Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence and Genetic Diversity of Grapevine Virus G in Croatian Vineyards

Grapevine virus G (GVG) is a recently discovered vitivirus infecting grapevines. Historically, viruses in the genus Vitivirus have been associated with the grapevine rugose wood disease. Based on new and previously reported GVG isolates, primers and probes were developed for real-time RT-PCR. The developed assay successfully detected the virus in infected plants during dormancy and the growing season. A field study of 4327 grapevines from Croatian continental and coastal wine-growing regions confirmed the presence of GVG in 456 (~10.5%) grapevines from three collection plantations and 77 commercial vineyards, with infection rates ranging from 2% to 100%. Interestingly, the virus was confirmed only in vines considered to be Croatian autochthonous cultivars, but not in introduced cultivars. A 564-nucleotide long portion of the coat protein gene from previously known and newly characterized GVG isolates had nucleotide and amino acid identities ranging from 89% to 100% and from 96.8% to 100%, respectively. Phylogenetic analysis revealed five distinct groups, with isolates originating from the same site being close to each other, indicating possible local infection. The information presented in this manuscript sets the stage for future studies to better understand the ecology and epidemiology of GVG and the possible need for inclusion in certification schemes.
Evaluating Autonomous Urban Perception and Planning in a 1/10th Scale MiniCity

We present the MiniCity, a multi-vehicle evaluation platform for testing perception hardware and software for autonomous vehicles. The MiniCity is a 1/10th scale city consisting of realistic urban scenery, intersections, and multiple fully autonomous 1/10th scale vehicles with state-of-the-art sensors and algorithms. The MiniCity is used to evaluate and test perception algorithms both upstream and downstream in the autonomy stack, in urban driving scenarios such as occluded intersections and avoiding multiple vehicles. We demonstrate the MiniCity’s ability to evaluate different sensor and algorithm configurations for perception tasks such as object detection and localization. For both tasks, the MiniCity platform is used to evaluate the task itself (accuracy in estimating obstacle pose and ego pose in the map) as well as the downstream performance in collision avoidance and lane following, respectively.
Biological Studies of New Implant Materials Based on Carbon and Polymer Carriers with Film Heterostructures Containing Noble Metals

This paper presents pioneering results on the evaluation of noble metal film hetero-structures to improve some functional characteristics of carbon-based implant materials: carbon-composite material (CCM) and carbon-fiber-reinforced polyetheretherketone (CFR-PEEK). Metal-organic chemical vapor deposition (MOCVD) was successfully applied to the deposition of Ir, Pt, and PtIr films on these carriers. A noble metal layer as thin as 1 µm provided clear X-ray imaging of 1–2.5 mm thick CFR-PEEK samples. The coated and pristine CCM and CFR-PEEK samples were further surface-modified with Au and Ag nanoparticles (NPs) through MOCVD and physical vapor deposition (PVD) processes, respectively. The composition and microstructural features, the NPs sizes, and surface concentrations were determined. In vitro biological studies included tests for cytotoxicity and antibacterial properties. A series of samples were selected for subcutaneous implantation in rats (up to 3 months) and histological studies. The bimetallic PtIr-based heterostructures showed no cytotoxicity in vitro, but were less biocompatible due to a dense two-layered fibrous capsule. AuNP heterostructures on CFR-PEEK promoted cell proliferation in vitro and exhibited a strong inhibition of bacterial growth (p < 0.05) and high in vitro biocompatibility, especially Au/Ir structures. AgNP heterostructures showed a more pronounced antibacterial effect, while their in vivo biocompatibility was better than that of the pristine CFR-PEEK, but worse than that of AuNP heterostructures.
Automated Fluid Intake Detection Using RGB Videos

Dehydration is a common, serious issue among older adults. It is important to drink fluid to prevent dehydration and the complications that come with it. As many older adults forget to drink regularly, there is a need for an automated approach, tracking intake throughout the day with limited user interaction. The current literature has used vision-based approaches with deep learning models to detect drink events; however, most use static frames (2D networks) in a lab-based setting, only performing eating and drinking. This study proposes a 3D convolutional neural network using video segments to detect drinking events. In this preliminary study, we collected data from 9 participants in a home simulated environment performing daily activities as well as eating and drinking from various containers to create a robust environment and dataset. Using state-of-the-art deep learning models, we trained our CNN using both static images and video segments to compare the results. The 3D model attained higher performance (compared to 2D CNN) with F1 scores of 93.7% and 84.2% using 10-fold and leave-one-subject-out cross-validations, respectively.
A Semantic Data-Based Distributed Computing Framework to Accelerate Digital Twin Services for Large-Scale Disasters

