Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of life and risk of depression in the youngest-old and middle-old women and 
men

Introduction and objective. The currently observed progressive aging of societies has an impact on the occurrence of problems affecting the quality of life in many spheres. While focused on a mixed gender population, deliberately stratified into the youngest-oldest and middle-aged individuals, the study aimed to determine whether their subjective assessment of overall quality of life was in any way correlated with the risk of depression. Materials and method. A study sample of 164 free-living community dwellers aged 65–84 years was investigated with the use of WHOQOL-BREF and GDS-15 surveys. The data collected was subsequently analysed by Chi-square test, Student’s t test, Mann Whitney U test, and Spearman rank correlation. Results. Significant differences were established in the overall quality of life in terms of select psychological variables in women from Groups I and II (p=0.006). In the groups stratified by age angender, significant negative correlations were noted (p<0.001) between respective domains under study. Conclusions. Women from the youngest-oldest group rated their quality of life higher in the psychological domain, compared with the middle-aged group. Neither age nor gender differentiated individual susceptibility to depression, even though approx. 30% of all respondents were ultimately established as being at such risk. Individuals established as less prone to depression rated their overall quality of life as higher in the respective domains. The respondents‘ subjective assessment indicated no correlation with the risk of depression.
Toxicological profile for 1,1,1-trichloroethane

DISCLAIMER The use of company or product name(s) is for identification only and does not imply endorsement by the Agency for Toxic Substances and Disease Registry. A Toxicological Profile for 1,1,1-Trichloroethane, Draft for Public Comment, was released in September 2004. This edition supersedes any previously released draft or final profile. Toxicological profiles are revised and republished as necessary. For information regarding the update status of previously released profiles, contact ATSDR at: FOREWORD This toxicological profile is prepared in accordance with guidelines developed by the Agency for Toxic Substances and Disease Registry (ATSDR) and the Environmental Protection Agency (EPA). The original guidelines were published in the Federal Register on April 17, 1987. Each profile will be revised and republished as necessary. The ATSDR toxicological profile succinctly characterizes the toxicologic and adverse health effects information for the hazardous substance described therein. Each peer-reviewed profile identifies and reviews the key literature that describes a hazardous substance's toxicologic properties. Other pertinent literature is also presented, but is described in less detail than the key studies. The profile is not intended to be an exhaustive document; however, more comprehensive sources of specialty information are referenced. The focus of the profiles is on health and toxicologic information; therefore, each toxicological profile begins with a public health statement that describes, in nontechnical language, a substance's relevant toxicological properties. Following the public health statement is information concerning levels of significant human exposure and, where known, significant health effects. The adequacy of information to determine a substance's health effects is described in a health effects summary. Data needs that are of significance to protection of public health are identified by ATSDR and EPA. Each profile includes the following: (A) The examination, summary, and interpretation of available toxicologic information and epidemiologic evaluations on a hazardous substance to ascertain the levels of significant human exposure for the substance and the associated acute, subacute, and chronic health effects; (B) A determination of whether adequate information on the health effects of each substance is available or in the process of development to determine levels of exposure that present a significant risk to human health of acute, subacute, and chronic health effects; and (C) Where appropriate, identification of toxicologic testing needed to identify the types or levels of exposure that may present significant risk of adverse health effects in humans. The principal audiences for the toxicological profiles are health professionals at the Federal, State, …
Sonoelastographic assessment of the normal uterus and ovaries using the Shear Wave Elastography according to different menstrual phases

Objective: In most of the gynecological studies conducted using the Shear Wave Elastography (SWE) method in the literature, the menstrual cycle period was not taken into account. Current study, we aimed to describe the sonoelastographic features of normal myometrium and ovaries in healthy women and to define their variability during the different phases of the menstrual cycle using the SWE method. Material and methods: All cases were selected from individuals between the ages of 24-31, with regular menstrual cycles and no systemic disease. Each case was called in, 1-5th, 12-16th, 21-24th day of their menstrual cycles and was evaluated by B-mode imaging and SWE in pelvic ultrasonography. The relationship of menstrual phases with uterine and ovarian elasticity was investigated by comparing all measurements made in different menstrual phases. Results: No statistically significant difference was observed between the volume of right and left ovaries in terms (p> 0.05). There was no statistically significant difference in terms of elastography measurements obtained from the uterus, right and left ovaries for each menstrual phase according to Bonferroni Correction (p> 0.0163). Conclusion: Although there was a slight decrease in myometrial SWE measurements in the follicular phase, there was no significant difference regarding the SWE measurements of uterus and ovaries in early follicular, peri-ovulatory, and luteal menstrual stages. Further studies with a large number of participants are needed to suggest whether gynecological studies planned to be carried out with the shear wave elastography method should be planned in a specific menstrual phase.
Methods of clustering parameters in the creation of neural network multi-mode dynamic models of aircraft engines

