobacter and Arthrobacter. And the most of fungal belonged to the Penicillium, Cladosporium and Aspergillus genera. The composition and structure of airborne bacteria and fungi outside the tomb were both significantly different from that inside the tomb. The variation trends of airborne bacterial and fungal concentrations at different sampling sites were remarkably similar. Bacillus frigoritolerans, Bacillus halotolerans, Bacillus safensis, Exiguobacterium mexicanum, Microbacterium trichothecenolyticum, and Micrococcus yunnanensis were bacterial species commonly isolated from both the mural and air environments. Fungal species commonly isolated from aerosol samples and mural painting samples were Alternaria alternata, Cladosporium cladosporioides, Penicillium brevicompactum, and Peyronellaea glomerata. The prediction of the ecological functions of the bacteria revealed that chemoheterotrophy or aerobic_chemoheterotrophy accounted for substantial relative proportions in all sample types.ConclusionThese results suggest that the aerosol circulation between the inside and outside environments of the tomb was weak and that the outside environment had yet to have an impact on the air microbial community inside the tomb. Selective colonization of microorganisms, which is mediated by interaction between microorganisms and special microenvironmental factors, is an important reason for the biodeterioration of murals.

Ustilago crameri is a pathogenic basidiomycete fungus that causes foxtail millet kernel smut (FMKS), a devastating grain disease in most foxtail-millet-growing regions of the world. Here, we report an assembled high-quality genome sequence of U. crameri strain SCZ-6 isolated from the diseased grains of foxtail millet in Changzhi, Shanxi Province, China. The genome size is 19.55 Mb, consisting of 73 contigs (N50 = 840,209 bp) with a G + C content of 54.09%, and encoding 6576 predicted genes and 6486 genes supported by RNA-seq. Evolutionarily, U. crameri lies close to the barley smut U. hordei, and an obvious co-linearity was observed between these two smut fungi. We annotated the genome of U. crameri strain SCZ-6 using databases, identifying 1827 pathogen–host interaction (PHI)-associated genes, 1324 genes encoding fungal virulence factors, 259 CAZy-related genes, 80 genes encoding transporters, and 206 putative cytochrome P450 genes; their expression profiles at different inoculation time points were also detected. Additionally, 70 candidate pathogen effectors were identified according to their expression patterns and predicted functions. In summary, our results provide important insights into the pathogenic mechanisms of the pathogenesis-related genes of U. crameri and a robust foundation for further investigation.

BackgroundSarcopenia and osteoporosis are closely interconnected and associated with adverse health outcomes. Osteosarcopenia is the concurrent presence of the two conditions and has rarely been reported in hemodialysis patients. Whether hemodialysis patients with osteosarcopenia are at greater risk of mortality than those with either condition alone remains unknown. The aim of this study was to explore the prevalence of sarcopenia and its association with osteoporosis and to determine its impact on survival risk in hemodialysis patients.MethodsA total of 209 adults undergoing hemodialysis were enrolled from the dialysis center in the West China Hospital of Sichuan University, and our study was registered at the Chinese Clinical Trial Register (number: ChiCTR2100043932). Muscle mass, handgrip strength, bone mineral density (BMD), and biochemical parameters were assessed. All deaths were recorded during a follow-up of 35.15 ± 15.37 months.ResultsSeventy-eight patients were diagnosed with sarcopenia, with a prevalence of 37.3%. After adjustment for potential confounders, age (OR=1.094, P

The recent Russia–Ukraine conflict has raised significant concerns about global food security, leaving many countries with restricted access to imported staple food crops, particularly wheat and sunflower oil, sending food prices soaring with other adverse consequences in the food supply chain. This detrimental effect is particularly prominent for low-income countries relying on grain imports, with record-high food prices and inflation affecting their livelihoods. This review discusses the role of Russia and Ukraine in the global food system and the impact of the Russia–Ukraine conflict on food security. It also highlights how diversifying four areas of agrifood systems—markets, production, crops, and technology can contribute to achieving food supply chain resilience for future food security and sustainability.

