3D human ear modelling with parameterization technique and variation analysis

Anthropometry is vital to provide design references when seeking proper product fit. Nowadays, 3D anthropometry is widely used to provide more size and shape details for improving product designs. However, 3D ear anthropometry is still at an explorative stage, considering the complex ear morphology and other technical obstacles. The proposed research method in this study is applicable to analyse the 3D point cloud of the entire external ear. With the cross-parameterisation technique, the dataset was used to explore the morphological characteristics of the ear. Ear dimensions were automatically extracted and further analysed to explore the gender and symmetry differences using two-way ANOVA. The 3D ear models were investigated through Principal Component Analysis (PCA). The most significant variation was found in the helix and concha region, and the overall ear size is the second important factor determining ear variance. The statistical models were generated as 3D design references for ear-related products.Practitioner summary: This study revealed the morphological variations of the entire 3D external ear with a parameterised 3D ear dataset. Based on the PCA findings, a set of statistical models were generated as design references for product evaluation digitally or physically.

Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses

Objectives

This study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques.

Methods

Data were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches.

Results

PCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01).

Conclusion

The study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

Copaifera mildbraedii Desf.: Phytochemical Composition of Extracts, Essential Oil, and In Vitro Biological Activities of Bark

Copaifera mildbraedii Desf. is an evergreen tree with an umbrella-like crown. It is distributed from south-eastern Nigeria eastward to the Central African Republic (CAR). The aim of this study was to assess the chemical composition and biological activities of C. mildbraedii bark, as well as the chemical composition of the essential oil. Ethyl acetate (EtOAc) and methanol (MeOH) extracts showed a high total phenolic content (TPC) (149.9 and 148.8 mg GAE/g dry residue (dr), respectively), which was related to good antioxidant activity (DPPH) with an IC50 of 21.2 and 12.9 µg/mL, respectively. High-performance liquid chromatography coupled with diode array detector (HPLC-DAD) analysis revealed seven phenolic compounds with myricitrin (13.3 mg/g dr) and 2,4-dihydroxy-3,6-dimethyl benzoic acid (30.7 mg/g dr) as major compounds, while gas chromatography-mass spectrometry (GC-MS) analysis enabled detection of 13 volatile compounds (3 before and 10 after derivatization). Thirty compounds were identified in the essential oil, which corresponds to 65% of all identified compounds. Among the latter, E,E-farnesylacetone and γ-gurjunene were considered as major compounds (8.08 and 10.43%, respectively). The EtOAc extract showed a potent potential, simultaneously, against anti-acetylcholinesterase (AChE), anti-15-lipoxygenase (15-LOX), anti-xanthine oxidase (XOD), and cytotoxic (OVCAR) activities, whereas cyclohexane (CYHA) and dichloromethane (DCM) extracts showed a cytotoxic effect with high percentages of inhibition (95.2%).

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Graphene Oxide, Carbon Nanotubes, and Polyelectrolytes-Based Impedanciometric E-Tongue for Estrogen Detection in Complex Matrices

Currently, it is necessary to maintain the quality of aquifers and water bodies, which means the need for sensors that detect molecules as emerging pollutants (EPs) at low concentrations in aqueous complex solutions. In this work, an electronic tongue (e-tongue) prototype was developed to detect 17β-estradiol in tap water. To achieve such a prototype, an array of sensors was prepared. Each sensor consists of a solid support with interdigitated electrodes without or with thin films prepared with graphene oxide, nanotubes, and other polyelectrolytes molecules adsorbed on them. To collect data from each sensor, impedance spectroscopy was used to analyze the electrical characteristics of samples of estrogen solutions with different concentrations. To analyze the collected data from the sensors, principal components analysis (PCA) method was used to create a three-dimensional plane using the calculated principal components, namely PC1 and PC2, and the estrogen concentration values. Then, damped least squares (DLS) was used to find the optimal values for the hyperplane calibration, as the sensitivity of this e-tongue was not represented by a straight line but by a surface. For the collected data, from nanotubes and graphene oxide sensors, a calibration curve for concentration given by the 10PC1×0.492-PC2×0.14-14.5 surface was achieved. This e-tongue presented a detection limit of 10-16 M of 17β-estradiol in tap water.

Associations of urinary caffeine and caffeine metabolites with metabolic syndrome in US adults

Aims

The relationship between caffeine and metabolic syndrome (MetS) has only been evaluated from the perspective of caffeine consumption. The association between urinary caffeine and MetS is still unclear. This study examined the associations between urinary caffeine and its metabolites and MetS and its components among adults.

Methods

Data from the United States (US) National Health and Nutrition Examination Survey (NHANES) 2011-2014 was analyzed. NHANES is a stratified, multi-stage survey of all non-institutionalized persons in the US. A total of 2,394 subjects aged ≥ 18 years without missing data were selected in this study. Urinary caffeine and caffeine metabolite levels were quantified using high-performance liquid chromatography-electrospray ionization-tandem quadrupole mass spectrometry (HPLC-ESI-MS/MS) with stable isotope-labeled internal standards. We performed principal components analysis (PCA) to investigate the underlying correlation structure of 15 features of urinary caffeine and its metabolites and then used these principal components (PCs) as independent variables to conduct logistic regression analysis with or without restricted cubic spline (RCS) terms to explore the associations between caffeine metabolites and MetS.

Results

Two main PCs that were derived from the PCA explained 90.67% of the total variance of caffeine and its metabolites. The first PC (PC1, strongly correlated with 1-MU, 1,3-DMU, 1,7-DMU, 1,3,7-TMU, 1-MX, 1,3-DMX, 1,7-DMX, 1,3,7-TMX, and AAMU) was positively correlated with risk of MetS (OR = 1.27, p < 0.001) and all its components (all ORs > 1, all p-values < 0.001) in the unadjusted models, while in the adjusted models, it was positively correlated with MetS (OR = 1.16, p = 0.042) and central obesity (OR = 1.22, p < 0.001). In the unadjusted model, there were significant associations between the second PC (PC2, correlated with 3-MU, 7-MU, 3,7-DMU, 3-MX, 7-MX, and 3,7-DMX) and MetS (OR = 1.11, P = 0.030) and central obesity (OR = 1.16, P < 0.001), while in the adjusted models (adjustment variables include gender, age, race/ethnicity, education level and income-poverty ratio, smoking status, drinking, and physical activity), PC2 was positively associated with MetS (OR = 1.15, p = 0.035) and central obesity (OR = 1.15, p = 0.005) and negatively associated with raised triglycerides (TG) (OR = 0.84, p = 0.008). Moreover, we observed U-shaped associations between PC1 and the risk of raised TG both in unadjusted (Pnon-linear = 0.017) and adjusted (Pnon-linear = 0.014) models.

Conclusion

Urinary caffeine metabolites were positively associated with the risk of MetS and its components through different linear or non-linear patterns.

New bands in the sleep stages of spider monkeys (Ateles geoffroyi): Electroencephalographic correlations and spatial distribution

The study of electroencephalographic (EEG) signals in nonhuman primates has led to important discoveries in neurophysiology and sleep behavior. Several studies have analyzed digital EEG data from primate species with prehensile tails, like the spider monkey, and principal component analysis has led to the identification of new EEG bands and their spatial distribution during sleep and wakefulness in these monkeys. However, the spatial location of the EEG correlations of these new bands during the sleep-wake cycle in the spider monkey has not yet been explored. Thus, the objective of this study was to determine the spatial distribution of EEG correlations in the new bands during wakefulness, rapid eye movement (REM) sleep, and non-REM sleep in this species. EEG signals were obtained from the scalp of six monkeys housed in experimental conditions in a laboratory setting. Regarding the 1-21 Hz band, a significant correlation between left frontal and central regions was recorded during non-REM 2 sleep. In the REM sleep, a significant correlation between these cortical areas was seen in two bands: 1-3 and 3-13 Hz. This reflects a modification of the degree of coupling between the cortical areas studied, associated with the distinct stages of sleep. The intrahemispheric EEG correlation found between left perceptual and motor regions during sleep in the spider monkey could indicate activation of a neural circuit for the processing of environmental information that plays a critical role in monitoring the danger of nocturnal predation.

