Miracle-dz/UniFAR-FTUs huggingface dataset:
Activation of the Wnt/β-Catenin Signaling Pathway by Mechanical Ventilation Is Associated with Ventilator-Induced Pulmonary Fibrosis in Healthy Lungs

Background Mechanical ventilation (MV) with high tidal volumes (VT) can cause or aggravate lung damage, so-called ventilator induced lung injury (VILI). The relationship between specific mechanical events in the lung and the cellular responses that result in VILI remains incomplete. Since activation of Wnt/β-catenin signaling has been suggested to be central to mechanisms of lung healing and fibrosis, we hypothesized that the Wnt/β-catenin signaling plays a role during VILI. Methodology/Principal Findings Prospective, randomized, controlled animal study using adult, healthy, male Sprague-Dawley rats. Animals (n = 6/group) were randomized to spontaneous breathing or two strategies of MV for 4 hours: low tidal volume (VT) (6 mL/kg) or high VT (20 mL/kg). Histological evaluation of lung tissue, measurements of WNT5A, total β-catenin, non-phospho (Ser33/37/Thr41) β-catenin, matrix metalloproteinase-7 (MMP-7), cyclin D1, vascular endothelial growth factor (VEGF), and axis inhibition protein 2 (AXIN2) protein levels by Western blot, and WNT5A, non-phospho (Ser33/37/Thr41) β-catenin, MMP-7, and AXIN2 immunohistochemical localization in the lungs were analyzed. High-VT MV caused lung inflammation and perivascular edema with cellular infiltrates and collagen deposition. Protein levels of WNT5A, non-phospho (Ser33/37/Thr41) β-catenin, MMP-7, cyclin D1, VEGF, and AXIN2 in the lungs were increased in all ventilated animals although high-VT MV was associated with significantly higher levels of WNT5A, non-phospho (Ser33/37/Thr41) β-catenin, MMP-7, cyclin D1, VEGF, and AXIN2 levels. Conclusions/Significance Our findings demonstrate that the Wnt/β-catenin signaling pathway is modulated very early by MV in lungs without preexistent lung disease, suggesting that activation of this pathway could play an important role in both VILI and lung repair. Modulation of this pathway might represent a therapeutic option for prevention and/or management of VILI.
Low Area & Memory Efficient VLSI Architecture of 1D/2D DWT for Real Time Image Decomposition

There is a dearth of high quality reconfigurable hardware for DWT which can be extensively used in various real time signal and image processing applications. In this article, we have focused on proposing a dedicated customizable hardware for DWT applicable to 1D/2D signal processing. We have proposed bit-serial Distributed Arithmetic (DA) based VLSI architectures for ID/2D DWT. Exploitation of DA enables us to make our designs Multiplierless, thereby consuming less area. Bit serial configuration of DA is also exploited to introduce modularity and pipelining in the proposed convolution DWT based ID/2D architectures. Though the speed of the proposed designs may not be suitable for certain applications, a number of parallel channels can be introduced for the same. The provision of mode selection can efficiently be used to realize 9/7 and also 5/3 DWT filters in the same proposed architecture. In the proposed 1D/2D architecture, we have achieved about 50% memory size reduction, 30% reduction in area consumption and comparable speed in comparison to the other latest notable DWT architecture so far. We have verified the viability of our proposed 2D architecture by utilizing it in real time image decomposition.
Real-time Fault Diagnosis of Rotating Machinery Using 1-D Convolutional Neural Network

Fast, accurate and robust fault diagnosis is an indispensable part of condition-based maintenance (CBM) and intelligent methods are widely used for this purpose. However, the intelligent methods with satisfied diagnosis accuracy are always accompanied by high computing cost, which limits their application in the industrial applications with real-time requirements. In this paper, we propose a fast accurate and robust rotating machinery diagnosis method based on one-dimensional(1-D) convolutional neural network, which works directly on the raw signal without any preprocessing. Besides, the proposed method uses single full-connected layer for reducing computing cost. The experimental results obtained using bearing and gearbox dataset demonstrate that the proposed method can realize fast, accurate and robust fault diagnosis, which is suitable for real-time diagnosis applications.
Self-supervised Sequence-to-sequence ASR using Unpaired Speech and Text

