Miracle-dz/UniFAR-FTUs huggingface dataset:
FRAPpuccino: Fault-detection through Runtime Analysis of Provenance

We present FRAPpuccino (or FRAP), a provenance-based fault detection mechanism for Platform as a Service (PaaS) users, who run many instances of an application on a large cluster of machines. FRAP models, records, and analyzes the behavior of an application and its impact on the system as a directed acyclic provenance graph. It assumes that most instances behave normally and uses their behavior to construct a model of legitimate behavior. Given a model of legitimate behavior, FRAP uses a dynamic sliding window algorithm to compare a new instance's execution to that of the model. Any instance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and experimental results showing that it can accurately detect application anomalies.
Gas source localisation by constructing concentration gridmaps with a mobile robot

This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps with a mobile robot equipped with a gas-sensitive system ("mobile nose"). By contrast to metric gridmaps extracted from sonar or laser range scans, a gas sensor measurement provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. The structure of the mapped features is discussed with respect to the parameters of the applied density function, the evolution of the gas distribution over time, and the capability to locate a gas source.
How pigeons couple three-dimensional elbow and wrist motion to morph their wings

Birds change the shape and area of their wings to an exceptional degree, surpassing insects, bats and aircraft in their ability to morph their wings for a variety of tasks. This morphing is governed by a musculoskeletal system, which couples elbow and wrist motion. Since the discovery of this effect in 1839, the planar ‘drawing parallels’ mechanism has been used to explain the coupling. Remarkably, this mechanism has never been corroborated from quantitative motion data. Therefore, we measured how the wing skeleton of a pigeon (Columba livia) moves during morphing. Despite earlier planar assumptions, we found that the skeletal motion paths are highly three-dimensional and do not lie in the anatomical plane, ruling out the ‘drawing parallels’ mechanism. Furthermore, micro-computed tomography scans in seven consecutive poses show how the two wrist bones contribute to morphing, particularly the sliding ulnare. From these data, we infer the joint types for all six bones that form the wing morphing mechanism and corroborate the most parsimonious mechanism based on least-squares error minimization. Remarkably, the algorithm shows that all optimal four-bar mechanisms either lock, are unable to track the highly three-dimensional bone motion paths, or require the radius and ulna to cross for accuracy, which is anatomically unrealistic. In contrast, the algorithm finds that a six-bar mechanism recreates the measured motion accurately with a parallel radius and ulna and a sliding ulnare. This revises our mechanistic understanding of how birds morph their wings, and offers quantitative inspiration for engineering morphing wings.
Fast Exact Stochastic Simulation Algorithms Using Partial Propensities

We review the class of partial‐propensity exact stochastic simulation algorithms (SSA) for chemical reaction networks. We show which modules partial‐propensity SSAs are composed of and how partial‐propensity variants of known SSAs can be constructed by adjusting the sampling strategy used. We demonstrate this on the example of two instances, namely the partial‐propensity variant of Gillespie’s original direct method and that of the SSA with composition‐rejection sampling (SSA‐CR). Partial‐propensity methods may outperform the corresponding classical SSA, particularly on strongly coupled reaction networks. Changing the different modules of partial‐propensity SSAs provides flexibility in tuning them to perform particularly well on certain classes of reaction networks. The framework presented here defines the design space of such adaptations.
Experimental Assessment and Thermal Characterization of Ethylene TetraFluoroEthylene ETFE Foil

Co-polymer facade materials have been a recent and popular option in the building industry as an alternative to glazing. Ethylene TetraFluoroEthylene (ETFE) is a promising case in this category. ETFE has been successfully used in many high-profile projects as an innovative solution to energy-conscious design challenges. In addition, ETFE presents significant savings in cost and structural support requirements, compared with conventional glazing, due to its low weight, and the potential for energy performance benefits due to its relatively high visible light transmittance. There is a lack of detailed published data reporting its thermal behaviour. This study focuses on the examination of heat transfer through the ETFE membrane, surface temperatures, heat losses and solar gains. The paper examines the impact of the material on the overall energy use of a building, as well as thermal comfort and interior conditions. Through field-testing the research will inspect the material’s thermal properties to obtain results that will assist in evaluating the suitability of ETFE use in a broader spectrum of building applications. Such an assessment of performance will provide information for further investigation to improve the material’s features and optimise energy performance.
Tent caterpillars are robust to variation in leaf phenology and quality in two thermal environments

