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In face of large-scale disasters, persons with fewer assets are at greater risk of persistent poorer mental health than persons with more assets. Everyday daily routine disruptions and financial hardship could mediate this association.This prospective population-representative study in Hong Kong aimed to investigate the relation between assets during the acute phase of COVID-19 (February-August 2020, T1) and persistent probable depression from T1 to March-August 2021 (T2), as well as the mediating effects of daily routine disruptions and financial hardship on the assets-depression association.Low assets at T1 prospectively related to persistent probable depression from T1 to T2. Primary routine disruptions (i.e., healthy eating and sleep) at T1 and financial hardship at T2 were found to fully mediate the association between T1 assets and persistent probable depression.Persistent probable depression reported on the PHQ-9 should be further verified with clinical diagnoses/interviews.The COVID-19 pandemic was accompanied by a global economic downturn. Persons who have fewer assets could be at greater risk of depression during this period. Our findings suggest a need to provide behavioral and financial assistance to persons with fewer assets in the short run and a need to ensure that everyone has adequate assets to mitigate the mental health consequences of the COVID-19 pandemic in the long run.

As the health impacts of climate change take on a more serious form, this study for the first time investigates the effect of meteorological factors on the risk of death from respiratory diseases (RD) in Wuhu, a representative city along the Yangtze River in subtropical humid region. Daily meteorological element data and RD deaths in Wuhu City were collected from 2014 to 2020. Time series analysis was conducted using distributed lagged nonlinear model (DLNM) combined with generalized additive model (GAM), and stratified by age and gender. In 7 years, a total of 8016 RD death cases were collected in Wuhu, China. The results demonstrated that the maximum impacts of short-term exposure to exceedingly low temperatures mean (Tmean) were at lag 9, with the maximum relative risk (RR) of 1.044 (lag 1, 95% CI: 1.001, 1.098). The risk of exceedingly high Tmean reached its maximum at lag 0 (RR = 1.070, 95% CI: 1.018, 1.125). Low relative humidity (RH) was negatively associated with the risk of RD death, with the lowest RR values occurring at lag 12 (RR = 0.987, 95% CI: 0.975, 0.999). No significant correlation was found for diurnal temperature range (DTR). Stratified analysis showed that Tmean exposure remained statistically significant for male, female and elderly, while RH and DTR only seemed to increase the mortality risk in the young. In a word, short-term exposure to extreme temperatures may increase the RD mortality risk in the population, and young people needed to be aware that exposure to exceedingly high RH and DTR also increased the risk.

Currently little is known about the interrelations between changes in psychiatric symptoms and changes in resources (personal, social, financial) amid large-scale disasters. This study investigated trajectories of psychiatric symptoms and their relationships with different patterns of changes in personal, social, and financial resources between 2020 and 2022 amid the COVID-19 pandemic. A population-representative sample (N = 1333) was recruited to complete self-report instruments at the pandemic’s acute phase (February–July 2020, T1), and again at 1-year (March–August 2021, T2) and 1.5-year (September 2021–February 2022, T3) follow-ups. Respondents reported depressive and anxiety symptoms, self-efficacy, perceived social support, and financial capacity. Growth mixture modeling (GMM) identified four trajectories of depressive and anxiety symptoms: resilience (72.39–74.19%), recovery (8.40–11.93%), delayed distress (7.20–7.35%), and chronic distress (8.33–10.20%). Four patterns were demonstrated in resource changes: persistent high resources (40.89–47.64%), resource gain (12.08–15.60%), resource loss (6.30–10.43%), and persistent low resources (28.73–36.61%). Loss and gain in financial resources characterized chronic distress and resilience, respectively. Loss in personal resources characterized delayed distress, whereas loss or no gain in social resources was related to chronic/delayed distress. Respondents in resilience were also more likely to have persistent high resources while those with delayed/chronic distress were more likely to have persistent low resources. These results provide an initial evidence base for advancing current understanding on trajectories of resilience and psychopathology in the context of resource changes during and after large-scale disasters.

