Miracle-dz/UniFAR-FTUs huggingface dataset:
Paxilline Prevents the Onset of Myotonic Stiffness in Pharmacologically Induced Myotonia: A Preclinical Investigation

Reduced Cl− conductance causes inhibited muscle relaxation after forceful voluntary contraction due to muscle membrane hyperexcitability. This represents the pathomechanism of myotonia congenita. Due to the prevailing data suggesting that an increased potassium level is a main contributor, we studied the effect of a modulator of a big conductance Ca2+- and voltage-activated K+ channels (BK) modulator on contraction and relaxation of slow- and high-twitch muscle specimen before and after the pharmacological induction of myotonia. Human and murine muscle specimens (wild-type and BK−/−) were exposed to anthracene-9-carboxylic acid (9-AC) to inhibit CLC-1 chloride channels and to induce myotonia in-vitro. Functional effects of BK-channel activation and blockade were investigated by exposing slow-twitch (soleus) and fast-twitch (extensor digitorum longus) murine muscle specimens or human musculus vastus lateralis to an activator (NS1608) and a blocker (Paxilline), respectively. Muscle-twitch force and relaxation times (T90/10) were monitored. Compared to wild type, fast-twitch muscle specimen of BK−/− mice resulted in a significantly decreased T90/10 in presence of 9-AC. Paxilline significantly shortened T90/10 of murine slow- and fast-twitch muscles as well as human vastus lateralis muscle. Moreover, twitch force was significantly reduced after application of Paxilline in myotonic muscle. NS1608 had opposite effects to Paxilline and aggravated the onset of myotonic activity by prolongation of T90/10. The currently used standard therapy for myotonia is, in some individuals, not very effective. This in vitro study demonstrated that a BK channel blocker lowers myotonic stiffness and thus highlights its potential therapeutic option in myotonia congenital (MC).
A Social Distancing Index: Evaluating Navigational Policies on Human Proximity using Crowd Simulations

The importance of social distancing for public health is well established. However, the policies and regulations regarding occupancy rates have not been designed with this in mind. While there are analytical tools and related measures that are used in practice to evaluate how the design of a built environment serves the needs of its intended occupants, these metrics cannot directly apply to the problem of preventing the spread of infectious diseases such as COVID-19. By using a crowd-based simulator using three levels of behavior and agent control in a given environment, a novel evaluation metric for a space layout can be calculated to reflect the proclivity of maintaining a safe distance throughout the shopping experience. We refer to this metric as the Social Distancing Index (SDI), accounting for the occupancy throughput and number of distance-based violations found. Through a case study of a realistic retail store, we demonstrate the proposed platforms performance and output on multiple scenarios by changing agent-behavior, occupancy rate, and navigational guidelines.
The Anti-atherogenic Role of Exercise Is Associated With the Attenuation of Bone Marrow-Derived Macrophage Activation and Migration in Hypercholesterolemic Mice

An early event in atherogenesis is the recruitment and infiltration of circulating monocytes and macrophage activation in the subendothelial space. Atherosclerosis subsequently progresses as a unresolved inflammatory disease, particularly in hypercholesterolemic conditions. Although physical exercise training has been a widely accepted strategy to inhibit atherosclerosis, its impact on arterial wall inflammation and macrophage phenotype and function has not yet been directly evaluated. Thus, the aim of this study was to investigate the effects of aerobic exercise training on the inflammatory state of atherosclerotic lesions with a focus on macrophages. Hypercholesterolemic LDL-receptor-deficient male mice were subjected to treadmill training for 8 weeks and fed a high-fat diet. Analyses included plasma lipoprotein and cytokine levels; aortic root staining for lipids (oil red O); macrophages (CD68, MCP1 and IL1β); oxidative (nitrotyrosine and, DHE) and endoplasmic reticulum (GADD) stress markers. Primary bone marrow-derived macrophages (BMDM) were assayed for migration activity, motility phenotype (Rac1 and F-actin) and inflammation-related gene expression. Plasma levels of HDL cholesterol were increased, while levels of proinflammatory cytokines (TNFa, IL1b, and IL6) were markedly reduced in the exercised mice. The exercised mice developed lower levels of lipid content and inflammation in atherosclerotic plaques. Additionally, lesions in the exercised mice had lower levels of oxidative and ER stress markers. BMDM isolated from the exercised mice showed a marked reduction in proinflammatory cytokine gene expression and migratory activity and a disrupted motility phenotype. More importantly, bone marrow from exercised mice transplanted into sedentary mice led to reduced atherosclerosis in the recipient sedentary mice, thus suggesting that epigenetic mechanisms are associated with exercise. Collectively, the presented data indicate that exercise training prevents atherosclerosis by inhibiting bone marrow-derived macrophage recruitment and activation.
Exploring the Allosteric Mechanism of Src Homology-2 Domain-Containing Protein Tyrosine Phosphatase 2 (SHP2) by Molecular Dynamics Simulations

