bluuebunny/crossref_metadata_2025 huggingface dataset:
Gut Microbial Community and Host Thermoregulation in Small Mammals
Authors: Zhang Xue-Ying , Wang De-Hua
Published: April 2022

The endotherms, particularly the small mammals living in the polar region and temperate zone, are faced with extreme challenges for maintaining stable core body temperatures in harsh cold winter. The non-hibernating small mammals increase metabolic rate including obligatory thermogenesis (basal/resting metabolic rate, BMR/RMR) and regulatory thermogenesis (mainly nonshivering thermogenesis, NST, in brown adipose tissue and skeletal muscle) to maintain thermal homeostasis in cold conditions. A substantial amount of evidence indicates that the symbiotic gut microbiota are sensitive to air temperature, and play an important function in cold-induced thermoregulation, via bacterial metabolites and byproducts such as short-chain fatty acids and secondary bile acids. Cold signal is sensed by specific thermosensitive transient receptor potential channels (thermo-TRPs), and then norepinephrine (NE) is released from sympathetic nervous system (SNS) and thyroid hormones also increase to induce NST. Meanwhile, these neurotransmitters and hormones can regulate the diversity and compositions of the gut microbiota. Therefore, cold-induced NST is controlled by both Thermo-TRPs—SNS—gut microbiota axis and thyroid—gut microbiota axis. Besides physiological thermoregulation, small mammals also rely on behavioral regulation, such as huddling and coprophagy, to maintain energy and thermal homeostasis, and the gut microbial community is involved in these processes. The present review summarized the recent progress in the gut microbiota and host physiological and behavioral thermoregulation in small mammals for better understanding the evolution and adaption of holobionts (host and symbiotic microorganism). The coevolution of host-microorganism symbionts promotes individual survival, population maintenance, and species coexistence in the ecosystems with complicated, variable environments.
Prediction of Photosynthetic, Biophysical, and Biochemical Traits in Wheat Canopies to Reduce the Phenotyping Bottleneck
Authors: Robles-Zazueta Carlos A. , Pinto Francisco , Molero Gemma , Foulkes M. John , Reynolds Matthew P. , Murchie Erik H.
Published: April 2022

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R2 = 0.48, RMSE = 5.24 μmol m–2 s–1 and stomatal conductance: R2 = 0.36, RMSE = 0.14 mol m–2 s–1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R2 = 0.3, p < 0.05; R2 = 0.29, p < 0.05) and at 7 days after anthesis (R2 = 0.15, p < 0.05; R2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.
Functional Differentiation of BnVTE4 Gene Homologous Copies in α-Tocopherol Biosynthesis Revealed by CRISPR/Cas9 Editing
Authors: Zhang Haiyan , Shi Yuqin , Sun Mengdan , Hu Xuezhi , Hao Mengyu , Shu Yu , Zhou Xue-Rong , Hu Qiong , Li Chao , Mei Desheng
Published: April 2022

Tocopherols are essential nutrients for human health known as vitamin E. Vitamin E deficiency can have a profound effect on human health, including the central nervous system and cardiovascular and immune protection. Multiple enzymatic steps are involved in the conversion between different forms of tocopherols. Among them, γ-tocopherol methyltransferase encoded by gene VTE4 catalyzes the conversion of γ- to α-tocopherol or δ- to β-tocopherol isoforms. However, the gene copies and their functional contribution of VTE4 homologs in Brassica napus were not elucidated. To this end, different mutation combinations of four putative BnVTE4 homologous copies were generated by using CRISPR/Cas9 genome editing technology. Editing of those BnVTE4 homologs led to a significant change of the α-tocopherol content and the ratio between α- and γ-tocopherol compared with wide-type control. Analysis of the different combinations of BnVTE4-edited homologs revealed that the contribution of the BnVTE4 individual gene displayed obvious functional differentiation in α-tocopherol biosynthesis. Their contribution could be in order of VTE4.C02-2 (BnaC02G0331100ZS) > VTE4.A02-1 (BnaA02G0247300ZS) > VTE4.A02-2 (BnaA02G0154300ZS). Moreover, the VTE4.A02-1 and VTE4.A02-2 copies might have severe functional redundancies in α-tocopherol biosynthesis. Overall, this study systemically studied the different effects of BnVTE4 homologs, which provided a theoretical basis for breeding high α-tocopherol content oilseed rape.
Comparative Transcriptome Analysis of Onion in Response to Infection by Alternaria porri (Ellis) Cifferi
Authors: Khandagale Kiran , Roylawar Praveen , Kulkarni Onkar , Khambalkar Pravin , Ade Avinash , Kulkarni Abhijeet , Singh Major , Gawande Suresh
Published: April 2022

