Bramsenbloom3792

The standard deviation of the interval between QRS complexes recorded over 24 h (SDNN24) is an important metric of cardiovascular health. Wrist-worn fitness wearable devices record heart beats 24/7 having a complete overview of users' heart status. Due to motion artefacts affecting QRS complexes recording, and the different nature of the heart rate sensor used on wearable devices compared to ECG, traditionally used to compute SDNN24, the estimation of this important Heart Rate Variability (HRV) metric has never been performed from wearable data. We propose an innovative approach to estimate SDNN24 only exploiting the Heart Rate (HR) that is normally available on wearable fitness trackers and less affected by data noise. The standard deviation of inter-beats intervals (SDNN24) and the standard deviation of the Average inter-beats intervals (ANN) derived from the HR (obtained in a time window with defined duration, i.e., 1, 5, 10, 30 and 60 min), i.e., ANN=60HR (SDANNHR24), were calculated over 24 h. Power spectrum analysis using the Lomb-Scargle Peridogram was performed to assess frequency domain HRV parameters (Ultra Low Frequency, Very Low Frequency, Low Frequency, and High Frequency). Due to the fact that SDNN24 reflects the total power of the power of the HRV spectrum, the values estimated from HR measures (SDANNHR24) underestimate the real values because of the high frequencies that are missing. Subjects with low and high cardiovascular risk show different power spectra. In particular, differences are detected in Ultra Low and Very Low frequencies, while similar results are shown in Low and High frequencies. For this reason, we found that HR measures contain enough information to discriminate cardiovascular risk. Semi-continuous measures of HR throughout 24 h, as measured by most wrist-worn fitness wearable devices, should be sufficient to estimate SDNN24 and cardiovascular risk.Risk of fall (ROF) is a worldwide major concern for its prevalence and consequent dramatic outcomes in the elderly population. The growing age-related risk appears to be associated with increasing motor, sensory, and cognitive problems in the elderly population. There is a consensus on the need to screen for these balance dysfunctions, but the available methods are largely based on subjectively assessed performances. The instrumented Romberg test using a force plate represents a validated assessment process for the evaluation of balance performances. The purpose of this study is to propose an innovative instrumental method to identify balance deficits, assess their severity, and give an automated indication of the most likely etiology. The proposed new method was applied to the instrumented Romberg test, using force plate data recorded in a cohort of 551 females aged >65 participating in adapted physical activity courses. The method allowed us to identify 145 dysfunctional subjects and to determine the likely origin of their deficit 21 central, 5 vestibular, 9 visual, 59 proprioceptive (musculoskeletal etiology), and 51 functional. Based on the preliminary findings of the study, this test could be an efficient and cost-effective mass screening tool for identifying subjects at risk of fall, since the procedure proves to be rapid, non-invasive, and apparently devoid of any contraindications.Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, a new system and algorithm is required to predict the green tide phenomenon and also minimize the related damage before the green tide occurs. For this purpose, we consider a new network model using smart sensor-based federated learning which is able to use distributed observation data with geologically separated local models. Moreover, we design an optimal scheduler which is beneficial to use real-time big data arrivals to make the overall network system efficient. The proposed scheduling algorithm is effective in terms of (1) data usage and (2) the performance of green tide occurrence prediction models. The advantages of the proposed algorithm is verified via data-intensive experiments with real water quality big-data.Inadequate food and nutrition affect human well-being, particularly for many poor subpopulations living in rural areas. The purpose of this research was to analyze the factors that determine the Household Dietary Diversity Score (HDDS) in the rural area of the Paute River Basin, Azuay Province, Ecuador. The sample size of 383 surveys was determined by a stratified random sampling method with proportional affixation. Dietary diversity was measured through the HDDS, with 12 food groups (cereals; roots and tubers; fruits; sugar/honey; meat and eggs; legumes or grains; vegetables; oils/fats; milk and dairy products; meats; miscellaneous; fish and shellfish) over a recall period of 7 days. A Poisson regression model was used to determine the relationship between the HDDS and sociodemographic variables. The results show that the average HDDS of food consumption is 10.89 foods. Of the