Multivariate Exploratory Analysis of the Bulgarian Soil Quality Monitoring Network

The goal of the present study is to assess the soil quality in Bulgaria using (i) an appropriate set of soil quality indicators, namely primary nutrients (C, N, P), acidity (pH), physical clay content and potentially toxic elements (PTEs: Cu, Zn, Cd, Pb, Ni, Cr, As, Hg) and (ii) respective data mining and modeling using chemometrical and geostatistical methods. It has been shown that five latent factors are responsible for the explanation of nearly 70% of the total variance of the data set available (principal components analysis) and each factor is identified in terms of its contribution to the formation of the overall soil quality-the mountain soil factor, the geogenic factor, the ore deposit factor, the low nutrition factor, and the mercury-specific factor. The obtained soil quality patterns were additionally confirmed via hierarchical cluster analysis. The spatial distribution of the patterns throughout the whole Bulgarian territory was visualized via the mapping of the factor scores for all identified latent factors. The mapping of identified soil quality patterns was used to outline regions where additional measures for the monitoring of the phytoavailability of PTEs were required. The suggested regions are located near to thermoelectric power plants and mining and metal production facilities and are characterized by intensive agricultural activity.

An analysis of the maxillary beak shape variation between 2 pure layer lines and its relationship to the underlying premaxillary bone, feather cover, and mortality

Beak shape varies considerably within and between intact-beak laying hens, and aspects of beak shape appear to be heritable. As an alternative to beak treatment (an effective method of reducing damage from severe feather pecking (SFP)), this variation could be used to genetically select hens whose beak shapes are less apt to cause damage. To be able to select certain phenotypes, the beak shape variation that exists within laying hen flocks must first be characterized. The objectives of this study were to 1) describe the maxillary beak shape variation in 2 pure White Leghorn layer lines with intact beaks using geometric morphometrics to analyze images, and 2) examine the beak shape's relationship to the premaxillary bone, feather cover, and mortality. A lateral head image was taken of each hen (n = 710), and 20 landmarks were placed along each image's dorsal and ventral margins of the maxillary beak. Landmark coordinates were standardized by Procrustes superimposition, and the covariation was analyzed by principal components analysis and multivariate regression. Feather cover was scored at 3 ages and mortality was monitored throughout the production cycle. Three principal components (PCs) explained 83% of the maxillary beak shape variation and the first PC partially separated the 2 lines. Maxillary beak shapes ranged from long and narrow with pointed tips to short and wide with more curved tips. Moderate correlations were found between the maxillary beak and premaxillary bone shape (rs = 0.44) and size (rs = 0.52). Line A hens had better feather cover than Line B at all ages. Line A hens also had less total and cannibalism-related mortality than Line B (10.7 and 0.4% vs. 16.7 and 2.4%, respectively). Beak shape may be one factor contributing to the observed differences in feather cover and mortality. The results suggest that distinct maxillary beak phenotypes within each line could be selected to help reduce SFP damage and improve bird welfare.

Fundamental limits in structured principal component analysis and how to reach them

How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is rotation invariant, we show that standard spectral PCA is optimal. However, for more general priors, both PCA and the existing approximate message-passing algorithm (AMP) fall short of achieving the information-theoretic limits, which we compute using the replica method from statistical physics. We thus propose an AMP, inspired by the theory of adaptive Thouless-Anderson-Palmer equations, which is empirically observed to saturate the conjectured theoretical limit. This AMP comes with a rigorous state evolution analysis tracking its performance. Although we focus on specific noise distributions, our methodology can be generalized to a wide class of trace matrix ensembles at the cost of more involved expressions. Finally, despite the seemingly strong assumption of rotation-invariant noise, our theory empirically predicts algorithmic performance on real data, pointing at strong universality properties.

Association of maternal dietary patterns derived by multiple approaches with gestational diabetes mellitus: a prospective cohort study

We used two a priori diet scores [Mediterranean diet (aMed) and Diet Balance Index (DBI)] and two a posteriori approaches [principal components analysis (PCA) and reduced-rank regression (RRR)] to examine the association of maternal dietary patterns with risk of gestational diabetes mellitus (GDM) and blood glucose among 2202 pregnant women in the Tongji Birth Cohort. Compared to the highest quartile of the aMed and legumes-vegetables-fruits (derived by PCA) scores, the fasting blood glucose (FBG) levels were higher in the lower quartiles (p-trend < 0.05). Lower scores of the meats-eggs-dairy (derived by PCA) and eggs-fish patterns (derived by RRR; characterised by higher intakes of freshwater fish, eggs, and lower intakes of leafy and cruciferous vegetables and fruits) were associated with decreased FBG levels (p-trend < 0.05). Similarities were found across approaches that some dietary patterns were associated with FBG, but not with postprandial glucose and GDM risk.

Genetic association models are robust to common population kinship estimation biases

Common genetic association models for structured populations, including principal component analysis (PCA) and linear mixed-effects models (LMMs), model the correlation structure between individuals using population kinship matrices, also known as genetic relatedness matrices. However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and linear algebra that LMM association tests are invariant to these kinship biases, and PCA approximately so. Our proof shows that the intercept and relatedness effect coefficients compensate for the kinship bias, an argument that extends to generalized linear models. As a corollary, association testing is also invariant to changing the reference ancestral population of the kinship matrix. Lastly, we observed that all kinship estimators, except for popkin ratio-of-means, can give improper non-positive semidefinite matrices, which can be problematic although some LMMs handle them surprisingly well, and condition numbers can be used to choose kinship estimators. Overall, we find that existing association studies are robust to kinship estimation bias, and our calculations may help improve association methods by taking advantage of this unexpected robustness, as well as help determine the effects of kinship bias in related problems.

High Dimensional Mode Hunting Using Pettiest Components Analysis

Principal components analysis has been used to reduce the dimensionality of datasets for a long time. In this paper, we will demonstrate that in mode detection the components of smallest variance, the pettiest components, are more important. We prove that for a multivariate normal or Laplace distribution, we obtain boxes of optimal volume by implementing "pettiest component analysis," in the sense that their volume is minimal over all possible boxes with the same number of dimensions and fixed probability. This reduction in volume produces an information gain that is measured using active information. We illustrate our results with a simulation and a search for modal patterns of digitized images of hand-written numbers using the famous MNIST database; in both cases pettiest components work better than their competitors. In fact, we show that modes obtained with pettiest components generate better written digits for MNIST than principal components.

Hierarchical clustering of prolonged post-concussive symptoms after 12 months: symptom-centric analysis and association with functional impairments

BACKGROUND

Following a concussion, approximately 15% of individuals experience persistent symptoms that can lead to functional deficits. However, underlying symptom-clusters that persist beyond 12 months have not been adequately characterized, and their relevance to functional deficits are unclear. The aim of this study was to characterize the underlying clusters of prolonged post-concussive symptoms lasting more than 12 months, and to investigate their association with functional impairments.

METHODS

Although hierarchical clustering is ideally suited in evaluating subjective symptom severities, it has not been applied to the Rivermead Post-Concussion Questionnaire (RPQ). The RPQ and functional impairments questions were administered via a smartphone application to 445 individuals who self-reported prolonged post-concussive symptoms. Symptom-clusters were obtained using agglomerative hierarchical clustering, and their association with functional deficits were investigated with sensitivity analyses, and corrected for multiple comparisons.

RESULTS

Five symptom-clusters were identified: headache-related, sensitivity to light and sound, cognitive, mood-related, and sleep-fatigue. Individuals with more severe RPQ symptoms were more likely to report functional deficits (p < 0.0001). Whereas the headache and sensitivity clusters were associated with at most one impairment, at-least-mild sleeping difficulties and fatigue were associated with four, and moderate-to-severe cognitive difficulties with five (all p < 0.01).

CONCLUSIONS

Symptom-clusters may be clinically useful for functional outcome stratification for targeted rehabilitation therapies. Further studies are required to replicate these findings in other cohorts and questionnaires, and to ascertain the effects of symptomatic intervention on functional outcomes.

Longitudinal Reading Measures and Genome Imputation in the National Child Development Study: Prospects for Future Reading Research

Reading difficulties are prevalent worldwide, including in economically developed countries, and are associated with low academic achievement and unemployment. Longitudinal studies have identified several early childhood predictors of reading ability, but studies frequently lack genotype data that would enable testing of predictors with heritable influences. The National Child Development Study (NCDS) is a UK birth cohort study containing direct reading skill variables at every data collection wave from age 7 years through to adulthood with a subsample (final n = 6431) for whom modern genotype data are available. It is one of the longest running UK cohort studies for which genotyped data are currently available and is a rich dataset with excellent potential for future phenotypic and gene-by-environment interaction studies in reading. Here, we carry out imputation of the genotype data to the Haplotype Reference Panel, an updated reference panel that offers greater imputation quality. Guiding phenotype choice, we report a principal components analysis of nine reading variables, yielding a composite measure of reading ability in the genotyped sample. We include recommendations for use of composite scores and the most reliable variables for use during childhood when conducting longitudinal, genetically sensitive analyses of reading ability.

