Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms and Predictions of Drug-Drug Interactions of the Hepatitis C Virus Three Direct-Acting Antiviral Regimen: Paritaprevir/Ritonavir, Ombitasvir, and Dasabuvir

To assess drug-drug interaction (DDI) potential for the three direct-acting antiviral (3D) regimen of ombitasvir, dasabuvir, and paritaprevir, in vitro studies profiled drug-metabolizing enzyme and transporter interactions. Using mechanistic static and dynamic models, DDI potential was predicted for CYP3A, CYP2C8, UDP-glucuronosyltransferase (UGT) 1A1, organic anion-transporting polypeptide (OATP) 1B1/1B3, breast cancer resistance protein (BCRP), and P-glycoprotein (P-gp). Perpetrator static model DDI predictions for metabolizing enzymes were within 2-fold of the clinical observations, but additional physiologically based pharmacokinetic modeling was necessary to achieve the same for drug transporters. When perpetrator interactions were assessed, ritonavir was responsible for the strong increase in exposure of sensitive CYP3A substrates, whereas paritaprevir (an OATP1B1/1B3 inhibitor) greatly increased the exposure of sensitive OATP1B1/1B3 substrates. The 3D regimen drugs are UGT1A1 inhibitors and are predicted to moderately increase plasma exposure of sensitive UGT1A1 substrates. Paritaprevir, ritonavir, and dasabuvir are BCRP inhibitors. Victim DDI predictions were qualitatively in line with the clinical observations. Plasma exposures of the 3D regimen were reduced by strong CYP3A inducers (paritaprevir and ritonavir; major CYP3A substrates) but were not affected by strong CYP3A4 inhibitors, since ritonavir (a CYP3A inhibitor) is already present in the regimen. Strong CYP2C8 inhibitors increased plasma exposure of dasabuvir (a major CYP2C8 substrate), OATP1B1/1B3 inhibitors increased plasma exposure of paritaprevir (an OATP1B1/1B3 substrate), and P-gp or BCRP inhibitors (all compounds are substrates of P-gp and/or BCRP) increased plasma exposure of the 3D regimen. Overall, the comprehensive mechanistic assessment of compound disposition along with mechanistic and PBPK approaches to predict victim and perpetrator DDI liability may enable better clinical management of nonstudied drug combinations with the 3D regimen.
The Thioredoxin Encoded by the Rod-Derived Cone Viability Factor Gene Protects Cone Photoreceptors Against Oxidative Stress.

AIMS Rod-derived cone viability factor long (RdCVFL) is an enzymatically active thioredoxin encoded by the nucleoredoxin-like-1 (Nxnl1) gene. The second product of the gene, RdCVF, made by alternative splicing is a novel trophic factor secreted by rods that protects cones in rodent models of retinitis pigmentosa, the most prevalent inherited retinal disease. It acts on cones by stimulating aerobic glycolysis through its interaction with a complex containing basigin-1 and the glucose transporter GLUT1. We studied the role of Nxnl1 in cones after its homologous recombination using a transgenic line expressing Cre recombinase under the control of a cone opsin promoter. RESULTS We show that the cones of these mice are dysfunctional and degenerate by 8 months of age. The age-related deficit in cones is exacerbated in young animals by exposure to high level of oxygen. In agreement with this phenotype, we found that the cones express only one of the two Nxnl1 gene products, the thioredoxin RdCVFL. Administration of RdCVFL to the mouse carrying a deletion of the Nxnl1 gene in cones reduces the damage produced by oxidative stress. Silencing the expression of RdCVFL in cone-enriched culture reduces cell viability, showing that RdCVFL is a cell-autonomous mechanism of protection. INNOVATION This novel mode of action is certainly relevant for the therapy of retinitis pigmentosa since the delivery into cones of the rd10 mouse, a recessive model of the disease, rescues cones. CONCLUSION Our work highlights the duality of the Nxnl1 gene, which protects the cones by two distinct mechanisms. Antioxid. Redox Signal. 24, 909-923.
Practical RSA Threshold Decryption for Things That Think

Progressing towards an era of ever more small devices with computational power and storage capabilities, a new kind of security approach is needed. We propose a practical scheme to achieve threshold security for Things That Think. The private data of the user remains protected as long as the number of corrupted devices is lower than the threshold. We describe a procedure for key distribution, key redistribution, encryption and decryption. To show that our solution is feasible, we have implemented a proof of concept.
Bug Prediction with Neural Nets Using regression-and classification-based approaches Bachelor Thesis

Bugs can often be hard to identify and developers spend a large amount of time locating and fixing them. Bug prediction strives to identify code defects using machine learning and statistical analysis and therefore decreasing time spent on bug localization. With bug prediction, awareness of bugs can be increased and software quality can be improved in significantly less time. Machine learning models are used in most modern bug prediction tools and with recent advances in machine learning, new models and possibilities have arisen that further improve the possibilities and performance in bug prediction. In our studies, we test the performance of “Doc2Vec” – a current model that is used to vectorize plain text – on source code to perform classification. Instead of relying on code metrics, we analyze and vectorize plain-text source code and try to identify bugs based on similarity to learned paragraph vectors. Testing two different implementations of the Doc2Vec model, we find that no usable results can be achieved by using plain text classification models for bug prediction. Even after abstracting the code and applying parameter tuning on our model, all experiments deliver a constant 50% accuracy, so no learning can be achieved by any of the models. The experiments clearly show that code should not be treated as plain text and should instead contain more code-specific information like metrics about the code. Our second setup of experiments consists of a 3-layer feed forward neural network that performs classificationand regressionbased approaches on code metrics, using datasets that contain a discrete number of bugs as a response variable. There have already been many successful experiments using metrics to perform classification based on code metrics. In our studies we compare the performance of a standard regression and standard multi-class classification model to the models “classification by regression” (CbR) and “regression by classification” (RbC). In the RbC model, we use the output from classification to predict an accurate number of bugs and then calculate the root-mean-square error (RMSE). In the CbR model we use the output of regression to perform binary classification and calculate area under the receiver operating characteristic curve (ROC AUC) to compare the results. In our experiments we find that a neural network delivers better results when using the CbR model on an estimated defect count compared to the results using standard multi-class classification. We also suggest that the RMSE can significantly be decreased by using the RbC model compared to standard regression.
Conversational Pointing Gestures for Virtual Reality Interaction: Implications from an Empirical Study

Interaction in conversational interfaces strongly relies on the system's capability to interpret the user's references to objects via deictic expressions. Deictic gestures, especially pointing gestures, provide a powerful way of referring to objects and places, e.g., when communicating with an embodied conversational agent in a virtual reality environment. We highlight results drawn from a study on pointing and draw conclusions for the implementation of pointing-based conversational interactions in partly immersive virtual reality.
Analysis of the Wind Field Evolution Associated with the Extratropical Transition of Bonnie (1998)

Abstract Extratropical transition brings about a number of environmentally induced structural changes within a transitioning tropical cyclone. Of particular interest among these changes is the acceleration of the wind field away from the cyclone’s center of circulation along with the outward movement of the radial wind maximum, together termed wind field expansion. Previous informal hypotheses aimed at understanding this evolution do not entirely capture the observed expansion, while a review of the literature shows no formal work done upon the topic beyond analyzing its occurrence. This study seeks to analyze the physical and dynamical mechanisms behind the wind field expansion using model simulations of a representative transition case, North Atlantic Tropical Cyclone Bonnie of 1998. The acceleration of the wind field along the outer periphery of the cyclone is found to be a function of the net import of absolute angular momentum within the cyclone’s environment along inflowing trajectories. This evolut.
Repeatable aversion across threat types is linked with life-history traits but is dependent on how aversion is measured

Personality research suggests that individual differences in risk aversion may be explained by links with life-history variation. However, few empirical studies examine whether repeatable differences in risk avoidance behaviour covary with life-history traits among individuals in natural populations, or how these links vary depending on the context and the way risk aversion is measured. We measured two different risk avoidance behaviours (latency to enter the nest and inspection time) in wild great tits (Parus major) in two different contexts—response to a novel object and to a predator cue placed at the nest-box during incubation---and related these behaviours to female reproductive success and condition. Females responded equally strongly to both stimuli, and although both behaviours were repeatable, they did not correlate. Latency to enter was negatively related to body condition and the number of offspring fledged. By contrast, inspection time was directly explained by whether incubating females had been flushed from the nest before the trial began. Thus, our inferences on the relationship between risk aversion and fitness depend on how risk aversion was measured. Our results highlight the limitations of drawing conclusions about the relevance of single measures of a personality trait such as risk aversion.
The role of a food policy coalition in influencing a local food environment: an Australian case study

Abstract Objective To explore how an Australian rural food policy coalition acts to influence a local food environment, focusing specifically on its composition, functions and processes as well as its food-related strategies and policy outputs. Design A qualitative case study approach was undertaken. Three sources were used to triangulate data: eleven semi-structured in-depth interviews with coalition members, analysis of thirty-seven documents relating to the coalition and observation at one coalition meeting. Data were analysed using a thematic and constant comparison approach. Community Coalition Action Theory provided a theoretical framework from which to interpret findings. Setting Two rural local government areas on the south-eastern coast of Victoria, Australia. Subjects Eleven members of the food policy coalition. Results Five themes emerged from the data analysis. The themes described the coalition’s leadership processes, membership structure, function to pool resources for food system advocacy, focus on collaborative cross-jurisdictional strategies and ability to influence policy change. Conclusions This Australian case study demonstrates that with strong leadership, a small-sized core membership and focus on collaborative strategies, food policy coalitions may be a mechanism to positively influence local food environments.
The Dangers of Rooting: Data Leakage Detection in Android Applications

Mobile devices are widely spread all over the world, and Android is the most popular operative system in use. According to Kaspersky Lab’s threat statistic (June 2017), many users are tempted to root their mobile devices to get an unrestricted access to the file system, to install different versions of the operating system, to improve performance, and so on. The result is that unintended data leakage flaws may exist. In this paper, we (i) analyze the security issues of several applications considered relevant in terms of handling user sensitive information, for example, financial, social, and communication applications, showing that 51.6% of the tested applications suffer at least of an issue and (ii) show how an attacker might retrieve a user access token stored inside the device thus exposing users to a possible identity violation. Notice that such a token, and a number of other sensitive information, can be stolen by malicious users through a man-in-the-middle (MITM) attack.
Comparative in vitro toxicity assessment of perfluorinated carboxylic acids

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are synthetic fluorinated compounds that are highly bioaccumulative and persistent organic pollutants. Perfluorooctanoic acid (PFOA), an eight‐carbon chain perfluorinated carboxylic acid, was used heavily for the production of fluoropolymers, but concerns have led to its replacement by shorter carbon chain homologues such as perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA). However, limited toxicity data exist for these substitutes. We evaluated the toxicity of PFOA, PFHxA and PFBA on a zebrafish liver cell line and investigated the effects of exposure on cell metabolism. Gross toxicity after 96 h of exposure was highest for PFOA and PFO–, while PFHxA and PFBA exhibited lower toxicity. Although the structural similarity of these compounds to fatty acids suggests the possibility of interference with the transport and metabolism of lipids, we could not detect any differential expression of peroxisome proliferator‐activated receptor (ppar‐α, ‐β and ‐γ), fabp3 and crot genes after 96 h exposure to up to 10 ppm of the test compounds. However, we observed localized lipid droplet accumulation only in PFBA‐exposed cells. To study the effects of these compounds on cell metabolism, we conducted fluorescence lifetime imaging microscopy using naturally fluorescent biomarkers, NADH and FAD. The fluorescence lifetimes of NADH and FAD and the bound/free ratio of each of these coenzymes decreased in a dose‐ and carbon length‐dependent manner, suggesting disruption of cell metabolism. In sum, our study revealed that PFASs with shorter carbon chains are less toxic than PFOA, and that exposure to sublethal dosage of PFOA, PFHxA or PFBA affects cell metabolism. Copyright © 2016 John Wiley & Sons, Ltd.
Securing communication in inter domains Internet of Things using identity-based cryptography

The advance of Internet and smart devices, which is often called Internet of Things (IoT), has made the interaction between virtual and physical worlds to be more seamless. IoT allows the information collected by smart devices to be transmitted to the people across the globe through Internet and enhancing people's lives in many aspects, such as health, energy, transportation, etc. In IoT, the communication between device-to-device or device-to-people very often go through different domains each having separate trust authority. Therefore, maintaining trust in the federated network of IoT is really important. However, IoT is vulnerable to numerous threats like tampering of information and key distribution issue, while considering the constraint in its resource. This paper proposes a method to secure communication that manages trust of IoT in federated fashion while minimizing usage of resources in IoT devices. Other aspects of loosely/tightly coupled network federation, dynamic nature of IoT devices in the network and the security analysis will also be discussed in the paper.
Implementation and evaluation of a prototype consumer reporting system for patient safety events

Objective No methodologically robust system exists for capturing consumer-generated patient safety reports. To address this challenge, we developed and pilot-tested a prototype consumer reporting system for patient safety, the Health Care Safety Hotline. Design Mixed methods evaluation. Setting The Hotline was implemented in two US healthcare systems from 1 February 2014 through 30 June 2015. Participants Patients, family members and caregivers associated with two US healthcare systems. Intervention A consumer-oriented incident reporting system for telephone or web-based administration was developed to elicit medical mistakes and care-related injuries. Main Outcomes Measures Key informant interviews, measurement of website traffic and analysis of completed reports. Results Key informants indicated that Hotline participation was motivated by senior leaders' support and alignment with existing quality and safety initiatives. During the measurement period from 1 October 2014 through 30 June 2015, the home page had 1530 visitors with a unique IP address. During its 17 months of operation, the Hotline received 37 completed reports including 20 mistakes without harm and 15 mistakes with injury. The largest category of mistake concerned problems with diagnosis or advice from a health practitioner. Hotline reports prompted quality reviews, an education intervention, and patient follow-ups. Conclusion While generating fewer reports than its capacity to manage, the Health Care Safety Hotline demonstrated the feasibility of consumer-oriented patient safety reporting. Further research is needed to understand how to increase consumers' use of these systems.
Cytokine Autoantibody Screening in the Swedish Addison Registry Identifies Patients With Undiagnosed APS1

Context Autoimmune polyendocrine syndrome type 1 (APS1) is a monogenic disorder that features autoimmune Addison disease as a major component. Although APS1 accounts for only a small fraction of all patients with Addison disease, early identification of these individuals is vital to prevent the potentially lethal complications of APS1. Objective To determine whether available serological and genetic markers are valuable screening tools for the identification of APS1 among patients diagnosed with Addison disease. Design We systematically screened 677 patients with Addison disease enrolled in the Swedish Addison Registry for autoantibodies against interleukin-22 and interferon-α4. Autoantibody-positive patients were investigated for clinical manifestations of APS1, additional APS1-specific autoantibodies, and DNA sequence and copy number variations of AIRE. Results In total, 17 patients (2.5%) displayed autoantibodies against interleukin-22 and/or interferon-α4, of which nine were known APS1 cases. Four patients previously undiagnosed with APS1 fulfilled clinical, genetic, and serological criteria. Hence, we identified four patients with undiagnosed APS1 with this screening procedure. Conclusion We propose that patients with Addison disease should be routinely screened for cytokine autoantibodies. Clinical or serological support for APS1 should warrant DNA sequencing and copy number analysis of AIRE to enable early diagnosis and prevention of lethal complications.
One-class Differential Expression Analysis using Tensor Decomposition-based Unsupervised Feature Extraction Applied to Integrated Analysis of Multiple Omics Data from 26 Lung Adenocarcinoma Cell Lines

Because usually there are no normal control cell lines, cancer cell lines can be examined only in a comparison between treatment and no-treatment conditions. Thus, characterization of cancer cell lines by themselves is impossible. To address this problem, one-class differential expression (DE) analysis, which can evaluate samples without a reference, is proposed here using tensor decomposition (TD)-based unsupervised feature extraction (FE) extended from recently proposed principal component analysis-based unsupervised FE. This one-class DE analysis was applied to multi-omics datasets of 26 lung adenocarcinoma cell lines. Enrichment analysis of selected genes identified multiple biological terms or concepts including signal recognition particles and nonsense-mediated decay (Reactome, Gene Ontology [GO] biological process), cadherin, poly(A) RNA binding (GO molecular function), eukaryotic translation initiation factors (Reactome), aberrant histone protein expression (Reactome and Human Protein Atlas [HPA]), and 163 transcription factors including E2F, PAX5, ARNT, AHR, and CREB, all of which are known to be related to non-small cell lung cancer and are expected to function cooperatively in lung adenocarcinoma oncogenesis. ,,,, These data not only indicate usefulness of one-class DE analysis using TD-based unsupervised FE but also point to new therapeutic targets in lung adenocarcinoma.
Glioblastoma: new therapeutic strategies to address cellular and genomic complexity

Glioblastoma (GBM) is the most invasive and devastating primary brain tumor with a median overall survival rate about 18 months with aggressive multimodality therapy. Its unique characteristics of heterogeneity, invasion, clonal populations maintaining stem cell-like cells and recurrence, have limited responses to a variety of therapeutic approaches, and have made GBM the most difficult brain cancer to treat. A great effort and progress has been made to reveal promising molecular mechanisms to target therapeutically. Especially with the emerging of new technologies, the mechanisms underlying the pathology of GBM are becoming more clear. The purpose of this review is to summarize the current knowledge of molecular mechanisms of GBM and highlight the novel strategies and concepts for the treatment of GBM.
Heuristic relevance learning for web image annotation

Automatic annotation can automatically annotate images with semantic labels to significantly facilitate image retrieval and organization. Traditional web image annotation methods often estimate specific label relevance to image, and neglect the relevance of the assigned label set as a whole. In this paper, A novel image annotation method by heuristic relevance learning is proposed. Label-to-image relevance and label-to-label correlation are formulated into a joint framework. Measures that can estimate the relevance are designed, and the assigned label set can provide a more precise description of the image. To reduce the complexity, a heuristic algorithm is introduced, thus making the framework more applicable to the large scale web image dataset. Experimental results demonstrate the general applicability of the algorithm.
A New Approach for Calculating the Band Gap of Semiconductors within the Density Functional Method

Within the framework of the density functional theory, the method was developed to calculate the band gap of semiconductors. We have evaluated the band gap for a number of monoatomic and diatomic semiconductors (Sn, Ge, Si, SiC, GaN, C, BN, AlN). The method gives the band gap of almost experimental accuracy. An important point is the fact that the developed method can be used to calculate both localized states (energy deep levels of defects in crystal), and electronic properties of nanostructures.