Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-Enabled Radio Access Network: Multi-Mode Communication and Trajectory Design

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled radio access network (RAN) with the UAV acting as an aerial platform to communicate with a set of ground users (GUs) in a variety of modes of practical interest, including data collection in the uplink, data transmission in the downlink, and data relaying between GUs involving both the uplink and downlink. Under this general framework, two UAV operation scenarios are considered: periodic operation, where the UAV serves the GUs in a periodic manner by following a certain trajectory repeatedly, and one-time operation where the UAV serves the GUs with one single fly and then leaves for another mission. In each scenario, we aim to minimize the UAV periodic flight duration or mission completion time, while satisfying the target data requirement of each GU via a joint UAV trajectory and communication resource allocation design approach. Iterative algorithms are proposed to find efficient locally optimal solutions by utilizing successive convex optimization and block coordinate descent techniques. Moreover, as the quality of the solutions obtained by the proposed algorithms critically depends on the initial UAV trajectory adopted, we propose new methods to design the initial trajectories for both operation scenarios by leveraging the existing results for solving the classic Traveling Salesman Problem and Pickup-and-Delivery Problem. Numerical results show that the proposed trajectory initialization designs lead to significant performance gains compared to the benchmark initialization based on circular trajectory.
Risk profile of young people admitted to hospital for suicidal behaviour in Melbourne, Australia

Self‐harm and suicidal behaviour is most prevalent during adolescence, but little is known about the risk profile of adolescents admitted to hospital for suicidal behaviour. Young people who self‐harm are at an increased risk of mortality compared to those who do not self‐harm; adolescents admitted to hospital for suicidal behaviour are particularly at risk. The aim of this study was to generate a risk profile of adolescents admitted to hospital with suicidal behaviour.
Expression of ZO‐1 and claudin‐1 in a 3D epidermal equivalent using canine progenitor epidermal keratinocytes

BACKGROUND Previous studies indicate that tight junctions are involved in the pathogenesis of canine atopic dermatitis (cAD). An in vitro skin model is needed to elucidate the specific role of tight junctions in cAD. A 3D epidermal equivalent model using canine progenitor epidermal keratinocytes (CPEK) has been established; the expression of tight junctions within this model is uncharacterized. HYPOTHESIS/OBJECTIVES To investigate the expression of tight junctions in the 3D epidermal equivalent. ANIMALS Two normal laboratory beagle dogs served as donors of full-thickness skin biopsy samples for comparison to the in vitro model. METHODS Immunohistochemical techniques were employed to investigate the expression of tight junctions including zonula occludens (ZO)-1 and claudin-1 in normal canine skin, and in the CPEK 3D epidermal equivalent. RESULTS Results demonstrated the expression of ZO-1 and claudin-1 in the CPEK 3D epidermal equivalent, with staining patterns that were similar to those in normal canine skin. CONCLUSIONS AND CLINICAL IMPORTANCE The CPEK 3D epidermal equivalent has the potential to be a suitable in vitro research tool for clarifying the specific role of tight junctions in cAD.
Evaluation of PCR primers targeting the groEL gene for the specific detection of Streptococcus agalactiae in the context of aquaculture

The aim of this study was to design a set of primers for specific detection and identification of Streptococcus agalactiae in polymerase chain reaction (PCR) that can detect a diverse range of S. agalactiae isolates from different hosts and that it is capable of discriminating between S. agalactiae and other species that are closely related or potentially present in aquaculture environments, notably Streptococcus iniae.
Episodic Memory Deep Q-Networks

Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interactions with the environments to obtain satisfactory performances. Recently, episodic memory based RL has attracted attention due to its ability to latch on good actions quickly. In this paper, we present a simple yet effective biologically inspired RL algorithm called Episodic Memory Deep Q-Networks (EMDQN), which leverages episodic memory to supervise an agent during training. Experiments show that our proposed method leads to better sample efficiency and is more likely to find good policy. It only requires 1/5 of the interactions of DQN to achieve many state-of-the-art performances on Atari games, significantly outperforming regular DQN and other episodic memory based RL algorithms.
Unsupervised Deep Learning for Optical Flow Estimation

Recent work has shown that optical flow estimation can be formulated as a supervised learning problem. Moreover, convolutional networks have been successfully applied to this task. However, supervised flow learning is obfuscated by the shortage of labeled training data. As a consequence, existing methods have to turn to large synthetic datasets for easily computer generated ground truth. In this work, we explore if a deep network for flow estimation can be trained without supervision. Using image warping by the estimated flow, we devise a simple yet effective unsupervised method for learning optical flow, by directly minimizing photometric consistency. We demonstrate that a flow network can be trained from end-to-end using our unsupervised scheme. In some cases, our results come tantalizingly close to the performance of methods trained with full supervision.
Significant Permission Identification for Machine-Learning-Based Android Malware Detection

