Miracle-dz/UniFAR-FTUs huggingface dataset:
Diminished nuclear RNA decay upon Salmonella infection upregulates antibacterial noncoding RNAs

Cytoplasmic mRNA degradation controls gene expression to help eliminate pathogens during infection. However, it has remained unclear whether such regulation also extends to nuclear RNA decay. Here, we show that 145 unstable nuclear RNAs, including enhancer RNAs (eRNAs) and long noncoding RNAs (lncRNAs) such as NEAT1v2, are stabilized upon Salmonella infection in HeLa cells. In uninfected cells, the RNA exosome, aided by the Nuclear EXosome Targeting (NEXT) complex, degrades these labile transcripts. Upon infection, the levels of the exosome/NEXT components, RRP6 and MTR4, dramatically decrease, resulting in transcript stabilization. Depletion of lncRNAs, NEAT1v2, or eRNA07573 in HeLa cells triggers increased susceptibility to Salmonella infection concomitant with the deregulated expression of a distinct class of immunity‐related genes, indicating that the accumulation of unstable nuclear RNAs contributes to antibacterial defense. Our results highlight a fundamental role for regulated degradation of nuclear RNA in the response to pathogenic infection.
Task Parallelism in the WRF Model Through Computation Offloading to Many-Core Devices

In the last decade the use of hybrid hardware (e.g., multicore processors + coprocessors) has been growing on the HPC field. However, this evolution in the HPC hardware has not been fully exploited by the WRF model since it shows limitations in the scalability when a large number of computing units are used. In a previous work, we proposed an asynchronous architecture for the WRF that overlaps the radiation computation with the execution of the rest of the model. In this work, we extend this idea with the aim of exploiting the computational power offered by hybrid hardware platforms. Specifically, we implement an OpenMP version of the asynchronous architecture and include the use of two types of coprocessors, a Xeon Phi and a GPU. The experimental evaluation performed shows that our proposal is able to adequately exploit these secondary computation devices, reaching interesting runtime reductions when solving tests cases from real scenarios.
Application of Near-Infrared Spectroscopy in Determination of Schaftoside Acid in Desmodium styracifolium (Osb.) Merr.

The potential of Near Infrared Spectroscopy (NIRS) in quality control of Traditional Chinese Medicine has been evaluated. This study focuses on a rapid and non-destructive quantitative method for the determination of schaftoside acid content in Desmodium styracifolium by near-infrared spectroscopy. The schaftoside acid contents of 103 samples were determined by HPLC. The NIRS quantitative model of the ethanol extracts content was performed by partial least-squares (PLS) regression as linear regression with analysis values as calibration reference using 2nd derivative method as spectral preprocessing options. The model was verified by internal cross validation and external predictive validation with 22 Desmodium styracifolium samples. The correlation coefficients (r), the root-mean-square error of calibration (RMSEC), the root-mean-square error of prediction (RMSEP) and the root-mean-square error of cross-validation (RMSECV) of the calibration model for ethanol extracts content were 0.97201, 0.147, 0.162 and 0.433, respectively. The average recovery was 99.82%, and t-test showed that P > 0.5. Precision and repeatability RSD were 1.75% and 1.17%, RPD > 2.5. The data obtained from results showed that calibration model of NIRS was verified to be reliable, suggesting that the quantitative NIR models can be qualified to accurately quantify the ethanol extracts content of Desmodium styracifolium rapidly.
Slotted patch and truncated edge techniques on microstrip antenna for CP-SAR S-band data transmitter

The Circularly Polarized Synthetic Aperture Radar (CP-SAR) uses Circular Polarization radio wave to transmit the Radar signal. With this polarization, the power consumption of CP-SAR is lower than common Synthetic Aperture Radar (SAR). Therefore, CP-SAR can be placed in microsatellite. Low power data transmitter also required in CP-SAR system, so circular polarization can also be used in the transmitter system. Circular polarization radio wave is produced by circularly polarized antenna. In this paper, slotted patch and truncated edge techniques to design circularly polarized microstrip antenna are evaluated. The results show that both techniques have similar performance. But, truncating the patch causes a further frequency response shift than adding slot to patch, so truncated edge technique is more difficult to design than slotted patch technique.
“iCub, clean the table!” A robot learning from demonstration approach using deep neural networks