Statistical methods for cis-Mendelian randomization with two-sample summary-level data

Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-MR with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis, and Bayesian variable selection. In a simulation study, we show that the various methods have comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of low-density lipoprotein-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions, respectively.

The influence of weather types over northern Greece on respiratory and cardio-vascular mortality

Extreme ambient temperatures are well-known for their adverse impact on public health, in the form of increased mortality and morbidity due to respiratory and cardio-vascular diseases. However, to capture the total impact of weather on cause-specific mortality/morbidity, the synoptic atmospheric conditions over the region under study need to be taken into account. The objective of this work is to identify weather types over Thessaloniki, Greece, statistically associated with mortality from circulatory and respiratory diseases, in an attempt to holistically determine the impact of weather on cause-specific mortality in the region. For this purpose, we employed datasets from the NCEP/NCAR Reanalysis comprising intrinsic daily data, gridded at a resolution of 2.5°×2.5° and covering a 41-year period (1980-2020). The first set used contains data of 500 hPa and 1,000 hPa geopotential heights for the main geographical domain of the Mediterranean region (30°N-45°N, 10°Ε-35°E). The second set comprises meteorological variables (2 m temperature, specific humidity, 2 m zonal and 2 m meridional wind and total cloud cover) for a geographical domain of north Greece (40.95°Ν, 22.50°Ε-26.25°E). We applied a combination of principal components analysis (PCA) as a dimensionality reduction tool and k-means cluster analysis (CA) in order to group days with homogeneous synoptic meteorological parameters. The derived weather types were statistically correlated with respiratory and mortality data for the time-period 1999-2018. It was concluded that the most fatal conditions for public health in Thessaloniki were associated with weather types bringing low/extremely low ambient temperature over north Greece.

Body Fat Is Superior to Body Mass Index in Predicting Cardiometabolic Risk Factors in Adolescents

BACKGROUND

Excess adiposity is one of the main risk factors for the development of cardiovascular and metabolic diseases. The purpose of this study is to compare cardiometabolic risk factors in eutrophic adolescents with a high body fat percentage (%BF) with eutrophic adolescents with adequate %BF and those with excess weight and %BF.

METHODS

Cross-sectional study with 1043 adolescents. This study presented power equal to 99.75%. Body fat and anthropometric, clinical and biochemical indicators were evaluated. Participants were grouped according to body composition classified by body mass index (BMI) and body fat percentage. Statistical analyses were performed using R software version 4.0.2, adopting a significance level of 5%. The Mann-Whitney test, principal components analysis and logistic regression were performed.

RESULTS

It was observed that the SG was more similar to GC2 than to GC1 for both sexes, demonstrating that there was a greater similarity between these groups in relation to the evaluated factors. Higher values for TC, SBP and TG were associated with the SG when the CG1 was used as reference, controlled for sex and age. Likewise, higher TC values and lower levels of SBP, TG and LDL were related to SG when the CG2 was used as reference.

CONCLUSION

Body fat assessment is more effective in predicting risk factors and cardiometabolic diseases than BMI alone.

Influence of handling in corrals on the temperament of different breeds of beef cattle raised in Brazil

The objective of the present study was to verify the effect of handling in corrals on the stress of beef cattle of Nelore, Caracu, and Guzerá breeds through the analysis of the relationship between behavioral traits, growth, and blood parameters. A total of 778 records of entry scores, chute scores, exit scores, flight speed, cortisol, glucose, lactate, live weight, and body condition scores were collected on steers and heifers of the three breeds, born between September and December. The animals came from the Advanced Beef Cattle Research Center of the Animal Science Institute. The variance analyses were performed using the GENMOD procedure. The PROC CORR estimated Spearman correlation coefficients for all traits studied, and the principal components analysis was performed according to the procedure PRINCOMP. Heifers had higher reactivity than steers, with higher behavioral scores and blood parameters. Overall, Caracu individuals demonstrated lower reactivity in comparison to Nelore and Guzerá. However, they presented high stress when they were restrained. The principal component analysis highlighted that behavioral traits and blood parameters were contrasted with growth traits; thus, this analysis can help selectors (livestock farmers) understand the importance of selecting less reactive animals and more easily choose the animals they want to select for breeding. Our results indicate that handling in corrals could be considered a stressful event for bovines. For this reason, measuring blood parameters and behavioral traits can compose necessary decision-making tools regarding changes and adequacies while handling events to reduce stress and consequently improve the herd's reactivity.

Simultaneous quantitative analysis and in vitro anti-arthritic effects of five polyphenols from Terminalia chebula

Background: The fruit of Terminalia chebula has been widely used for a thousand years for treating diarrhea, ulcers, and arthritic diseases in Asian countries. However, the active components of this Traditional Chinese medicine and their mechanisms remain unclear, necessitating further investigation. Objectives: To perform simultaneous quantitative analysis of five polyphenols in T. chebula and evaluate their anti-arthritic effects including antioxidant and anti-inflammatory activity in vitro. Materials and methods: Water, 50% water-ethanol, and pure ethanol were used as extract solvents. Quantitative analysis of gallic acid, corilagin, chebulanin, chebulagic acid, and ellagic acid in the three extracts was performed using high-performance liquid chromatography (HPLC). Antioxidant activity was assessed by the 2,2-diphenylpicrylhydrazyl (DPPH) radical-scavenging assay, and anti-inflammatory activity was evaluated by detecting interleukin (IL)-6 and IL-8 expression in IL-1β-stimulated MH7A cells. Results: The 50% water-ethanol solvent was the optimal solvent yielding the highest total polyphenol content, and the concentrations of chebulanin and chebulagic acid were much higher than those of gallic acid, corilagin, and ellagic acid in the extracts. The DPPH radical-scavenging assay showed that gallic acid and ellagic acid were the strongest antioxidative components, while the other three components showed comparable antioxidative activity. As for the anti-inflammatory effect, chebulanin and chebulagic acid significantly inhibited IL-6 and IL-8 expression at all three concentrations; corilagin and ellagic acid significantly inhibited IL-6 and IL-8 expression at high concentration; and gallic acid could not inhibit IL-8 expression and showed weak inhibition of IL-6 expression in IL-1β-stimulated MH7A cells. Principal component analysis indicated that chebulanin and chebulagic acid were the main components responsible for the anti-arthritic effects of T. chebula. Conclusion: Our findings highlight the potential anti-arthritic role of chebulanin and chebulagic acid from T. chebula.

Do Smart Cities Represent the Key to Urban Resilience? Rethinking Urban Resilience

The pandemic disrupted all activities, so it became necessary to understand, but also rethink, the complexity of economic resilience to better deal with future shocks. A component that can signal the resilience potential of a socio-economic system is smart city response, using technology to make services more efficient. This paper aims to analyze the relationship between smart cities and urban resilience to determine whether urban resilience is significantly influenced by urban smartness. Given the EU SDGs Strategy and the Implementation of RRF Programs, we have also identified the main driving forces that can amplify the impact of smart city development policies on local resilience. The results highlighted that at the European level, smart cities are significantly correlated with urban resilience; urban resilience is explained by the variation in urban smartness; resilience is correlated with all smart city dimensions, highly in (post-)pandemic, redefining a "new normal" in resilience approaches for smart cities. We also stressed the emerging, more complex content of the economic resilience concept and the new structural approach of smart cities resilience for the post-COVID-19 period.

