Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathogenic and non-pathogenic Escherichia coli colonization and host inflammatory response in a defined microbiota mouse model

ABSTRACT Most Escherichia coli strains in the human intestine are harmless. However, enterohemorrhagic E. coli (EHEC) is a foodborne pathogen that causes intestinal disease in humans. Conventionally reared (CONV) mice are inconsistent models for human infections with EHEC because they are often resistant to E. coli colonization, in part due to their gastrointestinal (GI) microbiota. Although antibiotic manipulation of the mouse microbiota has been a common means to overcome colonization resistance, these models have limitations. Currently, there are no licensed treatments for clinical EHEC infections and, thus, new tools to study EHEC colonization need to be developed. Here, we used a defined microbiota mouse model, consisting of the altered Schaedler flora (ASF), to characterize intestinal colonization and compare host responses following colonization with EHEC strain 278F2 or non-pathogenic E. coli strain MG1655. Significantly higher (P<0.05) levels of both strains were found in feces and cecal and colonic contents of C3H/HeN ASF compared to C3H/HeN CONV mice. GI inflammation was significantly elevated (P<0.05) in the cecum of EHEC 278F2-colonized compared to E. coli MG1655-colonized C3H/HeN ASF mice. In addition, EHEC 278F2 differentially modulated inflammatory-associated genes in colonic tissue of C3H/HeN ASF mice compared to E. coli MG1655-colonized mice. This approach allowed for prolonged colonization of the murine GI tract by pathogenic and non-pathogenic E. coli strains, and for evaluation of host inflammatory processes. Overall, this system can be used as a powerful tool for future studies to assess therapeutics, microbe-microbe interactions, and strategies for preventing EHEC infections. Summary: Mice harboring a defined microbiota of the altered Schaedler flora were identified as a reliable tool to assess prolonged Escherichia coli intestinal colonization and activation of the host inflammatory response.
A Review of Cyber Security Controls from An ICS Perspective

As cyber attacks on national critical infrastructure have increased, researches are actively conducted to protect industrial control systems(ICS). Compared with existing IT systems, research on special attributes of ICS has been performed mainly, and based on this, many security requirements for ICSs have been derived. However, there is insufficient research on security control to understand and improve the security status of target ICS. To the best of our knowledge, only a comprehensive guide to applying existing security controls to ICSs is being presented. Therefore, this study suggests implications for the current security control items from the ICS point of view. For this purpose, each viewpoint for categorizing and analyzing information security control items was established and based on this, an integrated matrix of ICS security control was derived. Through the ICS security control matrix, the information security control items currently being guided are analyzed from the viewpoint of the ICS, and the present status and implications are derived. The findings can be used as an indicator to effectively apply appropriate controls to appropriate locations where security is required. It is also expected to be a useful reference for improving and supplementing security control items currently being used and used.
Bilirubin increases the expression of glucose transporter-1 and the rate of glucose uptake in vascular endothelial cells.

BACKGROUND AND OBJECTIVES The close contact between the endothelial cell monolayer in blood vessels and blood plasma allows free diffusion of the hydrophobic unconjugated bilirubin (BR) into these cells. BR can exert both anti- and pro-oxidative effects in various types of cells in a dose-dependent manner. High glucose levels downregulate the expression of the glucose transporter-1 (GLUT-1) and the rate of glucose uptake in vascular endothelial cell (VEC). Pro-oxidants, on the other hand, up-regulate this system in VEC. We aimed to investigate potential effects of BR on the glucose transport system in VEC. METHODS Primary cultures of bovine aortic endothelial cells were exposed to BR, and the rate of hexose transport, GLUT-1 expression and plasma membrane localization were determined. RESULTS BR induced oxidative stress in VEC, and significantly augmented the rate of glucose transport and GLUT-1 expression and plasma membrane localization in these cells. BR also reversed the high glucose-induced downregulation of the glucose transport system in VEC. CONCLUSION The pro-oxidative properties of BR are responsible for its effects on the regulation of glucose transport in vascular endothelium. Pathological concentrations of BR in the vascular compartment (jaundice) may influence the cellular handling of glucose in diabetes.
Conceptual framework for the identification of influential contexts of the adoption decision

The decision to adopt new technologies is the most important stage in integrating a new technology into the ongoing processes of the organization and also to obtain benefits from its routine use. This paper proposes an integrated framework that combines Diffusion of Innovations (DOI) Theory, Technology-Organization–Environment (TOE) framework and Institutional Theory (INT) to characterize the critical factors influencing advanced technologies adoption in manufacturing context. This conceptual framework identifies three contextual environments - innovation, internal organizational and external environmental - that can influence the adoption decision along with some sub-contexts from the literature that may be considered. This framework can be used as starting point to explore in depth influential factors in advanced technologies in manufacturing contexts. Additionally, this framework can assist companies to develop adoption process plans as well as managerial practices that consider the role of these factors and thus lead to successful implementations.
Towards Automated Tissue Characterization Using Parallel Bag-of-Features Experts Dealing with Two-Photon Excitation Fluorescence and Second Harmonic Generation Microscopy Datasets