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.
Relationship between smoking and periodontal clinical findings and gene expression of IL-6 and TNF-α in severe periodontitis (clinical and laboratory data)

Abstract The association between the periodontal conditions and smoking is attracting increasing attention. There is little information about the significance of the gene expression levels of interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) for the severity of attachment loss in patients with smoking habits. The aim of this study was to evaluate the relationship between the severity of periodontitis and the gene expression levels of IL-6 and TNF-α in the presence of a main risk factor – smoking. A total of 20 individuals (periodontitis patients) participated in this study. The following clinical parameters were studied: hygiene index, gingival bleeding index, probing depth, clinical attachment loss, furcation involvement and tooth mobility, in patients of mean age of 50.25 ± 8.45 years with untreated periodontitis. Gingival biopsies were taken adjacent to shallow (<4 mm), moderate (4–6 mm) and deep (> 6 mm) periodontal pockets and similar attachment loss for Real-Time polymerase chain reaction (PCR) assessment of IL-6 and TNF-α gene expression levels. The obtained results demonstrated higher values for attachment loss and periodontal pocket depth in the presence of smoking. Furthermore, the results indicated smoking as an important risk factor which could greatly influence the studied gene expression levels. Smoking affected the gene expression mainly in conjunction with deep periodontal pockets. Our results suggest that smoking is an important risk factor for attachment loss in periodontitis patients demonstrated by clinical measurements and IL-6 and TNF-α gene expression.
Practical and Accurate Indoor Localization System Using Deep Learning

Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.
Food hypersensitivity - classification, pathogenesis, diagnosis. What are food allergies?

Introduction: Food hypersensitivity is a pathological, increased and inadequate reaction of the body to a particular substance, the consumption of which can cause various types of body symptoms. Purpose: The purpose of this study is to distinguish different types of hypersensitivity mechanisms, resulting in the introduction of appropriate diagnosis and treatment. Results and conclusions: Food hypersensitivity is becoming an epidemic of the 21st century. This is due to many individual factors as well as environmental pollution. It is important to identify the type of mechanism involved in a given food hypersensitivity and to eliminate the causative factors, or incorporate appropriate treatment. Understanding this forms the basis of therapy. Methods: Data for the article were retrieved using PubMed setting the time descriptors to 2014-2020.
Symmetry Breaking in Stock Demand

Scale-free distributions and correlation functions found in financial data are reminiscent of the scale invariance of physical observables in the vicinity of a critical point. Here, we present empirical evidence for a transition phenomenon, accompanied by a symmetry breaking, in the investors' demand for stocks. We study the volume imbalance $\Omega$ -- difference between the number of shares traded in buyer-initiated and seller-initiated trades in a time interval $\Delta t$ -- conditioned on $\Sigma$ which is defined as the local first moment of $\Omega$ in $\Delta t$. We find that the conditional distribution $P(\Omega | \Sigma)$ undergoes a qualitative change in behavior as $\Sigma$ increases beyond a critical threshold $\Sigma_c$. For $\Sigma \Sigma_c$, $\Omega=0$ becomes a local minimum and two new maxima $\Omega_{+}$ and $\Omega_{-}$ appear at non-zero values of $\Omega$, i.e., trades in $\Delta t$ are either predominantly buyer initiated or predominantly seller initiated. We interpret these results using a Langevin equation with multiplicative noise.
VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency.
The complication rate, but not the mortality rate, lower after percutaneous endoscopic gastrostomy than after open surgical gastrostomy: comparison of two methods in a high volume group of patients