The COVID-19 pandemic presents challenges to psychological wellbeing, but how can we predict when people suffer or cope during sustained stress? Here, we test the prediction that specific types of momentary emotional experiences are differently linked to psychological wellbeing during the pandemic. Study 1 used survey data collected from 24,221 participants in 51 countries during the COVID-19 outbreak. We show that, across countries, wellbeing is linked to individuals’ recent emotional experiences, including calm, hope, anxiety, loneliness, and sadness. Consistent results are found in two age, sex, and ethnicity-representative samples in the United Kingdom (N = 971) and the United States (N=961) with pre-registered analyses (Study 2). A prospective 30-day daily diary study conducted in the United Kingdom (N = 110) confirms the key role of these five emotions, and demonstrates that emotional experiences precede changes in wellbeing (Study 3). Our findings highlight differential relationships between specific types of momentary emotional experiences and wellbeing, and point to the cultivation of calm and hope as candidate routes for wellbeing interventions during periods of sustained stress.

The increasing penetration of renewable energy resources in power systems inadvertently leads to a surge in the number of random states. The calculation of optimal load shedding for all the proliferated states is rather overwhelming. It has been considered a bottleneck in the reliability evaluation process. To address this issue, a deep-learning-based approach is proposed to evaluate system reliability more efficiently considering the fluctuation of generation and loads. In the proposed power flow model, a stacked denoising auto-encoder (SDAE) serves as a multilayer neural network for the deep-learning process. Its stacked structure and encoding-decoding recursion enable it to extract high-order features even from non-linear equations. The features of contingency states are crucial to directly acquire the minimum load curtailment without the time-consuming optimal power flow (OPF). The after-trained model is applied to states simulated with the Monte-Carlo method in the RTS-79 system. The numerical result reveals the great advantage of the SDAE-based method in computation time while ensuring high accuracy. As an alternative way to classic OPF algorithms, it can be integrated with the impact-increment method and other state selection approaches.

Interfacial solar-driven evaporators have presented great potential for water purification owing to their low energy consumption and high steam generation efficiency. However, their further applications are hindered by the high costs and complicated fabrication processes. Here, a scalable bilayer interfacial evaporator was constructed via an affordable technique, in which carbon black deposited nonwoven fabric (CB@NF) was employed as the upper photothermal layer, as well as PVA/starch hybrid hydrogel for self-floating and water transport. Under simulated one sun irradiation, CB@NF layer displayed excellent photothermal conversion performance, whose temperature could increase 30.4 °C within 15 min. Moreover, the introduction of starch into PVA endowed the hybrid hydrogels with considerable water-absorption capability on the premise of ensuring mechanical properties. The resultant CB@NF/PVA/starch composites achieved superior interfacial adhesion performance with interfacial toughness at about 200 J m−2. Combining with good evaporation performance, salt-rejection property and high purification efficiency on pollutants, this evaporation system would become a promising candidate to alleviate water shortage.

The optimal load shedding calculations of an enormous number of contingency states are the most challenging impediments for power system reliability assessment, especially in a large-scale system with multiple types of loads. To deal with that, this paper proposes a fast method that accelerates the calculation of optimal load shedding based on the shadow price. This method calculates the optimal load shedding of systems by shadow-price-based linear functions, rather than the time-consuming OPF algorithms. The hundred percentage criterion is used to look for the matching shadow price of states. Case studies are performed on the RTS-79 system. The results show that the proposed methodology can significantly simplify the solving process and reduce the calculation time.

Reduced graphene oxide (rGO)-based fibers with high electrochemical performance have recently showed great potential in the field of flexible energy storage devices. However, they still suffer from low capacitance due to the severe stacking of graphene sheets. Hybrids with nanofillers are an efficient way to improve the electrochemical performance of rGO fibers. Nevertheless, controlling the distribution of nanoparticles in the matrix is still an enormous challenge due to the strong attraction among these nanoparticles which results into agglomeration. Here, we continually prepared rGO hybrid fibers via non-liquid-crystal spinning, accompanied by chemical reduction. Manganic oxide (MnOX) nanoparticles remained well-dispersed in GO dispersion during the continuous spinning of rGO/MnOX hybrid fibers. Results showed that rGO/MnOX-20 hybrid fibers possessed the best capacitance of 123.3 F g−1 (87.6 F cm−3) and 97.1 F g−1 (68.9 F cm−3) at the current density of 0.2 A g−1, and 0.5 A g−1 respectively. Furthermore, a fiber-shaped all-solid-state supercapacitor assembly from the optimized hybrid fibers demonstrated an energy density of 2.67 mWh cm−3 (3.76 mWh g−1) at the power density of 24.76 mWh cm−3 (34.89 mWh g−1). These fiber-based devices show great potential for application in the fields of wearable electronics and energy storage devices.