Purple blotch (PB) is one of the most destructive foliar diseases of onion and other alliums, caused by a necrotrophic fungal pathogenAlternaria porri. There are no reports on the molecular response of onion to PB infection. To elucidate the response of onion toA. porriinfection, we consequently carried out anRNAseqanalysis of the resistant (Arka Kalyan; AK) and susceptible (Agrifound rose; AFR) genotype after an artificial infection. Through differential expression analyses between control and pathogen-treated plants, we identified 8,064 upregulated and 248 downregulated genes in AFR, while 832 upregulated and 564 downregulated genes were identified in AK. A further significant reprogramming in the gene expression profile was also demonstrated by a functional annotation analysis. Gene ontology (GO) terms, which are particularly involved in defense responses and signaling, are overrepresented in current analyses such as “oxidoreductase activity,” “chitin catabolic processes,” and “defense response.” Several key plant defense genes were differentially expressed onA. porriinfection, which includes pathogenesis-related (PR) proteins, receptor-like kinases, phytohormone signaling, cell-wall integrity, cytochrome P450 monooxygenases, and transcription factors. Some of the genes were exclusively overexpressed in resistant genotype, namely,GABA transporter1,ankyrin repeat domain-containing protein,xyloglucan endotransglucosylase/hydrolase, andPR-5(thaumatin-like). Antioxidant enzyme activities were observed to be increased after infection in both genotypes but higher activity was found in the resistant genotype, AK. This is the first report of transcriptome profiling in onion in response to PB infection and will serve as a resource for future studies to elucidate the molecular mechanism of onion-A. porriinteraction and to improve PB resistance in onions.
Impact of Thin-Ideals in Influencer Posts Promoting Healthy vs. Unhealthy Foods on Tweens’ Healthy Food Choice Behavior
Authors: De Jans Steffi , Hudders Liselot , Naderer Brigitte , De Pauw Valentina
Published: April 2022

The current study examines how social media influencers can be deployed to promote healthy food choice behavior among tweens. In particular, we investigated whether tweens’ healthy food choice behavior can be stimulated by using a thin-ideal influencer in a sponsored influencer post promoting unhealthy vs. healthy food. A two-by-two, between-subjects experimental study (influencer weight: thin-ideal vs. overweight; snack-type: unhealthy vs. healthy) was conducted with 146 tweens (11–13 years old, 73 boys). Results show that tweens’ choice for a healthy snack was higher when a (female) overweight influencer promoted an unhealthy snack (compared to a healthy snack). Using a thin-ideal influencer promoting an unhealthy vs. healthy snack did not affect tweens’ healthy food choices. While there were no interaction effects of influencer weight and snack type on source effects (influencer credibility, influencer admiration, and trans-parasocial interactions), the results did show that the influencer was perceived as less credible and was admired less when she was overweight vs. when she had a thin-ideal body-type.
High Commitment Work System and Employee Proactive Behavior: The Mediating Roles of Self-Efficiency and Career Development Prospect
Authors: Shi Yang , Cao Man
Published: April 2022

At present, proactive behavior has become a major concern in the field of organizational behavior. Drawing from the proactive motivation theory, this article proposes the influence of a high commitment work system (HCWS) on employees’ proactive behavior and constructs the mediation model, including self-efficacy and career development prospect. Moreover, conformity values as a micro context factor are used to illustrate the process that affects employees’ proactive behavior. Analyzing the matched data from 117 enterprises and 1,055 employees, this article finds that HCWS are positively related to employees’ proactive behavior. This article also finds that self-efficacy and career development prospect are mediated by the relationship between HCWS and employees’ proactive behavior. Conformity value moderates the positive relationship between self-efficacy and employees’ proactive behavior, but it does not moderate the positive relationship between career development prospect and employees’ proactive behavior. This study sheds light on whether and how line managers’ leadership influences the human resource management (HRM) process.
Evaluation of Prediction-Oriented Model Selection Metrics for Extended Redundancy Analysis
Authors: Kim Sunmee , Hwang Heungsun
Published: April 2022

Extended redundancy analysis (ERA) is a statistical method that relates multiple sets of predictors to response variables. In ERA, the conventional approach of model evaluation tends to overestimate the performance of a model since the performance is assessed using the same sample used for model development. To avoid the overly optimistic assessment, we introduce a new model evaluation approach for ERA, which utilizes computer-intensive resampling methods to assess how well a model performs on unseen data. Specifically, we suggest several new model evaluation metrics for ERA that compute a model’s performance on out-of-sample data, i.e., data not used for model development. Although considerable work has been done in machine learning and statistics to examine the utility of cross-validation and bootstrap variants for assessing such out-of-sample predictive performance, to date, no research has been carried out in the context of ERA. We use simulated and real data examples to compare the proposed model evaluation approach with the conventional one. Results show the conventional approach always favor more complex ERA models, thereby failing to prevent the problem of overfitting in model selection. Conversely, the proposed approach can select the true ERA model among many mis-specified (i.e., underfitted and overfitted) models.
How Wealth Inequality Affects Happiness: The Perspective of Social Comparison
Authors: Gao Lingxi , Sun Bochi , Du Ziqing , Lv Guangming
Published: April 2022

