Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Tropism of SARS-CoV and SARS-CoV-2 in Bat Cells

Severe acute respiratory syndrome coronavirus 2 did not replicate efficiently in 13 bat cell lines, whereas severe acute respiratory syndrome coronavirus replicated efficiently in kidney cells of its ancestral host, the Rhinolophus sinicus bat, suggesting different evolutionary origins. Structural modeling showed that RBD/RsACE2 binding may contribute to the differential cellular tropism.
Development of a swimming robot for pipeline leak detection

In this work a swimming robot is designed for the purpose of leak detection and localization in nontrivial pipeline manifold configuration. It has a simple design configuration given by plastic spherical shell internally loaded with a microcontroller (MCU) board, an inertial measurement unit (IMU) and a microphone module. We apply the processing of data obtained from the gyroscope and accelerometer measurements collected by the IMU to reconstruct the position trajectory of the swimming robot. At the same time, the sound data is wavelet transformed to extract feature vectors, and then the Bayesian classifier is adopted to classify each feature to finally identify the leak. Finally, the swimming robot outputs data which provide for the accurate calculation of the trajectory map and the location of the leakage.
Two Beam Resource Scheduling Strategies for Multi-RF-Chain Based V2I Communication

Recently, many researches based on millimeter wave (mmWave) together with analog beamforming technology have been done to provide higher transmission throughput in vehicle-to-everything (V2X) communication. While hybrid beamforming technology, which can generate multi radio frequency (RF) chains with reduced hardware complexity, continues attracting attention. Hence, this paper focuses on a hybrid-beamforming-based roadside unit (RSU) beam resource scheduling problem in vehicle-to-infrastructure (V2I) communication and intends to improve transmission fairness. Then, this paper proposes an indicator $Q$ to evaluate transmission fairness. Furthermore, a Power-allocated-based Beam resource Scheduling strategy (PBS) and a Time-allocated-based Beam resource Scheduling strategy (TBS) are designed for the multi-RF-chain based V2I communication to enhance transmission fairness. Simulation results prove that, the two proposed beam resource scheduling strategies can effectively improve the transmission fairness in V2I communication.
A Flexible-Time Iterative Learning Control Framework for Linear, Time-Based Performance Objectives

This paper describes a method for optimizing a user-defined, time-based, linear system performance index through the use of a flexible-time iterative learning control (ILC) framework. This method utilizes a two stage design wherein a point-to-point ILC procedure is conducted to improve sparse reference tracking performance, followed by a linear programming optimization that updates the system timing to minimize the time-based performance cost. A guarantee of strictly monotonic improvement of system performance cost is presented. The technique is applied to a simulated servo positioning system subject to input saturation constraints in order to minimize the time required to track a sequence of waypoints. This framework relaxes restrictions of traditional ILC techniques that require a fixed trial length to allow ILC to be applied effectively to a broader range of system objectives.
Trends in Australian inguinal hernia repair rates: a 15‐year population study

An inguinal hernia is one of the most common surgical pathologies, and therefore the repair of an inguinal hernia is one of the most common general surgical procedures. The aim of this study was to assess the trend in inguinal hernia repair (IHR) rates in Australia between 2000/2001 and 2014/2015 using population data from public and private hospitals.
Direct perception of time-to-contact : temporal and visuo-spatial constraints