Autonomous service robots have become a key research topic in robotics, particularly for household chores. A typical home scenario is highly unconstrained and a service robot needs to adapt constantly to new situations. In this paper, we address the problem of autonomous cleaning tasks in uncontrolled environments. In our approach, a human instructor uses kinestethic demonstrations to teach a robot how to perform different cleaning tasks on a table. Then, we use Task Parametrized Gaussian Mixture Models (TP-GMMs) to encode the demonstrations variability, while providing appropriate generalization abilities. TP-GMMs extend Gaussian Mixture Models with an auxiliary set of reference frames, in order to extrapolate the demonstrations to different task parameters such as movement locations, amplitude or orientations. However, the reference frames (that parametrize TP-GMMs) can be very difficult to extract in practice, as it may require segmenting the cluttered images of the working table-top. Instead, in this work the reference frames are automatically extracted from robot camera images, using a deep neural network that was trained during human demonstrations of a cleaning task. This approach has two main benefits: (i) it takes the human completely out of the loop while performing complex cleaning tasks; and (ii) the network is able to identify the specific task to be performed directly from image data, thus also enabling automatic task selection from a set of previously demonstrated tasks. The system was implemented on the iCub humanoid robot. During the tests, the robot was able to successfully clean a table with two different types of dirt (wiping a marker's scribble or sweeping clusters of lentils).
Safe Use of Vasopressin and Angiotensin II for Patients with Circulatory Shock

Circulatory shock is a medical emergency that requires rapid intervention to optimize patient outcomes. Although catecholamine vasopressors are considered life‐sustaining therapy, they are associated with adverse reactions, and vasopressin and angiotensin II may be used to minimize these adverse effects. However, vasopressin and angiotensin II are also associated with adverse reactions that must be known to the clinician to mitigate risk for patients. This review focuses on the known adverse drug effects of vasopressin and angiotensin II while offering potential solutions to minimize harm with these agents. Future directions with vasoactive medication safety including optimization of the electronic medical record, clinical decision support, prediction analytics, and precision medicine for patients with circulatory shock are also discussed.
MIMO Scattered Pilot Performance and Optimization for ATSC 3.0

ATSC 3.0 is the latest digital terrestrial television (DTT) standard, and it allows a higher spectral efficiency and/or a transmission robustness with multiple-input multiple-output (MIMO) technology compared to existing DTT standards. Regarding MIMO channel estimation, two pilot encoding algorithms known as Walsh–Hadamard encoding and Null Pilot encoding are possible in ATSC 3.0. The two MIMO pilot algorithms are standardized so as to have the same pilot positions and the same pilot boosting as single-input single-output, and the optimum pilot configuration has not been fully evaluated for MIMO. This paper focuses on the performance evaluation and optimization of the pilot boosting and the pilot patterns for two MIMO pilot encoding algorithms in ATSC 3.0 using physical layer simulations. This paper provides a great benefit to broadcasters to select the MIMO pilot configuration including pilot boosting, pilot pattern, and pilot encoding algorithm that better suits their service requirements. Several channel interpolation algorithms have been taken into account as a typical receiver implementation in both fixed SFN reception and mobile reception.
My Dog Is Not My Cat: Owner Perception of the Personalities of Dogs and Cats Living in the Same Household

Simple Summary A growing number of dogs and cats live together, sharing both a common home and common owner. Nevertheless, how do owners of both cats and dogs living in the same household perceive their pets’ personalities? We tried to answer this question by using a questionnaire targeted at people who owned both dogs and cats. Sociability, protectiveness, reactivity, neuroticism, and fearfulness were the traits that emerged and that diversified themselves according to species. Moreover, intrinsic animal factors, such as age and gender, demographic characteristics of the owner, and environmental context seem to modulate the perceived personality traits in a species-specific way. As personality might influence pets’ welfare and adaptability to the home environment, owners of multiple pets should know and take into consideration both common and differential aspects of their pet’s personality to optimise cohabitation among dogs and cats. Abstract This study aims to define the personality traits perceived by the owners of multiple pets and to evaluate how they are modulated by experiential-environmental factors. A questionnaire was administered to 1270 owners of multiple pets (dogs and cats) to collect data on the demographics, management, and personality of their pets. Data were analysed by principal component analysis, bivariate, and multivariable models. Five personality traits emerged in dogs and cats: sociability, reactivity, protectiveness, neuroticism, and fearfulness. The owners perceived differences in the personality of their pet: dogs scored higher in sociability, protectiveness, and reactivity, while lower in the neuroticism dimension compared with cats (p < 0.001). Age similarly affected sociability (p < 0.01), and reactivity (p < 0.001) in both dogs and cats, while species-specific gender differences were found as to fearfulness (p < 0.05) and neuroticism (p < 0.001). The age of acquisition modulated several traits in dog personality, while living with conspecifics especially influenced cats. Physiological, behavioural, and evolutionary characteristics could explain species differences. Moreover, intrinsic and extrinsic factors modulated the five dimensions of dogs and cats in a diversified fashion, suggesting complex interactions between species and the environment. However, owners could have had different attitudes with their animals which could have influenced personality perception.
Modified ground microstrip antenna design for near field communication application at frequency 350 GHz