Both COVID-19 and unrest are posing a significant threat to population mental health across the globe. This study examined trends of probable depression and anxiety during a time of civil unrest and concurrent COVID-19 in Hong Kong. Four random digit dialing telephone surveys were conducted in July 2019 (n = 1112), February–March 2020 (n = 2003), April–May 2020 (n = 2008), and July–August 2020 (n = 2034). The prevalence of probable depression increased from 25.7% (95% CI: 23.2–28.3) in July 2019 to 28.2% (95% CI: 26.2–30.1) in February–March 2020, and then decreased to 15.3% (95% CI: 14.0–17.0) in April–May 2020 and 13.7% (95% CI: 12.2–15.2) in July–August 2020. The prevalence of probable anxiety was 19.2% (95% CI: 17.5–20.9) in February–March 2020 and then stabilized in April–May 2020 and July–August 2020 (14.1%, 95% CI: 12.0–15.8). Probable depression and anxiety were more prevalent among persons with high relative to low daily routine disruptions. Combined high unrest-COVID-19 stress was associated with probable depression and anxiety across all persons; high unrest stress alone was associated with probable mental disorders at high daily routine disruptions. Civil unrest and COVID-19 are jointly associated with depression and anxiety among Hong Kong citizens. While population mental health improved, daily routine disruptions is a risk factor of mental disorders at every time-point.

Utilizing energy storage systems have been considered as a feasible pathway to achieve carbon neutrality. However, the common battery type for energy storage systems is the cheap lithium iron phosphate battery, which has low output efficiency and is almost impossible to charge in cold areas. Lithium titanate battery has high output efficiency and charge efficiency in cold areas. Meanwhile, the price of a lithium titanate battery is three times that of a lithium iron phosphate battery with the same capacity. To achieves the complementary advantages of lithium iron phosphate battery and lithium titanate battery, this paper proposes the dual battery framework of energy storage systems. Based on this, the operation model of energy storage systems is formulated. Besides, five cases are designed to demonstrate the effectiveness of the proposed method and the results show that the dual battery framework has high output efficiency with a cheaper cost than lithium titanate battery.

Salinization of groundwater is a major challenge for groundwater management in long-term irrigation areas, decoupling its complex influencing factors can provide insights for the sustainable development of irrigation areas. In this study, the natural-human driving factors of groundwater salinization in the Yinchuan Plain, a typical irrigated area, were identified using isotope analysis, information entropy, and self-organizing map. Results show that groundwater in the study area is seriously salinized with obvious spatial heterogeneity. Multiple natural conditions and frequent human activities complicate the salinization characteristics of groundwater. On this basis, four typical natural influence units of groundwater were identified, namely, an evaporation and upward leakage zone, a runoff zone, an evaporation zone, and a runoff and upward leakage zone. Information entropy was proposed to quantify the complexity of groundwater resulting from human activities: The complexity difference between densely populated areas and natural dominant areas is mainly reflected in Na

Bacteriophage TaidaOne was isolated from soil collected in Taipei, Taiwan, using the host Streptomyces griseus . It is a siphovirus with a 56,183-bp genome that contains 86 protein-coding genes. Based on gene content similarity, it was assigned to actinobacteriophage subcluster BI1, within which only TaidaOne and GirlPower genomes contain an acetyltransferase homolog gene.

UDP-glycosyltransferases (UGTs) play key roles in modulating plant responses to environmental challenges. In this study, we characterized a novel glycosyltransferase, UGT2, which plays an important role in salt stress responses in rice (Oryza sativa L). We found that seedlings overexpressing UGT2 exhibited better growth than wild type in shoot and root under hydroponic culture with salt stress treatments, while ugt2ko mutant lines suffered much more growth inhibition. When the soil-grown UGT2 transgenic plants were subjected to salt stress, we also found that ugt2ko mutant lines were severely withered and most of them died, while the overexpression lines grew well and had higher survival rate. Compared with wild type plants, UGT2 overexpression greatly increased the expression levels of the reactive oxygen species scavenging genes and stress responsive genes. Furthermore, the upstream regulatory mechanism of the UGT2 gene was identified and we found that a bZIP transcription factor, OsbZIP23, can bind to the UGT2 promoter and enhance the UGT2 transcription levels. This work reveals that OsbZIP23-UGT2 module may play a major role in regulating the salt stress tolerance in rice.