AimTo explore the change characteristics and related factors of various indexes of GABAergic system in peripheral blood of patients with insomnia disorder.MethodsIn this study, a total of 30 patients who met the DSM-5 diagnostic criteria for insomnia disorder and 30 normal controls were included. All subjects had a structured clinical interview with the Brief International Neuropsychiatric Disorder Interview, and PSQI was used to evaluate the sleep status of the subjects. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum γ-aminobutyric acid (GABA), and RT-PCR was used to detect GABAA receptor α1 and α2 subunit mRNA. All data were statistically analyzed using SPSS 23.0.ResultsCompared with the normal control group, the mRNA levels of GABAA receptor α1 and α2 subunits in the insomnia disorder group were significantly lower, but there was no significant difference in the serum GABA levels between the two groups. And in the insomnia disorder group, there was no significant correlation between the GABA levels and the mRNA expression levels of α1 and α2 subunits of GABAA receptors. Although no significant correlation was found between PSQI and serum levels of these two subunit mRNAs, its component factors sleep quality and sleep time were negatively correlated with GABAA receptor α1 subunit mRNA levels, and daytime function was inversely correlated with GABAA receptor α2 subunit mRNA levels.ConclusionThe inhibitory function of serum GABA in patients with insomnia may be impaired, and the decreased expression levels of GABAA receptor α1 and α2 subunit mRNA may become a reliable indicator of insomnia disorder.

With rapid social and economic development, high-efficiency welding technology has become an important development direction in the field of welding. In recent years, scholars and professionals in many countries have devoted themselves to further increasing the welding efficiency by improving welding materials, welding process, and arc-welding equipment. The welding efficiency can be increased using two approaches: one is increasing the welding speed, the other is increasing the welding deposition rate. Considering these two methods, typical technologies such as multiwire submerged arc welding (SAW) and multiwire gas metal arc welding (GMAW) were proposed. A twin-arc integrated cold wire hybrid welding system was established. The mechanical effect of the cold wire position on the welding process was studied, including its effects on heating, melting, and weld surface formation. Results show that the melting of cold wire depends on the front end of the weld pool, and the melting effect of the weld pool on the cold wire is not sufficient when the cold wire is fed in front of two leading wires. The end of the cold wire makes contact with the bottom surface of the weld pool with the continuous feeding of the cold wire. Droplets melted at the wire ends are ejected and fall on the base metal surface to generate a globular spatter with the backward motion of the base metal. The thermal distribution on the side of the weld pool decreases as the cold wire is fed inside of the two leading wires. Hence, the flow of molten metal is affected, ultimately leading to an uneven weld formation. The cold wire is stably inserted into the weld pool when fed behind the two leading wires, representing the optimum cold wire position. Moreover, the deposition rates increase with an increase in the cold wire feed speed and show little change under two-pulse phase differences (in-phase and reverse-phase pulse differences). The effect of arc heating and melting on the cold wire was most intense at the in-phase pulse current, followed by the reverse-phase pulse current and subsequently direct current.

ObjectiveGPHB5 has been found to be associated with glucose and lipid metabolism in animal studies. However, the association of GPHB5 with IR and metabolic disorders remains unknown, and there is a lack of research in humans. Our aim in this study was to investigate the relationship between circulating GPHB5 and metabolic disorders in humans.MethodsBioinformatics analysis was performed to understand the relationship between GPHB5 and metabolic disorders. GPHB5 mRNA expression in mice and rats was determined using RT-qPCR. Circulating GPHB5 concentrations were measured with an ELISA kit. EHC and OGTT were performed in humans.ResultsBioinformatics analysis shows that GPHB5 is associated with metabolic disorders and PCOS. GPHB5 mRNA expression levels in the metabolic-related tissues of HFD-fed mice, db/db and ob/ob mice, and PCOS rats were significantly higher than those of WT mice or rats. In human studies, we find that circulating GPHB5 levels were significantly higher in women with IR and PCOS. GPHB5 levels were positively correlated with age, BMI, WHR, BP, FBG, 2 h-BG, FIns, 2 h-Ins, TC, LDL-C, HbA1c, and FFA, but negatively correlated with adiponectin. Furthermore, GPHB5 was positively correlated with DHEAS and FAI, while negatively correlated with SHBG, FSH, SHBG and FSH. The increased GPHB5 concentration was related to IR and PCOS. After the treatment of metformin, GLP-1RA (Lira), and TZDs, circulating GPHB5 levels were decreased.ConclusionsOur results reveal that circulating GPHB5 could be a biomarker and potential therapeutic target for IR and PCOS in women.