Label-free tissue imaging with Multiphoton Microscopy (MPM) techniques such as Two-Photon Excitation Fluorescence Microscopy (TPEF) or Second Harmonic Generation Microscopy (SHG) can provide cues of similar pathologic relevance to the information collected for characterization / confirmation purposes with traditional histopathology protocols based on fixation and staining. To date, various approaches for the automated characterization of MPM datasets have been proposed, but usually these address the outputs of a single technique, while MPM imaging sessions can simultaneously yield multiple information categories associated to distinct modalities. We discuss here the main concepts of the Bag-of-Features (BoF) paradigm used for automated image classification and retrieval, and a series of past experiments that dealt with BoF based tissue characterization. Further on, we present some of our connected efforts placed on the problem of human epithelial tissue characterization with TPEF & SHG and introduce a concept for MPM data classification based on multiple BoF experts that utilize in a parallel manner complementary information categories.
An Adaptive FECG Extraction and Analysis Method Using ICA, ICEEMDAN and Wavelet Shrinkage

The extraction of Fetal ECG (FECG) is still a difficult task in non-invasive approach since the frequency components of dominant maternal ECG and fetal ECG signals are generally overlapped. Besides, the baseline wander and high-frequency noise make the clear FECG difficult to be extracted. In this paper, we proposed a new combination of independent component analysis (ICA), improved complete ensemble empirical mode decomposition (ICEEMDAN), and wavelet shrinkage (WS) de-noising (ICA-ICEEMDAN-WS) to extract FECG while reducing the noise. ICA algorithm as the first step of our proposed method separated the mixed abdominal ECG signal to obtain the noisy FECG. Then for further denoisng and obtain higher SNR, noisy FECG was decomposed by the newly proposed ICEEMDAN that is more accurate for non-linear and non-stationary bio-signal processing. The informative extracted components were then determined based on the statistical significance test. WS de-noising removed the remained noisy subcomponents and finally the baseline wander was reduced by partial reconstruction. The performance of ICA-ICEEMDAN-WS method was evaluated using simulated and real data sets. Our results showed that our proposed method outperformed ICA-EEMD-WS, the recently proposed algorithm based on ensemble empirical mode decomposition
Opportunities for Innovation in Genetic Transformation of Forest Trees

The incorporation of DNA into plant genomes followed by regeneration of non-chimeric stable plants (transformation) remains a major challenge for most plant species. Forest trees are particularly difficult as a result of their biochemistry, aging, desire for clonal fidelity, delayed reproduction, and high diversity. We review two complementary approaches to transformation that appear to hold promise for forest trees.
Recurrent knowledge graph embedding for effective recommendation

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.
Mild therapeutic hypothermia protects against cerebral ischemia/reperfusion injury by inhibiting miR-15b expression in rats

OBJECTIVE: Mild hypothermia has a protective effect on ischemic stroke, but the mechanisms remain elusive. Here, we investigated microRNA (miRNA) profiles and the specific role of miRNAs in ischemic stroke treated with mild hypothermia. MATERIALS AND METHODS: Male adult Sprague Dawley rats were subjected to focal transient cerebral ischemia. Mild hypothermia was induced by applying ice packs around the neck and head of the animals. miRNAs expression profiles were detected in ischemic stroke treated with mild therapeutic hypothermia through miRNA chips. Reverse transcription-polymerase chain reaction (RT-PCR) was used to verify the change of miRNA array. Western blot and adenosine triphosphate (ATP) assay kits were used to detect the changes of protein expression and ATP levels, respectively. miR-15b mimic and its control were injected into the right lateral ventricle 60 min before the induction of ischemia. RESULTS: The results showed that mild hypothermia affected miRNAs profiles expression. We verified the expression of miR-15b and miR-598-3p by miRNA RT-PCR. miR-15b mimic inhibited the expression of its target, ADP ribosylation factor-like 2 (Arl2) protein, and decreased ATP levels in PC12 cells. Compared with the control, miR-15b mimic increased the infarct volume and aggravated the neurological function under normothermia or hypothermia treatment. Furthermore, the expression of Arl2 was decreased in the miR-15b mimic group under normothermia or hypothermia treatment. CONCLUSIONS: Mild therapeutic hypothermia affected miRNA profiles and protected against cerebral ischemia/reperfusion by inhibiting miR-15b expression in rats. miR-15b may be a potential target for therapeutic intervention in stroke.
A Descriptor System Approach to Stability and Stabilization of Discrete-Time Switched PWA Systems

