Development of a Real-time PCR test for porcine group A rotavirus diagnosis

Group A Rotavirus (RVA) is one of the most common causes of diarrhea in humans and several animal species. A SYBR-Green Real-Time polymerase chain reaction (PCR) was developed to diagnose RVA from porcine fecal samples, targeting amplification of a 137-bp fragment of nonstructural protein 5 (NSP5) gene using mRNA of bovine NADH-desidrogenase-5 as exogenous internal control. Sixty-five samples were tested (25 tested positive for conventional PCR and genetic sequencing). The overall agreement (kappa) was 0.843, indicating 'very good' concordance between tests, presenting 100% of relative sensitivity (25+ Real Time PCR/25+ Conventional PCR) and 87.5% of relative sensitivity (35- Real Time PCR/40- Conventional PCR). The results also demonstrated high intra- and inter-assay reproducibility (coefficient of variation ≤1.42%); thus, this method proved to be a fast and sensitive approach for the diagnosis of RVA in pigs

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Analysis of Pedestrian Clearance Time at Signalized Crosswalks in Japan

AbstractAt signalized crosswalks, pedestrian clearance time is a key design parameter for ensuring safe pedestrian crossing. It is generally defined as the time required by pedestrians who enter crosswalks at the end of the green indication to complete crossing before conflicting vehicular traffic movements are released. In Japan, pedestrian green indications are followed by pedestrian flashing green (PFG) indications during which time pedestrians are not allowed to start crossing and those in the crosswalk have to finish crossing to either side of the crosswalk; as such, some pedestrians are expected to return to the side they came from. Therefore, PFG intervals are designed to be shorter than the necessary clearance time. Instead, relatively longer red buffer intervals (BI) are provided between the end of the PFG and the succeeding vehicle green indication. This study clarifies the differences between signal setting concepts in various countries and analyses pedestrian clearing behaviors under the Japanese signal control system. Empirical analyses show that the current PFG and BI settings in Japan are shorter than the necessary clearance time and the settings in the US and Germany. As a result, most observed pedestrians who started crossing after the onset of PFG cannot finish crossing before its end and cannot even finish before the succeeding vehicle green indication

On approximating the TSP with intersecting neighborhoods

In the TSP with neighborhoods problem we are given a set of $n$ regions (neighborhoods) in the plane, and seek to find a minimum length TSP tour that goes through all the regions. We give two approximation algorithms for the case when the regions are allowed to intersect: We give the first $O(1)$-factor approximation algorithm for intersecting convex fat objects of comparable diameters where we are allowed to hit each object only at a finite set of specified points. The proof follows from two packing lemmas that are of independent interest. For the problem in its most general form (but without the specified points restriction) we give a simple $O(\log n)$-approximation algorithm

Can automated driving prevent crashes with distracted Pedestrians? An exploration of motion planning at unsignalized Mid-block crosswalks

Pedestrian distraction may provoke severe difficulties in automated vehicle (AV) control, which may significantly affect the safety performance of AVs, especially at unsignalized mid-block crosswalks (UMCs). However, there is no available motion-planning model for AVs that considers the effect of pedestrian distraction on UMCs. This study aims to explore innovative approaches for safe and reasonable automated driving in response to distracted pedestrians with various speed profiles at UMCs. Based on two common model design concepts, two new models are established for AVs: a rule-based model that solves motion plans through a fixed calculation procedure incorporating several optimization models, and a learning-based model that replaces the deterministic optimization process with policy-gradient reinforcement learning. The developed models were assessed through simulation experiments in which pedestrian speed profiles were defined using empirical data from field surveys. The results reveal that the learning-based model has outstanding safety performance, whereas the rule-based model leads to remarkable safety problems. For distracted pedestrians with significant crossing-speed changes, rule-based AVs lead to a 5.1% probability of serious conflict and a 1.4% crash probability. The learning-based model is oversensitive to risk and always induces high braking rates, which results in unnecessary efficiency loss. To overcome this, a hybrid model based on the learning-based model was developed, which introduces a rule-based acceleration value to regularize the action space of the proposed learning-based model. The results indicate that the hybrid approach outperforms the other two models in preventing crash hazards from distracted pedestrians by employing appropriate braking behaviors. The high safety performance of the hybrid models can be attributed to the spontaneous slowing down of the vehicle that initiates before detecting pedestrians on UMCs. Although such a cautious driving pattern leads to extra delay, the time cost of the hybrid model is acceptable considering the significant improvements in ensuring pedestrian safety.This publication was made possible by the Qatar–Japan Research Collaboration Application Award [M-QJRC-2020-8] from Qatar University. The statements made herein are the sole responsibility of the authors. This research was also partially supported by Kurata Grants No. 1397 of the Hitachi Global Foundation