Miracle-dz/UniFAR-FTUs huggingface dataset:
TORC1 Inhibition by Rapamycin Promotes Antioxidant Defences in a Drosophila Model of Friedreich’s Ataxia

Friedreich’s ataxia (FRDA), the most common inherited ataxia in the Caucasian population, is a multisystemic disease caused by a significant decrease in the frataxin level. To identify genes capable of modifying the severity of the symptoms of frataxin depletion, we performed a candidate genetic screen in a Drosophila RNAi-based model of FRDA. We found that genetic reduction in TOR Complex 1 (TORC1) signalling improves the impaired motor performance phenotype of FRDA model flies. Pharmacologic inhibition of TORC1 signalling by rapamycin also restored this phenotype and increased the lifespan and ATP levels. Furthermore, rapamycin reduced the altered levels of malondialdehyde + 4-hydroxyalkenals and total glutathione of the model flies. The rapamycin-mediated protection against oxidative stress is due in part to an increase in the transcription of antioxidant genes mediated by cap-n-collar (Drosophila ortholog of Nrf2). Our results suggest that autophagy is indeed necessary for the protective effect of rapamycin in hyperoxia. Rapamycin increased the survival and aconitase activity of model flies subjected to high oxidative insult, and this improvement was abolished by the autophagy inhibitor 3-methyladenine. These results point to the TORC1 pathway as a new potential therapeutic target for FRDA and as a guide to finding new promising molecules for disease treatment.
Trace-Driven Simulation and Design Space Exploration of Network-on-Chip Topologies on FPGA

Networking On Chips is now becoming an extremely important part of the present and future of electronic technology. It is extensively used in Multiprocessor System-on-Chips and in Chip Multiprocessors. Using an NoC, the backend wiring involved has drastically reduced in an SoC. Further, SoCs with NoC interconnect operates at a higher operating frequency, mainly because the hardware required for switching and routing are simplified. The NoC researchers have relied on simulators based on performance and power to study the different factors of NoC such as algorithm in place, the topology, the buffer management and location schemes, the flow control and routing among others. In this paper, we present a trace-driven NoC architecture that gives the user access to realistic details about the resource utilization of NoC architectures and their individual components. This includes exploration of various design decision parameters of NoC by modeling them on a FPGA. The paper also presents the performance of these architectures by conducting trace-driven simulations using benchmarks like PARSEC. Different topologies are considered for experimentation purposes with different routing algorithms.
Abalone Life Phase Classification with Deep Learning

In this paper, we present algorithmic and architectural comparison of deep learning models for predicting abalone age range. While abalone age can be determined through very detailed steps in a laboratory, we present an efficient method for determining its age through machine learning models. We present a precise and an efficient method for converting data to a computable version through binary encoding and normalization. We experiment with various topological variances in neural network architectures, convolutional approach to the task at hand and recently succeeding residual neural network architecture for finding the optimal prediction accuracy and efficiency. Although the conventional machine learning methods showed success in this field, our deep learning model tests yield an accuracy of 79.09% accuracy, surpassing the conventional machine learning algorithms as we incorporated methods for preventing over-fitting and methods for normalizing the output throughout the network.
Regulation of Ubiquitin Enzymes in the TGF-β Pathway

The transforming growth factor-β (TGF-β) pathway has a tumor suppressor role in normal and premalignant cells but promotes oncogenesis in advanced cancer cells. Components of the pathway are tightly controlled by ubiquitin modifying enzymes and aberrations in these enzymes are frequently observed to dysregulate the pathway causing diseases such as bone disorders, cancer and metastasis. These enzymes and their counterparts are increasingly being tested as druggable targets, and thus a deeper understanding of the enzymes is required. This review summarizes the roles of specific ubiquitin modifying enzymes in the TGF-β pathway and how they are regulated.
60 GHz Voltage Controlled Load for LINC PA Analogue SCS in SiGe BiCMOS

The single-input, single-output LINC outphasing PA topology requires nonlinear loads in the analogue signal component separators (SCS) to symmetrically outphase a complex signal. As a solution to a nonlinear load at mm-wave frequencies, a voltage controlled load (VCL) is designed and prototyped, producing 5.25 Ω real impedance and 13.85 Ω reactance shift over 0.2 V voltage tuning range in 130nm SiGe BiCMOS, which is required in the outphasing SCS for envelope elimination and restoration PA topologies.
Listening to and mimicking respiration: Understanding and synchronizing joint actions

