Dimensions of Self-Reported Driving Difficulty in Autistic and Non-Autistic Adults and their Relationship with Autistic Traits

A survey asked autistic and non-autistic people about the driving difficulties they experience and their autistic traits. Principle components analysis was used to identify how reported difficulties clustered together in each group, and regression was used to determine which subscales of the Autism Spectrum Quotient predict these factors. For autistic drivers three factors of driving difficulty emerged: a Driving Executive factor, predicted by Attention Switching; a Driving Understanding factor, predicted by Communication; and a Driving Social Interaction factor, predicted by Attention Switching. For non-autistic drivers only one Driving General factor emerged, predicted by Communication. This suggests autistic people may experience at least three distinct domains of difficulty when driving which may relate to their particular profile of autistic features.

Prediction Poverty Levels of College Students Using a Machine Learning Model

Nowadays, poverty-stricken college students have become a special group among the college students and occupied higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this manuscript, a novel model that combined Random Forest with Principle Components Analysis (RF-PCA) is proposed prediction poverty levels of college students. To build this model, data was firstly collected to establish datasets including 4 classed of poverty levels and 21 features of poverty-stricken college students. Then, feature dimension reduction includes two steps: the first step we selected the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF); the second step of feature extraction through Principle Components Analysis (PCA) extracted 11 dimensions. Finally, confusion metrics and receiver operating characteristic (ROC) curves were used to evaluate the performance of the proposed model, the accuracy of the model achieved 78.61%. Furthermore, compared with seven different classification algorithms, the model has a higher prediction accuracy, the result has great potential to identify the poverty levels of college students.

Identify Attractive and Unattractive Individuals Based on Geometric Features Using Neural Network

This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive individuals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).

Chemometric Analyses for the Characterization of Raw and Stir-Frying Processed Drynariae Rhizoma Based on HPLC Fingerprints

The processing of traditional Chinese medicine (TCM) is a necessary practice and usually occurs before most herbs are prescribed. According to Chinese medicine theory, raw (RDR) and stir-frying processed (PDR) Drynariae Rhizoma have different clinical applications. The purpose of this study was to establish HPLC fingerprints coupled with chemometric methods to evaluate the differences between RDR and PDR. Multivariate chemometric methods were applied to analyze the obtained HPLC fingerprints, including hierarchical cluster analysis (HCA), principle components analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results indicated that RDR and PDR samples showed a clear classification of the two groups, and seven chemical markers having great contributions to the differentiation were screened out. The findings suggested that 5-hydroxymethyl-2-furaldehyde (5-HMF) with a variable importance in the project (VIP > 1) can be used to differentiate between RDR and PDR. Moreover, 5-HMF, naringin, and neoeriocitrin were determined to evaluate their contents in RDR and PDR. The chemometrics combined with the quantitative analysis based on HPLC fingerprint results indicated that stir-frying processing may change the contents and types of components in Drynariae Rhizoma. These changes are probably responsible for the various pharmacological effects of RDR and PDR.

Attitude scale towards literature for high school students: A study of validity and reliability

The purpose of this research is to compensate the lack of research regarding the above-mentioned fields, and literature in particular, and to take a leading part to open the path for new studies regarding the subject. With regards to this purpose, it is planned to measure the attitude of high school students towards literature via an objective tool and thus, Attitude Scale Towards Literature (ASTL) was generated. The scale, which was implemented on 739 high school students during 2019-2020, consists of total 33 statements of which 20 are positive and 13 are negative. Respondents of the survey are students of three different schools and are from 4 different grade levels, namely 9, 10, 11 and 12th grades. With the aim of determining the factor pattern of the scale, principle components analysis was preferred as method of factoring and varimax was chosen as the spinning method. In accordance with these data, explanatory factor analysis was carried out on the scale and it was determined that the scale has a structure of two factors. The first factor is Attitude Towards Personal Development; and the second factor is Attitude Towards Internalization of Literature. Keywords: Literature, high school students, validity, reliability

