Miracle-dz/UniFAR-FTUs huggingface dataset:
Population-based stroke surveillance using big data: state-wide epidemiological trends in admissions and mortality in New South Wales, Australia

ABSTRACT Objectives Epidemiological trends for major causes of death and disability, such as stroke, may be monitored using administrative data to guide public health initiatives and service delivery. Methods We calculated admissions rates for ischaemic stroke, intracerebral haemorrhage and subarachnoid haemorrhage between 1 January 2005 and December 31st, 2013 and rates of 30-day mortality and 365-day mortality in 30-day survivors to 31 December 2014 for patients aged 15 years or older from New South Wales, Australia. Annual Average Percentage Change in rates was estimated using negative binomial regression. Results Of 81,703 eligible admissions, 64,047 (78.4%) were ischaemic strokes and 13,302 (16.3%) and 4,778 (5.8%) were intracerebral and subarachnoid haemorrhages, respectively. Intracerebral haemorrhage admissions significantly declined by an average of 2.2% annually (95% Confidence Interval = −3.5% to −0.9%) (p < 0.001). Thirty-day mortality rates significantly declined for ischaemic stroke (Average Percentage Change −2.9%, 95% Confidence Interval = −5.2% to −1.0%) (p = 0.004) and subarachnoid haemorrhage (Average Percentage Change = −2.6%, 95% Confidence Interval = −4.8% to −0.2%) (p = 0.04). Mortality at 365-days amongst 30-day survivors of ischaemic stroke and intracerebral haemorrhage was stable over time and increased in subarachnoid haemorrhage (Annual Percentage Change 6.2%, 95% Confidence Interval = −0.1% to 12.8%), although not significantly (p = 0.05). Discussion Improved prevention may have underpinned declining intracerebral haemorrhage rates while survival gains suggest that innovations in care are being successfully translated. Mortality in patients surviving the acute period is unchanged and may be increasing for subarachnoid haemorrhage warranting investment in post-discharge care and secondary prevention.
False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection in Multi-Parametric MRI Scans

Prostate cancer (PCa) is the most prevalent and one of the leading causes of cancer death among men. Multi-parametric MRI (mp-MRI) is a prominent diagnostic scan, which could help in avoiding unnecessary biopsies for men screened for PCa. Artificial intelligence (AI) systems could help radiologists to be more accurate and consistent in diagnosing clinically significant cancer from mp-MRI scans. Lack of specificity has been identified recently as one of weak points of such assistance systems. In this paper, we propose a novel false positive reduction network to be added to the overall detection system to further analyze lesion candidates. The new network utilizes multiscale 2D image stacks of these candidates to discriminate between true and false positive detections. We trained and validated our network on a dataset with 2170 cases from seven different institutions and tested it on a separate independent dataset with 243 cases. With the proposed model, we achieved area under curve (AUC) of 0.876 on discriminating between true and false positive detected lesions and improved the AUC from 0.825 to 0.867 on overall identification of clinically significant cases.
Three-Way Ensemble Clustering for Incomplete Data

There are many incomplete data sets in all fields of scientific studies due to random noise, data lost, limitations of data acquisition, data misunderstanding etc. Most of the clustering algorithms cannot be used for incomplete data sets directly because objects with missing values need to be preprocessed. In this paper, we present a new imputation algorithm for incomplete data and a three-way ensemble clustering algorithm based on the imputation result. In the proposed imputation algorithm, the objects with nonmissing values are firstly clustered by using hard clustering methods. For each missing objects, the mean attribute’s value of each cluster are used to fill the missing attribute’s value, respectively. Perturbation analysis of cluster centroid is applied to search the optimal imputation. As an application of proposed imputation method, we develop a three-way ensemble clustering algorithm by using the ideas of clustering ensemble and three-way decision. The objects with the same cluster label in different clustering results are assigned the core region of corresponding cluster while the objects with different clustering labels are assigned to the fringe region. Therefore, a three-way clustering is naturally formed. The experimental results on UCI data sets can verify that the algorithm is effective in revealing cluster structures.
Critical transition to woody plant dominance through microclimate feedbacks in North American coastal ecosystems.

