Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and Safety of N-Acetylcysteine for Chronic Obstructive Pulmonary Disease and Chronic Bronchitis

Background Patients with chronic obstructive pulmonary disease (COPD) and chronic bronchitis are associated with poor clinical outcomes. N-acetylcysteine (NAC) is a widely used therapeutic option for such patients; however, the clinical efficacy of NAC has not been conclusively determined. We hypothesized that high-dose oral NAC can improve the clinical outcomes for patients with concurrent chronic bronchitis and COPD. Objective and Methods. This was a randomized, double-blind, placebo-controlled trial evaluating the efficacy of high-dose NAC for COPD patients with concurrent chronic bronchitis. Study participants were randomized into two groups and administered with NAC (900 mg) twice daily or matching placebo for 3 months. Then, respiratory health status was evaluated using the St. George's Respiratory Questionnaire (SGQR), which was set as the primary end point. Results A total of 143 COPD patients with chronic bronchitis were screened, and as a result, only 100 patients were enrolled in this study (50 participants were randomized to receive placebo, and others were randomized to receive NAC). After treatment, differences in SGQR scores between the placebo and NAC groups were not significant. Moreover, differences in secondary end points between the two groups after treatment were insignificant. Discussion. High-dose NAC has no marked clinical benefits for COPD patients with concurrent chronic bronchitis.
Climate Change; Our Impact on the Climate and its Consequences; A Cross-sectional Study

Climate change, sometimes called global warming, is the observed century-scale rise in the average temperatures of the earth’s climate systems and its related effects. There are both natural and anthropogenic contributors to climate change. For example, volcanic eruptions are natural contributors and the burning of fossil fuels with the consequent release of greenhouse gases, such as water vapor, carbon dioxide, and methane. This is the largest human influence on global warming. This study attempted to assess the knowledge, contributory community practices, and perceived effects of climate change in a characteristic suburban community in Africa, where the majority of people are expected to have poor knowledge about the phenomenon. It was a cross-sectional study. The study population comprised townies in the Okada community, excluding students of Igbinedion University. Pre-tested structured questionnaires were the tools for data collection. Data analysis was done with IBM SPSS Version 21 and the level of statistical significance was set at p<0.05. A total of 274 respondents participated. 82.1% of respondents had knowledge of climate change with TV/radio (51.1%) being the most widely reported source of information. More than half of the respondents recognized human activities and natural variability as responsible factors. Bush burning (33.3%), burning of fossil fuels (15.3%) [especially cooking gas] and deforestation (12.4%) were recognized anthropogenic contributors to climate change in Okada. Burning (70.1%) was the predominant method of waste disposal among respondents. Of the perceived effects, poor harvest and crop yield (77%) were the most feared effects of climate change to respondents.
International Journal of Renewable Energy Development

The use of hybrid photovoltaic/thermal (PV/T) and low concentrating photovoltaic/thermal (LCPV/T) systems can significantly enhance the overall solar energy conversion efficiency by delivering electricity and thermal energy. This paper presents a case study using a standing PV system's theoretical and modeling approach that can be modified to adapt to the hybrid technology. Firstly, a single-pass conventional PV/T air-cooled collector is investigated based on heat transfer and electrical models under the climatic conditions of Zarqa, Jordan. The performance parameters are evaluated using thermal and electrical properties of the considered PV installation and measured meteorological data. Results show that the total energy produced varies between a maximum of 134.6 kWh/m2 in July and a minimum of 81.7 kWh/m2 in January. The annual average hourly variation of overall energy efficiency ranges between 79.2% and 88.4%. Moreover, the dissipated thermal energy can meet 63.6% of the total energy required to ventilate the Hashemite University Presidency Building during the winter months. Finally, the performance of the modeled PV/T system air system coupled with flat boosters to provide a low irradiation concentration ratio (CR) is explored. The maximum electric output of the resulting LCPV/T system is compared with the uncooled system. It is found that the percentage improvement due to air cooling ranges between 0.72% at CR=1 and 2.77% at CR=2.5.
Neutrinos and Abelian Gauge Symmetries

