Miracle-dz/UniFAR-FTUs huggingface dataset:
A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting

Precipitation nowcasting underlying various public services from rainstorm warning to flight safety is quite important and remains challenging due to the fast change in convective weather. Although some deep learning models have been proposed to make prediction automatically, most of them just deal with a single radar echo data source, making them hard to adapt to heterogeneous and diverse data in practice. In this article, a heterogeneous spatiotemporal attention fusion prediction (HST-AFP) network is proposed for radar echo extrapolation (deterministic output) and further precipitation nowcasting, which deals with mining and fusing knowledge from multiple heterogeneous spatiotemporal (ST) data sources, including history radar echo observations and numerical weather prediction data. With the help of the proposed attention-based ST diffusion module, the multiencoder is designed to extract information from both dense ST tensor and sparse ST tensor. On the other hand, the fusion decoder achieves very deep trainable residual fusion prediction by integrating scalewise attention fusion module and deep residual spatial and temporal attention mechanism. It can adaptively blend multisource ST features and rescale the multiscale temporalwise and spatialwise features for better prediction. Experiments in a real-world dataset of South China show that compared with the ingenious recurrent-neural-network-based methods and newly proposed UNet-based methods, our HST-AFP network can handle complex input with heterogeneity in both space and time domains, and performs better on the precipitation nowcasting metrics as well as requires remarkable shorter forecast time.
Workshop on Atmospheric Deposition of Nitrogen - Chesapeake Bay Program, Science and Technical Advisory Committee

This project is part of the Cornell University Agricultural Ecosystems Program: Understanding Sources and Sinks of Nutrients and Sediment in the Upper Susquehanna River Basin, funded by the US Department of Agriculture Cooperative State Research, Education, and Extension Service under award number 2005-34244-15740. Additional support provided by the Chesapeake Bay Program.
Review on PLGA Polymer Based Nanoparticles with Antimicrobial Properties and Their Application in Various Medical Conditions or Infections

The rise in the resistance to antibiotics is due to their inappropriate use and the use of a broad spectrum of antibiotics. This has also contributed to the development of multidrug-resistant microorganisms, and due to the unavailability of suitable new drugs for treatments, it is difficult to control. Hence, there is a need for the development of new novel, target-specific antimicrobials. Nanotechnology, involving the synthesis of nanoparticles, may be one of the best options, as it can be manipulated by using physicochemical properties to develop intelligent NPs with desired properties. NPs, because of their unique properties, can deliver drugs to specific targets and release them in a sustained fashion. The chance of developing resistance is very low. Polymeric nanoparticles are solid colloids synthesized using either natural or synthetic polymers. These polymers are used as carriers of drugs to deliver them to the targets. NPs, synthesized using poly-lactic acid (PLA) or the copolymer of lactic and glycolic acid (PLGA), are used in the delivery of controlled drug release, as they are biodegradable, biocompatible and have been approved by the USFDA. In this article, we will be reviewing the synthesis of PLGA-based nanoparticles encapsulated or loaded with antibiotics, natural products, or metal ions and their antibacterial potential in various medical applications.
Editorial: Fibrotic tissue remodeling as a driver of disease pathogenesis