Climate warming is facilitating the expansion of many cold-sensitive woody species in woodland-grassland ecotones worldwide. Recent research has demonstrated that this range expansion can be further enhanced by positive vegetation-microclimate feedbacks whereby woody canopies induce local nocturnal warming, which reduces freeze-induced damage and favors the establishment of woody plants. However, this local positive feedback can be counteracted by biotic drivers such as browsing and the associated consumption of shrub biomass. The joint effects of large-scale climate warming and local-scale microclimate feedbacks on woody vegetation dynamics in these ecotones remain poorly understood. Here, we used a combination of experimental and modelling approaches to investigate the effects of woody cover on microclimate and the consequent implications on ecological stability in North American coastal ecosystems. We found greater browsing pressure and significant warming (~2°C) beneath shrub canopies compared to adjacent grasslands, which reduces shrub seedlings' exposure to cold damage. Cold sensitivity is evidenced by the significant decline in xylem hydraulic conductivity in shrub seedlings when temperatures dropped below -2°C. Despite the negative browsing-vegetation feedback, a small increase in minimum temperature can induce critical transitions from grass to woody plant dominance. Our framework also predicts the threshold temperature of -7°C for mangrove-salt marsh ecotones on the Atlantic coast of Florida. Above this reference temperature a critical transition may occur from salt marsh to mangrove vegetation, in agreement with empirical studies. Thus, the interaction between ongoing global warming trends and microclimate feedbacks may significantly alter woody vegetation dynamics and ecological stability in coastal ecosystems where woody plant expansion is primarily constrained by extreme low temperature events.
AHFNet: Aggregation of Hierarchical Features for Crowd Counting

Accurately estimating crowds is a particularly tricky problem for public safety. In this paper, we propose a pyramid network called AHFNet (aggregation of hierarchical features network) to effectively overcome this challenge. Different convolution layers extract rich features, which are then aggregated to generate useful crowd density maps. To further improve the method’s performances, we also introduce a unique technique named PVBS (projection variance and background suppression) to obtain more effective labels. In the PVBS, the projection relation of people is considered to estimate variance of Gaussian kernels in the ground truths, and the background pixels are set to a negative value directly. Extensive experiments are conducted on the ShanghaiTech dataset, and the results demonstrate our method outperforms those state-of-the-art methods in terms of crowd counting accuracy, which shows that our method has greater practical potential.
Simulation-Based Ultralow Energy and High-Speed LIF Neuron Using Silicon Bipolar Impact Ionization MOSFET for Spiking Neural Networks

The silicon bipolar impact ionization MOSFET offers potential for the realization of leaky integrated fire (LIF) silicon neuron due to the presence of parasitic bipolar junction transistor (BJT) in the floating body. In this article, we have proposed an L-shaped gate bipolar impact ionization MOS (L-BIMOS) with reduced breakdown voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{B} = {1.68}$ </tex-math></inline-formula> V) and demonstrated the functioning of LIF neuron based on the positive feedback mechanism of parasitic BJT. Using the 2-D TCAD simulations, we manifest that the proposed L-BIMOS exhibits a low threshold voltage (0.2 V) for firing a spike, and the minimum energy required to fire a single spike for L-BIMOS is calculated to be 0.18 pJ, which makes the proposed device 194 times more energy efficient than the PD-SOI MOSFET silicon neuron and <inline-formula> <tex-math notation="LaTeX">${5}\times {10}^{{3}}$ </tex-math></inline-formula> times more energy efficient than the analog/digital circuit-based conventional neurons. Furthermore, the proposed L-BIMOS silicon neuron exhibits spiking frequency in the gigahertz range when the drain is biased at <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DG}} = {2.0}$ </tex-math></inline-formula> V.
Analysis of milk constituents of Indian sheep, goat, camel and buffalo