Previous research has shown that observers can judge their time to contact (TTC) with an approaching target represented by an expanding patch on a computer monitor. This study examined the visuo-spatial and temporal determinants of such judgements. The Prediction Motion (PM) method (Tresilian, 1995a) was used to measure estimated TTC: the view of a constant-speed target was occluded beyond a particular point in the simulated approach and the observer judged the moment of arrival. The main issue concerned the way in which the momentary retinal size and expansion rate at a given point in the target approach are combined to determine the TTC at that point. An experimental examination of this question was proceeded by a series of experiments determining the effect of reductions in viewing time on TTC estimates. These experiments were intended to establish whether estimation of TTC at each moment in the approach is indeed based on the sensory information available at that moment. The following results were obtained: (1) Viewing time above 120 ms had no effect on TTC estimation. This was taken as strong evidence supporting claims of a mechanism which directly extracts TTC from the optic flow pattern, and as supporting the conclusion that TTC estimation in the PM procedure was based on the stimulus information at, or immediately preceding the moment of occlusion. (2) Estimates of TTC were not based on either the final retinal size or the final expansion rate of the target alone, but on the relationship between these two variables. (3) TTC estimates were not based on the ratio of retinal size to expansion rate, as hypothesised by tau-theory, but, rather, on their difference. 111 ( 4) There was an effect of stimulus magnitude on TTC estimates. For a given TTC, small magnitude stimuli (low expansion rate and small retinal size) gave larger TTC estimates than large magnitude stimuli. This effect was due to small retinal size, rather than low expansion rate, and occuned when final retinal size fell below 2 deg.
Latent Fingerprint Image Enhancement Based on Progressive Generative Adversarial Network

Latent fingerprints, a kind of fingerprints which are captured from the finger skin impressions at the crime scene, have been adopted to identify suspected criminals for a long time. However, poor latent fingerprint image quality owing to unstructured overlapping patterns, unclear ridge structure, and various background noise has brought a challenge to the recognition of latent fingerprints. Therefore, image enhancement is a crucial step for more accurate fingerprint recognition. In this paper, a latent fingerprint enhancement method based on the progressive generative adversarial network (GAN) is proposed. The powerful GAN structure provides an efficient translation from latent fingerprint to high-quality fingerprint. Our method consists of two stages: Progressive Offline Training (POT) and Iterative Online Testing (IOT). Progressive training makes our model not only focus on the local features such as minutiae but also preserve structure feature such as the orientation field. We extensively evaluate our model on NIST SD27 latent fingerprint dataset. With the help of orientation estimation task and progressive training scheme, our model achieves better recognition accuracy.
The Emerging Role of Galectins and O-GlcNAc Homeostasis in Processes of Cellular Differentiation

Galectins are a family of soluble β-galactoside-binding proteins with diverse glycan-dependent and glycan-independent functions outside and inside the cell. Human cells express twelve out of sixteen recognized mammalian galectin genes and their expression profiles are very different between cell types and tissues. In this review, we summarize the current knowledge on the changes in the expression of individual galectins at mRNA and protein levels in different types of differentiating cells and the effects of recombinant galectins on cellular differentiation. A new model of galectin regulation is proposed considering the change in O-GlcNAc homeostasis between progenitor/stem cells and mature differentiated cells. The recognition of galectins as regulatory factors controlling cell differentiation and self-renewal is essential for developmental and cancer biology to develop innovative strategies for prevention and targeted treatment of proliferative diseases, tissue regeneration, and stem-cell therapy.
Causal Deep CASA for Monaural Talker-Independent Speaker Separation

Talker-independent monaural speaker separation aims to separate concurrent speakers from a single-microphone recording. Inspired by human auditory scene analysis (ASA) mechanisms, a two-stage deep CASA approach has been proposed recently to address this problem, which achieves state-of-the-art results in separating mixtures of two or three speakers. A main limitation of deep CASA is that it is a non-causal system, while many speech processing applications, e.g., telecommunication and hearing prosthesis, require causal processing. In this study, we propose a causal version of deep CASA to address this limitation. First, we modify temporal connections, normalization and clustering algorithms in deep CASA so that no future information is used throughout the deep network. We then train a <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula>-speaker (<inline-formula><tex-math notation="LaTeX">$C \geq 2$</tex-math></inline-formula>) deep CASA system in a speaker-number-independent fashion, generalizable to speech mixtures with up to <inline-formula><tex-math notation="LaTeX">$C$</tex-math></inline-formula> speakers without the prior knowledge about the speaker number. Experimental results show that causal deep CASA achieves excellent speaker separation performance with known or unknown speaker numbers.
Augmented Reality and Virtual Reality in Education. Myth or Reality?

