Miracle-dz/UniFAR-FTUs huggingface dataset:
Threat Intelligence Sharing Platforms: An Exploratory Study of Software Vendors and Research Perspectives

In the last couple of years, organizations have demonstrated an increased willingness to exchange information and knowledge regarding vulnerabilities, threats, incidents and mitigation strategies in order to collectively protect against today’s sophisticated cyberattacks. As a reaction to this trend, software vendors started to create offerings that facilitate this exchange and appear under the umbrella term “Threat Intelligence Sharing Platforms”. To which extent these platforms provide the needed means for exchange and information sharing remains unclear as they lack a common definition, innovation in this area is mostly driven by vendors and empirical research is rare. To close this gap, we examine the state-of-the-art software vendor landscape of these platforms, identify gaps and present arising research perspectives. Therefore, we conducted a systematic study of 22 threat intelligence sharing platforms and compared them. We derived eight key findings and discuss how existing gaps should be addressed by future research.
Incomplete Lineage Sorting in Mammalian Phylogenomics

Abstract The impact of incomplete lineage sorting (ILS) on phylogenetic conflicts among genes, and the related issue of whether to account for ILS in species tree reconstruction, are matters of intense controversy. Here, focusing on full‐genome data in placental mammals, we empirically test two assumptions underlying current usage of tree‐building methods that account for ILS. We show that in this data set (i) distinct exons from a common gene do not share a common genealogy, and (ii) ILS is only a minor determinant of the existing phylogenetic conflict. These results shed new light on the relevance and conditions of applicability of ILS‐aware methods in phylogenomic analyses of protein coding sequences.
Social patterning of cardiovascular and metabolic risk in Colombian adults

ABSTRACT Objectives: To test for differences in cardiovascular and metabolic risk (CMR) by educational attainment and physical capital. To compare CMR among black, indigenous, and mixed populations, accounting for socioeconomic status (SES). Design: We conducted multivariate analyses using cross-sectional data from a national survey of Colombian adults (n = 10,814) to examine the social patterning of CMR. In sex/gender-stratified models, a CMR index was regressed on educational attainment, physical capital, ethnicity/race, and age. Results: Women with a primary education (OR = 1.64, 95% CI: 1.25, 2.15) had higher age- and ethnicity/race-adjusted odds of CMR than women with more than secondary education. Men with a primary education (OR = 0.67, 95% CI: 0.48, 0.92) had significantly lower adjusted odds of CMR than men with more than secondary education; these associations did not remain significant after adjustments for physical capital. Men in the first (OR = 0.45, 95% CI: 0.36, 0.57) and second (OR = 0.72, 95% CI: 0.57, 0.91) physical capital tertiles had significantly lower adjusted odds of CMR than those in the highest tertile. There was not a significant patterning of CMR by ethnicity/race for women or men, or by physical capital for women. Conclusions: Findings suggest that for Colombian adults CMR is patterned by SES; these associations differ by sex/gender.
Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process

Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model to incorporate the time delay aspect of influence diffusion in social networks. We show that time-critical influence maximization under the time-delayed IC model maintains desired properties such as submodularity, which allows a greedy algorithm to achieve an approximation ratio of 1-1/e, to circumvent the NP-hardness of the problem. To overcome the inefficiency of the approximation algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures, while the second one converts the problem to one in the original IC model and then applies existing fast heuristics to it. Our simulation results demonstrate that our heuristics achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing algorithms that disregard the deadline constraint and delays in diffusion.
Familial monophasic acute transverse myelitis due to the pathogenic variant in VPS37A

Objective To identify genetic differences among siblings with a family history of idiopathic transverse myelitis (ITM). Methods We compared whole-exome sequencing (WES) on germline samples from the 2 affected sisters with ITM with 3 of their healthy siblings. Results The 2 sisters with ITM both had acute onset of sensory loss in the legs, weakness, and bowel/bladder dysfunction. The first developed ITM at age 15 years with a clinical nadir of complete paralysis, which slowly recovered over a few years. MRI demonstrated a persistent T2 lesion in the lower thoracic cord. The second developed ITM at age 50 years with a nadir of sensory loss from T6 down and paraparesis in the legs, associated with an MRI lesion at T6. She also made a partial recovery with treatment. Both sisters are homozygous for a missense variant in VPS37A (c.700C>A, p.Leu234Ile) identified by WES. We performed targeted sequencing of VPS37A in an additional 86 samples from patients with ITM and 175 with other diseases to investigate the p.Leu234Ile variant. We identified another patient with ITM homozygous for the same rare variant. No patients with multiple sclerosis, neuromyelitis optica, other neurologic conditions, or any healthy controls in public databases were homozygous for this variant. Conclusions A rare missense variant in VPS37A may predispose to development of ITM. Further studies are necessary to determine the frequency of this variant in the patient population and the mechanism through which it contributes to the risk of disease.
Knowledge Representation and Management, It’s Time to Integrate!

Summary Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query. Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN). Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development.
Understanding and predicting importance in images

What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e.g. size, location, 2) factors related to semantics, e.g. category of object or scene, and 3) contextual factors related to the likelihood of attribute-object, or object-scene pairs. We explore these factors using what people describe as a proxy for importance. Finally, we build models to predict what will be described about an image given either known image content, or image content estimated automatically by recognition systems.
Effects of Alcohol on Inhibitory Avoidance Learning in Zebrafish ( Danio rerio )

The zebrafish (Danio rerio) can be used in studies addressing the effects of drugs on learning, memory, and anxiety. In the present study, we investigated the effect of different alcohol treatments (chronic and acute) on the learning and anxiety response of zebrafish in an inhibitory avoidance paradigm. Zebrafish were initially exposed to different alcohol treatments and submitted to an inhibitory avoidance protocol, where an electroshock was applied to the fish as they swam from the white to the black side of a shuttle box tank (naturally preferred environment of zebrafish). Animals from the control and 0.5% acute alcohol groups exhibited high latency to enter the black side of the tank after the first exposure to electroshock, in addition to higher freezing and a shorter distance from the bottom of the tank, suggesting acute alcohol exposure did not affect aversive learning in zebrafish. However, chronic exposure and alcohol withdrawal impaired the fish’s capacity to properly respond to the aversive stimulus. Overall, our results show the harmful effects of chronic alcohol exposure, both continued intake and its cessation, but avoidance behavior persisted and anxiety increased following acute alcohol exposure.
Tolerogenic Transcriptional Signatures of Steady-State and Pathogen-Induced Dendritic Cells

Dendritic cells (DCs) are key directors of tolerogenic and immunogenic immune responses. During the steady state, DCs maintain T cell tolerance to self-antigens by multiple mechanisms including inducing anergy, deletion, and Treg activity. All of these mechanisms help to prevent autoimmune diseases or other hyperreactivities. Different DC subsets contribute to pathogen recognition by expression of different subsets of pattern recognition receptors, including Toll-like receptors or C-type lectins. In addition to the triggering of immune responses in infected hosts, most pathogens have evolved mechanisms for evasion of targeted responses. One such strategy is characterized by adopting the host’s T cell tolerance mechanisms. Understanding these tolerogenic mechanisms is of utmost importance for therapeutic approaches to treat immune pathologies, tumors and infections. Transcriptional profiling has developed into a potent tool for DC subset identification. Here, we review and compile pathogen-induced tolerogenic transcriptional signatures from mRNA profiling data of currently available bacterial- or helminth-induced transcriptional signatures. We compare them with signatures of tolerogenic steady-state DC subtypes to identify common and divergent strategies of pathogen induced immune evasion. Candidate molecules are discussed in detail. Our analysis provides further insights into tolerogenic DC signatures and their exploitation by different pathogens.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD). Unlike the choices of snapshot and starting points in SVRG and its proximal variant, Prox-SVRG, the two vectors of VR-SGD are set to the average and last iterate of the previous epoch, respectively. The settings allow us to use much larger learning rates, and also make our convergence analysis more challenging. We also design two different update rules for smooth and non-smooth objective functions, respectively, which means that VR-SGD can tackle non-smooth and/or non-strongly convex problems directly without any reduction techniques. Moreover, we analyze the convergence properties of VR-SGD for strongly convex problems, which show that VR-SGD attains linear convergence. Different from most algorithms that have no convergence guarantees for non-strongly convex problems, we also provide the convergence guarantees of VR-SGD for this case, and empirically verify that VR-SGD with varying learning rates achieves similar performance to its momentum accelerated variant that has the optimal convergence rate <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(1/T^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="shang-ieq1-2878765.gif"/></alternatives></inline-formula>. Finally, we apply VR-SGD to solve various machine learning problems, such as convex and non-convex empirical risk minimization, and leading eigenvalue computation. Experimental results show that VR-SGD converges significantly faster than SVRG and Prox-SVRG, and usually outperforms state-of-the-art accelerated methods, e.g., Katyusha.
ROBUST STABILITY ANALYSIS OF LINEAR SYSTEMS WITH AFFINE PARAMETER UNCERTAINTIES

