Indices of Loading and Propulsive Ability in the Gait of Patients With Chronic Stroke With Equinus Foot Deviation: A Correlation Study

In literature, indices of overall walking ability that are based on ground reaction forces have been proposed because of their ease of administration with patients. In this study, we analyzed the correlation between the indices of dynamic loading and propulsion ability of 40 chronic hemiparetic post-stroke patients with equinus foot deviation and a set of clinical assessments of ankle joint deviations and walking ability. Ankle passive and active range of motion (ROM) and triceps surae spasticity were considered, along with walking speed and three complementary scales of walking ability focusing respectively on the need for assistance on functional mobility, including balance and transfers, and the limitation in social participation. The correlation between the ground reaction force-based indices and both clinical and functional variables was carried out using the non-parametric Spearman correlation coefficient. Both indices were correlated to 8 of the 10 investigated variables, thus supporting their use. In particular, the dynamic propulsive ability was correlated with all functional scales (rho = 0.5, p < 0.01), and has the advantage of being a continuous variable. Among clinical assessments, limited ankle ROM affected walking ability the most, while spasticity did not. Since the acquisition of ground reaction forces does not require any patient prepping, the derived indices can be used during the rehabilitation period to quickly detect small improvements that, over time, might lead to the broad changes detectable by clinical scales, as well as to immediately highlight the lack of these improvements, thus suggesting adjustments to the ongoing rehabilitation approach.

Astrogliosis mapping in individual brains using multidimensional MRI

There are currently no noninvasive imaging methods available for astrogliosis mapping in the central nervous system despite its essential role in the response to injury, disease, and infection. We have developed a machine learning-based multidimensional MRI framework that provides a signature of astrogliosis, distinguishing it from normative brain at the individual level. We investigated ex vivo cortical tissue specimen derived from subjects who sustained blast induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathology. By performing a combined postmortem radiology and histopathology correlation study we found that astrogliosis induces microstructural changes that are robustly detected using our framework, resulting in MRI neuropathology maps that are significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition. The demonstrated high spatial sensitivity in detecting reactive astrocytes at the individual level has great potential to significantly impact neuroimaging studies in diseases, injury, repair, and aging.

THE RELATIONSHIP OF ANXIETY WITH HYPERTENSION FOR ELDERLY IN LAMPE WORKING AREA OF EAST RASANAE PUBLIC HEALTH CENTER BIMA

The increasing number of elderly people pays special attention to those who are experiencing an aging process. There are some problems that need special attention as the results of aging, such as physical, cognitive, emotional, social, and sexual changes. The purpose of this study is to find out the relationships between anxiety with hypertension of the elderly in subdistrict of Lampe working area of East Rasanae Public Health Center, Bima City.The research design of this study is cross sectional with correlation study approach. Around 23 elderly with hypertension was used as sample in the study. Sampling techniques used was total Sampling. The instrument used was questionnaires and spermank rank ws used for data analysis used.Based on the results of the questionnaire, 12 respondents showed no symptoms at all (52.2%), mild anxiety was amounted to 10 respondents (43.5%). 16 respondents (69.6%) were considered to have Hypertension at Stage I. This could also be seen from the test value analysis between anxiety and hypertension of the elderly where p value was greater than the significant level of 0.05 (p value>α) so that Ha was rejected and H0 was accepted, meaning that there was no relationship between anxiety and hypertension of the elderly in the subdistrict of Lampe working area of East Rasanae Public Health Center, Bima City.From the analysis, it can be concluded that there was no relationship between anxiety and hypertension of the elderly. Anxiety in the elderly is not a major factor that can trigger hypertension, yet there are other factors that should be considered by the elderly such as lifestyle and hereditary factors.

Supervised Learning Predictive Models for Automated Fracturing Treatment Design: A Workflow Based on Algorithm Comparison and Multiphysics Model Validation

Diagnostic pumping techniques are used routinely in proppant fracturing design. The pumping process can be time consuming; however, it yields technical confidence in treatment and productivity optimization. Recent developments in data analytics and machine learning can aid in shortening operational workflows and enhance project economics. Supervised learning was applied to an existing database to streamline the process and affect the design framework. Five classification algorithms were used for this study. The database was constructed through heterogeneous reservoir plays from the injection/falloff outputs. The algorithms used were support vector machine, decision tree, random forest, multinomial, and XGBoost. The number of classes was sensitized to establish a balance between model accuracy and prediction granularity. Fifteen cases were developed for a comprehensive comparison. A complete machine learning framework was constructed to work through each case set along with hyperparameter tuning to maximize accuracy. After the model was finalized, an extensive field validation workflow was deployed. The target outputs selected for the model were crosslinked fluid efficiency, total proppant mass, and maximum proppant concentration. The unsupervised clustering technique with t-SNE algorithm that was used first lacked accuracy. Supervised classification models showed better predictions. Cross-validation techniques showed an increasing trend of prediction accuracy. Feature selection was done using one-variable-at-a-time (OVAT) and a simple feature correlation study. Because the number of features and the dataset size were small, no features were eliminated from the final model building. Accuracy and F1 score calculations were used from the confusion matrix for evaluation, XGBoost showed excellent results with an accuracy of 74 to 95% for the output parameters. Fluid efficiency was categorized into three classes and yielded an accuracy of 96%. Proppant concentration and proppant mass predictions showed 77% and 86% accuracy, respectively, for the six-class case. The combination of high accuracy and fine granularity confirmed the potential application of machine learning models. The ratio of training to testing (holdout) across all cases ranged from 80:20 to 70:30. Model validations were done through an inverse problem of predicting and matching the fracture geometry and treatment pressures from the machine learning model design and the actual net pressure match. The simulations were conducted using advanced multiphysics simulations. The advantages of this innovative design approach showed four areas of improvement: reduction in polymer consumption by 30%, reduction of the flowback time by 25%, reduction of water usage by 30%, and enhanced operational efficiency by 60 to 65%.

