Miracle-dz/UniFAR-FTUs huggingface dataset:
Pan-genomic analysis provides novel insights into the association of E.coli with human host and its minimal genome

MOTIVATION Bacteria can usually acquire certain advantageous genes that enable the bacteria to adapt to rapidly changing niches, thereby leading to a wide range of intraspecific genome content and genetic redundancy. The minimal genome of Escherichia coli, which is the most important bacterial species, and the association between E.coli and its human host are worthy of further exploration. RESULTS We used gene prediction and phylogenetic analysis to reveal a rich phylogenetic diversity among 491 E.coli strains and to reveal substantial differences between these strains with respect to gene number and genome length. We used pan-genomic analysis to accurately identify 867 core genes, in which only 243 genes are shared by essential genes. This analysis revealed that core genes mainly provide essential functions to the basic lifestyle of E.coli, and accessory genes are likely to confer selective advantages such as niche adaptation or the ability to colonize specific hosts. By association analysis, we found that E.coli strains in non-human hosts may more easily utilize foreign genetic materials to adapt to their surroundings, but the population in human hosts has higher demands for the control of population density, indicating that highly accurate quorum-sensing behavior is very important for harmony between E.coli and its human host. By considering core genes and previous deletions together, we proposed a potential direction for further reduction of the E.coli genome. AVAILABILITY AND IMPLEMENTATION The data, analysis process and detailed information on software tools used in this study are all available in the supplementary material. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Feedback-Aware Precoding for Millimeter Wave Massive MIMO Systems

Millimeter wave (mmWave) communication is a promising solution for coping with the ever-increasing mobile data traffic because of its large bandwidth. To enable a suffi-cient link margin, a large antenna array employing directional beamforming, which is enabled by the availability of channel state information at the transmitter (CSIT), is required. However, CSIT acquisition for mmWave channels introduces a huge feedback overhead due to the typically large number of transmit and receive antennas. Leveraging properties of mmWave channels, this paper proposes a precoding strategy which enables a flexible adjustment of the feedback overhead. In particular, the optimal unconstrained precoder is approximated by selecting a variable number of elements from a basis that is constructed as a function of the transmitter array response, where the number of selected basis elements can be chosen according to the feedback constraint. Simulation results show that the proposed precoding scheme can provide a near-optimal solution if a higher feedback overhead can be afforded. For a low overhead, it can still provide a good approximation of the optimal precoder.
Hardware Design and Optimization of Multimode Pipeline Based FFT for IEEE 802.11ax WLAN Devices

In this paper, a new architecture of a multi-mode Fast Fourier Transform hardware for IEEE 802.11ax WLAN standard is presented. The proposed architecture is based on Radix-2 Multipath Delay Commutator (MDC), Radix-22 and Radix 24 Single-path Delay Feedback(SDF) stages. To optimize the throughput and area of the FFT hardware, we applied two design techniques such as compression of redundant twiddle factors, and optimization of twiddle factor multiplication. In FPGA implementation using Altera Stratix IV EP4SGX530KH40C3, the proposed FFT achieved 1.2 GSamples/s throughput and met the requirements of the 802.11ax standard. The synthesis results show that the proposed circuit is 6.19% lower latency and 30.2% lower area compared to a recently presented work while maintaining higher working frequency.
Formal Limitations on the Measurement of Mutual Information

Measuring mutual information from finite data is difficult. Recent work has considered variational methods maximizing a lower bound. In this paper, we prove that serious statistical limitations are inherent to any method of measuring mutual information. More specifically, we show that any distribution-free high-confidence lower bound on mutual information estimated from N samples cannot be larger than O(ln N ).
Analyzing Factors Influencing Continuance Intention of E-Payment Adoption Using Modified UTAUT 2 Model