Sequence-to-sequence ASR models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for self-supervised and supervised training in such models. This work builds upon recent results showing notable improvements in self-supervised training using cycle-consistency and related techniques. Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with text-to-speech (TTS) models. In particular, this work proposes a new self-supervised loss combining an end-to-end differentiable ASR$\rightarrow$TTS loss with a point estimate TTS$\rightarrow$ASR loss. The method is able to leverage both unpaired speech and text data to outperform recently proposed related techniques in terms of \%WER. We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora. Our code is provided to reproduce the experiments.
Distributed Representation Learning of Knowledge Graph with Diverse Information

Knowledge graph is a type of network structure in which nodes represent entities and edges indicate relations. However, as the network size explosively increases, the issues of data sparsity and computation inefficiency on large-scale knowledge graph become more difficult to manipulate and manage. Knowledge graph embedding, which is a representation technique of embedding entities and relations in the knowledge graph into continuous, dense, and low-dimensional semantics vector spaces to tackle these challenges and endow the model with the abilities of knowledge fusion and inference, has recently attracted much attention. In this paper, we firstly introduce the overall framework and specific idea of embedding models. We then introduce two applications that apply KG embedding, compare the performance of the methods in these applications. Finally, we summarize several challenges to overcome, and provide some prospective future research directions such as deep learning network for approaches and applications.
Performance Metrics of Imprecise Multipliers Based on Proximate Compressors for IIR Filters

Confined power and exceptional accomplishment are the essential prerequisites for compact appliances whereby imprecise estimation is drawing attention in computer arithmetic units, digital signal processing algorithms and multimedia applications. In the above devoted schemes, multipliers are the principal module that require huge silicon area and absorbs more power. On account of this, approximations were being enforced in the form of 4:2 compressors, considering that the compressors are critical sections of the multipliers. This paper demonstrates unique proposed approximate 4:2 compressor to embed it in 8 x 8 and 16 x 16 Dadda multiplier and analysis of proposed compressor based multiplier with exact one is being done which exhibits that the power of the proposed circuit is declined by 61.03% and 74.72% respectively. This multiplier has been positioned in an Infinite Impulse Response (IIR) filter, to regulate the speed of the output signal of this digital filter, an eye pattern measurements have been anticipated and the fundamental specifications such as jitters, mean and standard deviations of the time and amplitude distortions have been disclosed. Finally, elementary Bit Error Rate (BER) assessment has been carried out from eye diagrams. The simulations of these designs have been performed in 45nm technology node using cadence spectre©simulator.
Increased inter-hemispheric functional connectivity for concrete word imagery compared to abstract word imagery

Many behavioral and neuroimaging studies have shown that the human brain represents, recall and learn concrete words more accurately and easier than abstract words. This phenomenon is defined as the concreteness effect. Dual coding theory is one of the main important theories which is often used to justify the concreteness effect. Several neuroimaging studies tried to localize and compare activated areas during concrete and abstract word processing, but brain connectivity was not studied in the above tasks. In a fMRI experiment, we acquired fMRI data from 11 healthy volunteers in the visual mental imagery of concrete and abstract characteristics. In a novel work, we then used inter-hemispheric connectivity to compare the brain hemispheres integration between concrete and abstract word processing. Twelve important brain regions reported in previous studies of abstract or concrete concept processing were selected. Then the correlations of BOLD activity of each region with its corresponding contralateral region were calculated and compared for the time intervals of concrete and abstract word processing. Results revealed that the inter-hemispheric connectivity during concrete word imagery was significantly different compared to abstract word imagery (FDR correction, q = 0.05) in three contrasting regions which were middle occipital gyrus, inferior occipital gyrus and temporal pole (superior temporal gyrus). Notably, in all of these three areas, the inter-hemispheric functional connectivity was significantly stronger during concrete word imagery to that of abstract. These results imply that the synchronization between brain hemispheres within visual and language processing areas is significantly stronger for imagery of concrete words (more imageable words) than that of abstract words (less imageable words).
cAMP Response Element-Binding Protein Is a Primary Hub of Activity-Driven Neuronal Gene Expression

Long-lasting forms of neuronal plasticity require de novo gene expression, but relatively little is known about the events that occur genome-wide in response to activity in a neuronal network. Here, we unveil the gene expression programs initiated in mouse hippocampal neurons in response to different stimuli and explore the contribution of four prominent plasticity-related transcription factors (CREB, SRF, EGR1, and FOS) to these programs. Our study provides a comprehensive view of the intricate genetic networks and interactions elicited by neuronal stimulation identifying hundreds of novel downstream targets, including novel stimulus-associated miRNAs and candidate genes that may be differentially regulated at the exon/promoter level. Our analyses indicate that these four transcription factors impinge on similar biological processes through primarily non-overlapping gene-expression programs. Meta-analysis of the datasets generated in our study and comparison with publicly available transcriptomics data revealed the individual and collective contribution of these transcription factors to different activity-driven genetic programs. In addition, both gain- and loss-of-function experiments support a pivotal role for CREB in membrane-to-nucleus signal transduction in neurons. Our data provide a novel resource for researchers wanting to explore the genetic pathways associated with activity-regulated neuronal functions.
Increasing response rates among coaches: the role of prenotification methods.

