Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Real-Time Quaking-Induced Conversion (RT-QuIC) Assay, Immunohistochemistry and ELISA for Detection of Chronic Wasting Disease under Field Conditions in White-Tailed Deer: A Bayesian Approach

Chronic wasting disease (CWD) is a transmissible prion disease of the cervidae family. ELISA and IHC tests performed postmortem on the medial retropharyngeal lymph nodes (RPLN) or obex are considered diagnostic gold standards for prion detection. However, differences in CWD transmission, stage of infection, pathogenesis, and strain can limit performance. To overcome these uncertainties, we used Bayesian statistics to assess the accuracy of RT-QuIC, an increasingly used prion amplification assay, to diagnose CWD on tonsil (TLN), parotid (PLN) and submandibular lymph nodes (SMLN), and ELISA/IHC on RPLN of white-tailed deer (WTD) sampled from Minnesota. Dichotomous RT-QuIC and ELISA/IHC results from wild (n = 61) and captive (n = 46) WTD were analyzed with two-dependent-test, one-population models. RT-QuIC performed on TLN and SMLN of the wild WTD population had similar sensitivity (median range (MR): 92.2–95.1) to ELISA/IHC on RPLN (MR: 91.1–92.3). Slightly lower (4–7%) sensitivity estimates were obtained from farmed animal and PLN models. RT-QuIC specificity estimates were high (MR: 94.5–98.5%) and similar to ELISA/IHC estimates (MR: 95.7–97.6%) in all models. This study offers new insights on RT-QuIC and ELISA/IHC performance at the population level and under field conditions, an important step in CWD diagnosis and management.
WF-SLAM: A Robust VSLAM for Dynamic Scenarios via Weighted Features

The assumption of a static environment is typical in many visual simultaneous localization and mapping (VSLAM) systems. However dynamic objects in open scenes will mislead feature association and even fail to match, which reduces the accuracy of localization. For dynamic scenarios, a robust visual SLAM system that utilizes weighted features, namely, named WF-SLAM is proposed in this paper, which is based on ORB-SLAM2. First, WF-SLAM applies the tightly coupled semantic and geometric dynamic target detection algorithm to obtain the dynamic information in the scene. Then, WF-SLAM defines feature point weights and initializes them with the dynamic information. Finally, the pose optimization in ORB-SLAM2 is changed to weight-based joint optimization. WF-SLAM significantly decreases mismatch and improves the accuracy of localization. Experiments are performed on the benchmark RGB-D dataset TUM and real-world scenarios, and the results demonstrate that WF-SLAM realizes significant improvements in term of localization accuracy compared to ORB-SLAM2 in dynamic environments and a more robust performance compared with state-of-the-art dynamic SLAM methods.
IEEE Invited Paper - An Overview of Cyber Security Considerations and Vulnerabilities in Critical Infrastructure Systems and Potential Automated Mitigation - A Review

: Executive leadership in government, military and industry are faced with many difficult challenges when trying to understand the complex interaction of public and government security policies, the vulnerabilities in the wide array of key technologies supporting critical infrastructure upon which society is vitally dependent, and the identification of key cyber security trends that will need to be considered in the future. This invited paper discusses public policy issues related to the threat environment and provides a comprehensive description of the various cyber vulnerabilities and risks arising from a broad range of technologies supporting critical infrastructure and highlights key requirements and design principles desired from next generation automated defence capabilities. This document provides a unique review of key aspects related to these separate but interrelated subject areas that will hopefully provide greater context, background and clarity for senior decision makers responsible for shaping development agendas for their organizations.
Inter-individual differences in Hindi–English code-switching: A quantitative approach

