Miracle-dz/UniFAR-FTUs huggingface dataset:
Human DnaJB6 Antiamyloid Chaperone Protects Yeast from Polyglutamine Toxicity Separately from Spatial Segregation of Aggregates

Polyglutamine (polyQ) aggregates are associated with pathology in protein-folding diseases and with toxicity in the yeast Saccharomyces cerevisiae. Protection from polyQ toxicity in yeast by human DnaJB6 coincides with sequestration of aggregates. ABSTRACT Polyglutamine (polyQ) aggregates are associated with pathology in protein-folding diseases and with toxicity in the yeast Saccharomyces cerevisiae. Protection from polyQ toxicity in yeast by human DnaJB6 coincides with sequestration of aggregates. Gathering of misfolded proteins into deposition sites by protein quality control (PQC) factors has led to the view that PQC processes protect cells by spatially segregating toxic aggregates. Whether DnaJB6 depends on this machinery to sequester polyQ aggregates, if this sequestration is needed for DnaJB6 to protect cells, and the identity of the deposition site are unknown. Here, we found DnaJB6-driven deposits share characteristics with perivacuolar insoluble protein deposition sites (IPODs). Binding of DnaJB6 to aggregates was necessary, but not enough, for detoxification. Focal formation required a DnaJB6-Hsp70 interaction and actin, polyQ could be detoxified without sequestration, and segregation of aggregates alone was not protective. Our findings suggest DnaJB6 binds to smaller polyQ aggregates to block their toxicity. Assembly and segregation of detoxified aggregates are driven by an Hsp70- and actin-dependent process. Our findings show sequestration of aggregates is not the primary mechanism by which DnaJB6 suppresses toxicity and raise questions regarding how and when misfolded proteins are detoxified during spatial segregation.
Training Student Networks for Acceleration with Conditional Adversarial Networks

There is an increasing interest on accelerating neural networks for real-time applications. We study the student-teacher strategy, in which a small and fast student network is trained with the auxiliary information learned from a large and accurate teacher network. We propose to use conditional adversarial networks to learn the loss function to transfer knowledge from teacher to student. The proposed method is particularly effective for relatively small student networks. Moreover, experimental results show the effect of network size when the modern networks are used as student. We empirically study the trade-off between inference time and classiﬁcation accuracy, and provide suggestions on choosing a proper student network.
Structure Aligning Discriminative Latent Embedding for Zero-Shot Learning

We address the problem of zero-shot visual recognition in this paper and particularly focus on learning a discriminative latent embedding space where the visual image descriptors and the respective semantic class representations can be projected with coinciding alignment. While a supervised dimension reduction strategy which simultaneously optimizes the intra-class compactness and between-class separation is used to learn the latent space for the visual features, the semantic class prototypes are further projected onto this latent space via a multi-stage non-linear mapping function for re-alignment purposing. Furthermore, it is ensured that the visual and semantic class prototypes are likely to overlap in the latent space such that the overall similarity between samples from both the domains is maximized. Apart from remarkably reducing the so-called semantic gap, the discriminative property of the learned latent layer representations entails improved classiﬁcation performance on both the standard zero-shot learning (ZSL) and the challenging generalized ZSL (GZSL) setups on three benchmark datasets (AWA, CUB, SUN) where the proposed method surpasses the state of the art results.
On the origin of the elusive first intermediate of CO2 electroreduction

Significance The understanding of a catalytic reaction starts with understanding its first elementary step. Surprisingly, despite the large number of studies, it is unclear whether one common or two different first intermediates control the selectivity of CO2 electroreduction to formate and CO. We settle this controversy for Cu, which is best known for its unique capacity to synthesize C1+ products but is just emerging as a superior earth-abundant catalyst for CO and formate. We provide solid experimental and theoretical support of the one common first-intermediate (Hori’s) model, the first intermediate being carboxylate. This outcome is an essential milestone toward accurate specification of the reaction descriptors in the growing effort to accelerate the discovery of a viable CO2 electroreduction catalyst. We resolve the long-standing controversy about the first step of the CO2 electroreduction to fuels in aqueous electrolytes by providing direct spectroscopic evidence that the first intermediate of the CO2 conversion to formate on copper is a carboxylate anion *CO2− coordinated to the surface through one of its C–O bonds. We identify this intermediate and gain insight into its formation, its chemical and electronic properties, as well as its dependence on the electrode potential by taking advantage of a cutting-edge methodology that includes operando surface-enhanced Raman scattering (SERS) empowered by isotope exchange and electrochemical Stark effects, reaction kinetics (Tafel) analysis, and density functional theory (DFT) simulations. The SERS spectra are measured on an operating Cu surface. These results advance the mechanistic understanding of CO2 electroreduction and its selectivity to carbon monoxide and formate.
Learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties

Modeling biomechanical musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. In this work, we not only investigate whether machine learning approaches are capable of learning a controller for such systems. We are especially interested in the question if the structure of the musculoskeletal apparatus exhibits properties that are favorable for the learning task. In particular, we consider learning a control policy from target positions to muscle stimulations. To account for the high actuator redundancy of biomechanical systems, our approach uses a learned forward model represented by a neural network and sequential quadratic programming to obtain the control policy, which also enables us to alternate the co-contraction level and hence allows to change the stiffness of the system and to include optimality criteria like small muscle stimulations. Experiments on both a simulated musculoskeletal model of a human arm and a real biomimetic muscle-driven robot show that our approach is able to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency.
Random Distance Method for Generating Unimodal and Multimodal Cancelable Biometric Features

The cancelable biometric-based template protection method proposed in this paper addresses security and privacy concerns emerging from the phenomenal usage of biometric systems. Cancelable biometric transforms the original biometric identity of a user to a pseudo-biometric identity that is used for storage and matching purposes. The use of pseudo-identity mitigates privacy risks and allows revocability in case of compromise. This paper proposes a novel template transformation technique named random distance method which not only generates discriminative and privacy preserving revocable pseudo-biometric identities, but also reduces their size by 50%. Extensive experimentation is performed to analyze recognition and protection performance on unimodal and multimodal pseudo-identities generated with various biometric modalities such as face, thermal face, palmprint, palmvein, and fingervein. It is observed that the matching performance obtained with the proposed cancelable templates in the worst-case is closer to the performance achieved in the original domain. Also, multimodal cancelable biometric templates generated with the proposed method are observed for improved performance. Furthermore, the proposed approach is successfully analyzed for non-invertibilty, unlinkability, as well as its resistance for various types of attacks like attacks via record multiplicity, dictionary, false accepts, and brute force.
Apk2vec: Semi-Supervised Multi-view Representation Learning for Profiling Android Applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semisupervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for large-scale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.
Force Control of Series Elastic Actuators-Driven Parallel Robot

This paper proposes a novel parallel robot - Virtual Ground Robot (VGR) - that is driven by three Series Elastic Actuators (SEAs) to interact with a human. The proposed Virtual Ground Robot provides a virtual ground on which a human can stand on and interact in three directions: the pitch, the roll and the height directions. The most significant features of the proposed VGR are that 1) it is driven by RFSEAs (Reaction Force-sensing Series Elastic Actuator), and thus it can provide precise forces and torques, 2) the size of the VGR is small enough for a human to stand on with ease, and 3) it can generate torque/force large to support a weight of a human. Taking advantage of RFSEAs utilized in the proposed VGR, Spatial Force control algorithm is proposed in this paper. In order to design this controller, the motions of VGR are defined in the task space, the joint space and the RFSEA level. Based on the Kinematics, force control of VGR in the task level, which is named Spatial Force Control is designed and verified using experiments.
A histological study of the long‐term response to trauma of squamate integument

Scale neogenesis following excision of pieces of skin of various sizes and shapes has been studied in several squamate species. In small wounds, neogenesis does not occur, the wound region contracts, and increased areas of hinge differentiation are seen. In large wounds, the time taken for neogenesis to occur, and the size, shape, distribution, and number of the reformed units, are variable. We confirm previous reports that neogenesis accompanies a renewal phase of the shedding cycle, and suggests that this is because both types of morphogenesis require germinal stability.
Goats prefer positive human emotional facial expressions

Domestication has shaped the physiology and the behaviour of animals to better adapt to human environments. Therefore, human facial expressions may be highly informative for animals domesticated for working closely with people, such as dogs and horses. However, it is not known whether other animals, and particularly those domesticated primarily for production, such as goats, are capable of perceiving human emotional cues. In this study, we investigated whether goats can distinguish human facial expressions when simultaneously shown two images of an unfamiliar human with different emotional valences (positive/happy or negative/angry). Both images were vertically attached to a wall on one side of a test arena, 1.3 m apart, and goats were released from the opposite side of the arena (distance of 4.0 m) and were free to explore and interact with the stimuli during the trials. Each of four test trials lasted 30 s. Overall, we found that goats preferred to interact first with happy faces, meaning that they are sensitive to human facial emotional cues. Goats interacted first, more often and for longer duration with positive faces when they were positioned on the right side. However, no preference was found when the positive faces were placed on the left side. We show that animals domesticated for production can discriminate human facial expressions with different emotional valences and prefer to interact with positive ones. Therefore, the impact of domestication on animal cognitive abilities may be more far-reaching than previously assumed.
Cloud, Fog, and Edge Computing: A Software Engineering Perspective

