Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing

Variants in the TGFBR2 kinase domain cause several human diseases and can increase propensity for cancer. The widespread application of next generation sequencing within the setting of Individualized Medicine (IM) is increasing the rate at which TGFBR2 kinase domain variants are being identified. However, their clinical relevance is often uncertain. Consequently, we sought to evaluate the use of molecular modeling and molecular dynamics (MD) simulations for assessing the potential impact of variants within this domain. We documented the structural differences revealed by these models across 57 variants using independent MD simulations for each. Our simulations revealed various mechanisms by which variants may lead to functional alteration; some are revealed energetically, while others structurally or dynamically. We found that the ATP binding site and activation loop dynamics may be affected by variants at positions throughout the structure. This prediction cannot be made from the linear sequence alone. We present our structure-based analyses alongside those obtained using several commonly used genomics-based predictive algorithms. We believe the further mechanistic information revealed by molecular modeling will be useful in guiding the examination of clinically observed variants throughout the exome, as well as those likely to be discovered in the near future by clinical tests leveraging next-generation sequencing through IM efforts.
K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Indoor localization in multi-floor buildings is an important research problem. Finding the correct floor, in a fast and efficient manner, in a shopping mall or an unknown university building can save the users' search time and can enable a myriad of Location Based Services in the future. One of the most widely spread techniques for floor estimation in multi- floor buildings is the fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WLAN and BLE (Bluetooth Low Energy). The clear advantage of RSS-based floor estimation is its ease of implementation on a multitude of mobile devices at the Application Programming Interface (API) level, because RSS values are directly accessible through API interface. However, the downside of a fingerprinting approach, especially for large-scale floor estimation and positioning solutions, is their need to store and transmit a huge amount of fingerprinting data. The problem becomes more severe when the localization is intended to be done on mobile devices (smart phones, tablets, etc.) which have limited memory, power, and computational resources. An alternative floor estimation method, which has lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) method. The trade-off is however paid in terms of a lower accuracy than the one obtained with traditional fingerprinting with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based method for floor estimation via fingerprint clustering of WiFi and various other positioning sensor outputs is introduced. Our method achieves a floor estimation accuracy close to the one with NN fingerprinting, while significantly improves the complexity and the speed of the floor detection algorithm. The decrease in the database size is achieved through storing and transmitting only the cluster heads (CH's) and their corresponding floor labels. The performance of the proposed methods is evaluated using real-life indoor measurements taken from four multi-floor buildings. The numerical results show that the proposed K-means-based method offers an excellent trade-off between the complexity and performance.
Analysis of Interrelationships Among Application Popularity, Application Stability, and Potential Risk in e-WOM

During the last decade, electronic word of mouth (e-WOM) has drawn much attention from practitioners and researchers with regard to three emerging areas of research interest – “popularity,” “potential risk,” and “behavioral target ing. ” Although literature already exists on e-WOM from the perspective of these three areas, little research has been done, to the best of our knowledge, on the interrelationship among application popularity, application stability, potential risk, and behavioral targeting. This paper, with its focus on Android applications, aims to fill this gap by analyzing the correlation structure of the four factors,
Problem‐based learning: rationale and description

Problem‐based learning is an instructional method that is said to provide students with knowledge suitable for problem solving. In order to test this assertion the process of problem‐based learning is described and measured against three principles of learning: activation of prior knowledge, elaboration and encoding specificity. Some empirical evidence regarding the approach is presented.
Learning Polytrees

We consider the task of learning the maximum-likelihood polytree from data. Our first result is a performance guarantee establishing that the optimal branching (or Chow-Liu tree), which can be computed very easily, constitutes a good approximation to the best polytree. We then show that it is not possible to do very much better, since the learning problem is NP-hard even to approximately solve within some constant factor.
Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.
Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks

Presents a fully automated process for segmentation and classification of multispectral magnetic resonance (MR) images. This hybrid neural network method uses a Kohonen self-organizing neural network for segmentation and a multilayer backpropagation neural network for classification. To separate different tissue types, this process uses the standard T1-, T2-, and PD-weighted MR images acquired in clinical examinations. Volumetric measurements of brain structures, relative to intracranial volume, were calculated for an index transverse section in 14 normal subjects (median age 25 years; 7 male, 7 female). This index slice was at the level of the basal ganglia, included both genu and splenium of the corpus callosum, and generally, showed the putamen and lateral ventricle. An intraclass correlation of this automated segmentation and classification of tissues with the accepted standard of radiologist identification for the index slice in the 14 volunteers demonstrated coefficients (r/sub i/) of 0.91, 0.95, and 0.98 for white matter, gray matter, and ventricular cerebrospinal fluid (CSF), respectively. An analysis of variance for estimates of brain parenchyma volumes in 5 volunteers imaged 5 times each demonstrated high intrasubject reproducibility with a significance of at least p<0.05 for white matter, gray matter, and white/gray partial volumes. The population variation, across 14 volunteers, demonstrated little deviation from the averages for gray and white matter, while partial volume classes exhibited a slightly higher degree of variability. This fully automated technique produces reliable and reproducible MR image segmentation and classification while eliminating intra- and interobserver variability.
Emergence of highly designable protein‐backbone conformations in an off‐lattice model

Despite the variety of protein sizes, shapes, and backbone configurations found in nature, the design of novel protein folds remains an open problem. Within simple lattice models it has been shown that all structures are not equally suitable for design. Rather, certain structures are distinguished by unusually high designability: the number of amino acid sequences for which they represent the unique lowest energy state; sequences associated with such structures possess both robustness to mutation and thermodynamic stability. Here we report that highly designable backbone conformations also emerge in a realistic off‐lattice model. The highly designable conformations of a chain of 23 amino acids are identified and found to be remarkably insensitive to model parameters. Although some of these conformations correspond closely to known natural protein folds, such as the zinc finger and the helix‐turn‐helix motifs, others do not resemble known folds and may be candidates for novel fold design. Proteins 2002;47:506–512. © 2002 Wiley‐Liss, Inc.
Two-Stage SEPIC-Buck Topology for Neighborhood Electric Vehicle Charger

It is no exaggeration to consider electric vehicles a reality in our current society. Comparing to combustion vehicles they have better efficiency and monetary profitability by distance traveled. However, the necessary infrastructure for the proper functioning of EVs, as batteries, individual chargers, charge stations, etc., are still precarious in several places. This paper introduces a new interleaved model for a low voltage battery pack charger for neighborhood electric vehicles (NEV). The system has two power stages, a SEPIC PFC rectifier and a DC-DC Buck converter, which process 1.5 kW per cell a total of 4.5 kW. The proposed system is adequate to lower power single-phase systems, which the battery pack voltage is lower than the grid.
Tissue-Specific Transcriptome Analysis Reveals Multiple Responses to Salt Stress in Populus euphratica Seedlings

Salt stress is one of the most crucial factors impacting plant growth, development and reproduction. However, information regarding differences in tissue-specific gene expression patterns, which may improve a plant’s tolerance to salt stress, is limited. Here, we investigated the gene expression patterns in tissues of Populus euphratica Oliv. seedlings using RNA sequencing (RNA-Seq) technology. A total of 109.3 million, 125bp paired-end clean reads were generated, and 6428, 4797, 2335 and 3358 differentially expressed genes (DEGs) were identified in leaf, phloem, xylem and root tissues, respectively. While the tissue-specific DEGs under salt stress had diverse functions, “membrane transporter activity” was the most significant leaf function, whereas “oxidation–reduction process” was the most significant function in root tissue. Further analysis of the tissue-specific DEGs showed that the expression patterns or functions of gene families, such as SOS, NHX, GolS, GPX, APX, RBOHF and CBL, were diverse, suggesting that calcium signaling, reactive oxygen species (ROS) and salt overly sensitive (SOS) pathways are all involved in ionic homeostasis in tissues from P. euphratica seedlings. The DEGs, for example the up-regulated antioxidant genes, contribute to ROS-scavenging induced by salt stress but result in decreased Na+ concentrations in root vasculature cells and in xylem sap, while the down-regulated rbohF leads to the reverse results. These results suggest that the divergence of DEGs expression patterns contribute to maintenance of ionic and ROS homeostasis in tissues and improve plant salinity tolerance. We comprehensively analyzed the response of P. euphratica seedlings to salt stress and provide helpful genetic resources for studying plant-abiotic stress interactions.
Intensive correction orders vs other penalties: offender profiles

