Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering software artifacts based on frequent common changes

Changes of software systems are less expensive and less error-prone if they affect only one subsystem. Thus, clusters of artifacts that are frequently changed together are subsystem candidates. We introduce a two-step method for identifying such clusters. First, a model of common changes of software artifacts, called co-change graph, is extracted from the version control repository of the software system. Second, a layout of the co-change graph is computed that reveals clusters of frequently co-changed artifacts. We derive requirements for such layouts, and introduce an energy model for producing layouts that fulfill these requirements. We evaluate the method by applying it to three example systems, and comparing the resulting layouts to authoritative decompositions.
The Transcriptional Response to Oxidative Stress during Vertebrate Development: Effects of tert-Butylhydroquinone and 2,3,7,8-Tetrachlorodibenzo-p-Dioxin

Oxidative stress is an important mechanism of chemical toxicity, contributing to teratogenesis and to cardiovascular and neurodegenerative diseases. Developing animals may be especially sensitive to chemicals causing oxidative stress. The developmental expression and inducibility of anti-oxidant defenses through activation of NF-E2-related factor 2 (NRF2) affect susceptibility to oxidants, but the embryonic response to oxidants is not well understood. To assess the response to chemically mediated oxidative stress and how it may vary during development, zebrafish embryos, eleutheroembryos, or larvae at 1, 2, 3, 4, 5, and 6 days post fertilization (dpf) were exposed to DMSO (0.1%), tert-butylhydroquinone (tBHQ; 10 µM) or 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD; 2 nM) for 6 hr. Transcript abundance was assessed by real-time qRT-PCR and microarray. qRT-PCR showed strong (4- to 5-fold) induction of gstp1 by tBHQ as early as 1 dpf. tBHQ also induced gclc (2 dpf), but not sod1, nqo1, or cyp1a. TCDD induced cyp1a but none of the other genes. Microarray analysis showed that 1477 probes were significantly different among the DMSO-, tBHQ-, and TCDD-treated eleutheroembryos at 4 dpf. There was substantial overlap between genes induced in developing zebrafish and a set of marker genes induced by oxidative stress in mammals. Genes induced by tBHQ in 4-dpf zebrafish included those involved in glutathione synthesis and utilization, signal transduction, and DNA damage/stress response. The strong induction of hsp70 determined by microarray was confirmed by qRT-PCR and by use of transgenic zebrafish expressing enhanced green fluorescent protein (EGFP) under control of the hsp70 promoter. Genes strongly down-regulated by tBHQ included mitfa, providing a molecular explanation for the loss of pigmentation in tBHQ-exposed embryos. These data show that zebrafish embryos are responsive to oxidative stress as early as 1 dpf, that responsiveness varies with development in a gene-specific manner, and that the oxidative stress response is substantially conserved in vertebrate animals.
Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics

Significance Ecological and evolutionary investigations require accurate and high-resolution biodiversity information. Conventional morphological approaches to identifying species in species-rich tropical ecosystems are often unavailable or incapable of timely, cost-effective identification. We show that next-generation sequencing (NGS) of cytochrome c oxidase subunit I (COI) DNA barcodes can accurately detect 83.5% of individually sequenced species (corresponding to 91% of individuals) in a bulk sample of terrestrial arthropods from a Costa Rican species-rich site. Additionally, the 16S and 18S ribosomal DNA gene regions obtained also provide an assessment of the bacteria and protozoa in the bulk sample. This metasystematic approach provides the initial infrastructure for a next generation of biodiversity assessment and environmental monitoring. It can lead to more effective understanding, appreciation, and management of complex ecosystems. Conventional assessments of ecosystem sample composition are based on morphology-based or DNA barcode identification of individuals. Both approaches are costly and time-consuming, especially when applied to the large number of specimens and taxa commonly included in ecological investigations. Next-generation sequencing approaches can overcome the bottleneck of individual specimen isolation and identification by simultaneously sequencing specimens of all taxa in a bulk mixture. Here we apply multiple parallel amplification primers, multiple DNA barcode markers, 454-pyrosequencing, and Illumina MiSeq sequencing to the same sample to maximize recovery of the arthropod macrobiome and the bacterial and other microbial microbiome of a bulk arthropod sample. We validate this method with a complex sample containing 1,066 morphologically distinguishable arthropods from a tropical terrestrial ecosystem with high taxonomic diversity. Multiamplicon next-generation DNA barcoding was able to recover sequences corresponding to 91% of the distinguishable individuals in a bulk environmental sample, as well as many species present as undistinguishable tissue. 454-pyrosequencing was able to recover 10 more families of arthropods and 30 more species than did conventional Sanger sequencing of each individual specimen. The use of other loci (16S and 18S ribosomal DNA gene regions) also added the detection of species of microbes associated with these terrestrial arthropods. This method greatly decreases the time and money necessary to perform DNA-based comparisons of biodiversity among ecosystem samples. This methodology opens the door to much cheaper and increased capacity for ecological and evolutionary studies applicable to a wide range of socio-economic issues, as well as a basic understanding of how the world works.
Kinect Based Electronic Assisting System to Facilitate People with Disabilities Using KXPRUM Agile Model M . Rizwan