Since Easterlin pointed out that economic growth in nations does not guarantee increasing happiness for the average citizen, the underlying reason has remained controversial. The present study focuses on income inequality to explain the “Easterlin Paradox,” ignoring the permanent inequality that long-term wealth accumulation brings. Based on social comparison theory, the literature aims to determine how wealth inequality, which accompanies economic growth, diminishes one’s happiness (inequality aversion). Specifically, we conduct this study in which we split the wealth inequality into the upward wealth inequality and the downward wealth inequality as measures of upward comparison and downward comparison, respectively. The upward wealth inequality measures the average gap between one and the better-off in wealth while the downward wealth inequality measures the average gap between one and the worse-off in wealth. Furthermore, the heterogeneity of the area of respondent is analyzed and the family life cycle is tested as a moderator. The main findings of the paper are as follows: (1) The empirical test results of hypothesis 1 indicate that the upward wealth inequality aversion (jealousy effect: people envy who is richer than themselves) is stronger than the downward wealth inequality inclination (proud effect: people enjoy having a superior position in the wealth hierarchy). It is due to the psychological preference: loss aversion. As an increase in upward distance implies a loss in relative status and an increase in downward distance implies a gain in relative status, people focus more on loss rather than gain. (2) The empirical test results of hypothesis 2 indicate that residents who live in rural areas do not have a proud effect as much as those who live in urban areas. There is a huge urban-rural wealth gap in China. With the expansion of the social network, people living in rural areas realize that even he is almost the rich in rural areas but still the lower classes in the whole society. It is hard for rural residents to have a proud effect. (3) The empirical test results of hypothesis 3 indicate that family members have the strongest upward inequality aversion in the middle-stage phase of the life cycle (when the family head is approximately 50). During the family life cycle, inequality aversion will be different in different life stages due to the changes in economic status expectations. At the beginning of the family life cycle, family members assume their life has limitless possibilities, and they have high expectations for the future. Logically, they can be easily satisfied by achieving a little more than their peers. In later periods, with increasing age, the members will pay more attention to health instead of wealth. The results shed light on how macroeconomics influence changes in individual psychology.
Advancing the Study of “Goals of Best Practice”: Toward Achieving Optimal Best – Educational Implications to Developments in Flow Research and Positive Optimal Psychology
Authors: Phan Huy P. , Ngu Bing Hiong
Published: April 2022

The paradigm of positive psychology is significant in introducing positive psychological concepts such as “flourishing,” “optimal best,” and “a state of flow.” In terms of research development of positive psychology, the researchers of this article have made extensive theoretical, empirical, and methodological contributions by advancing the study of optimal best. One aspect of this research, notably, consists of advancement of the psychological process of optimization. Optimization, in brief, provides a theoretical account into the “optimization” of a person’s state of functioning. Non-academically, a Buddhist nun’s seeking to successfully achieve an optimal state of enlightenment or, academically, a first-year student’s seeking to achieve an A grade in Psych 101 would require some form of optimization. Recent research development has, interestingly, considered a related concept known as “goals of best practice” (GsBP), which may co-exist with the process of optimization and/or assist to account for the optimization of learning experiences. This conceptual analysis article, by utilizing the paradigm of philosophical psychology, advances the study of optimal best practice by focusing on three major aspects: (i) to consider conceptually and philosophically how and/or the extent to which GsBP could, in fact, relate to the nature of flow, flourishing, and optimal best; (ii) to consider a methodological account, which could help to measure and assess the concept GsBP; and (iii) to consider the potential practicality of GsBP in educational contexts, which may assist to facilitate and motivate the achievement of optimal best. These three aspects, we firmly believe, are of significance as they provide grounding for implementation and continuing research development into the area of best practice.
The Multivariate Physical Activity Signatures Associated With Self-Regulation, Executive Function, and Early Academic Learning in 3–5-Year-Old Children
Authors: Vabø Kristoffer Buene , Aadland Katrine Nyvoll , Howard Steven James , Aadland Eivind
Published: April 2022

