Objectives
This study developed multivariate law enforcement officer (LEO) body models for digital simulation of LEO accommodation in police cruiser cabs.
Background
Anthropometrically accurate ...digital LEO body models, representing the United States LEOs, for computerized LEO cruiser interface simulations are lacking.
Methods
Twenty body dimensions (with and without gear combined) of 756 male and 218 female LEOs were collected through a stratified national survey using a data collection trailer that traveled across the US. A multivariate Principal Component Analysis (PCA) approach was used to develop digital LEO body models.
Results
Fifteen men and 15 women representing unique body size and shape composition of the LEO population were identified. A combined set of 24 male and female models (removal of 6 redundant models for which female and male models overlapped) is suggested.
Conclusions
A set of 24 digital LEO body models in 3-dimensional form, along with their anthropometric measurements, were developed to facilitate LEO cruiser cab design.
Application
Digital modeling software developers can use the models and their anthropometric data to build digital avatars for simulated evaluation of LEO cruiser cab configuration, console communication-equipment fitting, and cruiser ingress/egress access arrangement. LEO vehicle and equipment designers also can use eight key body dimensions (i.e., stature, buttock-popliteal length, eye height sitting, knee height sitting, shoulder-grip length, popliteal height, sitting height, and body weight) of the body models to recruit 24 human subjects to physically evaluate their vehicle prototypes for improved vehicle and equipment design.
The gut microbiome modulates seizure susceptibility and the anti-seizure effects of the ketogenic diet (KD) in animal models, but whether these relationships translate to KD therapies for human ...epilepsy is unclear. We find that the clinical KD alters gut microbial function in children with refractory epilepsy. Colonizing mice with KD-associated microbes promotes seizure resistance relative to matched pre-treatment controls. Select metagenomic and metabolomic features, including those related to anaplerosis, fatty acid β-oxidation, and amino acid metabolism, are seen with human KD therapy and preserved upon microbiome transfer to mice. Mice colonized with KD-associated gut microbes exhibit altered hippocampal transcriptomes, including pathways related to ATP synthesis, glutathione metabolism, and oxidative phosphorylation, and are linked to susceptibility genes identified in human epilepsy. Our findings reveal key microbial functions that are altered by KD therapies for pediatric epilepsy and linked to microbiome-induced alterations in brain gene expression and seizure protection in mice.
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•Colonizing mice with KD-associated human gut microbes increases seizure resistance•Human donor and mouse recipients share select metagenomic and metabolomic features•Mice colonized with KD-associated gut microbes exhibit altered brain transcriptomes•Multi-omic analysis shows candidate microbial key drivers of seizure protection
Lum et al. find that ketogenic diets (KDs) alter the gut microbiome in children with drug-resistant epilepsy. Mice colonized with KD-associated microbes exhibit seizure resistance. Human donors and mouse recipients share functional features of the KD-associated microbiome linked to hippocampal transcriptomic signatures, epilepsy susceptibility genes, and seizure protection.
In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable ...populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytics to enhance prediction accuracy, including generalized regression to identify factors that contributed to the differences between the two measures. The mobile app underestimated most macro- and micro-nutrients compared to FFQ (ranges: -5% for total calories, -19% for cobalamin, -33% for vitamin E). The average correlations between the two measures were 0.87 for macro-nutrients and 0.84 for micro-nutrients. Factors that contributed to the differences between the two measures using total calories as an example, included caloric range (1000-2000 versus others), carbohydrate, and protein; for cobalamin, included caloric range, protein, and Chinese diet. Future studies are needed to validate actual intakes and reporting of various diets, and to examine the accuracy of mobile App. Thus, a mobile app can be used to support personalized nutrition in the mHealth era, considering adjustments with sources that could contribute to the inaccurate estimates of nutrients.
Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for precision nutrition. We, therefore, validated the ...accuracy of an internet-based app against the Nutrition Data System for Research (NDSR), assessing these essential nutrients among various social-ethnic diet types. The agreement between the two measures using intraclass correlation coefficients was good (0.85) for total calories, but moderate for caloric ranges outside of <1000 (0.75) and >2000 (0.57); and good (>0.75) for most macro- (average: 0.85) and micro-nutrients (average: 0.83) except cobalamin (0.73) and calcium (0.51). The app underestimated nutrients that are associated with protein and fat (protein: −5.82%, fat: −12.78%, vitamin B12: −13.59%, methionine: −8.76%, zinc: −12.49%), while overestimated nutrients that are associated with carbohydrate (fiber: 6.7%, B9: 9.06%). Using artificial intelligence analytics, we confirmed the factors that could contribute to the differences between the two measures for various essential nutrients, and they included caloric ranges; the differences between the two measures for carbohydrates, protein, and fat; and diet types. For total calories, as an example, the source factors that contributed to the differences between the two measures included caloric range (<1000 versus others), fat, and protein; for cobalamin: protein, American, and Japanese diets; and for folate: caloric range (<1000 versus others), carbohydrate, and Italian diet. In the e-Health era, the internet-based app has the capacity to enhance precision nutrition. By identifying and integrating the effects of potential contributing factors in the algorithm of output readings, the accuracy of new app measures could be improved.
The current study examines the relationship between parents' and children's reports of parenting and their effects on children's mental health symptoms. Six hundred and sixty-six parent-child dyads ...in Taiwan participated in this study. The parents and the children filled out the parenting questionnaires, and the children also reported their general mental health. The results demonstrated that parental-reported and child-perceived parenting were positively correlated, but parents tended to report lower scores on authoritarian parenting and higher scores on Chinese parenting than did their children. There were also significant gender differences: The mothers reported higher authoritative parenting than did the fathers; and the boys perceived higher authoritarian and Chinese-culture specific parenting than did the girls. Moreover, the Chinese parenting had a negative effect on children's mental health outcomes. Finally, our results showed that children's perception of parenting had a stronger effect on children's mental health symptoms than did parental reports on parenting, urging future research to include the children's report when investigating the effects of parenting on children's mental health outcomes.
Objectives
This study investigated anthropometric changes of national law enforcement officers (LEOs) in 46 years, compared the differences between LEO data and civilian anthropometry, and identified ...the magnitude of differences in dimensions measured with gear versus semi-nude measurements.
Background
The best available 46-year-old anthropometric dataset of LEOs has largely become outdated due to demographic changes. Additionally, anthropometric data of female LEOs and LEO measurements with gear are lacking.
Method
Thirty-four traditional body dimensions and 15 with gear measurements of 756 male and 218 female LEOs were collected through a stratified national survey using a data collection trailer that traveled across the U.S. and the data were compared to the LEO anthropometric data from 1975 and existing civilian anthropometric databases.
Results
LEO body size and shape have evolved over the past 46 years - an increase of 12.2 kg in body weight, 90 mm in chest circumference, and 120 mm in waist circumference for men. No previous data was available for comparison for females. Compared to civilians, both male and female LEOs have a larger upper body build. LEO gear added 91 mm in waist breadth for men and 120 mm for women, and 11 kg in weight for men and 9 kg for women.
Conclusion
The study reveals that equipment design based on the existing civilian datasets or 46-year-old LEO dataset would not accommodate the current LEO population. The new data fill this gap. Application: The differences reported above are important for LEO body gear, vehicle console, and vehicle ingress/egress design.
This study aims to examine the prevalence of multiple types of child victimization and the effects of multiple types of victimization on children’s mental health and behavior in Taiwan. The study ...also examines the child-protection rate and its correlates among children experiencing various types of victimization. This study collected data with a self-report questionnaire from a national proportionately stratified sample of 6,233 fourth-grade students covering every city and county in Taiwan in 2014. After calculating the 1-year prevalence of child victimization, the study found that bullying was the most prevalent (71%), followed by physical neglect (66%), psychological violence (43%), inter-parental violence (28%), community violence (22%), physical abuse (21%), and sexual violence (9%). As the number of victimization types increased, children were more likely to report greater posttraumatic symptoms, psychiatric symptoms, suicide ideation, self-harm thoughts, and violent behaviors. Gender, neonatal status, parental marital status, and other family risks were significantly associated with elevated incidences of the victimization types. Only 20.6% of the children who had experienced all seven types of victimization had received child protective services. A child was more likely to receive child protective services if he or she had experienced sexual violence, community violence, inter-parental violence exposure, higher family risks, higher suicidal ideation, or living in a single-parent or separated family. In conclusion, this study demonstrates the cumulative effects and the harmful effects that children’s experience of multiple types of victimization can have on the children’s mental health and behavior. The present findings also raise alarms regarding the severity of under-serving in child-victimization cases. These results underscore the importance of assessing, identifying, and helping children with multiple victimization experiences.
Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for ...clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records.
Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640.
A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered.
ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.
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