Miracle-dz/UniFAR-FTUs huggingface dataset:
BZLF1 interacts with chromatin remodelers promoting escape from latent infections with EBV

How herpesviruses escape from their latent states is enigmatic, but BZLF1, an important transcription factor of EBV, recruits cellular chromatin remodelers to viral DNA to overcome its epigenetic repression and initiate viral transcription. A hallmark of EBV infections is its latent phase, when all viral lytic genes are repressed. Repression results from a high nucleosome occupancy and epigenetic silencing by cellular factors such as the Polycomb repressive complex 2 (PRC2) and DNA methyltransferases that, respectively, introduce repressive histone marks and DNA methylation. The viral transcription factor BZLF1 acts as a molecular switch to induce transition from the latent to the lytic or productive phase of EBV’s life cycle. It is unknown how BZLF1 can bind to the epigenetically silenced viral DNA and whether it directly reactivates the viral genome through chromatin remodeling. We addressed these fundamental questions and found that BZLF1 binds to nucleosomal DNA motifs both in vivo and in vitro. BZLF1 co-precipitates with cellular chromatin remodeler ATPases, and the knock-down of one of them, INO80, impaired lytic reactivation and virus synthesis. In Assay for Transposase-Accessible Chromatin-seq experiments, non-accessible chromatin opens up locally when BZLF1 binds to its cognate sequence motifs in viral DNA. We conclude that BZLF1 reactivates the EBV genome by directly binding to silenced chromatin and recruiting cellular chromatin-remodeling enzymes, which implement a permissive state for lytic viral transcription. BZLF1 shares this mode of action with a limited number of cellular pioneer factors, which are instrumental in transcriptional activation, differentiation, and reprogramming in all eukaryotic cells.
The Secrecy Capacity Optimization Artificial Noise: A New Type of Artificial Noise for Secure Communication in MIMO System

In multiple-input multiple-output wireless communication system, artificial noise (AN) is designed to be aligned into the null space of the legitimate channel so that it has no effect on the legitimate channel. Meanwhile, AN will reduce the secrecy capacity of the eavesdropping channel so that AN increase the secrecy capacity of the wireless communication system. Nevertheless, AN technology only pays attention to the CSI of the legitimate channel but does not pay attention to the CSI of the eavesdropping channel so that the secrecy capacity of the system can be further improved. In this paper, we analyze the performance of traditional AN. A secrecy capacity optimization artificial noise for the whole communication system to further improve the system’s secrecy capacity is proposed. The secrecy capacity optimization artificial noise is considered as a kind of artificial noise which is disadvantageous to the legitimate receiver but more disadvantageous to the eavesdropper. Furthermore, we further analyzed the technical details of AN technology and found the key factor affecting the secrecy capacity. Meanwhile, the secrecy capacity optimization artificial noise is effectively designed by applying these conclusions and the key factors of secrecy capacity optimization artificial noise affecting the secrecy capacity of the wireless communication system are described. The analysis and simulation results in practical environments show that the proposed method has a good performance on improving the secrecy capacity.
Multi-Agent Base Evacuation Support System Considering Altitude

In this paper, we propose an extension of an evacuation support system that we have previously proposed (Taga, 2018). The system suggests evacuation routes in cases of disasters. We have confirmed the usefulness of the system. When a disaster occurs, we anticipate that the current popular wireless communication based on the Internet may not be very reliable. In order to accommodate such a problem, our proposed system employs multiple mobile agents and static agents on smartphones that use a mobile ad hoc network (MANET). The proposed system collects information by mobile agents as well as diffuses information by mobile agents so that the system provides an optimal evacuation route for each user in a dynamically changing disaster environment. In simulations, our system successfully guides evacuation users to safe areas. The system, however, does not consider the altitude of the evacuation routes. Therefore, the system may not be very useful in cases of flood. When a tsunami or a flood tide occurs, low altitude location may be under water. Therefore, evacuees need to move along high altitude routes. In this paper, we also take account of the altitude information for constructing evacuation routes.
Modeling Mobile Edge Computing Deployments for Low Latency Multimedia Services

