Miracle-dz/UniFAR-FTUs huggingface dataset:
Density and Population Size of Mammals in Remnants of Brazilian Atlantic Forest

Abstract: From October 1994 to April 1996, I surveyed six Atlantic forest reserves, two large (approximately 20,000 ha each), two medium‐sized (approximately 2000 ha each), and two small (approximately 200 ha each), located in northern Espírito Santo, southeastern Brazil, for mammals openface> 1 kg body weight. My main objective was to assess the effects of fragmentation on population sizes and densities of five abundant species of forest‐dwelling species (three primates and two rodents). I used line‐transect sampling to survey mammals and estimated densities through the DISTANCE program. The average density of mammals increased from small to medium to large fragments, with small fragments having an average mammal density ( 7.6 individuals/km2) significantly lower than that of large fragments (20.9 individuals/km2). This trend was not significant when species that were extinct or absent in some fragments were excluded from the analysis. Shortage of food ( fruits) and predation by small cats ( Leopardus sp.) are the likely factors contributing to the demise of mammals, most of which are frugivores, in small fragments. Estimates of population sizes were on the order of several hundred to thousands of individuals for the large reserves, whereas comparable estimates for the other areas indicate a few hundred individuals for the medium‐sized reserves and <100 individuals per species in the small reserves. The estimates provided here indicate that only Atlantic forest reserves of ≥20,000 ha can sustain viable populations ( Ne≅ 500 individuals) of the five species studied, which represent only about 14% of all medium‐sized to large species of mammals known to be present in fragments of this size. Given that only about 20% of all protected areas remaining in the Atlantic forest are equal to or larger than this size, management measures are badly needed to improve mammal conservation in this biome.
Association Between Dynamic Functional Connectivity and Intelligence

Several studies have explored the relationship between intelligence and neuroimaging features. However, little is known about whether the temporal variations of functional connectivity of the brain regions at rest are relevant to the differences in intelligence. In this study, we have used the fMRI data and intelligence scores of 50 healthy adult subjects from the Human Connectome Project (HCP) database. We have investigated the correlation between individual intelligence scores and the total power of the high frequency components of the Fast Fourier transform (FFT) of the dynamic functional connectivity time series of the brain regions. We have found temporal variations of specific functional connections highly correlated with the individual intelligence scores. In other words, functional connections of individuals with high levels of the intelligence have smoother temporal variation or higher temporal stability than those of the individuals with low intelligence levels.
Risk-Averse Home Energy Management System

Transformation of conventional energy systems into smart grids enables the integration of residential buildings with distributed generations, electro-thermal storages and demand response policies. Further, it improves the household comfort level and helps to preserve the ecological system. Keeping in view the techno-financial impact of residential energy management, optimal handling of residential customers may prove meaningful for peak load reduction, valley filling, and energy conservation. In this regard, this paper devises a residential energy management system (EMS) to optimally schedule appliances, energy sources and electro-thermal storage for reduction of consumption cost and greenhouse (GHG) emissions. Building integrates national grid, natural gas network and solar energy as input carriers, whereas, electricity, heat and cooling as output carriers. To resolve the risk of loss of load, conditional value at risk (CVaR) has been incorporated in the objective function. Comparison demonstrates that under risk-averse approach, energy retaining capability of electric vehicle and thermal energy storage increases by 28.56% and 53.34%, respectively. This stored energy acts as a reserve during absence of solar irradiance and outages on electric and natural gas networks. Moreover, to make EMS more efficient in tracking optimum solutions with faster convergence speed, a hybrid algorithm has been devised by concatenating the modified flower pollination algorithm with mixed-integer linear programming. The proposed algorithm has been validated by comparing its results with the Salp Swarm Algorithm, Grasshopper Optimization Algorithm, Polar Bear Algorithm, Coyote Optimization and Two Cored Flower Pollination Algorithm (FPA). Results manifest that the cost, GHG emissions and execution time drop by 8.98%, 10.81% and 35.064%, respectively.
Disease and community structure: white‐nose syndrome alters spatial and temporal niche partitioning in sympatric bat species