The alarming growth rate of malicious apps has become a serious issue that sets back the prosperous mobile ecosystem. A recent report indicates that a new malicious app for Android is introduced every 10 s. To combat this serious malware campaign, we need a scalable malware detection approach that can effectively and efficiently identify malware apps. Numerous malware detection tools have been developed, including system-level and network-level approaches. However, scaling the detection for a large bundle of apps remains a challenging task. In this paper, we introduce Significant Permission IDentification (SigPID), a malware detection system based on permission usage analysis to cope with the rapid increase in the number of Android malware. Instead of extracting and analyzing all Android permissions, we develop three levels of pruning by mining the permission data to identify the most significant permissions that can be effective in distinguishing between benign and malicious apps. SigPID then utilizes machine-learning-based classification methods to classify different families of malware and benign apps. Our evaluation finds that only 22 permissions are significant. We then compare the performance of our approach, using only 22 permissions, against a baseline approach that analyzes all permissions. The results indicate that when a support vector machine is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which are about the same as those produced by the baseline approach while incurring the analysis times that are 4–32 times less than those of using all permissions. Compared against other state-of-the-art approaches, SigPID is more effective by detecting 93.62% of malware in the dataset and 91.4% unknown/new malware samples.
Improved Cognitive Performance and Reduced Monocyte Activation in Virally Suppressed Chronic HIV After Dual CCR2 and CCR5 Antagonism

Objective: To evaluate changes in neuropsychological (NP) performance and in plasma and cell surface markers of peripheral monocyte activation/migration after treatment with cenicriviroc (CVC), a dual C-C chemokine receptor type 2 (CCR2) and type 5 (CCR5) antagonist, in treatment-experienced, HIV-infected individuals. Setting: Single-arm, 24-week, open-label clinical trial. Methods: HIV-infected individuals on antiretroviral therapy ≥1 year with plasma HIV RNA ⩽50 copies per milliliter and below-normal cognitive performance [defined as age-, sex-, and education-adjusted NP performance (NPZ) <−0.5 in a single cognitive domain or in global performance] were enrolled. Changes over 24 weeks were assessed for global and domain-specific NPZ scores, plasma markers of monocyte/macrophage activation [neopterin, soluble (s)CD14, and sCD163] quantified by ELISA, and CCR2 and CCR5 expression on monocytes, and T cells measured by flow cytometry. Results: Seventeen of 20 enrolled participants completed the study. Improvements over 24 weeks were observed in global NPZ [median change (&Dgr;) = 0.24; P = 0.008], and in cognitive domains of attention (&Dgr;0.23; P = 0.011) and working memory (&Dgr;0.44; P = 0.017). Plasma levels of sCD163, sCD14 and neopterin decreased significantly (P's < 0.01). CCR2 and CCR5 monocyte expression remained unchanged; however, CCR5 levels on CD4+ and CD8+ T cells and CCR2 expression on CD4+ T cells increased (P's < 0.01). Conclusions: CVC given over 24 weeks was associated with improved NP test performance and decreased plasma markers of monocyte immune activation in virally suppressed, HIV-infected participants. These data potentially link changes in monocyte activation to cognitive performance. Further study of CVC for HIV cognitive impairment in a randomized controlled study is warranted.
Veterans' experiences initiating VA-based mental health care.

Military veterans who could benefit from mental health services often do not access them. Research has revealed a range of barriers associated with initiating United States Department of Veterans Affairs (VA) care, including those specific to accessing mental health care (e.g., fear of stigmatization). More work is needed to streamline access to VA mental health-care services for veterans. In the current study, we interviewed 80 veterans from 9 clinics across the United States about initiation of VA mental health care to identify barriers to access. Results suggested that five predominant factors influenced veterans' decisions to initiate care: (a) awareness of VA mental health services; (b) fear of negative consequences of seeking care; (c) personal beliefs about mental health treatment; (d) input from family and friends; and (e) motivation for treatment. Veterans also spoke about the pathways they used to access this care. The four most commonly reported pathways included (a) physical health-care appointments; (b) the service connection disability system; (c) non-VA care; and (d) being mandated to care. Taken together, these data lend themselves to a model that describes both modifiers of, and pathways to, VA mental health care. The model suggests that interventions aimed at the identified pathways, in concert with efforts designed to reduce barriers, may increase initiation of VA mental health-care services by veterans. (PsycINFO Database Record
Past Life and Future Effects—How Heterologous Infections Alter Immunity to Influenza Viruses

