Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermococcus kodakarensis modulates its polar membrane lipids and elemental composition according to growth stage and phosphate availability

We observed significant changes in the elemental and intact polar lipid (IPL) composition of the archaeon Thermococcus kodakarensis (KOD1) in response to growth stage and phosphorus supply. Reducing the amount of organic supplements and phosphate in growth media resulted in significant decreases in cell size and cellular quotas of carbon (C), nitrogen (N), and phosphorus (P), which coincided with significant increases in cellular IPL quota and IPLs comprising multiple P atoms and hexose moieties. Relatively more cellular P was stored as IPLs in P-limited cells (2–8%) compared to control cells (<0.8%). We also identified a specific IPL biomarker containing a phosphatidyl-N-acetylhexoseamine headgroup that was relatively enriched during rapid cell division. These observations serve as empirical evidence of IPL adaptations in Archaea that will help to interpret the distribution of these biomarkers in natural systems. The reported cell quotas of C, N, and P represent the first such data for a specific archaeon and suggest that thermophiles are C-rich compared to the cell carbon-to-volume relationship reported for planktonic bacteria.
Marathon Race Affects Neutrophil Surface Molecules: Role of Inflammatory Mediators

The fatigue induced by marathon races was observed in terms of inflammatory and immunological outcomes. Neutrophil survival and activation are essential for inflammation resolution and contributes directly to the pathogenesis of many infectious and inflammatory conditions. The aim of this study was to investigate the effect of marathon races on surface molecules related to neutrophil adhesion and extrinsic apoptosis pathway and its association with inflammatory markers. We evaluated 23 trained male runners at the São Paulo International Marathon 2013. The following components were measured: hematological and inflammatory mediators, muscle damage markers, and neutrophil function. The marathon race induced an increased leukocyte and neutrophil counts; creatine kinase (CK), lactate dehydrogenase (LDH), CK-MB, interleukin (IL)-6, IL-10, and IL-8 levels. C-reactive protein (CRP), IL-12, and tumor necrosis factor (TNF)-α plasma concentrations were significantly higher 24 h and 72 h after the marathon race. Hemoglobin and hematocrit levels decreased 72 h after the marathon race. We also observed an increased intercellular adhesion molecule-1 (ICAM-1) expression and decreasedTNF receptor-1 (TNFR1) expression immediately after and 24 h after the marathon race. We observed an increased DNA fragmentation and L-selectin and Fas receptor expressions in the recovery period, indicating a possible slow rolling phase and delayed neutrophil activation and apoptosis. Marathon racing affects neutrophils adhesion and survival in the course of inflammation, supporting the “open-window” post-exercise hypothesis.
On matching digital face images against scanned passport photos

In this paper the problem of matching high-resolution digital face images against low-resolution passport photos scanned from the original document is studied. The challenges involved in such a problem are quite different from those encountered by classical facial matching systems described in the literature. These challenges can be person-related, document-related or scanning device-related. Thus, the matching performance of face recognition algorithms can be significantly impacted when tested on passport photos scanned by an external device. The purpose of this paper is to illustrate the complexity of the passport facial matching problem and to provide a preliminary solution to it. The contributions of this work are two-fold. Firstly, a database of 25 subjects is assembled and used to illustrate the challenges associated with passport facial matching. Secondly, a pre-processing scheme is proposed in order to denoise and eliminate watermark traces that may be present in scanned passport photos. The application of such a pre-processing scheme is observed to improve the performance of classical face matching systems. To the best of our knowledge, this is the first time that this problem is being investigated in the open literature in the context of international passports exhibiting a variety of facial image qualities and security marks.
Face detection based on generic local descriptors and spatial constraints

We present an algorithm for face detection that is based on generic local descriptors (e.g. eyes). A generic descriptor captures the distribution of individual descriptors over a set of samples (training images). This distribution is assumed to be a Gaussian mixture model and is learnt using the minimum description length principle. A descriptor of an unknown image may then be classified as one of the generic local descriptors. Robustness is achieved by using spatial constraints between locations of descriptors. Experiments show very promising results.
Realizing Common Communication Patterns in Partitioned Optical Passive Stars (POPS) Networks

We consider the problem of realizing several common communication structures in the all-optical Partitioned Optical Passive Stars (POPS) topology. We show that, often, the obvious or "natural" method of implementing a communication pattern in the POPS does not efficiently utilize its communication capabilities. We present techniques which distribute the communication load uniformly in the POPS for four of the most common communication patterns (all-to-all personalized, global reduction operations, ring, and torus). We prove that these techniques provide optimal performance in the sense that they minimize the time required to deliver the messages from each node to its neighbors.
A Ranking-based KNN Approach for Multi-Label Classification