There is a need of electronic assisting system to facilitate people with disabilities to perform their daily life tasks. Microsoft Kinect technology was introduced with Xbox to allow the children to play games using motions instead of using joysticks. The success of Kinect technology came up heuristically to scientists to use this technology in other disciplines like medical field. This research is conducted to develop an electronic assisting system to facilitate patients to make them free of attendants to perform routine tasks like on coffee maker, on/off air conditioner/light and control wheelchair. A customized KXPRUM model is proposed to develop the electronic system. The results are reported on the first four releases of electronic assisting system. Five patients act as customer to conduct case study to validate the proposed KXPRUM model. 80% customer satisfaction is achieved to show the effectiveness of the proposed model. [Qureshi MRJ, Barnawi A. Kinect Based Electronic Assisting System to Facilitate People with Disabilities Using KXPRUM Agile Model. Life Sci J 2014;11(10):57-62] (ISSN:1097-8135). http://www.lifesciencesite. com. 10
MAC-oriented programmable terahertz PHY via graphene-based Yagi-Uda antennas

Graphene is enabling a plethora of applications in a wide range of fields due to its unique electrical, mechanical, and optical properties. In the realm of wireless communications, graphene shows great promise for the implementation of miniaturized and tunable antennas in the terahertz band. These unique advantages open the door to new reconfigurable antenna structures which, in turn, enable novel communication protocols at different levels of the stack. This paper explores both aspects by, first, presenting a terahertz Yagi-Uda-like antenna concept that achieves reconfiguration both in frequency and beam direction simultaneously. Then, a programmable antenna controller design is proposed to expose the reconfigurability to the PHY and MAC layers, and several examples of its applicability are given. The performance and cost of the proposed scheme is evaluated through full-wave simulations and comparative analysis, demonstrating reconfigurability at nanosecond granularity with overheads below 0.02 mm2 and 0.2 mW.
An NLMS algorithm for the identification of bilinear forms

In a recent work, we addressed the identification poblem of bilinear forms with the Wiener filter. In this context, a different approach was introduced, by defining the bilinear term with respect to the impulse responses of a spatiotemporal model, which resembles a multiple-input/single-output (MISO) system. However, in practice, the Wiener filter may not be always very efficient or convenient to use. Consequently, in this paper, we further develop a normalized least-mean-square (NLMS) adaptive filter tailored for bilinear forms. Many simulations, which are performed from a system identification perspective, indicate the good performance of the proposed algorithm.
Explore to see, learn to perceive, get the actions for free: SKILLABILITY

How can a humanoid robot autonomously learn and refine multiple sensorimotor skills as a byproduct of curiosity driven exploration, upon its high-dimensional unprocessed visual input? We present SKILLABILITY, which makes this possible. It combines the recently introduced Curiosity Driven Modular Incremental Slow Feature Analysis (Curious Dr. MISFA) with the well-known options framework. Curious Dr. MISFA's objective is to acquire abstractions as quickly as possible. These abstractions map high-dimensional pixel-level vision to a low-dimensional manifold. We find that each learnable abstraction augments the robot's state space (a set of poses) with new information about the environment, for example, when the robot is grasping a cup. The abstraction is a function on an image, called a slow feature, which can effectively discretize a high-dimensional visual sequence. For example, it maps the sequence of the robot watching its arm as it moves around, grasping randomly, then grasping a cup, and moving around some more while holding the cup, into a step function having two outputs: when the cup is or is not currently grasped. The new state space includes this grasped/not grasped information. Each abstraction is coupled with an option. The reward function for the option's policy (learned through Least Squares Policy Iteration) is high for transitions that produce a large change in the step-functionlike slow features. This corresponds to finding bottleneck states, which are known good subgoals for hierarchical reinforcement learning - in the example, the subgoal corresponds to grasping the cup. The final skill includes both the learned policy and the learned abstraction. SKILLABILITY makes our iCub the first humanoid robot to learn complex skills such as to topple or grasp an object, from raw high-dimensional video input, driven purely by its intrinsic motivations.
A study on characteristics evaluation to control quality of asphalt mixture using X-ray CT