Introduction Patients on maintenance hemodialysis (MHD) are highly predisposed to low bone mineral density (BMD). This study aims to assess the value of quantitative ultrasound (QUS), bioelectrical impedance analysis (BIA), and their combination in detecting high-risk patients for low BMD in MHD. Methods Patients’ BMD of the total hip, femoral neck, and lumbar spine were measured using dual-energy X-ray absorptiometry (DXA). Bone mineral content (BMC) was assessed using BIA. Calcaneal BMD was measured using QUS. Patients with a T-score of ≤-2.5 were recorded as ‘low BMD.’ Results Overall, 93 subjects (62.37% female; mean age, 60.8 ± 16.2 years) were included in this cross-sectional study; approximately 36.56% met the ‘low BMD’ criteria. QUS-T score predicted low BMD with an area under the curve (AUC) value of 0.738, sensitivity of 70.59%, and specificity of 76.27%. The AUC for low BMD diagnosis using the BMC index (BMCI) measured through BIA was 0.679 (sensitivity, 91.18%; specificity, 38.98%). On the other hand, the combination of QUS-T score and BMCI yielded a higher AUC value of 0.762 with an improved specificity of 88.14%. Compared with the QUS and BIA alone, the net reclassification improvement (NRI) of the combination model increased by 47.16% (p = 0.022) and 78.36% (p

BackgroundAnimal studies have found that GPHB5 has a similar effect on system metabolism as TSH. However, the relationship between GPHB5 and metabolic diseases remains unknown. This study investigates the relationship between GPHB5 and MetS in young women.MethodsBioinformatics analysis was undertaken to explore the relationship between GPHB5 and metabolic-related genes and signaling pathways. EHC and OGTT were performed on all individuals. Lipid-infusion, physical activity, and cold-exposure tests were performed on healthy individuals. Serum GPHB5 concentrations were measured by an ELISA kit.ResultsPPI network showed that 11 genes interacted with GPHB5, in which POMC and KISS1R were involved in glucose and lipid metabolism. GO analysis showed 56 pathways for BP and 16 pathways for MF, in which OPRM1 and MCR families were related to energy metabolism. KEGG analysis found that GPHB5 is associated with lipolysis and neuroactive ligand-receptor interaction pathways. The levels of circulating GPHB5 were significantly increased, while serum adiponectin levels were lower in MetS women compared with healthy women. Obese/overweight individuals had lower adiponectin levels and higher GPHB5 levels. Circulating GPHB5 levels were positively correlated with BMI, WHR, blood pressure, FBG, 2 h-BG, HbA1c, FIns, 2h-Ins, LDL-C, FFA, HOMA-IR, and AUCg, etc. but negatively correlated with HDL-C, adiponectin, and M-values. Serum GPHB5 levels did not change significantly during the OGTT, EHC, and lipid infusion. Physical activity and cold-exposure tests did not lead to changes in GPHB5 levels. GLP-1RA treatment resulted in a significant decrease in serum GPHB5 levels.ConclusionsGPHB5 may be a biomarker for MetS.

