Recognition and prediction of driver’s whole body posture model

The driver’s whole-body posture at the time of a collision is a key factor in determining the magnitude of injury to the driver. However, current researchs on driver posture models only consider the upper body posture of the driver, and the lower body area which is not perceived by sensors is not studied. This paper investigates the driver’s posture and establishes a 3D posture model of the driver’s whole body through the application of machine vision algorithms and regression model statistics. This study proposes an improved Kinect-OpenPose algorithm for identifying the 3D spatial coordinates of nine keypoints of the driver’s upper body. The posture prediction regression model of four keypoints of the lower body is established by conducting volunteer posture acquisition experiments on the developed simulated driving seat and analyzing the volunteer posture data through using the principal components of the upper body keypoints and the seat parameters. The experiments proved that the error of the regression model in this paper is minor than that of current studies, and the accuracy of the keypoint location and the keypoint connection length of the established driver whole body posture model is high, which provides implications for future studies.

Predictors of Glycemic Outcomes at 1 Year Following Pediatric Total Pancreatectomy With Islet Autotransplantation

Objective: Total pancreatectomy with islet autotransplantation (TPIAT) is indicated to alleviate debilitating pancreas-related pain and mitigate diabetes in patients with acute recurrent and chronic pancreatitis when medical/endoscopic therapies fail. Our aim was to evaluate predictors of insulin requirement at one year following TPIAT in a cohort of children. <p>Research Design and Methods: This was a review of 43 pediatric patients followed after TPIAT for one year or longer. Primary outcome was insulin use at one year, categorized as: insulin independent, low (< 0.5 u/kg/day) or high insulin (≥ 0.5 u/kg/day) requirement. </p> <p>Results: At one year after TPIAT, 12/41 (29%) patients were insulin independent, 21/41 (51%) had low and 8/41 (20%) had high insulin requirement. Insulin independent patients were younger than those with low and high insulin requirement (median age 8.2 vs. 14.6 vs. 13.1 years, respectively; p=0.03). Patients with insulin independence had higher transplanted IEQ/kg (p=0.03) and lower body surface area (p=0.02), compared to those with insulin dependence. Preoperative exocrine insufficiency was associated with high insulin requirement (p=0.03). Higher peak C-peptide measured by stimulated mixed meal tolerance testing (MMTT) at 3 and 6 months post-TPIAT was predictive of lower insulin requirement at one year (p=0.006 and 0.03, respectively). </p> <p>Conclusions: We conclude that insulin independence following pediatric TPIAT is multifactorial and associated with younger age, higher IEQ/kg transplanted and lower body surface area at time of operation. Higher peak C-peptide measured by MMTT following TPIAT confers a higher likelihood of low insulin requirement. </p>

Predictors of Glycemic Outcomes at 1 Year Following Pediatric Total Pancreatectomy With Islet Autotransplantation

Objective: Total pancreatectomy with islet autotransplantation (TPIAT) is indicated to alleviate debilitating pancreas-related pain and mitigate diabetes in patients with acute recurrent and chronic pancreatitis when medical/endoscopic therapies fail. Our aim was to evaluate predictors of insulin requirement at one year following TPIAT in a cohort of children. <p>Research Design and Methods: This was a review of 43 pediatric patients followed after TPIAT for one year or longer. Primary outcome was insulin use at one year, categorized as: insulin independent, low (< 0.5 u/kg/day) or high insulin (≥ 0.5 u/kg/day) requirement. </p> <p>Results: At one year after TPIAT, 12/41 (29%) patients were insulin independent, 21/41 (51%) had low and 8/41 (20%) had high insulin requirement. Insulin independent patients were younger than those with low and high insulin requirement (median age 8.2 vs. 14.6 vs. 13.1 years, respectively; p=0.03). Patients with insulin independence had higher transplanted IEQ/kg (p=0.03) and lower body surface area (p=0.02), compared to those with insulin dependence. Preoperative exocrine insufficiency was associated with high insulin requirement (p=0.03). Higher peak C-peptide measured by stimulated mixed meal tolerance testing (MMTT) at 3 and 6 months post-TPIAT was predictive of lower insulin requirement at one year (p=0.006 and 0.03, respectively). </p> <p>Conclusions: We conclude that insulin independence following pediatric TPIAT is multifactorial and associated with younger age, higher IEQ/kg transplanted and lower body surface area at time of operation. Higher peak C-peptide measured by MMTT following TPIAT confers a higher likelihood of low insulin requirement. </p>