We study the intimate connection between neutrinos and simple abelian gauge symmetries U(1)', starting from the observation that the full global symmetry group of the Standard Model, G = U(1)(B-L) x U(1)(L_e-L_mu) x U(1)(L_mu-L_tau), can be promoted to a local symmetry group by introducing three right-handed neutrinos---automatically making neutrinos massive. The unflavored part U(1)(B-L) is linked to the Dirac vs. Majorana nature of neutrinos; we discuss the B-L landscape---including lepton-number-violating Dirac neutrinos---and implications for neutrinos, the baryon asymmetry, and experiments. Flavored subgroups U(1)' < G can shed light on the peculiar leptonic mixing pattern and mass ordering; we show how normal, inverted, and quasi-degenerate mass hierarchy can arise from a U(1)' in a simple and testable manner. We furthermore present all U(1)' < G that can enforce viable texture zeros in the neutrino mass matrices. Beyond G, symmetries U(1)(DM) in the dark matter sector can give rise to naturally light sterile neutrinos, which provide a new portal between visible and dark sector, and also resolve some longstanding anomalies in neutrino experiments. Further topics under consideration are the mixing of vector bosons with the Z boson, as well as the Stuckelberg mechanism. The latter raises the question why the photon should be massless---or stable for that matter!
The Role of the APC/C and Its Coactivators Cdh1 and Cdc20 in Cancer Development and Therapy

To sustain genomic stability by correct DNA replication and mitosis, cell cycle progression is tightly controlled by the cyclic activity of cyclin-dependent kinases, their binding to cyclins in the respective phase and the regulation of cyclin levels by ubiquitin-dependent proteolysis. The spindle assembly checkpoint plays an important role at the metaphase-anaphase transition to ensure a correct separation of sister chromatids before cytokinesis and to initiate mitotic exit, as an incorrect chromosome distribution may lead to genetically unstable cells and tumorigenesis. The ubiquitin ligase anaphase-promoting complex or cyclosome (APC/C) is essential for these processes by mediating the proteasomal destruction of cyclins and other important cell cycle regulators. To this end, it interacts with the two regulatory subunits Cdh1 and Cdc20. Both play a role in tumorigenesis with Cdh1 being a tumor suppressor and Cdc20 an oncogene. In this review, we summarize the current knowledge about the APC/C-regulators Cdh1 and Cdc20 in tumorigenesis and potential targeted therapeutic approaches.
Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke

Quantifying the extent and evolution of cerebral edema developing after stroke is an important but challenging goal. Lesional net water uptake (NWU) is a promising CT-based biomarker of edema, but its measurement requires manually delineating infarcted tissue and mirrored regions in the contralateral hemisphere. We implement an imaging pipeline capable of automatically segmenting the infarct region and calculating NWU from both baseline and follow-up CTs of large-vessel occlusion (LVO) patients. Infarct core is extracted from CT perfusion images using a deconvolution algorithm while infarcts on follow-up CTs were segmented from non-contrast CT (NCCT) using a deep-learning algorithm. These infarct masks were flipped along the brain midline to generate mirrored regions in the contralateral hemisphere of NCCT; NWU was calculated as one minus the ratio of densities between regions, removing voxels segmented as CSF and with HU outside thresholds of 20–80 (normal hemisphere and baseline CT) and 0–40 (infarct region on follow-up). Automated results were compared with those obtained using manually-drawn infarcts and an ASPECTS region-of-interest based method that samples densities within the infarct and normal hemisphere, using intraclass correlation coefficient (ρ). This was tested on serial CTs from 55 patients with anterior circulation LVO (including 66 follow-up CTs). Baseline NWU using automated core was 4.3% (IQR 2.6–7.3) and correlated with manual measurement (ρ = 0.80, p < 0.0001) and ASPECTS (r = −0.60, p = 0.0001). Automatically segmented infarct volumes (median 110-ml) correlated to manually-drawn volumes (ρ = 0.96, p < 0.0001) with median Dice similarity coefficient of 0.83 (IQR 0.72–0.90). Automated NWU was 24.6% (IQR 20–27) and highly correlated to NWU from manually-drawn infarcts (ρ = 0.98) and the sampling-based method (ρ = 0.68, both p < 0.0001). We conclude that this automated imaging pipeline is able to accurately quantify region of infarction and NWU from serial CTs and could be leveraged to study the evolution and impact of edema in large cohorts of stroke patients.
Capturing Phonotactic Learning Biases with a Simple RNN