The stability and stabilization problems for a class of switched discrete-time nonlinear systems are studied in this paper. Each nonlinear subsystem of the presented switched system is modeled as a piecewise affine (PWA) one by splitting the state space into polyhedron regions. With the aid of a simple searching strategy for active state transition pairs at a switching instant, i.e., the so-called $\mathbb {S}$ -arbitrary switching approach, the stability criteria are derived via the relaxed piecewise quadratic Lyapunov function technique. Then, using the descriptor system approach, a family of PWA stabilizing controllers are designed to guarantee exponential stability of the resulting closed-loop control system, and the corresponding PWA controller gains could be calculated using numerical software. The validity and potential of the developed techniques are verified through a numerical example.
Towards calibration of outdoor multi-camera visual monitoring system

This paper proposes a method for calibrating of multi-camera systems where no natural reference points exist in the surrounding environment. Monitoring the air space at wind farms is our test case. The goal is to monitor the trajectories of flying birds to prevent them from colliding with rotor blades. Our camera calibration method is based on the observation of a portable artificial reference marker made out of a pulsed light source and a navigation satellite sensor module. The reference marker can determine and communicate its position in the world coordinate system at centimeter precision using navigartion sensors. Our results showed that simultaneous detection of the same marker in several cameras having overlapping field of views allowed us to determine the markers position in 3D world coordinate space with an accuracy of 3-4 cm. These experiments were made in the volume around a wind turbine at distances from cameras to marker within a range of 70 to 90 m.
Performance Improvement for Devanagari Script Input Based P300 Speller

The performance of P300 speller-based BCI systems are evaluated based on its classification accuracy as well as the time required to spell the target character which is known as information transfer-rate (ITR). Though, previous works on English, Chinese and Devanagari script (DS) based P300 spellers have presented different approaches to improve the ITR. However, conventional machine learning approaches requires more number of trials for accurate detection of the target P300 component which increases the time to spell a character. The main aim of this work is to improve the information learning rate in DS based P300 speller by improving the classification accuracy in less number of trials. The deep convolution neural network was implemented for the detection of the target characters. The experimental results shown that the proposed work is able to provide higher accuracy for less number of trials which improved the ITR of existing DS based P300 speller.
Graph Diffusion-Embedding Networks

We present a novel graph diffusion-embedding networks (GDEN) for graph structured data. GDEN is motivated by our closed-form formulation on regularized feature diffusion on graph. GDEN integrates both regularized feature diffusion and low-dimensional embedding simultaneously in a unified network model. Moreover, based on GDEN, we can naturally deal with structured data with multiple graph structures. Experiments on semi-supervised learning tasks on several benchmark datasets demonstrate the better performance of the proposed GDEN when comparing with the traditional GCN models.
Composing Letters with a Simulated Listening Typewriter

Speech recognition is not yet advanced enough to provide people with a reliable listening typewriter with which they could compose documents. The aim of this experiment was to determine if an imperfect listening typewriter would be useful. Participants dictated either in isolated words or in continuous speech, and used a simulated listening typewriter which recognized a limited vocabulary as well as one which recognized an unlimited one. Results suggest some versions may be at least as good as writing.
Detecting User's Likes and Dislikes for a Virtual Negotiating Agent

This article tackles the issue of the detection of the user's likes and dislikes in a negotiation with a virtual agent for helping the creation of a model of user's preferences. We introduce a linguistic model of user's likes and dislikes as they are expressed in a negotiation context. The identification of syntactic and semantic features enables the design of formal grammars embedded in a bottom-up and rule-based system. It deals with conversational context by considering simple and collaborative likes and dislikes within adjacency pairs. We present the annotation campaign we conduct by recruiting annotators on CrowdFlower and using a dedicated annotation platform. Finally, we measure agreement between our system and the human reference. The obtained scores show substantial agreement.
Explore, exploit, and explain: personalizing explainable recommendations with bandits

The multi-armed bandit is an important framework for balancing exploration with exploitation in recommendation. Exploitation recommends content (e.g., products, movies, music playlists) with the highest predicted user engagement and has traditionally been the focus of recommender systems. Exploration recommends content with uncertain predicted user engagement for the purpose of gathering more information. The importance of exploration has been recognized in recent years, particularly in settings with new users, new items, non-stationary preferences and attributes. In parallel, explaining recommendations ("recsplanations") is crucial if users are to understand their recommendations. Existing work has looked at bandits and explanations independently. We provide the first method that combines both in a principled manner. In particular, our method is able to jointly (1) learn which explanations each user responds to; (2) learn the best content to recommend for each user; and (3) balance exploration with exploitation to deal with uncertainty. Experiments with historical log data and tests with live production traffic in a large-scale music recommendation service show a significant improvement in user engagement.
First-Order Rear-Wall Multipath Positioning and Suppression for Through-Wall Imaging Radar