Introduction Psychological resilience can be viewed as a personality trait, an ability to bounce back, or a positive outcome after life adversities. Although there have been increasing reports of the importance of sleep and circadian factors in contributing to an individual’s resilience, current studies were devoid of objective measurements and in-depth analysis. Whether psychological resilience is related to the slow wave sleep that facilitates recovery processes is of particular interest. Methods This study focused on the association between resilience and sleep in healthy young individuals free of physical and mental disorders. 160 young adults (61.7% female, mean age: 29.55±4.94) were included. Psychological resilience was defined as both a trait and a positive health outcome despite adversities. The trait resilience was measured by the Connor-Davidson Resilience Scale (CDRS-10), at which scores exceeding the 50th percentile of CDRS-10 were categorized as a “High resilient trait”. The outcome resilience was measured by the residual score of the linear regression model with psychological health level as the dependent variable and accumulative adversities as the independent variable. Those with a positive residual value were categorized as “Resilient outcome”. The accumulative adversities included childhood adversities (e.g., emotional and physical neglect, sexual abuse) and lifetime stressors (e.g., accident, death of families or close friends). Sleep architecture was assessed by polysomnography, and sleep microstructure was computed by spectrum power analysis and slow wave sleep autodetection. Results Resilient participants had more slow wave sleep (SWS; N3%: 15.69% vs 12.89%, p = 0.021) and higher delta power in N3 (Absolute: 750.36 uV2 vs 617.55 uV2, p = 0.059) than those non-resilient participants. Linear regression models showed that increased SWS was associated with higher resilient trait (SWS density: B=0.25, p=0.006; Absolute power of N3 delta: B=0.003, p=0.004) and resilient outcome (N3%: B=0.02, p=0.058; SWS density: B: 0.03, p=0.073; Delta power in N3: B=0.0004, p=0.020). Conclusion Psychological resilience was related to greater slow wave sleep. The findings suggested a critical role of sleep quality in maintaining psychological resilience. Longitudinal studies are need to examine the underlying mechanism of the link between resilience and slow wave sleep. Support (if any) The Collaborative Research Fund of Hong Kong, China (C7069-19GF).

Self-compassion has been shown to be protective against stress. Whether its stress buffering effect differs between cancer caregivers and non-caregivers is unknown. This study examined the moderating effect of self-compassion among cancer caregivers relative to non-caregivers by recruiting cancer caregivers from the hospital and a community sample matching on sex and age. Participants completed a questionnaire which comprised the self-reported anxiety (STAI-6) and self-compassion (SCS-SF) measures, and administered cortisol sample collections with Salivette tubes at home. Whereas caregivers experienced higher diurnal cortisol level, the differential protective effects of self-compassion were only present against anxiety symptoms but not physiological stress.

Recent studies have shown that global metabolic reprogramming is a common event in plant innate immunity; however, the relevant molecular mechanisms remain largely unknown. Here, we identified a pathogen-induced glycosyltransferase, UGT73C7, that plays a critical role in Arabidopsis disease resistance through mediating redirection of the phenylpropanoid pathway. Loss of UGT73C7 function resulted in significantly decreased resistance to Pseudomonas syringae pv. tomato DC3000, whereas constitutive overexpression of UGT73C7 led to an enhanced defense response. UGT73C7-activated immunity was demonstrated to be dependent on the upregulated expression of SNC1, a Toll/interleukin 1 receptor-type NLR gene. Furthermore, in vitro and in vivo assays indicated that UGT73C7 could glycosylate p-coumaric acid and ferulic acid, the upstream metabolites in the phenylpropanoid pathway. Mutations that lead to the loss of UGT73C7 enzyme activities resulted in the failure to induce SNC1 expression. Moreover, glycosylation activity of UGT73C7 resulted in the redirection of phenylpropanoid metabolic flux to biosynthesis of hydroxycinnamic acids and coumarins. The disruption of the phenylpropanoid pathway suppressed UGT73C7-promoted SNC1 expression and the immune response. This study not only identified UGT73C7 as an important regulator that adjusts phenylpropanoid metabolism upon pathogen challenge, but also provided a link between phenylpropanoid metabolism and an NLR gene.

Stroke is a common disease with high mortality and disability worldwide. Different forms of cell deaths, including apoptosis and necrosis, occur in ischemic or hemorrhagic brain tissue, among which pyroptosis, a newly discovered inflammation-related programmed cell death, is generally divided into two main pathways, the canonical inflammasome pathway and the non-canonical inflammasome pathway. Caspase-mediated pyroptosis requires the assembly of inflammasomes such as NLRP3, which leads to the release of inflammatory cytokines IL-1β and IL-18 through the pores formed in the plasma membrane by GSDMD followed by neuroinflammation. Recently, pyroptosis and its relationship with inflammation have attracted more and more attention in the study of cerebral ischemia or hemorrhage. In addition, many inhibitors of pyroptosis targeting caspase, NLRP3, and the upstream pathway have been found to reduce brain tissue damage after stroke. In this review, we mainly introduce the pathology of stroke, the molecular mechanism, and process of pyroptosis, as well as the pivotal roles of pyroptosis in stroke, in order to provide new insights for the treatment of stroke.