The Src homology-2 (SH2) domain-containing protein tyrosine phosphatase 2 (SHP2, encoded by PTPN11) is a critical allosteric phosphatase for many signaling pathways. Programmed cell death 1 (PD-1) could be phosphorylated at its immunoreceptor tyrosine-based inhibitory motif (ITIM) and immunoreceptor tyrosine-based switch motif (ITSM) and can bind to SHP2 to initiate T cell inactivation. Although the interaction of SHP2-PD-1 plays an important role in the immune process, the complex structure and the allosteric regulation mechanism remain unknown. In this study, molecular dynamics (MD) simulations were performed to study the binding details of SHP2 and PD-1, and explore the allosteric regulation mechanism of SHP2. The results show that ITIM has a preference to bind to the N-SH2 domain and ITSM has almost the same binding affinity to the N-SH2 and C-SH2 domain. Only when ITIM binds to the N-SH2 domain and ITSM binds to the C-SH2 domain can the full activation of SHP2 be obtained. The binding of ITIM and ITSM could change the motion mode of SHP2 and switch it to the activated state.
Is the Moon the future of infrared astronomy?

Infrared astronomy, particularly in spectroscopy, could benefit in a decisive way from an implementation of telescopes on the Moon since the largest telescopes on Earth are practically limited to 40 m and in space to 10 m. On the Moon, a collector larger than on Earth becomes conceivable, thanks to the low gravity and the absence of wind, in having the advantages of space. Passively cooled in the bottom of a permanently shadowed crater at the northern or the southern pole, it could reach unprecedented spectral sensitivity on a large part of the infrared domain, making possible spectral analysis of the most primitive galaxies and of the terrestrial exoplanet atmospheres. A project aiming at the detection of the weak cosmic microwave background spectral distortions is also presented. Several identical 1.5 m cryo-cooled telescopes at 2.5 K to fit in a launcher, with an imaging Fourier transform spectrometer in each unit, deposited in a cold crater and pointing in the same direction in lunar survey mode, would build for this fundamental goal the equivalent of a large telescope at an extremely low temperature. Last, the feasibility of these projects is discussed. This article is part of a discussion meeting issue ‘Astronomy from the Moon: the next decades'.
Iron and copper homeostasis in Staphylococcus aureus