Introduction Percutaneous endoscopic gastrostomy (PEG) has become the primary procedure for long-term enteral nutrition of most, but not all patients with dysphagia. Still in some patients gastrostomy may only be performed with open surgical technique (SG). Finally, in some patients due to relative contraindications to both methods, surgeons have to choose one of them. Aim To compare PEG with SG in terms of effectiveness and safety. Material and methods A retrospective study of 612 patients with dysphagia, who underwent PEG (573) or SG (39) was conducted. Authors analysed effectiveness of PEG and SG procedures as well as the type, frequency and treatment methods of complications classified according to Clavien-Dindo Classification. Results The rate of all complications was significantly lower for PEG than for SG and a significant effect of the treatment type on the probability of serious complications was observed – notably lower after PEG (OR = 0.21, 95% CI: 0.05–0.8, p = 0.02). The 30-day mortality rate was 1.74% for PEG and 0% for SG. PEG patients who required laparotomy were over 30 times more likely to die than others. No significant effect of the nutrition status on the probability of serious complications was observed (OR = 0.83, 95% CI: 0.51–1.34, p = 0.46). Conclusions A significant effect of the treatment type on the probability of serious complications was confirmed. This result was robust to the preoperative patients’ nutrition status which was found to be insignificant. A lower risk of postoperative complications, a relatively easy procedure make PEG a procedure of choice in patients with dysphagia.
Weight change and risk of depression in patients with diabetic kidney disease: a nationwide population-based study

Background Several studies have reported that depression is prevalent in patients with diabetes or chronic kidney disease. However, the relationship between weight changes and the risk of depression has not been elucidated in patients with diabetic kidney disease (DKD). Methods From the Korean National Health Insurance Service database, we selected 67,866 patients with DKD and body weight data from two consecutive health examinations with a 2-year interval between 2009 and 2012. Weight change over 2 years was categorized into five groups: ≥–10%, <–10% to ≥–5%, <–5% to <5%, ≥5% to <10%, and ≥10%. The occurrence of depression was monitored via the codes of International Statistical Classification of Diseases, 10th revision through the end of 2018. Results During the 5.24-year follow-up, 17,023 patients with DKD developed depression. Weight change and the risk of depression had a U-shaped relationship: patients with ≥–10% weight change (hazard ratio [HR], 1.12) and those with ≥10% weight change (HR, 1.11) showed higher HRs for depression than those with <–5% to <5% weight change, even after adjusting for several confounding factors. In the subgroup analyses, the risk of depression tended to increase as weight gain or weight loss increased in all subgroups. Conclusion Both weight loss and weight gain increased the risk of depression in patients with DKD.
Mango Industry Development Status of China in 2020

China has been the second largest mango cultivation country in the world in 2020.Mango has become the main source of income for mango farmers in some southern provinces and regions such as Guangxi, Yunnan, Hainan and Sichuan. At present, there are more than 40 different varieties cultivated in China. Through the combination of varieties and different climatic regions, and the adoption of comprehensive technical measures such as moving the harvest time forward and postponing the harvest time, China has become the only country in the world that can produce mango annually. At the same time, China has widely adopted a series of technical measures, such as topworking, bagging, integrated disease and pest control, water and fertilizer integration, intercropping cultivation and so on, which has significantly improved the quality of mango fruit, increased its market competitiveness，which can not only meet the domestic market, but also export to foreign countries. In the future, China will further improve the quality of fresh mango fruits, increase the proportion of processed products, and increase the income of mango practitioners.
Effect of Practicing Meditation, Pranayama, and Yoga on the Mental Health of Female Undergraduate Medical Students: An Interventional Study

Background Medical education is a rigorous formal education with a vast syllabus. Burnout and stresses are common among medical students and more prominent in females. Female medical students suffer from a higher level of stress than male medical students. For the improvement of physical and mental health, meditation, pranayama, and yoga are some of the ancient techniques. Meditation is a technique of focusing the mind on a target like an object, activity, or any thought. Pranayama is an ancient yogic practice focusing on the breath. Yoga is a combination of physical, mental, and spiritual dimensions that has the potential to improve mental and physical health. Objective This study aimed to find the effect of meditation, pranayama, and yoga on the improvement of mental health among female undergraduate medical students. Materials and methods This was an interventional study. A total of 105 females with a median age of 19 years (first quartile - third quartile: 18-20) first-year undergraduate medical students were recruited for this study. They were randomly allocated to control, meditation, pranayama, and yoga groups. The control group did not practice any form of meditation, pranayama, or yoga. The rest of the group practiced a designated program for their group, six days a week for 12 weeks. The anxiety, depression, anger, and sense of well-being were assessed by a validated self-administered questionnaire developed by the Defence Institute of Physiology and Allied Sciences, New Delhi before starting the study, at six weeks, and at 12 weeks after the intervention. Inter-group levels of anxiety, depression, anger, and well-being were compared by the Kruskal-Wallis test with Dunn’s posthoc test. Intra-group parameters at baseline, at six weeks, and at 12 weeks after the intervention was tested by Friedman’s test. Result The age (years) (p = 0.07), height (cm) (p = 0.98), and weight (kg) (p = 0.26) of participants among groups were similar. Anxiety, depression, and anger significantly decreased after six weeks in all three intervention groups. A further decrement was seen after 12 weeks of meditation, pranayama, and yoga. The maximum effect was seen in the yoga group. A sense of well-being was improved after practicing all types of interventions. However, meditation was found to increase a sense of well-being to the highest level compared to pranayama and yoga. Conclusion Introduction and sustainment of meditation, pranayama, and yoga programs for first-year female undergraduate medical students may help reduce anxiety, depression, and anger and promote a sense of well-being. Although a six-week program helps to improve mental health, a 12-week program helps in further improvement. A yoga program is more effective for improving the mental health of the students in comparison with pranayama and meditation.
Mathematics under the Microscope