Background and PurposeThe systemic immune-inflammation index, a new index based on platelets, neutrophils and lymphocytes, has been shown to be associated with outcomes of patients with venous sinus thrombosis and cancer. However, its application in acute ischemic stroke has rarely been reported. Therefore, we examined the relationship between systemic immune-inflammation index levels at hospital admission and the outcomes of patients 3 months after onset, and plotted a nomogram to predict the probability of adverse outcomes in patients with acute ischemic stroke.MethodsWe retrospectively analyzed a total of 208 patients with acute ischemic stroke who were admitted between January 2020 and December 2020, and recorded the modified Rankin score 3 months later. A modified Rankin score ≥ 3 was defined as an adverse outcome. Age, sex, NIHSS score, SII, hypertension and coronary heart disease were included in the binary logistic regression, and the nomogram was plotted with a regression equation.ResultsReceiver operating characteristic (ROC) curve analysis indicated that the best cutoff value of the systemic immune-inflammation index was 802.8, with a sensitivity of 70.9% and specificity of 58.2% (area under the curve: 0.657, 95% confidence interval: 0.572–0.742). The nomogram had a C index of 0.802. The average error of the calibration curves of the training set and the validation set was 0.021 and 0.034, respectively.ConclusionThe systemic immune-inflammation index is associated with short-term adverse outcomes in patients with acute ischemic stroke, and the nomograms can predict the risk of adverse outcomes in patients with acute ischemic stroke.

Weedy crop relatives are among the world’s most problematic agricultural weeds, and their ability to rapidly evolve can be enhanced by gene flow from both domesticated crop varieties and wild crop progenitor species. In this study, we examined the role of modern commercial crop cultivars, traditional landraces, and wild relatives in the recent emergence and proliferation of weedy rice in East Malaysia on the island of Borneo. This region of Malaysia is separated from the Asian continent by the South China Sea, and weedy rice has become a major problem there more recently than on the Malaysian peninsular mainland. Using 24 polymorphic SSR loci and genotype data from the awn-length domestication gene An-1, we assessed the genetic diversity, population structure and potential origins of East Malaysian weeds; 564 weedy, cultivated and wild rice accessions were analyzed from samples collected in East Malaysia, Peninsular Malaysia and neighboring countries. While there is considerable evidence for contributions of Peninsular Malaysian weed ecotypes to East Malaysian populations, we find that local crop cultivars and/or landraces from neighboring countries are also likely contributors to the weedy rice infestations. These findings highlight the implications of genetic admixture from different cultivar source populations in the spread of weedy crop relatives and the urgent need for preventive measurements to maintain sustainable crop yields.

The core-1 β1-3galactosyltransferase-specific chaperone 1 (Cosmc) is a unique molecular chaperone of core-1 β1-3galactosyltransferase(C1GALT1), which typically functions inside the endoplasmic reticulum (ER). Cosmc helps C1GALT1 to fold correctly and maintain activity. It also participates in the synthesis of the T antigen, O-glycan, together with C1GALT1. Cosmc is a multifaceted molecule with a wide range of roles and functions. It involves platelet production and the regulation of immune cell function. Besides that, the loss of function of Cosmc also facilitates the development of several diseases, such as inflammation diseases, immune-mediated diseases, and cancer. It suggests that Cosmc is a critical control point in diseases and that it should be regarded as a potential target for oncotherapy. It is essential to fully comprehend Cosmc’s roles, as they may provide critical information about its involvement in disease development and pathogenesis. In this review, we summarize the recent progress in understanding the role of Cosmc in normal development and diseases.

Rapid industrialization and urbanization have accelerated land-use changes in mountainous areas, with dramatic impacts on ecosystem health. In particular, the Qinling-Daba Mountains, as China’s central water tower, ecological green lung, and biological gene bank, have rich resource endowments and extremely high ecological value and are an important protective wall to China’s ecological security. Therefore, understanding the level of ecosystem health and its drivers in the research area contributes to the conservation and restoration of the mountain ecosystem. Based on remote sensing image data and land-use data from 2000 to 2020, we explored the spatial characteristics of ecosystem health, and supplemented with socio-economic data to explore its driving factors. The results show that (1) the ecosystem health in the study area has been continuously improved during the study period, and the regional differences in ecological organization are the most prominent; (2) the level of ecosystem health in the Qinling-Daba Mountains has been spatially improved from the peripheral areas to the central area, showing significant spatial autocorrelation and local spatial aggregation; (3) the ecosystem health is influenced by a combination of natural and anthropogenic factors, among which the negative effect of GRDP is mainly concentrated in the eastern region, the negative effect of the proportion of built-up land gradually spreads to the western region, and the positive effect of the proportion of forest land has a large scale. This study contributes to a better understanding of ecosystem health in mountainous counties in China and provides useful information for policymakers to formulate ecological and environmental management policies.

