Miracle-dz/UniFAR-FTUs huggingface dataset:
Distinct Effects of Repeated-Sprint Training in Normobaric Hypoxia and β-Alanine Supplementation

Abstract Objective: The present study evaluated the effects of repeated-sprint training in normobaric hypoxia and β-alanine supplementation (BA) on aerobic and anaerobic performance in recreationally active men. Methods: Participants were randomly assigned to one of the following groups: normoxia/β-alanine (NB, n = 11), normoxia/placebo (NP, n = 8), normobaric hypoxia/β-alanine (HB, n = 10) and normobaric hypoxia/placebo (HP, n = 9). All participants completed 8 training sessions over 4 weeks on a cycle ergometer either in normobaric hypoxia (oxygen fraction: FiO2 = 14.2%) or normoxia (FiO2 = 20.9%). Participants were instructed to consume a daily dosage of 6.4 g of BA or placebo. Changes in performance in a graded exercise test, repeated-sprint test (RST), and 3-minute all-out test (3MT) were examined before and after training and supplementation. Results: No between-group differences were observed for training volume or supplementation compliance. Anthropometric and hematological measures remained unchanged before and after intervention in all groups. A main effect of training condition was shown for oxygen consumption and power output at respiratory compensation point, average power output during the last sprint of the RST, heart rate recovery following the RST, and total work during the 3MT. These measures in the normobaric hypoxia groups were significantly (p < 0.05) higher than the normoxia groups, except for the heart rate recovery following the RST. A main effect of supplement was detected in anaerobic working capacity, with postintervention values in the BA groups being significantly (p < 0.05) higher than the placebo groups. Conclusions: Repeated-sprint training in hypoxia improved aerobic performance, exercise tolerance, cardiovascular recovery, and overall working capacity, while BA maintained the anaerobic working capacity. However, BA did not provide additional benefits with respect to attenuating fatigue or enhancing repeated-sprint performance.
Tumor Segmentation and Gradation for MR Brain Images

MRI is a non-invasive technology that is currently used for analyzing brain tumors, which can only conclusively be graded by invasive tissue extraction. Techniques like perfusion used for analysis of tumors are time taking, therefore, there is increased focus towards automated computational techniques to segment and grade tumors using standard MR sequences like T1 and T2 weighted images. These are intensity based images and are not enough to grade tumors, thus, are not used in clinical practice. This work automates tumor segmentation without the use of templates and training sets, by using vertical symmetry comparison. Once the tumor is segmented, fuzzy c means clustering is used on T1 and T2 maps, that are generated by using three intensity based sequences namely T1, T2 and proton density (PD) fat saturated images, for tumor grading. The clustered sections are compared with cerebral blood volume (CBV) images that are obtained by perfusion parameters to find correlation. 15 patients, 5 each from 4th, 3rd and 2nd grade tumor categories, were tested. Some correlation is seen between T1, T2 map values of tumor region and CBV values showing that this area can be explored further for tumor grading.
Measuring anti-relevance: a study on when recommendation algorithms produce bad suggestions

Typically, performance of recommender systems has been measured focusing on the amount of relevant items recommended to the users. However, this perspective provides an incomplete view of an algorithm's quality, since it neglects the amount of negative recommendations by equating the unknown and negatively interacted items when computing ranking-based evaluation metrics. In this paper, we propose an evaluation framework where anti-relevance is seamlessly introduced in several ranking-based metrics; in this way, we obtain a different perspective on how recommenders behave and the type of suggestions they make. Based on our results, we observe that non-personalized approaches tend to return less bad recommendations than personalized ones, however the amount of unknown recommendations is also larger, which explains why the latter tend to suggest more relevant items. Our metrics based on anti-relevance also show the potential to discriminate between algorithms whose performance is very similar in terms of relevance.
Unique size-dependent nanocatalysis revealed at the single atomically precise gold cluster level

