Miracle-dz/UniFAR-FTUs huggingface dataset:
Configuration Synthesis and Performance Analysis of Finger Soft Actuator

Compared with the traditional rigid finger actuator, the soft actuator has the advantages of light weight and good compliance. This type of finger actuator can be used for data acquisition or finger rehabilitation training, and it has broad application prospects. The motion differences between the soft actuator and finger may cause extrusion deformation at the binding point, and the binding forces along nonfunctional direction may reduce drive efficiency. In order to reduce the negative deformation of soft structure and improve comfort, the configuration synthesis and performance analysis of the finger soft actuator were conducted for the present work. The configuration synthesis method for soft actuator was proposed based on the analysis of the physiological structure of the finger, and the soft actuator can make the human-machine closed-loop structure including n joints (n = 1, 2, 3) meet the requirement of DOF (degrees of freedom). Then the typical feasible configurations were enumerated. The different typical configurations were analyzed and compared based on the establishment of mathematical models and simulation analysis. Results show that the configuration design method is feasible. This study offers a theoretical basis for designing the configuration of finger soft actuator.
Stressful Life Memories Relate to Ruminative Thoughts in Women With Sexual Violence History, Irrespective of PTSD

More than one in every four women in the world experience sexual violence (SV) in their lifetime, most often as teenagers and young adults. These traumatic experiences leave memories in the brain, which are difficult if not impossible to forget. We asked whether women with SV history experience stronger memories of their most stressful life event than women without SV history and if so, whether strength relates to ruminative and trauma-related thoughts. Using the Autobiographical Memory Questionnaire (AMQ), women with SV history (n = 64) reported this memory as especially strong (p < 0.001), remembering more sensory and contextual details, compared to women without SV history (n = 119). They further considered the event a significant part of their personal life story. The strength of the memory was highly correlated with posttraumatic cognitions and ruminative thoughts, as well as symptoms of depression and anxiety (p's < 0.001, n = 183). A third (33%) of the women with SV history were diagnosed with posttraumatic stress disorder (PTSD), but PTSD alone did not account for the increase in memory strength (p's < 0.001). These data suggest that the experience of SV increases the strength of stressful autobiographical memories, which are then reexperienced in everyday life during posttraumatic and ruminative thoughts. We propose that the repeated rehearsal of vivid stressful life memories generates more trauma memories in the brain, making the experience of SV even more difficult to forget.
3D hydrogel breast cancer models for studying the effects of hypoxia on epithelial to mesenchymal transition

Solid tumors are 3D assemblies of cancer cells, together with multiple stromal cell types within an extracellular matrix. Yet, the vast majority of cell-based studies to characterize oncogenesis and discovery of new anti-cancer drugs is conducted using conventional 2D monolayer culture systems, where cells are grown on plastic substratum under normoxic environments. In current study, we generated 3D breast cancer cell culture platform consists of photocrosslinkable hydrogels and encapsulated isogenic primary (21PT) and a metastatic (21MT-2) breast cancer cell lines derived from the primary tumor and pleural effusion from the same patient. We demonstrated that hypoxia decreased cellular assembly size and density, and promoted epithelial to mesenchymal transition (EMT) process, without affecting cell viability. Next, we showed hypoxia enhanced breast cancer cell migration, and expression and secretion of lysyl oxidase (LOX), which is copper-dependent amine oxidase and has the primary function to drive the crosslinking of collagen and elastin and is regulated by hypoxia. Furthermore, to recapitulate in vivo situation, we generated breast cancer and lung cells (derived from the same patient) contact model by stacking 3D hydrogel constructs with breast cancer cells onto lung mesenchymal cells (LMC) laden-hydrogel and then showed breast cancer cells migrated towards LMC during hypoxia. Lastly, as a validation of this model for future screen of therapeutic agents, we demonstrated that LOX inhibitor exhibited a significant decrease in breast cancer cell viability, migration, and EMT. Taken together, these results validate the use of hydrogels based models to examine hypoxia related EMT in breast cancer cells.
Disentangling Host-Microbiota Regulation of Lipid Secretion by Enterocytes: Insights from Commensals Lactobacillus paracasei and Escherichia coli