Milk constituents of different species viz. dromedary camel (n=35), goat (n=32), sheep (n=31) and buffalo (n=17) were evaluated. Camel milk samples were collected from National Research Centre on Camel, Bikaner and goat, sheep and buffalo milk samples were collected from the surrounding villages of Bikaner. Milk samples were analyzed for fat, protein, lactose, ash and pH. It was observed that buffalo milk had highest fat (8.36%) and protein (3.56%) concentration whereas sheep milk had highest lactose (4.34%) and ash (0.87%) content. The pH (6.96) values of camel milk were the lowest in comparison to other species.
A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network

Among the various maintenance technologies of wind turbines, online fault prediction technology is a kind of more cost-effective and reliable method. It may also be the most promising method for wind turbines with potential mechanical faults. SCADA data-based online condition monitoring technology has become a hot spot in current researches. Therefore, a novel fault prediction method based on the Pair-Copula model is proposed in this study. First, the conditional mutual information method is introduced to screen out useful variables from a number of variables. Then aiming at the limitation that the conventional Copula model can only deal with two-dimensional variables, the Pair-Copula model is introduced. In addition, the complexity of the prediction model and the dimension of the input variables are greatly reduced by the Pair-Copula model. So, the BP neural network is selected to complete the prediction model. A combined model based on BP neural network and Pair-Copula model is proposed. In order to solve the problem that the conventional Pair-Copula model cannot process real-time data which must be required in fault prediction, a kind of improved Pair-Copula model combined with the kernel density estimation is used to calculate the real-time data. Finally, the proposed method is validated with real data from a 1.5 MW wind turbine, and the effectiveness is confirmed.
Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data

Six baleen whale species are found in the temperate western North Atlantic Ocean, with limited information existing on the distribution and movement patterns for most. There is mounting evidence of distributional shifts in many species, including marine mammals, likely because of climate‐driven changes in ocean temperature and circulation. Previous acoustic studies examined the occurrence of minke (Balaenoptera acutorostrata) and North Atlantic right whales (NARW; Eubalaena glacialis). This study assesses the acoustic presence of humpback (Megaptera novaeangliae), sei (B. borealis), fin (B. physalus), and blue whales (B. musculus) over a decade, based on daily detections of their vocalizations. Data collected from 2004 to 2014 on 281 bottom‐mounted recorders, totaling 35,033 days, were processed using automated detection software and screened for each species' presence. A published study on NARW acoustics revealed significant changes in occurrence patterns between the periods of 2004–2010 and 2011–2014; therefore, these same time periods were examined here. All four species were present from the Southeast United States to Greenland; humpback whales were also present in the Caribbean. All species occurred throughout all regions in the winter, suggesting that baleen whales are widely distributed during these months. Each of the species showed significant changes in acoustic occurrence after 2010. Similar to NARWs, sei whales had higher acoustic occurrence in mid‐Atlantic regions after 2010. Fin, blue, and sei whales were more frequently detected in the northern latitudes of the study area after 2010. Despite this general northward shift, all four species were detected less on the Scotian Shelf area after 2010, matching documented shifts in prey availability in this region. A decade of acoustic observations have shown important distributional changes over the range of baleen whales, mirroring known climatic shifts and identifying new habitats that will require further protection from anthropogenic threats like fixed fishing gear, shipping, and noise pollution.
Genetic Control of Flavonoid Biosynthesis in Barley