Improvement in Injection Molding Quality Performance with Innovative Cyclone Mixers Used in Polypropylene with Spherical Silicon Dioxide Composites

This research proposes an innovative design of a new cyclone mixer for the quality of polymer materials, and it presents a systematic optimization model of process parameters for plastic injection molding. Thermo gravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used to determine the appropriate thermal properties of processing in order to select appropriate control factors and level values for a Taguchi orthogonal array. The injection molding machine was used to make sample test pieces for tensile strength, hardness and impact strength. Significant factors were found by the signal-to-noise (S/N) ratio with an analysis of variation (ANOVA), and the single-quality optimal parameter combination was obtained. The reproducibility of the experiment was evaluated, and various quality weights were evaluated by principal components analysis (PCA). The multi-quality optimal parameter combination was found, and the comprehensive scores were compared. Finally, the process capability indices were combined with a multi-process capability analysis chart (MPCAC) to compare the process yields of cyclone mixing and screw mixing. The mechanical properties of products were evaluated to verify the performance of cyclone mixing and to provide perfect information for the injection molding quality performance of cyclone mixing and screw mixing. It was concluded that the overall quality of the cyclone mixing products is 42.72, and the total quality of the screw mixing products is 41.85. The total number of defects for the cyclone mixing is 9659 ppm, and that of the screw mixing is 10688 ppm. It can be seen that, for the overall product quality performance, cyclone mixing can be applied in the plastic injection molding process instead of screw mixing.

ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli (E. Coli), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed.

The structure of the Italian version of the Practice Environment Scale of the Nursing Work Index

AIM

To investigate the factor structure of an Italian translation of the Practice Environment Scale of the Nursing Work Index.

BACKGROUND

Italy suffers from high turnover rates, yet validated tools in Italian to study and manage the problem are lacking. The Practice Environment Scale of the Nursing Work Index measures aspects of nurses' working environment. This study reports on the translation and investigation of the structure of the translated Practice Environment Scale of the Nursing Work Index scale.

METHODS

A survey was conducted with 3667 nurses from 182 hospitals across 13 Italian regions using the Practice Environment Scale of the Nursing Work Index. The structure of this scale was investigated using principal components analysis and other properties, including internal consistency (Cronbach's alpha). Construct validity was investigated using logistic regression.

RESULTS

The Italian translation of the Practice Environment Scale of the Nursing Work Index showed a five-factor structure and Cronbach's alpha of the factors ranged between 0.80-0.92. Logistic regression showed that the Practice Environment Scale of the Nursing Work Index was positively related to job satisfaction (Odds Ratio =10.3; CI 8.62-12.35) and negatively related to intention to leave (Odds Ratio =0.14; CI 0.12-0.16).

CONCLUSIONS

The original five-factor structure of the Practice Environment Scale of the Nursing Work Index was evident in the present study, albeit with some different factor loadings.

IMPLICATIONS FOR NURSING MANAGEMENT

The Italian version of the Practice Environment Scale of the Nursing Work Index can be used by nurse managers to tackle the workforce issues related to nurses' work environments in a major European country.

Association between Air Pollution and Squamous Cell Lung Cancer in South-Eastern Poland

Air pollution is closely associated with the development of respiratory illness. The aim of the present study was to assess the relationship between long-term exposure to PM2.5, PM10, NO₂, and SO₂ pollution and the incidence of lung cancer in the squamous subtype in south-eastern Poland from the years 2004 to 2014. We collected data of 4237 patients with squamous cell lung cancer and the level of selected pollutants. To investigate the relationship between the level of concentrations of pollutants and the place of residence of patients with lung cancer in the squamous subtype, proprietary pollution maps were applied to the places of residence of patients. To analyze the data, the risk ratio was used as well as a number of statistical methods, i.e., the pollution model, inverse distance weighted interpolation, PCA, and ordered response model. Cancer in women and in men seems to depend in particular on the simultaneous inhalation of NO₂ and PM10 (variable NO₂PM10) and of NO₂ and SO₂ (variable NO₂ SO₂), respectively. Nitrogen dioxide exercises a synergistic leading effect, which once composed with the other elements it becomes more persistent in explaining higher odds in the appearance of cancers and could constitute the main cause of squamous cancer.

Two-dimensional correlated spectroscopy distinguishes clear cell renal cell carcinoma from other kidney neoplasms and non-cancer kidney

Background

Routinely used clinical scanners, such as computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US), are unable to distinguish between aggressive and indolent tumor subtypes in masses localized to the kidney, often leading to surgical overtreatment. The results of the current investigation demonstrate that chemical differences, detected in human kidney biopsies using two-dimensional COrrelated SpectroscopY (2D L-COSY) and evaluated using multivariate statistical analysis, can distinguish these subtypes.

Methods

One hundred and twenty-six biopsy samples from patients with a confirmed enhancing kidney mass on abdominal imaging were analyzed as part of the training set. A further forty-three samples were used for model validation. In patients undergoing radical nephrectomy, biopsies of non-cancer kidney cortical tissue were also collected as a non-cancer control group. Spectroscopy data were analyzed using multivariate statistical analysis, including principal component analysis (PCA) and orthogonal projection to latent structures with discriminant analysis (OPLS-DA), to identify biomarkers in kidney cancer tissue that was also classified using the gold-standard of histopathology.

Results

The data analysis methodology showed good separation between clear cell renal cell carcinoma (ccRCC) versus non-clear cell RCC (non-ccRCC) and non-cancer cortical tissue from the kidneys of tumor-bearing patients. Variable Importance for the Projection (VIP) values, and OPLS-DA loadings plots were used to identify chemical species that correlated significantly with the histopathological classification. Model validation resulted in the correct classification of 37/43 biopsy samples, which included the correct classification of 15/17 ccRCC biopsies, achieving an overall predictive accuracy of 86%, Those chemical markers with a VIP value >1.2 were further analyzed using univariate statistical analysis. A subgroup analysis of 47 tumor tissues arising from T1 tumors revealed distinct separation between ccRCC and non-ccRCC tissues.

Conclusions

This study provides metabolic insights that could have future diagnostic and/or clinical value. The results of this work demonstrate a clear separation between clear cell and non-ccRCC and non-cancer kidney tissue from tumor-bearing patients. The clinical translation of these results will now require the development of a one-dimensional (1D) magnetic resonance spectroscopy (MRS) protocol, for the kidney, using an in vivo clinical MRI scanner.

Chemometric Investigation and Antimicrobial Activity of Salvia rosmarinus Spenn Essential Oils

To ensure the better production and sustainable management of natural resources, a chemometric investigation was conducted to examine the effect of cooperative and harvesting periods on the crop yields and chemical compositions of Salvia rosmarinus Spenn essential oils in the Oriental region of Morocco. The samples were collected from three cooperatives over nine time periods from January 2018 to April 2019. The chemical composition of Salvia rosmarinus Spenn essential oils was analyzed by gas chromatography coupled with mass spectrometry. The data from this study were processed by multivariate analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The disc diffusion technique and a determination of the minimal inhibitory concentration were performed to study the antibacterial properties of the oils. Statistical analysis showed that the cooperative and harvest period have a significant effect on yields. The highest yield of essential oil was recorded in April 2019 at cooperative C1. The PCA and the HCA results were divided into two groups: Group A for the summer season and group B for the winter season. The samples collected during summer were characterized by a high amount of 1,8-cineole component and a high yield of essential oil, whereas the samples collected during winter were qualified by a high amount of α-pinene component and a low yield of essential oil. The antibacterial activity of Salvia rosmarinus Spenn essential oils showed that Mycobacterium smegmatis ATCC23857 and Bacillus subtilis ATCC 23857 are the most susceptible strains, stopping growth at 1/500 (v/v). The least susceptible strain is Escherichia coli ATCC25922, with an MIC value corresponding to 1/250 (v/v). The findings of this study could have a positive economic impact on the exploitation of rosemary in the Oriental region, especially during the best harvest periods, as they indicate how to obtain the best yields of oils richest in 1,8-cineole and α-pinene chemotypes.

A Metacognitive Awareness Inventory for Surgical Educators: Creation of the MAISE

OBJECTIVE

Metacognition plays an important role in the professional development and growth of surgical educators. This work presents the development and pilot of the MAISE (Metacognitive Awareness Inventory for Surgical Educators). The MAISE is a novel tool for surgeons who are teaching residents in workplace-based learning environments and provides a framework for surgeons to consider their roles as educators and prompt reflection on potential areas for self-improvement.

DESIGN

The MAISE was developed for the surgical context by a general surgery resident by revising 2 existing validated instruments designed to measure metacognitive processes related to teaching. The first version of the MAISE consisted of 35 statements designed to measure 7 subdimensions of metacognition (factors). A principal components analysis with a varimax rotation was conducted, and factor loadings greater than 0.4 were used to assign items to factors.

SETTING

The initial version of the MAISE was piloted by recruiting participants via email and social media platforms (Facebook and Twitter).

PARTICIPANTS

Thirty participants who identified as attending surgeons with resident teaching responsibilities completed the initial version of the MAISE.

RESULTS

Twenty-four of the 35 original items were included in the final version of the MAISE, which includes 5 factors. Final factors were determined based on item loadings. When items loaded on more than one factor, interpretability was used to decide which factor to which an item was assigned. Some factors were excluded due to lack of interpretability. This model explained 85% of the variance in the data.

CONCLUSIONS

MAISE is a valid tool for assessing metacognition among surgical educators. Improving surgeons' metacognition of their teaching practices may advance the surgical field by promoting reflection on teaching processes and areas for growth. This will help surgical educators develop their teaching practices and to employ and model effective teaching with trainees.

Regulation of dynamics and densities of whitefly Bemisia tabaci by agricultural landscapes in south China

Agricultural landscape patterns can affect the population dynamics of pest insects. We selected four landscapes (flower field, mountain, river and urban) based on principal components analysis in Yunnan Province, south China. Through systematic investigation carried out in tomato fields, we intended to clarify the population dynamics and densities of Bemisia tabaci under different landscape types. During the main activity period of B. tabaci, the population densities of B. tabaci nymphs in tomato fields in the river and the urban landscape types were the highest compared to the other landscape types; the population densities of female adults in tomato fields in the river landscape type were also the highest. While the population densities of B. tabaci nymphs and female adults in the flower landscape type, no more than five individuals (ind.) 100 cm^-2 leaf in both years, were the lowest. The density of B. tabaci nymphs in the middle position of tomato plants was higher than those in the other positions, while the density of adults in the upper position of tomato plants was higher, regardless of landscape types. Our findings showed that the population growth of B. tabaci can be easily controlled by the flower landscape type.

Impact of the R292K Mutation on Influenza A (H7N9) Virus Resistance towards Peramivir: A Molecular Dynamics Perspective

In March 2013, a novel avian influenza A (H7N9) virus emerged in China. By March 2021, it had infected more than 1500 people, raising concerns regarding its epidemic potential. Similar to the highly pathogenic H5N1 virus, the H7N9 virus causes severe pneumonia and acute respiratory distress syndrome in most patients. Moreover, genetic analysis showed that this avian H7N9 virus carries human adaptation markers in the hemagglutinin and polymerase basic 2 (PB2) genes associated with cross-species transmissibility. Clinical studies showed that a single mutation, neuraminidase (NA) R292K (N2 numbering), induces resistance to peramivir in the highly pathogenic H7N9 influenza A viruses. Therefore, to evaluate the risk for human public health and understand the possible source of drug resistance, we assessed the impact of the NA-R292K mutation on avian H7N9 virus resistance towards peramivir using various molecular dynamics approaches. We observed that the single point mutation led to a distorted peramivir orientation in the enzyme active site which, in turn, perturbed the inhibitor's binding. The R292K mutation induced a decrease in the interaction among neighboring amino acid residues when compared to its wild-type counterpart, as shown by the high degree of fluctuations in the radius of gyration. MM/GBSA calculations revealed that the mutation caused a decrease in the drug binding affinity by 17.28 kcal/mol when compared to the that for the wild-type enzyme. The mutation caused a distortion of hydrogen bond-mediated interactions with peramivir and increased the accessibility of water molecules around the K292 mutated residue.

Inferring Postmortem Submersion Interval in Rats Found in Water Based on Vitreous Humor Metabolites

OBJECTIVES

The metabolomics technique of LC-MS/MS combined with data analysis was used to detect changes and differences in metabolic profiles in the vitreous humor of early rat carcasses found in water, and to explore the feasibility of its use for early postmortem submersion interval (PMSI) estimation and the cause of death determination.

METHODS

The experimental model was established in natural lake water with 100 SD rats were randomly divided into a drowning group (n=50) and a postmortem (CO₂ suffocation) immediately submersion group (n=50). Vitreous humor was extracted from 10 rats in each group at 0, 6, 12, 18 and 24 h postmortem for metabolomics analyses, of which 8 were used as the training set to build the model, and 2 were used as test set. PCA and PLS multivariate statistical analysis were performed to explore the differences in metabolic profiles among PMSI and causes of death in the training set samples. Then random forest (RF) algorithm was used to screen several biomarkers to establish a model.

RESULTS

PCA and PLS analysis showed that the metabolic profiles had time regularity, but no differences were found among different causes of death. Thirteen small molecule biomarkers with good temporal correlation were selected by RF algorithm. A simple PMSI estimation model was constructed based on this indicator set, and the data of the test samples showed the mean absolute error (MAE) of the model was 0.847 h.

CONCLUSIONS

The 13 metabolic markers screened in the vitreous humor of rat corpses in water had good correlations with the early PMSI. The simplified PMSI estimation model constructed by RF can be used to estimate the PMSI. Additionally, the metabolic profiles of vitreous humor cannot be used for early identification of cause of death in water carcasses.

Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach

Hyperspectral remote sensing has tremendous potential for monitoring land cover and water bodies from the rich spatial and spectral information contained in the images. It is a time and resource consuming task to obtain groundtruth data for these images by field sampling. A semi-supervised method for labeling and classification of hyperspectral images is presented. The unsupervised stage consists of image enhancement by feature extraction, followed by clustering for labeling and generating the groundtruth image. The supervised stage for classification consists of a preprocessing stage involving normalization, computation of principal components, and feature extraction. An ensemble of machine learning models takes the extracted features and groundtruth data from the unsupervised stage as input and a decision block then combines the output of the machines to label the image based on majority voting. The ensemble of machine learning methods includes support vector machines, gradient boosting, Gaussian classifier, and linear perceptron. Overall, the gradient boosting method gives the best performance for supervised classification of hyperspectral images. The presented ensemble method is useful for generating labeled data for hyperspectral images that do not have groundtruth information. It gives an overall accuracy of 93.74% for the Jasper hyperspectral image, 100% accuracy for the HSI2 Lake Erie images, and 99.92% for the classification of cyanobacteria or harmful algal blooms and surface scum. The method distinguishes well between blue green algae and surface scum. The full pipeline ensemble method for classifying Lake Erie images in a cloud server runs 24 times faster than a workstation.

Preconception Dietary Patterns and Associations With IVF Outcomes: An Ongoing Prospective Cohort Study

There is a lack of research on preconception diet and reproductive outcomes conducted in the Chinese population using individual assessment. Between April 2017 and April 2020, 2,796 couples undergoing in vitro fertilization treatment were recruited in this ongoing prospective cohort, and 1,500 eligible couples were included in the final analysis. A validated semi-quantitative food frequency questionnaire was used to evaluate the maternal preconception diet. Other lifestyle factors, including smoking status, psycho-mental status, sleep quality, and physical activity, were also assessed. Five dietary patterns were identified using principal component analysis, namely "Fruits-Vegetables-Dairy-Eggs," "Fish/Seafood-Animal blood," "Tubers-Beans-Cereals," "Puffed food-Candy-Bakery," and "Dried Fruits-Organs-Rice." After adjusting for multiple confounders, we detected that the women who are more inclined to the "Fruits-Vegetables-Dairy-Eggs" pattern and less adherent to the "Tubers-Beans-Cereals" were more likely to achieve normally fertilized eggs and transferable embryos. Regarding pregnancy outcomes, we observed that a lower "Puffed food-Candy-Bakery" score and a higher "Dried fruits-Organs-Rice" score were related to a higher likelihood to achieve biochemical pregnancy. In terms of pregnancy complications, an inverse association between "Fish/Seafood-Animal blood" and hypertensive disorders was observed. We further clustered the dietary patterns based on the proportion of food groups consumed and found that dairy intake was beneficial to embryo quality, while frequent rice consumption was associated with a higher risk of macrosomia. Notably, in the stratified analysis, we observed that the positive relationship between the "Fruit-Dairy-Vegetables-Eggs" score and normal fertilization and the inverse association of the "Fish/Seafood-Animal blood" score with hypertensive disorders during pregnancy were exhibited only among women with body mass index ≥25 kg/m2. In conclusion, pre-treatment diets might be an important target for intervention to achieve a better reproductive outcome.