Recent artiﬁcial language learning experiments have used the six pattern types de-ﬁned by Shepard et al. (1961) to explore what learning biases govern the acquisition of linguistic sound patterns (Moreton and Pertsova, 2016; Moreton et al., 2017). Moreton et al. (2017) went on to show that a Maximum Entropy Grammar (Goldwater and Johnson, 2003; Hayes and Wilson, 2008) could correctly predict these biases, due to the structure of its phonological constraints. In this paper we show that a simple recurrent neural network can also predict the biases observed by Moreton et al. (2017). Not only does this demonstrate that the prespeciﬁed constraint set used by the Maximum Entropy model is not crucial for such a prediction, but because our learner is only exposed to positive evidence, unlike the model used by Moreton et al. (2017), its acquisition task is more analogous to the one performed by humans in the experiment.
Raspberry Shake-Based Rapid Structural Identification of Existing Buildings Subject to Earthquake Ground Motion: The Case Study of Bucharest

The Internet of things concept empowered by low-cost sensor technologies and headless computers has upscaled the applicability of vibration monitoring systems in recent years. Raspberry Shake devices are among those systems, constituting a crowdsourcing framework and forming a worldwide seismic network of over a thousand nodes. While Raspberry Shake devices have been proven to densify seismograph arrays efficiently, their potential for structural health monitoring (SHM) is still unknown and is open to discovery. This paper presents recent findings from existing buildings located in Bucharest (Romania) equipped with Raspberry Shake 4D (RS4D) devices, whose signal recorded under multiple seismic events has been analyzed using different modal identification algorithms. The obtained results show that RS4D modules can capture the building vibration behavior despite the short-duration and low-amplitude excitation sources. Based on 15 RS4D device readings from five different multistorey buildings, the results do not indicate damage in terms of modal frequency decay. The findings of this research propose a baseline for future seismic events that can track the changes in vibration characteristics as a consequence of future strong earthquakes. In summary, this research presents multi-device, multi-testbed, and multi-algorithm evidence on the feasibility of RS4D modules as SHM instruments, which are yet to be explored in earthquake engineering.
Towards a Quantitative Model of Eggshell Growth: A Two-Dimensional Study

A two-stage, minimal model of eggshell growth is proposed. In a first stage growth is on isolated columns by aggregation on elliptical nucleation sites, the mammillae. It is controlled by a single parameter, the growth velocity. In a second stage, columns touch and the surface growth as a whole with a second parameter, surface relaxation. A differential equation that incorporates tehse features is numerically solved for various values of said parameters, in one space and one time dimension. Level curves are presented for various values of the parameters, indicating the dependence of column and pore geometry on them.
Permanent Magnet Assisted Synchronous Reluctance Machine validation for NDEC standard assesment

Electric and hybrid-electric vehicles have a tendency to switch from the classic 12, 24 or 48V batteries to ones delivering high voltage ranging from 400V to 800V. At the same time, the reduction or elimination of critical materials such as Neodymium used in the manufacturing of Permanent Magnets (PM) is a very important factor, considering the low availability within the EU. In this context, a 560 Vdc fed Permanent Magnet Assisted Synchronous Reluctance Machine (PMASynRM) with interior Ferrite PM for light electric vehicles propulsion will be controlled using the Field Oriented Control (FOC) strategy and subjected to the New European Driving Cycle (NEDC) in order to evaluate its performances in a dynamic simulation scenario required for any automobile propulsion system in the EU.
(Low) Energy Availability and Its Association with Injury Occurrence in Competitive Dance: Cross-Sectional Analysis in Female Dancers