This study determined whether, in a population of intercollegiate head coaches, prenotification had a significant influence on the return rate of mailed questionnaires. Acknowledging the growing use of e-mail and the Internet for survey distribution, the current study evaluated the effectiveness of e-mail as a prenotification technique, Response rates of Intercollegiate Head Coaches to mailed questionnaires from two separate samples were analyzed. Comparisons were made based upon the categorization of coaches into groups of prenotification by e-mail, formal letter, and a non-prenotified control group. Results indicated that prenotification of the survey recipients significantly increased response rates, with the group receiving e-mail prenotification having the highest response rate among the three groups. In addition to being cost effective for researchers, e-mail prenotification was an effective way to increase both the number and variety of contacts with survey recipients.
Known-class Aware Self-ensemble for Open Set Domain Adaptation

Existing domain adaptation methods generally assume different domains have the identical label space, which is quite restrict for real-world applications. In this paper, we focus on a more realistic and challenging case of open set domain adaptation. Particularly, in open set domain adaptation, we allow the classes from the source and target domains to be partially overlapped. In this case, the assumption of conventional distribution alignment does not hold anymore, due to the different label spaces in two domains. To tackle this challenge, we propose a new approach coined as Known-class Aware Self-Ensemble (KASE), which is built upon the recently developed self-ensemble model. In KASE, we first introduce a Known-class Aware Recognition (KAR) module to identify the known and unknown classes from the target domain, which is achieved by encouraging a low cross-entropy for known classes and a high entropy based on the source data from the unknown class. Then, we develop a Known-class Aware Adaptation (KAA) module to better adapt from the source domain to the target by reweighing the adaptation loss based on the likeliness to belong to known classes of unlabeled target samples as predicted by KAR. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of our approach.
The Cross-Cultural Transferability of a Peer-Based Employee Assistance Program (EAP): A Case Study

Member Assistance Programs (MAPs) are peer-based Employee Assistance Programs (EAPs) that originated in the United States and have been transferred to other countries. The current case study aims at exploring the cross-cultural transferability of MAPs by comparing the first MAP established in Israel with the prototypical American program. The observed differences are explained on the basis of cross-cultural organizational theory. In addition, empirically-testable hypotheses regarding the cross-cultural variations of MAPs are developed. The implications for EAP and MAP research and practice are discussed.
Detection of Rare Genetic Diseases using Facial 2D Images with Transfer Learning

Around 6-8% of people are affected by rare genetic disorders in the world. Due to unavailability of proper identification of gene variants causing these rare disorders, the genetic tests for many of them do not exist with an exception to some common conditions like Down’s Syndrome. A limited number of individuals have the proper training and ability to recognize these disorders and have to depend on the pronounced facial features which occur in about 30-40% of these diseases. Fine-tuning of the pre-trained model VGGFace(with ResNet50 network architecture) on our dataset comprising of 12 classes of rare genetic syndromes leads to a top accuracy of 97.66% and an F1 score of 0.86. On using a smaller dataset with 8 classes of syndromes same as Clinical Face Phenotype Space, the state-of-the-art, our model produces a top accuracy of 98.1% and an F1 score of 0.92. This could be used to screen for rare genetic diseases in newborns.
Optimal Paths for a Mobile Manipulator using the Leapfrog Method

This paper deals with finding optimal paths for a mobile manipulator in the presence of obstacles. The motion planning of the robot kinematic system is formulated as an optimal control problem. Applying Pontryagin’s minimum principle, indirect conditions of optimality are derived for the optimal motion planning problem and solved numerically using the Leapfrog method. Simulation results for the mobile manipulator are presented to demonstrate the effectiveness of the proposed method.
Development of pipe inspection robot

This paper describes a robot capable of inspecting a pipe. The robot consists of a fore leg system, a rear leg system and a body. The fore and rear leg systems are constructed by using three worm gear systems that are arranged at an angle of 120 degrees with respect to each other to operate inside pipes of different diameters. Also, the body is different diameters, and the effectiveness of the mechanism of the robot is confirmed.
Thinking emotions with two-year-old children: an educational programme to improve emotional knowledge in young preschoolers / Pensando las emociones con niños de dos años: un programa educativo para mejorar el conocimiento emocional en primer ciclo de Educación Infantil