Significance Here, by real-time monitoring with single-molecule fluorescence microscopy the size-dependent catalytic process of individual Au clusters at single-turnover resolution, we study the size-dependent catalytic behaviors of gold (Au) clusters at the single-cluster level, and then observe the strong unique size effect on the catalytic properties of individual Au clusters, in both catalytic product formation and dissociation processes. Such a unique size effect on the nanocatalysis could be attributed intrinsically to the size-dependent electronic structure of Au clusters, leading to a more comprehensive understanding of the catalytic mechanism of Au particles. Atomically precise metal clusters have attracted increasing interest owing to their unique size-dependent properties; however, little has been known about the effect of size on the catalytic properties of metal clusters at the single-cluster level. Here, by real-time monitoring with single-molecule fluorescence microscopy the size-dependent catalytic process of individual Au clusters at single-turnover resolution, we study the size-dependent catalytic behaviors of gold (Au) clusters at the single-cluster level, and then observe the strong size effect on the catalytic properties of individual Au clusters, in both catalytic product formation and dissociation processes. Surprisingly, indicated by both experiments and density functional theory (DFT) calculations, due to such a unique size effect, besides observing the different product dissociation behaviors on different-sized Au clusters, we also observe that small Au clusters [i.e., Au15(MPA)13; here, MPA denotes 3-mercaptopropionic acid] catalyze the product formation through a competitive Langmuir–Hinshelwood mechanism, while those relatively larger Au clusters [e.g., Au18(MPA)14 and Au25(MPA)18] or nanoparticles catalyze the same process through a noncompetitive Langmuir–Hinshelwood mechanism. Such a size effect on the nanocatalysis could be attributed intrinsically to the size-dependent electronic structure of Au clusters. Further analysis of dynamic activity fluctuation of Au clusters reveals more different catalytic properties between Au clusters and traditional Au nanoparticles due to their different size-dependent structures.
Hand-Eye Camera Calibration with an Optical Tracking System

This paper presents a method for hand-eye camera calibration via an optical tracking system (OTS) faciltating robotic applications. The camera pose cannot be directly tracked via the OTS. Because of this, a transformation matrix between a marker-plate pose, tracked via the OTS, and the camera pose needs to be estimated. To this end, we evaluate two different approaches for hand-eye calibration. In the first approach, the camera is in a fixed position and a 2D calibration plate is displaced. In the second approach, the camera is also fixed, but now a 3D calibration object is moved. The first step of our method consists of collecting N views of the marker-plate pose and the calibration plates, acquired via OTS. This is achieved by keeping the camera fixed and moving the calibration plate, while taking a picture of the calibration plate using the camera. A dataset is constructed that contains marker-plate poses and the relative camera poses. Afterwards, the transformation matrix is then computed, following a least-squares minimization. Accuracy in hand-eye calibration is computed in terms of re-projection error, calculated based on camera homography transformations. For both approaches, we measure the changes in accuracy as a function of the number of poses used for each calibration, while we define the minimum number of poses required to obtain a good camera calibration. Results of the experiments show similar performances for the two evaluated methods, achieving a median value of the re-projection error at N = 25 poses of 0.76 mm for the 2D calibration plate and 0.70 mm for the 3D calibration object. Also, we have found that minimally 15 poses are required to achieve a good camera calibration.
Osmolytes in vaccine production, flocculation and storage: a critical review

ABSTRACT Small molecule osmolytes, responsible for protecting stresses have long been known to rescue proteins and enzymes from loss of function. In addition to protecting macromolecules integrity, many osmolytes also act as potential antioxidant and also help to prevent protein aggregation, amyloid formation or misfolding, and therefore are considered promising molecules for neurodegenerative and many other genetic diseases. Osmolytes are also known to be involved in the regulation of several key immunological processes. In the present review we discuss in detail the effect of these compounds on important aspects of vaccines i.e., increasing the efficiency, production and purification steps. The present review therefore will help researchers to make a better strategy in vaccine production to formulation by incorporating specific and appropriate osmolytes in the processes.
High-Performance Enhancement-Mode AlGaN/GaN High Electron Mobility Transistors Combined With TiN-Based Source Contact Ledge and Two-Step Fluorine Treatment

High performance enhancement-mode (E-mode) AlGaN/GaN high electron mobility transistors (HEMTs) were achieved by using the TiN-based source contact ledge and two-step fluorine treatment. Due to the effective reduction of the source resistance by TiN-based source contact ledge, the peak extrinsic trans-conductance improved by 21.2% from 340 to 412 mS/mm. In addition, due to the enhancement of the back barrier by the two-step fluorine treatment, the three-terminal OFF-state breakdown voltage (BV<sub>DS</sub>) improved by 11.5% from 162 to 183 V. The proposed E-mode AlGaN/GaN HEMTs exhibit a maximum drain current of 845 mA/mm (@<inline-formula> <tex-math notation="LaTeX">${V} _{\text {GS}} =3$ </tex-math></inline-formula> V), a threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V} _{\text {th}}$ </tex-math></inline-formula>) of 0.6 V, a high current-gain cutoff frequency <inline-formula> <tex-math notation="LaTeX">${f} _{\text {T}}$ </tex-math></inline-formula> of 61 GHz, and a high power-gain cutoff frequency <inline-formula> <tex-math notation="LaTeX">${f} _{\text {MAX}}$ </tex-math></inline-formula> of 130 GHz. Approximately 5% current collapse occurs at a drain quiescent bias of 30 V. The calculated Johnson’s figure of merit (J-FOM = <inline-formula> <tex-math notation="LaTeX">$\text{BV}_{\text {DS}}\times {f}_{T}$ </tex-math></inline-formula>) is 11.2 THz-V, the highest so far for GaN-based HEMTs. The calculated <inline-formula> <tex-math notation="LaTeX">$\text {BV}_{\text {DS}} \times {f}_{\text {MAX}}$ </tex-math></inline-formula> value is 23.8 THz-V, indicating significantly improved the speed and power performance of the E-mode GaN-based HEMT.
Comorbidities and length of stay in chronic obstructive pulmonary disease patients

