Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Evaluation of Toxic Polyglycine Biosynthesis and Aggregation in Cell Models Expressing Expanded CGG Repeats

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by expanded CGG (CGGexp) trinucleotides in the 5′UTR of the FMR1 gene encoding fragile X mental retardation protein (FMRP). The patients, with the number of the repeats ranging from 55 to 200, show specific manifestation of clinical symptoms that include intention tremor, gait ataxia, cognitive deficits, and brain atrophy. Accumulation of toxic polyglycine (FMRpolyG), a by-product of the CGGexp repeat-associated non-ATG (RAN) translation, is considered to be one of the main factors triggering neurodegenerative processes in FXTAS patients. Nevertheless, the nature of the FMRpolyG-induced cell damage, especially in the context of its soluble and inclusion-associated forms, is still elusive. Targeting either biosynthesis, cellular stability or aggregation capacity of toxic FMRpolyG could be considered as a potential therapeutic strategy for FXTAS. Therefore, we tested a variety of quantitative methods based on forced expression of genetic constructs carrying CGGexp repeats in the context of the FMR1 5′UTR fused to GFP, mCherry or Firefly luciferase gene in or out of frame to the polyglycine encoding sequence. We show that FMRpolyG translation either from native or an AUG-induced start codon as well as the translation yield of the FMRP open reading frame equivalent located downstream of the CGGexp element can be effectively estimated using fluorescence microscopy, flow cytometry or luciferase assay. We also quantitatively estimated soluble fraction and insoluble form of FMRpolyG aggregated in foci using an electrophoretic separation of cell lysates and fluorescence microscopy, respectively. Importantly, we show that dependent on a fusion tag, FMRpolyG has a different potential for aggregate formation. Our established protocols enable sensitive tracking of FMRP and FMRpolyG quantitative and qualitative changes after treatment with potential therapeutic agents for FXTAS. Furthermore, they can be modified for application to other RAN translation- and aggregation-related diseases.
Perceval: Software Project Data at Your Will

Software development projects, in particular open source ones, heavily rely on the use of tools to support, coordinate and promote development activities. Despite their paramount value, they contribute to fragment the project data, thus challenging practitioners and researchers willing to derive insightful analytics about software projects. In this demo we present Perceval, a loyal helper able to perform automatic and incremental data gathering from almost any tool related with contributing to open source development, among others, source code management, issue tracking systems, mailing lists, forums, and social media. Perceval is an industry strong free software tool that has been widely used in Bitergia, a company devoted to offer commercial software analytics of software projects. It hides the technical complexities related to data acquisition and eases the definition of analytics. A video showcasing the main features of Perceval can be found at https://youtu.be/eH1sYF0Hdc8.
The GlucoCoper – a Tool for the Assessment of Coping Mechanisms

Abstract Diabetes distress – commonly encountered as a part of life with diabetes mellitus – can be effectively managed by coping skills training. Systematic assessment and documentation of the existing coping skills (and lack thereof) is a pre-requisite for planning such coping skills training. A six-item tool, the GlucoCoper – which assesses two negative (resistance, blame) and four positive (acceptance, optimism, planning and action) coping mechanisms – was administered to English-literate adult participants with type 2 diabetes (T2DM) at an endocrine centre in India. Each item was graded on a 10-point Likert scale. The GlucoCoper was administered to 338 participants. The tool demonstrated high internal validity. Total score on the GlucoCoper correlated with fasting and postprandial glucose. The GlucoCoper is a valid tool which can be used to assess and rate coping in persons with T2DM. It has potential in helping plan, deliver and monitor coping skills training in persons with diabetes distress.
Craving to Quit: A Randomized Controlled Trial of Smartphone app-based Mindfulness Training for Smoking Cessation.

