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.

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.

A critical review of on-site inorganic arsenic screening methods.

Approximately 94 to 220 million people worldwide are at risk of drinking well water containing arsenic > 10 µg/L, the WHO guideline value. To identify non-compliant domestic wells, assess health risks and reduce exposure, accurate and rapid on-site inorganic arsenic screening methods are desirable because all domestic wells worldwide need to be tested. Here, the principles, advantages and limitations of commonly used colorimetry, electrochemistry, and biosensing methods are critically reviewed, with the performance compared with laboratory-based benchmark methods. Most commercial kits are based on the classic Gutzeit reaction. Despite being semi-quantitative, the more recent and more expensive products display improved and acceptable accuracy and shorter testing time (∼10 min). Carried out by trained professionals, electrochemical methods are also feasible for on-site analysis, although miniaturization is desirable yet challenging. Biosensing using whole bacterial cells or bio-engineered materials such as aptamers is promising, if incorporated with function specific nanomaterials and biomaterials. Since arsenic is frequently found as arsenite in reducing groundwater and subject to oxidation during sampling, transportation and storage, on-site separation and sample preservation are feasible but the specific methods should be chosen based on sample matrix and tested before use. To eliminate arsenic exposure among hundreds of millions of mostly rural residents worldwide, we call for concerted efforts in research community and regulatory authority to develop accurate, rapid, and affordable tests for on-site screening and monitoring of arsenic in drinking water. Access to affordable testing will benefit people who are socioeconomically disadvantaged.

Factors Influencing Intergovernmental Cooperation on Emission Reduction in Chengdu-Chongqing Urban Agglomeration: An Evolutionary Game Theory Perspective.

In this study, we introduced the realistic problem of a dynamic carbon tax, built several evolutionary game models for cooperative emission reduction by local governments, and determined the factors that influence governments' willingness to cooperate in emission reduction. The findings revealed that, first, the probability of governments opting for cooperative emission reduction strategies increases at different rates depending on the benefits and costs of cooperation. Second, externalities influence governments' willingness to cooperate in emission reduction during cooperative carbon emission reduction. Furthermore, the emergence of a free-riding situation reduces the effectiveness of intergovernmental cooperation in reducing carbon emissions. Third, carbon tax policy can influence the likelihood that local governments will choose cooperative emission reduction, and different carbon tax sizes have different effects on their willingness to choose cooperative emission reduction.

Adjusting statistical benchmark risk analysis to account for non-spatial autocorrelation, with application to natural hazard risk assessment.

We develop and study a quantitative, interdisciplinary strategy for conducting statistical risk analyses within the 'benchmark risk' paradigm of contemporary risk assessment when potential autocorrelation exists among sample units. We use the methodology to explore information on vulnerability to natural hazards across 3108 counties in the conterminous 48 US states, applying a place-based resilience index to an existing knowledgebase of hazardous incidents and related human casualties. An extension of a centered autologistic regression model is applied to relate local, county-level vulnerability to hazardous outcomes. Adjustments for autocorrelation embedded in the resiliency information are applied via a novel, non-spatial neighborhood structure. Statistical risk-benchmarking techniques are then incorporated into the modeling framework, wherein levels of high and low vulnerability to hazards are identified.

Auction mechanism on construction land quota with selection on land location.

Due to unreasonable pricing, farmers have low enthusiasm for reclaiming their homesteads, which can be used to generate construction land quota. This paper studies how to design a feasible pricing mechanism to stimulate the enthusiasm of farmers. First, we analyze the practice that the local government gives the developers with quota the selection, the right to select the location of the land to be auctioned. Then, applying sequential auction theory, we propose first- and second-price sealed-bid sequential auction models and design quota auction pricing mechanisms. Through theoretical and numerical analysis, we obtain the equilibrium strategy and analyze the impact of selection and on developers' bidding pricing on quota. The results show that the selection can enhance the developer's quota bidding price and farmers' income. And the higher the value of selection to the developer, the higher the quota bidding price and farmer's income. Contrarily, the larger the number of developers, the smaller the quota bidding price and farmers' income. Finally, the quota bidding price and farmers' income in the second-price sealed-bid sequential auction are higher than in the first-price sealed-bid sequential auction.