Multi-access edge computing (MEC) technologies bring important improvements in terms of network bandwidth, latency, and use of context information and critical for services like multimedia streaming, augmented, and virtual reality. In future deployments, operators will need to decide how many MEC points of presence (PoPs) are needed and where to deploy them, also considering the number of base stations needed to support the expected traffic. This paper presents an application of inhomogeneous Poisson point processes with hard-core repulsion to model feasible MEC infrastructure deployments. With the presented methodology a mobile network operator knows where to locate the MEC PoPs and associated base stations to support a given set of services. We evaluate our model with simulations in realistic scenarios, namely Madrid City Center, an industrial area and a rural area.
Diversity and behavior of flower visitors insects of Malus komarovii in the southwest slop of Changbai Mountain

We investigated the visiting frequency,visiting duration,behavior and diversity about flower visitors insects of Malus komarovii in the southwest slop of Changbai Mountain from 2009 to 2010.The results showed that there were 46 species of pollinators,which belonged to 6 orders,19 families,33 genera,and Diptera,Lepidoptera,Coleoptera and Hymenoptera were major visitors.There was a certain rule for each insect to daily visit flowers.Hymenoptera visiting activity was mainly in the forenoon,while Coleoptera,Diptera and Lepidoptera visiting activity lasted longer,and their visiting activity existed complementary effect,and Hymenoptera visited frequently and carried more pollen.We also noted that Coleoptera stayed a short time on the flowers and Lepidoptera stayed longest on the flowers.The diversity indexes demonstrated that community diversity was in the upper level.The population of flower visitors insects first increased and then decreased with temperature increasing,while decreased with air humidity increasing which had a upward trend in a day.Regression analysis indicated that the number of insects conformed to normal distribution under the influence of temperature and humidity.
Cortical mitochondria regulate insulin secretion by local Ca2+ buffering in rodent beta cells

ABSTRACT Mitochondria play an essential role in regulating insulin secretion from beta cells by providing the ATP needed for the membrane depolarization that results in voltage-dependent Ca2+ influx and subsequent insulin granule exocytosis. Ca2+, in turn, is also rapidly taken up by the mitochondria and exerts important feedback regulation of metabolism. The aim of this study was to determine whether the distribution of mitochondria within beta cells is important for the secretory capacity of these cells. We find that cortically localized mitochondria are abundant in rodent beta cells, and that these mitochondria redistribute towards the cell interior following depolarization. The redistribution requires Ca2+-induced remodeling of the cortical F-actin network. Using light-regulated motor proteins, we increased the cortical density of mitochondria twofold and found that this blunted the voltage-dependent increase in cytosolic Ca2+ concentration and suppressed insulin secretion. The activity-dependent changes in mitochondria distribution are likely to be important for the generation of Ca2+ microdomains required for efficient insulin granule release. Highlighted Article: In rodent beta cells, cortical F-actin rearrangements control the density of a cortical pool of mitochondria, which, in turn, regulates insulin secretion through local Ca2+ buffering.
Real-Time Encoding/Decoding for Pairwise Communication Over an Unreliable Sensor Network

The length of time that a wireless sensor can be deployed is limited by its internal power supply. To increase the deployment lifetime of these sensors we must find ways to conserve power. In this paper, we propose an algorithm that reduces the amount of energy the transceiver consumes by compressing the bytes that are sent and received over the network. The algorithm compresses a data stream by exploiting its temporal locality and is designed to function efficiently on an unreliable network in real-time. A stream is compressed by using fewer bits to represent elements that frequently recur. We evaluate the proposed compression algorithm using a collection of independently collected traces from the crawdad database. We calculated the compression ratio for each trace and found that we were able to reduce the number of bytes transmitted by an average of 60%, resulting in a 30% increase in energy savings.
Proteomic discovery of substrates of the cardiovascular protease ADAMTS7