Multi-label classication has attracted a great deal of attention in recent years. This paper presents an interesting nding, namely, being able to identify neighbors with trustable labels can signicantly improve the classication accuracy. Based on this nding, we propose a k-nearest-neighbor-based ranking approach to solve the multi-label classication problem. The approach exploits a ranking model to learn which neighbor’s labels are more trustable candidates for a weighted KNN-based strategy, and then assigns higher weights to those candidates when making weighted-voting decisions. The weights can then be determined by using a generalized pattern search technique. We collect several real-word data sets from various domains for the experiment. Our experiment results demonstrate that the proposed method outperforms state-of-the-art instance-based learning approaches. We believe that appropriately exploiting k-nearest neighbors is useful to solve the multi-label problem.
On the Gap Between the Complex Structured Singular Value and Its Convex Upper Bound

The gap between the complex structured singular value of a complex matrix $M$ and its convex upper bound is considered. New necessary and sufficient conditions for the existence of the gap are derived. It is shown that determining whether there exists such a gap is as difficult as evaluating a structured singular value of a reduced rank matrix (whose rank is equal to the multiplicity of the largest singular value of $M$). Furthermore, if an upper bound on this reduced rank problem can be obtained, it is shown that this provides an upper bound on the original problem that is lower than the convex relaxation upper bound. An example that illustrates our procedure is given. We also give the solution of several structured-approximation problems of independent interest.
Fast Selection of Small and Precise Candidate Sets from Dictionaries for Text Correction Tasks

Lexical text correction relies on a central step where approximate search in a dictionary is used to select the best correction suggestions for an ill-formed input token. In previous work we introduced the concept of a universal Levenshtein automaton and showed how to use these automata for efficiently selecting from a dictionary all entries within a fixed Levenshtein distance to the garbled input word. In this paper we look at refinements of the basic Levenshtein distance that yield more sensible notions of similarity in distinct text correction applications, e.g. OCR. We show that the concept of a universal Levenshtein automaton can be adapted to these refinements. In this way we obtain a method for selecting correction candidates which is very efficient, at the same time selecting small candidate sets with high recall.
Coronary Heart Disease Risk Factors: Concordance Between Patients and Partners Before and After Bypass Grafting Surgery

Background:Coronary heart disease (CHD) risk factor reduction is required to maximize the benefits to be gained from coronary artery bypass grafting. Risk factor reduction after surgery, however, is often incomplete and adherence rates are poor. The health behaviors of the cardiac partner can be supportive or can act to undermine the patient’s motivation for change in risk factors. Concordance in health behaviors in couples can make it more difficult for patients to engage in positive lifestyle changes. Objectives:The aims of this study were to increase understanding of the role of concordance in CHD risk factors and common medical conditions in patients and partners before and 4 months after bypass grafting and to examine changes in the pattern of concordance over time. Methods:A prospective study of patients’ and partners’ CHD risk factors was conducted in the outpatient clinic before and at home 4 months after bypass grafting. Results:There was significant concordance for preoperative physical activity, body mass index, and diabetes mellitus, and postoperatively, there was significant concordance for smoking status, physical activity, body mass index, cholesterol, and diabetes mellitus. There were significant associations between patients’ preoperative and postoperative physical activity and cholesterol and between the partners’ preoperative and postoperative physical activity. There was a significant change in the pattern of concordance for physical activity from preoperation to postoperation, with more patients but not partners increasing their physical activity levels. Conclusions:Results revealed significant concordance in CHD risk factors and common medical conditions in patients and partners before and 4 months after coronary artery bypass grafting. This indicates that the behaviors of some couples can make it more difficult for patients to change their lifestyle. The health professionals involved in educating patients before and after bypass grafting need to target the patient and partner as a couple to help achieve more successful risk factor reduction.
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.
GNSS‐R nonlocal sea state dependencies: Model and empirical verification

Global Navigation Satellite System Reflectometry (GNSS-R) is an active, bistatic remote sensing technique operating at L-band frequencies. GNSS-R signals scattered from a rough ocean surface are known to interact with longer surface waves than traditional scatterometery and altimetry signals. A revised forward model for GNSS-R measurements is presented which assumes an ocean surface wave spectrum that is forced by other sources than just the local near-surface winds. The model is motivated by recent spaceborne GNSS-R observations that indicate a strong scattering dependence on significant wave height, even after controlling for local wind speed. This behavior is not well represented by the most commonly used GNSS-R scattering model, which features a one-to-one relationship between wind speed and the mean-square-slope of the ocean surface. The revised forward model incorporates a third generation wave model that is skillful at representing long waves, an anchored spectral tail model, and a GNSS-R electromagnetic scattering model. In comparisons with the spaceborne measurements, the new model is much better able to reproduce the empirical behavior.
RR interval variation, the QT interval index and risk of primary cardiac arrest among patients without clinically recognized heart disease.