Introduction Mindfulness training may reduce smoking rates and lessen the association between craving and smoking. This trial tested the efficacy of mindfulness training via smartphone app to reduce smoking. Experience sampling was used to measure real time craving, smoking and mindfulness. Methods A researcher-blind, parallel randomized controlled trial compared the efficacy of mobile mindfulness training with experience sampling (MMT-ES; Craving to Quit) vs. experience sampling-only (ES) to (1) increase one-week point-prevalence abstinence rates at 6 months, and (2) lessen the association between craving and smoking. A modified intent-to-treat approach was used for treatment starters (MMT-ES n=143; ES n=182; 72% female, 81% white, age 41±12yr.). Results No group difference was found in smoking abstinence at 6 months (overall, 11.1%; MMT-ES, 9.8%; ES, 12.1%; χ2(1)=. 43, p=. 51). From baseline to 6 months, both groups showed a reduction in cigarettes per day (p<.0001), craving strength (p<.0001) and frequency (p<.0001), and an increase in mindfulness (p<.05). Using experience sampling data, a craving by group interaction (F(1,3785)=3.71, p=. 05) was observed, driven by a stronger positive association between craving and cigarettes per day for ES (t=4.96, p<.0001) versus MMT-ES (t=2.03, p=.04). Within MMT-ES, the relationship between craving and cigarettes per day decreased as treatment completion increased (F(1,104)=4.44, p=. 04). Conclusions Although mindfulness training via smartphone app did not lead to reduced smoking rates compared with control, our findings provide preliminary evidence that mindfulness training via smartphone app may help lessen the association between craving and smoking, an effect that may be meaningful to support quitting in the longer-term. Trial registration Clinicaltrials.gov NCT02134509. Registered 7 May 2014. Implications This is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based mindfulness training with experience sampling may lessen the association between craving and smoking, an effect that did not lead to reduced smoking abstinence rates compared with control but may be meaningful to support quitting and prevent relapse in the longer-term.
Using a Separable Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.
Sirt3 Ameliorates Oxidative Stress and Mitochondrial Dysfunction After Intracerebral Hemorrhage in Diabetic Rats

Aim: Sirtuin3 (sirt3) plays a pivotal role in improving oxidative stress and mitochondrial dysfunction which directly induced neuronal apoptosis after intracerebral hemorrhage (ICH). Reactive oxygen species (ROS) is also a critical activator in triggering NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasomes activation which can regulate inflammatory responses in brain. Moreover, hyperglycemia can aggravate the ICH-induced damage. Hence, this study was designed to investigate the mechanisms of neuroprotection of sirt3 in hyperglycemic ICH. Methods: ICH model was established by autologous blood injection. Hyperglycemia was induced by intraperitoneal injection with streptozotocin. Honokiol (HKL, a pharmacological agonist of sirt3) was injected intraperitoneally at doses of 2.5, 5, or 10 mg/kg. Sirt3 small interfering RNA transfection was implemented through intracerebroventricular injection. The expression of sirt3 and its downstream signaling molecules were detected using Western blotting or immunofluorescence staining. Morphological changes of mitochondria were detected by electron microscopy. SH-SY5Y cells were incubated with 10 μM oxyhemoglobin for 48 h to establish an in vitro ICH model, and then JC-1 staining was used to determine mitochondrial membrane potential (Δψm). Results: Hyperglycemia could suppress sirt3 expression after ICH when compared with non-diabetic rats. Sirt3 protein expression was decreased to the minimum at 24 h in perihematoma tissues. Electron microscope analysis indicated that hyperglycemic ICH induced extensive mitochondrial vacuolization. HKL attenuated ROS accumulation, adenosine triphosphate reduction, and Δψm through Sirt3–superoxide dismutase 2 (SOD2) and Sirt3–NRF1–TFAM pathway. Sirt3 knockdown could exacerbate the neuronal apoptosis and reverse the positive effects of HKL. Sirt3 activation could decrease NLRP3 and interleukin-1β levels through deacetylating SOD2 and scavenging ROS. Conclusion: HKL protects against hyperglycemic ICH-induced neuronal injury via a sirt3-dependent manner.
Reemergence of Reston ebolavirus in Cynomolgus Monkeys, the Philippines, 2015

In August 2015, a nonhuman primate facility south of Manila, the Philippines, noted unusual deaths of 6 cynomolgus monkeys (Macaca fascicularis), characterized by generalized rashes, inappetence, or sudden death. We identified Reston ebolavirus (RESTV) infection in monkeys by using serologic and molecular assays. We isolated viruses in tissues from infected monkeys and determined viral genome sequences. RESTV found in the 2015 outbreak is genetically closer to 1 of the 4 RESTVs that caused the 2008 outbreak among swine. Eight macaques, including 2 also infected with RESTV, tested positive for measles. Concurrently, the measles virus was circulating throughout the Philippines, indicating that the infection of the macaques may be a reverse zoonosis. Improved biosecurity measures will minimize the public health risk, as well as limit the introduction of disease and vectors.
Runtime Adjustment of IoT System-on-Chips for Minimum Energy Operation

