Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term effect of a smart nebulizing device on adherence to inhaled corticosteroid therapy in Asthma Predictive Index-positive wheezing children

Objective To explore the effect of a smart nebulizing device on the rate of adherence to inhaled corticosteroid (ICS) in children with positive Asthma Predictive Index. Methods In total, 65 children with positive Asthma Predictive Index and under the age of 5 years who visited our hospital from October 2015 through October 2016, were randomly assigned to receive conventional nebulization or smart nebulization. The smart nebulizer was connected to smart phones via an App. The following information was collected: rate of adherence to ICS, frequency of emergency visits or hospitalizations, application of antibiotics or oral steroids, and wheezing progression or improvement. Results The rate of adherence to ICS was 86.67% (26/30), 76.67% (23/30), and 67.33% (20/30) in the smart nebulization group, and 62.86% (22/35), 51.42% (18/35), and 40.00% (14/35) in the conventional nebulization group after 4-, 8-, and 12-week therapy, respectively. There were significant differences between the 2 groups at all of the time points (P<0.05). Both day- and night-time wheezing scores were significantly lower in the smart nebulization group than those of the conventional nebulization group after 4-, 8-, and 12-week therapy (P<0.05). The frequency of emergency visits, comorbidity of respiratory infection, antibiotics or systemic steroid usage, and therapeutic cost for additional treatment during the 12-week study period, was significantly lower in the smart nebulization group than that in the conventional nebulization group (P<0.05). Conclusion A smart electronic nebulization device could significantly improve the rate of adherence to ICS in children under the age of 5 years, and thus could significantly reduce the frequency of emergency visits and respiratory infections as well as the usage of antibiotics or systemic steroids.
Demographic and Clinical Predictors of Trait Impulsivity in Parkinson's Disease Patients

Background Impulsive behaviour has become increasingly recognised as a neuropsychiatric complication of Parkinson's disease (PD). Thought to be a product of compromised cognitive control, the spectrum of impulsive behaviours in PD ranges from cognitive disinhibition to impulse control disorders (ICDs). Objective At present, there are no indicators for trait impulsivity in PD. The objective of the current study was to identify demographic and clinical predictors of susceptibility to trait impulsivity in a cohort of PD patients. Methods The current study assessed impulsivity using the Barratt Impulsiveness Scale 11 (BIS-11) in a cohort of 87 PD patients. General linear models (GLMs) were used to identify clinical and demographic variables predictive of heightened BIS-11 second-order attentional and nonplanning subscale scores. Results Male gender, no history of smoking, postsecondary education, and heightened disease severity were predictive of increased BIS-11 attentional scores (p < 0.05). Similarly, male gender, after secondary education, and disease severity were predictive of increased BIS-11 nonplanning scores (p < 0.05). Contrary to previous reports, dopaminergic medication use was not a significant determinant of either BIS-11 subscale scores. Conclusions Several demographic and clinical variables including male gender, no history of past smoking, after secondary education, and elevated disease severity are associated with impulsivity in PD.
Distributed Cartesian Power Graph Segmentation for Graphon Estimation

We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models. The power graph fused lasso (PGFL) segments a matrix by exploiting a known graphical structure, $G$, over the rows and columns. Our main results shows that for any connected graph, under subGaussian noise, the PGFL achieves the same mean-square error rate as 2D total variation denoising for signals of bounded variation. We study the use of the PGFL for denoising an observed network $H$, where we learn the graph $G$ as the $K$-nearest neighborhood graph of an estimated metric over the vertices. We provide theoretical and empirical results for estimating graphons, a non-parametric exchangeable network model, and compare to the state of the art graphon estimation methods.
Choosing the appropriate treatment setting: which information and decision-making needs do adult inpatients with mental disorders have? A qualitative interview study