CD146 is a Novel ANGPTL2 Receptor that Promotes Obesity by Manipulating Lipid Metabolism and Energy Expenditure.

Obesity and its related complications pose an increasing threat to human health; however, targetable obesity-related membrane receptors are not yet elucidated. Here, the membrane receptor CD146 is demonstrated to play an essential role in obesity. In particular, CD146 acts as a new adipose receptor for angiopoietin-like protein 2 (ANGPTL2), which is thought to act on endothelial cells to activate adipose inflammation. ANGPTL2 binds to CD146 to activate cAMP response element-binding protein (CREB), which then upregulates CD146 during adipogenesis and adipose inflammation. CD146 is present in preadipocytes and mature adipocytes, where it is mediated by its ligands ANGPTL2 and galectin-1. In preadipocytes, CD146 ablation suppresses adipogenesis, whereas the loss of CD146 in mature adipocytes suppresses lipid accumulation and enhances energy expenditure. Moreover, anti-CD146 antibodies inhibit obesity by disrupting the interactions between CD146 and its ligands. Together, these findings demonstrate that ANGPTL2 directly affects adipocytes via CD146 to promote obesity, suggesting that CD146 can be a potential target for treating obesity.

An improved sequential auction with complementarity for pricing the construction land quota.

The paradox between idle homesteads in rural areas and the shortage of construction land in urban areas is concerning. Hence, local governments encourage farmers to reclaim their idle homesteads and farmlands to generate quota. However, the current quota price, which is based on the land reclamation cost, is often inadequate to motivate farmers. This study proposed that the construction land quota and construction land are complementary items, and hence, their pricing should be studied simultaneously instead of independently. Therefore, the classical sequential auction model with complementarity was improved using the core characteristics of quota transactions and those were applied to design optimal pricing mechanisms. Subsequently, the influence of relevant factors on the bidding price was analysed. The results indicated that the bidding price decreases with an increase in the number of bidders and that a bidder's revenue is at a loss when they acquire the quota but fail to obtain the land; hence, the revenue probability is lost. However, bidding prices increase when the fine of delaying construction increases. To raise the quota price and encourage relatively more farmers to reclaim their idle homesteads, local governments should appropriately increase the delaying construction fines or repurchase the quota at a premium from the bidder who acquires the quota but fails to obtain the land, which is beneficial for easing conflict between construction land demands and farmland protection and for promoting the sustainable development of China's social economy.

A symptom-based continuum of psychosis explains cognitive and real-world functional deficits better than traditional diagnoses.

BACKGROUND: Patients with psychotic spectrum disorders share overlapping clinical/biological features, making it often difficult to separate them into a discrete nosology (i.e., Diagnostic and Statistical Manual of Mental Disorders [DSM]). METHODS: The current study investigated whether a continuum classification scheme based on symptom burden would improve conceptualizations for cognitive and real-world dysfunction relative to traditional DSM nosology. Two independent samples (New Mexico [NM] and Bipolar and Schizophrenia Network on Intermediate Phenotypes [B-SNIP]) of patients with schizophrenia (NM: N = 93; B-SNIP: N = 236), bipolar disorder Type I (NM: N = 42; B-SNIP: N = 195) or schizoaffective disorder (NM: N = 15; B-SNIP: N = 148) and matched healthy controls (NM: N = 64; B-SNIP: N = 717) were examined. Linear regressions examined how variance differed as a function of classification scheme (DSM diagnosis, negative and positive symptom burden, or a three-cluster solution based on symptom burden). RESULTS: Symptom-based classification schemes (continuous and clustered) accounted for a significantly larger portion of captured variance of real-world functioning relative to DSM diagnoses across both samples. The symptom-based classification schemes accounted for large percentages of variance for general cognitive ability and cognitive domains in the NM sample. However, in the B-SNIP sample, symptom-based classification schemes accounted for roughly equivalent variance as DSM diagnoses. A potential mediating variable across samples was the strength of the relationship between negative symptoms and impaired cognition. CONCLUSIONS: Current results support suggestions that a continuum perspective of psychopathology may be more powerful for explaining real-world functioning than the DSM diagnostic nosology, whereas results for cognitive dysfunction were sample dependent.