Augmented Reality and Virtual Reality are not new technologies. But several constraints prevented their actual adoption. Recent technological progresses added to the proliferation of affordable hardware and software have made AR and VR more viable and desirable in many domains, including educa-tion; they have been relaunched with new promises previously unimaginable. The nature of AR and VR promises new teaching and learning models that better meet the needs of the 21st century learner. We’re now on a path to re-invent education. This work consists of explaining the reasons behind the new rise of AR and VR and why their actual adoption in education will be a reality in a near fu-ture.
Increases in the Service Connection Disability and Treatment Costs Associated With Posttraumatic Stress Disorder and/or Traumatic Brain Injury in United States Veterans Pre- and Post-9/11: The Strong Need for a Novel Therapeutic Approach

Objective: To evaluate the relative distribution of service connection disability ratings among veterans with a diagnosis of Posttraumatic Stress Disorder (PTSD) and/or Traumatic Brain Injury (TBI) from all branches of service who went to war before and after a historic attack on US by a terrorist group on September 11, 2001 (9/11); and to determine the associated cost to the nation. Methods: The Veterans Health Administration (VHA) provides care to veterans at over 1,400 medical establishments across the United States. We used clinical data from these establishments, provided by researching the Veterans Administrations Informatics and Computing Infrastructure (VINCI). First, we identified all veterans with the ICD-9 code diagnosis of PTSD and/or TBI who received services within the VA between years 1998 to 2014. We excluded those who had no listed service connection disability rating. The remaining cohort was divided into four groups based on their period of service: Pre-9/11 Group – those who entered and left service before September 11, 2001; Post–9/11 Group– those who first entered the service after September 11, 2001; Overlap-9/11 Group – those who entered service before 2001 and left service after September 11, 2001; Reentered Post-9/11 Group – those who entered and then left service before September 11, 2001 and then reentered after September 11, 2001. Both annual and projected 50-year compensation costs were calculated for these groups. Results: We identified 1,067,691 veterans with PTSD and/or TBI diagnosis who were service connected during the period under study. The relative distribution was highest in Pre-9/11 Group (N=797,285; 74.7%), followed by Post-9/11 Group (N=174,134; 16.3%), Overlap-9/11 Group (N=82,450; 7.7%) and Reentered Post-9/11 Group (N=13,822; 1.3%). Majority of veterans in all groups were male. Veterans in Pre-9/11 Group were oldest (66.3 ± 11.3), those in the Post-9/11 Group were the youngest (36.1 ± 8.7), with the Overlap-9/11 Group (41.2 ± 8.4) and the Reentered Post-9/11 Group (46.7 ± 8.9) showing intermediate average ages. The projected service connection disability compensation cost will be at least $20.28 billion/year and total $515 billion in coming 50 years. Based on the 2014 numbers, we project inpatient and outpatient costs to be $3.63 billion and $1.48 billion annually, and this represents $106.4 billion and 40.4 billion in over the next 50 years, respectively. Conclusions: PTSD/TBI continues to be a devastating problem for veterans, their families, and our society. Rising costs and limited success with currently mandated therapies calls for a new therapeutic approach to help manage this unsustainable cost to the nation.
Sheep and Goats Respond Differently to Feeding Strategies Directed to Improve the Fatty Acid Profile of Milk Fat