Cloud, Fog, and Edge Computing paradigms have been introduced for data-driven organizations in order to facilitate data computation and processing in an easier manner. Considering that there has been a huge increase in the amount of data produced over the past couple of years, and it is expected that the amount of data produced will exponentially grow, recent research has focused on utilizing these paradigms in order to satisfy the growing demand of fast computation and data storage. In order to recognize the most suitable use for these models, this paper will evaluate the three computing paradigms: cloud computing, fog computing, and edge computing in terms of their architectures. Furthermore, a comparative analysis of the non-functional requirements is conducted and used to propose the use of each paradigm in real-life applications.
Working memory predicts subsequent episodic memory decline during healthy cognitive aging: evidence from a cross-lagged panel design

ABSTRACT Aging adults experience declines in working memory and episodic memory, however, it is unclear how these declines operate over time. Decreased working memory may be associated with early changes in episodic memory, by reducing older adults’ ability to meaningfully integrate new information into pre-existing schemas and recall information without the assistance of cues. Given the increased prevalence of Alzheimer’s disease, and concerns based on subjective memory changes, it is important to understand how these processes interact over time. To assess the relationship between working memory and episodic memory during healthy cognitive aging, we performed neuropsychological assessments at multiple time points in a sample of 310 community-dwelling older adults. Using a cross-lagged panel design, we demonstrated that the lagged associations between working memory and later episodic free recall were 50% larger than the lagged associations between episodic recall and later working memory, suggesting working memory may be a useful metric of future episodic memory decline.
Signboard Saliency Detection in Street Videos

During the last few decades researchers in computer vision have proposed various saliency models for images with the common goal of classifying the image content by using the measure of importance. However, compared to still images, there is only a limited number of saliency detection algorithms proposed for video signals. However, predicting where a person looks in a video is relevant for applications such as advertisement design, video re-targeting and editing. In this work, we propose a novel method for video saliency detection that aims to detect the relative ranking of saliencies of signboards in street videos. For that reason, we collected eye-gaze data of participants viewing various street videos in free viewing and task viewing scenarios, where the task was to identify a place to have lunch at. Further, we quantitatively analyzed the collected eye-gaze data in order to generate the relative ranking of the signboards in the free viewing and the task viewing scenario. Based on the analysis' results, we propose a video saliency detection algorithm which can more accurately predict the relative saliencies of signboards in street videos. It can be seen that the prediction accuracy of our proposed model outperforms the existing video saliency detection algorithms.
Bovine Epithelial in vitro Infection Models for Mycoplasma bovis

Mycoplasma bovis causes bovine mycoplasmosis. The major clinical manifestations are pneumonia and mastitis. Recently an increase in the severity of mastitis cases was reported in Switzerland. At the molecular level, there is limited understanding of the mechanisms of pathogenicity of M. bovis. Host–pathogen interactions were primarily studied using primary bovine blood cells. Therefore, little is known about the impact of M. bovis on other cell types present in infected tissues. Clear in vitro phenotypes linked to the virulence of M. bovis strains or tissue predilection of specific M. bovis strains have not yet been described. We adapted bovine in vitro systems to investigate infection of epithelial cells with M. bovis using a cell line (MDBK: Madin-Darby bovine kidney cells) and two primary cells (PECT: bovine embryonic turbinate cells and bMec: bovine mammary gland epithelial cells). Two strains isolated before and after the emergence of severe mastitis cases were selected. Strain JF4278 isolated from a cow with mastitis and pneumonia in 2008 and strain L22/93 isolated in 1993 were used to assess the virulence of M. bovis genotypes toward epithelial cells with particular emphasis on mammary gland cells. Our findings indicate that M. bovis is able to adhere to and invade different epithelial cell types. Higher titers of JF4278 than L22/93 were observed in co-cultures with cells. The differences in titers reached between the two strains was more prominent for bMec cells than for MDBK and PECT cells. Moreover, M. bovis strain L22/93 induced apoptosis in MDBK cells and cytotoxicity in PECT cells but not in bMec cells. Dose-dependent variations in proliferation of primary epithelial cells were observed after M. bovis infection. Nevertheless, an indisputable phenotype that could be related to the increased virulence toward mammary gland cells is not obvious.
The Effects of Motor Imagery on Pain and Range of Motion in Musculoskeletal Disorders: A Systematic Review Using Meta-Analysis