Staphylococcus aureus is a pathogenic bacterium that causes a wide spectrum of human diseases and is a leading cause of nosocomial infection in the UK. Metal homeostasis is an important aspect of bacterial biology as transition metals such as copper and iron are required as enzyme cofactors but can also be toxic to cells at high concentrations. These metal homeostasis systems can be important for virulence. However, several important aspects of S. aureus metal homeostasis remain to be defined. This project focuses on novel S. aureus iron/Fur gene regulation and copper homeostasis. Fur is a well-described DNA binding repressor protein, found in many pathogenic bacteria. In S. aureus, Fur has been seen to both activate and repress genes in iron replete and iron restrictive conditions, and there is also Fur independent iron regulation. However, the regulatory mechanisms involved remain undefined. This investigation into novel iron regulation identified a new S. aureus iron regulator, LysR. lysR expression was found to be auto-regulated and activated by Fur in low iron. Phenotypic analysis suggested a possible role for LysR in the control of genes of the histidine utilisation pathway, as well as oxidative stress resistance. Two copper responsive operons have been found in S. aureus; copAZ and copB/mco. However, many important aspects of the S. aureus response to copper remain undefined. In this study, copper tolerance was shown to vary between strains and ATCC 12600 was identified as the first hyper copper-tolerant S. aureus, due to a transferable copper-resistance plasmid. A new S. aureus regulator, CsoR, was found to control the copper response of copAZ and both chromosomal and plasmid encoded copB/mco. Finally, this data shows that H2O2 scavenging is an essential S. aureus copper resistance mechanism and that extracellular surface copper toxicity is important in S. aureus.
A Rigorous Benchmarking and Performance Analysis Methodology for Python Workloads

Computer architecture and computer systems research and development is heavily driven by benchmarking and performance analysis. It is thus of paramount importance that rigorous methodologies are used to draw correct conclusions and steer research and development in the right direction. While rigorous methodologies are widely used for native and managed programming language workloads, scripting language workloads are subject to ad-hoc methodologies which lead to incorrect and misleading conclusions. In particular, we find incorrect public statements regarding different virtual machines for Python, the most popular scripting language. The incorrect conclusion is a result of using the geometric mean speedup and not making a distinction between start-up and steady-state performance. In this paper, we propose a statistically rigorous benchmarking and performance analysis methodology for Python workloads, which makes a distinction between start-up and steady-state performance and which summarizes average performance across a set of benchmarks using the harmonic mean speedup. We find that a rigorous methodology makes a difference in practice. In particular, we find that the PyPy JIT compiler outperforms the CPython interpreter by 1.76 × for steady-state while being 2% slower for start-up, which refutes the statement on the PyPy website that ‘PyPy outperforms CPython by 4.4× on average’ based on the geometric mean speedup and not making a distinction between start-up and steady-state. We use the proposed methodology to analyze Python workloads which yields several interesting findings regarding PyPy versus CPython performance, start-up versus steady-state performance, the impact of a workload's input size, and Python workload execution characteristics at the microarchitecture level.
Trusted Boot for Embedded Systems Using Hypothesis Testing Benchmark

Security has become a crucial consideration and is one of the most important design goals for an embedded system. This paper examines the type of boot sequence, and more specifically a trusted boot which utilizes the method of chain of trust. After defining these terms, this paper will examine the limitations of the existing safe boot, and finally propose the method of trusted boot based on hypothesis testing benchmark and the cost it takes to perform this method.
An improved Visual Indoor Navigation Method based on Fully convolutional Neural Network

The indoor positioning algorithm based on visual object recognition is a new kind of indoor positioning solution, in which, user’s position can be determined by steps as identifying objects around, matching general positions by a digital map and precise positioning through nonlinear calculation. However, limited by low objects detection rate and low detection accuracy, the availability of the localization algorithm is greatly affected. Therefore, this paper presents a novel method that utilizes Fully Convolutional Networks (FCN), which brings a great improvement in contour recognition accuracy when identifying objects in an image, and thus improving the final positioning results. In additional, a pre-trained CNN (Convolution Neural Network) is also proposed in the paper, which is used to get a coarse location for solving the problem of fuzzy floor and location brought by similar object distribution in the existence algorithm. Verifying by experimental tests, The results show that compared with the previous TM-based (template matching) and the R-CNN based localization algorithm (Region-based Convolution Neural Network), the methods proposed in this paper can effectively improve the success rate from 85% to 93% and the positioning error drop from 2.6m to 1.4m of the indoor positioning algorithm.
Risk Classification of Peer-to-Peer Lending Platform Using SVM Algorithm