Simple Summary Sheep and goat milk, as well as dairy products, are considered good sources of high-quality nutrients, particularly proteins and fats. Many positive effects on human health have been attributed to the consumption of dairy containing specific fatty acids, including some compounds originating from the polyunsaturated FA (PUFA) biohydrogenation operated by rumen microbes. In this bibliographic review, several nutritional strategies able to improve the milk fatty acids (FA) profile, in terms of an increase in the concentration of fatty acids considered beneficial to human health, are presented and discussed, with special attention to the differences between the two species. Abstract This bibliographic review presents and discusses the nutritional strategies able to increase the concentration of beneficial fatty acids (FA) in sheep and goat milk, and dairy products, with a particular focus on the polyunsaturated FA (PUFA), and highlights differences between the two species. In fact, by adopting appropriate feeding strategies, it is possible to markedly vary the concentration of fat in milk and improve its FA composition. These strategies are based mostly on the utilization of herbage rich in PUFA, or on the inclusion of vegetable, marine, or essential oils in the diet of lactating animals. Sheep respond more effectively than goats to the utilization of fresh herbage and to nutritional approaches that improve the milk concentration of c9,t11-conjugated linoleic acid (c9,t11-CLA) and α-linolenic acid. Dietary polyphenols can influence milk FA profile, reducing or inhibiting the activity and growth of some strains of rumen microbes involved in the biohydrogenation of PUFA. Although the effectiveness of plant secondary compounds in improving milk FA composition is still controversial, an overall positive effect has been observed on the concentration of PUFA and RA, without marked differences between sheep and goats. On the other hand, the positive effect of dietary polyphenols on the oxidative stability of milk fat appears to be more consistent.
Deep Reinforcement Learning Based Offloading for Mobile Edge Computing with General Task Graph

In this paper, we consider a mobile-edge computing (MEC) system, where an access point (AP) assists a mobile device (MD) to execute an application consisting of multiple tasks following a general task call graph. The objective is to jointly determine the offloading decision of each task and the resource allocation (e.g., CPU computing power) under time-varying wireless fading channels and stochastic edge computing capability, so that the energy-time cost (ETC) of the MD is minimized. Solving the problem is particularly hard due to the combinatorial offloading decisions and the strong coupling among task executions under the general dependency model. To address the issue, we propose a deep reinforcement learning (DRL) framework based on the actor-critic learning structure. In particular, the actor network utilizes a deep neural network (DNN) to learn the optimal mapping from the input states (i.e., wireless channel gains and edge CPU frequency) to the binary offloading decision of each task. Meanwhile, for the critic network, we show that given the offloading decision, the remaining resource allocation problem becomes convex, where we can quickly evaluate the ETC performance of the offloading decisions output by the actor network. Accordingly, we select the best offloading action and store the state-action pair in an experience replay memory as the training dataset to continuously improve the action generation DNN. Numerical results show that for various types of task graphs, the proposed algorithm achieves up to 99.5% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization methods.
Ventral hernia and obesity: is there a consensus?

The prevalence of obesity worldwide is increasing. With it, obesity associated co-morbidities are also on the rise. On such complication concerning the abdominal wall is “hernia”. Similarly, annually a high volume of abdominal laparotomies are performed worldwide, especially in young. Rising life expectancy, increasing prevalence of obesity and comorbidities like diabetes has ultimately led to an increasing prevalence of incisional hernias. When combined together, general surgeons and bariatric surgeons are faced with a new dilemma: an obese patient with an abdominal wall hernia. This review briefly summarizes the impact of obesity on the natural history of hernia, its associated complication, management strategies and options.
How patient-specific do internal carotid artery inflow rates need to be for computational fluid dynamics of cerebral aneurysms?

Background Computational fluid dynamics (CFD) has become a popular tool for studying ‘patient-specific’ blood flow dynamics in cerebral aneurysms; however, rarely are the inflow boundary conditions patient-specific. We aimed to test the impact of widespread reliance on generalized inflow rates. Methods Internal carotid artery (ICA) flow rates were measured via 2D cine phase-contrast MRI for 24 patients scheduled for endovascular therapy of an ICA aneurysm. CFD models were constructed from 3D rotational angiography, and pulsatile inflow rates imposed as measured by MRI or estimated using an average older-adult ICA flow waveform shape scaled by a cycle-average flow rate (Qavg) derived from the patient’s ICA cross-sectional area via an assumed inlet velocity. Results There was good overall qualitative agreement in the magnitudes and spatial distributions of time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and spectral power index (SPI) using generalized versus patient-specific inflows. Sac-averaged quantities showed moderate to good correlations: R2=0.54 (TAWSS), 0.80 (OSI), and 0.68 (SPI). Using patient-specific Qavg to scale the generalized waveform shape resulted in near-perfect agreement for TAWSS, and reduced bias, but not scatter, for SPI. Patient-specific waveform had an impact only on OSI correlations, which improved to R2=0.93. Conclusions Aneurysm CFD demonstrates the ability to stratify cases by nominal hemodynamic ‘risk’ factors when employing an age- and vascular-territory-specific recipe for generalized inflow rates. Qavg has a greater influence than waveform shape, suggesting some improvement could be achieved by including measurement of patient-specific Qavg into aneurysm imaging protocols.
Welfare of broiler breeder and layer females subjected to food and water control during rearing: Quantifying the degree of restriction

Abstract 1. A factorial experiment was conducted with females of two broiler breeder and one layer strains. The birds were fed ad libitum or restricted and were provided with free or limited access to water to simulate commercial rearing practices. 2. There was little difference between the broiler breeder strains except in body weight at 16 and 18 weeks of age. 3. Food intake relative to body weight was higher in the broiler breeders compared with the layer strain. 3. The degree of restriction was defined as the food intake of re‐stricted birds as a proportion of the estimated intake of birds of the same body weight fed ad libitum. The value declined from 0.60 to 0.45 during the rearing period. 5. In the ad libitum treatments broiler breeders drank more than layers, whereas when restricted, layers drank more than broiler breeders. 6. Water intake was 2.50 times higher in restricted compared with ad libitum‐fed layers and 1.25 times higher in restricted than ad libitum‐fed broiler breeders, with free acce.
Resource Allocation in Energy-Efficient URLLC Multi-user Multicarrier AF Relay Networks

Ultra-reliable and low-latency communication (URLLC) is one of the key applications in fifth generation (5G) cellular networks, which requires extremely high reliability ($\sim$99.9999%) and low latency (<1 ms). In this paper, the energy efficiency (EE) of multi-user multicarrier amplify-and-forward (AF) networks is maximized under short packet transmission. Accordingly, we formulate an energy-efficient resource allocation problem to jointly optimize the transmit power, subcarrier pairing and allocation, and error probability with finite block-length codes subject to the constraints of the decoding error probability of each user pair, subcarrier pairing and allocation and total transmission power. The formulated problem is non-convex and hence difficult to solve. We analyze the structure of the problem and hence convert it into a convex problem which is approximately equivalent to the original one. An efficient algorithm is also proposed which is capable of producing a near-optimal solution. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC, by showing the impact of various system parameters on EE.
Using Mixture of Expert Models to Gain Insights into Semantic Segmentation

Not only correct scene understanding, but also ability to understand the decision making process of neural networks is essential for safe autonomous driving. Current work mainly focuses on uncertainty measures, often based on Monte Carlo dropout, to gain at least some insight into a models confidence. We investigate a mixture of experts architecture to achieve additional interpretability while retaining comparable result quality.By being able to use both the overall model output as well as retaining the possibility to take into account individual expert outputs, the agreement or disagreement between those individual outputs can be used to gain insights into the decision process. Expert networks are trained by splitting the input data into semantic subsets, e.g. corresponding to different driving scenarios, to become experts in those domains. An additional gating network that is also trained on the same input data is consequently used to weight the output of individual experts. We evaluate this mixture of expert setup on the A2D2 dataset and achieve similar results to a baseline FRRN network trained on all available data, while getting additional information.