The presence on modern aviation gas-turbine engines of dozens and even hundreds of sensors for continuous registration of various parameters of their operation makes it possible to collect and process large amounts of information. This stimulates the development of monitoring and diagnostic systems. At the same time the presence of great volumes of information is not always a sufficient condition for making adequate managerial decisions, especially in the case of evaluation of the technical condition of aviation engines. Thus it is necessary to consider, that aviation engines it is objects which concern to individualized, i.e. to such which are in the sort unique. Therefore, the theory of creating systems to assess the technical state of aircraft engines is formed on the background of the development of modern neural network technology and requires the formation of specific methodological apparatus. From these positions in the article the methods which are used at carrying out clustering of the initial information received at work of modern systems of an estimation and forecasting of a technical condition of aviation gas-turbine engines are considered. This task is particularly relevant for creating neural network multimode models of aircraft engines used in technical state estimation systems for identification of possible failures and damages. Metric, optimization and recurrent methods of input data clustering are considered in the article. The main attention is given to comparison of clustering methods in order to choose the most effective of them for the aircraft engine condition evaluation systems and suitable for implementation of systems with meta-learning. The implementation of clustering methods of initial data allows us to breakdown diagnostic images of objects not by one parameter, but by a whole set of features. In addition, cluster analysis, unlike most mathematical-statistical methods do not impose any restrictions on the type of objects under consideration, and allows us to consider a set of raw data of almost arbitrary nature, which is very important when assessing the technical condition of aircraft engines. At the same time cluster analysis allows one to consider a sufficiently large volume of information and sharply reduce, compress large arrays of parametrical information, make them compact and visual.
Interactive comment on “Tropospheric column amount of ozone retrieved from SCIAMACHY limb-nadir-matching observations” by F. Ebojie

Major points: 1.The estimation of errors is admirable and in some ways goes beyond what has been done in previous studies; however, key assumptions are questionable. Several of the error terms given in Eq. 6 will not produce Gaussian errors (for example cross section, aerosol, and tangent height errors). Therefore, using a root-sum-square approach to get an estimate of the total error is not appropriate and will lead to an underestimate of errors.
Modelling streamflow and sediment yield using Soil and Water Assessment Tool: A case study of Lidder watershed in Kashmir Himalayas, India

The conjunction of heavy snowfall during winters and intensive rainfall during monsoons along with the mountainous topography expose the Lidder watershed to serious erosion and flood aggravation issues. Barely any attempts have been made for an in-depth examination of the Lidder watershed for precise estimation of sub-basin level runoff and erosion. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated using the Sequential Uncertainty Fitting algorithm (SUFI-2) for modelling streamflow and sediment yield of the Lidder watershed. Daily runoff and sediment event data from 2003–2013 were used in this study; data from 2003–2008 was used for calibration and 2009–2013 for validation. Model performance was evaluated using various statistical tools, which showed good results revealing excellent potential of the SWAT model to simulate streamflow and sediment yield for both calibration and validation periods. The annual rate of average upland sediment drawn from the watershed was approximately 853.96 Mg/ha for an average surface runoff of 394.15 mm/year. This study identifies the vulnerable areas of the Lidder watershed, which can be thoroughly examined by decision-makers for effective management and planning. Further, the calibrated model can be applied to other watersheds with similar characterization to influence strategies in the management of watershed processes.
Prediction of Steering Angle for Autonomous Vehicles Using Pre-Trained Neural Network