Several studies provided evidence about the mutual influence between respiration and performance: breathing influences and is in turn influenced both by motor (Rasler & Kohl, 2000) and mental processes (Wientjes, Grossman, & Gaillard, 1998). Field experiences suggest that participants engaged in joint performances requiring a high degree of synchronization tend to breathe together to manage precise temporal coordination. This paper presents two studies aimed to explore if breathing sounds convey information about the activity being performed and to describe interpersonal breathing coordination during a joint action. In Study 1, 180 participants listened to ecological tracks of breathing sounds related to six activities different in degree of mental and physical effort in two conditions: listening vs. imitation. Most demanding activities were identified the most. Imitation significantly improved identification accuracy. Study 2 was aimed to develop a multilayer analysis to relate partners’ respiratory behaviour during joint actions and to describe their respiratory and acoustic features. Audio recording of breathing sounds of a dyad was taken during a baseline and a joint obstacle course, both video-recorded. Respiratory, acoustic, and coordination indices were extracted and related to six action units. The multilayer analysis provided quantitative measurements of respiratory behaviour that enable descriptions and comparisons between conditions and actions.
Alterations in in vitro function and protein oxidation of rat sarcoplasmic reticulum Ca2+‐ATPase during recovery from high‐intensity exercise

The hypothesis tested in this study was that the extent to which sarcoplasmic reticulum (SR) Ca2+‐ATPase is oxidized would correlate with a decline in its activity. For this purpose, changes in the SR Ca2+‐sequestering ability and the contents of carbonyl and sulfhydryl groups during recovery after exercise were examined in the superficial portions of vastus lateralis muscles from rats subjected to 5 min running at an intensity corresponding to maximal oxygen uptake (50 m min−1, 10% gradient). A single bout of exercise elicited a 22.4% reduction (P < 0.05) in SR Ca2+‐ATPase activity. The decreased activity progressively reverted to normal levels during recovery after exercise, reaching normal levels after 60 min of recovery. This change was paralleled by a depressed SR Ca2+‐uptake rate, and the proportional alteration in these two variables resulted in no change in the ratio of Ca2+‐uptake rate to Ca2+‐ATPase activity. The contents of SR Ca2+‐ATPase protein and sulfhydryl groups in microsomes were unchanged after exercise and during recovery periods. In contrast, the content of carbonyl groups in SR Ca2+‐ATPase behaved in an opposite manner to that of SR Ca2+‐ATPase activity. An approximately 80% augmentation (P < 0.05) in the carbonyl group content occurred immediately after exercise. The elevated carbonyl content decreased towards normal levels during 60 min of recovery. These results are strongly suggestive that oxidation of SR Ca2+‐ATPase is responsible, at least in part, for a decay in the SR Ca2+‐pumping function produced by high‐intensity exercise and imply that oxidized proteins may be repaired during recovery from exercise.
Disentangling Factors of Variation Using Few Labels

Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a limited amount of supervision, for example through manual labeling of (some) factors of variation in a few training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 52000 models under well-defined and reproducible experimental conditions. We observe that a small number of labeled examples (0.01--0.5\% of the data set), with potentially imprecise and incomplete labels, is sufficient to perform model selection on state-of-the-art unsupervised models. Further, we investigate the benefit of incorporating supervision into the training process. Overall, we empirically validate that with little and imprecise supervision it is possible to reliably learn disentangled representations.
Spatial Pattern Formation During Aggregation of the Slime Mould Dictyostelium discoideum

Stream formation and spiral wave behaviour during the aggregation of Dictyostelium discoideum (Dd) are studied in a model based on the Martiel-Goldbeter equations for cAMP relay, combined with chemotactic motion of Dd cells. The results show that stream formation occurs if the turnover rate of intracellular cAMP is increased. This increase in the turnover rate of cAMP[in] leads to a dependence of the speed of the cAMP wave on the cell density. We propose that this dependence of wave speed on cell density is the underlying mechanism for stream formation. Besides stream formation, increasing the turnover rate of cAMP[in] also results in a spiral wave period that decreases during aggregation, a phenomenon that is commonly observed in situ. Furthermore, the dependence of wave speed on cell density is measured empirically. The speed of the cAMP wave is found to decrease as the wave travels from high to low cell density. This indicates that in situ, wave speed does depend on cell density. 7 1996 Academic Press Limited
Analyzing graph-based algorithms employed to generate test cases from finite state machines

Context: Model-Based Testing (MBT) is a technique that employs formal models to represent reactive systems’ behav-ior and generates test cases. Such systems are mostly specified and verified using Finite State Machines (FSMs). There is a plethora of test generation algorithms in the literature. Most of them are graph-based once an FSM can be formally defined as a graph. Nevertheless, there is a lack of studies on analyzing cost and efficiency of FSM-based test generation algorithms. Objective: This study aims to compare graph-based algorithms employed to generate test cases from FSM models. In particular, we compare the Chinese Postman Problem (CPP) and H-Switch Cover (HSC) algorithms with the well-known depth-first and breadth-first searches algorithms in the context of covering all-transitions and all-transition-pairs criteria in an FSM. Method: First, a systematic literature mapping was conducted to summarize the algorithms that have been adopted in MBT, considering FSM. The main algorithms found were implemented and analyzed in different experiments. Results: CPP presented better results with the FSMs used in terms of number of test cases and test suite size. In addition, CPP also presented low distribution of average length compared to other algorithms.
Measuring personality from keyboard and mouse use

