Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield of Bromus inermis.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha-1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha-1, MAE = 29.43 kg ha-1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.

Neutrophil-derived nanovesicles deliver IL-37 to mitigate renal ischemia-reperfusion injury via endothelial cell targeting.

Renal ischemia-reperfusion injury (IRI) is one of the most important causes of acute kidney injury (AKI). Interleukin (IL)-37 has been suggested as a novel anti-inflammatory factor for the treatment of IRI, but its application is still limited by its low stability and delivery efficiency. In this study, we reported a novel engineered method to efficiently and easily prepare neutrophil membrane-derived vesicles (N-MVs), which could be utilized as a promising vehicle to deliver IL-37 and avoid the potential side effects of neutrophil-derived natural extracellular vesicles. N-MVs could enhance the stability of IL-37 and targetedly deliver IL-37 to damaged endothelial cells of IRI kidneys via P-selectin glycoprotein ligand-1 (PSGL-1). In vitro and in vivo evidence revealed that N-MVs encapsulated with IL-37 (N-MV@IL-37) could inhibit endothelial cell apoptosis, promote endothelial cell proliferation and angiogenesis, and decrease inflammatory factor production and leukocyte infiltration, thereby ameliorating renal IRI. Our study establishes a promising delivery vehicle for the treatment of renal IRI and other endothelial damage-related diseases.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

The effects of micro- and nanoplastics on the central nervous system: A new threat to humanity?

Given the widespread production and use of plastics, poor biodegradability, and inadequate recycling, micro/nanoplastics (MNPs) have caused widespread environmental pollution. As a result, humans inevitably ingest MNPs through various pathways. However, there is still no consensus on whether exposure to MNPs has adverse effects on humans. This article aims to provide a comprehensive overview of the knowledge of MNPs and the potential mechanisms of their impact on the central nervous system. Numerous in vivo and in vitro studies have shown that exposure to MNPs may pass through the blood-brain barrier (BBB) and lead to neurotoxicity through impairments in oxidative and inflammatory balance, neurotransmitter alternation, nerve conduction-related key enzymes, and impact through the gut-brain axis. It is worth noting that MNPs may act as carriers and have more severe effects on the body when co-exposed with other substances. MNPs of smaller sizes cause more severe harm. Despite the scarcity of reports directly relevant to humans, this review brings together a growing body of evidence showing that exposure to MNPs disturbs neurons and has even been found to alter the memory and behavior of organisms. This effect may lead to further potential negative influence on the central nervous system and contribute to the development of other diseases such as central nervous system inflammation and Parkinson 's-like neurodegenerative disorders. There is a need further to investigate the threat of MNPs to human health.

Causal effect between gut microbiota and gastroesophageal reflux disease: a bidirectional two-sample Mendelian randomization study.

Previous observational studies have found that the gut microbiota is closely related to the pathogenesis of gastroesophageal reflux disease (GERD), while their causal relationship is unclear. A two-sample multivariate Mendelian randomization analysis was implemented to estimate the causal effect of gut microbiota on GERD. The gut microbiota aggregated statistics were derived from a meta-analysis of the largest available genome-wide association studies (GWAS) conducted by the MiBioGen alliance (n = 13 266). GERD aggregated statistics were derived from published GWAS (129 080 cases and 473 524 controls). A bidirectional two-sample Mendelian randomization study was conducted to explore the causal relationship between gut microbiota and GERD using the inverse variance weighted (IVW), Mendelian randomization Egger, single model, weighted median, and weighted model. To verify the stability of the main results of Mendelian randomization analysis, we performed sensitivity analysis. Based on the results of IVW, we found that Anaerostipes was causally associated with an increased risk of GERD [odds ratio (OR): 1.09, P = 0.018]. Eight gut microbiota taxa (Actinobacteria, Bifidobacteriales, Bifidobacteriaceae, Clostridiales vadin BB60 group, Rikenellaceae, Lachnospiraceae UCG004, Methanobrevibacter, and unknown genus id.1000000073) are predicted to act causally in suppressing the risk of GERD (P < 0.05). In addition, reverse Mendelian randomization analyses revealed that the abundance of 15 gut microbiota taxon was found to be affected by GERD. No significant estimation of heterogeneity or pleiotropy is detected. Our study presents a complicated causal relationship between gut microbiota and GERD that offers guidance on the selection of appropriate probiotics as clinical interventions for GERD.