The evidence regarding associations between intensity-specific physical activity and cognitive and learning outcomes in preschoolers is inconsistent and limited by low sample sizes and analytical approaches that cannot handle the multicollinearity among multiple physical activity intensity variables. We aimed to determine the multivariate physical activity intensity signatures associated with self-regulation, executive function, and early academic learning in preschool children aged 3–5 years. A 711 Norwegian preschool children (mean age 4.6 years, 52% boys) provided valid data on physical activity (ActiGraph GT3X+), self-regulation, executive function, and early academic learning during 2019–2020. Multivariate pattern analysis was used to determine associations between uniaxial and triaxial intensity spectra (time spent in intensities from 0–99 to ≥15,000 counts per minute) and the outcomes in the total sample and in subgroups split by sex and age (median split). Uniaxial data led to the highest explained variances (R2) and were reported as the primary findings. We found significant association patterns between physical activity and numeracy (R2 = 4.28%) and inhibition (R2 = 1.48%) in the total sample. The associations with numeracy were negative for time spent sedentary (0–99 counts per minute) and positive for time spent in moderate to vigorous intensities (≥ 1,000 counts per minute). The associations with inhibition were positive for time spent sedentary (0–99 counts per minute) and in vigorous intensities (≥ 8,500 counts per minute) and negative for time spent in low to moderate intensities (100–3,499 counts per minute). Associations with numeracy were stronger in boys (R2 = 5.58%) and older children (R2 = 7.27%), and associations with inhibition were stronger in girls (R2 = 3.12%) and older children (R2 = 3.33%). In conclusion, we found weak associations with numeracy and inhibition across the physical activity intensity spectrum in preschool children.
Reliability and Validity of the Urdu Version of Psychosomatic Symptoms Scale in Pakistani Patients
Authors: Shah S. Mudasser , Jahangir Muhammad , Xu Wei , Yuan Yonggui
Published: April 2022

The current study was aimed to assess the reliability and validity of the Urdu version of the Psychosomatic Symptoms Scale (PSSS) in Pakistani patients. The PSSS is time-saving and easy to administer. The field experts drafted the translated version of PSSS. The Urdu version of PSSS, Patient Health Questionnaire- 9 (PHQ-9), and Symptom Checklist-90 (SCL-90) Urdu version were used for assessment. The translation procedure was comprised of three steps, namely forward translation, back translation, and expert panel discussion. A sample of 982 (men = 50.5% and women = 49.5%) was collected with a convenient sample technique from a general hospital and private clinic. The Cronbach’s alpha for PSSS was 0.974. The confirmatory factor analysis (CFA) revealed that all the items factor loading of PSSS were more than 0.35, the root mean square error of approximation (RMSEA) was = 0.062, the standardized root mean residual (SRMR) was = 0.043, and the comparative fit index (CFI) was = 0.97 with 90% CI. The results also showed that women (M = 72.08, SD = 6.79) are more likely to have psychosomatic symptoms than men (M = 51.21, SD = 13.36) on P < 0.001. The PSSS Urdu version is proven to be a useful and reliable instrument for screening, monitoring, and assessing Pakistani patients’ psychosomatics symptoms.
Establishing Normative Data for the Number Cancelation Test Among Children in Kindergartens and Primary Schools in China
Authors: Xie Yachun , Wang Hongan , Chen Yuxin , Liu Fulin , Yao Mengmeng , Zhang Lei , Liu Panting , Hong Qin , Chi Xia , Yu Dongchuan
Published: April 2022

This study aimed to suggest an attention assessment tool using a Digital Pen for measuring the temporal-spatial parameters during the Number Cancelation Test (NCT), and then to establish the normative data for the NCT among children in kindergartens and primary schools in China by recruiting a total of 989 children (496 males). Four measures, i.e., selective attention (SA), speed of cognitive processing (SpC), averaged time of circlings (ATC), and averaged circumference of circled curves (ACCC), were proposed to evaluate the NCT performance. They basically have a development trend with fast speed in the beginning before Grade 1 or 2 of primary schools, and then enter an extremely slow development period (with ceiling or floor effect). SA and SpC have gender and grade main effects, while ATC and ACCC have the grade main effect, only. In particular, females have higher SA scores than males in middle class of kindergarten, and Grade 2–Grade 5 of primary school, but no gender differences in other grades; females have higher SpC scores than males in middle class of kindergarten, and Grade 3–4 of primary school, but no gender differences in other grades. More importantly, in clinical practice, if SA or SpC measure of a child is below than the 5th centile (i.e., p5 level) of his/her grade-specific normative data, then this child may be predicted to have a high-risk of learning disabilities. Findings suggest that the proposed method can be used for early screening of learning disabilities by setting appropriate cut-off values.
Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods
Authors: Lee Dong Yun , Kim Chungsoo , Lee Seongwon , Son Sang Joon , Cho Sun-Mi , Cho Yong Hyuk , Lim Jaegyun , Park Rae Woong
Published: April 2022

Background

Identifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.

Methods

Clinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.

Results

A total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.

Conclusions

We developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.
Do Migrant Residents Really Achieve Health Equity by Obtaining Urban Hukou? A Comparative Study on Health Service Utilization and Urbanization in Central China
Authors: Min Rui , Fang Zi , Zi Chunyan , Tang Changmin , Fang Pengqian
Published: April 2022

Introduction

With more than 120 million rural-to-urban migrants, urbanization of the rural population requires deeply exploration in China.

