Miracle-dz/UniFAR-FTUs huggingface dataset:
Huntington's Disease: Prevalence and Psychological Indicators of Pain

Huntington's disease (HD) is a genetic neurodegenerative condition that involves impairments in movement, cognition, and mood. Research is lacking in HD with regard to the prevalence of pain and the relationships between psychological factors and pain. The aim of this research was to investigate the prevalence of pain and identify the psychological factors associated with pain severity in people with HD.
Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning

This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques for heterogeneous wireless networking, referred to as carrier-sense deep-reinforcement learning multiple access (CS-DLMA). Existing CSMA protocols, such as the medium access control (MAC) of WiFi, are designed for a homogeneous network environment in which all nodes adopt the same protocol. Such protocols suffer from severe performance degradation in a heterogeneous environment where there are nodes adopting other MAC protocols. This paper shows that DRL techniques can be used to design efficient MAC protocols for heterogeneous networking. In particular, in a heterogeneous environment with nodes adopting different MAC protocols (e.g., CS-DLMA, TDMA, and ALOHA), a CS-DLMA node can learn to maximize the sum throughput of all nodes. Furthermore, compared with WiFi’s CSMA, CS-DLMA can achieve both higher sum throughput and individual throughputs when co-existing with other MAC protocols. Last but not least, a salient feature of CS-DLMA is that it does not need to know the operating mechanisms of the co-existing MACs. Neither does it need to know the number of nodes using these other MACs.
The balance between food and dietary supplements in the general population

In the past, vitamins and minerals were used to cure deficiency diseases. Supplements nowadays are used with the aim of reducing the risk of chronic diseases of which the origins are complex. Dietary supplement use has increased in the UK over recent decades, contributing to the nutrient intake in the population, but not necessarily the proportion of the population that is sub-optimally nourished; therefore, not reducing the proportion below the estimated average requirement and potentially increasing the number at risk of an intake above the safety limits. The supplement nutrient intake may be objectively monitored using circulation biomarkers. The influence of the researcher in how the supplements are grouped and how the nutrient intakes are quantified may however result in different conclusions regarding their nutrient contribution, the associations with biomarkers, in general, and dose–response associations specifically. The diet might be sufficient in micronutrients, but lacking in a balanced food intake. Since public-health nutrition guidelines are expressed in terms of foods, there is potentially a discrepancy between the nutrient-orientated supplement and the quality of the dietary pattern. To promote health, current public-health messages only advocate supplements in specific circumstances, but not in optimally nourished populations.
Micronutrient Status in Sri Lanka: A Review

Micronutrients include vitamins, minerals and, trace elements that are required in minute quantities but play a vital role in normal human growth, development and physiological functioning. Micronutrient deficiencies, also known as hidden hunger, are a global issue, with particularly high prevalence rates in developing countries. Currently, Sri Lanka is experiencing the double burden of over- and undernutrition. This review describes the micronutrient status of Sri Lanka based on results of national surveys and related articles published from 2000. The available data suggest a higher prevalence of iron, zinc, calcium, folate, and vitamin A deficiencies. The prevalence of iodine deficiency has declined gradually following the implementation of a universal salt iodization program. Iron deficiency is the most common cause of anemia and low red blood cell indices. Females are more vulnerable to micronutrient deficiencies than males. The coexistence of multiple micronutrient deficiencies and concurrent macro- and micronutrient deficiencies is common. Studies have shown an association between micronutrient deficiencies and different demographic, socioeconomic, and dietary factors. Therefore, there is a need for comprehensive studies, nutritional policies, and nationwide intervention programs in Sri Lanka to improve the micronutrient status of the population.
Deep Learning-Based Channel Estimation

In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.
Sparse Super-Resolution Method Using Accelerated Cross-Validation

Super-resolution is a method for reconstructing high-resolution images from low resolution images. In recent years, in the advancement of image hardware such as image display technology, recording devices and data communication, super-resolution technology using image information processing on software has great demand. Recent machine learning approaches using sparse representation of images have provided sparse super-resolution method based on dictionary learning of essential basis images using image database. However, conventional sparse super-resolution methods include parameter tuning for regularization parameter by using cross-validation with high computational costs. In this paper, we propose a machine learning method for super-resolution with a high speed cross-validation method in order to realize high quality and high-speed sparse super-resolution. We show the effectiveness of proposed method with accelerated cross validation on standard image. Our proposed method is shown to provide more speedy super-resolution than conventional one while keeping high image quality.
Model-driven data-intensive Enterprise Information Systems