The causal impact of gut microbiota on circulating adipokine concentrations: a two-sample Mendelian randomization study.

PURPOSE: Evidence from previous experimental and observational research demonstrates that the gut microbiota is related to circulating adipokine concentrations. Nevertheless, the debate as to whether gut microbiome composition causally influences circulating adipokine concentrations remains unresolved. This study aimed to take an essential step in elucidating this issue. METHODS: We used two-sample Mendelian randomization (MR) to causally analyze genetic variation statistics for gut microbiota and four adipokines (including adiponectin, leptin, soluble leptin receptor [sOB-R], and plasminogen activator inhibitor-1 [PAI-1]) from large-scale genome-wide association studies (GWAS) datasets. A range of sensitivity analyses was also conducted to assess the stability and reliability of the results. RESULTS: The composite results of the MR and sensitivity analyses revealed 22 significant causal associations. In particular, there is a suggestive causality between the family Clostridiaceae1 (IVW: ß = 0.063, P = 0.034), the genus Butyrivibrio (IVW: ß = 0.029, P = 0.031), and the family Alcaligenaceae (IVW: ß=-0.070, P = 0.014) and adiponectin. Stronger causal effects with leptin were found for the genus Enterorhabdus (IVW: ß=-0.073, P = 0.038) and the genus Lachnospiraceae (NK4A136 group) (IVW: ß=-0.076, P = 0.01). Eight candidate bacterial groups were found to be associated with sOB-R, with the phylum Firmicutes (IVW: ß = 0.235, P = 0.03) and the order Clostridiales (IVW: ß = 0.267, P = 0.028) being of more interest. In addition, the genus Roseburia (IVW: ß = 0.953, P = 0.022) and the order Lactobacillales (IVW: ß=-0.806, P = 0.042) were suggestive of an association with PAI-1. CONCLUSION: This study reveals a causal relationship between the gut microbiota and circulating adipokines and may help to offer novel insights into the prevention of abnormal concentrations of circulating adipokines and obesity-related diseases.

CD146, a therapeutic target involved in cell plasticity.

Since its identification as a marker for advanced melanoma in the 1980s, CD146 has been found to have multiple functions in both physiological and pathological processes, including embryonic development, tissue repair and regeneration, tumor progression, fibrosis disease, and inflammations. Subsequent research has revealed that CD146 is involved in various signaling pathways as a receptor or co-receptor in these processes. This correlation between CD146 and multiple diseases has sparked interest in its potential applications in diagnosis, prognosis, and targeted therapy. To better comprehend the versatile roles of CD146, we have summarized its research history and synthesized findings from numerous reports, proposing that cell plasticity serves as the underlying mechanism through which CD146 contributes to development, regeneration, and various diseases. Targeting CD146 would consequently halt cell state shifting during the onset and progression of these related diseases. Therefore, the development of therapy targeting CD146 holds significant practical value.

Elucidation of antioxidant activities of intracellular and extracellular polysaccharides from Cordyceps militaris in vitro and their protective effects on ulcerative colitis in vivo.

In this study, we extracted the polysaccharides from C. militaris fruiting bodies (CFIPs), mycelial intracellular polysaccharides (CMIPs), and fermentation broth extracellular polysaccharides (CFEPs) to investigate their physicochemical properties, antioxidant capacities, and effects on oxazolone-induced zebrafish ulcerative colitis (UC). Our results revealed differences in monosaccharide composition and surface structure among CFIPs, CMIPs, and CFEPs. The molar ratios of glucose to mannose in CFIPs, glucose to xylose in CMIPs, and xylose to glucose in CFEPs were 7.57: 1.6, 7.26: 1.81, and 5.44: 2.98 respectively. Moreover, CFEPs exhibited significantly (p < 0.05) higher chemical antioxidant capacity compared to CMIPs and CFIPs. Surprisingly, CFEP treatment didn't show a significant effect in protecting against H2O2-induced oxidative damage in RAW 264.7 cells. After 3 d of treatment, the levels of ROS, MDA, and MPO in the CFIPs group exhibited a significant (p < 0.05) reduction by 37.82 %, 68.15 %, and 22.77 % respectively. Additionally, the ACP and AKP increased by 60.33 % and 96.99 %. Additionally, C. militaris polysaccharides (CMPs) were found to effectively improve UC by activating the MyD88/NF-κB signaling pathway in vivo. These findings confirm the distinct physicochemical properties of these three types of CMP and their potential for development into antioxidant-rich anti-inflammatory health foods.

