Miracle-dz/UniFAR-FTUs huggingface dataset:
WORKING PAPER SERIES VALUING THE TIME OF THE SELF-EMPLOYED

People’s value for their own time is a key input in evaluating public policies: evaluations should account for time taken away from work or leisure as a result of policy. Using rich choice data collected from farming households in western Kenya, we show that households exhibit nontransitive preferences consistent with behavioral features such as loss aversion and self-serving bias. As a result, neither market wages nor standard valuation techniques (such as the BeckerDeGroot-Marschak—BDM—mechanism of Becker et al., 1964) correctly measure participants’ value of time. Using a structural model, we identify the mix of behavioral features driving our choice data. We find that these features distort choices when exchanging cash either for time or for goods. Our model estimates suggest that valuing the time of the self-employed at 60% of the market wage is a reasonable rule of thumb. Daniel J. Agness University of California at Berkeley dagness@berkeley.edu Travis Baseler University of Rochester travis.baseler@rochester.edu Sylvain Chassang Department of Economics Princeton University Julis Romo Rabinowitz Building Princeton, NJ 08544 and NBER chassang@princeton.edu Pascaline Dupas Department of Economics Stanford University 579 Jane Stanford Way Stanford, CA 94305-6072 and CEPR and also NBER pdupas@stanford.edu Erik Snowberg David Eccles School of Business University of Utah Salt Lake City, UT 84112-8914 and University of British Columbia and also NBER Erik.Snowberg@ubc.ca
Evaluating the success of the company designing the TQM System

This paper proposes a methodology for evaluating the success of the designed and implemented system for TQM (audit) and its application in some Macedonian companies. Successful application of this methodology can only be achieved if business processes are designed and implemented with optimal utilization of resources, in order to work out the best from the first time, no defects, no loss of time and to the satisfaction of all users. This methodology has found a practical application within same of the Macedonian companies. The results displayed universal possibility of application and visible effects.
An Analytical Design Method for a Regenerative Braking Control System for DC-electrified Railway Systems under Light Load Conditions

A DC-electrified railway system that is fed by diode rectifiers at a substation is unable to return the electric power to an AC grid. Accordingly, the braking cars have to restrict regenerative braking power when the power consumption of the powering cars is not sufficient. However, the characteristics of a DC-electrified railway system, including the powering cars, is not known, and a mathematical model for designing a controller has not been established yet. Hence, the object of this study is to obtain the mathematical model for an analytical design method of the regenerative braking control system. In the first part of this paper, the static characteristics of this system are presented to show the position of the equilibrium point. The linearization of this system at the equilibrium point is then performed to describe the dynamic characteristics of the system. An analytical design method is then proposed on the basis of these characteristics. The proposed design method is verified by experimental tests with a 1kW class miniature model, and numerical simulations.
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network

Abstract Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.
Association between lifestyle factors and metabolic syndrome in general populations with depressive symptoms in cross-setional based cohort study of Ansung-Ansan

Background Metabolic syndrome (MetS) is caused by both genetic and environmental factors, such as daily calorie intake, smoking, and alcohol consumption. Lifestyle factors, such as alcohol consumption, are considered to be related to the prevalence of MetS and plays an essential role in the pathogenesis and prognosis of depression. Methods We investigated the bidirectional association between lifestyle factors and MetS among Korean adults with depressive symptoms in third wave of a community-based cohort study. A total of 1,578 individuals, aged 39–72 years, who had MetS at baseline were recruited. Participants were divided into two groups according to depressive symptoms. Logistic regression models were used to estimate the risk of MetS. Results The percentage of heavy drinkers was lower in men with depressive symptoms compared to those who did not (7.0% vs. 7.1%), while the percentage of current smokers were higher in participants who had depressive symptoms (40.2% vs. 30.0%). After adjusting for age, education, monthly income, body mass index (BMI), sleep duration, and volume of drinking and smoking status, logistic regression analysis demonstrated that male heavy drinkers with depressive symptoms were 2.75 times more likely to have MetS than those without depressive symptom. Conversely, depressive women with a high BMI were 3.70 times more likely to have MetS than in those with lower BMI. Limitations The cross-sectional nature of the study, and the study population ethnicity and ages were limitations. Conclusions Lifestyle factors, such as alcohol consumption, may be associated with the risk of MetS in adults with depressive symptoms.
In a team forgiveness climate, the influence of paradoxical thinking of leaders on the team voice behavior: Mediated by team cooperation

