Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing dimer metadynamics using gromacs

We develop a Gromacs implementation of dimer metadynamics (DM) (JCTC 13, 425 [2017]) for enhanced sampling through artificial delocalization effects. This implementation is based entirely on a Plumed collective variable developed for this purpose, the fine tuning of Gromacs input parameters, modified forcefields and custom nonbonded interactions. We demonstrate this implementation on alanine dipeptide in vacuum and in water, and on the 12‐residue alanine polypeptide in water and compare the results with a standard multiple‐replica technique such as parallel tempering. In all the considered cases, this comparison is consistent and the results with DM are smoother and require shorter simulations, thus proving the consistency and effectiveness of this Gromacs implementation. © 2018 Wiley Periodicals, Inc.
Automated Risk Analysis on Privacy in Social Networks. (Analyse automatisée des risques sur la vie privée dans les réseaux sociaux)

In this thesis we shed the light on the danger of privacy leakage on social network. We investigate privacy breaches, design attacks, show their feasibility and study their accuracies. This approach helps us to track the origin of threats and is a first step toward designing effective countermeasures. We have first introduced a subject sensitivity measure through a questionnaire survey. Then, we have designed on-line friendship and group membership link disclosure (with certainty) attacks on the largest social network “Facebook”. These attacks successfully uncover the local network of a target using only legitimate queries. We have also designed sampling techniques to rapidly collect useful data around a target. The collected data are represented by social-attribute networks and used to perform attribute inference (with uncertainty) attacks. To increase the accuracy of attacks, we have designed cleansing algorithms. These algorithms quantify the correlation between subjects, select the most relevant ones and combat data sparsity. Finally, we have used a shallow neural network to classify the data and infer the secret values of a sensitive attribute of a given target with high accuracy measured by AUC on real datasets. The proposed algorithms in this work are included in a system called SONSAI that can help end users analyzing their local network to take the hand over their privacy
Dynamic Analysis for JavaScript Code

Author(s): Gong, Liang | Advisor(s): Sen, Koushik | Abstract: The effectiveness of the widely adopted static analysis tools is often limited by JavaScript’s dynamic nature and the need to over-approximate runtime behaviors. To tackle this challenge, we research robust dynamic analysis techniques for real-world JavaScript code.To analyze front-end web applications, we first extend Jalangi which is a dynamic analysis framework based on source code instrumentation. Our extension of Jalangi intercepts and rewrites JavaScript code during network transmission. We also develop NodeSec, which is a dynamic instrumentation framework that traces and sandboxes the interactions between a Node.js program and the operating system. Based on the two frameworks, we research dynamic analysis techniques to detect correctness, performance, and security issues in JavaScript code.First, we present DLint, a dynamic analysis approach to check code quality rules in JavaScript. DLint consists of a generic framework and an extensible set of checkers that each addresses a particular rule. We formally describe and implement 28 checkers that address problems missed by state-of-the-art static approaches. Applying the approach in an empirical study on over 200 popular websites shows that static and dynamic checking complement each other. On average per website, DLint detects 49 problems that are missed statically, including visible bugs on the websites of IKEA, Hilton, eBay, and CNBC.Second, we present JITProf, a profiling framework to dynamically identify JIT-unfriendly code, which prohibits profitable JIT optimizations. The key idea is to associate meta-information with JavaScript objects and code locations, to update this information whenever particular runtime events occur, and to use the meta-information to identify JIT-unfriendly operations. We use JITProf to analyze widely used JavaScript web applications and show that JIT-unfriendly code is prevalent in practice. We show that refactoring JIT-unfriendly code identified by JITProf leads to statistically significant performance improvements of up to 26.3% in 15 popular benchmarks.Finally, we conduct the first large-scale empirical study of security issues on over 330,000 npm packages. We adopted an iterative approach to dynamically analyze those packages and identified 360 previously unknown malicious or vulnerable packages, 315 of which have been validated by the community so far; 258 of those issues are considered as highly severe. All those packages with security issues in aggregate have 2,138 downloads per day, stressing the risks for the Node.js ecosystem.
MutT homologue 1 (MTH1) catalyzes the hydrolysis of mutagenic O6-methyl-dGTP