Background: Histone acetylation modification has been found to be correlated the development of renal carcinoma; however, its role in clear cell renal carcinoma (ccRCC) remains to be investigated. Thus, this study aimed to identify the molecular subtypes and establish a relevant score based on histone acetylation modification in ccRCC.Methods: Gene expression and mutation data were retrieved from The Cancer Genome Atlas database. Molecular subtypes were identified by unsupervised clustering based on histone acetylation regulators expression, and the molecular and clinical characteristics including survival, tumor microenvironment, gene set variation, immune cell infiltration, and immune checkpoints in each subtype were investigated. Next, we employed univariate Cox analysis to analyze these genes and established acetylation-related score by lasso regression analysis. Furthermore, we investigated the differences including survival, signaling pathways, mutational landscape, and tumor mutation burden (TMB) between high-risk and low-risk groups. The established score was validated by receiver operating curve and univariate and multivariate Cox regression analyses. We also established a nomogram including acetylation score, age, gender, grade, and stage and verified it by decision curve analysis and calibration plot. The E-MTAB-1980 cohort from the ArrayExpress database was employed as a reference to validate the established score.Results: Thirty-three types of histone acetylation regulators were employed in this study, and two clusters were identified. The two clusters presented significant differences in survival, tumor microenvironment, immune cell infiltration, immune checkpoints, and signaling pathways. Furthermore, an acetylation-related score, composed of six genes (BRD9, HDAC10, KAT2A, KAT5, BRDT, SIRT1, KAT6A, HDAC5), was verified to be significantly associated with prognosis and TMB. Thus, the established scores were successfully verified by the validated cohort, and the nomogram was constructed and successfully validated.Conclusion: The identification of the histone acetylation-related subtypes and score in our study may help reveal the potential relation between histone acetylation and immunity and provide novel insights for the development of individualized therapy for ccRCC.

Brassica crop diseases caused by various pathogens, including viruses, bacteria, fungi and oomycetes, have devastating effects on the plants, leading to significant yield loss. This effect is worsened by the impact of climate change and the pressure to increase cultivation worldwide to feed the burgeoning population. As such, managing Brassica diseases has become a challenge demanding a rapid solution. In this review, we provide a detailed introduction of the plant immune system, discuss the evolutionary pattern of both dominant and recessive disease resistance (R) genes in Brassica and discuss the role of epigenetics in R gene evolution. Reviewing the current findings of how R genes evolve in Brassica spp. provides further insight for the development of creative ideas for crop improvement in relation to breeding sustainable, high quality, disease-resistant Brassica crops.

Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the issue of vehicle-assisted UAV delivery, we did not find any studies on the scenario of an UAV serving several customers. This study aims to design a new vehicle-assisted UAV delivery solution that allows UAVs to serve multiple customers in a single take-off and takes energy consumption into account. A multi-UAV task allocation model and a vehicle path planning model were established to determine the task allocation of the UAVs as well as the path of UAVs and the vehicle, respectively. The model also considered the impact of changing the payload of the UAV on energy consumption, bringing the results closer to reality. Finally, a hybrid heuristic algorithm based on an improved K-means algorithm and ant colony optimization (ACO) was proposed to solve the problem, and the effectiveness of the scheme was proven by multi-scale experimental instances and comparative experiments.

Insomnia is a type of sleep disorder which is associated with various diseases’ development and progression, such as obesity, type II diabetes and cardiovascular diseases. Recent investigation of the gut-brain axis enhances our understanding of the role of the gut microbiota in brain-related diseases. However, whether the gut microbiota is associated with insomnia remains unknown. In the present investigation, leveraging the 16S rDNA amplicon sequencing of V3-V4 region and the novel bioinformatic analysis, it was demonstrated that between insomnia and healthy populations, the composition, diversity and metabolic function of the gut microbiota are significantly changed. Other than these, redundancy analysis, co-occurrence analysis and PICRUSt underpin the gut taxa composition, signaling pathways, and metabolic functions perturbed by insomnia disorder. Moreover, random forest together with cross-validation identified two signature bacteria, which could be used to distinguish the insomnia patients from the healthy population. Furthermore, based on the relative abundance and clinical sleep parameter, we constructed a prediction model utilizing artificial neural network (ANN) for auxiliary diagnosis of insomnia disorder. Overall, the aforementioned study provides a comprehensive understanding of the link between the gut microbiota and insomnia disorder.