It is an unusual book which casts new and paradoxical light on the nature of mathematics. The book will be interesting -- perhaps for different reasons -- to school teachers of mathematics and maths majors at universities, to graduate students in mathematics and computer science, to research mathematicians and computer scientists, to philosophers and historians of mathematics, to psychologists and neurophysiologists. The author attempts to start a dialogue between mathematicians and cognitive scientists. He discusses, from a working mathematician's point of view, the mystery of mathematical intuition: why are certain mathematical concepts are more intuitive than the others? To what extent the "small scale" structure of mathematical concepts and algorithms reflects the workings of the human brain? What are the "elementary particles'' of mathematics which build up the mathematical universe? One of the principal points of the book is the essential vertical unity of mathematics, the natural integration of its simplest objects and concepts into the complex hierarchy of mathematics as a whole. The same ideas and patterns of thinking can be found in elementary school arithmetic and in the cutting edge mathematical theories. There are no boundaries between "recreational'', "elementary'', "undergraduate'' and "research'' mathematics; the book freely moves throughout the whole range. Nevertheless, the author takes great care of keeping the book as non-technical as possible. The book is saturated with amusing examples from a wide range of disciplines -- from turbulence to error-correcting codes to logic -- as well as just puzzles and brainteasers. Despite the very serious subject matter, the author's approach is lighthearted and entertaining.
Rituximab in Pemphigus – An Observational Study from a Tertiary Care Center of North India

Abstract Background: Pemphigus is a group of potentially fatal autoimmune mucocutaneous blistering diseases. Rituximab (RTX) is a chimeric anti-CD20 (anti-cluster of differentiate 20) monoclonal antibody being increasingly used and becoming the first-line therapy in the management of pemphigus. Aims and Objectives: This was an observational study to evaluate the efficacy and safety of rituximab in patients of pemphigus vulgaris (PV) who either did not respond or relapsed after conventional therapeutic regimens and in treatment naive pemphigus patients. Materials and Methods: The study included pemphigus patients coming to our immunobullous clinic who did not respond to conventional therapy or relapsed after receiving conventional therapy as well as fresh cases between January 2019 and October 2021. All enrolled patients received two doses of rituximab (1 gram in each) as intravenous infusions two weeks apart as per the rheumatoid arthritis protocol. The efficacy and safety were evaluated by assessing pemphigus area and activity score (PAAS) before and after the therapy, clinical response, and any adverse events during follow-up. Results: Sixteen (ten males and six females) patients were included in the study. The age of these patients ranged from 27 to 60 years, with a mean of 43.8 ± 9.8 years. There were 15 (93.75%) patients with PV (14 mucocutaneous type and 01 mucosal) and one (6.25%) with pemphigus foliaceus. Among these patients, nine (56.25%) were relapse cases, four (25%) were non-responders, and three (18.75%) were fresh cases who received rituximab as first-line therapy. Fourteen (87.5%) patients reached complete remission off therapy over a median time of 6.36 months (ranging from 18 weeks to 35 weeks). Rituximab was well-tolerated by our patients, and no serious adverse events were observed. The main limitation of our study was the small sample size and the lack of a comparison group. Conclusion: Rituximab is a safe and effective treatment for pemphigus.