Autonomous driving is one promising research area that would not only revolutionize the transportation industry but would as well save thousands of lives. accurate correct Steering angle prediction plays a crucial role in the development of the autonomous vehicle . This research attempts to design a model that would be able to clone a drivers behavior using transfer learning from pretrained VGG16, the results showed that the model was able to use less training parameters and achieved a low mean squared error(MSE) of less than 2% without overfitting to the training set hence was able to drive on new road it was not trained on.
Lane Line Detection for Autonomous Cars using Python and OpenCV

: - Driver’s safety is one of the main reasons why autonomous cars has been evolving these days. There are many accidents which takes place due to the negligence of the drivers and driving on the wrong lane or a sudden change in the lane. To overcome this problem autonomous car has come into the market. Here the foremost concern is the safety of the passengers and so for that reason lanes detection could be used as a safe driving option. The Lane detection device can be implemented using OpenCV and python programming. This system needs a front camera which can be mounted on a car and using the view from the camera and applying few processes, lanes can be detected easily. The lanes can be extracted using Hough transform through a pair of hyperbolas. The proposed system can be utilized on both painted and unpainted roads. This system can be used in any car for safe lane driving.
Analysis on Factors Influencing Geography Teachers’ Ability in Constructing High-Order Thinking Skills (HOTS) Assessment Instrument

The teacher's ability to construct assessment instruments is a focus that needs to be considered. Furthermore, the demand of the 21st century directs teachers to set questions that are oriented to train students' abilities in higher-order thinking. However, several factors affect the ability to construct HOTS-oriented assessment instruments. This study aims to investigate what factors influence geography teachers’ ability to develop higher-order thinking skills (HOTS) instruments to measure learning outcomes. This study used a survey method with a quantitative approach. The data collection technique was field observation, and multiple linear regression was used for analysis. The results showed the linearity of teacher education regarding the length of teaching geography was 0.904, the tertiary institution was 0.009, and the background of education was 0.019. Also, teachers' certification was 0.007, their training was 0.032, and their experience in making HOTS questions was 0.047. The coefficient value of determination R, namely 0.635 means the relationship between teaching length, the linearity of their education in tertiary institutions, background, certification, training, and experience regarding the ability to develop HOTS-oriented assessment instruments is 63.5%. This means the relationship is strong, and 36.5% is another factor. This study concludes that the factors that significantly affect the ability of geography teachers in developing HOTS-oriented assessment instruments are the linearity factor of teacher education in universities, education background, certification, teacher training and experience in making HOTS questions. Keywords: Teacher ability factors; Assessment, Higher-order thinking skills Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
Isolation and Simultaneous Determination of Three Biologically Active Flavonoids from Some Indigenous Cordia Species by Thin-Layer Chromatography with UV Absorption Densitometry Method

The aim of this study was to isolate and identify three biologically active flavonoids, quercetin, kaempferol, and apigenin, from the hydrolyzed methanolic extract of three indigenous medicinal Cordia species, i.e., Cordia macleodii, Cordia dichotoma, and Cordia rothi barks using preparative thin-layer chromatography (TLC) and high-performance (HP)TLC methods. The isolation of flavonoid fraction was accomplished by preparative TLC method using reference standard. For achieving good separation, a mobile phase of toluene—ethyl acetate—GAA—formic acid (5:5:0.5:1) was used. The ultraviolet (UV)-based densitometry determination was carried out at 254 nm in reflection—absorption mode. The antioxidant compounds in the samples were screened through 2,2-diphenyl- 1-picrylhydrazyl (DPPH) derivatization method. The method was partially validated in terms of linearity, specificity, and sensitivity. All three standards showed good linearity in the range of 2500–7500 ng with respect to area. Limit of detection (LOD) and limit of quantitation (LOQ) for quercetin were 9.4 and 28 ng; for apigenin, 30 and 92 ng; and for kaempferol, 57 and 173 ng. From the analysis, quercetin (0.21%) and kaempferol (0.19%) in C. rothi and apigenin (0.28%) in C. dichotoma were found to be the highest. The isolation and identification of these flavonoids have not yet been found in the literature and they are reported in this study for first time. From this, it is concluded that planar chromatography has a potential as a rapid and simple tool for the identification and quantification of phytochemicals in complex mixtures and marker- based samples.
Crecimiento dimórfico y caracterización molecular de Candida guillermondi aislado de Pannicum maximun