Energy-constrained Systems-on-Chips (SoC) are becoming major components of many emerging applications, especially in the Internet of Things (IoT) domain. Although the best energy efficiency is achieved when the SoC operates in the near-threshold region, the best operating point for maximum energy efficiency could vary depending on operating temperature, workload, and the power-gating state (power modes) of various SoC components at runtime. This paper presents a lightweight machine-learning based scheme to predict and tune the SoC to the most energy efficient supply voltage at the firmware level during runtime, considering the impacts of temperature variation and power-gating of SoC components while meeting the performance and reliability requirements. Simulation results indicate that the proposed method can determine the most energy efficient supply voltage of a circuit with high-accuracy (RMSE = 7mV), while considering the runtime performance and reliability constraints.
A Multichannel High-Frequency Power-Isolated Neural Stimulator With Crosstalk Reduction

In neuroprostheses applications requiring simultaneous stimulations on a multielectrode array, electric crosstalk, the spatial interaction between electric fields from various electrodes is a major limitation to the performance of multichannel stimulation. This paper presents a multichannel stimulator design that combines high-frequency current stimulation (using biphasic charge-balanced chopped pulse profile) with a switched-capacitor power isolation method. The approach minimizes crosstalk and is particularly suitable for fully integrated realization. A stimulator fabricated in a 0.6 μm CMOS high-voltage technology is presented. It is used to implement a multichannel, high-frequency, power-isolated stimulator. Crosstalk reduction is demonstrated with electrodes in physiological media while the efficacy of the high-frequency stimulator chip is proven in vivo. The stimulator provides fully independent operation on multiple channels and full flexibility in the design of neural modulation protocols.
Elevated oxidative DNA damage in patients with coronary artery disease and its association with oxidative stress biomarkers

Abstract Objective: The objective of the present study was to evaluate oxidative DNA damage in peripheral blood leukocytes (PBLs) of patients with coronary artery disease (CAD) and to explore the relationship of oxidised purine and pyrimidine with oxidative stress. Methods: The study participants (n = 100) included 50 patients and unrelated 50 age-, sex- and population-subgroup (Jat Sikhs)-matched healthy controls. Oxidative DNA damage using the modified enzymatic comet in PBLs, and malondialdehyde (MDA) levels, total oxidant status (TOS) and total antioxidant status (TAS) in blood serum samples using spectrophotometric methods was determined. Results: The basal DNA damage of percent tail DNA (T-DNA%) was increased as were tail moment (TM) and olive tail moment (OTM). Oxidative DNA damage in terms of oxidised purines and oxidised pyrimidines was also significantly (p < .001) elevated in patients. Rather the advanced stages of CAD, unstable angina and acute myocardial infarction had significantly more basal and oxidative DNA damage (p < .05) compared to stable angina. MDA levels (p < .01) and TOS (p < .001) were increased significantly in patients with significant (p < .001) decrease in TAS. There was positive correlation of oxidised purines (T-DNA% r = 0.399, p = .004; TM r = 0.623, p = .001; OTM r = 0.456, p= .001) and of total oxidative damage (TM r = 0.515, p = .001; OTM r = 0.463, p = .001) with disease severity, and, with TOS (r = 0.279, p = .050) and negative with TAS (r = −0.341, p = .015). Multiple linear regression analysis revealed TOS and disease severity as independent predictors of oxidative DNA damage. Conclusions: There was significant increase in oxidative DNA damage and oxidative stress in CAD patients compared to levels in healthy controls.
Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images

Alzheimer’s disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG-PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. These methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNNs), which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The 2D CNNs are built to capture the features of image slices while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter-slice features for image classification. No rigid registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG-PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance.
Impact of Nurse Practitioner Practice Regulations on Rural Population Health Outcomes

Background: For decades, U.S. rural areas have experienced shortages of primary care providers. Nurse practitioners (NPs) are helping to reduce that shortage. However, NP scope of practice regulations vary from state-to-state ranging from autonomous practice to direct physician oversight. The purpose of this study was to determine if clinical outcomes of older rural adult patients vary by the level of practice autonomy that states grant to NPs. Methods: This cross-sectional study analyzed data from a sample of Rural Health Clinics (RHCs) (n = 503) located in eight Southeastern states. Independent t-tests were performed for each of five variables to compare patient outcomes of the experimental RHCs (those in “reduced practice” states) to those of the control RHCs (in “restricted practice” states). Results: After matching, no statistically significant difference was found in patient outcomes for RHCs in reduced practice states compared to those in restricted practice states. Yet, expanded scope of practice may improve provider supply, healthcare access and utilization, and quality of care (Martsolf et al., 2016). Conclusions: Although this study found no significant relationship between Advanced Registered Nurse Practitioner (ARNP) scope of practice and select patient outcome variables, there are strong indications that the quality of patient outcomes is not reduced when the scope of practice is expanded.
Challenges with automotive test case specifications