Background Decisions on medical treatment setting are perceived as important but often difficult to make for patients with mental disorders. Shared decision-making as a strategy to decrease decisional conflict has been recommended, but is not yet widely implemented. This study aimed to investigate the information needs and the decision-making preferences of patients with mental disorders prior to the decision for a certain treatment setting. The results will serve as a prerequisite for the development of a high-quality patient decision aid (PtDA) regarding the treatment setting decision. Methods We conducted retrospective individual semi-structured interviews with n=24 patients with mental disorders in three psychotherapeutic inpatient care units. The interviews were audiotaped, transcribed, coded, and content-analyzed. Results The majority of the patients wanted to be involved in the decision-making process. They reported high information needs regarding treatment options in order to feel empowered to participate adequately in the decision for a certain treatment setting. However, some patients did not want to participate or receive information, for example, because of their high burden of mental disorder. Whereas the majority were satisfied with the extent they were involved in the decision, few participants felt sufficiently informed about treatment options. Most patients reported that a decision aid regarding an appropriate treatment setting would have been helpful for them. Important information that should be included in a PtDA was general information about mental illness, effective treatment options, specific information about the different treatment settings, and access to treatment. Discussion The identified information and decision-making needs provide a valuable basis for the development of a PtDA aiming to support patients and caregivers regarding the decision for an adequate treatment setting. As preferences for participation vary among patients and also depend on the current mental state, a flexible approach is needed to meet patients’ individual wishes and needs.
Intrusion detection method based on the sensory traps system

In this paper, intrusion detection system based on touch traps is presented. Aim of the work — investigate and design a software product for monitoring intruders in various systems for further analysis and prevention of repeated attacks. The work is designed for intrusion detection system that includes procedures for analyzing network honeypots and procedure for systematization received data. The results may be used for: Improving intrusion detection system; Providing users of intrusion detection system tool for operational monitoring of network honeypots; Providing additional tools for systematization of input data. The efficiency of the above-mentioned procedures verified by computer experiment.
Clinical Effects of Regular Dry Sauna Bathing: A Systematic Review

Introduction Many health benefits are claimed by individuals and facilities promoting sauna bathing; however the medical evidence to support these claims is not well established. This paper aims to systematically review recent research on the effects of repeated dry sauna interventions on human health. Methods A systematic search was made of medical databases for studies reporting on the health effects of regular dry sauna bathing on humans from 2000 onwards. Risk of bias was assessed according to the Cochrane Collaboration guidelines. Results Forty clinical studies involving a total of 3855 participants met the inclusion criteria. Only 13 studies were randomized controlled trials and most studies were small (n < 40). Reported outcome measures were heterogeneous with most studies reporting beneficial health effects. Only one small study (n = 10) reported an adverse health outcome of disrupted male spermatogenesis, demonstrated to be reversible when ceasing sauna activity. Conclusions Regular dry sauna bathing has potential health benefits. More data of higher quality is needed on the frequency and extent of adverse side effects. Further study is also needed to determine the optimal frequency and duration of distinct types of sauna bathing for targeted health effects and the specific clinical populations who are most likely to benefit.
Effects of the Competitive Season on the Isokinetic Muscle Parameters Changes in World-Class Handball Players.