Efficiency of Numerical Images Analysis in Selection of Durum Wheat [Triticum turgidum (L.) ssp. durum (Desf.) Husn.] Genotypes Growing under Semi-arid Conditions in Algeria

Background: This study was conducted during the 2020/2021 cropping season at Setif Agricultural Experimental Station, it aims to assess the efficiency of using numerical image analysis (NIA) in the selection of durum wheat genotypes in semi-arid areas. Methods: The genetic materials used in this study consist of 11 advanced lines and 4 genotypes of which 3 are local landraces used as control to evaluate their performance, the genotypes tested were sown in a randomized block design (RDB) with three replications. each plot consisted of 6 lines of 10 m long spaced of 0.2 m witdth makes 12 m2 as plot area. Result: Analysis of variance showed that all the parameters measured numerically (senescence and total reflectance) had a very high genotypic significance. The chlorophyll content at full heading showed a very highly significant genotypic effect. Thousand kernels weight, number of spikes per meter square, number of days to heading and plant height had a significant genotypic effect. The correlation study showed that all senescence parameters were significantly correlated. A significant and negative correlation was observed between chlorophyll contents; average of velocity and total reflectance. Grain yield was highly and significantly correlated with thousand kernels weight and number of spikes per meter square. Number of spikes per meter square was significantly and positively correlated with average of velocity and negatively correlated with sum of temperatures at mid-senescence. Number of days to heading was significantly and negatively correlated with senescence average and maximum of senescence average. A significant correlation was observed between plant height and sum of temperatures at mid-senescence.

Influence of environmental factors on the spread of COVID-19 in Saudi Arabia

Background Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide. The infection is mostly spread through the inhalation of infected droplets. Saudi Arabia is a vast country having different climatic conditions. Methods The study evaluated the influence of environmental factors on the spread of COVID-19. Six zones (A to F) were classified depending on the climatic conditions. The study was conducted by retrospective analysis of COVID-19 records from the ministry of health between the months of September 2020 and August 2021. The environmental data such as average temperature (°C), humidity (%), wind speed (m/s) and sun exposure (kwh/m2) were retrieved from official sites. The data was analyzed to determine the effect of these factors on the spread of COVID-19. SPSS IBM 25 software was used to conduct the analysis and p < 0.05 was considered to indicate the significance of the results. Results According to the findings, the rate of infection was greater between April and July 2021. Six climatic zones experienced high temperatures, little humidity, consistent wind flow, and intense sun exposure throughout this time. The correlation study revealed a significant (p < 0.05) relationship between the environmental factors and the spread of COVID-19. The data suggested that during summer condition when the weather is hot, less humid, and steady wind flow with lots of sun exposure, the COVID-19 infection rate got augmented in Saudi Arabia. Poor ventilation and closed-door habitats in an air-conditioned atmosphere during this period could have played a role in human transmission. More research on air quality, population mobility and diseased condition is essential, so that precise proactive measures can be designed to limit the spread of infection in specific climatic seasons.

Knowledge and Practice Regarding Breastfeeding among Primipara Mothers of Obstetrics Ward, Birganj, Nepal

Breastfeeding is an ideal form of feeding to neonate. It is most precious gift a mother can give and is free of cost. It should be started as soon as possible after birth. Breast milk is a species-specific complete food. Human milk facilitates effortless digestion for infant and is well absorbed by the newborn. It helps in stimulating the production of breast milk, protect against infection and facilitate mother infant bonding and promotes better brain growth. For mothers breastfeeding helps in involution of uterus, delays pregnancy and lower risk of breast and ovarian cancer. Every year around 57,000 children below 5 years of age lose their lives, among which 54 percent die within the first month of life. The data stated that 22 percent of newborn death can be prevented through breastfeeding within 1st hour of birth. Mother play a very important roles in reducing neonatal mortality and neonatal morbidity rate by their knowledge and practice while feeding their baby. Method: Descriptive correlation study design was conducted in the month of August 2021 National Medical College and Teaching Hospital, Birganj, Nepal. Sample size was 50 primi para mothers. Purposive sampling techniques was used to select the sample. Semi structure interview schedule and observation checklist was prepared according to objectives which comprised questions related to demographic variables, Knowledge to assess breastfeeding and observational checklist to assess practice Data was collected by administering this structured questionnaire to the primipara mothers. Result: In the study 31 (62.0%) had adequate, 19 (38.0%) had moderate and none of them had inadequate knowledge regarding breastfeeding. 23 (46.0 had good, 27 (54.0%) had satisfactory and none of them had poor practice on breastfeeding. The results show that there was moderate degree positive correlation between knowledge and practice. Key words: Knowledge, practice, breastfeeding and primipara mothers.