Online business of Indonesia continuous to grow rapidly. This rapid growth encourages developments for electronic payment systems. Moreover, Bank Indonesia is running a program to increase the awareness of society to become cash-less. One of the e-payment that emerging in Indonesia is Go-Pay, a Go-Jek payment system. Go-Pay successfully becomes one of the fifth largest e-money in Indonesia less than a year of its operation. Therefore, Bank Indonesia awarded Go-Jek as the most active company that increase society's awareness in conducting non-cash transactions in 2017. The successful of Go-Pay creates opportunities to upgrade the positions of Go-Pay service by knowing factors that influence its customers in continuing using Go-Pay services. This study intended to analyze factors influencing continuance intention of Go-Pay adoption in Indonesia by using a Modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) Model with Trust as a new variable. The data were gathered from 507 valid respondents in Indonesia chosen by purposive sampling technique. The result revealed that the factors influence continuance intention adoption of Go-Pay from the highest to the lowest respectively are Habit, Trust, Social Influence, Price Saving Orientation, Hedonic Motivation, and Performance Expectancy. The model can predict strongly the continuance intention of consumers towards Go-Pay services in Indonesia since the R2 is 72.8%. This model can be used by Go-Pay management in making decisions to maintain the continuance intention of consumers towards Go-Pay adoption by paying attention to those factors and their indicators.
PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction

Traffic problems have seriously affected people’s life quality and urban development, and forecasting short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that: 1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays and 2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN, which is based on a deep convolutional neural network, modeling periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a 2-D matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then, with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale, whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a real-world urban traffic data set confirm that folding time series data into a 2-D matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction.
Epidemiology of pericardial diseases in Africa: a systematic scoping review

Objectives This scoping review sought to summarise available data on the prevalence, aetiology, diagnosis, treatment and outcome of pericardial disease in Africa. Methods We searched PubMed, Scopus and African Journals Online from 1 January 1967 to 30 July 2017 to identify all studies published on the prevalence, aetiologies, diagnosis, treatment and outcomes of pericardial diseases in adults residing in Africa. Results 36 studies were included. The prevalence of pericardial diseases varies widely according to the population of interest: about 1.1% among people with cardiac complaints, between 3.3% and 6.8% among two large cohorts of patients with heart failure and up to 46.5% in an HIV-infected population with cardiac symptoms. Tuberculosis is the most frequent cause of pericardial diseases in both HIV-uninfected and HIV-infected populations. Patients with tuberculous pericarditis present mostly with effusive pericarditis (79.5%), effusive constrictive pericarditis (15.1%) and myopericarditis (13%); a large proportion of them (up to 20%) present in cardiac tamponade. The aetiological diagnosis of pericardial diseases is challenging in African resource-limited settings, especially for tuberculous pericarditis for which the diagnosis is not definite in many cases. The outcome of these diseases remains poor, with mortality rates between 18% and 25% despite seemingly appropriate treatment approaches. Mortality is highest among patients with tuberculous pericarditis especially those coinfected with HIV. Conclusion Pericardial diseases are a significant cause of morbidity and mortality in Africa, especially in HIV-infected individuals. Tuberculosis is the most frequent cause of pericardial diseases, and it is associated with poor outcomes.
Career planning development in system engineering students

it is well known that an economy based on knowledge has a strong potential for human capital. In this sense, universities have a key role in student’s professional development and performance, as well as to increase the number of professional population in science, technology, engineer, and mathematics. The aim of the present study is to obtain student’s individual, structural and external characteristics by considering their context, which represents a condition that shape student’s interest, persistence and goals in computing engineering discipline through career development. To accomplish this, we used self-reports based on Social Cognitive Career Theory in a sample of system engineering students (n=270) from three universities in the north of Lima. In addition to this, we evaluate the model through Structural Equation Modeling (SEM) under the partial least squared (PLS-SEM) approach. Results showed different associations and effects in individual and contextual variables among system engineering students.
Multilevel Wavelet Packet Entropy and Support Vector Machine for Epileptic EEG Classification