The specific contribution of each bacterial species within a complex microbiota to the regulation of host lipid metabolism remains largely unknown. Using two model commensal microorganisms, L. paracasei and E. coli, we demonstrated that both bacterial species impacted host lipid metabolism in a diet-dependent manner and, notably, that L. paracasei-colonized mice but not E. coli-colonized mice resisted high-fat-diet-induced body weight gain. In addition, we set up cellular models of fatty acid absorption and secretion by enterocytes cocultured with bacteria and showed that, in vitro, both L. paracasei and E. coli inhibited lipid secretion, through increased intracellular fat storage and enhanced lipid catabolism, respectively. ABSTRACT The gut microbiota contributes to nutrients absorption and metabolism by enterocytes, but the molecular mechanisms involved remain poorly understood, and most conclusions are inferred from studies comparing germfree and conventional animals colonized with diverse bacterial species. We selected two model commensal microorganisms, Escherichia coli and Lactobacillus paracasei, to assess the role of the small-intestinal microbiota in modulating lipid absorption and metabolism by the epithelium. Using an integrated approach encompassing cellular and murine models and combining metabolic parameters measurement, lipid droplet imaging, and gene expression analysis, we demonstrated that under homeostatic conditions, L. paracasei promotes fat storage in enterocytes, whereas E. coli enhances lipid catabolism and reduces chylomicron circulating levels. The Akt/mammalian target of sirolimus (mTOR) pathway is inhibited by both bacterial species in vitro, indicating that several regulatory pathways are involved in the distinct intracellular lipid outcomes associated with each bacterial species. Moreover, soluble bacterial factors partially reproduce the effects observed with live microorganisms. However, reduction of chylomicron circulating levels in E. coli-colonized animals is lost under high-fat-diet conditions, whereas it is potentiated by L. paracasei colonization accompanied by resistance to hypercholesterolemia and excess body weight gain. IMPORTANCE The specific contribution of each bacterial species within a complex microbiota to the regulation of host lipid metabolism remains largely unknown. Using two model commensal microorganisms, L. paracasei and E. coli, we demonstrated that both bacterial species impacted host lipid metabolism in a diet-dependent manner and, notably, that L. paracasei-colonized mice but not E. coli-colonized mice resisted high-fat-diet-induced body weight gain. In addition, we set up cellular models of fatty acid absorption and secretion by enterocytes cocultured with bacteria and showed that, in vitro, both L. paracasei and E. coli inhibited lipid secretion, through increased intracellular fat storage and enhanced lipid catabolism, respectively.
Resting regional brain activity and connectivity vary with resting blood pressure but not muscle sympathetic nerve activity in normotensive humans: An exploratory study

Blood pressure is tightly controlled by the central nervous system, particularly the brainstem. The aim of this study was to investigate the relationship between mean blood pressure (MBP), muscle sympathetic nerve activity (MSNA) and resting regional brain activity in healthy human subjects. Pseudocontinuous arterial spin labeling and functional magnetic resonance imaging of the brain were performed immediately following a laboratory microneurography recording of MSNA and BP measurement in 31 young, healthy normotensive subjects. Regional cerebral blood flow (CBF) correlated significantly with resting MBP levels in the region encompassing the rostroventrolateral medulla (RVLM), dorsolateral pons, and insular, prefrontal and cingulate cortices. Functional connectivity analysis revealed that the ventrolateral prefrontal cortex displayed greater resting connectivity strength within the RVLM in the lower compared with the higher MBP group. No significant differences in CBF were found when subjects were divided based on their MSNA levels. These results suggest that even subtle differences in resting MBP are associated with significant differences in resting activity in brain regions, which are well known to play a role in cardiovascular function. These data raise the question of the potential long-term consequences of differences in regional brain activity levels and their relationship with systemic blood pressure.
Contextual Encoding for Translation Quality Estimation

The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and which are wrong. In this paper, propose a method to effectively encode the local and global contextual information for each target word using a three-part neural network approach. The first part uses an embedding layer to represent words and their part-of-speech tags in both languages. The second part leverages a one-dimensional convolution layer to integrate local context information for each target word. The third part applies a stack of feed-forward and recurrent neural networks to further encode the global context in the sentence before making the predictions. This model was submitted as the CMU entry to the WMT2018 shared task on QE, and achieves strong results, ranking first in three of the six tracks.
Future-Prediction-Based Model for Neural Machine Translation

We propose a novel model for Neural Machine Translation (NMT). Different from the conventional method, our model can predict the future text length and words at each decoding time step so that the generation can be helped with the information from the future prediction. With such information, the model does not stop generation without having translated enough content. Experimental results demonstrate that our model can significantly outperform the baseline models. Besides, our analysis reflects that our model is effective in the prediction of the length and words of the untranslated content.
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization

Recently deep reinforcement learning (DRL) has achieved outstanding success on solving many difficult and large-scale RL problems. However the high sample cost required for effective learning often makes DRL unaffordable in resource-limited applications. With the aim of improving sample efficiency and learning performance, we will develop a new DRL algorithm in this paper that seamless integrates entropy-induced and bootstrap-induced techniques for efficient and deep exploration of the learning environment. Specifically, a general form of Tsallis entropy regularizer will be utilized to drive entropy-induced exploration based on efficient approximation of optimal action-selection policies. Different from many existing works that rely on action dithering strategies for exploration, our algorithm is efficient in exploring actions with clear exploration value. Meanwhile, by employing an ensemble of Q-networks under varied Tsallis entropy regularization, the diversity of the ensemble can be further enhanced to enable effective bootstrap-induced exploration. Experiments on Atari game playing tasks clearly demonstrate that our new algorithm can achieve more efficient and effective exploration for DRL, in comparison to recently proposed exploration methods including Bootstrapped Deep Q-Network and UCB Q-Ensemble.
miR1432‐OsACOT (Acyl‐CoA thioesterase) module determines grain yield via enhancing grain filling rate in rice

Summary Rice grain filling rate contributes largely to grain productivity and accumulation of nutrients. MicroRNAs (miRNAs) are key regulators of development and physiology in plants and become a novel key target for engineering grain size and crop yield. However, there is little studies, so far, showing the miRNA regulation of grain filling and rice yield, in consequence. Here, we show that suppressed expression of rice miR1432 (STTM1432) significantly improves grain weight by enhancing grain filling rate and leads to an increase in overall grain yield up to 17.14% in a field trial. Molecular analysis identified rice Acyl‐CoA thioesterase (OsACOT), which is conserved with ACOT13 in other species, as a major target of miR1432 by cleavage. Moreover, overexpression of miR1432‐resistant form of OsACOT (OXmACOT) resembled the STTM1432 plants, that is, a large margin of an increase in grain weight up to 46.69% through improving the grain filling rate. Further study indicated that OsACOT was involved in biosynthesis of medium‐chain fatty acids. In addition, RNA‐seq based transcriptomic analyses of transgenic plants with altered expression of miR1432 demonstrated that downstream genes of miR1432‐regulated network are involved in fatty acid metabolism and phytohormones biosynthesis and also overlap with the enrichment analysis of co‐expressed genes of OsACOT, which is consistent with the increased levels of auxin and abscisic acid in STTM1432 and OXmACOT plants. Overall, miR1432‐OsACOT module plays an important role in grain filling in rice, illustrating its capacity for engineering yield improvement in crops.
Deep Learning of Human Perception in Audio Event Classification

In this paper, we introduce our recent studies on human perception in audio event classification. In particular, the pre-trained model VGGish is used as feature extractor to process audio data, and DenseNet is trained by and used as feature extractor for our electroencephalography (EEG) data. The correlation between audio stimuli and EEG is learned in a shared space. In the experiments, we record brain activities (EEG signals) of several subjects while they are listening to music events of 8 audio categories selected from Google AudioSet. Our experimental results demonstrate that i) audio event classification can be improved by exploiting the power of human perception, and ii) the correlation between audio stimuli and EEG can be learned to complement audio event understanding.
A Review of Software-Defined WLANs: Architectures and Central Control Mechanisms

The significant growth in the number of WiFi-enabled devices as well as the increase in the traffic conveyed through wireless local area networks (WLANs) necessitate the adoption of new network control mechanisms. Specifically, dense deployment of access points, client mobility, and emerging QoS demands bring about challenges that cannot be effectively addressed by distributed mechanisms. Recent studies show that software-defined WLANs (SDWLANs) simplify network control, improve QoS provisioning, and lower the deployment cost of new network control mechanisms. In this paper, we present an overview of SDWLAN architectures and provide a qualitative comparison in terms of features such as programmability and virtualization. In addition, we classify and investigate the two important classes of centralized network control mechanisms: 1) association control and 2) channel assignment. We study the basic ideas employed by these mechanisms, and in particular, we focus on the metrics utilized and the problem formulation techniques proposed. We present a comparison of these mechanisms and identify open research problems.
Metabolize Neural Network

The metabolism of cells is the most basic and important part of human function. Neural networks in deep learning stem from neuronal activity. It is self-evident that the significance of metabolize neuronal network(MetaNet) in model construction. In this study, we explore neuronal metabolism for shallow network from proliferation and autophagy two aspects. First, we propose different neuron proliferate methods that constructive the selfgrowing network in metabolism cycle. Proliferate neurons alleviate resources wasting and insufficient model learning problem when network initializes more or less parameters. Then combined with autophagy mechanism in the process of model self construction to ablate under-expressed neurons. The MetaNet can automatically determine the number of neurons during training, further, save more resource consumption. We verify the performance of the proposed methods on datasets: MNIST, Fashion-MNIST and CIFAR-10.
Hepatitis B Virus Induces Autophagy to Promote its Replication by the Axis of miR‐192‐3p‐XIAP Through NF kappa B Signaling