The protease ADAMTS7 functions in the extracellular matrix (ECM) of the cardiovascular system. However, its physiological substrate specificity and mechanism of regulation remain to be explored. To address this, we conducted an unbiased substrate analysis using terminal amine isotopic labeling of substrates (TAILS). The analysis identified candidate substrates of ADAMTS7 in the human fibroblast secretome, including proteins with a wide range of functions, such as collagenous and noncollagenous extracellular matrix proteins, growth factors, proteases, and cell-surface receptors. It also suggested that autolysis occurs at Glu-729–Val-730 and Glu-732–Ala-733 in the ADAMTS7 Spacer domain, which was corroborated by N-terminal sequencing and Western blotting. Importantly, TAILS also identified proteolysis of the latent TGF-β–binding proteins 3 and 4 (LTBP3/4) at a Glu-Val and Glu-Ala site, respectively. Using purified enzyme and substrate, we confirmed ADAMTS7-catalyzed proteolysis of recombinant LTBP4. Moreover, we identified multiple additional scissile bonds in an N-terminal linker region of LTBP4 that connects fibulin-5/tropoelastin and fibrillin-1–binding regions, which have an important role in elastogenesis. ADAMTS7-mediated cleavage of LTBP4 was efficiently inhibited by the metalloprotease inhibitor TIMP-4, but not by TIMP-1 and less efficiently by TIMP-2 and TIMP-3. As TIMP-4 expression is prevalent in cardiovascular tissues, we propose that TIMP-4 represents the primary endogenous ADAMTS7 inhibitor. In summary, our findings reveal LTBP4 as an ADAMTS7 substrate, whose cleavage may potentially impact elastogenesis in the cardiovascular system. We also identify TIMP-4 as a likely physiological ADAMTS7 inhibitor.
Does synaesthesia protect against age-related memory loss?

Synaesthesia is known to be linked to enhanced episodic memory abilities, across a variety of stimuli and tests, but the evidence has tended to come from younger adults. This enhanced cognitive ability in early adult life, together with the known brain-related differences linked to synaesthesia (e.g., in both grey and white matter structure), makes it an ideal candidate for exploring the notion of 'reserve'. That is, synaesthetes may be able to utilize additional cognitive and/or neural resources to mitigate against the effects of age-related decline. This was explored in a 2 × 2 design contrasting age (young, old) against the presence/absence of synaesthesia in two different studies: recognition memory for digits, snowflakes, and music; and visual associative learning. Synaesthesia and age had independent, non-interacting, effects on memory ability suggesting that, while synaesthetes show a memory advantage and maintain this advantage in later life, the presence of synaesthesia is not able to act as a reserve to protect against the effects of ageing. On our tasks, the benefit of having synaesthesia (enhancing memory) was of a similar magnitude to the effects of age (impairing memory); in other words, elderly synaesthetes present with 'youthful' memory abilities. It is important for future research on elderly cohorts to consider the presence of synaesthesia as an individual difference.
Study on Inoculation Methods and Condition of Clubroot in Qingmaye Chinese Cabbage

For the disease-resistant breeding of Qingmaye chinese cabbage,five kinds of inoculation methods,different soil p H,different inoculation concentration,illumination and root secretion were compared by use Qiulu 60 as material in this article. The results showed that the effect of soil inoculation was more excellent and stable. In contrast,seed-immersion method was the poorest. The acid soil p H was fit to clubroot break out and the disease index reached the maximum when p H = 6. 5. 0. 050 g / g was the most suitable inoculation concentration. After light treatment,the disease index was increased,it showed that light had a promoting effect on diseases. Root secretion of tomato,resistant and susceptible hosts could promote the happening of disease.
Optimization methods for MR image reconstruction

