More income, less depression? Revisiting the nonlinear and heterogeneous relationship between income and mental health

This paper uses a large-scale nationally representative dataset to examine the nonlinear effect of income on mental health. To investigate their causal relationship, the exogenous impact of automation on income is utilized as the instrument variable (IV). In addition, to explore their nonlinear relationship, both income and its quadratic term are included in regressions. It is found that the impact of income on mental health is U-shaped rather than linear. The turning point (7.698) of this nonlinear relation is near the midpoint of the income interval ([0, 16.113]). This suggests that depression declines as income increases at the lower-income level. However, beyond middle income, further increases in income take pronounced mental health costs, leading to a positive relationship between the two factors. We further exclude the possibility of more complex nonlinear relationships by testing higher order terms of income. In addition, robustness checks, using other instrument variables and mental health indicators, different IV models and placebo analysis, all support above conclusions. Heterogeneity analysis demonstrates that males, older workers, ethnic minorities and those with lower health and socioeconomic status experience higher levels of depression. Highly educated and urban residents suffer from greater mental disorders after the turning point. Religious believers and Communist Party of China members are mentally healthier at lower income levels, meaning that religious and political beliefs moderate the relationship between income and mental health

Influence of the electrografting method on the performances of a flow electrochemical sensor using modified electrodes for trace analysis of copper (II).

International audienceThe performances of carboxylate- and cyclam-modified graphite felt electrodes prepd. by different electrografting methods for trace anal. of copper (II) were compared to det. the influence of the immobilization process of the linkers on the sensor properties. The derivatization performed by cathodic redn. of diazonium salts and by anodic oxidn. of amines in org. and aq. media was first evaluated by cyclic voltammetry and XPS analyses, showing a higher surface coverage for the redn. process. Cyclam was subsequently attached on the COOH-modified graphite felts by a coupling reaction. The modified electrodes were then employed in a flow anal. system for trace anal. of copper (II) ions. The influence of the surface coverage and the nature of the linker on the electrochem. signal obtained by linear sweep stripping voltammetry anal. after a preconcn. step performed at open circuit was highlighted. The selectivity estd. in the presence of lead used as a common ion interferent was higher when a selective receptor was used and depends on the nature of the linker. [on SciFinder(R)

Endoplasmic Reticulum Aminopeptidase 1 Is Involved in Anti-viral Immune Response of Hepatitis B Virus by Trimming Hepatitis B Core Antigen to Generate 9-Mers Peptides

Endoplasmic reticulum aminopeptidase 1 (ERAP1) is a processing enzyme of antigenic peptides presented to major histocompatibility complex (MHC) class I molecules. ERAP1-dependent trimming of epitope repertoire determines an efficacy of adoptive CD8+ T-cell responses in several viral diseases; however, its role in hepatitis B virus (HBV) infection remains unknown. Here, we show that the serum level of ERAP1 in patients with chronic hepatitis B (CHB) (n = 128) was significantly higher than that of healthy controls (n = 44) (8.78 ± 1.82 vs. 3.52 ± 1.61, p < 0.001). Furthermore, peripheral ERAP1 level is moderately correlated with HBV DNA level in patients with CHB (r = 0.731, p < 0.001). HBV-transfected HepG2.2.15 cells had substantially increased ERAP1 expression and secretion than the germline HepG2 cells (p < 0.001). The co-culture of ERAP1-specific inhibitor ERAP1-IN-1 pretreated HepG2.2.15 cells or ERAP1 knockdown HepG2.2.15 cells with CD8+ T cells led to 14–24% inhibition of the proliferation of CD8+ T cells. Finally, liquid chromatography tandem mass spectrometry (LC-MS/MS) test demonstrated that ERAP1-IN-1 blocks completely the production of a 9-mers peptide (30–38, LLDTASALY) derived from Hepatitis B core antigen (HBcAg). The predictive analysis by NetMHCpan-4.1 server showed that human leukocyte antigen (HLA)-C*04:01 is a strong binder for the 9-mers peptide in HepG2.2.15 cells. Taken together, our results demonstrated that ERAP1 trims HBcAg to produce 9-mers LLDTASALY peptides for binding onto HLA-C*04:01 in HepG2.2.15 cells, facilitating the potential activation of CD8+ T cells

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

BackgroundPeople usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.MethodsIn this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.ResultsThe final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.ConclusionIn this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction

CD64 plays a key role in diabetic wound healing

IntroductionWound healing poses a clinical challenge in diabetes mellitus (DM) due to compromised host immunity. CD64, an IgG-binding Fcgr1 receptor, acts as a pro-inflammatory mediator. While its presence has been identified in various inflammatory diseases, its specific role in wound healing, especially in DM, remains unclear.ObjectivesWe aimed to investigate the involvement of CD64 in diabetic wound healing using a DM animal model with CD64 KO mice.MethodsFirst, we compared CD64 expression in chronic skin ulcers from human DM and non-DM skin. Then, we monitored wound healing in a DM mouse model over 10 days, with or without CD64 KO, using macroscopic and microscopic observations, as well as immunohistochemistry.ResultsCD64 expression was significantly upregulated (1.25-fold) in chronic ulcerative skin from DM patients compared to non-DM individuals. Clinical observations were consistent with animal model findings, showing a significant delay in wound healing, particularly by day 7, in CD64 KO mice compared to WT mice. Additionally, infiltrating CD163+ M2 macrophages in the wounds of DM mice decreased significantly compared to non-DM mice over time. Delayed wound healing in DM CD64 KO mice correlated with the presence of inflammatory mediators.ConclusionCD64 seems to play a crucial role in wound healing, especially in DM conditions, where it is associated with CD163+ M2 macrophage infiltration. These data suggest that CD64 relies on host immunity during the wound healing process. Such data may provide useful information for both basic scientists and clinicians to deal with diabetic chronic wound healing