Seed bulb size influences the effects of exogenous brassinolide on yield and quality of Pinellia ternata

In recent years, natural Pinellia ternata populations of have gradually been exhausted, while the cultivated yield has been limited due to lack of research and uncertain climate condition. Therefore, it is necessary to explore methods of improving yield and quality in P. ternata using brassinolide (BR) treatments and choice of a suitable seed bulb size. This article reports the effects of BR and two seed bulb sizes (diameter: 0.5-1.0 cm and 1.0-1.5 cm) on active and nutrient components and antioxidant activity in P. ternata. The experiment included six levels of BR (0, 0.05, 0.10, 0.50, 1.00 and 2.00 mg l^-1 ). The tuber yield of the two seed bulb sizes and bulbil yield of small seed bulbs increased 5.67%, 22.66% and 69.23% by day 105 after 0.50 mg l^-1 BR treatment, compared with the control. On day 105, only 0.05 mg l^-1 BR increased scores in principal components analysis (PCA) in tubers of small seed bulbs by 167.29%, and 0.05 and 0.50 mg l^-1 BR increased PCA score in bulbils of large seed bulbs by 145.66% and 252.97%, respectively, compared with the control. Significant BR × seed bulb size interactions were found on yield and quality of P. ternata. The results indicate that BR effects on yield and quality of tubers and bulbils of P. ternata are not only related to BR concentration but also to seed bulb size.

Variation in trauma-related behavioral effects using a preclinical rat model of three predator exposure stress

The underlying mechanisms of trauma-related disorders are challenging to discover and the disorders are difficult to treat, in part due to the complex and varied expressions of response to traumatic stress. These challenges will be easier to overcome as more is understood about the variability in response to trauma. Incorporating assessment of variability into animal models of traumatic stress may allow better translation to clinical research and treatment development. In this study, we characterized variability in behavioral responses following traumatic stress exposure using a predator exposure (PE) animal model. Elevated plus maze (EPM) and acoustic startle response (SR) were used to study avoidance and arousal symptoms in male Sprague-Dawley rats. Behavioral data were compared between control (n = 31) and predator-exposed (n = 30) rats. PE behavior was clustered using k-means principal components analysis. Four clusters and three distinct subgroups were identified and were characterized as avoidant (Cluster 4, 30%), moderately avoidant (Clusters 2 and 3, 49%), and unaffected (Cluster 1, 17%). These results demonstrate that rodent responses to PE are varied, similar to human presentations following traumatic stress, suggesting it may be possible to develop treatment strategies for varied symptom presentations using a preclinical animal model.

Torso kinematic patterns associated with throwing shoulder joint loading and ball velocity in Little League pitchers

The purpose of this study was to investigate the associations between kinematic patterns of the torso segment and shoulder joint loading as well as pitching performance in youth pitchers. Twenty-four Little League pitchers threw fastballs while motion capture and force plate data were collected and ball speed was measured with a radar gun. Three-dimensional torso segment kinematics (absolute angles and angular velocities) and shoulder net joint moments (NJM) and forces were calculated. The time-series kinematic data were used as inputs to a principal components analysis to extract torso movement patterns. Associations between torso movement patterns and discrete peak shoulder NJM, compressive force, and ball speed were investigated with nonparametric correlations. Torso segment motion patterns related to forward flexion, lateral flexion (away from pitching arm), and axial rotation and rotational velocities were associated with shoulder joint kinetics and ball speed. In addition, excessive axial (transverse plane) torso rotation at ball release correlated positively with shoulder joint loads but not ball speed, which may indicate the prospect for decreasing joint kinetics while maintaining pitching performance through targeted interventions. These results provide a deeper understanding about the interrelationships between torso kinematic patterns, shoulder kinetics, and pitching performance.

Identification of Phytoconstituents as Potent Inhibitors of Casein Kinase-1 Alpha Using Virtual Screening and Molecular Dynamics Simulations

Casein kinase-1 alpha (CK1α) is a multifunctional protein kinase that belongs to the serine/threonine kinases of the CK1α family. It is involved in various signaling pathways associated with chromosome segregation, cell metabolism, cell cycle progression, apoptosis, autophagy, etc. It has been known to involve in the progression of many diseases, including cancer, neurodegeneration, obesity, and behavioral disorders. The elevated expression of CK1α in diseased conditions facilitates its selective targeting for therapeutic management. Here, we have performed virtual screening of phytoconstituents from the IMPPAT database seeking potential inhibitors of CK1α. First, a cluster of compounds was retrieved based on physicochemical parameters following Lipinski's rules and PAINS filter. Further, high-affinity hits against CK1α were obtained based on their binding affinity score. Furthermore, the ADMET, PAINS, and PASS evaluation was carried out to select more potent hits. Finally, following the interaction analysis, we elucidated three phytoconstituents, Semiglabrinol, Curcusone_A, and Liriodenine, posturing considerable affinity and specificity towards the CK1α binding pocket. The result was further evaluated by molecular dynamics (MD) simulations, dynamical cross-correlation matrix (DCCM), and principal components analysis (PCA), which revealed that binding of the selected compounds, especially Semiglabrinol, stabilizes CK1α and leads to fewer conformational fluctuations. The MM-PBSA analysis suggested an appreciable binding affinity of all three compounds toward CK1α.

Genetic Diversity and Population Structure for Resistance and Susceptibility to Mastitis in Braunvieh Cattle

Mastitis is a disease that causes significant economic losses, since resistance to mastitis is a difficult trait to be improved due to its multifactorial occurrence. Therefore, our objective was to characterize a Mexican Braunvieh cattle population for genetic resistance and susceptibility to mastitis. We used 66 SNP markers for 45 candidate genes in 150 animals. The average heterozygosity was 0.445 ± 0.076, a value higher than those reported for some European breeds. The inbreeding coefficient was slightly negative for resistance to subclinical (−0.058 ± 0.055) and clinical (−0.034 ± 0.076) mastitis, possibly due to low selection for the immunological candidate genes that influence these traits. The genotypic profiles for the candidate loci per K-means group were obtained, as well as the group distribution through the graphics of the principal component analysis. The genotypic profiles showed high genetic diversity among groups. Resistance to clinical mastitis had the lowest presence of the heterozygous genotypes. Although the percentage of highly inbred animals (>50%) is up to 13.3%, there are highly heterozygous groups in terms of the studied traits, a favorable indicator of the presence of genetic diversity. The results of this study constitute evidence of the genetic potential of the Mexican Braunvieh population to improve mastitis-related traits.

Efficacy Profiles of Antimicrobials Evaluated against Staphylococcus Species Isolated from Canine Clinical Specimens

Simple Summary

Clinical cases associated with staphylococci infections are common among dogs and cats. There is evidence to suggest that staphylococci infections are increasingly becoming unresponsive to commonly used antimicrobials. This negatively impacts the ability of these infections to be treated successfully. Although resistance among these organisms has been linked to several factors, including sharing the same mechanism of action or belonging to the same group, there is evidence to suggest that cross resistance can occur between unrelated antimicrobials. The findings of this study not only confirm that antimicrobials that belong to the same group share the same mechanism of resistance and similar antimicrobial efficacy against staphylococcal infections, but also show that cross resistance occurs between unrelated antimicrobials. This should be taken into consideration when selecting antimicrobials for inclusion in the susceptibility testing panel as well as for the treatment of staphylococci infections.

Abstract

Cross-resistance occurs between antimicrobials with either similar mechanisms of action and/or similar chemical structures, or even between unrelated antimicrobials. This study employed a multivariate approach to investigate the associations between the efficacy profile of antimicrobials and the clustering of eleven different antimicrobial agents based on their efficacy profile. Records of the susceptibility of 382 confirmed Staphylococcus species isolates against 15 antimicrobials based on the disc diffusion method were included in this study. Tetrachoric correlation coefficients were computed to assess the correlations of antimicrobial efficacy profiles against Staphylococcus aureus. Principal components analysis and factor analysis were used to assess the clustering of antimicrobial susceptibility profiles. Strong correlations were observed among aminoglycosides, penicillins, fluroquinolones, and lincosamides. Three main factors were extracted, with Factor 1 dominated by the susceptibility profile of enrofloxacin (factor loading (FL) = 0.859), gentamicin (FL = 0.898), tylosin (FL = 0.801), and ampicillin (FL = −0.813). Factor 2, on the other hand, was dominated by the susceptibility profile of clindamycin (FL = 0.927) and lincomycin-spectinomycin (FL = 0.848) and co-trimazole (FL = −0.693). Lastly, Factor 3 was dominated by the susceptibility profile of amoxicillin-clavulanic acid (FL = 0.848) and cephalothin (FL = 0.824). Antimicrobials belonging to the same category or class of antimicrobial, tended to exhibit similar efficacy profiles, therefore, laboratories must choose only one of the antimicrobials in each group to help reduce the cost of antimicrobial susceptibility tests.