In order to clarify the influence of the paradoxical thinking of leaders on team voice behavior, a moderating mediation model was constructed to explore the mediating role of team cooperation and the moderating effect of team forgiveness climate based on the social exchange theory. Based on the "leader-employee" matching data of 477 employees from 101 teams, SPSS (Statistic Package for Social Science) and AMOS (a macro-micro model of Scotland) were used to analyze the three-stage data linear regression. The research conclusions indicate that: (1) The paradoxical thinking of leaders positively affects team voice behavior; (2) Team cooperation plays a completely mediating role in the relationship between the paradoxical thinking of leaders and team voice behavior; (3) The team forgiveness climate positively moderates the relationship between the paradoxical thinking of leaders and team cooperation, which means that their positive relationship is stronger in a higher forgiveness climate; (4) The team forgiveness climate moderates the mediating role of team cooperation between the paradoxical thinking of leaders and team voice behavior. Compared with a lower forgiveness climate, this moderating mediating effect is significant at a higher forgiveness level. This study clarifies the connection of the paradoxical thinking of leaders to the team voice behavior through team cooperation and has practical insights into how a team forgiveness climate promotes the team voice behavior.
Development and Implementation of an Advanced Practitioner–Led Survivorship Clinic for Patients Status Post Allogeneic Transplant

Background: Survivor recovery from hematopoietic cell transplantation (HCT) is long term, with significant physical and psychological morbidities that impact quality of life and reentry into personal and social lives. The optimal timing of when and how to deliver comprehensive HCT survivorship care is not well defined. Purpose: The purpose of this study was to design, implement, and evaluate an advanced practitioner (AP)-led pilot survivorship clinic incorporating an individual and group format for patients post HCT at the 1-year transition period. Methods: A survey assessing physical, social, emotional, and spiritual needs and concerns was mailed to a sample of patients who underwent HCT between 2009 and 2014. This phase 1 survey was utilized in the phase 2 design of an AP-led pilot survivorship clinic for patients post allogeneic HCT. A total of 15 patients were approached, out of which 7 enrolled over a 12-month period in the pilot survivorship clinic. Results: The needs assessment survey noted the most prevalent moderate to high concerns were in the emotional domain, with 52% of respondents identifying fear of cancer returning and new cancer developing. The pilot survivorship clinic incorporating a group visit format with multiple sessions was not feasible for both patients and APs within the context of a small- to medium-sized HCT program. Conclusion: The needs assessment survey underscored the importance of addressing all four quality of life domains in cancer survivors. A hybrid survivorship clinic with one comprehensive group visit may be beneficial for HCT survivors at the 1-year transition for small- to medium-sized HCT programs.
Childhood factors associated with suicidal ideation among South African youth: A 28-year longitudinal study of the Birth to Twenty Plus cohort

Background Although early life factors are associated with increased suicide risk in youth, there is a dearth of research on these associations for individuals growing up in disadvantaged socioeconomic contexts, particularly in low- and middle-income countries (LMICs). We documented the association between individual, familial, and environmental factors in childhood with suicidal ideation among South African youth. Methods and findings We used data from 2,020 participants in the Birth to Twenty Plus (Bt20+) study, a South African cohort following children born in Soweto, Johannesburg from birth (1990) to age 28 years (2018). Suicidal ideation was self-reported at ages 14, 17, 22, and 28 years, and the primary outcome of interest was suicidal ideation reported at any age. We assessed individual, familial, and socioeconomic characteristics at childbirth and during infancy, adverse childhood experiences (ACEs) between ages 5 and 13 years, and externalizing and internalizing problems between 5 and 10 years. We estimated odds ratios (ORs) of suicidal ideation for individuals exposed to selected childhood factors using logistic regression. Lifetime suicidal ideation was reported by 469 (23.2%) participants, with a 1.7:1 female/male ratio. Suicidal ideation rates peaked at age 17 and decreased thereafter. Socioeconomic adversity, low birth weight, higher birth order (i.e., increase in the order of birth in the family: first, second, third, fourth, or later born child), ACEs, and childhood externalizing problems were associated with suicidal ideation, differently patterned among males and females. Socioeconomic adversity (OR 1.13, CI 1.01 to 1.27, P = 0.031) was significantly associated with suicidal ideation among males only, while birth weight (OR 1.20, CI 1.02 to 1.41, P = 0.03), ACEs (OR 1.11, CI 1.01 to 1.21, P = 0.030), and higher birth order (OR 1.15, CI 1.07 to 1.243, P < 0.001) were significantly associated with suicidal ideation among females only. Externalizing problems in childhood were significantly associated with suicidal ideation among both males (OR 1.23, 1.08 to 1.40, P = 0.002) and females (OR 1.16, CI 1.03 to 1.30, P = 0.011). Main limitations of the study are the high attrition rate (62% of the original sample was included in this analysis) and the heterogeneity in the measurements of suicidal ideation. Conclusions In this study from South Africa, we observed that early life social and environmental adversities as well as childhood externalizing problems are associated with increased risk of suicidal ideation during adolescence and early adulthood.
Patterns of risk for diabetic retinopathy in the Mumbai slums: The Aditya Jyot Diabetic Retinopathy in Urban Mumbai Slums Study (AJ-DRUMSS) Report 3