Fibrosis has emerged as one of the main pathophysiological common end-point in organ failure. Maladaptive tissue remodeling and fibrosis are typical responses to injury which leads to distorted architecture, pathologic signaling, and extracellular matrix (ECM) remodeling leading ultimately to organ dysfunction (Henderson et al., 2020). Tissue fibrosis has remained the main pathophysiological endpoint outcome leading to organ failure causing severe morbidity worldwide (Bollong et al., 2017). Cardiac fibrosis leading to heart failure and other forms of cardiovascular diseases (CVD) is a major contributor to global mortality (Gulati et al., 2013). Clinically, patients suffering from CVDs are often attributed to cardio-renal and cardio-pulmonary complexities as well (Husain-Syed et al., 2015). A histological hallmark of many of these end-stage pathologies is tissue fibrosis, characterized by an excessive accumulation of extracellular matrix (ECM)molecules and de-regulation of ECM modifying enzymes (Lu et al., 2011; Bollong et al., 2017). The causeconsequence relation between this fibrotic remodeling and the patient’s outcome remains unclear—and likely depends on the type of tissue insult. Further, perturbed cellular signaling could lead to myofibroblast activation contributing to inherent fibrosis development (Duffield, 2014; Gibb et al., 2020). The degree of tissue fibrosis, comorbidities, and the standard of care therapies often vary among patients (Raghu et al., 2015; Wong et al., 2020). Further, the limited availability of effective anti-fibrotic therapies has remained a major bottleneck in the reversal of the tissue fibrosis prognosis. Therefore, effective, safe, andwell-tolerated pharmacological strategies that halt fibrotic remodeling of tissues are an area of unmet but high clinical need. The challenges are to prospectively define patient populations that benefit from anti-fibrotic therapies and gain a deeper mechanistic understanding of pro-fibrotic signaling mechanisms. These molecular mechanistic understandings eventually would lay the foundation for developing next-generation therapeutics to tackle pathological fibrosis in a spatio-temporal manner. In this Research Topic, our focus was to highlight studies that primarily addressed the factors involved in modulating fibrosis in cardiac repair as well as other tissue pathologies including chronic kidney diseases, keloids, type II diabetes and glaucoma. Both Drs. Haffner and Leifheit-Nestler’s groups have indicated the significance of FGF23 in cardiac and kidney pathologies (Leifheit-Nestler et al.; Eitner et al.). Dr. Haffner’s group identified that FGF23 expression increased in cardiac fibroblasts and endothelial cells following pressure-induced cardiac hypertrophy and not in cardiac myocytes (Eitner et al.). However, Cre-Flox mediated deletion of FGF23 in myocytes resulted in worsening of cardiac function followed by transverse aortic constriction (TAC) induced cardiac insult OPEN ACCESS
scenoRITA: Generating Diverse, Fully Mutable, Test Scenarios for Autonomous Vehicle Planning

Autonomous Vehicles (AVs) leverage advanced sensing and networking technologies (e.g., camera, LiDAR, RADAR, GPS, DSRC, 5G, etc.) to enable safe and efficient driving without human drivers. Although still in its infancy, AV technology is becoming increasingly common and could radically transform our transportation system and by extension, our economy and society. As a result, there is tremendous global enthusiasm for research, development, and deployment of AVs, e.g., self-driving taxis and trucks from Waymo and Baidu. The current practice for testing AVs uses virtual tests—where AVs are tested in software simulations—since they offer a more efficient and safer alternative compared to field operational tests. Specifically, search-based approaches are used to find particularly critical situations. These approaches provide an opportunity to automatically generate tests; however, systematically creating valid and effective tests for AV software remains a major challenge. To address this challenge, we introduce scenoRITA, a test generation approach for AVs that uses an evolutionary algorithm with (1) a novel gene representation that allows obstacles to be fully mutable, hence, resulting in more reported violations and more diverse scenarios, (2) 5 test oracles to determine both safety and motion sickness-inducing violations and (3) a novel technique to identify and eliminate duplicate tests. Our extensive evaluation shows that scenoRITA can produce test scenarios that are more effective in revealing ADS bugs and more diverse in covering different parts of the map compared to other state-of-the-art test generation approaches.
Type IV P-Type ATPases: Recent Updates in Cancer Development, Progression, and Treatment

Simple Summary Lipids are the main structural component of cellular membranes; they play an essential role in cellular functions. For the growth and progression of cancer, tumor cells require lipid-related metabolic and structural adaptations, such as altered lipid membrane composition, which is in part regulated by lipid flippases. Type IV P-type ATPases (P4-ATPases), the main class of flippases, are responsible for membrane lipid asymmetry, and thus they are closely involved in cancer-related lipid changes. Lately, flippases have gained more attention regarding the pathobiology of human disease. This review focuses on recent publications discussing the role of P4-ATPases in cancer development, progression, and prospective treatment. Abstract Adaptations of cancer cells for survival are remarkable. One of the most significant properties of cancer cells to prevent the immune system response and resist chemotherapy is the altered lipid metabolism and resulting irregular cell membrane composition. The phospholipid distribution in the plasma membrane of normal animal cells is distinctly asymmetric. Lipid flippases are a family of enzymes regulating membrane asymmetry, and the main class of flippases are type IV P-type ATPases (P4-ATPases). Alteration in the function of flippases results in changes to membrane organization. For some lipids, such as phosphatidylserine, the changes are so drastic that they are considered cancer biomarkers. This review will analyze and discuss recent publications highlighting the role that P4-ATPases play in the development and progression of various cancer types, as well as prospects of targeting P4-ATPases for anti-cancer treatment.
The art of not being governed : an anarchist history of upland Southeast Asia