Hepatitis B virus (HBV) is a major risk factor for the development and progression of hepatocellular carcinoma. It has been reported that viral infection can interfere with cellular microRNA (miRNA) expression and participate in the pathogenesis of oncogenicity. Here, we report that decreasing levels of the expression of the miRNA miR‐192‐3p is associated with rising levels of HBV DNA in the serum of HBV patients. We revealed that HBV infection repressed the expression of miR‐192‐3p through hepatitis B x protein interaction with c‐myc. We further showed that miR‐192‐3p was repressed by HBV transfection in vitro and in a mouse model, leading to cellular autophagy. Using an miRNA target prediction database miRBase, we identified X‐linked inhibitor of apoptosis protein (XIAP) as a target gene of miR‐192‐3p and demonstrated that miR‐192‐3p directly targeted the XIAP 3′‐untranslated region of XIAP messenger RNA. Importantly, we discovered that HBV promoted autophagy through miR‐192‐3p‐XIAP axis and that this process was important for HBV replication in vitro and in vivo. We demonstrated that miR‐192‐3p functioned through the nuclear factor kappa B signaling pathway to inhibit autophagy, thereby reducing HBV replication. Conclusions: Our findings indicate that miR‐192‐3p is a regulator of HBV infection and may play a potential role in hepatocellular carcinoma. It may also serve as a biomarker or therapeutic target for HBV patients.
Protein Phosphatase 1α and Cofilin Regulate Nuclear Translocation of NF-κB and Promote Expression of the Anti-Inflammatory Cytokine Interleukin-10 by T Cells

While several protein serine/threonine kinases control cytokine production by T cells, the roles of serine/threonine phosphatases are largely unexplored. Here, we analyzed the involvement of protein phosphatase 1α (PP1α) in cytokine synthesis following costimulation of primary human T cells. ABSTRACT While several protein serine/threonine kinases control cytokine production by T cells, the roles of serine/threonine phosphatases are largely unexplored. Here, we analyzed the involvement of protein phosphatase 1α (PP1α) in cytokine synthesis following costimulation of primary human T cells. Small interfering RNA (siRNA)-mediated knockdown of PP1α (PP1KD) or expression of a dominant negative PP1α (D95N-PP1) drastically diminished interleukin-10 (IL-10) production. Focusing on a key transcriptional activator of human IL-10, we demonstrate that nuclear translocation of NF-κB was significantly inhibited in PP1KD or D95N-PP1 cells. Interestingly, knockdown of cofilin, a known substrate of PP1 containing a nuclear localization signal, also prevented nuclear accumulation of NF-κB. Expression of a constitutively active nonphosphorylatable S3A-cofilin in D95N-PP1 cells restored nuclear translocation of NF-κB and IL-10 expression. Subpopulation analysis revealed that defective nuclear translocation of NF-κB was most prominent in CD4+ CD45RA− CXCR3− T cells that included IL-10-producing TH2 cells. Together these findings reveal novel functions for PP1α and its substrate cofilin in T cells namely the regulation of the nuclear translocation of NF-κB and promotion of IL-10 production. These data suggest that stimulation of PP1α could limit the overwhelming immune responses seen in chronic inflammatory diseases.
Adversarial Attack Type I: Generating False Positives

False positive and false negative rates are equally important for evaluating the performance of a classifier. Adversarial examples by increasing false negative rate have been studied in recent years. However, harming a classifier by increasing false positive rate is almost blank, since it is much more difficult to generate a new and meaningful positive than the negative. To generate false positives, a supervised generative framework is proposed in this paper. Experiment results show that our method is practical and effective to generate those adversarial examples on large-scale image datasets.
Modeling Topical Coherence in Discourse without Supervision

Coherence of text is an important attribute to be measured for both manually and automatically generated discourse; but well-defined quantitative metrics for it are still elusive. In this paper, we present a metric for scoring topical coherence of an input paragraph on a real-valued scale by analyzing its underlying topical structure. We first extract all possible topics that the sentences of a paragraph of text are related to. Coherence of this text is then measured by computing: (a) the degree of uncertainty of the topics with respect to the paragraph, and (b) the relatedness between these topics. All components of our modular framework rely only on unlabeled data and WordNet, thus making it completely unsupervised, which is an important feature for general-purpose usage of any metric. Experiments are conducted on two datasets - a publicly available dataset for essay grading (representing human discourse), and a synthetic dataset constructed by mixing content from multiple paragraphs covering diverse topics. Our evaluation shows that the measured coherence scores are positively correlated with the ground truth for both the datasets. Further validation to our coherence scores is provided by conducting human evaluation on the synthetic data, showing a significant agreement of 79.3%