Diabetes onset precedes diabetic retinopathy (DR) by 5–10 years, but many people with diabetes remain free of this microvascular complication. Our aim was to identify risk factors for DR progression in a unique and diverse population, the slums of Mumbai. We performed a nested case-control study of 1163 diabetics over 40 years of age from slums in 18 wards of Mumbai. Data was collected on 33 variables and assessed for association with DR using both univariate and multivariate analyses. Stratified analyses were also performed on males and females, separately. Among hypertensive individuals we also assessed whether duration of hypertension associated with DR. Of 31 non-correlated variables analysed as risk factors for DR, 15 showed evidence of significant association. The most prominent included sex, where being a female associated with decreased odds of DR, while longer duration of diabetes and poor glycaemic control associated with increased odds. The duration of diabetes effect was partially, but significantly, mediated by age of diabetes diagnoses (8.6% of variance explained, p = 0.012). Obesity as measured by several measures, including body mass index (BMI) and measures of central obesity had a negative association with DR; increased measures of obesity consistently reduced odds of DR. As in most earlier studies, DR was associated with the duration of diabetes and glycaemic control. However, other factors, especially obesity related measures were associated with DR, in ways that contrast with most prior studies. These results indicated that the overall pattern of association in the Mumbai slums was novel. Thus, in previously uncharacterized populations, such as the slums that we examined, it is important to evaluate all risk factors de novo to appropriately assess patterns of association as the patterns of association with DR can be complex and population specific.
Complete Genome Sequences of Five SO-1-Like Siphoviridae Bacteriophages That Infect Enterobacteriales

The Enterobacteriales order is composed of Gram-negative bacteria that range from harmless symbionts to well-studied pathogens. We announce complete genome sequences of five related SO-1-like Enterobacteriales bacteriophages (also known as the Dhillonvirus genus) isolated from wastewater that infect Escherichia coli (Opt-212, Over9000, Pubbukkers, and Teewinot) or Shigella boydii (StarDew). ABSTRACT The Enterobacteriales order is composed of Gram-negative bacteria that range from harmless symbionts to well-studied pathogens. We announce complete genome sequences of five related SO-1-like Enterobacteriales bacteriophages (also known as the Dhillonvirus genus) isolated from wastewater that infect Escherichia coli (Opt-212, Over9000, Pubbukkers, and Teewinot) or Shigella boydii (StarDew).
Insulin‐like growth factor 2 mRNA‐binding protein 2‐regulated alternative splicing of nuclear factor 1 C‐type causes excessive granulosa cell proliferation in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common reproductive endocrine disorder. Insulin‐like growth factor 2 mRNA‐binding protein 2 (IGF2BP2) serves as an HMGA2 target gene to promote the proliferation of granulosa cells (GCs). However, it is still unclear whether IGF2BP2 participates in the pathogenesis of PCOS as RNA binding protein (RBP). In this study, we aimed to elucidate IGF2BP2‐interacting transcripts, global transcriptome together with alternative splicing in GCs to eventually uncover potential mechanisms of PCOS pathogenesis.
Sustainable Thioetherification via Electron Donor-Acceptor Photoactivation using Thianthrenium Salts.

The synthesis of sulfides has been widely studied because this functional subunit is prevalent in biomolecules and pharmaceuticals, as well as being a useful synthetic platform for further elaboration. Thus, various methods to build C-S bonds have been developed, but typically they require the use of precious metals or harsh conditions. Electron donor-acceptor (EDA) complex photoactivation strategies have emerged as versatile and sustainable ways to achieve C-S bond formation, avoiding challenges associated with previous methods. This work describes an open-to-air, photoinduced, site-selective C-H thioetherification from readily available reagents via EDA complex formation, tolerating a wide range of different functional groups. Moreover, C(sp 2 )-halogen bonds remain intact using this protocol, allowing late-stage installation of the sulfide motif in various bioactive scaffolds, while allowing yet further modification through more traditional C-X bond cleavage protocols. Additionally, various mechanistic investigations support the envisioned EDA complex scenario.
Characteristics, goal‐oriented treatments and survival of pulmonary arterial hypertension in China: Insights from a national multicentre prospective registry