Aims and objectives: Variationist studies on code-switching (CS) have focused on community-level differences driven by variables such as age, ethnicity, and socio-economic status. Studies on inter-individual variation that make use of naturalistic speech need to be prioritised, especially in countries with large, diverse populations, such as India. This article presents a quantitative analysis of the Hindi–English CS (HECS) patterns among speakers of comparable socio-economic status and clusters them based on similarities in their CS strategies. Methodology: Television interviews of Hindi movie (Bollywood) personalities were transcribed and coded for Hindi and English lexemes. Bespoke software in Python was used to calculate five numerical indices to describe each speaker’s CS pattern. Data and analysis: The interviews from 28 speakers ranged between 20 and 40 minutes each. Cluster analysis was carried out in SPSS to produce a typology of CS strategies. Findings/conclusions: A total of six CS strategies were identified, with five being employed by both older and younger speakers and only one being used only by a single age group. Most CS strategies are therefore not age-specific. A number of hypothetical scenarios are presented, to demonstrate how individual CS strategies might change with time. Originality: This is the first study to systematically and quantitatively investigate inter-individual differences in HECS, using naturalistic speech in semi-controlled conditions. It is also the first to propose a typology of HECS strategies. Implications: There was significant variation in CS strategy even within the relatively homogeneous group of speakers investigated in this study. The overall variation in the whole of Hindi-speaking India must therefore be far greater. This suggests that certain predictions regarding the predictability of CS patterns from the contact situation (e.g., that postcolonial contexts result in insertional CS) may need to be refined.
Associations between Maternal Selenium Status and Cord Serum Vitamin D Levels: A Birth Cohort Study in Wuhan, China

Serum selenium (Se) has been reported to be associated with serum 25-hydroxyvitamin D [25(OH)D], but epidemiological findings are limited in pregnant women. We aimed to assess the associations between maternal urinary Se concentrations and cord serum 25(OH)D levels. We measured urinary concentrations of Se in the first, second, and third trimesters and cord serum 25(OH)D of 1695 mother-infant pairs from a prospective cohort study in Wuhan, China. The results showed that each doubling of urinary Se concentrations in the first, second, third trimester, and whole pregnancy (average SG-adjusted concentrations across three trimesters) were associated with 8.76% (95% confidence interval (CI): 4.30%, 13.41%), 15.44% (95% CI: 9.18%, 22.06%), 11.84% (95% CI: 6.09%, 17.89%), and 21.14% (95% CI: 8.69%, 35.02%) increases in 25(OH)D levels. Newborns whose mothers with low (<10 μg/L) or medium (10.92–14.34 μg/L) tertiles of urinary Se concentrations in whole pregnancy were more likely to be vitamin D deficient (<20 ng/mL) compared with those with the highest tertile (>14.34 μg/L). Our study provides evidence that maternal Se levels were positively associated with cord serum vitamin D status.
Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

: The alignment operation between many protein sequences or DNA sequences related to the scientific bioinformatics application is very complex. There is a trade-off in the objectives in the existing techniques of Multiple Sequence Alignment (MSA). The techniques that concern with speed ignore accuracy, whereas techniques that concern with accuracy ignore speed. The term alignment means to get the similarity in different sequences with high accuracy. The more growing number of sequences leads to a very complex and complicated problem. Because of the emergence; rapid development; and dependence on gene sequencing, sequence alignment has become important in every biological relationship analysis process. Calculating the number of similar amino acids is the primary method for proving that there is a relationship between two sequences. The time is a main issue in any alignment technique. In this paper, a more effective MSA method for handling the massive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method depends on Artificial Fish Swarm (AFS) algorithm that can break down the most challenges of MSA problems. The AFS is exploited to obtain high accuracy in adequate time. ASF has been increasing popularly in various applications such as artificial intelligence, computer vision,
PHYSIOLOGY AND MANAGEMENT Environmental Streptococcal Intramammary Infections of the Bovine Mammary Gland 1

Characteristics of environmental streptococcal IMI were investigated over a 7-yr period for a herd in total confine­ ment. A total of 374 new environmental streptococcal IMI was detected. Approxi­ mately 50.5% of IMI were new in the dry period, and 49.5% were new in lacta­ tion. The rate of new IMI was . 00312 IMIIcow day during the dry period and . 00054 IMIIcow day during lactation. The percentages of cows and quarters with an environmental streptococcal IMI present at calving were 10.6 and 3.2%, respectively. The percentage of heifers with an environmental streptococcal IMI at calving was similar to that for cows. The rate of new environmental strep­ tococcal IMI was greater during the 1st mo of lactation than during the re­ mainder of lactation. The rate of IMI during late lactation was higher for older cows than for either heifers or cows in second lactation. The rate of environ­ mental streptococcal IMI during the dry period and during lactation was greatest during summer. The mean days of lacta­ tion that cows were infected for all IMI was 12.3 d. Approximately 41% of IMI had a duration of <8 d. Stage of lacta­ tion, season of the year, and parity in­ fluenced the rate of new IMI.
Wraparound View System for Motor Vehicles