Abstract This article aims to present preliminary data to validate and show the effectiveness of the educational programme Thinking emotions with a sample of children aged two years. To test its effectiveness, a sample of 57 children was selected. The sample was divided into experimental group (N = 38) and control group (N = 19). Participants were evaluated for overall development (Brunet-Lezine) and emotional understanding through two tests (AKT and TEC) before and after the implementation of the programme in the classroom. The tutors implemented the programme during school hours in weekly 45-minute sessions over six months. The results show significant advances in emotion understanding of children in the experimental group versus the control group. Results are discussed considering the implications of early education on emotions and the consequences on psychological wellbeing.
The effect of pharmacotherapist characteristics on treatment outcome in panic disorder

A large number of studies suggest that individual characteristics of psychotherapists affect the outcome of psychosocial treatments for psychiatric illness, but little work has been done to see if this is also the case for pharmacotherapy. In the context of a multicenter study that compared psychosocial and medication treatments for panic disorder, we assessed whether such characteristics as age of the psychiatrist, number of years of experience, and gender influence the outcome of treatment with the antipanic drug imipramine. Data were examined by multiple and logistic regression analyses for eight psychiatrists who treated a total of 57 patients with panic disorder. More physician experience, measured as years since completing residency, was associated with better response to imipramine on one of two main dichotomous measures (the Clinical Global Impression‐Improvement Scale) and on six of nine continuous measure rating scales. Associations between psychiatrist age and outcome and between psychiatrist gender and outcome were also present but on fewer measures. Although these are post‐hoc analyses that were not planned when the multicenter study was originally designed and therefore there are limitations in the information available about the psychiatrists' characteristics, the findings suggest that even in the context of a clinical trial that employs a specific protocol and single medication, physician experience may influence patient outcome. Depression and Anxiety 17:88–93, 2003. © 2003 Wiley‐Liss, Inc.
Performance Comparison for Ballistocardiogram Peak Detection Methods

A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods’ authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants.
Challenges for Decentralized Congestion Control Mechanisms in Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networking (VANET) is an emerging technology defined with main motive of avoiding on road accidents and provide safety and life critical applications to users in a well-organized manner. We present current challenges in designing VANET protocols through extensive survey from the perspective of decentralized congestion control (DCC) strategies. The challenges in routing and security applications are also outlined briefly. Internet of things (IoT) will be the future networking system in which all things will be connected to interact. In the near future, VANETs will be the main part of IoT networks because of its safety and infotainment applications. A lot of work is done in this area, still, the industry thrusts for more efficient and robust design. This paper highlights various research challenges, which will help other researchers who wish to work in this area.
An Unsupervised Framework for Author-Paper Linking in Bibliographic Retrieval System

Author name ambiguity can significantly impact the accuracy of a bibliographic retrieval system, especially when author name served as a search keyword. In this paper, we propose an unsupervised approach addressing the name ambiguity problem by linking papers to their corresponding authors based on clustering result of word embeddings. Each cluster represents a collection of words in a certain research area. Papers and authors which to be disambiguated are then assigned a probability of each research area they belong to. We put those probabilities and some metadata of papers and authors as features into a graphic model and do the collective inference. Experiment shows that our entirely unsupervised method perform well for a Chinese Bibliographic Retrieval System even with a huge amount of noisy in its database.
Modeling and Robust Integral Sliding Mode Control for a Quadrotor Unmanned Aerial Vehicle

In the last years, many research studies have been presented on quadrotors due to their different applications in daily life. In this paper, a complete mathematical model of generalized flight is developed based on the Newton-Euler method. This one takes into account external disturbances (aero-dynamic forces and moments) acting on the vehicle. A nonlinear control strategy based on the sliding mode control technique has been designed. This controller is employed to control the drone attitude and position as well as to compensate for external disturbances acting on the quadrotor. The stability of the system in closed-loop is ensured by Lyapunov’s theorem and analysis. A control method integral sliding mode controller (ISMC) has been developed to improve the controller performance of the SMC. The problem of chattering in the suggested controllers was treated without degradation of the system performance (precision, rapidity, robustness). Finally, to validate the choice of controllers proposed, several simulations were developed with the presence of aerodynamic disturbances.