In this work, a fluorescence/colorimetric dual-mode detection method based on MnO

In the early stages of the COVID-19 pandemic, Italy, Spain, New York, and Hong Kong stood out for the ir high rates of infections. Given this scenario, a web-based international multisite and cross-sectional study was conducted between April and May 2020 to investigate the psychological impact of the pandemic and the restrictions imposed by the governments in these countries. We expected similar patterns in European countries, and no significant differences in terms of psychological impairment between Hong Kong (with a previous experience related to SARS, but subjected to restrictions for a longer time) and the other areas. Participants were 1955 adults from the above-mentioned areas. We assessed anxiety (GAD-7), depression (PHQ-9), COVID-19-related threats, and perceived burden of restrictive measures. Two-explorative factor analyses (EFAs) with Promax rotation identified COVID-19-related factors: personal physical threat, personal economic threat, global economic threat, and restriction-related burden. ANOVAs studied locations’ differences and two-separate hierarchical multiple regression analyses by location determined whether and how COVID-19-related variables were associated with anxiety and depression, adjusting for age and sex. Italy and Hong Kong showed higher anxiety than Spain (p p NY = 0.242; βHK = 0.116) and personal economic threat (βNY = 0.246; βHK = 0.145) in New York (Adj.R2 = 0.125) and Hong Kong (Adj.R2 = 0.079); by global economic threat (β = 0.199) and restriction-related burden (β = 0.124) in Italy (Adj.R2 = 0.108); and by personal physical threat (β = 0.144) in Spain (Adj.R2 = 0.049). Depression was predicted by restriction-related burden (βNY = 0.313; βHK = 0.120) and personal economic threat (βNY = 0.229; βHK = 0.204) in New York (Adj.R2 = 0.161) and Hong Kong (Adj.R2 = 0.089); by global economic threat (β = 0.209) in Italy (Adj.R2 = 0.149); and no predictors emerged in Spain. Findings could contribute to understanding the specific impact of the pandemic on people’s psychological health in each area, along with the factors that impacted mental health. This information may be useful to implementing prevention interventions in case of restrictions.

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

Lung cancer is the most prevalent cancer with high mortality in China, and it is associated with the dysbiosis of the lung microbiome. This study attempted to screen for specific microorganisms as potential biomarkers for distinguishing benign lung disease from lung cancer.Bronchoalveolar lavage fluid (BALF) sample was selected in the study instead of saliva to avoid contamination with oral microorganisms, and microbial taxonomic and functional differences in BALF samples from patients with lung cancer and those with those from patients with benign lung diseases were performed based on metagenomic next-generation sequencing, for the first time, so that microorganisms other than bacteria could be included.The results showed that the intrasample diversity of malignant samples was different from benign samples, and the microbial differences among malignant samples were smaller, with lower microbial diversity, significantly changed microbial abundance and metabolic functions. Metabolic function analysis revealed amino acid-related metabolism was more prevalent in benign samples, whereas carbohydrate-related metabolism was more prevalent in malignant samples. By LEfSe, Metastat and Random Forest analysis, we identified a series of important differential microorganisms. Importantly, the model combining five key genera plus one tumor marker (neuron-specific enolase) as indicators presented the optimal disease typing performance.Thus results suggest the value of these differential microorganisms enriched in tumors in mechanism research and may be potential new targets for lung cancer therapy. More importantly, the biomarkers identified in this study can be conducive to improve the clinical diagnosis of lung cancer and have good application prospects.