Brassica napus (canola/oilseed rape/rapeseed) is an economically important crop, mostly found in temperate and sub-tropical regions, that is cultivated widely for its edible oil. Major diseases of Brassica crops such as Blackleg, Clubroot, Sclerotinia Stem Rot, Downy Mildew, Alternaria Leaf Spot and White Rust have caused significant yield and economic losses in rapeseed-producing countries worldwide, exacerbated by global climate change, and, if not remedied effectively, will threaten global food security. To gain further insights into the host–pathogen interactions in relation to Brassica diseases, it is critical that we review current knowledge in this area and discuss how omics technologies can offer promising results and help to push boundaries in our understanding of the resistance mechanisms. Omics technologies, such as genomics, proteomics, transcriptomics and metabolomics approaches, allow us to understand the host and pathogen, as well as the interaction between the two species at a deeper level. With these integrated data in multi-omics and systems biology, we are able to breed high-quality disease-resistant Brassica crops in a more holistic, targeted and accurate way.

Brassica napus is an economically important crop across different continents including temperate and subtropical regions in Europe, Canada, South Asia, China and Australia. Its widespread cultivation also brings setbacks as it plays host to fungal, oomycete and chytrid pathogens that can lead to serious yield loss. For sustainable crop production, identification of resistance (R) genes in B. napus has become of critical importance. In this review, we discuss four key pathogens affecting Brassica crops: Clubroot (Plasmodiophora brassicae), Blackleg (Leptosphaeria maculans and L. biglobosa), Sclerotinia Stem Rot (Sclerotinia sclerotiorum), and Downy Mildew (Hyaloperonospora parasitica). We first review current studies covering prevalence of these pathogens on Brassica crops and highlight the R genes and QTL that have been identified from Brassica species against these pathogens. Insights into the relationships between the pathogen and its Brassica host, the unique host resistance mechanisms and how these affect resistance outcomes is also presented. We discuss challenges in identification and deployment of R genes in B. napus in relation to highly specific genetic interactions between host subpopulations and pathogen pathotypes and emphasize the need for common or shared techniques and research materials or tighter collaboration between researchers to reconcile the inconsistencies in the research outcomes. Using current genomics tools, we provide examples of how characterization and cloning of R genes in B. napus can be carried out more effectively. Lastly, we put forward strategies to breed resistant cultivars through introgressions supported by genomic approaches and suggest prospects that can be implemented in the future for a better, pathogen-resistant B. napus.

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans.

Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners. [ABSTRACT FROM AUTHOR]

Plant disease outbreaks continuously challenge food security and sustainability. Traditional chemical methods used to treat diseases have environmental and health concerns, raising the need to enhance inherent plant disease resistance mechanisms. Traits, including disease resistance, can be linked to specific loci in the genome and identifying these markers facilitates targeted breeding approaches. Several methods, including genome‐wide association studies and genomic selection, have been used to identify important markers and select varieties with desirable traits. However, these traditional approaches may not fully capture the non‐linear characteristics of the effect of genomic variation on traits. Machine learning, known for its data‐mining abilities, offers an opportunity to enhance the accuracy of the existing trait association approaches. It has found applications in predicting various agronomic traits across several species. However, its use in disease resistance prediction remains limited. This review highlights the potential of machine learning as a complementary tool for predicting the genetic loci contributing to pathogen resistance. We provide an overview of traditional trait prediction methods, summarize machine‐learning applications, and address the challenges and opportunities associated with machine learning‐based crop disease resistance prediction. [ABSTRACT FROM AUTHOR]