Objective

This study focused on settled citizens who obtained urban Hukou (household registration) during urbanization and investigated their perceptions of health services in China.

Method

A cross-sectional comparison study with an original, closed questionnaire was conducted in two major cities of Hubei, central China, covering health status and both the satisfaction with and utilization of health services. In total, 863 residents with urban Hukou participated in this study; migrants formed the study group and original city residents formed the control group. Propensity score matching (PSM) was used to reduce choice bias in the analysis steps. Besides basic description of the data, ordinary least squares regression (OLS regression) was used to discover the relationship between basic demographic indicators and health expenditure.

Results

PSM yielded 290 effective pairs for analysis. The results indicated an improvement in health status for migrant residents (study group) with a higher average score of self-reported health status and lower prevalence of chronic diseases than the control group. These scores were also better than the standard urban level in central China. The study group showed a higher clinic visit utility (69.63%), lower hospitalization utility (8.28%), less convenience of health service utility, and lower health expenditure than the control group. For the study group, the biggest difference was observed in satisfaction with health service costs, which was the least improved aspect after they obtained urban Hukou. The regression results demonstrated that age, family size, living expenditures, and marital status impacted health costs in the overall model and the influences of these factors differed between the study and control groups.

Conclusions

Obtaining urban Hukou helps migrant residents to meet their health service needs and receive equal access to health services. However, after obtaining urban Hukou, migrants also face great pressure in terms of health consumption. This study therefore offers guidance on the next steps for progressing China's urbanization.
Environmental Exposure to Triclosan and Male Fecundity: A Prospective Study in China
Authors: Zhu Wenting , Xie Chong , Zhao Shasha , Zhang Dan , Zhang Hao
Published: April 2022

Triclosan (2,4,4′-trichloro-2′-hydroxy-diphenyl ether, TCS) is widely used in personal care and household products. It is ubiquitous across the ecosystem nowadays. Several in vitro and in vivo studies have suggested the possible adverse effects of TCS on male reproductive health. However, little research has been done on human beings, especially in eastern countries. To assess the effects of TCS exposure on male fecundity, we recruited couples who planned to conceive and went to the preconception care clinics for physical examination in Shanghai, China. TCS was quantified in male urine samples collected at enrollment. For this study, 443 couples were included in the cohort, and 74.7% of couples (n = 331) were prospectively followed 12 months later. The outcomes of interest included the pregnancy status of their wives and time to pregnancy. Elevated male urinary TCS concentrations were found to be associated with diminished fecundability (fecundability odds ratio (FOR) 0.77; 95% CI, 0.62–0.97). The risk of infertility significantly increased (OR = 1.6; 95% CI, 1–2.6) as TCS levels elevated. Besides, we divided TCS concentration into tertiles a priori, and there tended to be a dose-response pattern in both analyses. Our findings suggest that environmental exposure to TCS may have an adverse impact on male fecundity.
Predicting Rural Women's Breast Cancer Screening Intention in China: A PLS-SEM Approach Based on the Theory of Planned Behavior
Authors: Sun Yanjun , Yuan Jiawei , Liu Wuqianhui , Qin Banghui , Hu Zhiqing , Li Jianwei , He Yuan
Published: April 2022

Background

It was reported that the incidence of breast cancer (BC) was the highest among cancers worldwide. The breast cancer screening (BCS) program is regarded as an effective preventive measure. However, rural women's willingness to participate in the BCS program is relatively low. To provide measures to prevent BC, it is necessary for the government to identify the influencing factors of rural women's BCS intention.

Methods

A cross-sectional study was conducted among 3,011 rural women by a convenience sampling method through face-to-face interviews on a self-designed questionnaire based on the theory of planned behavior (TPB). The partial least square structural equation model (PLS-SEM) was conducted to determine the predictors of BCS intention, and a multi-group analysis (MGA) of age was performed to identify if there were differences in all hypotheses between different age groups.

Results

There were still rural women who have not been screened for BC in five years (41.7%). The research model of rural women's intention to accept this prevention against BC was rational. All of the hypotheses are supported. Especially, subjective norm (SN) (β = 0.345, p < 0.001) is found to be the strongest predictor followed by the perceived behavioral control 1 (PBC 1) (personal factors, including distance, transportation, busyness, etc.) (β = 0.165, p < 0.001), attitude (β = 0.152, p < 0.001), past behavior (PB) (β = 0.150, p < 0.001), knowledge (β = 0.121, p < 0.001), and perceived behavioral control 2 (PBC 2) (pain and cultural-social factors including embarrassment from a physician, etc.) (β = 0.042, p < 0.05). The advocacy and education (A&E), medical level and service attitude (ML&SA) of township health centers and village clinics can affect behavior intention (BI) via attitude, SN, and PBC. The results of MGA of age indicate that there are significant differences among rural women of different ages regarding the relationship between A&E and PBC 2 (p < 0.01) and the effect of PB on BI (p < 0.001).