Abstract This paper is concerned with the problem of robust stability of a class of uncertain linear systems, where the system state-matrices considered are affinely dependent on the uncertain parameters. Affine parameter-dependent Lyapunov functions are exploited to prove stability, and a robust stability criterion for the above class of systems to be affinely quadratically stable (AQS) is given in terms of linear matrix inequalities (LMIs). A comparison with the existing tests for AQS is given, and it is shown that the robust stability criterion provides a test that is not more conservative than the existing tests. Numerical examples are given to illustrate the results.
Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.
Process‐based simulation of contrail cirrus in a global climate model

[1] Aviation induces changes in global cirrus cloudiness by producing contrails. In the past, line shaped contrail coverage has been parameterized relying on the scaling of contrail formation frequency to observed values. Coverage due to irregularly shaped contrail cirrus, that develop from line shaped contrails, could not be estimated with this method. We introduce a process-based parameterization of contrail cirrus in a global climate model that does not rely on scaling and that is not restricted to line shaped contrails. A new prognostic cloud class, contrail cirrus, is introduced that is allowed to develop in the parameterized, fractional ice supersaturated area. Initial dimensions of the contrails and a parameter controlling their spreading in a sheared flow are constrained by observational data. In an idealized experiment contrail cirrus coverage is found to be dominated by a major contrail outbreak and scales with supersaturation rather than contrail formation frequency. The global distribution of young contrail coverage is smoothed out due to transport but overall values are similar compared to older estimates. Interannual variability of young contrail coverage can be as large as the mean coverage. The sensitivity of the model simulations to physical model parameters and to parameters concerning the comparison with observational data is studied. The associated uncertainty of global line shaped contrail coverage can be as high as 60% of the reference estimate (0.05%). The simulated coverage due to young contrails agrees reasonably well with most satellite observations of regional line shaped contrail coverage considering the sensitivity to the above parameters and the interannual variability.
Effect of vaccine storage temperatures and dose rate on antibody responses to foot and mouth disease vaccination in Cambodia

Abstract A field study investigated the effects of foot and mouth disease vaccine storage temperature for 7 days (frozen, refrigerated or held at ambient temperature) and dose (half or full dose) on the serological response to vaccination. It utilised a complete factorial design replicated on 18 smallholder cattle farms in three villages in Pursat province, Cambodia. Antibody responses from the 108 cattle involved were assessed by serological examination of blood samples collected at primary vaccination (day 0), at booster vaccination (day 30) and finally at 60 days post primary vaccination. Vaccination responses to the inactivated vaccine were assessed by testing for antibodies directed against FMD structural proteins in a liquid‐phase blocking ELISA (LPBE test) and differentiated from responses to natural infection by examining antibody titres against non‐structural viral proteins (NSPE test). LPBE results indicated that the mean log10 LPBE antibody titres of all experimental cattle increased from below protective levels at day 0 to protective levels at 30 days post primary vaccination, and increased further at 60 days post primary vaccination. Storage at ambient temperature for 1 week had no effect on antibody response to vaccination. However, freezing the vaccine for a week or use of a half dose resulted in significant reduction in titres at day 60 (P = 0.04 and P = 0.02, respectively). The results of this study reinforce the need to store FMD vaccines within the range recommended by the manufacturers and to adhere to the specified dosage instructions.
On the Feasibility of Enhancing Interactivity for Real-time Communications using Overlay Routing

As real-time communications are developing rapidly on the Internet, overlay routing has been widely proposed as an easily deployable way to meet their stringent requirements of end-to-end (E2E) performance and interactivity. Contrasting to that much previous work focused on the design of novel protocols, some significant issues impacting overlay routing's feasibility of performance improvement and guidelines of practical implementation still lack a careful study. This paper contributes to the understanding of three of such technical issues. The first issue is how long it takes a relay node to forward a packet, and what are the determinant factors. The second one is how long the overlay path (OP) can persist to sustainedly outperforming the direct IP-path (DP), and how to select more persistent OPs. The last issue is that given the existence of performance correlation between E2E paths, whether it is possible to gain benefits by cooperatively manipulating multiple paths.
Childhood BMI and Adult Type 2 Diabetes, Coronary Artery Diseases, Chronic Kidney Disease, and Cardiometabolic Traits: A Mendelian Randomization Analysis

OBJECTIVE To test the causal effect of childhood BMI on adult cardiometabolic diseases using a Mendelian randomization analysis. RESEARCH DESIGN AND METHODS We used 15 single nucleotide polymorphisms as instrumental variables for childhood BMI to test the causal effect of childhood BMI on cardiometabolic diseases using summary-level data from consortia. RESULTS We found that a 1-SD increase in childhood BMI (kg/m2) was associated with an 83% increase in risk of type 2 diabetes (odds ratio [OR] 1.83 [95% CI 1.46, 2.30]; P = 2.5 × 10−7) and a 28% increase in risk of coronary artery disease (CAD) (OR 1.28 [95% CI 1.17, 1.39]; P = 2.1 × 10−8) at the Bonferroni-adjusted level of significance (P < 0.017) in adults. In addition, a 1-SD increase in childhood BMI was associated with a 0.587-SD increase in adulthood BMI (kg/m2), a 0.062-SD increase in hip circumference (cm), a 0.602-SD increase in waist circumference (cm), a 0.111 pmol/L increase in log fasting insulin, a 0.068 increase in log-transformed HOMA of ß-cell function (%), a 0.126 increase in log-transformed HOMA of insulin resistance (%), and a 0.109-SD increase in triglyceride (mg/dL) but a 0.138-SD decrease in HDL (mg/dL) in adults at the Bonferroni-adjusted level of significance (P < 0.0026). CONCLUSIONS A genetic predisposition to higher childhood BMI was associated with increased risk of type 2 diabetes and CAD in adult life. These results provide evidence supportive of a causal association between childhood BMI and these outcomes.
Adaptive contrast weighted learning for multi‐stage multi‐treatment decision‐making

Dynamic treatment regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. To directly identify the optimal DTR in a multi‐stage multi‐treatment setting, we propose a dynamic statistical learning method, adaptive contrast weighted learning. We develop semiparametric regression‐based contrasts with the adaptation of treatment effect ordering for each patient at each stage, and the adaptive contrasts simplify the problem of optimization with multiple treatment comparisons to a weighted classification problem that can be solved by existing machine learning techniques. The algorithm is implemented recursively using backward induction. By combining doubly robust semiparametric regression estimators with machine learning algorithms, the proposed method is robust and efficient for the identification of the optimal DTR, as shown in the simulation studies. We illustrate our method using observational data on esophageal cancer.