Influenza virus frequently mutates due to its error-prone polymerase. This feature contributes to influenza virus’s ability to evade pre-existing immunity, leading to annual epidemics and periodic pandemics. T cell memory plays a key protective role in the face of an antigenically distinct influenza virus strain because T cell targets are often derived from conserved internal proteins, whereas humoral immunity targets are often sites of increased mutation rates that are tolerated by the virus. Most studies of influenza T cell memory are conducted in naive, specific pathogen free mice and do not account for repetitive influenza infection throughout a lifetime, sequential acute heterologous infections between influenza infections, or heterologous chronic co-infections. By contrast to these mouse models, humans often experience numerous influenza infections, encounter heterologous acute infections between influenza infections, and are infected with at least one chronic virus. In this review, we discuss recent advances in understanding the effects of heterologous infections on the establishment and maintenance of CD8+ T cell immunological memory. Understanding the various factors that affect immune memory can provide insights into the development of more effective vaccines and increase reproducibility of translational studies between animal models and clinical results.
Psychological factors as predictors of early postoperative pain after open nephrectomy

Purpose There is an increasing interest in the identification of predictors for individual responses to analgesics and surgical pain. In this study, we aimed to determine psychological factors that might contribute to this response. We hence investigated patients undergoing a standardized surgical intervention (open nephrectomy). Patients and methods Between May 2014 and April 2015, we conducted a prospective observational cohort study. The following psychological tests were administered preoperatively: Mini-Mental State Examination, Amsterdam Preoperative Anxiety and Information Scale (APAIS), Hamilton Anxiety Rating Scale, Hamilton Depression Rating Scale, and Pain Catastrophizing Scale. The primary outcome, postoperative pain intensity (11-point numerical rating scale, [NRS]), was assessed in the “immediate early” (first 8 hours), “early” (12 and 24 hours), and “late early” periods (48 and 72 hours). Results A total of 196 patients were assessed, and 150 were finally included in the study. NRS scores improved from 4.9 (95% confidence interval [CI]: 4.7–5.1) in the “immediate early” to 3.1 (95% CI: 2.9–3.3) in the “early” and 2.3 (95% CI: 2.1–2.5) in the “late early” postoperative period. Most (87%) patients received intravenous opioids, while 13% received analgesics epidurally. Repeated measures analysis of variance indicated better pain management with epidural analgesia in the first two postoperative periods (F=15.01, p<0.00). Postoperative pain correlated strongly with analgesic strategy and preoperative psychological assessment. Multiple linear regression analysis showed “expected pain” was the only predictor in the “immediate early” phase, and “anxiety” was most important in the “early” postoperative period. In the “late early” phase, catastrophizing was the predominant predictor, alongside “preoperative analgesic usage” and “APAIS anxiety”. Conclusion After open nephrectomy, epidural analgesia conveys a clear advantage for pain management only within the first 24 hours. Moreover, as the psychological phenotype of patients changes distinctively in the first 72 postoperative hours, psychological variables increasingly determine pain intensity, even surpassing employed analgesic strategy as its main predictor.
Development and blood compatibility assessment of electrospun polyvinyl alcohol blended with metallocene polyethylene and plectranthus amboinicus (PVA/mPE/PA) for bone tissue engineering

Introduction Currently, the design of extracellular matrix (ECM) with nanoscale properties in bone tissue engineering is challenging. For bone tissue engineering, the ECM must have certain properties such as being nontoxic, highly porous, and should not cause foreign body reactions. Materials and methods In this study, the hybrid scaffold based on polyvinyl alcohol (PVA) blended with metallocene polyethylene (mPE) and plectranthus amboinicus (PA) was fabricated for bone tissue engineering via electrospinning. The fabricated hybrid nanocomposites were characterized by scanning electron microscopy (SEM), Fourier transform and infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), contact angle measurement, and atomic force microscopy (AFM). Furthermore, activated partial thromboplastin time (APTT), prothrombin time (PT), and hemolytic assays were used to investigate the blood compatibility of the prepared hybrid nanocomposites. Results The prepared hybrid nanocomposites showed reduced fiber diameter (238±45 nm) and also increased porosity (87%) with decreased pore diameter (340±86 nm) compared with pure PVA. The interactions between PVA, mPE, and PA were identified by the formation of the additional peaks as revealed in FTIR. Furthermore, the prepared hybrid nanocomposites showed a decreased contact angle of 51°±1.32° indicating a hydrophilic nature and exhibited lower thermal stability compared to pristine PVA. Moreover, the mechanical results revealed that the electrospun scaffold showed an improved tensile strength of 3.55±0.29 MPa compared with the pristine PVA (1.8±0.52 MPa). The prepared hybrid nanocomposites showed delayed blood clotting as noted in APTT and PT assays indicating better blood compatibility. Moreover, the hemolysis assay revealed that the hybrid nanocomposites exhibited a low hemolytic index of 0.6% compared with pure PVA, which was 1.6% suggesting the safety of the developed nanocomposite to red blood cells (RBCs). Conclusion The prepared nanocomposites exhibited better physico-chemical properties, sufficient porosity, mechanical strength, and blood compatibility, which favors it as a valuable candidate in bone tissue engineering for repairing the bone defects.
Solasodine, Solamargine and Mixtures of Solasodine Rhamnosides: Pathway to Expansive Clinical Anticancer Therapies

Cancer remains a major cause of mortality worldwide. Progresses have been made in the understanding of the molecular basis of cancer, cancer detection, and cancer treatment. Important strides by treating early-stage cancers have resulted in improved outcomes. Despite these achievements, mortality of cancer patients is high and still there is no cure. Some oncologists remain optimistic that cytotoxic chemotherapy will significantly improve cancer survival. However, notwithstanding the use of new and expensive single and more recently, combination drugs, the improvement of the response rates remains very low. In the United States, cancer death rates decreased by a mere 1.4% to 1.8% from 2004 to 2013. Compared to other serious diseases, the improvement of the cancer patient has been disappointingly lagging, and by far, most patients with advanced cancer eventually die of their disease. The need for improved cancer therapies is self-evident. This communication gives an overview of the overwhelming resurgence of solasodine and its glycosides in cancer therapy.
A universal framework for learning based on the elliptical mixture model (EMM)

An increasing prominence of unbalanced and noisy data highlights the importance of elliptical mixture models (EMMs), which exhibit enhanced robustness, flexibility and stability over the widely applied Gaussian mixture model (GMM). However, existing studies of the EMM are typically of \textit{ad hoc} nature, without a universal analysis framework or existence and uniqueness considerations. To this end, we propose a general framework for estimating the EMM, which makes use of the Riemannian manifold optimisation to convert the original constrained optimisation paradigms into an un-constrained one. We first revisit the statistics of elliptical distributions, to give a rationale for the use of Riemannian metrics as well as the reformulation of the problem in the Riemannian space. We then derive the EMM learning framework, based on Riemannian gradient descent, which ensures the same optimum as the original problem but accelerates the convergence speed. We also unify the treatment of the existing elliptical distributions to build a universal EMM, providing a simple and intuitive way to deal with the non-convex nature of this optimisation problem. Numerical results demonstrate the ability of the proposed framework to accommodate EMMs with different properties of individual functions, and also verify the robustness and flexibility of the proposed framework over the standard GMM.
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge

We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation before inferring the answer. This allows the model to attract and imply knowledge from an external knowledge source that is not explicitly stated in the text, but that is relevant for inferring the answer. Our model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of much more complex models. By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.
The fatty acid composition of Estonian and Latvian retail milk; implications for human nutrition compared with a designer milk

The study reported in this Research Communication compared retail milks’ FA profiles from two neighbouring countries, estimated the potential contributions of these milks and a designer milk (achieved by changing the diet of the dairy cow) to the recommended human dietary intake of FA, and predicted (based on the milk FA profile) methane emission from dairy cows. Retail milks in Estonia and Latvia were purchased from supermarkets monthly for one year. To compare the FA composition of retail milk with designer milk with an increased PUFA content, the bulk milk FA profile from a separate field trial was used. Milk FA concentrations of the two neighbouring countries were affected by state, season and their interaction, while the main influence on all these factors were different feeding practices (grazing availability, forage to concentrate ratio and legume-rich silages vs. maize silages). Three cups (600 mL; fat content 2·5 g/100 g) of Estonian, or Latvian retail milk or designer milk per day contributed more to the recommended intakes of saturated FA (SFA) (42·5, 42·7, 38·7%, respectively) than other FA. Compared to the retail milks, α-linolenic acid estimated intake was almost doubled by designer milk consumption (19·7% of adequate intake) without influencing summed intakes of SFA and trans FA. There were state and seasonal differences in the predicted methane outputs of dairy cattle based on retail milk FA. Although the FA profiles of retail milks in the two neighbouring countries were affected by state and season, an appreciable increase in human dietary intakes of beneficial fatty acids from milk, and concomitant reduction in methane emissions from dairy cows, can be achieved only by targeted feeding.