The development of compressed sensing methods for MR image reconstruction led to an explosion of research on models and optimization algorithms for MRI. Roughly 10 years after such methods first appeared in the MRI literature, the US FDA approved the commercial use of certain compressed sensing methods, making compressed sensing a clinical success story for MRI. This review paper summarizes several key models and optimization algorithms for MR image reconstruction, including both the type of methods that have FDA approval for clinical use, as well as more recent methods being considered in the research community that use data-adaptive regularizers. One impetus for this paper is that "off the shelf" optimization methods have rarely been the best choice for solving optimization problems in MR image reconstruction, due to the large volume of MRI data collected by clinical systems and practical time constraints on processing time. Instead, special purpose algorithms have been devised that exploit the structure of the system model and regularizers used in MRI; this paper strives to collect such algorithms in a single survey. Many of the ideas used in optimization methods for MRI are also useful for solving other inverse problems.
Ultrasound based Silent Speech Interface using Deep Learning

Silent Speech Interface (SSI) is a technology able to synthesize speech in the absence of any acoustic signal. It can be useful in cases like laryngectomy patients, noisy environments or silent calls. This thesis explores the particular case of SSI using ultrasound images of the tongue as input signals. A 'direct synthesis' approach based on Deep Neural Networks and Mel-generalized cepstral coefficients is proposed. This document is an extension of Csapo et al. "DNN-based Ultrasound-to-Speech Conversion for a Silent Speech Interface". Several deep learning models, such as the basic Feed-forward Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks are presented and discussed. A denoising pre-processing based on a Deep Convolutional Autoencoder has also been studied. A considerable number of experiments using a set of different deep learning architectures and an extensive hyperperameter optimization study have been realized. The different experiments have been testing and rating several objective and subjective quality measures. According to the experiments, an architecture based on a CNN and bidirectional LSTM layers has shown the best results in both objective and subjective terms.
Deep learning application using neural network classification for cyberspace dataset with backpropagation algorithm and log-linear models

This study aims to classify bloggers in the Kohkiloye and Boyer Ahmad Province in Iran where causes of users tend cyberspace on there. The database was got from UCI Machine Learning Repository. There are 100th object and 6th variables. All of the variables were Professional Bloggers, Political and Social Space (LPSS), Local Media Turnover (LMT), Political Caprice, Topics, and Degree. This study has using Artificial Neural Network with backpropagation algorithm and Log-linear models for classify Bloggers (Cyber Space). We classify blogger to two groups: professional bloggers and seasonal (temporary) bloggers. The result of this study is Neural network with backpropagation algorithm has been shown to be useful tool for prediction, especially for this case. From this study, we can see on the result that miss-classification with backpropagation algorithm less than using Log-Linear Models
Segmentation of Shoulder MRI Data for Musculoskeletal Model Adaptation

Applying image processing techniques to medical images has already brought many useful applications. This work is focused on using these methods in the process of adapting a musculoskeletal model of the shoulder joint. Comparing the model of healthy individuals and the patient with joint damage leads to a subjectspecific convalescence treatment. This work describes the procedure for segmentation of magnetic resonance imaging (MRI) of the shoulder joint. Firstly three bones inside the shoulder area: humerus, clavicle, scapula are identified and thereby it provides initial reference objects. A major step is the segmentation of the deltoid muscle needed for the subsequent adaptation of the musculoskeletal model. This step is challenging in terms of image processing due to the closeness of soft tissues, which are almost identical in intensity and the boundaries between them are often barely visible. The approach to resolving this problem is described and possible improvements and future work are described.
Procalcitonin: Promising biomarker to detect sepsis in burns – A prospective study

Background: Sepsis-3 guidelines published in the Journal of American Medical Association is thought to be a pathbreaking effort in the diagnosis of sepsis,[1],[2],[3],[4] but it has limitations with respect to burns population. [5] On the other hand, the American Burn Association (ABA) 2007 guideline[6] for sepsis in burn patients is noted for low specificity rate. However, an independent biomarker procalcitonin (PCT) could augment clinical findings and improve sepsis predictability rate in burn patients. This study is the first attempt to assess diagnostic validity of PCT in Indian population and to highlight its role in diagnosing sepsis in burns despite newer sepsis-3 guidelines. Materials and Methods: This prospective study involved 33 consecutive thermal burn patients with 66 samples estimated for PCT. Samples were sent at each suspected episode of sepsis based on ABA guidelines. Baseline PCT estimation was done when the patient did not have sepsis. The positive blood or tissue culture was taken as a reference standard. The sample was divided into sepsis and nonsepsis group. All the parameters were documented on the graphical sheet which was interpreted by an independent analyzer. The sensitivity and specificity of each coordinate were plotted on receiver operating characteristic curve to identify the predictive ability of the test. Results: The sensitivity and specificity of the test was 94.7% and 85.7%, respectively. The cutoff value to diagnose sepsis in burn patients was 3.5 ng/ml with an area under the curve of 0.974 at 95% confidence interval. Conclusion: Good correlation between documented sepsis and elevated PCT levels makes PCT a promising biomarker in burns population.
Photoacoustic signal enhancement using a novel adaptive filtering algorithm