Emerging infectious diseases present a major perturbation with apparent direct effects such as reduced population density, extirpation and/or extinction. Comparatively less is known about the potential indirect effects of disease that likely alter community structure and larger ecosystem function. Since 2006, white‐nose syndrome (WNS) has resulted in the loss of over 6 million hibernating bats in eastern North America. Considerable evidence exists concerning niche partitioning in sympatric bat species in this region, and the unprecedented, rapid decline in multiple species following WNS may provide an opportunity to observe a dramatic restructuring of the bat community.
ESPAR Antenna-Based WSN Node With DoA Estimation Capability

In this paper, we present a low-cost energy-efficient electronically steerable parasitic array radiator (ESPAR) antenna-based wireless sensor network (WSN) node designed for IEEE 802.15. 4 standard that is capable of performing direction of arrival (DoA) estimation in real-life outdoor environments. To this end, we propose the WSN node architecture, design and realization that utilizes NXP JN5168 radio frequency (RF) wireless transceiver and a microcontroller integrated with ESPAR antenna beam-switching circuits. To incorporate DoA estimation capability into the developed single-board WSN node, power-pattern cross-correlation (PPCC) algorithm, that relies solely on received signal strength (RSS) values measured by the transceiver at the antenna output for every considered directional antenna radiation pattern, has been adapted and implemented in a simple microcontroller embedded within NXP JN5168 integrated circuit. Measurements conducted in an outdoor environment show that the proposed low-cost WSN node can successfully provide DoA estimation results, which may be used to enhance WSN capabilities in practical applications. The obtained root mean square (RMS) DoA estimation errors are 7.91°, 6.58° and 9.47° for distances between WSN nodes equal to <inline-formula> <tex-math notation="LaTeX">$3~m$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$5~m$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$10~m$ </tex-math></inline-formula> respectively.
Lack of nutrient declarations and low nutritional quality of pre-packaged foods sold in Guatemalan supermarkets

Abstract Objective: To assess the presence of nutrition declarations and nutritional quality in pre-packaged food products sold in Guatemala. Design: We photographed nutrition labels of pre-packaged foods. We extracted information about declaration of energy, total/saturated/trans-fats, total/added sugars and Na content (critical nutrients). We classified all products according to their degree of processing (NOVA classification) and nutritional quality (PAHO and WHO-Europe nutrient profile models). Setting: Pre-packaged foods for sale in seven supermarkets in Guatemala City. Participants: This study did not involve human subjects. Results: We assessed 3459 pre-packaged foods, including 80 % ultra-processed, 7 % processed and 13 % unprocessed/minimally processed foods or culinary ingredients. Nutritional information was available in 3021 products (87·3 %). Energy content was declared in 87·0 %; total fats in 86·1 %; saturated fats in 81·5 %; trans-fats in 48·9 %; total sugars in 70·3 %; added sugars in 0·5 % and Na/salt in 85·5 % of products. Insufficient nutrient information made impossible to assess nutritional quality in 36·6 and 17·1 % of products with the PAHO and WHO-Europe models, respectively. Using PAHO and WHO nutrient profiles, we found that 66·2 and 50 % of food products did not meet the model’s nutritional criteria. Conclusions: A high proportion of pre-packaged foods with nutritional information available in Guatemalan supermarkets do not meet the nutritional criteria recommended by WHO and PAHO. Furthermore, a high proportion of products did not declare critical nutrients and many did not even provide any nutritional information. National regulations should consider making critical nutrient declarations (including trans-fats and sugars) mandatory for all products.
CTX‐M‐producing Escherichia coli in pigs from a Czech farm during production cycle