Electroencephalogram (EEG) is a bioelectric signal produced by brain activity. The abnormalities that occur in the brain, such as epilepsy, can be seen through a particular pattern on the EEG signal. A recurrent unprovoked seizure occurs in epilepsy patients as a result of excessive brain cell activity. EEG is a non-linear and non-stationary signal, so a visual interpretation is difficult to conduct. One method to measure EEG characteristics is the entropy that quantifies the signal complexity. Several studies have been conducted to classify epileptic EEG signal using entropy as the feature set. Previous studies has shown a promising result for epileptic EEG signal classification. However, to achieve effectiveness for the classification process, we propose a new method to reduce the number of features with a competitive accuracy. In this research, we propose a wavelet-based entropy method named multilevel wavelet packet entropy (MWPE) for automatic EEG signal analysis. MWPE is calculated from the wavelet packet entropy (WPE) which performed at some decomposition level. WPE was calculated from wavelet packet decomposition (WPD) which give more informations in every signal subbands compared to discrete wavelet transform (DWT). Using MWPE, we got informations about the distribution of subband energy in every level of signal decomposition. MWPE and support vector machine (SVM) are used as the feature extraction and classifier respectively. The result showed that the method is able to classify three classes of the EEG data set (normal, interictal, seizure). The best accuracy is 94.3% which achieved by using a 1–5 decomposition level with biorthogonal 2.8 wavelet, and cubic or quadratic SVM. MWPE provides high accuracy with relatively few features.
Melanoma Screening Using Deep Neural Networks

Deep learning algorithms, in particular convolutional neural networks, have rapidly become a methodology of choice for analyzing images. This choice arises due to fact that Deep Learning reduces the feature engineering task which is very important for automatic analysis of various kinds of images including medical images. In this work we have implemented various Deep Convolutional Neural Networks architectures for the process of binary classification task efficient enough in predicting either the input RGB image of the skin lesion is melanoma or not. Diagnosing the skin lesion is the first step towards its treatment. In this work we have assimilated two approaches on the pretrained Convolutional Neural Networks on the ImageNet dataset. Firstly, we use the transfer learning approach without any fine tuning and obtained features are fed for linear classification task. Secondly, we use the transfer learning approach with fine tuning. The adopted method requires no lesion segmentation or any other higher level image processing tasks. Finally, we have also developed a real time android application to perform the binary classification task. The result obtained using our android application were enthusiastic.
Transparent Edge Gateway for Mobile Networks

Advances in software-defined networking (SDN) enable a wave of innovation in a wide selection of networks ranging from data center networks to WAN. While existing standard bodies for mobile networks define stringent requirements, they too are embracing the flexibility of SDN in defining the specifications of the next generation of mobile networks. Mobile edge computing (MEC), in particular, is an emerging architecture to bring virtualized network functions and programmable network devices closer to the user. For instance, delay-sensitive or bandwidth-hungry computing resources are moved to the edge of the radio access network (RAN) to provide low latency computation and/or content for users while alleviating the backhaul pressure for network operators. In this paper, we propose an edge gateway (EGW) in the MEC that enables offloading of computation and storage resources to the edge of mobile networks. The EGW is backward compatible with components and protocols of LTE networks and does not require any modification in the user equipment, LTE software, or offloaded resources. We have designed and implemented the EGW using P4 language and verified its operation on a small testbed using a low-end P4 target and a reference LTE protocol stack.
Assessment of Radiofrequency Ablation Efficacy for Hepatocellular Carcinoma by Histology and Pretransplant Radiology

Radiofrequency ablation (RFA) represents a potentially curative option for early‐stage hepatocellular carcinoma (HCC). This study aims at evaluating the histologic response after RFA of small HCCs arising in cirrhosis. Data were reviewed from 78 patients with de novo HCCs who were treated with RFA and subsequently transplanted. The last radiological assessment before liver transplantation (LT) was used for comparison between modified Response Evaluation Criteria in Solid Tumors (mRECIST) and histological findings. A total of 125 de novo HCCs (median diameter, 20 mm) were treated with RFA only in 92 sessions. There were 98 nodules that did not show local recurrence during follow‐up (78.4%), and the remaining were retreated, except 1 because of subsequent LT. On explanted livers, complete pathological response (CPR) was observed in 61.6%, being 76.9% when <2 cm, 55.0% when 2‐3 cm, and 30.8% when >3 cm. Tumors near hepatic vessels had CPR in 50% of patients versus 69.3% for tumors distant from vessels (P = 0.039). Of the 125 HCCs, 114 had available radiological assessment within a median of 3 months before LT. Complete radiological response, according to mRECIST, was observed in 77.2% of nodules before LT. The Cohen κ was 0.48 (moderate agreement). The overall accuracy was 78.1%. A total of 18 complications were recorded with only 1 graded as major. In conclusion, RFA can provide high CPR for HCC, especially in smaller tumors distant from hepatic veins or portal branches. The agreement between mRECIST and histology is only moderate. Further refinements in radiological assessment are essential to accurately assess the true effectiveness of RFA.
Functional Feature Construction for Individualized Treatment Regimes