Near field communication (NFC) is one of various wireless communication technologies that can be enhanced as a solution to transfer high-speed multimedia data. An antenna has a critical role to achieve the purpose. To cover 10 cm distance and 20 Gbps data rates, an antenna has to have the bandwidth, gain, and beamwidth for both vertical and horizontal of more than 10 GHz, 0.16 dB, and 26 degrees, respectively. This paper discusses a proposed microstrip antenna design combined with modified ground technique to provide those specifications. The antenna has a rectangular shape, modified inset lines and slots. Design and simulation is conducted by using CST software. The antenna has a resonant frequency of 350 GHz to provide a wide bandwidth. The simulation results show that the return loss, bandwidth, gain, horizontal and vertical bandwidth are −37.39 dB, 30.28 GHz in the range of 337.09 to 367.37 GHz, 5.51 dB, 59.8° and 61.2°, respectively. From these results are expected that the proposed antenna design can be used for near field communication applications.
Optimal Training Design for MIMO Systems With General Power Constraints

Training design for general multiple-input multiple-output (MIMO) systems is investigated in this paper. Unlike prior designs that are applicable only for centralized MIMO systems with total power constraints, general power constraints are considered here. They cover total power constraints, individual power constraints, and mixed individual and per-user sum-power constraints as special cases. By writing the MIMO received signals in matrix and vector forms, respectively, and using Kronecker structured channel and noise statistics, three channel estimation schemes, i.e., right estimation, left estimation, and right-left estimation, are discussed. Their corresponding training designs are considered individually with the general power constraints. Under each channel estimation scheme, optimal training sequences to maximize the mutual information between the true channel and its estimated counterpart, and to minimize the mean square error (MSE) of the channel estimate are, respectively, proposed in semiclosed forms. The relationship between the two design criteria, i.e., the mutual information maximization and the MSE minimization, is clearly revealed. The optimal training designs under the three estimation schemes are also compared in depth. It is demonstrated that right estimation exploits less statistical information about the channel and noise, and provides worse performance than the left estimation but with lower computational complexity. On the other hand, right-left estimation performs in between the other two and provides a good compromise between complexity and performance. Finally, the optimality and effectiveness of the proposed training designs are verified by extensive simulations.
Auxin-Mediated Sterility Induction System for Longevity and Mating Studies in Caenorhabditis elegans

The ability to control both the means and timing of sexual reproduction provides a powerful tool to understand not only fertilization but also life history trade-offs resulting from sexual reproduction. However, precisely controlling fertilization has proved a major challenge across model systems. An ideal sterility induction system should be external, non-toxic, and reversible. Using the auxin-inducible degradation system targeting the spe-44 gene within the nematode Caenorhabditis elegans, we designed a means of externally inducing spermatogenesis arrest. We show that exposure to auxin during larval development induces both hermaphrodite self-sterility and male sterility. Moreover, male sterility can be reversed upon cessation of auxin exposure. The sterility induction system developed here has multiple applications in the fields of spermatogenesis and mating systems evolution. Importantly, this system is also a highly applicable tool for aging studies. In particular, we show that auxin-induced self-sterility is comparable to the commonly used chemically-induced FUdR sterility, while offering multiple benefits, including being less labor intensive, being non-toxic, and avoiding compound interactions with other experimental treatments.
LRF assisted SLAM for airborne platforms

This paper presents a one-dimensional Laser Range Finder (LRF) assisted visual Simultaneous Localization and Mapping (SLAM) technique in order to aid Inertial Navigation System (INS) for airborne platforms. Landmark initialization is a critical process of SLAM algorithm, which is registering features to the map by estimating their position. Due to scale ambiguity, precise undelayed landmark initialization may not be possible by using monocular 2D vision sensor. Although several delayed initialization methods have been proposed and used, number of observed frames of a scene does not suffice to both determine the position of landmarks accurately and aid INS for high speed airborne platforms with a 2D narrow field of view vision sensor. To achieve precise mapping of features and localization simultaneously within a few frames, we further develop LRF assisted visual SLAM which estimates the position of newly detected landmarks instantaneously providing more accurate updates to INS sooner. Fusing LRF and optical measurements improved accuracy of navigation solution by more than 95 percent over INS only mode for 15 minutes flight simulations.
A Big Data smart library recommender system for an educational institution