AIMS Autonomic tone influences RR interval variation (RRV) and the heart rate-corrected QT interval index (QTI). Together, QTI and RRV may improve characterization of sympathovagal control and estimation of risk of primary cardiac arrest. We therefore examined effects of QTI and short-term RRV from standard, 12-lead electrocardiograms on risk of primary cardiac arrest among persons without clinically recognized heart disease. METHODS AND RESULTS We analysed data from a case-control study of risk factors for primary cardiac arrest among enrollees in a large health plan. Cases (n=505) were enrollees aged 18 to 79 years without history of heart disease who had primary cardiac arrest between 1980 and 1994. Controls (n=529) were a demographically similar, stratified random sample of enrollees. We determined enrollee characteristics from ambulatory medical records, QTI and RRV from standard, 12-lead electrocardiograms, and medication use from automated pharmacy files. Low and high values of QTI and RRV were designated as the first and fifth quintiles of QTI (96% and 107%) and RRV (35 ms and 120 ms) among controls. In a model adjusting for clinical predictors of primary cardiac arrest, RRV modified the association between QTI and risk of primary cardiac arrest (P=0.05). Compared to high RRV and low QTI, the risk of primary cardiac arrest (odds ratio [95% CI]) was 0.95 [0.73-1.23] at low RRV and QTI, 1.23 [0.97-1.57] at high RRV and QTI, and 1.55 [1.16-2.06] at low RRV and high QTI. Risk remained elevated after adjustment for other electrocardiographic predictors and medication use. CONCLUSION Autonomic dysfunction, characterized by high QTI and low RRV on the standard, 12-lead electrocardiogram, is associated with an increased risk of primary cardiac arrest among persons without clinically recognized heart disease.
Clinical Profile of Acute Confusion in the Long-Term Care Setting

Aspects of acute confusion (AC) including risk factors, behavior patterns, and outcomes are not well documented in long-term care (LTC) residents. The purpose of this prospective study was to describe the clinical profile of AC in LTC including risk factors, behavior patterns, etiologies, and 3-month outcomes. Seventy-four elderly LTC residents were assessed for AC, depression, and global cognitive impairment. Risk factors associated with AC included hearing deficits, depression, pulmonary disorders, and abnormal serum sodium or potassium levels. Behavior patterns of acutely confused residents included hyperactive (n = 9, 31%), hypoactive (n = 8, 28%), and mixed (n = 7, 24%). In the majority of the AC cases, the etiology was multifactorial: Infections and dehydration were the most common causes. Residents with AC had very poor 3-month outcomes. Thirty-four percent (n = 10) of the residents with AC died within 3 months of the evaluation. This study highlights the complexity and serious nature of AC in this frail population.
Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications for Generating ROC Curves

It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is satisfactory for the task at hand. The authors have investigated the use of a niched Pareto multiobjective genetic algorithm (GA) for classifier optimization. With niched Pareto GA's, an objective vector is optimized instead of a scalar function, eliminating the need to aggregate classification objective functions. The niched Pareto GA returns a set of optimal solutions that are equivalent in the absence of any information regarding the preferences of the objectives. The a priori knowledge that was used for aggregating the objective functions in conventional classifier training can instead be applied post-optimization to select from one of the series of solutions returned from the multiobjective genetic optimization. The authors have applied this technique to train a linear classifier and an artificial neural network (ANN), using simulated datasets. The performances of the solutions returned from the multiobjective genetic optimization represent a series of optimal (sensitivity, specificity) pairs, which can be thought of as operating points on a receiver operating characteristic (ROC) curve. All possible ROC curves for a given dataset and classifier are less than or equal to the ROC curve generated by the niched Pareto genetic optimization.
Posttranslational regulation impacts the fate of duplicated genes