Pterostilbene nanoemulsion promotes Nrf2 signaling pathway to downregulate oxidative stress for treating Alzheimer's disease.

Pterostilbene, a stilbene compound, demonstrates neuroprotective effects through its antioxidant and anti-inflammatory properties. However, pterostilbene exhibits low bioavailability. We developed a pterostilbene nanoemulsion with better release stability and particle size. Behavioral tests, including the Y maze, new object recognition, and water maze, revealed that the pterostilbene nanoemulsion demonstrated a more significant effect on improving learning and memory function than pterostilbene. Immunofluorescence analysis revealed that pterostilbene nanoemulsion was more potent in safeguarding hippocampal neurons and inhibiting apoptosis and oxidative stress than pterostilbene. Further results from the Western blot and quantitative reverse transcription polymerase chain reaction indicated that the enhanced efficacy of pterostilbene nanoemulsion may be attributed to its stronger promotion of the nuclear factor erythroid 2-related factor 2 signaling pathway. Hence, enhanced drug delivery efficiency decreased dosage requirements and increased the bioavailability of pterostilbene, thereby potentially providing a safe, effective, and convenient treatment option for patients with Alzheimer's disease.

Polysaccharides from an edible mushroom, Hericium erinaceus, alleviate ulcerative colitis in mice by inhibiting the NLRP3 inflammasomes and reestablish intestinal homeostasis.

This study aimed to assess the effects of polysaccharides extracted from Hericium erinaceus fruiting bodies (HEFPs) on the inflammatory response to oxidative stress in a mouse model of ulcerative colitis (UC) induced by ingestion of dextran sodium sulfate. The results indicated reduced oxidative damage in the HEFPs groups, as evidenced by significantly decreased malondialdehyde levels and significantly increased levels of the antioxidant enzymes superoxide dismutase and catalase in colon homogenates, compared with those in the Model Control (MC) group. Additionally, compared with the levels in the MC group, the levels of the pro-inflammatory factors IL-6, IL-1ß, and TNF-α in the positive-control (PC) and HEFPs groups were significantly lower, and that of the anti-inflammatory factor IL-10 was significantly higher. qRT-PCR analyses revealed that the colon expression patterns of IL-6, IL-1ß, TNF-α, and IL-18 were consistent with the serum levels. Western-blotting results indicated significantly lower levels of NLRP3, ASC, and caspase 1 P20 in the HEFPs and PC groups than in the MC group. These findings suggest that HEFPs alleviate UC by suppressing the NLRP3 inflammasome/Caspase-1 pathway. Lachnospiraceae, Clostridiales, Parabacteroides, Oscillibacter, and Clostridium XlVa genera were more abundant in the gut microbiota of the HEFPs group than that of the MC group.

Combination Therapy of Bone Marrow Mesenchymal Stem Cell Transplantation and Electroacupuncture for the Repair of Intrauterine Adhesions in Rats: Mechanisms and Functional Recovery.

Transplantation of bone marrow mesenchymal stem cells (BMSCs) has demonstrated promising clinical utility in the treatment of endometrial injury and the restoration of fertility. However, since the efficacy of BMSCs after transplantation is not stable, it is very important to find effective ways to enhance the utilisation of BMSCs. Electroacupuncture (EA) has some positive effects on the chemotaxis of stem cells and diseases related to uterine injury. In this study, we established the intrauterine adhesion (IUA) model of the Sprague-Dawley rat using lipopolysaccharide infection and mechanical scratching. Phosphate-buffered saline, BMSCs alone, and BMSCs combined with EA were randomly administered to the rats. Fluorescent cell labelling showed the migration of transplanted BMSCs. H&E staining, Masson staining, Western blot, immunohistochemistry, ELISA, and qRT-PCR were utilised to detect changes in endometrial morphology and expressions of endometrial receptivity-related factors, endometrial pro-inflammatory factors, and fibrosis factors. Finally, we conducted a fertility test to measure the recovery of uterine function. The results showed that EA promoted transplanted BMSCs to migrate into the injured uterus by activating the SDF-1/CXCR4 axis. Endometrial morphology showed the most significant improvement in the BMSC + EA group. The expressions of endometrial pro-inflammatory factors and fibrosis indexes in the BMSC + EA group were lower than those in the model and BMSC groups. Further studies revealed that the expression of endometrial receptivity-related factors and the number of embryos implanted on day 8 of gestation increased in the BMSC + EA group compared with the model group and the BMSC group.