The study of extra-solar planets, or simply, exoplanets, planets outside our own Solar System, is fundamentally a grand quest to understand our place in the Universe. Discoveries in the last two decades have re-defined what we know about planets, and helped us comprehend the uniqueness of our very own Earth. In recent years, however, the focus has shifted from planet detection to planet characterisation, where key planetary properties are inferred from telescope observations using Monte Carlo-based methods. However, the efficiency of sampling-based methodologies is put under strain by the high-resolution observational data from next generation telescopes, such as the James Webb Space Telescope and the Ariel Space Mission. We propose to host a regular competition with the goal of identifying a reliable and scalable method to perform planetary characterisation. Depending on the chosen track, participants will provide either quartile estimates or the approximate distribution of key planetary properties. They will have access to synthetic spectroscopic data generated from the official simulators for the ESA Ariel Space Mission. The aims of the competition are three-fold. 1) To offer a challenging application for comparing and advancing conditional density estimation methods. 2) To provide a valuable contribution towards reliable and efficient analysis of spectroscopic data, enabling astronomers to build a better picture of planetary demographics, and 3) To promote the interaction between ML and exoplanetary science. The competition is open for all and is expected to run from July to October. We will provide a brief introduction to the competition, its aim and the different tracks available for participants. We will also be sharing preliminary results from the competition in this session.

Current endeavours in exoplanet characterisation rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of the technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation becomes more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in Machine Learning provide optimisation-based Variational Inference as an alternative approach to perform approximate Bayesian Posterior Inference. In this investigation we combined Normalising Flow-based neural network with our newly developed differentiable forward model, Diff-tau, to perform Bayesian Inference in the context of atmospheric retrieval. We demonstrated, with examples from real and simulated spectroscopic data, several advantages of our proposed framework: 1.) Our neural network does not require a large library of spectra, all it takes is a single observation 2.) It is able to produce distributions similar to sampling-based retrieval and 3.) It requires much less forward model computation to converge. Our proposed framework contribute towards the latest development of a neural-powered atmospheric retrieval. Its flexibility and speed hold the potential to complement sampling-based approaches in large and complex data sets in the future.

This is an exciting era for exo-planetary exploration. The recently launched JWST, and other upcoming space missions such as Ariel, Twinkle and ELTs are set to bring fresh insights to the convoluted processes of planetary formation and evolution and its connections to atmospheric compositions. However, with new opportunities come new challenges. The field of exoplanet atmospheres is already struggling with the incoming volume and quality of data, and machine learning (ML) techniques lands itself as a promising alternative. Developing techniques of this kind is an inter-disciplinary task, one that requires domain knowledge of the field, access to relevant tools and expert insights on the capability and limitations of current ML models. These stringent requirements have so far limited the developments of ML in the field to a few isolated initiatives. In this paper, We present the Atmospheric Big Challenge Database (ABC Database), a carefully designed, organised and publicly available database dedicated to the study of the inverse problem in the context of exoplanetary studies. We have generated 105,887 forward models and 26,109 complementary posterior distributions generated with Nested Sampling algorithm. Alongside with the database, this paper provides a jargon-free introduction to non-field experts interested to dive into the intricacy of atmospheric studies. This database forms the basis for a multitude of research directions, including, but not limited to, developing rapid inference techniques, benchmarking model performance and mitigating data drifts. A successful application of this database is demonstrated in the NeurIPS Ariel ML Data Challenge 2022., Comment: 21 pages, Accepted in RASTI, challenge accepted in NeurIPS 2022. Database link: https://doi.org/10.5281/zenodo.6770103

Organizational virtuousness is defined as collective positive attributes and behaviors supported by and characteristic of an organization that promote hedonic well-being, eudaimonic well-being, and optimal performance. The underlying mechanisms through which organizational virtuousness operate remain largely unknown. Drawing from the broaden-and-build theory, organizational virtuousness is proposed to broaden employees' attention and cognition toward positive stimuli and events, and over time, build psychological resources for managing future endeavors. Building resources in turn promotes well-being and organizational commitment. A 3-wave prospective study (baseline, T1; 3 months, T2; and 1 year, T3) was conducted with a sample of 444 primary and secondary schoolteachers in Hong Kong. Organizational virtuousness, cognitive reappraisal, psychological capital (PsyCap), life satisfaction, flourishing, affective commitment, and contextual performance were assessed. The results showed that cognitive reappraisal and PsyCap served as sequential mediators of the relationships between three components of organizational virtuousness and all four indicators of well-being and organizational commitment. Collective gratitude, kindness, and forgiveness at T1 had significant indirect effects through cognitive reappraisal at T2 and then PsyCap at T3 on satisfaction with life, state of flourishing, emotional attachment to the organization, and engagement in extra-role activities that contribute to the organization's efficacy. This study is among the first to provide prospective data on the effects of organizational virtuousness. The results lead to a discussion of how organizations might shape a grateful, kind, and forgiving work culture using virtue-based management to achieve optimal individual and organizational functioning.