Gene and genome duplications create novel genetic material on which evolution can work and have therefore been recognized as a major source of innovation for many eukaryotic lineages. Following duplication, the most likely fate is gene loss; however, a considerable fraction of duplicated genes survive. Not all genes have the same probability of survival, but it is not fully understood what evolutionary forces determine the pattern of gene retention. Here, we use genome sequence data as well as large-scale phosphoproteomics data from the baker’s yeast Saccharomyces cerevisiae, which underwent a whole-genome duplication ∼100 mya, and show that the number of phosphorylation sites on the proteins they encode is a major determinant of gene retention. Protein phosphorylation motifs are short amino acid sequences that are usually embedded within unstructured and rapidly evolving protein regions. Reciprocal loss of those ancestral sites and the gain of new ones are major drivers in the retention of the two surviving duplicates and in their acquisition of distinct functions. This way, small changes in the sequences of unstructured regions in proteins can contribute to the rapid rewiring and adaptation of regulatory networks.
Measuring Information Leakage in Website Fingerprinting Attacks

Tor is a low-latency anonymity system intended to provide low-latency anonymous and uncensored network access against a local or network adversary. Because of the design choice to minimize traffic overhead (and increase the pool of potential users) Tor allows some information about the client's connections to leak in the form of packet timing. Attacks that use (features extracted from) this information to infer the website a user visits are referred to as Website Fingerprinting (WF) attacks. We develop a methodology and tools to directly measure the amount of information about a website leaked by a given set of features. We apply this tool to a comprehensive set of features extracted from a large set of websites and WF defense mechanisms, allowing us to make more fine-grained observations about WF attack and defense mechanisms.
Characterization of a Novel Protein with Homology to C-type Lectins Expressed by the Cotesia rubecula Bracovirus in Larvae of the Lepidopteran Host, Pieris rapae*

Polydnaviruses are essential for the survival of many Ichneumonoid endoparasitoids, providing active immune suppression of the host in which parasitoid larvae develop. The Cotesia rubecula bracovirus is unique among polydnaviruses in that only four major genes are detected in parasitized host (Pieris rapae) tissues, and gene expression is transient. Here we describe a novel C. rubecula bracovirus gene (CrV3) encoding a lectin monomer composed of 159 amino acids, which has conserved residues consistent with invertebrate and mammalian C-type lectins. Bacterially expressed CrV3 agglutinated sheep red blood cells in a divalent ion-dependent but Ca2+-independent manner. Agglutination was inhibited by EDTA but not by biological concentrations of any saccharides tested. Two monomers of ∼14 and ∼17 kDa in size were identified on SDS-PAGE in parasitized P. rapae larvae. The 17-kDa monomer was found to be an N-glyscosylated form of the 14-kDa monomer. CrV3 is produced in infected hemocytes and fat body cells and subsequently secreted into hemolymph. We propose that CrV3 is a novel lectin, the first characterized from an invertebrate virus. CrV3 shows over 60% homology with hypothetical proteins isolated from polydnaviruses in two other Cotesia wasps, indicating that these proteins may also be C-type lectins and that a novel polydnavirus lectin family exists in Cotesia-associated bracoviruses. CrV3 is probably interacting with components in host hemolymph, resulting in suppression of the Pieris immune response. The high similarity of CrV3 with invertebrate lectins, as opposed to those from viruses, may indicate that some bracovirus functions were acquired from their hosts.
Does the Addition of Tramadol and Ketamine to Ropivacaine Prolong the Axillary Brachial Plexus Block?

Background and Objectives. A prospective, randomized, controlled, double-blind clinical trial to assess the effect of tramadol and ketamine, 50 mg, added to ropivacaine in brachial plexus anesthesia. Methods. Thirty-six ASA physical statuses I and II patients, between 18 and 60 years of age, scheduled for forearm and hand surgery under axillary brachial plexus block, were allocated to 3 groups. Group R received 0.375% ropivacaine in 40 mL, group RT received 0.375% ropivacaine in 40 mL with 50 mg tramadol, and group RK received 0.375% ropivacaine in 40 mL with 50 mg ketamine for axillary brachial plexus block. The onset times and the duration of sensory and motor blocks, duration of analgesia, hemodynamic parameters, and adverse events (nausea, vomiting, and feeling uncomfortable) were recorded. Results. The onset time of sensorial block was the fastest in ropivacaine + tramadol group. Duration of sensorial and motor block was the shortest in the ropivacaine + tramadol group. Duration of analgesia was significantly longer in ropivacaine + tramadol group. Conclusion. We conclude that when added to brachial plexus analgesia at a dose of 50 mg, tramadol extends the onset and duration time of the block and improves the quality of postoperative analgesia without any side effects.