The transition from traditional to digital of financial industry in Indonesia is supported by the role of peer-to-peer lending platform. Technology disruption on the peer-to-peer lending platform goes hand in hand with the high risks that must be faced by the users. In this study, text classification is conducted to find the risks that perceived by the users. Text classification process follows the CRISP-DM data mining process and uses SVM which generates better accuracy compared to the other algorithms. Twitter data of peer-to-peer lending platforms is used in this study and classified based on the perceived risk into six classes, namely performance, financial, time, psychological, social, and privacy. The result of text classification using SVM algorithm generate 81.51% accuracy. Classification results indicate that performance risk is the most felt risk by users and must be considered by peer-to-peer Lending platforms.
An SE approach for CoCo Learning of Virtual Labs

With increasing computing power, capacity and internet connectivity, the ability to learn has crossed classroom boundaries in the form of online learning. The adoption rate and effectiveness of online learning continues to be an area of research. Collaborative and Cooperative (CoCo) learning involves a joint effort by students to understand and solve problems or tasks. Adoption of open source software has also increased with technology-aided collaboration and cooperation. Leveraging the practices of crowdsourced software development, we propose CoCo Learning approach for technical education using virtual labs. About 145 undergraduate engineering students from various institutes participated in our study for a period of one full semester. About 92% of the students conducted an average of 24 virtual experiments with this approach. Nearly 90% of the faculty from these institutes also confirmed that their students' understanding of courses has improved while delivering tangible software artifacts as outcomes.
Development of Exercise Promotion System Using Smartphone and AI Speaker

The average human life expectancy has been gradually extended; thus, development of novel approaches to extend healthy life expectancy have gained considerable research attention. Although it is well-known that habitual exercise is an effective way of extending healthy life expectancy, we have found that few people actually continue exercising regularly. Consequently, we have devised a system for promoting exercise using a smartphone and an artificial intelligence (AI) speaker. After confirming that the number of repetitions of an exercise recommended to a user (via the exercise content presented to them) can be detected using smartphone sensors, we integrated the smartphone and AI speaker into one system via the cloud. This system allows the user to receive advice from the AI speaker after exercising. In the future, we will investigate whether the proposed method can significantly encourage changes in exercise-related awareness and behavior compared to existing smartphone apps used for health management.
Probability of default estimation, with a reject option

Many companies, such as credit granting companies, have to decide on granting or denying customer or invoice loans on a daily basis. Increasingly, machine learning is used to learn probability-of-default models from previously granted cases and, thus, whether the outcome was positive or negative for the company, i.e. whether the client paid back or defaulted. However, as the outcome can only be observed for the granted cases, the data inherently has sample selection bias and caution should be taken when applying the probability-of-default model to the full through-the-door population. In reject inference, this problem is studied with respect to whether using the unlabeled rejected instances can help improve a classifier that is only trained on granted instances, e.g. using semi-supervised learning. In contrast, we investigate under what circumstances a model trained on the granted instances, with known outcome, can be used on all possible instances. For this, we believe a model should indicate when it cannot reliably predict the outcome. That is, it should refrain from making predictions on instances unlike those on which it was trained. If not, the credit granting company would expose itself to great risk, and experts could lose their trust in the predictions. We discuss similarities and differences of this problem compared to novelty detection, classification with a reject option and reject inference. We compare a number of methods that combine novelty detection with classification, with decent results even for two-stage methods and especially when using data of existing instances with unknown outcome.
General Framework for Multi-Classification of EEG Signals Based on Multi-Scale Properties

Numerous computer automated diagnosis (CAD) systems have been proposed to detect epilepsy in electroencephalogram (EEG) signals. The aim of this paper is to look at multi-scaling properties obtained by multi-scale analysis (MSA) as main distinctive features to simultaneously distinguish between all categories of EEG signals that compose the popular database hosted by the department of epileptology, University of Bonn, Germany. Particularly, multi-scale analysis is employed to capture long-range properties of the EEG signal at different scales used to represent its short and long variations. Then, the obtained multi-scale properties are used to train four different classifiers; namely, k-nearest neighbor (k-NN), linear discriminant analysis (LDA), naïve Bayes (NB), and the support vector machine (SVM). Experimental results based on ten-fold cross-validation method show that each single classifier achieves 100% accuracy. In this respect, multi-scale properties are found to be effective as they outperformed existing works on the same database by achieving perfect accuracy to distinguish between all five distinct EEG categories. Overall, the obtained results are promising.
Contact Tracing of Infectious Diseases Using Wi-Fi Signals and Machine Learning Classification