Background and objectives: The risk of low energy availability is related to various health problems in sports. This cross-sectional study aimed to identify a possible association between various dance factors, anthropometrics/body build, and energy availability with injury occurrence in contemporary dancers. Materials and Methods: The participants were 50 female competitive dancers (19.8 ± 4.1 years of age). The independent variables included age, dance factors (amount of training and competitions per week–exposure time, experience in dance), anthropometrics/body composition (body height, mass, BMI, body fat percentage (BF%), and fat-free mass (FFM)), and energy availability score (EAS; evaluated by accelerometer-based measurement of energy expenditure and Dance Energy Availability Questionnaires). The dependent variables were the occurrence of (i) soft-tissue injuries and (ii) bone injuries. The measurements were obtained by experienced technicians during the pre-competition period for each specific dance discipline. Univariate and multivariate logistic regressions were calculated to identify the associations between independent variables and injury prevalence. Results: The results showed that EAS (OR = 0.81, 95% CI:0.65–0.91), age (OR = 1.65, 95% CI: 1.1–2.46), higher BF% (OR = 1.23, 95% CI: 1.04–1.46) and BMI (OR = 1.61, 95% CI: 1.05–2.47) were correlated with soft-tissue injuries. Dancers who suffered from bone injuries reported higher exposure time (OR = 1.21, 95% CI: 1.05–1.37) and had lower values of FFM (OR = 0.73, 95% CI: 0.56–0.98). Multivariate regression analyses evidenced a higher likelihood of soft-tissue injuries in older dancers (OR = 1.75, 95% CI: 1.21–2.95) and the ones who had lower EAS (OR = 0.84, 95% CI: 0.71–0.95) while the exposure time was associated with a higher likelihood of bone injuries (OR = 1.21, 95% CI: 1.05–1.39). Conclusions: In order to decrease the injury prevalence among dancers, special attention should be paid to maintaining adequate nutrition that will provide optimal available energy for the demands of training and performing. Additionally, the control of training volume should be considered in order to reduce traumatic bone injuries.
Evaluation of Transcriptomic Responses in Livers of Mice Exposed to the Short-Chain PFAS Compound HFPO-DA

HFPO-DA (ammonium 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate; CASRN 62037-80-3) is a component of the GenX technology platform used as a polymerization aid in the manufacture of some types of fluoropolymers. The liver is the primary target of toxicity for HFPO-DA in rodents and previous examination of hepatic transcriptomic responses in mice following oral exposure to HFPO-DA for 90 days showed induction of peroxisome proliferator-activated receptor signaling pathways, predominantly by PPARα, as well as increased gene expression of both peroxisomal and mitochondrial fatty acid metabolism. To further investigate the mechanism of liver toxicity, transcriptomic analysis was conducted on liver tissue from mice orally exposed to 0, 0.1, 0.5 or 5 mg/kg-bw/day HFPO-DA in a reproduction/developmental toxicity study. Hepatic gene expression changes demonstrated activation of the PPARα signaling pathway. Peroxisomal and mitochondrial fatty acid β-oxidation gene sets were enriched at lower HFPO-DA concentrations, and complement cascade, cell cycle and apoptosis related gene sets were enriched at higher HFPO-DA concentrations. These results support the reported histopathological findings in livers of mice from this study and indicate that the effects of HFPO-DA are mediated through rodent-specific PPARα signaling mechanisms regardless of reproductive status in mice.
Laser Cooling, Trapping, and Bose-Einstein Condensation of Atoms and Molecules

In this paper we first focus on the methods developed to control the position and the velocity of atoms, taking advantage of the radiative forces exerted on atoms placed in a laser beam. Temperatures in the range of μK can be reached for dilute atomic clouds trapped under vacuum in a very small region of space. The application to fountain clocks based on cold cesium atoms is presented. We then describe the characterization and the main features of Bose‐Einstein condensates, a new state of matter of purely quantum origin, which can be obtained by subsequent evaporative cooling. The methods in use for cooling molecules are considered, in particular the collision processes or the photoassociation of cold atoms. The possibility of changing interactions between ultracold particles is also explained and photoassociation is illustrated by the recent experiments of our group dealing with metastable helium atoms.
Utilizing Hidden Observations to Enhance the Performance of the Trained Agent