The special issue is motivated by the extensive growth of interest for embedding so-called Big Data, Wireless Sensor Networks (WSN), Cyber-Physical Systems (CPS) and Internet-ofThings (IoT) capabilities into traditional Enterprise Information Systems (EIS) architectures. In the EIS world, those new capabilities would significantly expand the scope of planning and monitoring to end-to-end processes, encompassing full supply chain, while also including new stakeholders or events having the impact to these processes. Networks of physical objects with sensing, data collection, transmission and actuation capabilities, and vast endpoints in the cloud, offering large amounts of heterogeneous data, become increasingly important enterprise resources and sources for fundamental analytics. The large number of identifiable devices, used also on a sharing basis, is expected to become a commodity in the future, also providing a technology tool for emergence of so-called ‘sensing enterprise’ (Noran, Romero, and Zdravković 2014). Technology stack seem to be already there, with large number of cloud-based IoT platforms, offering connectivity, storage, analytics and visualization in a secure way and at scale. However, critical issues still remain open. First, such diversity will pose tremendous challenges related to the interoperability issues. The new technological landscape, provided by the Future Internet systems will thus establish interoperability problems as critical and possibly consider the interoperability as an inherent capability of the future information systems. Second, while the interoperability is self-obvious challenge, implementation problem is inherited from the traditional EISs and even multiplied when considering significantly increased complexity of the EISs operating in IoT world. In order to capitalize on the technological trend of data sources commoditization and thus start evolving from digital to sensing enterprises, existing EISs need to change. Such change may imply any of the following: complete EIS re-design and re-deployment, full integration with off-the-shelf tools or empowering existing systems with specific interoperability infrastructures. The objective of this Special Issue is to present state-of-the-art research in using Model-Driven Engineering approaches, techniques, tools and practices to overcome issues arising from transition from traditional process-focused to data-intensive EIS, where this transition is facilitated by any of the three above mentioned approaches. The Issue is also expected to highlight new challenges and opportunities, related to the new role of EISs in the ‘datafied’ world we are living in, today.
Editorial: hepatitis B, fatty liver, and metabolic syndrome—interaction or co‐existence?

35 years or younger and those older than 35, or between men and women. Therefore, based on the previous results, we believe that the risk of these complications is not limited to young males but is linked to patients’ seronegative status, regardless of age or gender. The cost of the VCA‐IgG determination is low (about €7). Therefore, to determine EBV serological status at the diagnosis of IBD or, as recommended by ECCO, before initiation of immunomodulatory treatment, would not impose a major cost. In our series, the seroprevalence of EBV infection was 97.4%; 55% of patients had been treated with thiopurines. With these data, in a unit that cares for 2000 adult patients with IBD, there would be 52 seronegative patients, of whom 29 would have received thiopurines. Knowing the seronegative status of this small group of patients (1.5%) would allow clinicians to consider whether there were therapeutic alternatives as effective as thiopurines. However, ignorance of seronegative status may lead clinicians to fail to suspect primary EBV infection in an adult patient who is immunosuppressed, possibly resulting in HLH; discontinuation of immunosuppressive drugs and the early establishment of specific treatment could reverse the process. We agree with Hans and Scott that, in some patients with serious forms of IBD, thiopurines should continue to be used in combination with biological treatments, to achieve the highest efficacy. Seronegative patients in whom it is essential to use thiopurines, must be informed of the symptoms of primary EBV infection and advised to quickly seek treatment in an IBD unit.
Virtual honeypots and detection of telnet botnets

Despite recommendations to not use telnet, there is an increasing number of telnet-based botnets and a need to analyse these attacks. We deployed a network of high interaction honeypots that simulate telnet devices. From the collected data, we created a dataset that we analysed from different perspectives. In this paper, we focus on the infection phase of botnets. Based on the found signatures collected by our samples, we can divide the botnets into 9 families. We show dependencies between commands, and between commands and directories used to propagate botnets.
Novel Mitoviruses and a Unique Tymo-Like Virus in Hypovirulent and Virulent Strains of the Fusarium Head Blight Fungus, Fusarium boothii