Abstract Nucleotides in the free pool are more susceptible to nonenzymatic methylation than those protected in the DNA double helix. Methylated nucleotides like O6-methyl-dGTP can be mutagenic and toxic if incorporated into DNA. Removal of methylated nucleotides from the nucleotide pool may therefore be important to maintain genome integrity. We show that MutT homologue 1 (MTH1) efficiently catalyzes the hydrolysis of O6-methyl-dGTP with a catalytic efficiency similar to that for 8-oxo-dGTP. O6-methyl-dGTP activity is exclusive to MTH1 among human NUDIX proteins and conserved through evolution but not found in bacterial MutT. We present a high resolution crystal structure of human and zebrafish MTH1 in complex with O6-methyl-dGMP. By microinjecting fertilized zebrafish eggs with O6-methyl-dGTP and inhibiting MTH1 we demonstrate that survival is dependent on active MTH1 in vivo. O6-methyl-dG levels are higher in DNA extracted from zebrafish embryos microinjected with O6-methyl-dGTP and inhibition of O6-methylguanine-DNA methyl transferase (MGMT) increases the toxicity of O6-methyl-dGTP demonstrating that O6-methyl-dGTP is incorporated into DNA. MTH1 deficiency sensitizes human cells to the alkylating agent Temozolomide, a sensitization that is more pronounced upon MGMT inhibition. These results expand the cellular MTH1 function and suggests MTH1 also is important for removal of methylated nucleotides from the nucleotide pool.
Optimization of Growth and Carotenoid Production by Haloferax mediterranei Using Response Surface Methodology

Haloferax mediterranei produces C50 carotenoids that have strong antioxidant properties. The response surface methodology (RSM) tool helps to accurately analyze the most suitable conditions to maximize C50 carotenoids production by haloarchaea. The effects of temperature (15–50 °C), pH (4−10), and salinity (5–28% NaCl (w/v)) on the growth and carotenoid content of H. mediterranei were analyzed using the RSM approach. Growth was determined by measuring the turbidity at 600 nm. To determine the carotenoid content, harvested cells were lysed by freeze/thawing, then re-suspended in acetone and the total carotenoid content determined by measuring the absorbance at 494 nm. The analysis of carotenoids was performed by an HPLC system coupled with mass spectrometry. The results indicated the theoretical optimal conditions of 36.51 or 36.81 °C, pH of 8.20 or 8.96, and 15.01% or 12.03% (w/v) salinity for the growth of haloarchaea (OD600 = 12.5 ± 0.64) and production of total carotenoids (3.34 ± 0.29 mg/L), respectively. These conditions were validated experimentally for growth (OD600 = 13.72 ± 0.98) and carotenoid production (3.74 ± 0.20 mg/L). The carotenoid profile showed four isomers of bacterioruberin (89.13%). Our findings suggest that the RSM approach is highly useful for determining optimal conditions for large-scale production of bacterioruberin by haloarchaea.
Inter-rater Agreement Between Exposure Assessment Using Automatic Algorithms and Using Experts

Objectives To estimate the inter-rater agreement between exposure assessment to asthmagens in current jobs by algorithms based on task-based questionnaires (OccIDEAS) and by experts. Methods Participants in a cross-sectional national survey of exposure to asthmagens (AWES-Asthma) were randomly split into two subcohorts of equal size. Subcohort 1 was used to determine the most common asthmagen groups and occupational groups. From subcohort 2, a random sample of 200 participants was drawn and current occupational exposure (yes/no) was assessed in these by OccIDEAS and by two experts independently and then as a consensus. Inter-rater agreement was estimated using Cohen's Kappa coefficient. The null hypothesis was set at 0.4, because both the experts and the automatic algorithm assessed the exposure using the same task-based questionnaires and therefore an agreement better than by chance would be expected. Results The Kappa coefficients for the agreement between the experts and the algorithm-based assessments ranged from 0.37 to 1, while the agreement between the two experts ranged from 0.29 to 0.94, depending on the agent being assessed. After discussion by both experts the Kappa coefficients for the consensus decision and OccIDEAS were significantly larger than 0.4 for 7 of the 10 asthmagen groups, while overall the inter-rater agreement was greater than by chance (P < 0.0001). Conclusions The web-based application OccIDEAS is an appropriate tool for automated assessment of current exposure to asthmagens (yes/no), and requires less time-consuming work by highly-qualified research personnel than the traditional expert-based method. Further, it can learn and reuse expert determinations in future studies.
Type 1 diabetes mellitus: can coaching improve health outcomes?

ABSTRACT Objective: To evaluate the introduction of coaching in the interdisciplinary care of individuals with type 1 diabetes mellitus in the public health care system. Subjects and methods: Ten patients routinely attending a public health care service and with a glycated hemoglobin (HbA1c) level above 75% participated in eight coaching sessions. This study evaluated the patients' self-management of the disease and personal behavior. The participants were assessed at the beginning of the program and on two occasions after the intervention, with evaluation of biochemical and anthropometric data, and frequency of self-monitoring of blood glucose (SMBG). Questionnaires were applied during these evaluations to analyze emotional burden (B-PAID), medication adherence (Morisky Adherence Scale), and self-efficacy (IMDSES). Results HbA1c had a median level of 8.0% (range 76-10.3%) at the beginning of the study and reduced significantly 3 months after initiation of the intervention (7.78% [6.5-10%], p = 0.028), with no significant increase at 6 months (8.3% [713-9.27%], p = 0.386). SMBG improved significantly from the beginning to the end of the study, with the median number of glucose tests per week varying from 16.5 (range 0-42) at baseline to 29.0 (7-42) at 3 months and 27.5 (10-48) at 6 months (p = 0.047). No significant differences were observed in anthropometric parameters or in the scores of the instruments between the three measurements. Conclusion: A coaching intervention focused on patients' values and sense of purpose may provide added benefit to traditional diabetes education programs and could be an auxiliary method to help individuals with type 1 diabetes achieve their treatment goals.
Estrogen receptor‐based multi‐residue screening of bisphenol compounds in urine

Human exposure to bisphenol compounds (BPs) has been implicated in the development of several chronic diseases. Instead of exploiting the traditional methods for determination of BPs, this work confirms that the human estrogen receptor α ligand binding domain (hERα‐LBD) is a powerful recognition element that can be used to monitor multi‐residue of BPs in urine samples by fluorescence polarization (FP) assay. Test parameters were optimized for the best performance. Under the optimal conditions, the IC50 values of BPs are in the range of 0.04–1.61 μg mL−1. Recovery experiments were then performed to assess the accuracy and precision of the established method. The results detected by FP assay show good agreements with that of liquid chromatography–tandem mass spectrometry method with a fit of R2 = 0.9372 and 0.9640 for BPE and BPAP, respectively. A computational methodology, ligand‐based pharmacophore model, was also employed to further explore the broad‐specific of tested compounds. It was found that the two hydrogen bond acceptor features and one hydrophobic aliphatic feature were essential for the corresponding cross‐reactivity results from the FP assay. All these results suggest that the established method can be successfully applied to monitor the occurrence of BPs in urine.
Towards using Volunteered Geographic Information to monitor post-disaster recovery in tourist destinations

The aftereffects of disaster events are significant in tourist destinations where they do not only lead to destruction and casualties, but also long-lasting economic harms. The public perception causes tourists to refrain from visiting these areas and recovery of the tourist industry, a major economic sector, to become challenging. To improve this situation, current information about the tourist and infrastructure recovery is crucial for a “rebranding”information that is however time and cost-intensive in acquisition using traditional information sources. An alternative data source that has shown great potential for information gathering in other disaster management phases, which was less considered for disaster recovery purposes, is Volunteered Geographic Information (VGI). Therefore, this paper introduces a VGI-based methodology to address this task. Initial analyses conducted with Flickr data indicate a potential of VGI for recovery monitoring, whereas the analysis of OpenStreetMap data shows, that this form of VGI requires further quality assurance.
Recent structural advances in constrained helical peptides

Given the ubiquity of the ⍺‐helix in the proteome, there has been much research in developing mimics of ⍺‐helices, and most of this study has been toward developing protein‐protein interaction inhibitors. A common strategy for mimicking ⍺‐helices has been through the use of constrained, helical peptides. The addition of a constraint typically provides for conformational and proteolytic stability and, in some cases, cell permeability. Some of the most well‐known strategies included are lactam formation and hydrocarbon “stapling. ” Beyond those strategies, there have been many recent advances in developing constrained peptides. The purpose of this review is to highlight recent advances in the development of new helix‐stabilizing technologies, constraint diversification strategies, tether diversification strategies, and combination strategies that create new bicyclic helical peptides.
HIV-2 Primary Infection in a French 69-Year-Old Bisexual Man

Abstract More than 1 million individuals, mainly in West Africa, are thought to be infected with HIV-2. Acute HIV-2 infection is rarely observed, only 2 primary infections have been described to date. We report a detailed case of HIV-2 primary infection in a 69-year-old French bisexual Caucasian man, thereby providing valuable insights into HIV-2 early infection.
Distributed Data-Driven Decision Making in Uncertain Networked Systems with Applications in Smart Energy Systems

This dissertation aims to develop a rigorous distributed approach to decision making using scenario-based techniques for large-scale networks of interconnected uncertain dynamical systems (called agents). A scenario program is a finite-dimensional optimization problem in which an objective function is minimized under constraints that are associated with finitely many, independently and identically distributed (i.i.d.), scenarios of a random parameter. Theoretical and practical interest in scenario programs originates from the fact that these problems are typically efficiently solvable while being closely related to robust and chance-constrained programs. In the former, the constraint is enforced for all admissible random parameters, whereas in the latter, the constraint is enforced up to a given level of probability. However, finding solutions of the resulting large-scale scenario optimization problem for uncertain networked systems poses several difficulties, e.g., computational cost for a central control unit. The main contribution of this dissertation is the design of a technique to decompose a large-scale scenario program into small-scale distributed scenario programs for each agent. Building on existing results in literature, we provide novel guarantees to quantify the robustness of the resulting solutions in a distributed framework. In this setting, each agent needs to exchange some information with its neighboring agents that is necessary due to the statistical learning features of the proposed setup. However, this inter-agent communication scheme might give rise to some concerns about the agents' private information. We therefore present a novel privatized distributed framework, based on the so-called differential privacy concept, such that each agent can share requested information while preserving its privacy. In addition, a soft communication scheme based on a set parameterization technique, along with the notion of probabilistically reliable set, is introduced to reduce the required communication burden. Such a reliability measure is incorporated into the feasibility guarantees of agent decisions in a probabilistic sense. The theoretical guarantees of the proposed distributed scenario-based decision making framework coincide with the centralized counterpart, however the scaling of the results with the number of agents remains an issue.
Anterior resection for rectal cancer in Sweden: validation of a registry-based method to determine long-term stoma outcome

Abstract Background: A permanent stoma after anterior resection for rectal cancer is common. Nationwide registries provide sufficient power to evaluate factors influencing this phenomenon, but validation is required to ensure the quality of registry-based stoma outcomes. Methods: Patients who underwent anterior resection for rectal cancer in the Northern healthcare region of Sweden between 1 January 2007 and 31 December 2013 were reviewed by medical records and followed until 31 December 2014 with regard to stoma outcome. A registry-based method to determine nationwide long-term stoma outcomes, using data from the National Patient Registry and the Swedish Colorectal Cancer Registry, was developed and internally validated using the chart reviewed reference cohort. Accuracy was evaluated with positive and negative predictive values and Kappa values. Following validation, the stoma outcome in all patients treated with an anterior resection for rectal cancer in Sweden during the study period was estimated. Possible regional differences in determined stoma outcomes between the six Swedish healthcare regions were subsequently evaluated with the χ2 test. Results: With 312 chart reviewed patients as reference, stoma outcome was accurately predicted through the registry-based method in 299 cases (95.8%), with a positive predictive value of 85.1% (95% CI 75.8%–91.8%), and a negative predictive value of 100.0% (95% CI 98.4%–100.0%), while the Kappa value was 0.89 (95% CI 0.82–0.95). In Sweden, 4768 patients underwent anterior resection during the study period, of which 942 (19.8%) were determined to have a permanent stoma. The stoma rate varied regionally between 17.8–29.2%, to a statistically significant degree (p = .001). Conclusion: Using data from two national registries to determine long-term stoma outcome after anterior resection for rectal cancer proved to be reliable in comparison to chart review. Permanent stoma prevalence after such surgery remains at a significant level, while stoma outcomes vary substantially between different healthcare regions in Sweden.
Multi-resolution speech analysis for automatic speech recognition using deep neural networks: Experiments on TIMIT

Speech Analysis for Automatic Speech Recognition (ASR) systems typically starts with a Short-Time Fourier Transform (STFT) that implies selecting a fixed point in the time-frequency resolution trade-off. This approach, combined with a Mel-frequency scaled filterbank and a Discrete Cosine Transform give rise to the Mel-Frequency Cepstral Coefficients (MFCC), which have been the most common speech features in speech processing for the last decades. These features were particularly well suited for the previous Hidden Markov Models/Gaussian Mixture Models (HMM/GMM) state of the art in ASR. In particular they produced highly uncorrelated features of small dimensionality (typically 13 coefficients plus deltas and double deltas), which was very convenient for diagonal covariance GMMs, for dealing with the curse of dimensionality and for the limited computing resources of a decade ago. Currently most ASR systems use Deep Neural Networks (DNN) instead of the GMMs for modeling the acoustic features, which provides more flexibility regarding the definition of the features. In particular, acoustic features can be highly correlated and can be much larger in size because the DNNs are very powerful at processing high-dimensionality inputs. Also, the computing hardware has reached a level of evolution that makes computational cost in speech processing a less relevant issue. In this context we have decided to revisit the problem of the time-frequency resolution in speech analysis, and in particular to check if multi-resolution speech analysis (both in time and frequency) can be helpful in improving acoustic modeling using DNNs. Our experiments start with several Kaldi baseline system for the well known TIMIT corpus and modify them by adding multi-resolution speech representations by concatenating different spectra computed using different time-frequency resolutions and different post-processed and speaker-adapted features using different time-frequency resolutions. Our experiments show that using a multi-resolution speech representation tends to improve over results using the baseline single resolution speech representation, which seems to confirm our main hypothesis. However, results combining multi-resolution with the highly post-processed and speaker-adapted features, which provide the best results in Kaldi for TIMIT, yield only very modest improvements.
Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

Free-viewpoint video (FVV) systems allow the viewers to freely change the viewpoints of the scene. In such systems, view synthesis and compression are the two main sources of artifacts influencing the perceived quality. To assess this influence, quality evaluation studies are often carried out using conventional displays and generating predefined navigation trajectories mimicking the possible movement of the viewers when exploring the content. Nevertheless, as different trajectories may lead to different conclusions in terms of visual quality when benchmarking the performance of the systems, methods to identify critical trajectories are needed. This paper aims at exploring the impact of exploration trajectories [defined as hypothetical rendering trajectories (HRT)] on the perceived quality of FVV subjectively and objectively, providing two main contributions. First, a subjective assessment test including different HRTs was carried out and analyzed. The results demonstrate and quantify the influence of HRT in the perceived quality. Second, we propose a new full-reference objective video quality assessment measure to objectively predict the impact of HRT. This measure, based on sketch-token representation, models how the categories of the contours change spatially and temporally from a higher semantic level. Performance in comparison with existing quality metrics for the FVV highlight promising results for the automatic detection of most critical HRTs for the benchmark of immersive systems.