The aim of this paper is to propose X-ray computed tomography (CT) as a new evaluation method of asphalt mixtures. To achieve this aim, all sections of three benchmark and three core samples of porous asphalt mixture at an expressway were subjected to X-ray CT scanning, internal characteristics were analysed, and a minimum slice number was decided. As a result of internal characteristics, the internal density indicated a tendency to increase by a rise in the height in the core samples, whereas the internal density indicated a tendency to become larger at the centre than at the top or bottom in the benchmark samples. With slice number examination, a trustable average density will be obtained by CT scanning if five slices at least are scanned at equal interval.
Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.
Structural Principles Analysis of Host-Pathogen Protein-Protein Interactions: A Structural Bioinformatics Survey

Computational-intelligence methods in bioinformatics and systems biology show promising potential for leveraging abundant, large-scale molecular data. These methods can facilitate analysis and prediction of the principles of biological systems through the construction of statistical and visualized models. Specifically, structural data from exogenous and endogenous protein–protein interactions are of vital significance in this context, encompassing primarily 3-D structural information for a cohort of macromolecules underpinning the biological system. In this paper, we surveyed the main methodologies and algorithms for the reconstruction and modeling of the structural-interaction networks (SINs) of host–pathogen protein–protein interactions (HPPPIs), regarding how the protein domains interact with each other to constitute a SIN. Surveying the pattern and the organization of the SIN delivers a state-of-the-art view of HPPPIs and illustrates prospective future research directions. In addition to the binary PPI network, we distilled the relevant data sources into several branching research areas and further expanded the discussions into computational-intelligence methods according to the algorithms applied, including machine learning statistical models, to shed light on effective method design. In particular, atomic resolution level investigations can reveal novel insights into the underlying principles of the organization and the complexity of HPPPIs networks. Combining data analytics and machine-learning technologies, we anticipate that our systematic overview will serve as a useful guide for interested researchers to carry out related studies on this exciting and challenging research topic in system biology.
3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures.

Background The aim of our work is to assess the potential role of texture analysis (TA), applied to computed tomography (CT) simulation scans, in relation to the development of insufficiency fractures (IFs) in patients undergoing radiation therapy (RT) for pelvic malignancies. Methods We analyzed patients undergoing pelvic RT from Jan-2010 to Dec-2016, 31 of whom had developed IFs of the pelvis. We analyzed CT simulation scans using LifeX Software©, and in particular we selected three regions of interest (ROI): L5 body, the sacrum and both the femoral heads. The ROI were automatically contoured using the treatment planning software Raystation©. TA parameters included parameters from the gray-level histogram, indices from sphericity and from the matrix of GLCM (gray level co-occurrence matrix). The IFs patients were matched (1:1 ratio) with control patients who had not developed IFs, and were matched for age, sex, type of tumor, menopausal status, RT dose and use of chemotherapy. Univariate and multivariate analyses (logistic regression) were used for statistical analysis. Results Significant TA parameters on univariate analysis included both parameters from the histogram distribution, as well from the matrix of GLCM. On logistic regression analysis the significant parameters were L5-energy [P=0.033, odds ratio (OR): 1.997, 95% CI: 1.059-3.767] and FH-Skewness (P=0.014, OR: 2.338, 95% CI: 1.191-4.591), with a R2: 0.268. A ROC curve was generated from the binary logistic regression, and the AUC was 0.741 (95% CI: 0.627-0.855, P=0.001, S.E.: 0.058). Conclusions In our experience, 3D-bone CT TA can be used to stratify the risk of the patients to develop radiation-induced IFs. A prospective study will be conducted to validate these findings.
What Links Alice and Bob?: Matching and Ranking Semantic Patterns in Heterogeneous Networks

An increasing number of applications are modeled and analyzed in network form, where nodes represent entities of interest and edges represent interactions or relationships between entities. Commonly, such relationship analysis tools assume homogeneity in both node type and edge type. Recent research has sought to redress the assumption of homogeneity and focused on mining heterogeneous information networks (HINs) where both nodes and edges can be of different types. Building on such efforts, in this work we articulate a novel approach for mining relationships across entities in such networks while accounting for user preference (prioritization) over relationship type and interestingness metric. We formalize the problem as a top-$k$ lightest paths problem, contextualized in a real-world communication network, and seek to find the $k$ most interesting path instances matching the preferred relationship type. Our solution, PROphetic HEuristic Algorithm for Path Searching (PRO-HEAPS), leverages a combination of novel graph preprocessing techniques, well designed heuristics and the venerable A* search algorithm. We run our algorithm on real-world large-scale graphs and show that our algorithm significantly outperforms a wide variety of baseline approaches with speedups as large as 100X. We also conduct a case study and demonstrate valuable applications of our algorithm.
Management of Necrotizing Otitis Externa: Our Experience with Forty-Three Patients.