Photoacoustic (PA) signal experiences excessive background noise when generated using cost-effective, low-energy laser diodes. A denoising technique is essential in this case. Averaging is a common approach to increase the Signal-to-Noise Ratio (SNR) of PA signals. This technique requires numbers of data acquisition in hundreds and thousands and hence, demands more hardware and time consuming at the same time. Here, an adaptive method based on Adaptive Line Enhancers (ALE) algorithm to improve the SNR of PA signals has been presented. Our results validate the feasibility of the usage of an adaptive method and also indicate excellent improvement in terms of increasing the SNR of the PA signals. Additionally, this proposed algorithm requires way less number of acquisitions as compared to the conventional averaging techniques that leads to faster PA image processing.
Balancing Walking Gait For Small Size Humanoid Robot By Using Movable Mass

This paper describes a method used in stabilizing the walking gait of HUNO - a small size humanoid robot by using a movable mass. This method eliminates the movement of the robot’s pelvis when walking. In previous study, a model for the robot was derived and simulation was done based on that model. In this paper, the aforementioned model is implemented on real life robot. Then, a simple PID is deduced to control the real movable mass mechanism. Finally, the effectiveness of the proposed method is shown through experimental results.
IML: Towards an Instructional Modeling Language

Existing software modeling languages and tooling often contain features far beyond the comprehension of novice users. Additionally, tools focus on specific aspects of the model-driven software engineering (MDSE) process with little to no connection between various tools, phases, and applications. This paper describes our current work and project plans to develop a single modeling language that is aimed at teaching MDSE at the university level. The Instructional Modeling Language (IML), which implements both graphical and textual modeling paradigms, centres around a single tool that includes functionality for various modeling techniques and is not bloated with the full functionality that has been demonstrated to cause students to become overwhelmed when introduced to numerous tools at once. IML includes and describes crucial MDSE concepts and learning milestones including functionality for meta-modeling, instance creation, model transformations, real-time system modeling, code generation, model-based testing, and others. Ultimately, our goal is for IML to be used by instructors to introduce MDSE into their curriculum in a lightweight, easy-to-instruct manner. This includes industrial education to introduce employees with little or no modeling experience to MDSE concepts and applications. In this paper, we describe our plans for developing IML through four phases, our current progress including IML’s scope and meta-model, our prototype, and future plans and anticipated challenges. Our hope is to continue engaging the MDSE community at the conference for feedback, suggestions, and for volunteers for case study and trial adoption.
Unmanned Aerial Vehicle Remote Sensing Image Segmentation Method by Combining Superpixels with multi-features Distance Measure

Image segmentation is the foundation and key step of object-level classification and change detection. In this paper, a segmentation method of UAV remote sensing image based on multi-features distance measure and superpixels is proposed. First, the simple linear iterative clustering (SLIC) algorithm is used to segment the unmanned aerial vehicle (UAV) remote sensing image to obtain the initial superpixels. Then the distance measures of the spectral, texture, shape and area features are used as the criterion for initial superpixels merging. Finally, merger termination when the number of regions reaches the set number. Two groups of UAV remote sensing images are selected to evaluate the experimental results through visual evaluation. The experimental results show that the proposed method can be used to aggregate objects of different scales, and the segmentation effect is satisfactory.