Game theoretical model for sharing control in online social network

Today, with the popularity of Online Social Networks (OSNs) websites, more and more people share their information using social network services. To protect these shared informations, OSNs provide access control mechanisms to protect them from undesired access. Yet, there still exist many unsolved problems related to information sharing in OSNs. A key problem for current access control scheme is the lack of usage control for the shared informations.To solve this problem, this thesis proposes a new access control scheme called Personal Data Sharing Control, or PDSC for information sharing in OSNs based on the risk involved in the sharing process. The risk computation utilizes game theoretical model for sharing behaviors along with the historical interactions between OSNs users. Additionally, the users can specify the value of his or her personal data item and the access permission to the data item is determined by comparing the value of the data item and the risk involved in sharing it. Simulations of PDSC scheme are performed to analyze the effects of user interactions and social network structure. The result shows that PDSC protecting the data item by exploiting the trust that the data item owner can place on the requestor based on their interactions and social network structure.Aujourd'hui, avec la popularité croissante des réseaux sociaux en ligne, de plus en plus de gens partagent leurs informations en utilisant les services de réseaux sociaux. Pour protéger ces informations partagées, le réseautage social en ligne fournit des mécanismes de contrôle d'accès pour les protéger contre les accès indésirables. Pourtant, il existe encore de nombreux problèmes non résolus liés au partage d’informations dans les réseaux sociaux. Un problème clé pour les systèmes de contrôle d'accès actuels est le manque de contrôle de l'utilisation des informations partagées.Pour résoudre ce problème, cette thèse propose un nouveau système de contrôle d'accès appelé {Contrôle de partage des données personnelles} ou CPDP pour le partage des informations dans les réseaux sociaux. Il est basé sur le risque impliqué dans le processus du partage d'informations. Le calcul de risque utilise un modèle issu de la théorie des jeux pour partager les comportements ainsi que les interactions passées entre les utilisateurs des réseaux sociaux. En outre, les utilisateurs peuvent spécifier la valeur d’un élément de données à caractère personnel, et l'autorisation d'accès à cet élément de données est déterminée en comparant la valeur de l'élément de données et le risque lié à partager celles-ci. Des simulations de schéma de CPDP sont effectuées pour analyser les effets des interactions de l'utilisateur et la structure du réseau social. Le résultat montre que le CPDP protège l'élément de données en exploitant la confiance que le propriétaire de l'élément de données peut accorder au demandeur en fonction de leurs interactions et de la structure du réseau social

Multi-Sensor-Based Hierarchical Detection and Tracking Method for Inland Waterway Ship Chimneys

In the field of automatic detection of ship exhaust behavior, a deep learning-based multi-sensor hierarchical detection method for tracking inland river ship chimneys is proposed to locate the ship exhaust behavior detection area quickly and accurately. Firstly, the primary detection uses a target detector based on a convolutional neural network to extract the shipping area in the visible image, and the secondary detection applies the Ostu binarization algorithm and image morphology operation, based on the infrared image and the primary detection results to obtain the chimney target by combining the location and area features; further, the improved DeepSORT algorithm is applied to achieve the ship chimney tracking. The results show that the multi-sensor-based hierarchical detection and tracking method can achieve real-time detection and tracking of ship chimneys, and can provide technical reference for the automatic detection of ship exhaust behavior

Multi-Sensor-Based Hierarchical Detection and Tracking Method for Inland Waterway Ship Chimneys

In the field of automatic detection of ship exhaust behavior, a deep learning-based multi-sensor hierarchical detection method for tracking inland river ship chimneys is proposed to locate the ship exhaust behavior detection area quickly and accurately. Firstly, the primary detection uses a target detector based on a convolutional neural network to extract the shipping area in the visible image, and the secondary detection applies the Ostu binarization algorithm and image morphology operation, based on the infrared image and the primary detection results to obtain the chimney target by combining the location and area features; further, the improved DeepSORT algorithm is applied to achieve the ship chimney tracking. The results show that the multi-sensor-based hierarchical detection and tracking method can achieve real-time detection and tracking of ship chimneys, and can provide technical reference for the automatic detection of ship exhaust behavior

Research on Innovative Design of railway passenger service

In order to improve the quality of railway passenger service, the quality of railway passenger service is investigated through field investigation and questionnaire survey, the evaluation index system of railway passenger service quality is established, and the Delphi method is used to screen important service quality evaluation indicators, The model of railway passenger service quality evaluation is established by Delphi method and analytic hierarchy process based on passenger perception, the top five indicators in the weight ranking in the station service quality evaluation index system can show the importance of the station environment in improving the quality of passenger service. On this basis, 1088 valid questionnaires on the addition of lifting stools in station waiting halls were analyzed. The results show that most passengers are concerned about the comfort of the renovated seats and the safety of luggage storage, and the innovative design of the lifting stool greatly improves the space utilization of the waiting hall when improving the railway passenger service