High levels of fatty acid-binding protein 5 excessively enhances fatty acid synthesis and proliferation of granulosa cells in polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is one of the most complex endocrine disorders in women of reproductive age. Abnormal proliferation of granulosa cells (GCs) is an important cause of PCOS. This study aimed to explore the role of fatty acid-binding protein 5 (FABP5) in granulosa cell (GC) proliferation in polycystic ovary syndrome (PCOS) patients. METHODS: The FABP5 gene, which is related to lipid metabolism, was identified through data analysis of the gene expression profiles of GSE138518 from the Gene Expression Omnibus (GEO) database. The expression levels of FABP5 were measured by quantitative real-time PCR (qRT‒PCR) and western blotting. Cell proliferation was evaluated with a cell counting kit-8 (CCK-8) assay. Western blotting was used to assess the expression of the proliferation marker PCNA, and immunofluorescence microscopy was used to detect Ki67 expression. Moreover, lipid droplet formation was detected with Nile red staining, and qRT‒PCR was used to analyze fatty acid storage-related gene expression. RESULTS: We found that FABP5 was upregulated in ovarian GCs obtained from PCOS patients and PCOS mice. FABP5 knockdown suppressed lipid droplet formation and proliferation in a human granulosa-like tumor cell line (KGN), whereas FABP5 overexpression significantly enhanced lipid droplet formation and KGN cell proliferation. Moreover, we determined that FABP5 knockdown inhibited PI3K-AKT signaling by suppressing AKT phosphorylation and that FABP5 overexpression activated PI3K-AKT signaling by facilitating AKT phosphorylation. Finally, we used the PI3K-AKT signaling pathway inhibitor LY294002 and found that the facilitation of KGN cell proliferation and lipid droplet formation induced by FABP5 overexpression was inhibited. In contrast, the PI3K-AKT signaling pathway agonist SC79 significantly rescued the suppression of KGN cell proliferation and lipid droplet formation caused by FABP5 knockdown. CONCLUSIONS: FABP5 promotes active fatty acid synthesis and excessive proliferation of GCs by activating PI3K-AKT signaling, suggesting that abnormally high expression of FABP5 in GCs may be a novel biomarker or a research target for PCOS treatment.

Polychlorinated biphenyls exposure and type 2 diabetes: Molecular mechanism that causes insulin resistance and islet damage.

Polychlorinated biphenyls (PCBs) are typical persistent organic pollutants that have been associated with type 2 diabetes (T2DM) in cohort studies. This review aims to comprehensively assess the molecular mechanisms of PCBs-induced T2DM. Recent progress has been made in the research of PCBs in liver tissue, adipose tissue, and other tissues. By influencing the function of nuclear receptors, such as the aryl hydrocarbon receptor (AhR), pregnancy X receptor (PXR), and peroxisome proliferator activated receptor γ (PPARγ), as well as the inflammatory response, PCBs disrupt the balance of hepatic glucose and lipid metabolism. This is associated with insulin resistance (IR) in the target organ of insulin. Through androgen receptor (AR), estrogen receptor α/ß (ERα/ß), and pancreato-duodenal-homeobox gene-1 (PDX-1), PCBs affect the secretion of insulin and increase blood glucose. Thus, this review is a discussion on the relationship between PCBs exposure and the pathogenesis of T2DM. It is hoped to provide basic concepts for diabetes research and disease treatment.

Circadian disruption during fetal development promotes pathological cardiac remodeling in male mice.

Disruption of circadian rhythms during fetal development may predispose mice to developing heart disease later in life. Here, we report that male, but not female, mice that had experienced chronic circadian disturbance (CCD) in utero were more susceptible to pathological cardiac remodeling compared with mice that had developed under normal intrauterine conditions. CCD-treated males showed ventricular chamber dilatation, enhanced myocardial fibrosis, decreased contractility, higher rates of induced tachyarrhythmia, and elevated expression of biomarkers for heart failure and myocardial remodeling. In utero CCD exposure also triggered sex-dependent changes in cardiac gene expression, including upregulation of the secretoglobin gene, Scgb1a1, in males. Importantly, cardiac overexpression of Scgb1a1 was sufficient to induce myocardial hypertrophy in otherwise naive male mice. Our findings reveal that in utero CCD exposure predisposes male mice to pathological remodeling of the heart later in life, likely as a consequence of SCGB1A1 upregulation.