Abstract The synchrony between emergence of spring-active, insect herbivores and the budburst of their host plants could be affected by warming temperatures with influences on the availability and quality of foliage as it undergoes physical and chemical changes. This can affect the growth and survival of insects. Here, we used sun-exposed and shaded trees to determine whether the synchrony between egg hatch of western tent caterpillar, Malacosoma californicum pluviale Dyar (Lepidoptera: Lasiocampidae) and budburst of its host red alder, Alnus rubra Bongard (Betulaceae) changes with different thermal environments (temperature and light together). To explore the potential outcome of a shift in phenological synchrony, we used laboratory assays of larval growth and survival to determine the effect of variation in young, youthful and mature leaves from sun-exposed and shaded trees. While the average higher temperature of sun-exposed trees advanced the timing of budburst and egg hatch, synchrony was not disrupted. Leaf quality had no significant influence on growth or survival in the laboratory for early instars reared as family groups. Later instar larvae, however, performed best on mature leaves from sun-exposed trees. The robust relationship between leaf and larval development of western tent caterpillars suggests that warming climates may not have a strong negative impact on their success through shifts in phenological synchrony, but might influence other aspects of leaf quality and larval condition.
Structured networks and coarse-grained descriptions: a dynamical perspective

This chapter discusses the interplay between structure and dynamics in complex networks. Given a particular network with an endowed dynamics, our goal is to find partitions aligned with the dynamical process acting on top of the network. We thus aim to gain a reduced description of the system that takes into account both its structure and dynamics. In the first part, we introduce the general mathematical setup for the types of dynamics we consider throughout the chapter. We provide two guiding examples, namely consensus dynamics and diffusion processes (random walks), motivating their connection to social network analysis, and provide a brief discussion on the general dynamical framework and its possible extensions. In the second part, we focus on the influence of graph structure on the dynamics taking place on the network, focusing on three concepts that allow us to gain insight into this notion. First, we describe how time scale separation can appear in the dynamics on a network as a consequence of graph structure. Second, we discuss how the presence of particular symmetries in the network give rise to invariant dynamical subspaces that can be precisely described by graph partitions. Third, we show how this dynamical viewpoint can be extended to study dynamics on networks with signed edges, which allow us to discuss connections to concepts in social network analysis, such as structural balance. In the third part, we discuss how to use dynamical processes unfolding on the network to detect meaningful network substructures. We then show how such dynamical measures can be related to seemingly different algorithm for community detection and coarse-graining proposed in the literature. We conclude with a brief summary and highlight interesting open future directions.
Integrated Genomic and Immunophenotypic Classification of Pancreatic Cancer Reveals Three Distinct Subtypes with Prognostic/Predictive Significance

Purpose: Current clinical classification of pancreatic ductal adenocarcinoma (PDAC) is unable to predict prognosis or response to chemo- or immunotherapy and does not take into account the host reaction to PDAC cells. Our aim is to classify PDAC according to host- and tumor-related factors into clinically/biologically relevant subtypes by integrating molecular and microenvironmental findings. Experimental Design: A well-characterized PDAC cohort (n = 110) underwent next-generation sequencing with a hot spot cancer panel while next-generation tissue microarrays were immunostained for CD3, CD4, CD8, CD20, PD-L1, p63, hyaluronan-mediated motility receptor (RHAMM), and DNA mismatch repair proteins. Previous data on FOXP3 were integrated. Immune cell counts and protein expression were correlated with tumor-derived driver mutations, clinicopathologic features (TNM 8th edition, 2017), survival, and epithelial–mesenchymal transition (EMT)–like tumor budding. Results: Three PDAC subtypes were identified: the “immune escape” (54%), poor in T and B cells and enriched in FOXP3+ regulatory T cells (Treg), with high-grade budding, frequent CDKN2A, SMAD4, and PIK3CA mutations, and poor outcome; the "immune rich" (35%), rich in T and B cells and poorer in FOXP3+ Tregs, with infrequent budding, lower CDKN2A and PIK3CA mutation rate, and better outcome and a subpopulation with tertiary lymphoid tissue (TLT), mutations in DNA damage response genes (STK11 and ATM), and the best outcome; and the "immune exhausted" (11%), with immunogenic microenvironment and two subpopulations—one with PD-L1 expression and a high PIK3CA mutation rate and a microsatellite-unstable subpopulation with a high prevalence of JAK3 mutations. The combination of low budding, low stromal FOXP3 counts, presence of TLTs, and absence of CDKN2A mutations confers significant survival advantage in patients with PDAC. Conclusions: Immune host responses correlate with tumor characteristics, leading to morphologically recognizable PDAC subtypes with prognostic/predictive significance. Clin Cancer Res; 24(18); 4444–54. ©2018 AACR. See related commentary by Khalil and O'Reilly, p. 4355
Combined multivariate matching pursuit and support vector machine: A way forward to classify single-sweep evoked potentials?