Aim: To examine the profile of offenders given intensive correction orders in New South Wales and to compare these offenders with those who received other penalties. Method: Details of offenders' demographic and offence characteristics, prior convictions and penalties were examined. Logistic regression models were developed to compare those who received intensive correction orders with those who received other penalties. Results: Between 1 October 2010 and 30 September 2011, 488 offenders in NSW were given an intensive correction order. The majority of offenders were male (89%); on average, they were 32.7 years of age, with 5.3 prior proven court appearances, and most commonly they were convicted of traffic and vehicle regulatory offences (40%). When compared with offenders receiving periodic detention, a suspended sentence with supervision, a community service order or a sentence of imprisonment, those who received intensive correction orders were most similar to those who received periodic detention in the preceding year. However, they were more likely to be female, have a prior prison sentence and live in a major city than those who had received periodic detention. Conclusion: The profile of offenders receiving intensive correction orders was very similar to the profile of those who previously received periodic detention.
Lipidomics of cellular and secreted phospholipids from differentiated human fetal type II alveolar epithelial cellss⃞

Maturation of fetal alveolar type II epithelial cells in utero is characterized by specific changes to lung surfactant phospholipids. Here, we quantified the effects of hormonal differentiation in vitro on the molecular specificity of cellular and secreted phospholipids from human fetal type II epithelial cells using electrospray ionization mass spectrometry. Differentiation, assessed by morphology and changes in gene expression, was accompanied by restricted and specific modifications to cell phospholipids, principally enrichments of shorter chain species of phosphatidylcholine (PC) and phosphatidylinositol, that were not observed in fetal lung fibroblasts. Treatment of differentiated epithelial cells with secretagogues stimulated the secretion of functional surfactant-containing surfactant proteins B and C (SP-B and SP-C). Secreted material was further enriched in this same set of phospholipid species but was characterized by increased contents of short-chain monounsaturated and disaturated species other than dipalmitoyl PC (PC16:0/16:0), principally palmitoylmyristoyl PC (PC16:0/14:0) and palmitoylpalmitoleoyl PC (PC16:0/16:1). Mixtures of these PC molecular species, phosphatidylglycerol, and SP-B and SP-C were functionally active and rapidly generated low surface tension on compression in a pulsating bubble surfactometer. These results suggest that hormonally differentiated human fetal type II cells do not select the molecular composition of surfactant phospholipid on the basis of saturation but, more likely, on the basis of acyl chain length.
Fast Focus+Context Visualization of Large Scientific Data

Visualization of high-dimensional and time-dependent data, resulting from computational simulation, is a very challenging and resource-consuming task. Here, featurebased visualization approaches, which aim at a usercontrolled reduction of the data shown at one instance of time, proof to be useful. In this paper, we present a framework for interactive highdimensional visualization and feature specification. We put special emphasis on the design of the data access layer and the performance optimization of the highly interactive parts of our system.
Prediction with Partial Match using two-dimensional approximate contexts

The Prediction with Partial Match (PPM) is a context-based lossless compression scheme developed in the mid 80's. Originally it was targeted towards compressing text that can be viewed as a one-dimensional sequence of symbols. When compressing digital images, PPM usually breaks the two-dimensional data into a one-dimensional raster scan form. This paper extends PPM in order to take full advantage of the two-dimensional nature of digital images. Unlike the traditional two-dimensional raster scan contexts (i.e. concerning upper pixels and pixels to the left), the proposed context is determined using pixels from all directions, including pixels to the right and the lower pixels. Results show that this type of context yields a significant improvement over the traditional raster scan context.
Effects of HIV status and Linguistic Medium on the Test Performance of Rural Low-Literacy Adults: Implications for Neuropsychological Test Development in Zambia