A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age.

Aim: To develop an approach to evaluate multiple overlapping brain functional change patterns (FCPs) in functional network connectivity (FNC) and apply to study developmental changes in brain function. Introduction: FNC, the network analog of functional connectivity (FC), is commonly used to capture the intrinsic functional relationships among brain networks. Ongoing research on longitudinal changes of intrinsic FC across whole-brain functional networks has proven useful for characterizing age-related changes, but to date, there has been little focus on capturing multivariate patterns of FNC change with brain development. Methods: In this article, we introduce a novel approach to evaluate multiple overlapping FCPs by utilizing FNC matrices. We computed FNC matrices from the large-scale Adolescent Brain Cognitive Development data using fully automated spatially constrained independent component analysis (ICA). We next evaluated changes in these patterns for a 2-year period using a second-level ICA on the FNC change maps. Results: Our proposed approach reveals several highly structured (modular) FCPs and significant results including strong brain FC between visual and sensorimotor domains that increase with age. We also find several FCPs that are associated with longitudinal changes of psychiatric problems, cognition, and age in the developing brain. Interestingly, FCP cross-covariation, reflecting coupling between maximally independent FCPs, also shows significant differences between upper and lower quartile loadings for longitudinal changes in age, psychiatric problems, and cognition scores, as well as baseline age in the developing brain. FCP patterns and results were also found to be highly reliable based on analysis of data collected in a separate scan session. Conclusion: In sum, our results show evidence of consistent multivariate patterns of functional change in emerging adolescents and the proposed approach provides a useful and general tool to evaluate covarying patterns of whole-brain functional changes in longitudinal data. Impact statement In this article, we introduce a novel approach utilizing functional network connectivity (FNC) matrices to estimate multiple overlapping brain functional change patterns (FCPs). The findings demonstrate several well-structured FCPs that exhibit significant changes for a 2-year period, particularly in the functional connectivity between the visual and sensorimotor domains. In addition, we discover several FCPs that are associated with psychopathology, cognition, and age. Finally, our proposed approach for studying age-related FCPs represents a pioneering method that provides a valuable tool for assessing interconnected patterns of whole-brain functional changes in longitudinal data and may be useful to study change over time with applicability to many other areas, including the study of longitudinal changes within diagnostic groups, treatment effects, aging effects, and more.

Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification.

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.

A deep learning approach for mental health quality prediction using functional network connectivity and assessment data.

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint.

The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

The allocative efficiency of construction land quota in rural China: a perspective of bidders' behavior and regret psychology.

The construction land quota pricing mechanism with cost plus pricing method is not sufficient to reflect its intrinsic value. This diminishes the willingness of farmers to voluntarily reclaim abandoned residential and other rural construction land, leading to suboptimal efficiency in rural land utilization and an excessive squandering of rural land resources. Thus, a sequential auction model with two stages for complementary goods was constructed, which considered the synergic characteristics between the land and quota. Further, regret psychology of bidder was considered in the case of winning or losing. A rational pricing mechanism has been developed to allocate construction land quotas, aiming to enhance farmers' motivation to the vacant homesteads of reclamation and revitalizing the stock of rural construction land. The results revealed that the regret psychology in the case of winning would decrease the transaction price of the quota, i.e., the greater the perceived regret in the case of winning, the more significant the reduction in the bidding price offered. Moreover, the regret psychology in the event of losing/failure would increase the transaction price of quota. Furthermore, publishing only the winner's price after the auction leads to the highest price of the quota offered by the bidder. In contrast, publishing only the loser's bidding price leads to the lowest transaction price of the quota offered by the bidder. In addition, the fee for delayed construction would increase the bidding price of the construction land quota. Therefore, local governments should consider announcing only the winner of price after the quota auction has ended. In addition, imposing a fee for delayed construction would enhance the transaction price of land quota, increase farmers' revenue from land reclamation, and incentivize farmers to reclaim unused rural land.

Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population.

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.