This paper deals with the positioning and suppression problem for the first-order rear-wall multipath ghosts exploiting a time-division multiple-input multiple-output through-wall imaging radar (TWIR). Based on the specular reflecting assumption, the positions of the multipath ghosts are obtained mathematically, and the defocusing of the multipath ghosts in TWIR image is proved theoretically. In addition, a correlation-weighted sub-aperture-based multipath suppression algorithm is proposed to subdue the first-order rear-wall multipath ghosts, improving the quality of the TWIR image significantly. Numerical simulations and experimental data verify the positioning theory and validate the proposed algorithm.
Neuronal Activity-Dependent Activation of Astroglial Calcineurin in Mouse Primary Hippocampal Cultures

Astrocytes respond to neuronal activity by generating calcium signals which are implicated in the regulation of astroglial housekeeping functions and/or in modulation of synaptic transmission. We hypothesized that activity-induced calcium signals in astrocytes may activate calcineurin (CaN), a calcium/calmodulin-regulated protein phosphatase, implicated in neuropathology, but whose role in astroglial physiology remains unclear. We used a lentiviral vector expressing NFAT-EYFP (NY) fluorescent calcineurin sensor and a chemical protocol of LTP induction (cLTP) to show that, in mixed neuron-astrocytic hippocampal cultures, cLTP induced robust NY translocation into astrocyte nuclei and, hence, CaN activation. NY translocation was abolished by the CaN inhibitor FK506, and was not observed in pure astroglial cultures. Using Fura-2 single cell calcium imaging, we found sustained Ca2+ elevations in juxtaneuronal, but not distal, astrocytes. Pharmacological analysis revealed that both the Ca2+ signals and the nuclear NY translocation in astrocytes required NMDA and mGluR5 receptors and depended on extracellular Ca2+ entry via a store-operated mechanism. Our results provide a proof of principle that calcineurin in astrocytes may be activated in response to neuronal activity, thereby delineating a framework for investigating the role of astroglial CaN in the physiology of central nervous system.
Open-Ended Content-Style Recombination Via Leakage Filtering

We consider visual domains in which a class label specifies the content of an image, and class-irrelevant properties that differentiate instances constitute the style. We present a domain-independent method that permits the open-ended recombination of style of one image with the content of another. Open ended simply means that the method generalizes to style and content not present in the training data. The method starts by constructing a content embedding using an existing deep metric-learning technique. This trained content encoder is incorporated into a variational autoencoder (VAE), paired with a to-be-trained style encoder. The VAE reconstruction loss alone is inadequate to ensure a decomposition of the latent representation into style and content. Our method thus includes an auxiliary loss, leakage filtering, which ensures that no style information remaining in the content representation is used for reconstruction and vice versa. We synthesize novel images by decoding the style representation obtained from one image with the content representation from another. Using this method for data-set augmentation, we obtain state-of-the-art performance on few-shot learning tasks.
Classification of Hand Gestures from Wearable IMUs using Deep Neural Network

IMUs are gaining significant importance in the field of hand gesture analysis, trajectory detection and kinematic functional study. An Inertial Measurement Unit (IMU) consists of tri-axial accelerometers and gyroscopes which can together be used for formation analysis. The paper presents a novel classification approach using a Deep Neural Network (DNN) for classifying hand gestures obtained from wearable IMU sensors. An optimization objective is set for the classifier in order to reduce correlation between the activities and fit the signal-set with best performance parameters. Training of the network is carried out by feed-forward computation of the input features followed by the back-propagation of errors. The predicted outputs are analyzed in the form of classification accuracies which are then compared to the conventional classification schemes of SVM and kNN. A 3-5% improvement in accuracies is observed in the case of DNN classification. Results are presented for the recorded accelerometer and gyroscope signals and the considered classification schemes.
Data-driven modeling of group entitativity in virtual environments

We present a data-driven algorithm to model and predict the socio-emotional impact of groups on observers. Psychological research finds that highly entitative i.e. cohesive and uniform groups induce threat and unease in observers. Our algorithm models realistic trajectory-level behaviors to classify and map the motion-based entitativity of crowds. This mapping is based on a statistical scheme that dynamically learns pedestrian behavior and computes the resultant entitativity induced emotion through group motion characteristics. We also present a novel interactive multi-agent simulation algorithm to model entitative groups and conduct a VR user study to validate the socio-emotional predictive power of our algorithm. We further show that model-generated high-entitativity groups do induce more negative emotions than low-entitative groups.