Maurelli, O, Bernard, PL, Dubois, R, Ahmaidi, S, and Prioux, J. Effects of the competitive season on the isokinetic muscle parameters changes in world-class handball players. J Strength Cond Res XX(X): 000-000, 2018-The aim of this study is to investigate the effects of the competitive season on isokinetic muscular parameters of the lower limbs in world-class handball players. Nineteen, male, world-class, handball players (age, 26.6 ± 5.4 years) participated in the study. Two bilateral isokinetic tests of knee joint flexors (H; hamstring) and extensors (Q; quadriceps) were performed in the beginning and end of the competitive season to determine the peak torque (PT), the mean power, and agonist-antagonist ratio, dominant-nondominant ratio (DNDR), and combined ratio. The results showed a significant decrease in PT values at low angular velocity (60°·s) in concentric mode for Q on dominant leg (p < 0.001). The other PT values for dominant and nondominant legs at low and high angular velocities (240°·s) and in eccentric mode (30°·s) were not significantly different for Q and H. For mean power, values did not change at 60°·s. At 240°·s, we found a significant decrease in H for dominant leg (p < 0.001) but not for nondominant leg. In eccentric mode, the results showed a significant increase on both legs (p < 0.001). For the ratios, values significantly decreased for DNDR at 60°·s for Q (p < 0.03) and for agonist/antagonist ratio at 240°·s for the dominant leg (p < 0.01). The present results highlight the importance of integrating regular strength training sessions during the competitive season in world-class handball players. Accordingly, this study should help trainers to modify their planning to maximize strength and power qualities of the lower limbs of their players in addition to avoiding injuries.
Fisetin Enhances the Cytotoxicity of Gemcitabine by Down-regulating ERK-MYC in MiaPaca-2 Human Pancreatic Cancer Cells

Background: Pancreatic cancer is a highly lethal malignancy with a poor prognosis. This study was set up to investigate the combined effect of gemcitabine and fisetin, a natural flavonoid from plants, on human pancreatic cancer cells. Meterials and Methods: Cytotoxic effect of fisetin in combination with gemcitabine on MiaPaca-2 cells was evaluated by the MTT assay, caspase 3/7 assay and propidium iodide/Annexin V. Extracellular signal-regulated kinase (ERK)-v-myc avian myelocytomatosis viral oncogene homolog (MYC) pathway was investigated by western blotting and quantitative real-time polymerase chain reaction. Results: Combination treatment with fisetin and gemcitabine inhibited the proliferation of pancreatic cancer cells within 72 h and induced apoptosis, as indicated by activation of caspase 3/7. Fisetin down-regulated ERK at the protein and mRNA levels, and reduced ERK-induced MYC instability at the protein level. Conclusion: Fisetin sensitized human pancreatic cancer cells to gemcitabine-induced cytotoxicity through inhibition of ERK-MYC signaling. These results suggest that the combination of fisetin and gemcitabine could be developed as a novel potent therapeutic.
Corticospinal and intracortical excitability differ between athletes early after ACLR and matched controls

Neuromuscular impairments, such as quadriceps weakness and activation deficits, persist after anterior cruciate ligament reconstruction (ACLR). Recent research demonstrating changes in the function of the primary motor cortex after ACLR posits that quadriceps impairments may be influenced by reduced corticospinal excitability. The purpose of this study was to investigate whether the integrity of the neuromotor axis of the vastus medialis is altered in subjects 2 weeks post‐ACLR compared to uninjured control subjects. Eighteen athletes 2 weeks post‐ACLR and 18 age and sex matched uninjured control subjects participated in this cross‐sectional study. We quantified corticospinal (resting motor threshold, RMT; motor evoked potential amplitudes at 120% RMT, MEP120) and intracortical (inhibition and facilitation) excitability using single and paired pulse transcranial magnetic stimulation (TMS), respectively. We assessed spinal‐reflex excitability (H‐reflex amplitude normalized to maximal M‐wave, H/M ratio) using peripheral stimulation. Subjects post‐ACLR had higher RMTs (p = 0.001), greater MEP120 amplitudes (p = 0.001), and more asymmetric facilitation (p = 0.041) than the uninjured control subjects. No significant group differences were found for intracortical inhibition (p = 0.289) and H/M ratio (p = 0.332). Our findings indicate that both intracortical and corticospinal excitability of vastus medialis are bilaterally altered in subjects 2 weeks after ACLR. Given persistent neuromuscular deficits seen after ACLR, rehabilitation strategies targeting intracortical and corticospinal deficits may potentially improve clinical outcomes. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2941–2948, 2018.
Enhanced-alignment Measure for Binary Foreground Map Evaluation