We evaluated the prevalence and epidemiology of extended‐spectrum beta‐lactamase (ESBL)‐producing Escherichia coli isolates in pigs during production cycle on a Czech farm with the history of previous use of ceftiofur. ESBL‐producing E. coli isolates were obtained from rectal swabs from pigs of different age groups (suckling piglets, weaned piglets, growers and sows). Collected samples were directly cultivated on MacConkey agar with cefotaxime (2 mg l−1), whereas intestinal swabs of slaughtered pigs and surface swabs from pig carcasses were also pre‐enriched in buffered peptone water without antimicrobials before the cultivation. Clonal relationship of selected isolates was determined by XbaI pulse‐field gel electrophoresis and multi‐locus sequence typing. The transferability of plasmids carrying blaCTX‐M genes was tested by conjugation experiments. From all examined samples, 141 (43·7%, n = 323) were positive for ESBL‐producing E. coli. All ESBL‐producing isolates showed resistance to multiple antimicrobials and were positive for blaCTX‐M genes. The blaCTX‐M‐1 was carried by conjugative IncN/ST1 plasmids (c. 40–45 kb) while the blaCTX‐M‐15 was located on conjugative F plasmids with F:18:A5:B1 formula (c. 165 kb). This study demonstrated the persistence of CTX‐M‐positive E. coli isolates 2 months after banner of ceftiofur usage and indicated possible risk of transmission of these isolates to humans via the food chain.
MapTransfer : Urban Air Quality Map Generation for Downscaled Sensor Deployments

Dense deployments of commodity air quality sensors have proven effective to provide spatially-resolved information on urban air pollution in real-time. However, long-term operation of a dense sensor deployment incurs enormous maintenance expenses and efforts. A cost-effective alternative is to first collect measurements with an initial dense deployment and then rely on a small subset of sensors for air quality map generation. To avoid dramatic accuracy degradation in air quality maps generated using the downscaled sparse deployment, we design MapTransfer, an air quality map generation scheme which augments the current sensor measurements from the downscaled sparse deployment with appropriate historical data from the initial dense deployment. Due to the spatiotemporal complexity of air pollution, it is challenging to select the best historical data and fuse them with measurements from the downscaled deployment to accurate map generation. To overcome this challenge, MapTransfer adopts a learning-based data selection scheme and integrates the best historical data with the current measurements via a multi-output Gaussian process model at sub-region levels. Evaluations on a large-scale PM2.5 sensor deployment show that MapTransfer reduces the overall mean absolute error of air quality maps by 45.9%, compared with using data from the downscaled deployment alone.
TV-RSPIRiT:Total Variation Regularized Based Robust Self-Consistent Parallel Imaging Reconstruction

RSPIRIT is a magnetic resonance imaging method based on generalized Lasso that is more robust than SPIRiT in terms of calibration errors, but often suffers from noise. In this study, we present TV-RSPRiT that can generate higher signal-to-noise images through total variation regularization. The model contains two non-smooth problems that solved using first order primal dual algorithm. Comparisons with several recent parallel imaging algorithms indicate that the proposed method significantly improves the quality of reconstruction images
A State-Merging Scheduling Method for Bulk Transfers with Store-and-Forward over Inter-DC Optical Networks

The time- and space-varying nature of residual bandwidth in inter-datacenter networks (inter-DCNs) makes conventional end-to-end connections difficult to fully utilize the residual bandwidth. A promising solution is to introduce datacenter storage into data-plane paths. Delay-tolerant bulk data can be temporarily stored at intermediate sites and forwarded (SnF) at a later time when inter-DCN is less congested. However, the conventional methods attempt to involve multi-dimensional state information of the entire network in scheduling, which results in high computational complexity. In this paper, our studies reveal that there exist redundant states in scheduling, which cannot provide any performance benefit while imposing extra computational burden. Inspired by this finding, we propose a state-merging scheduling (SMS) method. By merging the state information of the pre-selected links, the SMS method naturally reduces the redundant states involved, which greatly improves the efficiency of SnF scheduling. Simulations demonstrate that the SMS method can outperform the conventional scheduling method, given a limit of the computational cost.
How manipulation of playing area dimensions in ball possession games constrains physical effort and technical actions in under-11, under-15 and under-23 soccer players