From the acclaimed author and scholar James C. Scott, the compelling tale of Asian peoples who until recently have stemmed the vast tide of state-making to live at arm's length from any organized state society For two thousand years the disparate groups that now reside in Zomia (a mountainous region the size of Europe that consists of portions of seven Asian countries) have fled the projects of the organized state societies that surround them-slavery, conscription, taxes, corvee labor, epidemics, and warfare. This book, essentially an "anarchist history," is the first-ever examination of the huge literature on state-making whose author evaluates why people would deliberately and reactively remain stateless. Among the strategies employed by the people of Zomia to remain stateless are physical dispersion in rugged terrain; agricultural practices that enhance mobility; pliable ethnic identities; devotion to prophetic, millenarian leaders; and maintenance of a largely oral culture that allows them to reinvent their histories and genealogies as they move between and around states. In accessible language, James Scott, recognized worldwide as an eminent authority in Southeast Asian, peasant, and agrarian studies, tells the story of the peoples of Zomia and their unlikely odyssey in search of self-determination. He redefines our views on Asian politics, history, demographics, and even our fundamental ideas about what constitutes civilization, and challenges us with a radically different approach to history that presents events from the perspective of stateless peoples and redefines state-making as a form of "internal colonialism. " This new perspective requires a radical reevaluation of the civilizational narratives of the lowland states. Scott's work on Zomia represents a new way to think of area studies that will be applicable to other runaway, fugitive, and marooned communities, be they Gypsies, Cossacks, tribes fleeing slave raiders, Marsh Arabs, or San-Bushmen.
Editorial: Autonomous magnetic resonance imaging

COPYRIGHT © 2023 Geethanath, Nunes and Nielsen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Autonomous magnetic resonance imaging
Artificial Intelligence in Lung Cancer Screening: The Future Is Now

Simple Summary Lung cancer is a widespread malignant tumour with a high mortality and morbidity rate and is frequently diagnosed in the middle and late stages when few therapies are available. Lung cancer screening allows an early-stage diagnosis and more effective therapies. Artificial intelligence (AI) plays a key role in lung cancer screening workflow for early diagnosis. Particularly, in low-dose computed tomography for screening programs, AI further reduces radiation dose maintaining an optimal image quality. AI also allows risk stratification and subsequent screening personalization. A computer-aided detection (CAD) system helps in lung nodule detection with a high sensibility, reducing imaging time interpretation. AI is additionally applied in nodule characterization (benign or malignant), using different approaches. This narrative review aims to provide an overall view of all possible AI applications in lung cancer screening. Abstract Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.
Assessment of Herd, Calf, and Colostrum Management Practices on Austrian Dairy Farms Using a Scoring System

Simple Summary As calves are born with a naïve immune system, they depend on the transfer of immunoglobulins via colostrum. If calves do not receive a sufficient supply of high-quality colostrum (>50 g/L of immunoglobulins; 100–300 g Immunoglobulins in total) within the first hours after birth, they are likely to suffer from Failure of Transfer of Passive Immunity. The objective of the present study was to evaluate herd, calf, and colostrum management on Austrian dairy farms, focusing on challenges and possibilities for improvement. A scoring system was implemented to compare and classify management practices. Farms in foothills/flatland regions of Austria, conventional producing farms, and full-time operated farms overall received a higher and therefore better score rating than farms in alpine regions of Austria, organic producing farms, and part-time operated farms. Abstract The objectives of the study were to describe colostrum management on Austrian dairy farms and to explore differences between regions (alpine/flatlands), organic and conventional producing farms, and full-time or part-time operated farms. An online survey (24 questions) on general farm characteristics and herd and calf management was sent to 16,246 farmers. In total, 2328 farmers (response rate 14.3%) answered the questionnaire. To allow an objective comparison, a scoring system was implemented. Farm size is, on average, smaller in the alpine regions than in the foothills/flatlands regions of Austria. Small farms were more often organic-producing farms (81.6%) and operated part-time (93.8%). In foothills/flatland regions, 70.0% of farms have a separate calving area, and in the alpine regions, it is solely 42.8%. Colostrum testing is still mostly done by visual appraisal (63.7%); only a few farmers use a colostrometer (8.8%), brix-refractometer (18.3%), or ColostroCheck® (9.2%, a cone-shaped device to rate the flow velocity of colostrum). The results of the present study using the scoring system showed differences in herd and calf management practices in all sectors. In the future, the findings and especially the scoring system can support Austrian dairy farmers or veterinarians to better assess areas of improvement on farms in order to prevent calves from suffering from Failure of Transfer of Passive Immunity.
ClimAlpTour - Climate Change and its Impacts on Tourism in the Alpine Space