Motivation -- To measure computer users' personality from their use of keyboard and mouse. Research approach -- Two explorative studies were conducted. In first study a background application was executed on 20 participants' computers to record keys pressed and mouse clicks on an average of eight days. In a second Study 15 participants' completed a programming task in an hour while a background application recorded keys pressed and mouse clicks. Participants were asked to complete the short form of IPIP-NEO personality inventory afterwards. Pearson correlation analysis was done between participants' behaviour on keyboard, mouse events and personality ratings. Findings/Design -- The results suggest that some of the main traits and sub traits of personality can be measured from keyboard and mouse use. Significant correlations were found between personality main traits and sub traits and the use of keyboard and mouse. The maximum and minimum significant correlations were r (20) = 0.62, p <= 0.01 and r (20) = 0.40, p <= 0.05 respectively in first study and were r (15) = 0.7, p < 0.01 and r (15) = 0.51, p <= 0.05 respectively in the second study. Personality trait activity level was found to be significantly correlated with behavioural measure 'standard deviation of average time between events' in both the studies with r (20) = 0.54, p <= 0.05 and r (15) = 0.58, p <= 0.05 respectively Research limitations/Implications -- The group of participants in both studies was relatively small. There was about 40% overlap of participants in the studies. Originality/Value -- Relatively little research has focussed on personality and behaviour and specifically on the measurement of personality from the user behaviour on keyboard and mouse. Take away message -- It might be possible to measure computer users' personality from their use of keyboard and mouse.
Deep Neural Network Regularization for Feature Selection in Learning-to-Rank

Learning-to-rank is an emerging area of research for a wide range of applications. Many algorithms are devised to tackle the problem of learning-to-rank. However, very few existing algorithms deal with deep learning. Previous research depicts that deep learning makes significant improvements in a variety of applications. The proposed model makes use of the deep neural network for learning-to-rank for document retrieval. It employs a regularization technique particularly suited for the deep neural network to improve the results significantly. The main aim of regularization is optimizing the weight of neural network, selecting the relevant features with active neurons at the input layer, and pruning of the network by selecting only active neurons at hidden layer while learning. Specifically, we use group <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> regularization in order to induce the group level sparsity on the network’s connections. Set of outgoing weights from each hidden layer represents the group here. The sparsity of network is measured by the sparsity ratio and it is compared with learning-to-rank models, which adopt the embedded method for feature selection. An extensive experimental evaluation considers the performance of the extended <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> regularization technique against classical regularization techniques. The empirical results confirm that sparse group <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> regularization is able to achieve competitive performance while simultaneously making the network compact with less number of input features. The model is analyzed with respect to evaluating measures, such as prediction accuracy, <italic>NDCG@n</italic>, <italic>MAP</italic>, and <italic>Precision</italic> on benchmark datasets, which demonstrate improved results over other state-of-the-art methods.
Escaping bullying: The simultaneous impact of individual and unit-level bullying on turnover intentions

In this study, we investigate the simultaneous impact of, and interaction between, being the direct target of bullying and working in an environment characterized by bullying upon employees’ turnover intentions. Hierarchical linear modeling analysis of a sample of 41 hospital units and 357 nurses demonstrates that working in an environment characterized by bullying increases individual employees’ turnover intentions. Importantly, employees report similarly high turnover intentions when they are either the direct target of bullying or when they work in work units characterized by high bullying. Results also suggest that the impact of unit-level bullying is stronger on those who are not often directly bullied themselves.
The evolution of galaxy intrinsic alignments in the MassiveBlackII universe