Mutations in genes associated with the pathway of flavonoid biosynthesis can readily be induced in barley and since 1977 more than 700 mutants have been isolated in which the biosynthesis of anthocyanin and/or proanthocyanidin is affected. Diallelic crosses have localized a majority of the mutants to 28 different complementation groups (Ant genes). Mutations in ten of the Ant genes result in a blocked synthesis of proanthocyanidins in the testa layer of the grains, whereas mutations in 18 Ant gene loci only affect anthocyanin synthesis in various organs of the plants. Seven Ant genes have been localized to a chromosome. A number of investigations have been carried out to characterize the ten known Ant genes acting on the biosynthesis of proanthocyanidins. Grains of mutants in different Ant genes and wildtypes were analyzed for contents of flavonoids and other phenolic compounds and the activity levels of several of the enzymes in the flavonoid pathway were studied. The gene locus Ant 18 has been isolated both in the form of cDNA and genomic clones. Ant 17, Ant 18, Ant 19, Ant 22, and Ant 26 are structural genes. Ant 13 is a regulatory gene coding for a transcription factor necessary for transcription of at least three structural genes in the flavonoid pathway. The action of the genes Ant 21, Ant 25, Ant 27, and Ant 28 is not fully understood. They might be regulatory genes.
Cutting up Text to Make Moveable, Magnetic Diagrams: A Way of Teaching & Assessing Biological Processes

1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ How-oDo-It : plicated biotechnology procedures of the polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) to in- struct and assess our students. We think this method can be applied to any laboratory process or proce- dure that has many steps, where knowing those steps is important for understanding the underlying biological process.
Range and Bearing Data Fusion for Precise Convex Network Localization

Hybrid localization in GNSS-challenged environments using measured ranges and angles is becoming increasingly popular, in particular with the advent of multimodal communication systems. Here, we address the hybrid network localization problem using ranges and bearings to jointly determine the positions of a number of agents through a single maximum-likelihood (ML) optimization problem that seamlessly fuses all the available pairwise range and angle measurements. We propose a tight convex surrogate to the ML estimator, we examine practical measures for the accuracy of the relaxation, and we comprehensively characterize its behavior in simulation. We found that our relaxation outperforms a state of the art SDP relaxation by one order of magnitude in terms of localization error, and is amenable to much more lightweight solution algorithms.
Transcatheter Aortic Valve Replacement in the Coronavirus Disease 2019 (COVID‐19) Era

On January 20, 2020, the first case of coronavirus disease 2019 (COVID19), caused by the severe acute respiratory syndrome coronavirus2, was identified in the United States.1 Since then, the pandemic has spread across the United States, and as of April 14, 2020, there were 600 000 cases and at least 25 000 deaths. As a result, cardiovascular societies have recommended postponing all nonemergent cardiac procedures.2 There is no clear consensus on what constitutes elective versus a timesensitive indication for some cardiac procedures. Transcatheter aortic valve replacement (TAVR) has expanded significantly since its approval in 2011, with >50 000 TAVR procedures done in 2017 and >550 hospitals performing TAVR in 2018. This was projected to further increase with the Food and Drug Administration approval of TAVR for lowrisk patients with severe symptomatic aortic stenosis (AS) in 2019.3 However, the COVID19 pandemic has raised tremendous challenges and practically brought to a halt the performance of TAVR in many centers in the United States and across the globe, with many hospitals transitioning patient care to telemedicine and virtual encounters. Furthermore, structural heart disease training, which is already fragmented and inconsistent between programs,4 is markedly affected by the lower number and limited scope of procedures performed in the COVID19 era. In many institutions, the interventional fellows are providing backup clinical support to clinical cardiology fellows and critical care physicians in their efforts combatting the COVID19 outbreak in their local community. This will undoubtedly result in diminished experience and exposure of fellows to structural heart disease procedures, even among those enrolled in the few dedicated structural heart disease training programs in the United States. If the COVID19 pandemic continues abated, societal guidance is needed to revisit the certification volume requirements and provide alternative means to complement the fellows’ training (eg, online didactic and case review sessions, webinars, and simulation programs). Given the COVID19 outbreak in the United States, the heart team should meticulously deliberate about the risks, benefits, and alternatives of aortic valve replacement (AVR) in patients with severe AS, as well as the best therapeutic strategy (TAVR versus surgical AVR) and its timing. The American College of Cardiology/Society for Cardiovascular Angiography and Interventions released a guidance statement for triaging patients referred for structural heart interventions that describes some scenarios for patients’ presentations and recommended management.5 The current COVID19 outbreak in the United States is placing an unparalleled strain and new challenges for the healthcare system in the United States. Therefore, when the heart team discusses whether to perform a TAVR procedure and its timing, 3 basic questions may arise and answers to those may help guide their decision making.
Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions

Semi-supervised approaches have been developed to improve brain lesion segmentation based on convolutional neural networks (CNNs) when annotated data is scarce. Existing methods have exploited unannotated images with lesions to improve the training of CNNs. In this work, we explore semi-supervised brain lesion segmentation by further incorporating images without lesions. Specifically, using information learned from annotated and unannotated scans with lesions, we propose a framework to generate synthesized lesions and their annotations simultaneously. Then, we attach them to normal-appearing scans using a statistical model to produce synthesized training samples, which are used together with true annotations to train CNNs for segmentation. Experimental results show that our method outperforms competing semi-supervised brain lesion segmentation approaches.
Genetic Variability in Two Tree Species, Acer platanoides L. and Betula pendula Roth, With Contrasting Life-history Traits

Genetic variation in adjacent Nordic populations of Acer platanoides L. and Betula pendula Roth was assessed by allozymes and in growth and phenology traits of juvenile trees grown in two field trials, and in growth chambers with free or restricted nutrient access. The objectives, based on the difference in the species' life-history traits, were to test the following hypotheses: (1) the population differentiation is higher in A. platanoides; (2) the genetic variability and (3) the phenotypic plasticity are larger in B. pendula. Analyses of variance revealed that, except for budburst, the growth and phenology traits generally supported the first hypothesis. The estimates of the coefficients of additive variance consistently disagreed with the second hypothesis. Allozyme data also supported the first hypothesis, but not the second. Finally, the phenotypic plasticity was larger in B. pendula.
Induction of resistance to Phytophthora sojae in soyabean (Glycine max) by salicylic acid and ethylene

This study investigated the protective effects of various phytohormones and plant activators on soyabean resistance to Phytophthora sojae, the cause of root and stem rot. It was found that application of benzothiadiazole (BTH, an activator of salicylic acid (SA) signalling) and 1-aminocyclopropane-1-carboxylic acid (ACC, a precursor of ethylene (ET) biosynthesis) markedly induced resistance to P. sojae. By contrast, gibberellin (GA) and abscisic acid (ABA) rendered soyabean seedlings more susceptible to P. sojae. Simultaneous application of ABA with BTH or ACC suppressed the protective effects of BTH and ACC, indicating that ABA acts antagonistically on the SA- and ET-signalling pathways. Neither BTH nor ACC directly inhibited growth of P. sojae. The protective effect of ACC was diminished by co-treatment with its analogue α-aminoisobutyric acid, suggesting that ET biosynthesis is required for ACC-induced soyabean resistance. Expression analysis of ET- and SA-responsive genes demonstrated the activation of ET- and SA-signalling pathways during P. sojae infection. Furthermore, ACC treatments augmented the expression of ET-responsive and pathogenesis-related genes. Taken together, the results indicate that ACC (ET)-induced soyabean resistance to P. sojae relies on transcriptional augmentation of defence-related genes.
Implementation of an Internet of Things System for Smart Hospitals

—With the rapid development of smart devices and the Internet of Things (IoT) technology, some traditional scenarios are exploring new possibilities. Especially in the ﬁeld of health-care, the mixed and large amount of people, the complex and professional data, and the strict environmental requirements on some medical scenes and equipment, all put forward extremely high requirements on the hospital management. Therefore, an efﬁcient and secure Internet of things system is greatly necessary. This paper develops an IoT system that could be deployed in hospitals for various applications. This system supports LoRa, Wi-Fi and other data collection methods, uploads the data to the cloud platform for further processing through secure connection, and ﬁnally feeds back to users in real-time through the user interface. This system supports accurate indoor positioning based on UWB, ECG signal detection, environmental monitoring, people ﬂow statistics and other functions.