The use of vehicle-mounted cameras, including one for the rearview monitor, has become increasingly popular recently. These cameras can help make driving easier and safer by reducing the number of blind spots around the vehicle. We have now developed a new wraparound view monitoring system that combines video images from four cameras and provides the driver with a full field of vision around the vehicle with no blind spots. This technology is built into a vehicle-mounted system, which consists of an MB86R01 System-on-a-Chip (SoC) graphics controller for automobiles supplied by Fujitsu Microelectronics Ltd. and a video processing chip FPGA that synchronizes the images from the four cameras and conducts multiplex synthesizing on those video images. The system is capable of processing the input video images at a rate of up to 30 ms per frame and showing the processed images to the driver. This technology thus can offer proper visual assistance to the driver in a variety of driving conditions, including when parking the vehicle, passing oncoming traffic on a narrow street, entering an intersection with poor visibility, and turning left or right. This paper describes our new wraparound view monitoring technology and its application to a vehicle-mounted system.
Review on the Testing Effect

The retrieval of information from memory through testing produces learning advantages that are superior to studying alone, a phenomenon called the testing effect. As an efficient learning strategy, the testing effect has received more attention in recent years. This paper reviews the researches on the factors influencing the testing effect and points out the future research directions.
Reduced Chemical Fertilizer Combined With Bio-Organic Fertilizer Affects the Soil Microbial Community and Yield and Quality of Lettuce

Reducing chemical fertilizers in combination with bio-organic fertilizers can limit the use of chemical fertilizers while maintaining soil fertility. However, the effects of combined fertilization on soil chemical properties, microbial community structure, and crop yield and quality are unknown. Using high-throughput sequencing, we conducted field experiments using lettuce plants subjected to five fertilization treatments: chemical fertilizer with conventional fertilization rate (CK), chemical fertilizer reduction by 30% + 6,000 kg ha–1 bio-organic fertilizer (T1), chemical fertilizer reduction by 30% + 9,000 kg ha–1 bio-organic fertilizer (T2), chemical fertilizer reduction by 40% + 6,000 kg ha–1 bio-organic fertilizer (T3), and chemical fertilizer reduction by 40% + 9,000 kg ha–1 bio-organic fertilizer (T4). Compared with CK, the T1–T4 had significantly higher soil pH and soil organic matter (SOM) and showed increased richness and diversity of the bacterial community, and decreased richness and diversity of the fungal community. Principal coordinate analysis evidenced that the bacterial and fungal communities of CK and T1–T4 were distinctly separated. The Kruskal-Wallis H-test demonstrated that the fungal community was more sensitive than the bacterial community to chemical fertilizer reduction combined with bio-organic fertilizer. Among the soil chemical parameters measured, only TN (total nitrogen) was significantly correlated with bacterial and fungal community composition. The T1 and T2 increased lettuce yield. Moreover, T1–T4 characterized reduced nitrate content and increased levels of soluble sugars and vitamin C in lettuce. Overall, the combined application of reduced chemical fertilizer and bio-organic fertilizer effectively improved soil fertility, microbial community structure, and lettuce yield and quality. These findings have valuable implications for vegetable safety and long-term environmental sustainability.
Solithromycin in Children and Adolescents With Community-acquired Bacterial Pneumonia