Edizioni tradotte: ClimAlpTour, Le Changement Climatique et son impact sur le tourisme dans l'Espace Alpin. ISBN 9789612543181 -- ClimAlpTour, Auswirkungen des Klimawandels auf den Tourismus im Alpenraum. ISBN 9789612543204 -- ClimAlpTour, Podnebne spremembe in njihov vpliv na turizem v Alpah. ISBN 9789612543167 -- ClimAlpTour, Il Cambiamento Climatico e i suoi impatti sul turismo nello Spazio Alpino. ISBN 9789612543198
Prevalence and Predictors of Cyberbullying in Middle and High School Students During the COVID-19 Pandemic

Schools were closed during the COVID-19 pandemic, and the learning process has changed dramatically. Students spent countless hours online for learning and leisure activities and risked themselves by engaging in cyberbullying. This study aims are twofold: (1) to investigate the prevalence of cyberbullying perpetration and victimization during the COVID-19 pandemic, and (2) to investigate predictors of cyberbullying perpetration and victimization. A cross-sectional survey method was used in this study. This study used three questionnaires named Cyberbullying Perpetration and Victimization, Problematic Internet Use Questionnaire Short Form (PIUQ-SF-6), and Cyberbullying Attitudes Measure. Participants are middle and high school students (N = 3,752; 52.4% were girls, 81.6% were middle school students). Data were analyzed using descriptive statistics and multivariate linear regression. Results showed that more students engaged in cyberbullying victimization than perpetration. Boys were more likely to engage in cyberbullying perpetration. Girls were more likely to engage in cyberbullying victimization. The most prevalent cyberbullying perpetration and victimization were posted mean or hurtful comments online. PIU, particularly more time spent online, harms both perpetrators and victims, as many as 3.4% for perpetrators and 4.5% for victims. Having fun teasing others online and feeling good attacking others online made the highest contributions to engaging in cyberbullying perpetration, as many as 10.9% and 10.1%, respectively. Do not accept harming others online and do not feel-good attacking others online, protecting the individuals from being cyberbullied as many as 4.2%. The attitude that school rules will be ineffective at stopping cyberbullying made the highest contribution to being cyberbullied, as many as 4.2%.
Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets

Many real‐world application datasets such as medical diagnostics, fraud detection, biological classification, risk analysis and so forth are facing class imbalance and overlapping problems. It seriously affects the learning of the classification model on these datasets because minority instances are not visible to the learner in the overlapped region and the performance of learners is biased towards the majority. Undersampling‐based methods are the most commonly used techniques to handle the above‐mentioned problems. The major problem with these methods is excessive elimination and information loss, that is, unable to retain potential informative majority instances. We propose a novel entropy and neighborhood‐based undersampling (ENU) that removed only those majority instances from the overlapped region which are having less informativeness (entropy) score than the threshold entropy. Most of such existing methods improved sensitivity scores significantly but not in many other performance contexts. ENU first computes entropy and threshold score for majority instances and, a local density‐based improved KNN search is used to identify overlapped majority instances. To tackle the problem effectively ENU defined four improved KNN‐based procedures (ENUB, ENUT, ENUC, and ENUR) for effective undersampling. ENU outperformed in sensitivity, G‐mean, and F1‐score average ranking with reduced information loss as compared to the existing state‐of‐the‐art methods.
Daily temperature prediction with CMIP6 in the historical period of Hong Kong

An ensemble forecast of historical climate simulations consisting of CMIP6 models was used to project years during World War II for which temperature data were missing for Hong Kong. In multi-model ensemble prediction, since the deep learning in this paper considers that GCM has dependencies between continuous values, the overall effect of prophecy may be better than machine learning. The daily temperature recorded by the Hong Kong Observatory and the extreme temperature index are used to evaluate the daily temperature forecast effect of CMIP6 in the historical period. In the 139-year record span of the Hong Kong Observatory, comparing the forecast results with and without CMIP6, the results show that except for the TN10p and TX10p cold indicators, all other indicators are on the rise, indicating that the temperature is rising.