With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. ,This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. ,As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. ,The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. ,A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.
Therapeutic drug monitoring

SUMMARY The cytochrome P450 iso-enzyme system plays a key role in the biotransformation of many drugs, including psychotropics. Its activity is determined by both genetic and environmental factors. The most important iso-enzymes for psychiatry in general are P450 IID6, 3A4 and 1A2. Knowledge about the involvement of these enzymes and biotransformation processes is mandatory because of the individual variability in their metabolic capacity. Regular measurement of plasmaconcentrations of (psycho)pharmacological compounds is therefore essential. In addition, the potential value of pheno- and/or genotyping has to be investigated.
Effects of a community-based multicomponent rehabilitation programme for patients with fibromyalgia: protocol for a randomised controlled trial

Introduction People with fibromyalgia (FM) suffer from symptoms such as widespread pain, non-refreshing sleep, fatigue and reduced quality of life. Effects of pharmacological treatment are questionable and non-pharmacological treatments are recommended as first-line therapy. To date the majority of patients with FM in Norway are not offered any targeted treatment. The aim of this randomised controlled trial is to investigate the effects of a community-based multicomponent rehabilitation programme comprising an acceptance-based and mindfulness-based group intervention, the Vitality Training Programme (VTP), followed by tailored physical activity counselling. Materials and methods General practitioners refer potential participants to a rheumatologist in specialist healthcare for diagnostic clarification and assessment of comorbidities. Inclusion criteria are widespread pain/FM ≥3 months, age 20–50 and work participation (minimum part-time) within the last 2 years. The intervention group attends the VTP comprising 10 weekly 4 hour group sessions plus a booster session after 6 months. Thereafter, they receive 12 weeks of individually tailored physical exercise counselled by physiotherapists at community-based Healthy Life Centers. The control group follows treatment as usual. The primary outcome is Patient Global Impression of Change. Secondary outcomes include self-reported pain, fatigue and sleep quality, psychological distress, mindfulness, health-related quality of life, physical activity, work ability and exercise beliefs and habits. To achieve a power of 80% and allow for 10% dropout, 70 participants are needed in each arm. All analyses will be conducted on intention-to-treat bases and measured as differences between groups at 12 months follow-up. Ethics and dissemination The study is approved and granted by the Norwegian South-Eastern Regional Health Authority (reference 2016015). Ethics approval was obtained from Regional Committee for Medical and Health Research Ethics (reference 2015/2447/REK sør-øst A). Results will be submitted to appropriate journals and presented in relevant conferences and social media. Trial registration ISRCTN96836577.
Edge Convolutional Network for Facial Action Intensity Estimation

In this paper, we propose a novel convolutional neural architecture for facial action unit intensity estimation. While Convolutional Neural Networks (CNNs) have shown great promise in a wide range of computer vision tasks, these achievements have not translated as well to facial expression analysis, with hand crafted features (e.g. the Histogram of Orientated Gradient) still being very competitive. We introduce a novel Edge Convolutional Network (ECN) that is able to capture subtle changes in facial appearance. Our model is able to learn edge-like detectors that can capture subtle wrinkles and facial muscle contours at multiple orientations and frequencies. The core novelty of our ECN model is in its first layer which integrates three main components: an edge filter generator, a receptive gate and a filter rotator. All the components are differentiable and our ECN model is end-to-end trainable and learns the important edge detectors for facial expression analysis. Experiments on two facial action unit datasets show that the proposed ECN outperforms state-of-the-art methods for both AU intensity estimation tasks.
Conditional probability calculation using restricted Boltzmann machine with application to system identification

There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extracted in probability forms. The biggest obstacle of the probability model is the probability distributions are not easy to be obtained. In this paper, we form the nonlinear system identification into solving the conditional probability. Then we modify the restricted Boltzmann machine (RBM), such that the joint probability, input distribution, and the conditional probability can be calculated by the RBM training. Binary encoding and continue valued methods are discussed. The universal approximation analysis for the conditional probability based modelling is proposed. We use two benchmark nonlinear systems to compare our probability modelling method with the other black-box modeling methods. The results show that this novel method is much better when there are big noises and the system dynamics are complex.