Nationally representative reports on the characteristics and long‐term survival of pulmonary arterial hypertension (PAH) from developing countries are scarce. The applicability of the current main risk stratifications and the longitudinal changes in goal‐oriented treatments have yet to be elucidated in real‐world settings. Therefore, we aimed to provide insights into the characteristics, goal‐oriented treatments and survival of PAH in China and to explore the applicability of the main risk stratifications in our independent cohort.
Identification of defined structural elements within TOR2 kinase required for TOR complex 2 assembly and function in Saccharomyces cerevisiae

The mammalian target of rapamycin (mTOR) is a large protein kinase that assembles into two multisubunit protein complexes, mTORC1 and mTORC2, to regulate cell growth in eukaryotic cells. While significant progress has been made in our understanding of the composition and structure of these complexes, important questions remain regarding the role of specific sequences within mTOR important for complex formation and activity. To address these issues, we have used a molecular genetic approach to explore TOR complex assembly in budding yeast, where two closely related TOR paralogues, TOR1 and TOR2, partition preferentially into TORC1 versus TORC2, respectively. We previously identified an ∼500-amino-acid segment within the N-terminal half of each protein, termed the major assembly specificity (MAS) domain, which can govern specificity in formation of each complex. In this study, we have extended the use of chimeric TOR1-TOR2 genes as a “sensitized” genetic system to identify specific subdomains rendered essential for TORC2 function, using synthetic lethal interaction analyses. Our findings reveal important design principles underlying the dimeric assembly of TORC2 as well as identifying specific segments within the MAS domain critical for TORC2 function, to a level approaching single-amino-acid resolution. Together these findings highlight the complex and cooperative nature of TOR complex assembly and function.
Family mealtime emotions and food parenting practices among mothers of young children: Development of the Mealtime Emotions Measure for Parents (MEM‐P)

Abstract Family mealtimes can be important for supporting children's healthy development, yet the emotional context of mealtimes can vary considerably, likely impacting their overall success and enjoyment. Yet, despite having an important role, little is known about how parents emotionally experience mealtimes with their family. The first aim of the current study was to assess the factor structure of a novel self‐report measure to assess parents’ emotional responses experienced during family mealtimes (Mealtime Emotions Measure for Parents; MEM‐P). The second aim was to examine relationships between maternal mealtime emotions and their food parenting practices. Mothers of children aged between 1.5 and 6 years participated in this study. Mothers were invited to complete an online questionnaire measuring family mealtime emotions, anxiety, depression and food parenting practices. Exploratory factor analysis produced a three‐factor solution comprising both positive and negative emotion subscales: MEM‐P Efficacy; MEM‐P Anxiety; MEM‐P Stress and Anger. Mothers' positive mealtime emotions (mealtime efficacy) were related to greater use of practices promoting autonomy, providing a healthy home food environment, and modelling healthy eating. Higher anxiety about mealtimes was related to greater reports of child control over eating, and mealtime stress and anger was associated with greater use of food to regulate emotions. These findings highlight novel relationships between how mothers emotionally experience family mealtimes and the food parenting practices they use with their children. It is important to develop resources to help promote positive maternal experiences of family mealtimes and food‐based interactions.
Prediction of Maximum Absorption Wavelength Using Deep Neural Networks

Fluorescent molecules are important tools in biological detection, and numerous efforts have been made to develop compounds to meet the desired photophysical properties. For example, tuning the wavelength allows an appropriate penetration depth with minimal interference from the autofluorescence/scattering for a better signal-to-noise contrast. However, there are limited guidelines to rationally design or computationally predict the optical properties from first principles, and factors like the solvent effects will make it more complicated. Herein, we established a database (SMFluo1) of 1181 solvated small-molecule fluorophores covering the ultraviolet-visible-near-infrared absorption window and developed new machine learning models based on deep neural networks for accurately predicting photophysical parameters. The optimal system was applied to 120 out-of-sample compounds, and it exhibited remarkable accuracy with a mean relative error of 1.52%. In this new paradigm, a deep learning algorithm is promising to complement conventional theoretical and experimental studies of fluorophores and to greatly accelerate the discovery of new dyes. Due to its simplicity and efficiency, data from newly developed fluorophores can be easily supplemented to this system to further improve the accuracy across various dye families.