Conclusion

The TPB with the addition of PB, knowledge, ML&SA, and A&E can provide the theoretical basis for the policy intervention that aims to enhance the rural women's BCS willingness. MGA of age is conducive to promoting the implementation of the BCS policy. The findings are of great significance to improve rural women's health levels.
The Impact of Artificial Intelligence on the Mental Health of Manufacturing Workers: The Mediating Role of Overtime Work and the Work Environment
Authors: Wei WanQing , Li LinYu
Published: April 2022

Background

Work-related mental health and psychological injuries are important issues related to people's livelihood and wellbeing. Currently, digitalization and intelligent technology have an extremely large impact on the workforce. China is actively promoting the deep integration of artificial intelligence (AI) and manufacturing, which may have important implications for the mental health of manufacturing workers. However, existing researches have paid little attention to the influence of AI on the mental wellbeing of workers in China. There is a lack of relevant empirical research, and the findings in existing studies are inconsistent.

Methods

Using data from the 2018 China Labor Force Dynamics Survey, this paper studies the impact of AI on the depressive symptoms of manufacturing workers and uses stepwise and bootstrapping methods to test whether overtime work and the work environment exhibit mediating effects. Robustness tests were performed by using alternative measures for the dependent and mediating variables. Finally, the heterogeneity in the impact of AI by skill level and generation was examined.

Results

AI can reduce the psychological depression scores of manufacturing workers by 1.643 points, which indicates that AI promotes workers' mental health. Working overtime is not a mediator between AI and mental health. However, the work environment is a mediator between AI and the mental health of manufacturing workers: it explains 11.509% of workers' mental health. The impact of AI on the mental health of manufacturing workers varies by skill level and generation. AI improves the mental health of low-skilled manufacturing workers by 2.342 points and that of manufacturing workers born before the 1980's by 2.070 points.

Conclusions

The application of AI is conducive to improvements in the mental health of manufacturing workers. Improving the work environment is a powerful way to increase the positive effects of AI on workers' mental health. The impact of AI on the mental health of manufacturing workers varies by skill level and generation. The mental health of low-skilled workers and workers born after 1980 is affected more positively by the adoption of AI.
Knowledge, Perceptions and Behaviors Related to COVID-19 in a University Setting in Malaysia
Authors: Lee Kai Wei , Yap Sook Fan , Ong Hooi Tin , Leong Pooi Pooi , Mohamad Hatta Nadia , Lye Munn Sann
Published: April 2022

Background

In Malaysia the COVID-19 disease (COVID-19) has continued to escalate since its first detection in late January 2020, despite widespread implementation of control measures. This study aims to determine the knowledge, perception and behaviors with respect to COVID-19 in the midst of the third wave of the infection.

Methods

A cross-sectional study was carried out among staffs and students of Universiti Tunku Abdul Rahman (UTAR). The survey consists of basic sociodemographic information, 22 items on knowledge on COVID-19, 3 items on perceived self-risk, 2 items on preparedness & perceived self-efficacy, 10 items on preventive (own) measures, 9 items assessing unwanted and desirable behaviors during the pandemic. Simple and multiple linear regression were performed to determine the factors associated with knowledge, preventive measures adopted, self-risk perception, preparedness & perceived self-efficacy, and behaviors.

Results

A total of 434 responded to the survey of whom the majority (85.1%) had high scores for knowledge (mean score of 18.72 out of 22). A significant positive association was found between knowledge and older age (adjusted B coefficient (SE) = 0.046 (0.022), p = 0.039), those from medical faculty (adjusted B coefficient (SE) = 0.870 (0.420), p = 0.039) and residence in high-risk areas (adjusted B coefficient (SE) = 0.831 (0.295), p = 0.005). Predictors for higher perception of COVID-19 risk included presence of COVID-19 cases among social contacts (adjusted B coefficient (SE) = 0.751 (0.308), p = 0.015) and living with elderly (adjusted B coefficient (SE) = 1.137 (0.296), p < 0.001), while that for perception of preparedness and self-efficacy were living with children (adjusted Beta coefficient (SE) = 0.440 (0.173), p = 0.011) and absence of positive cases among social contacts (adjusted B coefficient (SE) = 0.418 (0.183), p = 0.023). Good preventive measures among the respondents were positively associated with knowledge (adjusted B coefficient (SE) = 0.116 (0.025), p < 0.001), as well as with female gender (adjusted B coefficient (SE) = 0.348 (0.142), p = 0.014). Unwanted behavior was significantly associated with male gender (adjusted B coefficient (SE) = 0.664 (0.321), p = 0.039) and COVID-19 positive status (adjusted B coefficient (SE) = 9.736 (3.297), p = 0.003). Knowledge of COVID-19 (adjusted B coefficient (SE) = 0.069 (0.035), p = 0.048) and being married (adjusted B coefficient (SE) = 0.917 (0.462), p = 0.048) were the predictors of desirable behavior.