Retrospective Analysis of Training Intensity Distribution Based on Race Pace Versus Physiological Benchmarks in Highly Trained Sprint Kayakers

Background Research results on the training intensity distribution (TID) in endurance athletes are equivocal. This non-uniformity appears to be partially founded in the different quantification methods that are implemented. So far, TID research has solely focused on sports involving the lower-body muscles as prime movers (e.g. running). Sprint kayaking imposes high demands on the upper-body endurance capacity of the athlete. As there are structural and physiological differences between upper- and lower-body musculature, TID in kayaking should be different to lower-body dominant sports. Therefore, we aimed to compare the training intensity distribution during an 8-wk macrocycle in a group of highly trained sprint kayakers employing three different methods of training intensity quantification. Methods Heart rate (HR) and velocity during on-water training of nine highly trained German sprint kayakers were recorded during the final 8 weeks of a competition period leading to the national championships. The fractional analysis of TID was based on three zones (Z) derived from either HR (TIDBla-HR) or velocity (TIDBla-V) based on blood lactate (Bla) concentrations (Z1 ≤ 2.5 mmol L−1 Bla, Z2 = 2.5–4.0 mmol L−1 Bla, Z3 ≥ 4.0 mmol L−1 Bla) of an incremental test or the 1000-m race pace (TIDRace): Z1 ≤ 85% of race pace, Z2 = 86–95% and Z3 ≥ 95%. Results TIDBla-V (Z1: 68%, Z2: 14%, Z3: 18%) differed from TIDBla-HR (Z1: 91%, Z2: 6%, Z3: 3%) in each zone (all p < 0.01). TIDRace (Z1: 73%, Z2: 20%, Z3: 7%) differed to Z3 in TIDBla-V (p < 0.01) and all three TIDBla-HR zones (all p < 0.01). Individual analysis revealed ranges of Z1, Z2, Z3 fractions for TIDBla-HR of 85–98%, 2–11% and 0.1–6%. For TIDBla-V, the individual ranges were 41–82% (Z1), 6–30% (Z2) and 8–30% (Z3) and for TIDRace 64–81% (Z1), 14–29% (Z2) and 4–10% (Z3). Conclusion The results show that the method of training intensity quantification substantially affects the fraction of TID in well-trained sprint kayakers. TIDRace determination shows low interindividual variation compared to the physiologically based TIDBla-HR and TIDBla-V. Depending on the aim of the analysis TIDRace, TIDBla-HR and TIDBla-V have advantages as well as drawbacks and may be implemented in conjunction to maximize adaptation.

Promoting Judicious Antimicrobial Use in Beef Production: The Role of Quarantine

Judicious antimicrobial stewardship in livestock industry is needed to reduce the use of antimicrobials (AMU) and the associated risk of antimicrobial resistance. Biosecurity measures are acknowledged for their role against the spread of diseases and the importance in reducing AMU in different species. However, their effectiveness in beef production has been scarcely considered. The aim of this study was to investigate the effect of the quarantine strategy on AMU in beef cattle. A total of 1206 Charolaise animals in five farms were included in the trial. Roughly half of the animals followed the standard procedure of the fattening cycle (no-quarantine; NO-QUA group) and half followed a 30-day period of quarantine (QUA group) since their arrival. Performance and antimicrobial data were recorded and a treatment incidence 100 (TI100it) per animal was calculated. Penicillins was the most used class of antimicrobials. Differences between groups were significant for males only, with NO-QUA group having greater TI100it (3.76 vs. 3.24; p < 0.05) and lower body weight at slaughter (713.4 vs. 723.7 kg; p < 0.05) than QUA group. Results suggest that quarantine strategy can reduce AMU in males without compromising their performance, whereas further investigation is needed for females.