There is just a handful of interventions proven to curb the spread of infectious diseases. One of them is contact tracing that involves reaching infected people to investigate where they might have been infected and whom they might have exposed to the virus. Contact tracing has been identified as a core disease control measure by the World Health Organization and has been exercised by state health agencies for decades. In this research, we proposed a new contact tracing method based on machine learning classification algorithms, for infectious diseases, such as COVID-19. The proposed method uses the Wi-Fi signals data from a possible contact and a confirmed patient's smartphones to detect whether the two shared the same physical space. Simulation results show up to 95% tracing accuracy depending on area size.
A Multipath Matching Pursuit algorithm Based on Improved-Inner Product Matching Criterion

The traditional multipath matching pursuit (MMP) algorithm generally uses inner product matching criteria (IPMc) to select the best atom for signal reconstruction, and often lose some important information of atoms. Therefore, any two similar atoms of the observation matrix will affect the process of selecting the best atom for the residual signal, and reduce the quality of signal reconstruction. Using the improved inner product matching criteria (I-IPMc) improves the sensitivity of the MMP algorithm to atom values, optimizes the selection of support sets, and reduces the impact of similar atoms on the matching process. The simulation results show that under the same conditions, the MMP algorithm based on the I-IPMc has better reconstruction quality and higher signal reconstruction probability than the traditional MMP algorithm.
Dynamic Satisfactory Power Allocation for Multi-Class Ultra Dense 5G and Beyond

Power control is originally used in wireless networks, to compute optimal transmit power and deal with undesired interference. It is also a flexible mechanism that can provide Quality of Service (QoS) and strategically allow to meet the user requirements. This paper introduces a QoS-aware Satisfactory Power Allocation (SPA) for ultra dense networks, using a mean field perspective. In this setting, the user devices are partitioned into several classes based on their throughput requirements. Now, instead of seeking to maximize their QoS, the user devices from each class only aims to meet their respective throughput demands. Yet, by leveraging stochastic geometry analysis and mean field approximation, we investigate the uplink power control problem in a large scale ultra dense network that guarantees a satisfactory performance per class. Next, we formulate the problem as a mean field optimal control where the optimality conditions are derived using Lagrangian dual formulation. Finally, the effectiveness of the SPA policies is illustrated via extensive numerical analysis, and many insightful discussions are presented.
A Novel Approach for Seasonality and Trend Detection using Fast Fourier Transform in Box-Jenkins Algorithm

Forecasting is the first step to deal with the new generation of renewable energy systems. The accuracy of the forecasting techniques is very important. Time series technique is one of the powerful tools used for forecasting, but it works well with stationary data. In addition, non-stationary time series can cause unexpected behaviors or create a non-existing relationship between two variables. This work was motivated by the need of detecting the seasonality and trend for a given data. The trend and seasonality components are very important in dealing with forecasting. Based on the trend and seasonality we could use clustered regions and feed the clustered regions to a forecasting technique like ANN, WNN or Kalman Filtering. Then aggregating the forecasted data back again for a better performance. In this paper we use Fast Fourier Transform (FFT) in Box-Jenkins approach instead of Autocorrelation Function (ACF) for the seasonality and trend detection. The present study is validated using visual inspection, statistical tests and time series decomposition in identifying the trend and the seasonality by applying them to wind speed time series. The results of FFT technique and ACF are compared to the results of the most well-known techniques. The results show that some methods have minor limitations in determining either the trend or the seasonality compared to FFT.