Introduction: In recent years, there has been an increase in the use of motor imagery (MI) in the rehabilitation of musculoskeletal pain conditions. Across the literature, most reviews have yet to consider Laterality Judgement Task training as a form of MI method. This review aimed to evaluate the effectiveness of using MI as an adjunct to standard rehabilitation on the improvement of pain and range of motion parameters when managing patients with musculoskeletal pain conditions. Methods: Searches of 8 major electronic databases were conducted. Data for pain and range of motion were extracted. Meta-analyses (where possible) with either a fixed or random-effect(s) model, standardized mean differences (SMDs), and tests of heterogeneity were performed. Results: Eight clinical controlled trials were identified and included in the meta-analyses. When compared with standard rehabilitation alone, the adjunctive role of MI provided superior pain relief (pooled SMD, −2.25; 95% confidence interval, −4.11 to −0.4; P=0.02), and greater improvement in range of motion (pooled SMD, 3.04; 95% confidence interval, 0.66-5.43; P=0.01) in chronic musculoskeletal pain disorders. Discussion: The results suggest that MI may be effective for pain relief and improvement in range of motion among chronic musculoskeletal pain conditions, although conclusion is based on a limited certainty of evidence as assessed using the GRADES (Grading of Recommendation, Assessment, Development and Evaluation) approach.
XOR-Based Visual Cryptographic Schemes With Monotonously Increasing and Flawless Reconstruction Properties

A <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> out of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> visual cryptographic scheme [(<inline-formula> <tex-math notation="LaTeX">$k, n)$ </tex-math></inline-formula>-VCS] encodes a secret image <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> into <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> transparencies (shares) in such a way that any <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> shares reveal <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> in a visual sense when they are superimposed (equivalent to the “OR” operation), while any <inline-formula> <tex-math notation="LaTeX">$k-1$ </tex-math></inline-formula> or less cannot. A (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$n)$ </tex-math></inline-formula>-XVCS achieves the same goal with a better contrast on the reconstructed result of any <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> “XOR”ed shares. It has been proved that a (<inline-formula> <tex-math notation="LaTeX">$k, n)$ </tex-math></inline-formula>-VCS is also a (<inline-formula> <tex-math notation="LaTeX">$k, n)$ </tex-math></inline-formula>-XVCS. However, the monotonously increasing property (that any <inline-formula> <tex-math notation="LaTeX">$k+1, k+2, \ldots , n$ </tex-math></inline-formula> shares also reveal <inline-formula> <tex-math notation="LaTeX">$P)$ </tex-math></inline-formula> in most conventional (<inline-formula> <tex-math notation="LaTeX">$k, n)$ </tex-math></inline-formula>-VCSs is not guaranteed when they are applied as (<inline-formula> <tex-math notation="LaTeX">$k, n$ </tex-math></inline-formula>)-XVCSs. This paper aims at developing efficient and flexible constructions for (<inline-formula> <tex-math notation="LaTeX">$k, n)$ </tex-math></inline-formula>-XVCSs with minimal pixel expansions, such that the monotonously increasing property, or even the flawless reconstruction of <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> shares are “XOR”ed, could be additionally fulfilled. Property analyses and comparisons against Tuyls <italic>et al.</italic>’s schemes are examined to demonstrate the applicability and superiority (in terms of the pixel expansion) of our schemes.
Total Variation Iterative Linear Expansion of Thresholds with Applications in CT

The iterative linear expansion of threshold framework, or iLET, offers a new approach for solving image restoration problems under sparsity assumptions. Instead of estimating the reconstructed image directly, the iLET paradigm parametrizes the reconstruction process as a linear combination of elementary thresholding functions and optimizes over their coefficients. Here, we rely on the fast and accurate convergence of iLET, and propose an extension of this framework, under the assumption that the reconstructed object is approximately piece-wise constant. This assumption leads to a new total-variation framework of iLET. We demonstrate the applicability of our technique to bio-medical imaging problems, such as computerized tomography reconstruction. Our technique surpasses state-of-the-art reconstructions in terms of PSNR and SSIM, while offering an automatic way for tuning its regularization parameter.