Candida guilliermondii is an ascomicete yeast widely distributed in the natural environment, this yeast is attributed several properties that make it be of biotechnological interest, such as for biological control. In this project C. guillermondii was isolated from Panicum maximun grass, collected in soccer squares, street edges and pastures of the areas of Cartago and Heredia. Small, ovoid and budding colonies, 3 to 5 μm and cream colored, were purified at 48 h of culture at 25 ° C in PDA. They were grown in liquid medium, to stimulate the growth of biomass and to be able to carry out the molecular analysis, the DNA was extracted and the PCR tests with different primers were realized. The sequences generated were compared by alignment using the Blast tool from the National Center for Biotechnology Information (NCBI) and compared to the Gen Bank database, identifying Candida guillermondii. Pseudohyphae induction was carried out. Different culture conditions were tested: light, temperature and pH, and several culture media (carbon sources, nitrogen, medium poor in minerals and medium with only agar-water). Well-developed pseudomyelium formation was achieved from 96 h of incubation, pH and light, media such as water agar, supplemented with sucrose, which did not contain nitrogen source and Vogels, were determinants for the appearance of hyphae in the yeasts. Concluding that causing stress to the microorganism allows a morphological change in it.
Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17–April 25; T2: April 26–May 23; T3: May 24–June 25). The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06–0.75) during T2 and 0.14 times lower (95% CI: 0.04–0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks, and/or individual site characteristics.
Structural and diffusion weighted brain imaging biomarkers for ADHD and its symptomology in very young (4-7-year-old) children

The current study aimed to identify the key neurobiology of Attention-Deficit/Hyperactivity Disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. To do so, we adapted a predictive modeling approach to identify the key structural and diffusion weighted brain imaging measures, and their relative standing with respect to teacher ratings of executive function - EF (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function- BRIEF), negativity and emotion regulation - ER, (measured by the Emotion Regulation Checklist, ERC), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behavior and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus, the pericallosal sulcus, and the caudate as critical predictors of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.
Prevalence of Metabolic Syndrome in Urban South Indian Population

Because of changes in lifestyle, poor dietary habits, and obesity, the prevalence of non-communicable diseases such as hypertension, diabetes mellitus, and coronary artery disease is on the rise. The metabolic syndrome is a straightforward method for predicting the risk of diabetes and cardiovascular disease in the future. To compare the prevalence of metabolic syndrome in urban south Indian males and females and the prevalence of metabolic syndrome in various age groups above 20 years. To find out the most common metabolic abnormality among the study population and to find out specificity and sensitivity of any specific skin marker among the study population.
Comparison of four species diversity indices in mangrove community

Abstract. Mulya H, Santosa Y, Hilwan I. 2021. Comparison of four species diversity indices in mangrove community. Biodiversitas 22: 3648-3655. Mangrove communities have a tidal environment that affects their species composition, which may be finite but the abundance may vary between species individuals. Many ecologists have used indices to measure species diversity without knowing the best suitable index for the community and its statistical characteristics. Therefore, this study aimed to compare species diversity indices in mangrove communities to obtain the best and most appropriate index for mangrove diversity. The comparison was done using the Simpson (1-D), Shannon, Menhinick, and Margalef indices, which were widely used and considered the best measures in various communities. The data was obtained from the field sampling using a single plot of 80×40 m2, and then it was simulated to obtain standardized variables so that the indices got the same treatment. It was calculated with descriptive statistics and species diversity indices among other Simpson, Margalef, Shannon, and Menhinick indices. Pearson correlation and UPGMA cluster were used to get conformity based on the best index criteria. The results showed the Margalef index was the best in performance that passed the assessment criteria more than Simpson (1-D), Shannon, and Menhinick index. It had a low correlation with sample variance and normality marker but had high variability of the index value. It was also sensitive to the number of species, good reduced individual sample, and good correlation with similar indices. Thus, we concluded that the Margalef index was the best index for mangrove community diversity measurement. The index was recommended for monitoring and evaluating the species diversity assessment for the mangrove community. It should not use several species diversity indices with similar meaning.