The frame-skipping strategy has been widely employed in deep reinforcement learning (DRL) technology to train an agent. Specifically, this strategy repeats the action determined by the agent for a fixed number of frames. It increases computational efficiency by reducing the number of inferences by making the action decision sparse. However, previously, these consecutive changes in frames during the frame-skipping were hidden and ignored from the environment and did not affect the agent’s action decision. As a result, it can adversely affect the performance of trained agents, where the performance is more critical than computational efficiency. To alleviate these issues, we propose a new framework that utilizes these hidden frames during the frame-skipping, called <inline-formula><tex-math notation="LaTeX">$hidden$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$observation$</tex-math></inline-formula>, to enhance the performance of the trained agent. The proposed framework retrieves all hidden observations during frame skipping. It then combines batch inference and an exponentially weighted sum to calculate and merge the outputs from hidden observations. Through experiments, we validated the effectiveness of the proposed method in terms of both performance and stability with only a marginal increase in computation.
A Mechano-Chemical Computational Model of Deep Vein Thrombosis

Computational models of deep vein thrombosis (DVT) typically account for either the mechanical or biochemical factors involved in thrombus formation. Developing a model that accounts for both factors will improve our understanding of the coagulation process in this particular disease. The work presented in this study details the development of a CFD model that considers the biochemical reactions between thrombin and fibrinogen, pulsatile blood flow, and clot growth within a three-dimensional patient-specific common femoral vein. Thrombin is released into the bloodstream from an injury zone on the wall of the vein. The Michaelis–Menten equation is used to represent the conversion of thrombin and fibrinogen to fibrin, the final product of the coagulation process. The model development starts with a two-dimensional idealized geometry. At this stage, the model is used to conduct a parametric study to determine the effects of varying parameters such as inlet velocity, vein diameter, and peak thrombin concentration on the size and shape of the clot formed. Peak thrombin concentration is the key factor driving the initiation and propagation of clots in the vein. To demonstrate the potential use of the model, the two-dimensional model is then extended to an image-derived three-dimensional patient-specific geometry. Realistic clot growth was achieved using this model, and the clot was compared to a clot formed in vivo. The volume of the clot that formed in the patient was about 4% smaller than that formed in the simulation. This demonstrates that with further development and refinement, this model could be used for patient-specific interventional planning. The model provides a means for predicting clot formation under different physiological conditions in a non-invasive manner.
Parallelizing the all-nearest-neighbor algorithm for Travelling Salesman Problem

Travelling Salesman Problem (TSP) is an NP-hard problem in combinational optimization important in operations research and theoretical computer science. The all-nearest-neighbor (NN) algorithm is a typical algorithm to solve TSP due to its brevity and effectiveness. However, as graphs become larger, the cubic growth of computational time makes sequential algorithms unfavorable. This paper presents two parallel all-nearest neighbor algorithms - the coarse-grained parallel model (PnnCG) and the integrated parallel model (PnnIN) to optimize the runtime of the sequential NN for TSP on large graphs. PnnCG employs a top-level parallelization to simultaneously search for the shortest tours within subgroups of the cities. PnnIN adds a lower level of parallelization based on PnnCG by again dividing the cities into subgroups when searching for the nearest city of the current one. To verify the effectiveness of PnnCG and PnnIN, we compare them with NN on ten TSP benchmark graphs whose sizes range from 48 to 11849. PnnCG reaches a speedup of 2-3 on graphs with a size from around 50 to 11500. PnnIN, though slower in smaller graphs, outperforms PnnCG on graphs with a size larger than 11500. Our analysis demonstrates that the proposed parallel models significantly decrease the runtime needed.