OBJECTIVE To assess the impact of the introduction of a dedicated management protocol of necrotizing otitis externa patients with joint care between otorhinolaryngology and infectious diseases. MATERIALS AND METHODS Retrospective review of case notes and the otorhinolaryngology department database of all adults admitted with necrotizing otitis externa at our teaching hospital over a 5-year period. The patients were split into two groups (first group of 10 patients prior to the introduction of the dedicated management protocol, and a second group of 33 patients managed after the introduction of this protocol). RESULTS Of the 43 patients included in the study, diabetes mellitus was present in 83.7%. Pseudomonas aeruginosa was grown in 67.4% of patients. All 43 patients underwent computed tomography and magnetic resonance imaging (with contrast) scans. Surgical intervention was undertaken in 25.6% of patients. Mean follow-up was 10 months (SD, ±7 months). Of the 43 patients, 79.1% made a full recovery and were discharged. Relapse occurred in 9.3% of patients. One patient died because of a myocardial infarction 4 months after treatment. The mean length of stay was significantly lower after the protocol was introduced (25.6±5.3 vs. 14.2±3.8 days, p=0.001), and the duration of treatment was also significantly lower after the protocol (21.2±6.8 vs. 14.3±4.3 weeks, p=0.01). CONCLUSION The introduction of a dedicated management protocol and joint care with otorhinolaryngology and infectious diseases resulted in improved care and decreased length of stay in patients. Early diagnosis and involvement with the relevant teams as well as prompt intervention are the key factors that reduce morbidity and mortality.
Gold nano sphere based fiber optic LSPR probe for biosensing measurement

We present a localized surface plasmon resonance fiber optic biosensor based on an intensity interrogation mechanism. A layer of gold nano sphere is deposited on a fiber optic sensor probe which works as the sensing element and is immobilized on the sidewall of an unclad optical fiber via two different immobilization methods (amino silane method and layer by layer self-assembly method). Different self-assembly layers were also respectively investigated by using layer by layer self-assembly method to explore the optimum layer number. Experimental results reveal that PDDA/PSS/PAH layer self-assembly method provides the best LSPR response. We obtain a refractive index sensitivity as 6.57RIU-1 in a RI range of 1.3266~1.3730. We also conduct real-time and label free monitoring of Ribonuclease B/Con A biomolecular interaction by using this sensor prototype and demonstrate it can perform qualitative and quantitative detection in real-time biomolecular sensing.
Structural Basis for the dsRNA Specificity of the Lassa Virus NP Exonuclease

Lassa virus causes hemorrhagic fever characterized by immunosuppression. The nucleoprotein of Lassa virus, termed NP, binds the viral genome. It also has an additional enzymatic activity as an exonuclease that specifically digests double-stranded RNA (dsRNA). dsRNA is a strong signal to the innate immune system of viral infection. Digestion of dsRNA by the NP exonuclease activity appears to cause suppression of innate immune signaling in the infected cell. Although the fold of the NP enzyme is conserved and the active site completely conserved with other exonucleases in its DEDDh family, NP is atypical among exonucleases in its preference for dsRNA and its strict specificity for one substrate. Here, we present the crystal structure of Lassa virus NP in complex with dsRNA. We find that unlike the exonuclease in Klenow fragment, the double-stranded nucleic acid in complex with Lassa NP remains base-paired instead of splitting, and that binding of the paired complementary strand is achieved by “relocation” of a basic loop motif from its typical exonuclease position. Further, we find that just one single glycine that contacts the substrate strand and one single tyrosine that stacks with a base of the complementary, non-substrate strand are responsible for the unique substrate specificity. This work thus provides templates for development of antiviral drugs that would be specific for viral, rather than host exonucleases of similar fold and active site, and illustrates how a very few amino acid changes confer alternate specificity and biological phenotype to an enzyme.
On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear $H_{\infty}$ Control

In this paper, based on the adaptive critic learning technique, the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.