ABSTRACT Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple time points before treatment assignment should be included as input to the treatment regime. However, subject longitudinal measurements are typically sparse, irregularly spaced, noisy, and vary in number across subjects. Existing estimators for treatment regimes require equal information be measured on each subject and thus standard practice is to summarize longitudinal subject information into a scalar, ad hoc summary during data preprocessing. This reduction of the longitudinal information to a scalar feature precedes estimation of a treatment regime and is therefore not informed by subject outcomes, treatments, or covariates. Furthermore, we show that this reduction requires more stringent causal assumptions for consistent estimation than are necessary. We propose a data-driven method for constructing maximally prescriptive yet interpretable features that can be used with standard methods for estimating optimal treatment regimes. In our proposed framework, we treat the subject longitudinal information as a realization of a stochastic process observed with error at discrete time points. Functionals of this latent process are then combined with outcome models to estimate an optimal treatment regime. The proposed methodology requires weaker causal assumptions than Q-learning with an ad hoc scalar summary and is consistent for the optimal treatment regime. Supplementary materials for this article are available online.
Towards Governing Agent's Efficacy: Action-Conditional β-VAE for Deep Transparent Reinforcement Learning

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible to foresee all unwanted outcomes and penalize them with negative rewards beforehand. Unlike reverse analysis of learned neural features from previous works, our proposed method \nj{tackles the blackbox issue by encouraging} an RL policy network to learn interpretable latent features through an implementation of a disentangled representation learning method. Toward this end, our method allows an RL agent to understand self-efficacy by distinguishing its influences from uncontrollable environmental factors, which closely resembles the way humans understand their scenes. Our experimental results show that the learned latent factors not only are interpretable, but also enable modeling the distribution of entire visited state space with a specific action condition. We have experimented that this characteristic of the proposed structure can lead to ex post facto governance for desired behaviors of RL agents.
One Quantifiable Security Evaluation Model for Cloud Computing Platform

Whatever one public cloud, private cloud or a mixed cloud, the users lack of effective security quantifiable evaluation methods to grasp the security situation of its own information infrastructure on the whole. This paper provides a quantifiable security evaluation system for different clouds that can be accessed by consistent API. The evaluation system includes security scanning engine, security recovery engine, security quantifiable evaluation model, visual display module and etc. The security evaluation model composes of a set of evaluation elements corresponding different fields, such as computing, storage, network, maintenance, application security and etc. Each element is assigned a three tuple on vulnerabilities, score and repair method. The system adopts “One vote vetoed” mechanism for one field to count its score and adds up the summary as the total score, and to create one security view. We implement the quantifiable evaluation for different cloud users based on our G-Cloud platform. It shows the dynamic security scanning score for one or multiple clouds with visual graphs and guided users to modify configuration, improve operation and repair vulnerabilities, so as to improve the security of their cloud resources.
Aβ stimulates microglial activation through antizyme‐dependent downregulation of ornithine decarboxylase

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders. Its pathology is associated with the deposition of amyloid β (Aβ), an abnormal extracellular peptide. Moreover, its pathological progression is closely accompanied by neuroinflammation. Specifically, Aβ‐associated microglial overactivation may have the central role in AD pathogenesis. Interestingly, arginine metabolism may contribute to the equilibrium between M1 and M2 microglia. However, little is known about the involvement of arginine metabolism in Aβ‐induced microglial neuroinflammation and neurotoxicity. Moreover, the underlying mechanism by which Aβ induces the transition of microglia to the M1 phenotype remains unclear. In this study, we investigated the role of Aβ in mediating microglial activation and polarization both in vitro and in vivo. Our results demonstrated that under the Aβ treatment, ornithine decarboxylase (ODC), a rate‐limiting enzyme in the regulation of arginine catabolism, regulates microglial activation by altering the antizyme (AZ) + 1 ribosomal frameshift. Furthermore, the restoration of ODC protein expression levels has profound effects on inhibition of Aβ‐induced M1 markers and thus attenuates microglial‐mediated cytotoxicity. Altogether, our findings suggested that Aβ may contribute to M1‐like activation by disrupting the balance between ODC and AZ in microglia.