Evoked brain potentials averaged over multiple sweeps provide a valuable objective measure of abnormal pain processing due to sensitization of the central nervous system. However, the average procedure cancel out important information regarding phase resetting and non-phase locked oscillations. Hence, assessment of the pain processing could be optimized by analyzing single-sweeps. To develop improved methods to assess single-sweeps, we applied a new approach in one healthy volunteer participating in a placebo controlled study of widespread hyperalgesia induced by perfusion of acid and capsaicin in the esophagus. The evoked potentials were recorded during electrical stimulations in the rectosigmoid colon. Features from the single-sweeps were extracted by a multivariate matching pursuit algorithm with Gabor atoms, and features were discriminated by a support vector machine with a linear kernel. The classification performance for the optimal number of atoms was 95% when discriminating the sensitization response from the placebo response, which was above change level compared to the performance when discriminating the two baseline responses (P < 0.001). The discriminative capacity was increased power in the delta, theta, and alpha frequency bands. This result corresponds to previous characteristics seen in chronic pain patients who exhibit central sensitization. The new approach to classify single-sweeps on a single subject basis might in the future prove to be a useful tool in assessing mechanisms in central sensitization, and could be applied to improve enriched enrollment of study subjects in clinical trial units.
UCBoost: A Boosting Approach to Tame Complexity and Optimality for Stochastic Bandits

In this work, we address the open problem of finding low-complexity near-optimal multi-armed bandit algorithms for sequential decision making problems. Existing bandit algorithms are either sub-optimal and computationally simple (e.g., UCB1) or optimal and computationally complex (e.g., kl-UCB). We propose a boosting approach to Upper Confidence Bound based algorithms for stochastic bandits, that we call UCBoost. Specifically, we propose two types of UCBoost algorithms. We show that UCBoost($D$) enjoys $O(1)$ complexity for each arm per round as well as regret guarantee that is $1/e$-close to that of the kl-UCB algorithm. We propose an approximation-based UCBoost algorithm, UCBoost($\epsilon$), that enjoys a regret guarantee $\epsilon$-close to that of kl-UCB as well as $O(\log(1/\epsilon))$ complexity for each arm per round. Hence, our algorithms provide practitioners a practical way to trade optimality with computational complexity. Finally, we present numerical results which show that UCBoost($\epsilon$) can achieve the same regret performance as the standard kl-UCB while incurring only $1\%$ of the computational cost of kl-UCB.
Protein extraction from human anagen head hairs 1-millimeter or less in total length.

A simple method for extracting protein from human anagen (i.e., actively growing hair stage) head hairs was developed in this study for cases of limited sample availability and/or studies of specific micro-features within a hair. The distinct feature segments of the hair from one donor were divided lengthwise (i.e., each of ∼200-400 μm) and then pooled for three individual hairs to form a total of eight composite hair samples (i.e., each of ∼1 mm or less in total length). The proteins were extracted, digested using trypsin, and characterized via nano-flow liquid chromatography tandem-mass spectrometry (nLC-MS/MS). A total of 63 proteins were identified from all eight protein samples analyzed of which 60% were keratin and keratin-associated proteins. The major hair keratins identified are consistent with previous studies using fluorescence in situ hybridization and nLC-MS/MS while requiring over 400-8000-fold less sample. The protein extraction method from micro-sized human head hairs described in this study will enable proteomic analysis of biological evidence for cases of limited sample availability and will complement hair research. For example, research seeking to develop alternative non-DNA based techniques for comparing questioned to known hairs, and understanding the biochemistry of hair decomposition.
Patient and hospital characteristics predictive of inferior vena cava filter usage in venous thromboembolism patients

Abstract To examine the association between patient and hospital characteristics and inferior vena cava filter (IVCF) utilization in patients with venous thromboembolism (VTE). The 2013 to 2014 Nationwide Readmissions Database was used to define a cohort of patients with VTE aged ≥18 after a primary VTE diagnosis. Comorbidities of interest were classified via diagnosis codes and IVCF placement was identified via procedure code. Chi square analysis tested differences between patient and hospital-level characteristics and whether or not IVCFs were placed. A hierarchical logistic regression model estimated the relationship between patient-level factors (demographics, socioeconomic status, comorbidities), hospital-level factors (bed size, teaching status, urbanity) and whether or not IVCFs were placed. Additional models were specified to examine goodness of fit across methodological alternatives. There were 212,395 VTE hospitalizations, with 12.18% (n = 25,877) receiving IVCF placement. There were significant differences between those who did and did not receive IVCF placement; notably, those receiving IVCFs were older (P < .001), had Medicare insurance more than private (P < .001), longer lengths of stay (P < .001), and were in privately owned hospitals (P < .001). IVCF placement remained significantly associated with patient and hospital-level characteristics following multivariate adjustment via hierarchical logistic regression; notably, age >80 (adjusted Odds Ratio [aOR]: 2.53, 95% confidence interval [CI]: 2.25–2.85), ≥13 comorbid conditions (aOR: 3.85, 95% CI: 3.25–4.27), and privately owned hospitals (aOR: 1.21, 95% CI: 1.08–1.36). Optimal goodness-of-fit was achieved with a combination of random effects and patient-level fixed effects. These findings provide evidence that combinations of patient and hospital-level factors are related to whether patients with VTE receive IVCFs.
Deep-Learning-Assisted Volume Visualization