Characterization of Genetic Diversity in Dhofari Wild Gazelles

Wild gazelles are scattered in most of the arid and semi-arid areas of the Sultanate of Oman particularly in valleys, mountains and sandy zones of Rub' al Khali desert. Recently, gazelles’ populations have been facing reduction in the numbers. Consequently, gradual loss of genetic diversity is inevitable. This loss is considered one of the main threats that attribute to a loss of habitat and may lead to gazelle extinction. Till now, little is known about the status of the genetic diversity of the wild Dhofari gazelle. This project aimed to determine the extent of inbreeding, population structure and genetic diversity in the Dhofari gazelles’ populations. DNA was extracted from gazelles’ fecal samples using the human stool DNA extraction protocol. Following extraction, four microsatellite nuclear markers were used to calculate the level of inbreeding, population differentiation and genetic diversity. PCR inhibitors were significantly removed by the addition of Bovine Serum Albumin (BSA) and dimethyl sulfoxide (DMSO). The mean inbreeding for the population was 0.228 for all loci with a standard error of 0.09. It is therefore postulated that Dhofari gazelles are generally undergoing gradual inbreeding which could lead to lack of fitness in future generations. The genetic differentiation (Fst) ranged between 0.071 (between Gara and Stom) and 0.231 (between Gara and Ayon). On the other hand, the Fst estimate between Solot (most distant) versus other Dhofari gazelles populations (pooled together) was 3.7%. Principle Components Analysis clustered Ayon and Gara populations apart from one another and closer to Stom while placing Solot further than all other populations which is in agreement with the Fst results and the geographical distribution. In conclusion, the results of this preliminary study will provide insight towards the conservation of wild gazelles in Dhofar. This is the first study to report the genetic diversity and status of wild Gazelles and provides a reference point for future studies assessing their genetic diversity and variability.

Behavior-specific occurrence patterns of Pinyon Jays (Gymnorhinus cyanocephalus) in three Great Basin study areas and significance for pinyon-juniper woodland management

The Pinyon Jay is a highly social, year-round inhabitant of pinyon-juniper and other coniferous woodlands in the western United States. Range-wide, Pinyon Jays have declined ~ 3–4% per year for at least the last half-century. Occurrence patterns and habitat use of Pinyon Jays have not been well characterized across much of the species’ range, and obtaining this information is necessary for better understanding the causes of ongoing declines and determining useful conservation strategies. Additionally, it is important to better understand if and how targeted removal of pinyon-juniper woodland, a common and widespread vegetation management practice, affects Pinyon Jays. The goal of this study was to identify the characteristics of areas used by Pinyon Jays for several critical life history components in the Great Basin, which is home to nearly half of the species’ global population, and to thereby facilitate the inclusion of Pinyon Jay conservation measures in the design of vegetation management projects. To accomplish this, we studied Pinyon Jays in three widely separated study areas using radio telemetry and direct observation and measured key attributes of their locations and a separate set of randomly-selected control sites using the U. S. Forest Service’s Forest Inventory Analysis protocol. Data visualizations, principle components analysis, and logistic regressions of the resulting data indicated that Pinyon Jays used a distinct subset of available pinyon-juniper woodland habitat, and further suggested that Pinyon Jays used different but overlapping habitats for seed caching, foraging, and nesting. Caching was concentrated in low-elevation, relatively flat areas with low tree cover; foraging occurred at slightly higher elevations with generally moderate but variable tree cover; and nesting was concentrated in slightly higher areas with high tree and vegetation cover. All three of these Pinyon Jay behavior types were highly concentrated within the lower-elevation band of pinyon-juniper woodland close to the woodland-shrubland ecotone. Woodland removal projects in the Great Basin are often concentrated in these same areas, so it is potentially important to incorporate conservation measures informed by Pinyon Jay occurrence patterns into existing woodland management paradigms, protocols, and practices.

Measurement of Patient Confidence in Self-Management of Atrial Fibrillation: Initial Validation of The Confidence in Atrial fibriLlation Management (CALM) Scale

Background: The patient experience of atrial fibrillation (AF) involves several daily self-care behaviors and ongoing confidence to manage their condition. Currently, no standardized self-report measure of AF patient confidence exists. The purpose of this study is to establish the reliability and validity of a newly developed confidence in AF management measure. Methods: This study provides preliminary analysis of the Confidence in Atrial FibriLlation Management (CALM) scale, which was rationally developed to measure patient confidence related to self-management of AF. The scale was provided to a sample of AF patients N=120, (59% male) electronically through a patient education platform. Principle components analysis (PCA) and Cronbach’s alpha were employed to provide preliminary assessment of the validity and reliability of the measure. Results: PCA identified a four-factor solution. Internal consistency of the CALM was considered excellent with Cronbach’s α = .910. Additional PCA confirmed the value of a single factor solution to produce a total confidence score for improved utility and ease of clinical interpretation. Conclusions: Initial assessment of a novel scale measuring patient confidence in managing AF provided promising reliability and validity. Patient confidence in self-management of AF may prove useful as a key marker and endpoint of the patient experience beyond QOL.