ABSTRACT The aim of this study was to investigate the effects of playing area manipulation (20 × 15 m, 25 × 20 m and 30 × 25 m) on external workloads (total distance covered, distance covered while walking, running and sprinting, number of sprints, maximum sprint speed), internal load perceptions (rating of perceived exertion) and technical actions of passing (number of passes with dominant and non-dominant foot, and maximum passing speed) during 4v4 ball possession small-sided and conditioned games in under-11, under-15 and under-23 soccer players. Results showed higher values in the large playing area for under-11 in the distance covered in different speed zones, sprint number and RPE (all p <.001) for under-15 in sprints number (p <.01) and maximum sprint speed (p =.02), and for under-23 in both RPE and sprint numbers (p <.01). Although no significant differences were found on technical actions, it was still possible to notice some effects through pairwise comparison. High-intensity running was promoted on larger playing areas, where under-11 s were also able to perform more technical actions of passing. Opposite, under-23s were able to perform more passing on smaller playing areas, where under-11 s perceived the exercise more intense. The impact of different playing areas was reduced for the under-15.
Improving Lung Nodule Detection with Learnable Non-Maximum Suppression

Current lung nodule detection methods generate several candidate regions per nodule, such that a Non-Maximum Suppression (NMS) algorithm is required to select a single region per nodule while eliminating the redundant ones. GossipNet is a 1D Neural Network (NN) for NMS, which can learn the NMS parameters rather than relying on handcrafted ones. However, GossipNet does not take advantage of image features to learn NMS. We use Faster R-CNN with ResNet18 for candidate region detection and present FeatureNMS — a neural network that provides additional image features to the input of GossipNet, which result from a transformation over the voxel intensities of each candidate region in the CT image. Experiments indicate that FeatureNMS can improve nodule detection in 2.33% and 0.91%, on average, when compared to traditional NMS and the original GossipNet, respectively.
Constructing Novel Prognostic Biomarkers of Advanced Nasopharyngeal Carcinoma from Multiparametric MRI Radiomics Using Ensemble-Model Based Iterative Feature Selection

Although different treatment strategies have been developed for nasopharyngeal carcinoma (NPC), recurrence and distant metastasis remain major challenges to advanced NPC. This study aims to identify pre-treatment radiomics models to predict progression-free survival (PFS) using pre-treatment T2weighted short tau inversion recovery (STIR) magnetic resonance (MR) images and contrast-enhanced T1-weighted MR images (CET1-W) separately. To address the problem of imbalanced and small dataset in model training, we developed a novel method named as ensemble-model based iterative feature selection for determine the predictive feature sets. Least absolute shrinkage and selection operator (LASSO) was used in both feature selection and model construction. Model ensemble was constructed from the subset of patients during the process of feature selection and model construction. In model construction, selected models built from predictive feature sets were then internally validated using 1000-bootstrapping for whole-patient cohort. Corrected AUC of Joint CET1-w and T2-w model was the highest and corrected AUC of T2-w modes was the lowest. Rad-scores were calculated as a linear combination of selected features for each patient, and were evaluated by stratified Kaplan-Meier analysis and Cox proportional hazard regression. Significant differences $(\mathrm{p}\lt 0.001)$ were observed between survival curves of high-risk and low-risk patients stratified by Rad-scores. Our results demonstrated the capability of the ensemble-model based iterative feature selection method for imbalanced and small dataset when building MRI-based biomarkers to stratify patients into high risk and low risk.
Deep Learning Models to Study the Early Stages of Parkinson's Disease

Current physio-pathological data suggest that Parkinson's Disease (PD) symptoms are related to important alterations in subcortical brain structures. However, structural changes in these small regions remain difficult to detect for neuro-radiologists, in particular, at the early stages of the disease (de novo PD patients). The absence of a reliable ground truth at the voxel level prevents the application of traditional supervised deep learning techniques. In this work, we consider instead an anomaly detection approach and show that auto-encoders (AE) could provide an efficient anomaly scoring to discriminate de novo PD patients using quantitative Magnetic Resonance Imaging (MRI) data.
Analyzing Service Chaining of Virtualized Network Functions with SR-IOV