In the automotive domain standards like ISO 26262 require a structured test process. Test cases are usually derived from requirements and documented in test case specifications. They provide a necessary basis for test implementation and execution. Therefore, test case specifications are a fundamental part of the automotive test process. The aim of this work is to gain insights into the creation and processing of test case specifications from a practitioner's point of view. In order to identify challenges concerning automotive test case specifications, we conducted an explorative case study based on 17 semi-structured interviews at a German OEM and three automotive suppliers. The interviews were transcribed and analyzed qualitatively to identify the challenges. We summarized the challenge to a taxonomy consisting of nine main categories: (1) availability and (2) content-related problems with input artifacts, (3) knowledge-related problems, (4) test case related problems, (5) the test case specification content-related problems, (6) process-related problems, (7) communication-related problems, (8) quality-related problems, and (9) tool-related problems. In general, we noticed that the interviewees were aware of challenges in terms of test case specifications in the automotive domain. Nevertheless, some of the current solutions are not efficient and require a lot of manual work.
Ensemble learning: A survey

Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.
Adherence to diabetic self-care practices and its associated factors among patients with type 2 diabetes in Addis Ababa, Ethiopia

Background Self-care practices in diabetes patients are crucial to keep the illness under control and prevent complications. Effective management of diabetes will be a difficult task without adequate understanding of the existing level of practice related to diabetes self-care. This study is, therefore, aimed at assessing the self-care practice and its associated factors among patients with type 2 diabetes in Tikur Anbessa Specialized Hospital, Ethiopia. Materials and methods A health facility-based cross-sectional study was conducted among 419 type 2 diabetes patients from March 29, 2013, to May 16, 2013. The data were collected by face-to-face interview using structured and pretested questionnaire. Binary logistic regression was performed to assess the association between determinant factors and adherence to self-monitoring of blood glucose. Adjusted odds ratios (AOR) with its 95% confidence interval (CI) were estimated to identify factors associated with the outcome variables in the multivariable analysis. Result In this study, 318 (75.9%) diabetes patients did not adhere to the recommended diet management, 350 (83.5%) did not adhere to self-monitoring of blood glucose level, while 18 (4.3%) of the respondents did not adhere to the prescribed medications. Diabetic patients who were unemployed were 2.4 times more likely to practice blood glucose monitoring than merchants (AOR [95% CI] =2.4 [1.3–5.9]). Those who attended primary education were 70% less likely to adhere to blood glucose self-monitoring than those educated to a tertiary educational level (AOR [95% CI] =0.3 [0.1–0.9]). Respondents within the age group of 40–49 years were 11 times more likely to adhere to their medication than those aged 60–76 years (AOR [95% CI] =11 [1.03–13.6]). Conclusion The study showed that the extent to which individuals adhere to the recommended management of type 2 diabetes is substantially low. Improving awareness of patients and the community at large is imperative especially on medication adherence, glycemic control and diet management.
MU-MIMO Downlink Capacity Analysis and Optimum Code Weight Vector Design for 5G Big Data Massive Antenna Millimeter Wave Communication

Multiuser multiple input multiple output (MU-MIMO) wireless communication system provides substantial downlink throughput in millimeter wave (mmWave) communication by allowing multiple users to communicate at the same frequency and time slots. However, the design of the optimum beam-vector for each user to minimise interference from other users is challenging. In this paper, based on the concept of signal-to-leakage plus noise ratio (SLNR), we analyze the ergodic sum-rate capacity using statistical Eigen-mode (SE) and zero-forcing (ZF) models with Ricean fading channel. In the analysis, the orthogonality of channel vectors between users is assumed to guarantee interference cancelation from other cochannel users. The impact of the number of antenna elements on the achievable sum-rate capacity obtained by dirty paper coding (DPC) method considered as a nonlinear scheme for approximating average system capacity is studied. A power iterative precoding scheme that iteratively finds the most dominant eigenvector (optimum weight vector) for minimising cochannel interference (CCI), that is, maximising the SLNR for all users simultaneously, is designed resulting in enhancement of average system capacity. The average system capacities achieved by the proposed power iterative technique in this study compared with the singular value decomposition (SVD) method are in the ranges of 5–11 bps/Hz and 1–6 bps/Hz, respectively. Therefore, the proposed power iterative method achieves higher performance than the SVD regarding achievable sum-rate capacity.