Designing volume visualizations showing various structures of interest is critical to the exploratory analysis of volumetric data. The last few years have witnessed dramatic advances in the use of convolutional neural networks for identification of objects in large image collections. Whereas such machine learning methods have shown superior performance in a number of applications, their direct use in volume visualization has not yet been explored. In this paper, we present a deep-learning-assisted volume visualization to depict complex structures, which are otherwise challenging for conventional approaches. A significant challenge in designing volume visualizations based on the high-dimensional deep features lies in efficiently handling the immense amount of information that deep-learning methods provide. In this paper, we present a new technique that uses spectral methods to facilitate user interactions with high-dimensional features. We also present a new deep-learning-assisted technique for hierarchically exploring a volumetric dataset. We have validated our approach on two electron microscopy volumes and one magnetic resonance imaging dataset.
The Research Progress of Mechanical Propertiesof Composite Laminates

Composite materials are widely used in modern industry because of their high specific strength and specific quality. Based on composite material laminates is hot research topic in recent years to consult, discusses the modeling of piezoelectric fiber laminated plates with bending theory equation to solve the results, composite laminates of higher order shear deformation theory, the heat is abnormal load response for the bottom of the plywood and participation and viscous damping for laminated plates such aspects as the influence of the introduction of composite laminates research present situation and the future research direction.
Submicroscopic Plasmodium falciparum parasitaemia in human immunodeficiency virus–infected adults living in Gabon (Central Africa)—a pilot study

Background Submicroscopic malaria infections contribute to malaria transmission. Describing the extent of the parasite reservoir is of importance. In people living with human immunodeficiency virus (HIV), the frequency of subpatent malaria infections is rarely reported. The aim of the present study was to determine the frequency of submicroscopic infections in people living with HIV in Gabon and its relationship with cotrimoxazole (CTX) use. Methods A survey was conducted in two health care centres in rural areas (Koulamoutou and Oyem) and three in urban areas (Libreville) of Gabon from March 2015 to June 2016. Blood samples were collected from consenting people living with HIV with a negative blood smear. Information on CTX and antiretroviral therapy intake was recorded from the medical file of the patient and through an interview. For molecular analysis, the Plasmodium small subunit ribosomal RNA gene was amplified by nested polymerase chain reaction. Results Submicroscopic infections were detected in 10.1% (n=12/119) of the people living with HIV, more frequently in those residing in rural areas (15.1%) compared with urban areas (2.1%) (p<0.01). The proportion of anaemic patients was 1.74-fold more frequent in malaria-infected patients, although not statistically significant. Submicroscopic infections frequency did not vary according to CTX intake (p=0.6). Conclusions The present pilot study highlights a non-negligible frequency of submicroscopic malaria infections in people living with HIV from rural areas, but no relationship with CTX intake was found.
Management of adenovirus infection in patients after haematopoietic stem cell transplantation: State‐of‐the‐art and real‐life current approach

The important insights gained over the past years in diagnosis and treatment of invasive adenoviral infections provide new paradigms for the monitoring and clinical management of these life‐threatening complications. A meeting was held to discuss and subsequently disseminate the current advances in our understanding of the aetiology/pathogenesis and future treatment options facilitating effective control or prevention of adenovirus‐related diseases in the allogeneic haematopoietic stem cell transplant setting. Invited experts in the field discussed recent progress with leading members of the Infectious Diseases Working Party of the European Society of Blood and Marrow Transplantation at the “State‐of‐the‐art” Meeting in Poznan, Poland, in October 2017. In this review article, the panel of experts presents a concise summary of the current evidence based on published data from the last 15 years and on recent achievements resulting from real‐life practice. The present position statement reflects an expert opinion on current approaches to clinical management of adenovirus infections in patients undergoing allogeneic haematopoietic stem cell transplant and provides graded recommendations of the panel for diagnostic approaches and preemptive therapy reflecting the present state of knowledge.
Correlated network of networks enhances robustness against catastrophic failures

Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule.