Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation of an aryloxyphenoxy propanoate (AOPP) herbicide-degrading strain Rhodococcus ruber JPL-2 and the cloning of a novel carboxylesterase gene (feh)

The strain JPL-2, capable of degrading fenoxaprop-P-ethyl (FE), was isolated from the soil of a wheat field and identified as Rhodococcus ruber. This strain could utilize FE as its sole carbon source and degrade 94.6% of 100 mg L−1 FE in 54 h. Strain JPL-2 could also degrade other aryloxyphenoxy propanoate (AOPP) herbicides. The initial step of the degradation pathway is to hydrolyze the carboxylic acid ester bond. A novel esterase gene feh, encoding the FE-hydrolyzing carboxylesterase (FeH) responsible for this initial step, was cloned from strain JPL-2. Its molecular mass was approximately 39 kDa, and the catalytic efficiency of FeH followed the order of FE > quizalofop-P-ethyl > clodinafop-propargyl > cyhalofop-butyl > fluazifop-P-butyl > haloxyfop-P-methyl > diclofop-methy, which indicated that the chain length of the alcohol moiety strongly affected the hydrolysis activity of the FeH toward AOPP herbicides.
A Proposed Textual Model for i-Star

Despite software engineering community efforts, projects continue to fail. Researches have shown that modeling became more critical due to the growth of complexity and scale of the system. In this context, the i* Framework, a Goal-oriented Requirement Language, is used to model and analyze dependencies and intentions relationships of actors on the observed environment. However, in large-scale projects, i* graphical notation makes the model more complex due to lack of scalability and some ambiguous representations. This paper shows a proposed textual model as a complementary vision of the i* models. Its main objective is achieving a modeling approach that combines text and graphic to i* Framework. We discuss how this proposal can help to mitigate scalability and complexity problems.
Friend or foe? Brain systems involved in the perception of dynamic signals of menacing and friendly social approaches

Abstract During every social approach, humans must assess each other's intentions. Facial expressions provide cues to assist in these assessments via associations with emotion, the likelihood of affiliation, and personality. In this functional magnetic resonance imaging (fMRI) study, participants viewed animated male characters approaching them in a hallway and making either a happy or an angry facial expression. An expected increase in amygdala and superior temporal sulcus activation to the expression of anger was found. Notably, two other social brain regions also had an increased hemodynamic response to anger relative to happiness, including the lateral fusiform gyrus and a region centered in the middle temporal gyrus. Other brain regions showed little differentiation or an increased level of activity to the happy stimuli. These findings provide insight into the brain mechanisms involved in reading the intentions of other human beings in an overtly social context. In particular, they demonstrate brain regions sensitive to social signals of dominance and affiliation.
Three-dimensional electron crystallography of protein microcrystals

We demonstrate that it is feasible to determine high-resolution protein structures by electron crystallography of three-dimensional crystals in an electron cryo-microscope (CryoEM). Lysozyme microcrystals were frozen on an electron microscopy grid, and electron diffraction data collected to 1.7 Å resolution. We developed a data collection protocol to collect a full-tilt series in electron diffraction to atomic resolution. A single tilt series contains up to 90 individual diffraction patterns collected from a single crystal with tilt angle increment of 0.1–1° and a total accumulated electron dose less than 10 electrons per angstrom squared. We indexed the data from three crystals and used them for structure determination of lysozyme by molecular replacement followed by crystallographic refinement to 2.9 Å resolution. This proof of principle paves the way for the implementation of a new technique, which we name ‘MicroED’, that may have wide applicability in structural biology. DOI: http://dx.doi. org/10.7554/eLife.01345. 001
Particle Filter-Based Predictive Tracking of Futsal Players from a Single Stationary Camera

In this paper we study the use of computer vision techniques for visual tracking of futsal players. In the sports field, player tracking is an important task, as it can provide an estimate of the position of the athlete in a given time and thus compute his/her trajectories. This information can be used by coaches and sport professionals on tactical and physical analyses. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to predict their positions and track them using data from a single stationary camera. Experimental results show that our approach is capable to track players and compute their trajectories over time with errors below 20 cm, thus demonstrating a high potential to be used in a wide range of futsal match analyses.
Comorbidity of intellectual disability confounds ascertainment of autism: implications for genetic diagnosis

While recent studies suggest a converging role for genetic factors towards risk for nosologically distinct disorders including autism, intellectual disability (ID), and epilepsy, current estimates of autism prevalence fail to take into account the impact of comorbidity of these disorders on autism diagnosis. We aimed to assess the effect of comorbidity on the diagnosis and prevalence of autism by analyzing 11 years (2000–2010) of special education enrollment data on approximately 6.2 million children per year. We found a 331% increase in the prevalence of autism from 2000 to 2010 within special education, potentially due to a diagnostic recategorization from frequently comorbid features such as ID. The decrease in ID prevalence equaled an average of 64.2% of the increase of autism prevalence for children aged 3–18 years. The proportion of ID cases potentially undergoing recategorization to autism was higher (P = 0.007) among older children (75%) than younger children (48%). Some US states showed significant negative correlations between the prevalence of autism compared to that of ID while others did not, suggesting state‐specific health policy to be a major factor in categorizing autism. Further, a high frequency of autistic features was observed when individuals with classically defined genetic syndromes were evaluated for autism using standardized instruments. Our results suggest that current ascertainment practices are based on a single facet of autism‐specific clinical features and do not consider associated comorbidities that may confound diagnosis. Longitudinal studies with detailed phenotyping and deep molecular genetic analyses are necessary to completely understand the cause of this complex disorder. © 2015 Wiley Periodicals, Inc.
A Novel Shape from Shading Algorithm for Non-Lambertian Surfaces

3D shape reconstruction is a classical problem in the field of computer vision. Shape from shading (SFS) is one approach to obtain the 3D shape from a single shading image. And Lambertian surfaces are an important assumption in traditional SFS methods. However, Lambertian reflectance model used to express the reflectance property of diffuse reflection is proved to be inaccurate. In this paper, we present a novel SFS algorithm for non-Lambertian diffuse surfaces. The Oren-Nayer reflectance model is used to approximate the reflectance property of the diffuse reflection. The image irradiance equation for diffuse surfaces is established with the assumptions that the camera performs an orthographic projection and its direction is as same as the point light source’s. Then, the equation is derived as an Eikonal partial differential equation (PDE) which consists of the shape of the surfaces. The viscosity solution of the resultant PDE is solved by using the high-order Godunov fast sweeping scheme. Experiments are conducted on both synthetic and real images and the results show the effectiveness of the presented algorithm.
Large Scale Agreements via Microdebates

Argumentative debates are a powerful tool for reaching agreements in open environments. However, in large scale settings, such as social networks and massive multi-agent systems, making sense of ongoing debates may be a compelling task, and debates risk to lose their effectiveness. We thus propose “microdebates” to help organizing and confronting users’ opinions in an automated way.
Specification and Automated Detection of Code Smells using OCL

The words Code smells mean certain code lines which makes problems in source code. It also means that code lines in bad design shape or any code made by bad coding habits. There are a few studies found in code smells detecting field. But these studies have their own weaknesses which are detecting only particular kinds of code smells and lack of expressing what the problems are. In this paper, we newly define code smells by using specification language OCL and use these models in auto detection. To construct our new code smells model, we first transform java source into XMI document format based on JavaEAST metamodel which is expanded from JavaAST meta-model. The prepared OCL will be run through OCL component based on Eclipse plug-in. Finally the effectiveness of the model will be verified by applying on primitive smell and derived smell in java source code.
The Potential Nephrotoxic Effects of Intensity Modulated Radiotherapy Delivered to the Para-Aortic Area of Women With Gynecologic Malignancies: Preliminary Results

Objective:To assess kidney function via creatinine clearance before and after radiotherapy in gynecologic cancer patients treated to the para-aortic (PA) area via Intensity Modulated Radiotherapy (IMRT). Methods:Twenty-three patients underwent IMRT to the para-aortic area, were followed for at least 5 months, and had the necessary laboratory data to calculate creatinine clearance. Various patient-related factors and radiotherapy-treatment related factors were analyzed to determine their association with changes in CrCl. Results:Median follow-up was 10.9 months (range, 5–19 months). Median patient age was 51.7 years (range, 22–78). The average initial CrCl was noted to be 109.23 mL/min (range, 38.64–188.38) before radiotherapy and decreased to 90.00 mL/min (29.31–175.61) after radiotherapy (P = 0.004). Although 17 patients had a decrease in their CrCl, 6 were found to have a slight elevation. Five factors were associated with a decrement in CrCl greater than the average decrease (17.6%): presence of hydronephrosis, age <50, no history of cisplatin treatment, a BED to gross adenopathy exceeding mean BED, and salvage treatment of PA node recurrence. Subgroup analysis revealed that the only statistically significant change within the group of patient and/or treatment-related factors was between patients who were <50-year-old and patients who were ≥50 years of age (P = 0.03). No patient exhibited clinical signs of radiation-induced nephropathy. Conclusion:With a median follow-up of 10.9 months, the estimated CrCl decreased by 17.6% after IMRT to the para-aortic area ± cisplatin chemotherapy. The greatest decrease in CrCl occurred in patients who had a history of hydronephrosis. Subgroup analysis revealed that the decline in CrCl was significantly greater for patients younger than 50 years of age. Interestingly, a greater decline in CrCl was noted for those patients who did not have a history of cisplatin treatment. Our preliminary results indicate that IMRT ± cisplatin chemotherapy to the para-aortic area of women is safe and is not associated with any clinical sequelae of renal toxicity despite a small decrement in CrCl in most, but not all patients.
Ushering Buyers into Electronic Channels: An Empirical Analysis

Despite many success stories, B2B e-commerce penetration remains low. Many firms introduce electronic channels in addition to their traditional sales channels but find that buyer usage of the e-channel over time does not keep up with initial expectations. Firms must understand the underlying factors that drive channel usage and how these factors change over time and across buyers. Using panel data pertaining to the purchase histories of 683 buyers over a 43-month period, we estimate a dynamic discrete choice model in a B2B setting that (i) recognizes how price, channel inertia, and inventory change over time; (ii) allows buyers to dynamically trade off these factors when making e-channel adoption decisions; and (iii) takes into account buyer heterogeneity. We find that channel usage is both heterogeneous and dynamic across buyers. Our findings reveal the dynamic tradeoff between channel inertia and the adverse price effect, which interact in opposing directions as the e-channel grows more popular over time: price increases resulting from more bids deter buyers, whereas channel inertia built from sampling experience helps retain repeat buyers for the new channel. Second, we find that the buyers' size and diversity influence purchase decisions, and the e-channel appears more attractive to small and/or diversified buyers. Based on our analysis, we postulate that the seller's allocation decisions of products across channels, if not aligned with buyer behavior, can alienate some buyers. Based on the parameter estimates from the buyer response model, we propose an improved channel allocation that enables firms to selectively attract more buyers to the e-channel and improve revenues. Channel acceptance increases as a result of smart allocation when firms understand and account for individual buyers' channel usage behavior.
Linux Security Module Framework

Computer security is a chronic and growing problem, even for Linux, as evidenced by the seemingly endless stream of software security vulnerabilities. Security research has produced numerous access control mechanisms that help improve system security; however, there is little consensus on the best solution. Many powerful security systems have been implemented as research prototypes or highly specialized products, leaving systems operators with a dif-ﬁcult challenge: how to utilize these advanced features, without having to throw away their existing systems? The Linux Security Modules (LSM) project addresses this problem by providing the Linux kernel with a general purpose framework for access control. LSM enables loading enhanced security policies as kernel modules. By providing Linux with a standard API for policy enforcement modules, the LSM project hopes to enable widespread deployment of security hardened systems. This paper presents the de-∗
The development of social processing in young children : insights from somatosensory activations during observation and experience of touch in typically developing children and speech processing in children with autism spectrum disorders

This thesis explores the neural mechanisms underlying the observation of touch and tactile processing in adults and typically developing children and speech versus computerized speech processing in children with autism spectrum disorders (ASD). Chapter 1 reviews the literature on mirror functioning, embodied cognition and typical and atypical development of social and speech processing in infancy and childhood. Chapter 2 investigates the neural mechanisms underlying hand and object touch observation in adults. In Chapter 3, a similar procedure is employed to investigate tactile mirroring mechanisms in children. The findings demonstrate that these mechanisms are relatively developed in 4- to 5- year old children. Chapter 4 further explores somatosensory activity during touch in adults and children. The findings reveal the modulation of somatosensory beta (15-24 Hz) activity during touch in adults, but not in children. Chapter 5 examines the neural mechanisms underlying speech versus computerized speech perception in children with ASD. These results suggest an impaired classification of speech sounds preceded by computerized speech, and atypical lateralization of speech processing in children with ASD. Together, these findings make a notable contribution to our understanding of typical development of tactile mirroring and touch processing mechanisms, and social processing dysfunctions in children with ASD.
Automatic population of knowledge bases with multimodal data about named entities

Knowledge bases are of great importance for Web search, recommendations, and many Information Retrieval tasks. However, maintaining them for not so popular entities is often a bottleneck. Typically, such entities have limited textual coverage and only a few ontological facts. Moreover, these entities are not well populated with multimodal data, such as images, videos, or audio recordings. The goals in this thesis are (1) to populate a given knowledge base with multimodal data about entities, such as images or audio recordings, and (2) to ease the task of maintaining and expanding the textual knowledge about a given entity, by recommending valuable text excerpts to the contributors of knowledge bases. The thesis makes three main contributions. The first two contributions concentrate on finding images of named entities with high precision, high recall, and high visual diversity. Our main focus are less popular entities, for which the image search engines fail to retrieve good results. Our methods utilize background knowledge about the entity, such as ontological facts or a short description, and a visual-based image similarity to rank and diversify a set of candidate images. Our third contribution is an approach for extracting text contents related to a given entity. It leverages a language-model-based similarity between a short description of the entity and the text sources, and solves a budget-constraint optimization program without any assumptions on the text structure. Moreover, our approach is also able to reliably extract entity related audio excerpts from news podcasts. We derive the time boundaries from the usually very noisy audio transcriptions.
Distance-Constraint Reachability Computation in Uncertain Graphs

Driven by the emerging network applications, querying and mining uncertain graphs has become increasingly important. In this paper, we investigate a fundamental problem concerning uncertain graphs, which we call the distance-constraint reachability (DCR) problem: Given two vertices s and t, what is the probability that the distance from s to t is less than or equal to a user-defined threshold d in the uncertain graph? Since this problem is #P-Complete, we focus on efficiently and accurately approximating DCR online. Our main results include two new estimators for the probabilistic reachability. One is a Horvitz-Thomson type estimator based on the unequal probabilistic sampling scheme, and the other is a novel recursive sampling estimator, which effectively combines a deterministic recursive computational procedure with a sampling process to boost the estimation accuracy. Both estimators can produce much smaller variance than the direct sampling estimator, which considers each trial to be either 1 or 0. We also present methods to make these estimators more computationally efficient. The comprehensive experiment evaluation on both real and synthetic datasets demonstrates the efficiency and accuracy of our new estimators.
Detecting Activations in PET and fMRI: Levels of Inference and Power

This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtaining c, or more, clusters with k, or more, voxels, above a threshold u. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion of set-level inference. Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigning P values to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clusters c = 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e., k = 0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster-and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signal f (relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences if f is large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. If f is small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.
A region-based compilation technique for dynamic compilers

Method inlining and data flow analysis are two major optimization components for effective program transformations, but they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This article describes the design and implementation of a region-based compilation technique in our dynamic optimization framework, in which the compiled regions are selected as code portions without rarely executed code. The key parts of this technique are the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify and eliminate rare sections of code. The region selection process and method inlining decisions are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, allowing more method inlining to be performed. The inlining process can be performed for parts of a method, not just for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then use on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that our region-based compilation approach achieves approximately 4% performance improvement on average, while reducing the compilation overhead by 10% to 30%, in comparison to the traditional method-based compilation techniques.