Purpose of the study: The purpose of the study was to determine whether the familiar language (Chichewa version) could contribute to the early diagnosis of neurocognitive dysfunctions and develop a battery of locally valid tests capable of detecting early changes in the cognitive profile of neurocognitive dysfunctions among HIV positive patients. Research question one : What is the difference in performance between HIV negative and HIV positive individuals when they are subjected to the four verbal tests of the neuropsychological test battery using the English and Chichewa versions? Research question two: What is the interaction effect among the influences of HIV status, linguistic medium and gender on the four verbal tests of the neuropsychological test battery? Design : It was an experimental design that assessed the neuropsychological effects of HIV status and linguistic medium on the test performance. The Hopkins Verbal Learning Test (HVLT-R) for both immediate and delayed recall were used to test the verbal episodic memory from the Verbal Learning and Memory Recall Domain (Brandt and Benedict, 2001). Other tests included Animal and Action Naming. These tests were translated into Chichewa and administered to 28 HIV positive and 22 HIV negative rural low illiterate adults aged between 40 and 65 years. Results : On all the Neuropsychological tests administered, HIV positive respondents scored significantly lower than HIV negative respondents, and the mean scores on the English medium version were consistently lower than scores on the L1 (Chichewa) version across all tests and all groups. Conclusion : The study has shown that the primary language is best suited to test neurocognitive performance and especially when one is using test components that do not require reading or writing.
Educating CPE Supervisors: A Grounded Theory Study

This qualitative study was designed to cull the wisdom of CPE supervisors doing especially competent supervisory education and to develop a theory of CPE supervisory education. Grounded theory methodology included interviewing 11 supervisors and coding the data to identify themes. Four primary dimensions emerged along with a reciprocal core dimension, Supervisory Wisdom, which refers to work the supervisors do in terms of their continuing growth and development.
Robust predictive control of discrete-time systems with actuating delay

In this paper robust constrained regulation problem for a discrete-time system with actuation delay has been considered. Persistent but bounded measurement noise and disturbance input are present. Furthermore, a polytopic bound on estimation error is assumed to be given. A linear estimator is designed such that the given polytopic estimation error bound is achievable. Actuating delay is compensated with the use of a linear propagator. The information obtained from this propagator is then used to design a set-theoretic model predictive controller which guarantees ultimately bounded steady state response as well as robust constraint satisfaction.
Fast text classification with Naive Bayes method on Apache Spark

The increase in the number of devices and users online with the transition of Internet of Things (IoT), increases the amount of large data exponentially. Classification of ascending data, deletion of irrelevant data, and meaning extraction have reached vital importance in today's standards. Analysis can be done in various variations such as Classification of text on text data, analysis of spam, personality analysis. In this study, fast text classification was performed with machine learning on Apache Spark using the Naive Bayes method. Spark architecture uses a distributed in-memory data collection instead of a distributed data structure presented in Hadoop architecture to provide fast storage and analysis of data. Analyzes were made on the interpretation data of the Reddit which is open source social news site by using the Naive Bayes method. The results are presented in tables and graphs
AUTOMATIC FACE RECOGNITION APPROACHES

This paper deals with automatic face recognition, which means to use a computer for automatic identification of a person from a digital image or from a video frame. This field became intensively studied in the last two decades. Concerning other biometrics methods, automatic face recognition seems to be one of the most important ones. Therefore, automatic face recognition is used in many applications as for example access control to restricted areas, surveillance of persons, various programs for sharing and labelling of photographs, social networks and many others. The main goal of this paper is to review most important face recognition approaches with their theoretical and practical advantages and drawbacks. We further evaluate and compare these approaches with each other. We conclude that it is generally not possible to identify a best performing face recognition approach and that the choice of the optimal method is strictly related to the target application. We assume that the future research directions will address the main issue of the current approaches, insufficient recognition accuracy in the totally uncontrolled environment.
Fast predictive control of linear, time-invariant systems using an algorithm based on the fast gradient method and augmented Lagrange multipliers

We present an algorithm based on the fast gradient method and augmented Lagrange multipliers for model predictive control of linear, discrete-time, time-invariant, systems with constraints. In particular, the algorithm solves the underlying quadratic program in the so-called condensed form and takes advantage of the problem structure. At the end, we illustrate the performance of the algorithm, which is competitive with tailored interior-point methods, by an example.