Conclusion

Overall, the UTAR community had demonstrated a good level of knowledge and preventive behaviors, albeit with some areas for improvement.
Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning
Authors: de Lacy Nina , Ramshaw Michael J. , Kutz J. Nathan
Published: April 2022

Artificial intelligence and machine learning techniques have proved fertile methods for attacking difficult problems in medicine and public health. These techniques have garnered strong interest for the analysis of the large, multi-domain open science datasets that are increasingly available in health research. Discovery science in large datasets is challenging given the unconstrained nature of the learning environment where there may be a large number of potential predictors and appropriate ranges for model hyperparameters are unknown. As well, it is likely that explainability is at a premium in order to engage in future hypothesis generation or analysis. Here, we present a novel method that addresses these challenges by exploiting evolutionary algorithms to optimize machine learning discovery science while exploring a large solution space and minimizing bias. We demonstrate that our approach, called integrated evolutionary learning (IEL), provides an automated, adaptive method for jointly learning features and hyperparameters while furnishing explainable models where the original features used to make predictions may be obtained even with artificial neural networks. In IEL the machine learning algorithm of choice is nested inside an evolutionary algorithm which selects features and hyperparameters over generations on the basis of an information function to converge on an optimal solution. We apply IEL to three gold standard machine learning algorithms in challenging, heterogenous biobehavioral data: deep learning with artificial neural networks, decision tree-based techniques and baseline linear models. Using our novel IEL approach, artificial neural networks achieved ≥ 95% accuracy, sensitivity and specificity and 45–73% R2 in classification and substantial gains over default settings. IEL may be applied to a wide range of less- or unconstrained discovery science problems where the practitioner wishes to jointly learn features and hyperparameters in an adaptive, principled manner within the same algorithmic process. This approach offers significant flexibility, enlarges the solution space and mitigates bias that may arise from manual or semi-manual hyperparameter tuning and feature selection and presents the opportunity to select the inner machine learning algorithm based on the results of optimized learning for the problem at hand.
Socioeconomic Inequity in the Screening and Treatment of Hypertension in Kenya: Evidence From a National Survey
Authors: Oyando Robinson , Barasa Edwine , Ataguba John E.
Published: April 2022

Background

Non-communicable diseases (NCDs) account for 50% of hospitalisations and 55% of inpatient deaths in Kenya. Hypertension is one of the major NCDs in Kenya. Equitable access and utilisation of screening and treatment interventions are critical for reducing the burden of hypertension. This study assessed horizontal equity (equal treatment for equal need) in the screening and treatment for hypertension. It also decomposed socioeconomic inequalities in care use in Kenya.

Methods

Cross-sectional data from the 2015 NCDs risk factors STEPwise survey, covering 4,500 adults aged 18–69 years were analysed. Socioeconomic inequality was assessed using concentration curves and concentration indices (CI), and inequity by the horizontal inequity (HI) index. A positive (negative) CI or HI value suggests a pro-rich (pro-poor) inequality or inequity. Socioeconomic inequality in screening and treatment for hypertension was decomposed into contributions of need [age, sex, and body mass index (BMI)] and non-need (wealth status, education, exposure to media, employment, and area of residence) factors using a standard decomposition method.

Results

The need for hypertension screening was higher among poorer than wealthier socioeconomic groups (CI = −0.077; p < 0.05). However, wealthier groups needed hypertension treatment more than poorer groups (CI = 0.293; p <0.001). Inequity in the use of hypertension screening (HI = 0.185; p < 0.001) and treatment (HI = 0.095; p < 0.001) were significantly pro-rich. Need factors such as sex and BMI were the largest contributors to inequalities in the use of screening services. By contrast, non-need factors like the area of residence, wealth, and employment status mainly contributed to inequalities in the utilisation of treatment services.

Conclusion

Among other things, the use of hypertension screening and treatment services in Kenya should be according to need to realise the Sustainable Development Goals for NCDs. Specifically, efforts to attain equity in healthcare use for hypertension services should be multi-sectoral and focused on crucial inequity drivers such as regional disparities in care use, poverty and educational attainment. Also, concerted awareness campaigns are needed to increase the uptake of screening services for hypertension.
Designing Expandable-Structure Robots for Human-Robot Interaction
Authors: Hedayati Hooman , Suzuki Ryo , Rees Wyatt , Leithinger Daniel , Szafir Daniel
Published: April 2022