Hypovirulence of phytopathogenic fungi are often conferred by mycovirus(es) infections and for this reason many mycoviruses have been characterized, contributing to a better understanding of virus diversity. In this study, three strains of Fusarium head blight fungus (Fusarium boothii) were isolated from Ethiopian wheats as dsRNA-carrying strains: hypovirulent Ep-BL13 (>10, 3 and 2.5 kbp dsRNAs), and virulent Ep-BL14 and Ep-N28 (3 kbp dsRNA each) strains. The 3 kbp-dsRNAs shared 98% nucleotide identity and have single ORFs encoding a replicase when applied to mitochondrial codon usage. Phylogenetic analysis revealed these were strains of a new species termed Fusarium boothii mitovirus 1 in the genus Mitovirus. The largest and smallest dsRNAs in Ep-BL13 appeared to possess single ORFs and the smaller was originated from the larger by removal of its most middle part. The large dsRNA encoded a replicase sharing the highest amino acid identity (35%) with that of Botrytis virus F, the sole member of the family Gammaflexiviridae. Given that the phylogenetic placement, large genome size, simple genomic and unusual 3′-terminal RNA structures were far different from members in the order Tymovirales, the virus termed Fusarium boothii large flexivirus 1 may form a novel genus and family under the order.
Characterization of Household and Community Shedding and Transmission of Oral Polio Vaccine in Mexican Communities With Varying Vaccination Coverage

Abstract Background The World Health Assembly 2012 Polio Eradication and Endgame Strategic Plan calls for the eventual cessation of all oral polio vaccines (OPVs), to be replaced with inactivated polio vaccine (IPV); however, IPV induces less robust mucosal immunity than OPV. This study characterized household and community OPV shedding and transmission after OPV vaccination within primarily IPV-vaccinated communities. Methods Households in 3 IPV-vaccinated Mexican communities were randomized to receive 3 levels of OPV vaccination coverage (70%, 30%, or 10%). Ten stool samples were collected from all household members over 71 days. Analysis compared vaccinated subjects, household contacts of vaccinated subjects, and subjects in unvaccinated households. Logistic and Cox regression models were fitted to characterize transmission of OPV by coverage and household vaccination status. Results Among 148 vaccinated children, 380 household contacts, and 1124 unvaccinated community contacts, 78%, 18%, and 7%, respectively, shed OPV. Community and household contacts showed no differences in transmission (odds ratio [OR], 0.67; 95% confidence interval [CI], .37–1.20), in shedding trajectory (OR, 0.61; 95% CI, .35–1.07), or in time to shedding (hazard ratio, 0.68; 95% CI, .39–1.19). Transmission began as quickly as 1 day after vaccination and persisted as long as 71 days after vaccination. Transmission within unvaccinated households differed significantly across vaccination coverage communities, with the 70% community experiencing the most transmissions (15%), and the 10% community experiencing the least (4%). These trends persisted over time and in the time to first shedding analyses. Conclusions Transmission did not differ between household contacts of vaccinees and unvaccinated households. Understanding poliovirus transmission dynamics is important for postcertification control.
Panax notoginseng Saponins Protect Cerebral Microvascular Endothelial Cells against Oxygen-Glucose Deprivation/Reperfusion-Induced Barrier Dysfunction via Activation of PI3K/Akt/Nrf2 Antioxidant Signaling Pathway

Oxidative stress plays a critical role in cerebral ischemia/reperfusion (I/R)-induced blood-brain barrier (BBB) disruption. Panax notoginseng saponins (PNS) possess efficient antioxidant activity and have been used in the treatment of cerebral ischemic stroke in China. In this study, we determined the protective effects of PNS on BBB integrity and investigated the underlying mechanism in cerebral microvascular endothelial cells (bEnd.3) exposed to oxygen-glucose deprivation/reperfusion (OGD/R). MTT and LDH release assays revealed that PNS mitigated the OGD/R-induced cell injury in a dose-dependent manner. TEER and paracellular permeability assays demonstrated that PNS alleviated the OGD/R-caused disruption of BBB integrity. Fluorescence probe DCFH-DA showed that PNS suppressed ROS generation in OGD/R-treated cells. Immunofluorescence and western blot analysis indicated that PNS inhibited the degradation of tight junction proteins triggered by OGD/R. Moreover, mechanism investigations suggested that PNS increased the phosphorylation of Akt, the activity of nuclear Nrf2, and the expression of downstream antioxidant enzyme HO-1. All the effects of PNS could be reversed by co-treatment with PI3K inhibitor LY294002. Taken together, these observations suggest that PNS may act as an extrinsic regulator that activates Nrf2 antioxidant signaling depending on PI3K/Akt pathway and protects against OGD/R-induced BBB disruption in vitro.
Which velocity model is more suitable for the 2017 MS7.0 Jiuzhaigou earthquake?