analyzed food groups, the most consumed are cereals; roots and tubers; fruits; sugar/honey. In addition, the determinants that best explain the HDDS in the predictive model were housing size, household size, per capita food expenditure, area of cultivated land, level of education, and marital status of the head of household. The tools used in this research can be used to analyze food and nutrition security interventions. Furthermore, the results allow policymakers to identify applicable public policies in the fight against hunger.This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.Kiwifruit is very popular among consumers due to its high nutritional value. The increasing expansion in kiwifruit cultivation has led to the spread of rot diseases. To identify the pathogens causing kiwifruit ripe rots in China, 24 isolates were isolated from the diseased fruit and wart in trees. Botryosphaeria dothidea was recognized as the pathogen causing kiwifruit ripe rot and wart in the tree through internal transcribed spacer (ITS) sequencing, pathogenicity testing, morphological and microscopic characteristics. The rapid and accurate detection of this pathogen will lead to better disease monitoring and control efforts. A loop-mediated isothermal amplification (LAMP) method was then developed to rapidly and specifically identify B. dothidea. These results offer value to further research into kiwifruit ripe rot, such as disease prediction, pathogen rapid detection, and effective disease control.In the midst of the unceasing COVID-19 pandemic, the identification of immunogenic epitopes in the SARS-CoV-2 spike (S) glycoprotein plays a vital role in the advancement and development of intervention strategies. S is expressed on the exterior of the SARS-CoV-2 virion and contains two subunits, namely the N-terminal S1 and C-terminal S2. It is the key element for mediating viral entry as well as a crucial antigenic determinant capable of stimulating protective immune response through elicitation of anti-SARS-CoV-2 antibodies and activation of CD4+ and CD8+ cells in COVID-19 patients. Given that S2 is highly conserved in comparison to the S1, here, we provide a review of the latest findings on the SARS-CoV-2 S2 subunit and further discuss its potential as an attractive and promising target for the development of prophylactic vaccines and therapeutic agents against COVID-19.Multidirectional running has been described as an important factor in team sports performance. The aim of the present study was to determine changes in T-test, 505 time, 10 m sprint, squat jump (SJ), countermovement jump (CMJ), countermovement jump right leg (CMJRL), and countermovement jump left leg (CMJLL) following exposure to 12 sessions over 4 weeks of a multidirectional running sprint training intervention in male and female handball players. A total of 31 handball players (15 male and 16 female) were recruited for this study and then randomly assigned to an experimental group (EG) or control group (CG). Male EG players showed improvements in 505 Preferred Side (PS) (p ≤ 0.05), 505 Non-Preferred Side (NPS) (p ≤ 0.05), and 10 m sprint (p ≤ 0.05), while female EG players presented statistically significant improvements between pre- and post-test for the T-test (p ≤ 0.05), 505 PS (p ≤ 0.05), 505 NPS (p ≤ 0.05), and 10 m sprint (p ≤ 0.05). No statistically significant pre- and post-test differences were observed in CG (all p ≥ 0.05) or between male and female players. We found an improvement in handball players' agility and speed of movement following the intervention protocol, suggesting the need to introduce this program into our training sessions. It may also be necessary to select and develop more specific tests in order to evaluate multidirectional work in handball players.Despite the growing body of research regarding sexting and online sexual victimization, there is little evidence exploring cultural differences in association with those behaviors. The aim of this study was to examine cultural differences in sexting practices by comparing an American sample and a Spanish sample of university students. The original sample was composed of 1799 college students, including 1386 Spanish college students and 413 American Students, with 74% of female participants, and ages ranging from 18 to 64 years old (mean age = 21.26, SD= 4.61). Results indicate that American students sext more than Spanish students and have higher probabilities of being victims of nonconsensual dissemination of their sexual content. However, Spanish students receive more sexts than American students. Although our results show differences between the Spanish and the American samples that might be modulated by cultural factors, the vulnerability of females regarding sexting remains unchanged. 8-OH-DPAT cost Additionally, differences in specific characteristics of the behaviors (such as perceived risk, receiver of the sexual content, intensity of the sexual content, and motive for sexting) were also studied.