In this paper, we survey the emerging design space of expandable structures in robotics, with a focus on how such structures may improve human-robot interactions. We detail various implementation considerations for researchers seeking to integrate such structures in their own work and describe how expandable structures may lead to novel forms of interaction for a variety of different robots and applications, including structures that enable robots to alter their form to augment or gain entirely new capabilities, such as enhancing manipulation or navigation, structures that improve robot safety, structures that enable new forms of communication, and structures for robot swarms that enable the swarm to change shape both individually and collectively. To illustrate how these considerations may be operationalized, we also present three case studies from our own research in expandable structure robots, sharing our design process and our findings regarding how such structures enable robots to produce novel behaviors that may capture human attention, convey information, mimic emotion, and provide new types of dynamic affordances.
Robot Learning From Randomized Simulations: A Review
Authors: Muratore Fabio , Ramos Fabio , Turk Greg , Yu Wenhao , Gienger Michael , Peters Jan
Published: April 2022

The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot (sim-to-real). Despite becoming increasingly realistic, all simulators are by construction based on models, hence inevitably imperfect. This raises the question of how simulators can be modified to facilitate learning robot control policies and overcome the mismatch between simulation and reality, often called the “reality gap.” We provide a comprehensive review of sim-to-real research for robotics, focusing on a technique named “domain randomization” which is a method for learning from randomized simulations.
Evaluating the Carbon Footprint of Cement Plants Integrated With the Calcium Looping CO2 Capture Process
Authors: Carbone Claudio , Ferrario Daniele , Lanzini Andrea , Stendardo Stefano , Agostini Alessandro
Published: April 2022

Cement industry is estimated to account for ~6–7% of anthropogenic CO2 emissions globally. Therefore, the identification of innovative solutions for their mitigation is both a priority and a challenge. The integration of carbon capture and storage technologies into the industrial production process is considered among the most viable solutions for this purpose, and calcium looping (CaL) represents one of the most promising. A key research challenge points to maximize process efficiencies and minimize production cost to decouple cement production from carbon emissions. The carbon capture process proposed in this work is a looping system where CO2 is absorbed by calcium oxide (CaO) in the first reactor (carbonator) and the calcium carbonate (CaCO3) produced is regenerated in an oxy-fired calciner. During calcination, CO2 is released from the sorbents, purified, compressed, and then made available for geological storage. In this study, greenhouse gas (GHG) emissions related to two cement production systems with CaL carbon capture are evaluated: the tail-end CaL carbon capture and the integrated CaL carbon capture. The carbon footprint is complemented with the assessment of the resources depletion mineral and elements and the demand of primary energy. An eco-design approach was pursued by carrying out a life cycle assessment to identify the environmental hotspots and which CaL integration approach presents a higher potential for cement industry decarbonization. The results of the analysis were compared with a conventional cement production process. The results show that the GHG emissions may be reduced by 74% with a tail-end approach and 71% when the CaL is fully integrated into the cement production process. When a future perspective, with higher penetration of renewable energy resources into the electricity sector, was modeled, the results showed that CaL integrated into the clinker production process is more promising in terms of reduction of the carbon footprint, rather than the tail-end solutions. Primary energy consumption from non-renewables is substantially impacted by CaL, with the integrated CaL configuration showing to be a more efficient solution because of less primary energy consumption (coal).
Optimization of Protein Quality Assay in Normal, opaque-2, and Quality Protein Maize
Authors: Kaur Charanjeet , Singh Alla , Sethi Mehak , Devi Veena , Chaudhary Dharam Paul , Phagna Ramesh Kumar , Langyan Sapna , Bhushan Bharat , Rakshit Sujay
Published: April 2022

The development of quality protein maize (QPM) was considered a significant leap toward improvement in the nutritional status of rural masses in developing countries. The nutritional quality of QPM is attributed to the higher concentration of essential amino acids, particularly lysine and tryptophan, in its kernel endosperm. However, the similarity in the grains of QPM and normal maize necessitates the development of a standard protocol to assess the protein quality of maize. The present study aimed at improving the protocol of protein quality assessment in QPM. For this purpose, endosperm defatting and protein estimation procedures were restandardized and optimized with respect to the protocol duration and its amenability for high-throughput analysis. Unlike normal maize, QPM andopaque-2mutants were completely defatted within a 48 h period. It was observed that the tryptophan content, calculated at each defatting interval, increased in the samples defatted for a longer duration. No significant differences were observed in the tryptophan content analyzed in the samples defatted for 48 and 72 h. Moreover, the endosperm protein estimated by using the Bradford method with certain modifications strongly correlated with the micro-Kjeldahl method (r= 0.9). Relative to the micro-Kjeldahl method, the Bradford method was found to be precise, rapid, and hazard-free. The present findings enable a testing protocol of reduced time duration that can be used in resource-poor settings for the determination of a protein quality assay in QPM. Overall, the present study effectively helped in reducing the defatting time by 24 h and protein estimation by 3 h as compared to the already established International Maize and Wheat Improvement Center protocol. This is expected to enable the aggregation of high-protein-quality maize to facilitate its commercialization.