On Aug. 8, 2017, an MS7.0 earthquake struck Jiuzhaigou, a county of Sichuan province, China. A number of investigations and studies have been conducted, some of which involved local velocity models. However, the suitability of these models has not been properly addressed. Here we collect 11 already‐existing models, including those used in studies of the 2017 MS7.0 Jiuzhaigou earthquake, choose 10 local stations surrounding the earthquake, and employ the same technique (TRIT) to relocate the hypocenter. And furthermore, we choose a more suitable model from the 11 already‐existed models by analyzing the relocation process and the relocated results for reasonability. Finally, our conclusion is that the model Fang2018 is more suitable and the hypocenter parameters, 103.801°E, 33.192°N and 15.8 km for longitude, latitude and depth, respectively, and 2017‐08‐08 13:19:46.66 for its origin time, based on this model should be recommended for the 2017 MS7.0 Jiuzhaigou earthquake.
Mouse genome-wide association studies and systems genetics uncover the genetic architecture associated with hepatic pharmacokinetic and pharmacodynamic properties of a constrained ethyl antisense oligonucleotide targeting Malat1

Antisense oligonucleotides (ASOs) have demonstrated variation of efficacy in patient populations. This has prompted our investigation into the contribution of genetic architecture to ASO pharmacokinetics (PK) and pharmacodynamics (PD). Genome wide association (GWA) and transcriptomic analysis in a hybrid mouse diversity panel (HMDP) were used to identify and validate novel genes involved in the uptake and efficacy of a single dose of a Malat1 constrained ethyl (cEt) modified ASO. The GWA of the HMDP identified two significant associations on chromosomes 4 and 10 with hepatic Malat1 ASO concentrations. Stabilin 2 (Stab2) and vesicle associated membrane protein 3 (Vamp3) were identified by cis-eQTL analysis. HMDP strains with lower Stab2 expression and Stab2 KO mice displayed significantly lower PK than strains with higher Stab2 expression and the wild type (WT) animals respectively, confirming the role of Stab2 in regulating hepatic Malat1 ASO uptake. GWA examining ASO efficacy uncovered three loci associated with Malat1 potency: Small Subunit Processome Component (Utp11l) on chromosome 4, Rho associated coiled-coil containing protein kinase 2 (Rock2) and Aci-reductone dioxygenase (Adi1) on chromosome 12. Our results demonstrate the utility of mouse GWAS using the HMDP in detecting genes capable of impacting the uptake of ASOs, and identifies genes critical for the activity of ASOs in vivo.
Clinical Determinants of Parkinson's Disease‐associated Pain Using the King's Parkinson's Disease Pain Scale

Pain is a frequent feature in Parkinson's disease (PD). Current knowledge on pain and its associated factors in PD has been obtained using nondisease‐specific tools. Recently, the King's Parkinson's Disease Pain Scale (KPPS) was published as the first disease‐specific scale. The aim of this study was to assess PD‐associated pain and its main determinants using the KPPS.
Incremental learning abstract discrete planning domains and mappings to continuous perceptions

Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain. Consequently, the focus is on learning the transitions between states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the "life" of the agent. We define an algorithm that interleaves planning, acting, and learning, and allows the agent to update the planning domain depending on how much it trusts the model w.r. t. the new experiences learned by executing actions. We define a measure of coherence between the planning domain and the real world as perceived by the agent. We test our approach showing that the agent learns increasingly coherent models, and that the system can scale to deal with models with an order of $10^6$ states.
On the Off-Chip Memory Latency of Real-Time Systems: Is DDR DRAM Really the Best Option?

Predictable execution time upon accessing shared memories in multi-core real-time systems is a stringent requirement. A plethora of existing works focus on the analysis of Double Data Rate Dynamic Random Access Memories (DDR DRAMs), or redesigning its memory to provide predictable memory behavior. In this paper, we show that DDR DRAMs by construction suffer inherent limitations associated with achieving such predictability. These limitations lead to 1) highly variable access latencies that fluctuate based on various factors such as access patterns and memory state from previous accesses, and 2) overly pessimistic latency bounds. As a result, DDR DRAMs can be ill-suited for some real-time systems that mandate a strict predictable performance with tight timing constraints. Targeting these systems, we promote an alternative off-chip memory solution that is based on the emerging Reduced Latency DRAM (RLDRAM) protocol, and propose a predictable memory controller (RLDC) managing accesses to this memory. Comparing with the state-of-the-art predictable DDR controllers, the proposed solution provides up to 11× less timing variability and 6.4× reduction in the worst case memory latency.
Similarity Measurement for Sentiment Classification on Textual Reviews

Sentiment classification on textual reviews refers to classifying textual reviews based on whether they are positive or negative. This research focuses on classifying movie reviews, and is benchmarked on the IMDB dataset, which consists of long movie reviews, using accuracy as the evaluation metric. In sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This research proposes to train document embedding using cosine similarity instead of dot product. Experiments on the IMDB dataset show that accuracy is improved when using cosine similarity compared to using dot product, while using feature combination with Naïve-Bayes weighted bag of n-grams achieves a new state of the art accuracy of 97.4%.