Network Function Virtualization (NFV) along with Service Function Chaining (SFC) has proliferated the way that network functions and services are introduced, offered, and deployed. In particular, NFV and SFC are the basic components of the so called network softwarization era, promoting significant cost reductions and agility. Nonetheless, a constant debate exist regarding the network performance achieved and the holistic establishment of NFV as the key and sole component of the network infrastructure. To this end, this paper tries to shed light in remedy solutions when chaining Virtualized Network Functions (VNFs) internally on a standard x86 server using the Single-Root Input Output Virtualization (SR-IOV) technology, in order to maximize the throughput and delay achieved.
SEX RATIO VARIATION OF MYOTIS VELIFER (CHIROPTERA: VESPERTILIONIDAE) IN OKLAHOMA

Abstract Sex ratios of Myotis velifer populations were studied during 2 hibernating seasons within and among 42 caves across a portion of its geographic range in Oklahoma and Kansas. The number of females to males was significantly different than the expected 1∶1 sex ratio in 25 of 44 visits to hibernacula. The percentage of females at the Selman Cave System, one of the largest hibernacula in Oklahoma, was always greater than the percentage of males throughout the season and in clusters of different sizes. Neither females nor males were any more likely to be found at the center than on the periphery of large clusters. The sex ratio of volant young at a maternity roost did not differ significantly from the expected 1∶1 sex ratio.
Intelligent Assessment of Percutaneous Coronary Intervention Based on GAN and LSTM Models

Coronary artery calcification affects the arteries that supply the heart with blood, and percutaneous coronary intervention (PCI) is a direct and effective surgery to alleviate this symptom. In this paper, we propose a framework to judge if a patient requires surgery, based on cardiac computerized tomography scans. We adopt generative adversarial network to segment the calcified areas from slices. This architecture provides an environment for the generator to perform joint learning from ground truth images and the high-resolution discriminator. We use images reconstructed using two types of filters to test our method. An F1 score of 96.1% and 85.0% was achieved for the soft and sharp filters. In addition, we explored different recurrent neural networks for making the final decision. Including long short-term memory, which was ultimately used to deal with the calcium score normalized by the age and score threshold. Using the soft reconstruction image as the input, the whole framework achieved an accuracy of 76.6%. These results certify that our method can precisely locate lesion in artery, and make a reasonable risk assessment for PCI.
Hierarchy-Constrained Network for Corneal Tissue Segmentation Based on Anterior Segment OCT Images

Anterior segment optical coherence tomography (AS-OCT) is widely used to observe the corneal tissue structures in clinical ophthalmology. Accurate segmentation of corneal tissue interfaces is essential for corneal diseases diagnosis and surgical planning. However, most existing methods are used for healthy corneal data and cannot work effective due to noise and corneal diseases. In this paper, we propose a hierarchy-constrained network, which combines hierarchical features by using of an elegant progressive feature-extraction module and boundary constraint to overcome these challenges. In the meantime, multi-level prediction fusion module is integrated to the network that eventually enables the output node to sufficiently absorb features extracted from various levels. Extensive experimental results on two datasets containing cornea images with multiple lesions show that our proposed hierarchy-constrained network distinctly improves the performance of corneal tissue interfaces segmentation and outperforms other existing methods.
Unsupervised Learning for Compressed Sensing MRI Using Cyclegan

Recently, deep learning based approaches for accelerated MRI have been extensively studied due to its high performance and reduced run time complexity. The existing deep learning methods for accelerated MRI are mostly supervised methods, where matched subsampled k-space data and fully sampled k-space data are necessary. However, it is hard to acquire fully sampled k-space data because of long scan time of MRI. Therefore, unsupervised method without matched label data has become a very important research topic. In this paper, we propose an unsupervised method using a novel cycle-consistent generative adversarial network (cycleGAN) with a single deep generator. We show that the proposed cy-cleGAN architecture can be derived from a dual formulation of the optimal transport with the penalized least squares cost. The results of experiments show that our method can remove aliasing patterns in downsampled MR images without the matched reference data.