Gene functional networks and autism spectrum characteristics in young people with intellectual disability: a dimensional phenotyping study

Background The relationships between specific genetic aetiology and phenotype in neurodevelopmental disorders are complex and hotly contested. Genes associated with intellectual disability (ID) can be grouped into networks according to gene function. This study explored whether individuals with ID show differences in autism spectrum characteristics (ASC), depending on the functional network membership of their rare, pathogenic de novo genetic variants. Methods Children and young people with ID of known genetic origin were allocated to two broad functional network groups: synaptic physiology (n = 29) or chromatin regulation (n = 23). We applied principle components analysis to the Social Responsiveness Scale to map the structure of ASC in this population and identified three components—Inflexibility, Social Understanding and Social Motivation. We then used Akaike information criterion to test the best fitting models for predicting ASC components, including demographic factors (age, gender), non-ASC behavioural factors (global adaptive function, anxiety, hyperactivity, inattention), and gene functional networks. Results We found that, when other factors are accounted for, the chromatin regulation group showed higher levels of Inflexibility. We also observed contrasting predictors of ASC within each network group. Within the chromatin regulation group, Social Understanding was associated with inattention, and Social Motivation was predicted by hyperactivity. Within the synaptic group, Social Understanding was associated with hyperactivity, and Social Motivation was linked to anxiety. Limitations Functional network definitions were manually curated based on multiple sources of evidence, but a data-driven approach to classification may be more robust. Sample sizes for rare genetic diagnoses remain small, mitigated by our network-based approach to group comparisons. This is a cross-sectional study across a wide age range, and longitudinal data within focused age groups will be informative of developmental trajectories across network groups. Conclusion We report that gene functional networks can predict Inflexibility, but not other ASC dimensions. Contrasting behavioural associations within each group suggest network-specific developmental pathways from genomic variation to autism. Simple classification of neurodevelopmental disorder genes as high risk or low risk for autism is unlikely to be valid or useful.

Unconstrained representation of orthogonal matrices with application to common principal components

Many statistical problems involve the estimation of a $$\left( d\times d\right) $$ d × d orthogonal matrix $$\varvec{Q}$$ Q . Such an estimation is often challenging due to the orthonormality constraints on $$\varvec{Q}$$ Q . To cope with this problem, we use the well-known PLU decomposition, which factorizes any invertible $$\left( d\times d\right) $$ d × d matrix as the product of a $$\left( d\times d\right) $$ d × d permutation matrix $$\varvec{P}$$ P , a $$\left( d\times d\right) $$ d × d unit lower triangular matrix $$\varvec{L}$$ L , and a $$\left( d\times d\right) $$ d × d upper triangular matrix $$\varvec{U}$$ U . Thanks to the QR decomposition, we find the formulation of $$\varvec{U}$$ U when the PLU decomposition is applied to $$\varvec{Q}$$ Q . We call the result as PLR decomposition; it produces a one-to-one correspondence between $$\varvec{Q}$$ Q and the $$d\left( d-1\right) /2$$ d d - 1 / 2 entries below the diagonal of $$\varvec{L}$$ L , which are advantageously unconstrained real values. Thus, once the decomposition is applied, regardless of the objective function under consideration, we can use any classical unconstrained optimization method to find the minimum (or maximum) of the objective function with respect to $$\varvec{L}$$ L . For illustrative purposes, we apply the PLR decomposition in common principle components analysis (CPCA) for the maximum likelihood estimation of the common orthogonal matrix when a multivariate leptokurtic-normal distribution is assumed in each group. Compared to the commonly used normal distribution, the leptokurtic-normal has an additional parameter governing the excess kurtosis; this makes the estimation of $$\varvec{Q}$$ Q in CPCA more robust against mild outliers. The usefulness of the PLR decomposition in leptokurtic-normal CPCA is illustrated by two biometric data analyses.