Lower Body Temperature Independently Predicts Delayed Cerebral Infarction in the Elderly With Ruptured Intracranial Aneurysm

Purpose: To assess the correlation between admission body temperature and delayed cerebral infarction in elderly patients with ruptured intracranial aneurysm (IA).Methods: Patients with ruptured IA diagnosed between 2012 and 2020 were retrospectively analyzed. Patients were divided into a non-infarction and an infarction group based on the presence of cerebral infarction after treatment. The demographic and clinical information of the patients was gathered. Outcomes at the 3-month follow-up were assessed using the modified Rankin Scale. Correlation between admission body temperature and cerebral infarction was assessed using Spearman's rank correlation coefficient. A receiver operating characteristic (ROC) curve was used to assess the specificity and sensitivity of admission body temperature to predict cerebral infarction.Results: A total of 426 patients (142 men and 284 women) with ruptured IA were enrolled. Elderly patients with cerebral infarction (12.4%) had a lower body temperature at admission (p &lt; 0.001), higher prevalence of hypertension and diabetes (p = 0.051 and p = 0.092, respectively), and higher rate of poor outcomes (p &lt; 0.001). Admission body temperature was independently associated with cerebral infarction (odds ratio [OR] = 5.469, p &lt; 0.001); however, hypertension (OR = 0.542, p = 0.056), diabetes (OR = 0.750, p = 0.465), and aneurysm size (OR = 0.959, p = 0.060) showed no association. An inverse correlation between admission body temperature and the incidence of cerebral infarction was observed (Spearman's r =−0.195, p &lt; 0.001). An admission body temperature of 36.6°C was able to distinguish infarction and non-infarction patients. The area under the ROC curve was 0.669 (specificity, 64.15%; sensitivity, 81.50%; p &lt; 0.001).Conclusions: Lower body temperature at admission (≤36.6°C) is an independent predictor of delayed cerebral infarction in elderly patients who have undergone treatment for ruptured IA. Therefore, it could be a risk factor for adverse outcomes of IA.

Trunk in Stroke

The trunk is the part of the human body that provides basic mechanical stabilization. It provides strength transmission between the upper and lower body regions. Body control is the ability of the body muscles to maintain the upright posture, to adapt to weight transfers, and to maintain selective trunk and limb movements by maintaining the support surface in static and dynamic postural adjustments. Good proximal trunk control provides better distal limb movements, balance, and functional motion. There are many evaluation methods, devices, and scales for trunk function and performance. 3D kinematic, electromyography, hand-held dynamometer, isokinetic dynamometer, trunk accelerometer are some devices that measure trunk function. The motor assessment scale-trunk subscale, the stroke impairment assessment set- trunk control subscale, trunk control test, trunk impairment scale are the most used scales. This chapter explores the effect of strokes on the trunk.

House Plant Leaf Disease Detection and Classification Using Machine Learning

Hibiscus is a fantastic herb, and in Ayurveda, it is one of the most renowned herbs that have extraordinary healing properties. Hibiscus is rich in vitamin C, flavonoids, amino acids, mucilage fiber, moisture content, and antioxidants. Hibiscus can help with weight loss, cancer treatment, bacterial infections, fever, high blood pressure, lower body temperature, treat heart and nerve diseases. Automatic leaf disease detection is an essential task. Image processing is one of the popular techniques for the plant leaf disease detection and categorization. In this chapter, the diseased leaf is identified by concurrent k-means clustering algorithm and then features are extracted. Finally, reweighted KNN linear classification algorithms have been used to detect the diseased leaves categories.