Abstract Chronic Obstructive Pulmonary Disease (COPD) has a significant burden on patients and the healthcare system. There is a link between COPD and comorbidities such as congestive heart failure (CHF), fluid and electrolyte disorders, and renal failure. This adds to the complexity of healthcare in these patients. The objective of this study is to determine if certain comorbidities affect length of stay. A sample of 3,399 patients with COPD were assessed from the Premier© healthcare database. The cohort had a mean (standard deviation (SD)) age of 68.41 (10.85) years. The average number of comorbidities was 24.83 (10.46) with a mean length of stay (SD) of 11.64 (9.40) days. A negative binomial regression model was used to evaluate the impact that comorbidities have on the length of hospital stay. The authors found that the number of comorbidities was associated with an increased length of stay (r = .4596, p < .0001). Having at least one comorbidity was associated with a 13% greater length of stay (IRR = 1.13, 95% CI 1.11–1.15, p < 0.0001). CHF was associated with a 28% greater length of stay (IRR = 1.28, 95% CI 1.24–1.31, p < 0.0001). Fluid and electrolyte disorders were associated with a 2-fold greater length of stay (IRR = 2.57, 95% CI 2.52–2.62, p < 0.0001). Renal failure was associated with a 50% greater length of stay (IRR = 1.50, 95% CI 1.45–1.55, p < 0.0001). However, uncomplicated diabetes was associated with 13% shorter length of stay than not having uncomplicated diabetes (IRR = .87, 95% CI .82–.91, p < .0001). This study demonstrated that specific comorbidities have an impact on length of stay.
Energy Efficiency Maximization for MISO Systems with Total and Per-antenna Power Constraints

In wireless communications, the power of each antenna is typically constrained similar to the total power. In this paper, we consider a maximization problem of energy efficiency (EE) for multiple-input single-output (MISO) systems in the presence of both total and per-antenna power constraints. To solve this global non-concave optimization problem, we propose an iterative antenna selection and power allocation (IASPA) algorithm with two steps. In the first step, a set of optimal antennas are selected based on their channel gains. In the second step, by proving the concavity of this maximization problem of energy efficiency for fixed active antennas with respect to total power for transmission, we solve the power allocation by bidirectional search approach. Numerical results suggest that the proposed IASPA algorithm achieves the same performance as brute-force search and requires low computational burden.
Event-Based Control for Networked T–S Fuzzy Systems via Auxiliary Random Series Approach

This paper presents an auxiliary random series approach to model the effect of network induced problems, such as data losses and transmission delay subject to event-based communication scheme for nonlinear continuous time systems. T–S fuzzy model is employed to describe the nonlinear systems. In order to save the bandwidth and energy, we introduce the event-triggered mechanism to reduce the number of data for transmission and computation. Thus, it is necessary to consider the influence of data losses, data disorder, and transmission delay since the transmitted data packets become more important. Consequently, it is very complicated to analyze the performance of such networked system and one of the most difficult part, in the authors’ opinion, is to construct the mathematical model of closed-loop systems. In this paper, we present an auxiliary random series approach to describe the data transmitted in the system, and therefore, the closed-loop systems can be obtained. Associated with a tailor-made Lyapunov–Krasovskii functional, the stability analysis is processed and a fuzzy controller is designed. Asynchronous membership functions are considered to obtain more relaxed stability conditions. To clarify the effectiveness of the proposed method, a cart–damper–spring system is employed for simulation.
RangeQC: A Framework for Caching Range Predicate Query Results

Range predicates are important to many workloads and are supported by both SQL and NoSQL data stores. This study presents RangeQC, a caching framework to expedite processing of range predicates. RangeQC is a framework that scales to a large number of cache servers. It is conﬁgurable with alternative data stores, provides strong consistency and supports write-around, write-through, and write-back policies. In addition to presenting its design principles and an implementation for use in a data center, we evaluate RangeQC with both MySQL and MongoDB. Obtained results demonstrate superiority of using RangeQC as long as the size of result set produced by a range predicate is small . A surprising result is the superiority of RangeQC with write-heavy workloads.
meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays

Here we present an open-source R package ‘meaRtools’ that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL>=3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/. Author summary Cultured neuronal networks are widely used to study and characterize neuronal network activity. Among the many uses of neuronal cultures are the capabilities to evaluate neurotoxicity and the effects of pharmacological compounds on cellular physiology. Multi-well microelectrode arrays (MEAs) can collect high-throughput data from multiple neuronal cultures simultaneously, and thereby make possible hypotheses-driven inquiries into neurobiology and neuropharmacology. The analysis of MEA-derived information presents many computational challenges. High frequency data recorded simultaneously from hundreds of electrodes can be difficult to handle. The need to compare network activity across various drug treatments or genotypes recorded on the same plate from experiments lasting several weeks presents another challenge. These challenges inspired us to develop meaRtools; an MEA data analysis package that contains new methods to characterize network activity patterns, which are illustrated here using examples from a genetic mouse model of epilepsy. Among the highlights of meaRtools are novel algorithms designed to characterize neuronal activity dynamics and network properties such as bursting and synchronization, options to combine multiple recordings and use a robust statistical framework to draw appropriate statistical inferences, and finally data visualizations and plots. In summary, meaRtools provides a platform for the analyses of singular and longitudinal MEA experiments.
Long Short Term Memory Hyperparameter Optimization for a Neural Network Based Emotion Recognition Framework

Recently, emotion recognition using low-cost wearable sensors based on electroencephalogram and blood volume pulse has received much attention. Long short-term memory (LSTM) networks, a special type of recurrent neural networks, have been applied successfully to emotion classification. However, the performance of these sequence classifiers depends heavily on their hyperparameter values, and it is important to adopt an efficient method to ensure the optimal values. To address this problem, we propose a new framework to automatically optimize LSTM hyperparameters using differential evolution (DE). This is the first systematic study of hyperparameter optimization in the context of emotion classification. In this paper, we evaluate and compare the proposed framework with other state-of-the-art hyperparameter optimization methods (particle swarm optimization, simulated annealing, random search, and tree of Parzen estimators) using a new dataset collected from wearable sensors. Experimental results demonstrate that optimizing LSTM hyperparameters significantly improve the recognition rate of four-quadrant dimensional emotions with a 14% increase in accuracy. The best model based on the optimized LSTM classifier using the DE algorithm achieved 77.68% accuracy. The results also showed that evolutionary computation algorithms, particularly DE, are competitive for ensuring optimized LSTM hyperparameter values. Although DE algorithm is computationally expensive, it is less complex and offers higher diversity in finding optimal solutions.
Visceral obesity and hemostatic profile in patients with type 2 diabetes: the effect of gender and metabolic compensation.

BACKGROUND Type 2 diabetes (T2DM) patients are characterized by a very high risk of cardiovascular diseases. Among the factors that are responsible for this phenomenon are abdominal obesity and hemostatic abnormalities. AIM OF THE STUDY To examine the association of the markers of coagulation and fibrinolysis with the parameters of abdominal obesity and metabolic compensation in T2DM patients. METHODS 46 T2DM patients participated in the study: 24 men (mean age 61.1 +/- 7.9 years) and 22 postmenopausal women (mean age 62.6 +/- 8.7 years). In each patient the content and distribution of fatty tissue was measured by a dual energy X-ray absorptiometry method (DEXA). The central abdominal fat/gynoid hip fat (CAF/GF) ratio was calculated. The following hemostatic parameters were measured: fibrinogen (Fb), factor VII (fVII), antithrombin III (ATIII), C protein (pC), tissue plasminogen activator inhibitor (PAI-1) and alpha 2 antiplasmin (alpha2 AP). In addition, the biochemical indices of metabolic compensation were measured: HbA1c, glucose levels and lipids. RESULTS Patients of both genders were divided according to median CAF/GF ratio. The activity of PAI-1 was significantly higher in women with CAF/GF ratio >or= 0.88 as compared to those with CAF/GF < 0.88 (2.64 +/- 1.28 vs. 1.61 +/- 0.27 U/ml, p < 0.05). The activity of ATIII was significantly lower in men with CAF/GF ratio >or= 1.17, as compared to those with CAF/GF < 1.17 (105.10 +/- 10.02 vs. 113.42 +/- 10.72 %, p < 0.05). There was a significant correlation between the CAF/GF ratio and the activity of PAI-1 in women (r = 0.30, p < 0.05). In addition, in men the CAF/GF ratio was negatively correlated with ATIII activity (r = -0.44, p < 0.05). Multiple stepwise regression analysis demonstrated independent association between the CAF/ GF ratio and the activity of PAI-1 (p < 0.001), and between the CAF/GF ratio and the activity of alpha2 AP (p < 0.01). There was an independent association between the concentration of HbA1c and the concentration of Fb (p < 0.001) and between triglycerides and the activity of fVII (p < 0.01). CONCLUSIONS The results of our study show that the patients with T2DM and with higher markers of abdominal obesity measured by DEXA show fibrinolysis impairment and thrombinogenesis elevation compared to those with lower abdominal obesity markers. Independent factors determining hypercoagulation also include metabolic control and lipids. Hemostatic disorders place subjects with diabetes and abdominal obesity at risk of developing vascular complications.