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IntroductionPromoting the reemployment of retirees is important to effectively recognize the capacity of older adults and to help governments cope with an aging global population. Existing research on the factors that impact reemployment has mainly focused on the role of traditional forms of human capital, like education and experience, while ignoring non-cognitive skills.MethodsBased on 3,693 samples, this study examines the impact of non-cognitive skills on the reemployment of Chinese retirees using the Logit model through the lens of human capital theory.ResultsThe results show that non-cognitive skills incentivize retirees to seek reemployment. The incentive effect is greater for retirees who are male, live in a rural household, and are of lower age and education level. Further, the mediation effect model reveals the mediating role of social capital between non-cognitive skills and the reemployment of retirees. Social capital is important to the promotion of retiree reemployment.DiscussionThis study ultimately sheds light on the relationship between non-cognitive skills and the reemployment of retirees. Findings will help improve governments’ understandings of non-cognitive skills so that they may develop better policies on retiree reemployment.

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection.

Molecular markers are developed to accelerate deployment of genes for desirable traits segregated in a bi-parental population of recombinant inbred lines (RILs) or doubled haplotype (DH) lines for mapping. However, it would be the most effective if such markers for multiple traits could be identified in an F2 population. In this study, single nucleotide polymorphisms (SNP) chips were used to identify major genes for heading date and awn in an F2 population without developing RILs or DH lines. The population was generated from a cross between a locally adapted spring wheat cultivar “Ningmaizi119” and a winter wheat cultivar “Tabasco” with a diverse genetic background. It was found that the dominant Vrn-D1 allele could make Ningmaizi119 flowered a few months earlier than Tabasco in the greenhouse and without vernalization. The observed effects of the allele were validated in F3 populations. It was also found that the dominant Ali-A1 allele for awnless trait in Tabasco or the recessive ali-A1 allele for awn trait in Ningmaizi119 was segregated in the F2 population. The allelic variation in the ALI-A1 gene relies not only on the DNA polymorphisms in the promoter but also on gene copy number, with one copy ali-A1 in Ningmaizi119 but two copies Ali-A1 in Tabasco based on RT-PCR results. According to wheat genome sequences, cultivar “Mattis” has two copies Ali-A1 and cultivar “Spelta” has four copies Ali-A in a chromosome that was uncharacterized (ChrUN), in addition to one copy on chromosome 5A. This study rapidly characterized the effects of the dominant Vrn-D1 allele and identified the haplotype of Ali-A1 in gene copy number in the F2 segregation population of common wheat will accelerate their deployment in cycling lines in breeding.

Wheat production is increasingly threatened by the fungal disease, Fusarium head blight (FHB), caused by Fusarium spp. The introduction of resistant varieties is considered to be an effective measure for containment of this disease. Mapping of FHB-resistance quantitative trait locus (QTL) has promoted marker-assisted breeding for FHB resistance, which has been difficult through traditional breeding due to paucity of resistance genes and quantitative nature of the resistance. The lab of Ma previously cloned Fhb1, which inhibits FHB spread within spikes, and fine mapped Fhb4 and Fhb5, which condition resistance to initial infection of Fusarium spp., from FHB-resistant indigenous line Wangshuibai (WSB). In this study, these three QTLs were simultaneously introduced into five modern Chinese wheat cultivars or lines with different ecological adaptations through marker-assisted backcross in early generations. A total of 14 introgression lines were obtained. All these lines showed significantly improved resistance to the fungal infection and disease spread in 2-year field trials after artificial inoculation. In comparison with the respective recipient lines, the Fhb1, Fhb4, and Fhb5 pyramiding could reduce the disease severity by 95% and did not systematically affect plant height, productive tiller number, kernel number per spike, thousand grain weight, flowering time, and unit yield (without Fusarium inoculation). These results indicated the great value of FHB-resistance QTLs Fhb1, Fhb4, and Fhb5 derived from WSB, and the feasibility and effectiveness of early generation selection for FHB resistance solely based on linked molecular markers.