Monitoring of frying process in canola oil blend using fourier transform infrared and chemometrics techniques

The production of trans-fats and chemical changes during the process of frying are serious public health concerns and must be monitored efficiently. For this purpose, the canola oil was formulated with different ratio of extra virgin olive oil and palm olein using D-optimal mixture design, and the best formulation (67:22:11) based on free fatty acid (FFA) content, peroxide value (PV), and iodine value (IV) as responses was selected for multiple frying process. The data on FFA, PV, and IV along with Fourier transform-infrared (FT-IR) spectra were taken after each frying up to ten frying. The spectral data were preprocessed with standard normal variate followed by principal component analysis which is clearly showing the differentiation for various frying. Similarly, partial least square regression was applied to predict the FFA (0.37%-1.63%), PV (4.47-13.85 meqO2/kg), and IV (111.51-51.39 I2/100 g) which demonstrated high coefficient of determination (R2) 0.84, 0.83, and 0.81, respectively. It can be summarized that FT-IR can be used as a novel tool for fast and noninvasive quality determination of frying oils.

Evaluating the Presence and Contents of Phytochemicals in Honey Samples: Phenolic Compounds as Indicators to Identify Their Botanical Origin

Honey is a natural product well known for its beneficial properties. It contains phytochemicals, a wide class of nutraceuticals found in plants, including compounds with highly demonstrated antimicrobial and antioxidant capacities as phenolic compounds and flavonoids. The main goal of this work is the development of a miniaturized and environmentally friendly methodology to obtain the phenolic profile of Galician honeys (Northwest Spain) from different varieties such as honeydew, chestnut, eucalyptus, heather, blackberry and multi-floral. The total phenolic content (TPC) and antioxidant activity (AA) were also evaluated. As regards sample preparation, miniaturized vortex (VE) and ultrasound assisted extraction (UAE) employing aqueous-based solvents were performed. Individual quantification of 41 target phenolic compounds was carried out by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results revealed the presence of 25 phenolic compounds in the 91 analyzed samples, reaching concentrations up to 252 µg g⁻¹. Statistical tools such as analysis of variance (ANOVA) and principal component analysis (PCA) were employed to obtain models that allowed classifying the different honeys according to their botanical origin. Obtained results, based on TPC, AA and ∑phenolic compounds showed that significant differences appeared depending on the honey variety, being several of the identified phenol compounds being responsible of the main differentiation.

Dry Powder Mixing Is Feasible in Continuous Twin Screw Extruder: Towards Lean Extrusion Process for Oral Solid Dosage Manufacturing

Using discrete element method (DEM) modeling and near-infrared (NIR) spectroscopy, the feasibility of powder mixing in the initial pre-melting zones of a twin screw extruder using two independent feeders was studied. Previous work in the pharmaceutical and food industry has focused on mixing when materials are melted or on material homogeneity at the extruder's output. Depending on the formulation, ensuring a fully blended formulation prior to melting may be desired. Experiments were conducted using a Coperion ZSK-18 extruder to evaluate if blend uniformity can be achieved by exploring screw configuration, screw speed, and powder feed rate. As powder exited the extruder and deposited on a conveyor belt, an in-line NIR spectrophotometer measured spectra of material. Chemometric-based models predicted unknown concentrations to evaluate if blend uniformity was achieved. Using the EDEM software, Hertz-Mindlin contact model, and dimensions of the extruder, DEM simulations complemented the experimental work. The DEM computational models provided understanding of mixing patterns inside the extruder at particle scale and helped select the screw configuration before doing experimentation. The simulations showed good axial mixing for all the screw configurations studied, while good cross (radial) mixing was only observed for the screw configuration with 90-degree kneading elements. Therefore, the screw configuration with two 90-degree kneading elements was chosen for the experimental study. The RTD profiles when using a screw configuration with only conveying screw elements are comparable to a plug flow reactor (PFR), while the profiles when using kneading elements are more comparable to an ideal continuous stirred tank reactor (CSTR). For the screw configuration with 90 degrees kneading elements, the mean residence time (MRT) decreases with an increase in the screw speed. Experimental NIR spectra showed that concentrations can be predicted with an error of 2%. It was demonstrated that the twin screw extruder can provide proper dry powder mixing of two powder feed streams based on a unit dose scale, enabling continuous powder mixing prior to the melting zone in the extruder for the formulation studied with a cohesive API. This setup may also work for other types of formulations. These studies can help in developing lean hot melt as well as wet extrusion/granulation processes using twin screw extruders for the continuous manufacturing of oral solid dosage products.

Adoption Intention and Factors Influencing the Use of Gerontechnology in Chinese Community-Dwelling Older Adults: A Mixed-Methods Study

Objective: To explore the Chinese community-dwelling intention of older adults to adopt gerontechnology and its influencing factors.

Design: A mixed-methods sequential explanatory design with an inductive approach was employed. In phase 1, a self-made questionnaire was administered from August 2018 to December 2019. Multifactor logistic regression was used to analyze the adoption intention and factors influencing the use of gerontechnology. In phase 2, participants completed a semistructured interview to explore the adoption intention of a specific form of gerontechnology, Smart Aged Care Platform, from May to July 2020.

Setting: Twelve communities in three districts of Chongqing, China.

Participants: Community-dwelling older adults were included.

Results: A total of 1,180 older adults completed the quantitative study; two-thirds of them (68.7%) showed adoption intention toward gerontechnology. Nineteen participants (10 users and nine nonusers) completed the qualitative study and four themes were explored. Through a summarized understanding of the qualitative and quantitative data, a conceptual model of influencing factors, namely, predictive, enabling, and need factors, was constructed.

Conclusions: This study reveals that most Chinese community-dwelling older adults welcome the emergence of new technologies. However, there was a significant difference in the adoption intention of gerontechnology in Chinese community-dwelling older adults based on their sociodemographic and psychographic characteristics. Our findings extend previous technology acceptance models and theories and contribute to the existing resource base.

Data-Driven Prediction of Fatigue in Parkinson's Disease Patients

Introduction: Numerous non-motor symptoms are associated with Parkinson's disease (PD) including fatigue. The challenge in the clinic is to detect relevant non-motor symptoms while keeping patient-burden of questionnaires low and to take potential subgroups such as sex differences into account. The Fatigue Severity Scale (FSS) effectively detects clinically significant fatigue in PD patients. Machine learning techniques can determine which FSS items best predict clinically significant fatigue yet the choice of technique is crucial as it determines the stability of results. Methods: 182 records of PD patients were analyzed with two machine learning algorithms: random forest (RF) and Boruta. RF and Boruta calculated feature importance scores, which measured how much impact an FSS item had in predicting clinically significant fatigue. Items with the highest feature importance scores were the best predictors. Principal components analysis (PCA) grouped highly related FSS items together. Results: RF, Boruta and PCA demonstrated that items 8 ("Fatigue is among my three most disabling symptoms") and 9 ("Fatigue interferes with my work, family or social life") were the most important predictors. Item 5 ("Fatigue causes frequent problems for me") was an important predictor for females, and item 6 ("My fatigue prevents sustained physical functioning") was important for males. Feature importance scores' standard deviations were large for RF (14-66%) but small for Boruta (0-5%). Conclusion: The clinically most informative questions may be how disabling fatigue is compared to other symptoms and interference with work, family and friends. There may be some sex-related differences with frequency of fatigue-related complaints in females and endurance-related complaints in males yielding significant information. Boruta but not RF yielded stable results and might be a better tool to determine the most relevant components of abbreviated questionnaires. Further research in this area would be beneficial in order to replicate these findings with other machine learning algorithms, and using a more representative sample of PD patients.