The existing binary foreground map (FM) measures address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-level information independently, while cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes. In this paper, we take a detailed look at current binary FM evaluation measures and propose a novel and effective E-measure (Enhanced-alignment measure). Our measure combines local pixel values with the image-level mean value in one term, jointly capturing image-level statistics and local pixel matching information. We demonstrate the superiority of our measure over the available measures on 4 popular datasets via 5 meta-measures, including ranking models for applications, demoting generic, random Gaussian noise maps, ground-truth switch, as well as human judgments. We find large improvements in almost all the meta-measures. For instance, in terms of application ranking, we observe improvement ranging from 9.08% to 19.65% compared with other popular measures.
Analysis of Cigarette Tax Structure as a Requirement for an Effective Tax Policy: Evaluation and Simulation for Argentina

This study describes the cigarette demand and the tobacco tax structure in Argentina in order to identify which type of consumption tax can be increased by the government to reduce tobacco use in the short run. Based on the elasticity estimates and the cigarette tax structure, we analyze the possibility of implementing a tax increase government policy to reduce cigarette consumption. An analysis of each tobacco consumption tax is provided, with the twofold purpose of describe the variety of taxes affecting the consumption of cigarettes and to determine the impact of tobacco tax hikes. The cigarette tax structure in Argentina is very complex. Three excise duties plus VAT levied cigarettes consumption. Tobacco taxes have a dissimilar, vague origin, their bases differ significantly and are not applied similarly for cigarette and the other tobacco products. Additional Emergency Tax (a permanent emergency tax) and Special Tobacco Fund (that works as a subsidy to the tobacco production) are applied only to cigarettes; meanwhile internal tax rate is 60% for cigarettes but 16% or 20% to other tobacco products. Ad valorem taxes account for most of the tobacco tax structure in Argentina. The exception is a little component of the FET tax, which is a specific tax. The long-run cigarette consumption elasticity with respect to retail prices is -0.299, while the long-run income elasticity is 0.411. With these figures, a 10% increase in real prices will reduce long-run total cigarette consumption by 2.99%, and a 10% increase in real income will raise long-run consumption by 4.11%. Simulation exercises shows that increasing the price of cigarettes will increase government revenues, as well as a large decrease in cigarette consumption.
SNP Analysis of Caries and Initial Caries in Finnish Adolescents

Background Dental caries is the most common infection in the world and is influenced by genetic and environmental factors. Environmental factors are largely known, but the role of genetic factors is quite unknown. The aim was to investigate the genetic background of caries in Finnish adolescents. Materials and Methods This study was carried out at the Kotka Health Center in Eastern Finland. 94 participants aged 15–17 years gave approval for the saliva and DNA analyses. However, one was excluded in DNA analysis; thus, the overall number of participants in analysis was 93. Caries status was recorded clinically and from bite-wing X-rays to all 94 participants. Genomic DNA was extracted by genomic QIAamp® DNA Blood Mini Kit and genotyped for polymorphisms. The results were analyzed using additive and logistic regression models. Results No significant associations between caries and the genes studied were found. However, SNPs in DDX39B and MPO showed association tendencies but were not statistically significant after false discovery rate (FDR) analysis. SNPs in VDR, LTA, and MMP3 were not statistically significant with initial caries lesions after FDR analysis. Conclusion The present study could not demonstrate statistically significant associations between caries and the genes studied. Further studies with larger populations are needed.
A Case-Based Reasoning Approach to Learning State-Based Behavior

Learning from Observation involves creating agents that observe experts performing tasks and imitate them. Case-Based Reasoning (CBR) is a tool that can be used for this purpose. Regular CBR can only learn memoryless behavior: behavior that doesn't rely on the past. Temporal Backtracking (TB) is an approach to learning state-based behavior that uses re-cency as its inductive bias, which may or may not be relevant to the agent behavior. We show how TB can be viewed as a particular case of a more generalized case-based approach to learning state-based behavior that can accommodate other inductive biases. We then propose five alternative similarity metrics to learn three different state-based behaviors in a 2D vacuum cleaner domain, and compare their performance to the TB algorithm's performance. We show that none of the proposed metrics (nor TB) is a one-size-fits all algorithm for learning state-based behavior.
Heart Electrical Activity Recording and Transmission using Bluetooth