Parallel multi-parameter study of PEI-functionalized gold nanoparticle synthesis for bio-medical applications: part 1-a critical assessment of methodology, properties, and stability.

Cationic polyethyleneimine (PEI)-conjugated gold nanoparticles (AuNPs) that are chemically and physically stable under physiological conditions are an ideal candidate for certain bio-medical applications, in particular DNA transfection. However, the issue remains in reproducibly generating uniform stable species, which can cause the inadequate characterization of the resulting product under relevant conditions and timepoints. The principal objective of the present study was to develop an optimized and reproducible synthetic route for preparing stable PEI-conjugated AuNPs (Au-PEIs). To achieve this objective, a parallel multi-parametric approach involving a total of 96 reaction studies evaluated the importance of 6 key factors: PEI molar mass, PEI structure, molar ratio of PEI/Au, concentration of reaction mixtures, reaction temperature, and reaction time. Application of optimized conditions exhibited narrow size distributions with characteristic surface plasmon resonance absorption and positive surface charge. The optimized Au-PEI product generated by this study exhibits exceptional stability under a physiological isotonic medium (phosphate-buffered saline) over 48 h and shelf-life in ambient condition without any significant change or sedimentation for at least 6 months. Furthermore, the optimized Au-PEI product was highly reproducible. Contributions from individual factors were elucidated using a broad and orthogonal characterization suite examining size and size distribution, optical absorbance, morphological transformation (agglomeration/aggregation), surface functionalities, and stability. Overall, this comprehensive multi-parametric investigation, supported by thorough characterization and rigorous testing, provides a robust foundation for the nanomedicine research community to better synthesize nanomaterials for biomedical use.

Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes.

We develop a quantitative methodology to characterize vulnerability among 132 U.S. urban centers ('cities') to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centered autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for autocorrelation in the geospatial data. Risk-analytic 'benchmark' techniques are then incorporated into the modeling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new, translational adaptation of the risk-benchmark approach, including its ability to account for geospatial autocorrelation, is seen to operate quite flexibly in this socio-geographic setting.

Rare copy number deletions predict individual variation in intelligence.

Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in "mutation load" emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77) had been administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = -.30, p = .01). As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs. Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed.

Assessment of association of rs2200733 on chromosome 4q25 with atrial fibrillation and ischemic stroke in a Chinese Han population.

Atrial fibrillation (AF) is the most common arrhythmia in the clinical setting and an independent risk factor for stroke. Approximately 10 million Chinese people are affected by AF, but the genetic basis is largely unknown. A recent genome-wide association study in Iceland identified association between SNP rs2200733 on 4q25 and AF; however, many independent replication studies are essential to unequivocally validate this association. To assess the association between rs2200733 and AF as well as that between rs2200733 and ischemic stroke in a mainland Chinese Han population, we carried out case-control association studies with 383 AF patients versus 851 non-AF controls and 811 ischemic stroke patients versus 688 non-stroke controls. Highly significant association was detected between rs2200733 and AF in a Chinese Han population (allelic P = 3.7 × 10(-11) with OR = 1.81; genotypic P = 4.1 × 10(-12) with a dominant model). When the AF cases were divided into lone AF (32.6%) and other types of AF (67.4%), significantly stronger association was found with lone AF (OR = 2.40, P = 1.3 × 10(-9) compared to OR = 1.59, P = 6.2 × 10(-7) for other types of AF; P = 0.02 for two ORs). No significant association was found between rs2200733 and ischemic stroke. Our results suggest that SNP rs2200733 confers a highly significant risk of AF, but not ischemic stroke, in a more representative Chinese Han population in the mainland China.