We investigate the redshift evolution of the intrinsic alignments (IAs) of galaxies in the MassiveBlackII (MBII) simulation. We select galaxy samples above fixed subhalo mass cuts ($M_h\gt 10^{11,12,13}\,\mathrm{M}_{\odot }\, h^{-1}$) at z = 0.6 and trace their progenitors to z = 3 along their merger trees. Dark matter components of z = 0.6 galaxies are more spherical than their progenitors while stellar matter components tend to be less spherical than their progenitors. The distribution of the galaxy–subhalo misalignment angle peaks at ∼10 deg with a mild increase with time. The evolution of the ellipticity–direction (ED) correlation amplitude ω(r) of galaxies (which quantifies the tendency of galaxies to preferentially point towards surrounding matter overdensities) is governed by the evolution in the alignment of underlying dark matter (DM) subhaloes to the matter density of field, as well as the alignment between galaxies and their DM subhaloes. At scales $\sim 1~\mathrm{Mpc}\, h^{-1}$, the alignment between DM subhaloes and matter overdensity gets suppressed with time, whereas the alignment between galaxies and DM subhaloes is enhanced. These competing tendencies lead to a complex redshift evolution of ω(r) for galaxies at $\sim 1~\mathrm{Mpc}\, h^{-1}$. At scales $\gt 1~\mathrm{Mpc}\, h^{-1}$, alignment between DM subhaloes and matter overdensity does not evolve significantly; the evolution of the galaxy–subhalo misalignment therefore leads to an increase in ω(r) for galaxies by a factor of ∼4 from z = 3 to 0.6 at scales $\gt 1~\mathrm{Mpc}\, h^{-1}$. The balance between competing physical effects is scale dependent, leading to different conclusions at much smaller scales ($\sim 0.1~\mathrm{Mpc}\, h^{-1}$).
A high-resolution quadratic time-frequency distribution for multicomponent signals analysis

The paper introduces a new kernel for the design of a high resolution time-frequency distribution (TFD). We show that this distribution can solve problems that the Wigner-Ville distribution (WVD) or the spectrogram cannot. In particular, the proposed distribution can resolve two close signals in the time-frequency domain that the two other distributions cannot. Moreover, we show that the proposed distribution is more accurate than the WVD and the spectrogram in the estimation of the instantaneous frequency of a stepped FM signal embedded in additive Gaussian noise. Synthetic and real data collected from real-world applications are shown to validate the proposed distribution.
The wavelet based synthesis for fractional Brownian motion

Abstract The aim of this communication is to propose some complementary remarks and interpretation on the wavelet-based synthesis technique for fractional Brownian motion proposed by Sellan in 1995. These comments will lead us to propose a fast and efficient pyramidal filter bank-based Mallat-type algorithm, which permits an easy and efficient implementation of this synthesis technique.
On the Impact of Memory Allocation on High-Performance Query Processing

Somewhat surprisingly, the behavior of analytical query engines is crucially affected by the dynamic memory allocator used. Memory allocators highly influence performance, scalability, memory efficiency and memory fairness to other processes. In this work, we provide the first comprehensive experimental analysis on the impact of memory allocation for high-performance query engines. We test five state-of-the-art dynamic memory allocators and discuss their strengths and weaknesses within our DBMS. The right allocator can increase the performance of TPC-DS (SF 100) by 2.7x on a 4-socket Intel Xeon server.
An overview of robot vision

Robot vision is an interdisciplinary field that deals with how robots can be made to gain high-level understanding from digital images or videos. Understanding an image at the pixel level often does not provide enough information for decision making and action taking. In this case, higher level semantic information that describes the image is required. This helps the robot to accomplish complex tasks that require visual understanding.For robots to add value they need to be sufficiently effective at executing tasks in different settings. Despite many impressive advances in robot vision, robots still lack the ability to function as humans do in complex environments. Importantly, this includes being able to interpret and understand the perceptual complexities of the world.Robot vision is dependant on ideas from both computer vision and machine learning. In this paper we provide a overview of the advances in these disciplines and how they contribute to robot vision.
Variability of Cardiac Electromechanical Delay With Application to the Noninvasive Detection of Coronary Artery Disease

Heart rate variability (HRV), systolic period variability (SPV), and diastolic period variability (DPV) have shown potential for assessing cardiac function. It is unknown whether the time delay between the myocardial electrical and mechanical activities (i.e., electromechanical delay, EMD) also possesses variability, and if it does, whether this EMD variability (EMDV) could render additional value for cardiac function assessment. In this paper, we extracted the beat-to-beat EMD from 5-min simultaneously recorded electrocardiogram and phonocardiogram signals in 30 patients with coronary artery disease (CAD) and 30 healthy control subjects, and studied its variability using the same methods as applied for HRV including time-domain measures [mean and standard deviation (SD)], frequency-domain measures [normalized low- and high-frequency (LFn, HFn) and LF/HF)], and nonlinear measures [sample entropy (SampEn), permutation entropy (PE), and dynamical patterns]. In addition, we examined whether the addition of EMDV could offer improved performance for distinguishing between the two groups compared to using the HRV, SPV, and DPV features. Support vector machine with 10-fold cross-validation was used for classification. Results showed increased SD of SPV, increased mean, SD and decreased SampEn of EMDV in CAD patients. Besides, the dynamical pattern analysis showed that CAD patients had significantly increased fluctuated patterns and decreased monotonous patterns in EMDV. In particular, the addition of EMDV indices dramatically increased the classification accuracy from 0.729 based on HRV, SPV, and DPV features to 0.958. Our results suggest promising of the EMDV analysis that could potentially be helpful for detecting CAD noninvasively.