The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum Schwabe is an important disease of wheat that causes severe yield losses along with serious quality concerns. Incorporating the host resistance from either wild relatives, landraces, or exotic materials remains challenging and has shown limited success. Therefore, a better understanding of the genetic basis of native FHB resistance in hard winter wheat (HWW) and combining it with major quantitative trait loci (QTLs) can facilitate the development of FHB-resistant cultivars. In this study, we evaluated a set of 257 breeding lines from the South Dakota State University (SDSU) breeding program to uncover the genetic basis of native FHB resistance in the US hard winter wheat. We conducted a multi-locus genomewide association study (ML-GWAS) with 9,321 high-quality single-nucleotide polymorphisms (SNPs). A total of six distinct marker-trait associations (MTAs) were identified for the FHB disease index (DIS) on five different chromosomes including 2A, 2B, 3B, 4B, and 7A. Further, eight MTAs were identified for Fusarium-damaged kernels (FDK) on six chromosomes including 3B, 5A, 6B, 6D, 7A, and 7B. Out of the 14 significant MTAs, 10 were found in the proximity of previously reported regions for FHB resistance in different wheat classes and were validated in HWW, while four MTAs represent likely novel loci for FHB resistance. Accumulation of favorable alleles of reported MTAs resulted in significantly lower mean DIS and FDK score, demonstrating the additive effect of FHB resistance alleles. Candidate gene analysis for two important MTAs identified several genes with putative proteins of interest; however, further investigation of these regions is needed to identify genes conferring FHB resistance. The current study sheds light on the genetic basis of native FHB resistance in the US HWW germplasm and the resistant lines and MTAs identified in this study will be useful resources for FHB resistance breeding via marker-assisted selection. [ABSTRACT FROM AUTHOR]

In order to compensate for the limitations of traditional temperature measurement methods, glass plates were installed on both sides of the single-dome combustion chamber test piece, and two CCD cameras observed the combustion flame in the furnace from different positions. The obtained flame radiation images were combined into a single image through the video capture card, and then the images were input into the reconstruction algorithm software to calculate the three-dimensional temperature field in the combustion chamber in real-time. The software could dynamically display the temperature distribution at three different positions in the chamber. This marked the first time that the temperature field in the combustion chamber was obtained using this new temperature measurement technology, which preliminarily verifies the feasibility of reconstructing the temperature field in the single-dome combustion chamber through flame image temperature measurement technology.

IntroductionPulmonary fibrosis is a terminal lung disease characterized by fibroblast proliferation, extracellular matrix accumulation, inflammatory damage, and tissue structure destruction. The pathogenesis of this disease, particularly idiopathic pulmonary fibrosis (IPF), remains unknown. Macrophages play major roles in organ fibrosis diseases, including pulmonary fibrosis. The phenotype and polarization of macrophages are closely associated with pulmonary fibrosis. A new direction in research on anti-pulmonary fibrosis is focused on developing drugs that maintain the stability of the pulmonary microenvironment.MethodsWe obtained gene sequencing data and clinical information for patients with IPF from the GEO datasets GSE110147, GSE15197, GSE24988, GSE31934, GSE32537, GSE35145, GSE53845, GSE49072, GSE70864, and GSE90010. We performed GO, KEGG enrichment analysis and GSEA analysis, and conducted weighted gene co-expression network analysis. In addition, we performed proteomic analysis of mouse lung tissue. To verify the results of bioinformatics analysis and proteomic analysis, mice were induced by intratracheal instillation of bleomycin (BLM), and gavaged for 14 days after modeling. Respiratory function of mice in different groups was measured. Lung tissues were retained for histopathological examination, Western Blot and real-time quantitative PCR, etc. In addition, lipopolysaccharide, interferon-γ and interleukin-4 were used to induce RAW264.7 cells for 12h in vitro to establish macrophage inflammation and polarization model. At the same time, HG2 intervention was given. The phenotype transformation and cytokine secretion of macrophages were investigated by Western Blot, RT-qPCR and flow cytometry, etc.ResultsThrough bioinformatics analysis and experiments involving bleomycin-induced pulmonary fibrosis in mice, we confirmed the importance of macrophage polarization in IPF. The analysis revealed that macrophage polarization in IPF involves a change in the phenotypic spectrum. Furthermore, experiments demonstrated high expression of M2-type macrophage-associated biomarkers and inducible nitric oxide synthase, thus indicating an imbalance in M1/M2 polarization of pulmonary macrophages in mice with pulmonary fibrosis.DiscussionOur investigation revealed that the ethyl acetate extract (HG2) obtained from the roots of Prismatomeris connata Y. Z. Ruan exhibits therapeutic efficacy against bleomycin-induced pulmonary fibrosis. HG2 modulates macrophage polarization, alterations in the TGF-β/Smad pathway, and downstream protein expression in the context of pulmonary fibrosis. On the basis of our findings, we believe that HG2 has potential as a novel traditional Chinese medicine component for treating pulmonary fibrosis.

Skeletal muscle is one of the largest organs in the body and the largest protein repository. Mitochondria are the main energy-producing organelles in cells and play an important role in skeletal muscle health and function. They participate in several biological processes related to skeletal muscle metabolism, growth, and regeneration. Adenosine monophosphate-activated protein kinase (AMPK) is a metabolic sensor and regulator of systemic energy balance. AMPK is involved in the control of energy metabolism by regulating many downstream targets. In this review, we propose that AMPK directly controls several facets of mitochondrial function, which in turn controls skeletal muscle metabolism and health. This review is divided into four parts. First, we summarize the properties of AMPK signal transduction and its upstream activators. Second, we discuss the role of mitochondria in myogenesis, muscle atrophy, regeneration post-injury of skeletal muscle cells. Third, we elaborate the effects of AMPK on mitochondrial biogenesis, fusion, fission and mitochondrial autophagy, and discuss how AMPK regulates the metabolism of skeletal muscle by regulating mitochondrial function. Finally, we discuss the effects of AMPK activators on muscle disease status. This review thus represents a foundation for understanding this biological process of mitochondrial dynamics regulated by AMPK in the metabolism of skeletal muscle. A better understanding of the role of AMPK on mitochondrial dynamic is essential to improve mitochondrial function, and hence promote skeletal muscle health and function.

BackgroundThis study aimed to explore the impact of hypoxic hepatitis (HH) on survival in heart failure (HF) patients and to develop an effective machine learning model to predict 30-day mortality risk in HF patients with HH.MethodsIn the Medical Information Mart for Intensive Care (MIMIC)-III and IV databases, clinical data and survival situations of HF patients admitted to the intensive care unit (ICU) were retrospectively collected. Propensity Score Matching (PSM) analysis was used to balance baseline differences between HF patients with and without HH. Kaplan Meier analysis and multivariate Cox analysis were used to determining the effect of HH on the survival of CF patients. For developing a model that can predict 30-day mortality in CF patients with HH, the feature recurrence elimination (RFE) method was applied to feature selection, and seven machine learning algorithms were employed to model construction. After training and hyper-parameter optimization (HPO) of the model through cross-validation in the training set, a performance comparison was performed through internal and external validation. To interpret the optimal model, Shapley Additive Explanations (SHAP) were used along with the Local Interpretable Model-agnostic Explanations (LIME) and the Partial Dependence Plot (PDP) techniques.ResultsThe incidence of HH was 6.5% in HF patients in the MIMIC cohort. HF patients with HH had a 30-day mortality rate of 33% and a 1-year mortality rate of 51%, and HH was an independent risk factor for increased short-term and long-term mortality risk in HF patients. After RFE, 21 key features (21/56) were selected to build the model. Internal validation and external validation suggested that Categorical Boosting (Catboost) had a higher discriminatory capability than the other models (internal validation: AUC, 0.832; 95% CI, 0.819–0.845; external validation: AUC, 0.757 95% CI, 0.739–0.776), and the simplified Catboost model (S-Catboost) also had good performance in both internal validation and external validation (internal validation: AUC, 0.801; 95% CI, 0.787–0.813; external validation: AUC, 0.729, 95% CI, 0.711–0.745).ConclusionHH was associated with increased mortality in HF patients. Machine learning methods had good performance in identifying the 30-day mortality risk of HF with HH. With interpretability techniques, the transparency of machine learning models has been enhanced to facilitate user understanding of the prediction results.

The application of deep learning in the medical field has continuously made huge breakthroughs in recent years. Based on convolutional neural network (CNN), the U-Net framework has become the benchmark of the medical image segmentation task. However, this framework cannot fully learn global information and remote semantic information. The transformer structure has been demonstrated to capture global information relatively better than the U-Net, but the ability to learn local information is not as good as CNN. Therefore, we propose a novel network referred to as the O-Net, which combines the advantages of CNN and transformer to fully use both the global and the local information for improving medical image segmentation and classification. In the encoder part of our proposed O-Net framework, we combine the CNN and the Swin Transformer to acquire both global and local contextual features. In the decoder part, the results of the Swin Transformer and the CNN blocks are fused to get the final results. We have evaluated the proposed network on the synapse multi-organ CT dataset and the ISIC 2017 challenge dataset for the segmentation task. The classification network is simultaneously trained by using the encoder weights of the segmentation network. The experimental results show that our proposed O-Net achieves superior segmentation performance than state-of-the-art approaches, and the segmentation results are beneficial for improving the accuracy of the classification task. The codes and models of this study are available at https://github.com/ortonwang/O-Net.

Gastric cancer is the third most common cause of cancer-related death in the world. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. It is a repetitive, tedious, and highly subjective process. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.

The time to flowering (DF), pod beginning (DPB), seed formation (DSF), and maturity initiation (DMI) in soybean (Glycine max [L.] Merr) are important characteristics of growth stage traits (GSTs) in Chinese summer-sowing soybean, and are influenced by genetic as well as environmental factors. To better understand the molecular mechanism underlying the initiation times of GSTs, we investigated four GSTs of 309 diverse soybean accessions in six different environments and Best Linear Unbiased Prediction values. Furthermore, the genome-wide association study was conducted by a Fixed and random model Circulating Probability Unification method using over 60,000 single nucleotide polymorphism (SNP) markers to identify the significant quantitative trait nucleotide (QTN) regions with phenotypic data. As a result, 212 SNPs within 102 QTN regions were associated with four GSTs. Of which, eight stable regions were repeatedly detected in least three datasets for one GST. Interestingly, half of the QTN regions overlapped with previously reported quantitative trait loci or well-known soybean growth period genes. The hotspots associated with all GSTs were concentrated on chromosome 10. E2 (Glyma10g36600), a gene with a known function in regulating flowering and maturity in soybean, is also found on this chromosome. Thus, this genomic region may account for the strong correlation among the four GSTs. All the significant SNPs in the remaining 7 QTN regions could cause the significant phenotypic variation with both the major and minor alleles. Two hundred and seventy-five genes in soybean and their homologs in Arabidopsis were screened within ± 500 kb of 7 peak SNPs in the corresponding QTN regions. Most of the genes are involved in flowering, response to auxin stimulus, or regulation of seed germination, among others. The findings reported here provide an insight for genetic improvement which will aid in breeding of soybean cultivars that can be adapted to the various summer sowing areas in China and beyond.

2-Hyroxypropyl-beta-cyclodextrin (HPβCD) is being used to treat Niemann-Pick C1, a fatal neurodegenerative disease caused by abnormal cholesterol metabolism. HPβCD slows disease progression, but unfortunately causes severe, rapid onset hearing loss by destroying the outer hair cells (OHC). HPβCD-induced damage is believed to be related to the expression of prestin in OHCs. Because prestin is postnatally upregulated from the cochlear base toward the apex, we hypothesized that HPβCD ototoxicity would spread from the high-frequency base toward the low-frequency apex of the cochlea. Consistent with this hypothesis, cochlear hearing impairments and OHC loss rapidly spread from the high-frequency base toward the low-frequency apex of the cochlea when HPβCD administration shifted from postnatal day 3 (P3) to P28. HPβCD-induced histopathologies were initially confined to the OHCs, but between 4- and 6-weeks post-treatment, there was an unexpected, rapid and massive expansion of the lesion to include most inner hair cells (IHC), pillar cells (PC), peripheral auditory nerve fibers, and spiral ganglion neurons at location where OHCs were missing. The magnitude and spatial extent of HPβCD-induced OHC death was tightly correlated with the postnatal day when HPβCD was administered which coincided with the spatiotemporal upregulation of prestin in OHCs. A second, massive wave of degeneration involving IHCs, PC, auditory nerve fibers and spiral ganglion neurons abruptly emerged 4–6 weeks post-HPβCD treatment. This secondary wave of degeneration combined with the initial OHC loss results in a profound, irreversible hearing loss.

New onset diabetes mellitus (NODM) postliver transplantation (LT) is very common and may negatively affect patient and graft survival, but its causative mechanism is still unclear. This study was to analyze the connection between Hepatitis C virus (HCV) infection and NODM after LT by systematically reviewing published medical literature. We electronically searched databases of MEDLINE, EMBASE and the Cochrane Library from January 1980 to January 2008. Only retrospective studies could be identified. Seven of them were subjected to the meta-analysis. Analysis was performed by usingrevman 4.2 software. We found that HCV increased the prevalence of NODM [OR 2.46; 95%CI (1.44, 4.19)]. Then, we further analyzed the association between HCV and persistent-NODM (P-NODM) after LT. The result showed that prevalence of P-NODM was higher in HCV-positive group than in HCV-negative group with marginally statistical significance [OR = 1.39; 95%CI (1.06, 1.83)]. The present meta-analysis based on retrospective studies suggested a significant relationship between HCV and NODM after LT, and it seems that HCV infection might also increase the prevalence of P-NODM. Multicenter, large sized prospective studies are still needed to further confirm these results. [ABSTRACT FROM AUTHOR]

Calorie restriction (CR) is considered one of the most robust ways to extend life span and reduce the risk of age-related diseases, including cancer, as shown in many different organisms, whereas opposite effects have been associated with high fat diets (HFDs). Despite the proven contribution of sirtuins in mediating the effects of CR in longevity, the involvement of these nutrient sensors, specifically, in the diet-induced effects on tumorigenesis has yet to be elucidated. Previous studies focusing on SIRT1, do not support a critical role for this sirtuin family member in CR-mediated cancer prevention. However, the contribution of other family members which exhibit strong deacetylase activity is unexplored. To fill this gap, we aimed at investigating the role of SIRT2 and SIRT3 in mediating the anti and pro-tumorigenic effect of CR and HFD, respectively. Our results provide strong evidence supporting distinct, context-dependent roles played by these two family members. SIRT2 is indispensable for the protective effect of CR against tumorigenesis. On the contrary, SIRT3 exhibited oncogenic properties in the context of HFD-induced tumorigenesis, suggesting that SIRT3 inhibition may mitigate the cancer-promoting effects of HFD. Given the different functions regulated by SIRT2 and SIRT3, unraveling downstream targets/pathways involved may provide opportunities to develop new strategies for cancer prevention.

Background: Chronic stress is an important risk factor for depression. The nesfatin-1 (NES1)–oxytocin (OT)–proopiomelanocortin (POMC) neural pathway, which is involved in the stress response, was recently shown to have an anorectic effect in the hypothalamus. Our previous study showed that Xiaoyaosan, a well-known antidepressant used in traditional Chinese medicine, effectively relieved appetite loss induced by chronic immobilization stress (CIS). However, whether Xiaoyaosan ameliorates depression-like behaviors and anorexia by regulating the NES1-OT-POMC neural pathway remains unclear.Objective: To investigate whether the antidepressant-like and anti-anorexia effects of Xiaoyaosan are related to the NES1-OT-POMC neural pathway in the hypothalamus.Methods: Rats were randomly divided into control, CIS, Xiaoyaosan treatment, and fluoxetine treatment groups. The rats in the CIS, Xiaoyaosan treatment, and fluoxetine treatment groups were subjected to CIS for 21 consecutive days, during which they were administered distilled water, a Xiaoyaosan decoction [3.854 g/(kg·d)] or fluoxetine [1.76 mg/(kg·d)], respectively, by gavage, and their body weights and food intake were monitored daily. The rats were subsequently subjected to the open field test and sucrose preference test. Then, the expression levels of corticosterone and NES1 in the serum and the expression levels of NES1, OT, POMC, and melanocortin-4 receptor (MC4R) in the hypothalamus were determined by real-time fluorescence quantitative polymerase chain reaction, Western blot analysis, and immunochemistry. Furthermore, immunofluorescence double staining was used to determine whether related proteins in the hypothalamic NES1-OT-POMC neural pathway were co-expressed.Results: Compared to control rats, rats exposed to CIS exhibited gradually less food intake and lower body weights and significantly increased concentrations of NES1 in the serum and paraventricular nucleus. Moreover, the expression levels of POMC, OT, and MC4R in the hypothalamus were significantly higher in the CIS group than those in the control group. However, these changes were reversed by pretreatment with Xiaoyaosan and fluoxetine. Specifically, the expression levels of members of the NES1-OT-POMC neural pathway were lower in the Xiaoyaosan-treated group than in the CIS group.Conclusion: Xiaoyaosan ameliorates CIS-induced depression-like behaviors and anorexia by regulating the NES1-OT-POMC neural pathway in the hypothalamus.

The caudal region of the rodent striatum, called the tail of the striatum (TS), is a relatively small area but might have a distinct function from other striatal subregions. Recent primate studies showed that this part of the striatum has a unique function in encoding long-term value memory of visual objects for habitual behavior. This function might be due to its specific connectivity. We identified inputs to the rat TS and compared those with inputs to the dorsomedial striatum (DMS) in the same animals. The TS directly received anatomical inputs from both sensory structures and value-coding regions, but the DMS did not. First, inputs from the sensory cortex and sensory thalamus to the TS were found; visual, auditory, somatosensory and gustatory cortex and thalamus projected to the TS but not to the DMS. Second, two value systems innervated the TS; dopamine and serotonin neurons in the lateral part of the substantia nigra pars compacta (SNc) and dorsal raphe nucleus projected to the TS, respectively. The DMS received inputs from the separate group of dopamine neurons in the medial part of the SNc. In addition, learning-related regions of the limbic system innervated the TS; the temporal areas and the basolateral amygdala selectively innervated the TS, but not the DMS. Our data showed that both sensory and value-processing structures innervated the TS, suggesting its plausible role in value-guided sensory-motor association for habitual behavior.

To understand the molecular mechanism underlying salt stress response in radish, iTRAQ-based proteomic analysis was conducted to investigate the differences in protein species abundance under different salt treatments. In total, 851, 706, and 685 differential abundance protein species (DAPS) were identified between CK vs. Na100, CK vs. Na200, and Na100 vs. Na200, respectively. Functional annotation analysis revealed that salt stress elicited complex proteomic alterations in radish roots involved in carbohydrate and energy metabolism, protein metabolism, signal transduction, transcription regulation, stress and defense and transport. Additionally, the expression levels of nine genes encoding DAPS were further verified using RT-qPCR. The integrative analysis of transcriptomic and proteomic data in conjunction with miRNAs was further performed to strengthen the understanding of radish response to salinity. The genes responsible for signal transduction, ROS scavenging and transport activities as well as several key miRNAs including miR171, miR395, and miR398 played crucial roles in salt stress response in radish. Based on these findings, a schematic genetic regulatory network of salt stress response was proposed. This study provided valuable insights into the molecular mechanism underlying salt stress response in radish roots and would facilitate developing effective strategies toward genetically engineered salt-tolerant radish and other root vegetable crops.

Lead (Pb) is one of the most abundant heavy metal pollutants, which can penetrate the plant through the root and then enter the food chain causing in potential health risks for human beings. Radish is an important root vegetable crop worldwide. To investigate the mechanism underlying plant response to Pb stress in radish, the protein profile changes of radish roots were comprehensively analyzed using iTRAQ (Isobaric Tag for Relative and Absolute Quantification) respectively upon Pb(NO3)2 at 500 mg L-1(Pb500) and Pb(NO3)2 at 1000 mg L-1(Pb1000) exposure. A total of 3, 898 protein species were successfully detected and 2,141 were quantified. Among them, a subset of 721 protein species were differentially accumulated upon at least one Pb treatment, and 135 ones showed significantly abundance changes during both two Pb-stressed conditions. Many critical protein species related to protein translation, processing, and degradation, reactive oxygen species (ROS) scavenging, photosynthesis and respiration and carbon metabolism were successfully identified. Gene Ontology (GO) and pathway enrichment analysis of the 135 differential abundance protein species revealed that the overrepresented GO terms were include in ‘cell wall’, ‘apoplast’, ‘response to metal ion’, ‘vacuole’ and ‘peroxidase activity’, and the critical enriched pathways were involved in ‘citric acid (TCA) cycle and respiratory electron transport’, ‘pyruvate metabolism’, ‘phenylalanine metabolism’, ‘phenylpropanoid biosynthesis’ and ‘carbon metabolism’. Furthermore, the integrative analysis of transcriptomic, miRNA, degradome, metabolomic and proteomic data provided a strengthened understanding of radish response to Pb stress at multiple levels. Under Pb stress, many key enzymes (i.e. ATP citrate lyase, Isocitrate dehydrogenase, fumarate hydratase and malate dehydrogenase) involved in the glycolysis and TCA cycle were severely affected, which ultimately cause alteration of some metabolites including glucose, citrate, and malate. Meanwhile, a series of other defense responses including ascorbate (ASA)–glutathione (GSH) cycle for ROS scavenging and Pb-defense protein species (glutaredoxin, aldose 1-epimerase malate dehydrogenase and thioredoxin), were triggered to cope with Pb-induced injuries. The results would be helpful for further dissecting molecular mechanism underlying plant response to HM stresses, and facilitate effective management of HM contamination in vegetable crops by genetic manipulation.