Basic information about electrocardiography (ECG) is described in the paper, as well as a simple ECG monitoring system. This proposed system takes bio-potential signals using electrodes placed on the skin on arms and legs of the patient. The ECG system is comprised of bio-potential amplifiers, active filters, AD converter and block for digital signal processing. After being processed, digital signals are sent via Bluetooth communication to other devices for representation on and further analysis. I. ELECTROCARDIOGRAPHY (ECG) An electrocardiogram (ECG) is a recording of the heart electrical activity, which is monitored over a period of time. This is a common diagnostic method for heart activity examination, which quickly gives the accurate information about a patient’s heart condition. The Fig. 1. presents a typical ECG recording. Fig. 1. Typical ECG waveform. [1] An ECG is able to show every part of heart electrical activity. The heart muscles are controlled by electrical pulses, which are generated by SA node within heart. These pulses move across a heart, forcing heart to beat. The direction and amplitude of pulses are monitored using electrocardiogram [1]. ECG signals are acquired through a number of conductive electrodes, which are placed on the skin surface on both arms and legs. The standard ECG leads D1, D2, D3, aVR, aVL and aVF are obtained using limb electrodes. For precordial ECG leads (V1V6) the electrodes are placed in specific positions on the patients’ chest [1]. Namely, ten electrodes are used for a 12-lead standard ECG recording. In our less complex system only three 3 electrodes are used, producing a single ECG leadthe lead D1. The lead D1 amplifies the potential difference between left and right arm electrodes. The positions where electrodes are placed are shown in Figure 2 [1]. Fig. 2. Positioning of the electrodes. The electrocardiogram contains several waves (shown in Figure 3) which are used to measure the rate and regularity of heartbeats, the size and position of the heart’s chambers, the presence of any damage of the heart. The signals reveal various heart conditions such as heart attacks, heart rhythm problems, and heart failure. The ECG waveform consists of following components: P wave, T wave, QRS complex and ST segment (shown in Figure 3). The P wave occurs during atrium depolarization. QRS complex occurs during ventricular depolarization and T wave during ventricular re-polarization [2]. Fig. 3. Waves in typical ECG signal. [3] Marjan emanja Electronics at Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia, E-mail: trickovic.m@gmail.com Borisav s, Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia, E-mail: borisav.jovanovic@elfak.ni. ac.rs
DeepBugs: a learning approach to name-based bug detection

Natural language elements in source code, e.g., the names of variables and functions, convey useful information. However, most existing bug detection tools ignore this information and therefore miss some classes of bugs. The few existing name-based bug detection approaches reason about names on a syntactic level and rely on manually designed and tuned algorithms to detect bugs. This paper presents DeepBugs, a learning approach to name-based bug detection, which reasons about names based on a semantic representation and which automatically learns bug detectors instead of manually writing them. We formulate bug detection as a binary classification problem and train a classifier that distinguishes correct from incorrect code. To address the challenge that effectively learning a bug detector requires examples of both correct and incorrect code, we create likely incorrect code examples from an existing corpus of code through simple code transformations. A novel insight learned from our work is that learning from artificially seeded bugs yields bug detectors that are effective at finding bugs in real-world code. We implement our idea into a framework for learning-based and name-based bug detection. Three bug detectors built on top of the framework detect accidentally swapped function arguments, incorrect binary operators, and incorrect operands in binary operations. Applying the approach to a corpus of 150,000 JavaScript files yields bug detectors that have a high accuracy (between 89% and 95%), are very efficient (less than 20 milliseconds per analyzed file), and reveal 102 programming mistakes (with 68% true positive rate) in real-world code.