Determining the variation in premaxillary and dentary bone morphology that may underlie beak shape between two pure layer lines

Beak treatment is an effective method of reducing the damage inflicted by severe feather pecking (SFP) but there is significant pressure to eliminate these treatments and rely solely on alternative strategies. Substantial variation in beak shape exists within non-beak treated layer flocks and beak shape appears to be heritable. There is the potential to use this pre-existing variation and genetically select for hens whose beak shapes are less apt to cause damage during SFP. To do this, we must first understand the range of phenotypes that exist for both the external beak shape and the bones that provide its structure. The objective of this study was to determine the variation in premaxillary (within the top beak) and dentary (within the bottom beak) bone morphology that exists in 2 non-beak treated pure White Leghorn layer lines using geometric morphometrics to analyze radiographs. Lateral head radiographs were taken of 825 hens and the premaxillary and dentary bones were landmarked. Landmark coordinates were standardized by Procrustes superimposition and the covariation was analyzed by principal components analysis and multivariate regression using Geomorph (an R package). Three principal components (PCs) explained 85% of total premaxillary bone shape variation and showed that the shape ranged from long and narrow with pointed bone tips to short and wide with more curved tips. Two PCs explained 81% of total dentary bone shape variation. PC1 described the dentary bone length and width and PC2 explained the angle between the bone tip and its articular process. For both bones, shape was significantly associated with bone size and differed significantly between the two lines. Bone size accounted for 42% of the total shape variation for both bones. Together, the results showed a range of phenotypic variation in premaxillary and dentary bone shape, which in turn may influence beak shape. These bone phenotypes will guide further quantitative genetic and behavioral analyses that will help identify which beaks shapes cause the least damage when birds engage in SFP.

Psychometric Evaluation of the Creighton Competency Evaluation Instrument in a Population of Working Nurses

BACKGROUND AND PURPOSE

This study explored the psychometric properties of the Creighton Competency Evaluation Instrument (C-CEI), previously validated for use with nursing students, to assess simulation performance among registered nurses working 12-hour shifts. Valid and reliable measurements are needed to test clinical and simulation competencies and characterize the effects of fatigue on nursing performance.

METHODS

Trained raters scored nurses' patient care performance in simulation scenarios using the C-CEI. We analyzed the instrument's principal components, internal reliability, and construct validity.

RESULTS

Internal reliability of the C-CEI aggregate score and the Clinical Decision-making component were high (>.70). The latter robustly correlated with predicted cognitive effectiveness, a measure of fatigue.

CONCLUSIONS

The C-CEI is a reliable measure for use among registered nurses and its further development will be important for testing performance of working nurses and fatigue-mitigation innovations.

Mediterranean diet and prudent diet are both associated with low circulating esterified 3-hydroxy fatty acids, a proxy of LPS burden, among older adults

BACKGROUND

LPS-type endotoxins, naturally found in the gut microbiota, are recognized as triggers of inflammation and emerge as detrimental factors of healthy aging. Nutrition represents a promising strategy to reduce LPS burden, yet little is known about the relation of diet to circulating LPS concentrations.

OBJECTIVE

The aim was to evaluate the associations between food groups, dietary patterns, and circulating 3-hydroxy fatty acids (3-OH FAs), a proxy of LPS burden.

METHODS

In a cross-sectional study of 698 French older community-dwelling individuals, 3-OH FA concentrations were measured by LC-tandem MS. Dietary patterns were determined using food-frequency questionnaires. Adherence to a Mediterranean-type diet was computed according to the consumption of 8 food groups (fruits, vegetables, legumes, cereals, fish, olive oil, meat, and dairy products) and alcohol intake (range: 0, low adherence, to 18, high adherence). Three a posteriori dietary patterns were derived from factor analysis: complex carbohydrate (rich in rice, pasta, eggs, poultry, and potatoes), traditional (rich in alcohol, meat, processed meats-cold cuts, and legumes), and prudent (rich in vegetables and fruits and low in cookies) diets. Linear regression models were applied.

RESULTS

The frequency of consumption of each food group was not associated with 3-OH FA concentrations. Greater adherence to both the Mediterranean diet and the prudent diet were associated with lower circulating 3-OH FAs (β [95% CI] for each additional point of score: -0.12 [-0.22, -0.01] and -0.27 [-0.48, -0.07], respectively). In contrast, greater adherence to the traditional diet was associated with higher concentration of 3-OH FAs (β [95% CI] 0.22 [0.001, 0.46]). The adherence to the complex-carbohydrate diet was not associated with 3-OH FA concentrations.

CONCLUSIONS

Based on 2 complementary approaches, the identified plant-based dietary patterns were associated with lower 3-OH FA concentrations, and thus a lower LPS burden, which is considered a potent trigger of inflammatory response.

Multivariate analysis of metals contents in spices commonly consumed in republic of Korea

Concentrations of lead (Pb), cadmium (Cd), arsenic (As), mercury (Hg), zinc (Zn), calcium (Ca), iron (Fe), nickel (Ni) and copper (Cu) were determined in 310 samples of commonly consumed spices from the market in Korea. The content of metals was assayed by acid wet digestion followed by inductively coupled plasma mass spectrometry (ICP-MS) or ICP. The content of Hg was analysed using a direct mercury analyser (DMA). Leafy spices had a significantly higher content of Pb, Cd, As, Hg, Ca and Ni when compared to those of fruit spices. Principal component analysis/cluster analysis (PCA/CA) analyses showed a high positive correlation and close proximities in the content of Pb and As in all samples, Zn and Ca in leafy spices and Cd and Zn in fruit spices.

Meal Pattern Analysis in Nutritional Science: Recent Methods and Findings

There is a scarcity of dietary intake research focusing on the intake of whole meals rather than on the nutrients and foods of which those meals are composed. This growing area of research has recently begun to utilize advanced statistical techniques to manage the large number of variables and permutations associated with these complex meal patterns. The aim of this narrative review was to evaluate those techniques and the meal patterns they detect. The 10 observational studies identified used techniques such as principal components analysis, clustering, latent class analysis, and decision trees. They examined meal patterns under 3 categories: temporal patterns (relating to the timing and distribution of meals), content patterns (relating to combinations of foods within a meal and combinations of those meals over a day), and context patterns (relating to external elements of the meal, such as location, activities while eating, and the presence or absence of others). The most common temporal meal patterns were the 3 meals/d pattern, the skipped breakfast pattern, and a grazing pattern consisting of smaller but more frequent meals. The 3 meals/d pattern was associated with increased diet quality compared with the other 2 patterns. Studies identified between 7 and 12 content patterns with limited similarities between studies and no clear associations between the patterns and diet quality or health. One study simultaneously examined temporal and context meal patterns, finding limited associations with diet quality. No study simultaneously examined other combinations of meal patterns. Future research that further develops the statistical techniques required for meal pattern analysis is necessary to clarify the relations between meal patterns and diet quality and health.

Unraveling heterogeneity in pediatric atopic dermatitis: Identification of serum biomarker based patient clusters

BACKGROUND

Increasing evidence shows that pediatric atopic dermatitis (AD) differs from adult AD on a biologic level. Broad biomarker profiling across a wide range of ages of pediatric patients with AD is lacking.

OBJECTIVE

Our aim was to identify serum biomarker profiles in children with AD aged 0 to 17 years and compare these profiles with those previously found in adults with AD.

METHODS

Luminex multiplex immunoassays were used to measure 145 biomarkers in serum from 240 children with AD (aged 0-17 years). Principal components analysis followed by unsupervised k-means clustering were performed to identify patient clusters. Patients were stratified into age groups (0-4 years, 5-11 years, and 12-17 years) to assess association between age and cluster membership.

RESULTS

Children aged 0 to 4 years had the highest levels of T_H1 cell-skewing markers and lowest levels of T_H17 cell-related markers. T_H2 cell-related markers did not differ significantly between age groups. Similar to the pattern in adults, cluster analysis identified 4 distinct pediatric patient clusters (T_H2 cell/retinol-dominant, skin-homing-dominant, T_H1 cell/T_H2 cell/T_H17 cell/IL-1-dominant, and T_H1 cell/IL-1/eosinophil-inferior clusters). Only the T_H1 cell/T_H2 cell/T_H17 cell/IL-1-dominant cluster resembled 1 of the previously identified adult clusters. Although no association with age or age of onset seemed to be found, disease severity was significantly associated with the skin-homing-dominant cluster.

CONCLUSION

Four distinct patient clusters based on serum biomarker profiles could be identified in a large cohort of pediatric patients with AD, of which 1 was similar to previously identified adult clusters. The identification of endotypes driven by distinct underlying immunopathologic pathways might be useful to define pediatric patients with AD who are at risk of persistent disease and may necessitate different targeted treatment approaches.