Background: Solithromycin is a new macrolide-ketolide antibiotic with potential effectiveness in pediatric community-acquired bacterial pneumonia (CABP). Our objective was to evaluate its safety and effectiveness in children with CABP. Methods: This phase 2/3, randomized, open-label, active-control, multicenter study randomly assigned solithromycin (capsules, suspension or intravenous) or an appropriate comparator antibiotic in a 3:1 ratio (planned n = 400) to children 2 months to 17 years of age with CABP. Primary safety endpoints included treatment-emergent adverse events (AEs) and AE-related drug discontinuations. Secondary effectiveness endpoints included clinical improvement following treatment without additional antimicrobial therapy. Results: Unrelated to safety, the sponsor stopped the trial prior to completion. Before discontinuation, 97 participants were randomly assigned to solithromycin (n = 73) or comparator (n = 24). There were 24 participants (34%, 95% CI, 23%–47%) with a treatment-emergent AE in the solithromycin group and 7 (29%, 95% CI, 13%–51%) in the comparator group. Infusion site pain and elevated liver enzymes were the most common related AEs with solithromycin. Study drug was discontinued due to AEs in 3 subjects (4.3%) in the solithromycin group and 1 (4.2%) in the comparator group. Forty participants (65%, 95% CI, 51%–76%) in the solithromycin group achieved clinical improvement on the last day of treatment versus 17 (81%, 95% CI, 58%–95%) in the comparator group. The proportion achieving clinical cure was 60% (95% CI, 47%–72%) and 68% (95% CI, 43%–87%) for the solithromycin and comparator groups, respectively. Conclusions: Intravenous and oral solithromycin were generally well-tolerated and associated with clinical improvement in the majority of participants treated for CABP.
Drug Regimens of Novel Antibiotics in Critically Ill Patients with Varying Renal Functions: A Rapid Review

There is currently an increase in the emergence of multidrug-resistant bacteria (MDR) worldwide, requiring the development of novel antibiotics. However, it is not only the choice of antibiotic that is important in treating an infection; the drug regimen also deserves special attention to avoid underdosing and excessive concentrations. Critically ill patients often have marked variation in renal function, ranging from augmented renal clearance (ARC), defined as a measured creatinine clearance (CrCL) ≥ 130 mL/min*1.73 m2, to acute kidney injury (AKI), eventually requiring renal replacement therapy (RRT), which can affect antibiotic exposure. All novel beta-lactam (BLs) and/or beta-lactam/beta-lactamases inhibitors (BL/BLIs) antibiotics have specific pharmacokinetic properties, such as hydrophilicity, low plasma–protein binding, small volume of distribution, low molecular weight, and predominant renal clearance, which require adaptation of dosage regimens in the presence of abnormal renal function or RRT. However, there are limited data on the topic. The aim of this review was therefore to summarize available PK studies on these novel antibiotics performed in patients with ARC or AKI, or requiring RRT, in order to provide a practical approach to guide clinicians in the choice of the best dosage regimens in critically ill patients.
A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (-1), and neutral (0) emotions through different visualization. © 2022 Tech Science Press. All rights reserved.
Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

: Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users’ reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users’ reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users’ opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.
Retinal Diseases: The Next Frontier in Pharmacodelivery

The future continuous growth of the global older population augments the burden of retinal diseases worldwide. Retinal characteristics isolating and protecting the sensitive neuro-retina from the rest of the ocular tissues challenge drug delivery and promote research and development toward new horizons. In this review, we wish to describe the unmet medical needs, discuss the novel modes of delivery, and disclose to the reader a spectrum of older-to-novel drug delivery technologies, innovations, and the frontier of pharmacodelivery to the retina. Treating the main retinal diseases in the everlasting war against blindness and its associated morbidity has been growing steadily over the last two decades. Implants, new angiogenesis inhibitor agents, micro- and nano-carriers, and the anchored port delivery system are becoming new tools in this war. The revolution and evolution of new delivery methods might be just a few steps ahead, yet its assimilation in our daily clinical work may take time, due to medical, economical, and regulatory elements that need to be met in order to allow successful development and market utilization of new technologies. Therefore, further work is warranted, as detailed in this Pharmaceutics Special Issue.
Clinical Trial Endpoints and Their Clinical Meaningfulness in Early Stages of Alzheimer’s Disease

As the focus of Alzheimer’s disease (AD) therapeutic development shifts to the early stages of the disease, the clinical endpoints used in drug trials, and how these might translate into clinical practice, are of increasing importance. The clinical meaningfulness of trial outcome measures is often unclear, with a lack of conclusive evidence as to how these measures correlate to changes in disease progression and treatment response. Clarifying this would benefit all, including patients, care partners, primary care providers, regulators, and payers, and would enhance our understanding of the relationship between clinical trial endpoints and assessments used in everyday practice. At present, there is a wide range of assessment tools used in clinical trials for AD and substantial variability in measures selected as endpoints across these trials. The aim of this review is to summarize the most commonly used assessment tools for early stages of AD, describe their use in clinical trials and clinical practice, and discuss what might constitute clinically meaningful change in these measures in relation to disease progression and treatment response.