Miracle-dz/UniFAR-FTUs huggingface dataset:
Prostaglandin E2 and F2 alpha reduces urea reabsorption from the rat collecting duct.

The present study examines the possibility that prostaglandins affect the renal tubular handling of urea. Meclofenamate (1 mg.kg-1.h-1), an inhibitor of prostaglandin synthesis, decreased fractional urea clearance from 86 to 67%, increased urine osmolality, and decreased the fractional excretion of water in rats undergoing a hypertonic sodium chloride diuresis. The percentage of [14C]urea microinjected into distal convoluted tubules that was recovered in urine fell from 75 to 64% (P less than 0.01) after meclofenamate. The fraction of injected urea excreted like inulin (direct recovery) was reduced from 20 to 8% (P less than 0.0001) by meclofenamate. Addition of PGE2 (1.2 or 89 pmol) or PGF2 alpha (1.4 pmol) to the microinjectate returned the urinary recovery of the microinjected [14C]urea to the control level, but PGA2 (3 pmol) did not. Direct urea recovery was doubled by PGE2 or PGF2 alpha. These results indicate that prostaglandin E2 and F2 alpha inhibit the reabsorption of urea in the collecting duct. Prostaglandins may participate in the renal concentrating mechanism by altering the inner medullary influx of urea.
Damage detection from aerial images via convolutional neural networks

This paper explores the effective use of Convolutional Neural Networks (CNNs) in the context of washed-away building detection from pre- and post-tsunami aerial images. To this end, we compile a dedicated, labeled aerial image dataset to construct models that classify whether a building is washed-away. Each datum in the set is a pair of pre- and post-tsunami image patches and encompasses a target building at the center of the patch. Using this dataset, we comprehensively evaluate CNNs from a practical-application viewpoint, e.g., input scenarios (pre-tsunami images are not always available), input scales (building size varies) and different configurations for CNNs. The experimental results show that our CNN-based washed-away detection system achieves 94–96% classification accuracy across all conditions, indicating the promising applicability of CNNs for washed-away building detection.
Trends in Antifungal Susceptibility of Candida spp. Isolated from Pediatric and Adult Patients with Bloodstream Infections: SENTRY Antimicrobial Surveillance Program, 1997 to 2000

ABSTRACT From 1 January 1997 through 31 December 2000, 2,047 bloodstream infections (BSIs) due to Candida spp. were reported from hospitals in the United States, Canada, Latin America, and Europe participating in the SENTRY Antifungal Surveillance Program. Among individuals in four age groups (≤1, 2 to 15, 16 to 64, and ≥65 years) Candida albicans was the most common species, causing 60, 55, 55, and 50% of infections, respectively. C. glabrata caused 17 to 23% of BSIs in those ages 16 to 64 and ≥65 years, whereas it caused only 3% of BSIs in the individuals in the two younger age groups (P < 0.001). C. parapsilosis (which caused 21 to 24% of BSIs) and C. tropicalis (which caused 7 to 10% of BSIs) were more common than C. glabrata in individuals ages ≤1 year and 2 to 15 years. Isolates of Candida spp. showed a trend of decreasing susceptibility to fluconazole, itraconazole, and amphotericin B with increasing patient age (P ≤ 0.01). None of the C. glabrata isolates from individuals ≤1 year old were resistant to fluconazole, whereas they made up 5 to 9% of isolates from individuals ages 16 to 64 and ≥65 years. Isolates of C. tropicalis from patients ≤1 year old were more susceptible to flucytosine (MIC at which 90% of isolates are inhibited [MIC90], 0.5 μg/ml; 0% resistant isolates) than those from patients ≥65 years old (MIC90, 32 μg/ml; 11% resistant isolates). The investigational triazoles posaconazole, ravuconazole, and voriconazole were all highly active against all species of Candida from individuals in all age groups. These data demonstrate differences in the species distributions of pathogens and differences in antifungal resistance among isolates from individuals in the pediatric and adult age groups. Ongoing surveillance will enhance efforts to limit the extent of antifungal resistance in individuals in various age groups.
Apprentissage automatique pour la détection d'anomalies dans les données ouvertes : application à la cartographie. (Satellite images analysis for anomaly detection in open geographical data)

Dans cette these nous etudions le probleme de detection d’anomalies dans les donnees ouvertes utilisees par l’entreprise Qucit ; aussi bien les donnees metiers de ses clients, que celles permettant de les contextualiser. Dans un premier temps, nous nous sommes interesses a la detection de velos defectueux au sein des donnees de trajets du systeme de velo en libre service de New York. Nous cherchons des donnees refletant une anomalie dans la realite. Des caracteristiques decrivant le comportement de chaque velo observe sont partitionnes. Les comportements anormaux sont extraits depuis ce partitionnement et compares aux rapports mensuels indiquant le nombre de velos repares ; c’est un probleme d’apprentissage a sortie agregee. Les resultats de ce premier travail se sont averes insatisfaisant en raison de la pauvrete des donnees. Ce premier volet des travaux a ensuite laisse place a une problematique tournee vers la detection de bâtiments au sein d’images satellites. Nous cherchons des anomalies dans les donnees geographiques qui ne refletent pas la realite. Nous proposons une methode de fusion de modeles de segmentation ameliorant la metrique d’erreur jusqu’a +7% par rapport a la methode standard. Nous evaluons la robustesse de notre modele face a la suppression de bâtiments dans les etiquettes, afin de determiner a quel point les omissions sont susceptibles d’en alterer les resultats. Ce type de bruit est communement rencontre au sein des donnees OpenStreetMap, regulierement utilisees par Qucit, et la robustesse observee indique qu’il pourrait etre corrige.
Software Approaches to Manage Resource Tradeoffs of Power and Energy Constrained Applications

Power and energy efficiency have become an increasingly important design metric for a wide spectrum of computing devices. Battery efficiency, which requires a mixture of energy and power efficiency, is exceedingly important especially since there have been no groundbreaking advances in battery capacity recently. The need for energy and power efficiency stretches from small embedded devices to portable computers to large scale data centers. The projected future of computing demand, referred to as exascale computing, demands that researchers find ways to perform exaFLOPs of computation at a power bound much lower than would be required by simply scaling today’s standards. There is a large body of work on power and energy efficiency for a wide range of applications and at different levels of abstraction. However, there is a lack of work studying the nuances of different tradeoffs that arise when operating under a power/energy budget. Moreover, there is no work on constructing a generalized model of applications running under power/energy constraints, which allows the designer to optimize their resource consumption, be it power, energy, time, bandwidth, or space. There is need for an efficient model that can provide bounds on the optimality of an application’s resource consumption, becoming a basis against which online resource management heuristics can be measured. In this thesis, we tackle the problem of managing resource tradeoffs of power/energy constrained applications. We begin by studying the nuances of power/energy tradeoffs with the response time and throughput of stream processing applications. We then study the power performance tradeoff of batch processing applications to identify a power configuration that maximizes performance under a power bound. Next, we study the tradeoff of power/energy with network bandwidth and precision. Finally, we study how to combine tradeoffs into a generalized model of applications running under resource constraints. The work in this thesis presents detailed studies of the power/energy tradeoff with response time, throughput, performance, network bandwidth, and precision of stream and batch processing applications. To that end, we present an adaptive algorithm that manages stream processing tradeoffs of response time and throughput at the CPU level. At the tasklevel, we present an online heuristic that adaptively distributes bounded power in a cluster to improve performance, as well as an offline approach to optimally bound performance. We demonstrate how power can be used to reduce bandwidth bottlenecks and extend our offline approach to model bandwidth tradeoffs. Moreover, we present a tool that identifies parts of a program that can be downgraded in precision with minimal impact on accuracy, and maximal impact on energy consumption. Finally, we combine all the above tradeoffs into a flexible model that is efficient to solve and allows for bounding and/or optimizing the consumption of different resources.
Dose dependence of the excision of ultraviolet-induced pyrimidine dimers from nuclear deoxyribonucleic acids of haploid and diploid Saccharomyces cerevisiae

The yield of ultraviolet-induced dimers is similar for a fixed dose in both haploid and diploid Saccharomyces cerevisiae. The excision of these photo-products from the nuclear deoxyribonucleic acids of cells of both ploidies after ultraviolet incident doses of 2 times 10-3 to 4 times 10-3 ergs/mm2 decreased with the corresponding increasing dose. Postirradiation incubation in saline followed by a further incubation in nutrient medium increases the excision as compared to that seen in either nutrient medium or saline alone. Previous data regarding both pyrimidine dimer removal and the survival of haploid and diploid cells after ultraviolet irradiation and either immediate or delayed plating are discussed.
Audio-visual speech activity detection in a two-speaker scenario incorporating depth information from a profile or frontal view

Motivated by increasing popularity of depth visual sensors, such as the Kinect device, we investigate the utility of depth information in audio-visual speech activity detection. A two-subject scenario is assumed, allowing to also consider speech overlap. Two sensory setups are employed, where depth video captures either a frontal or profile view of the subjects, and is subsequently combined with the corresponding planar video and audio streams. Further, multi-view fusion is regarded, using audio and planar video from a sensor at the complementary view setup. Support vector machines provide temporal speech activity classification for each visually detected subject, fusing the available modality streams. Classification results are further combined to yield speaker diarization. Experiments are reported on a suitable audio-visual corpus recorded by two Kinects. Results demonstrate the benefits of depth information, particularly in the frontal depth view setup, reducing speech activity detection and speaker diarization errors over systems that ignore it.
A dynamic architecture for runtime adaptation of service-based applications

Service-Based Applications (SBA) offer flexible functionalities in wide range of environments. Therefore they should dynamically adapt to different quality concerns such as security, performance, etc. For example, we may add particular delivery service for the golden customers, or provide secure services for the specific partners, or change service invocation based on context information. Unlike other adaptation methods which substitute a faulty service or negotiate for service level objectives, we modify the architecture of SBA, that is, the underlying services structure and the runtime services implementation. In this regard, we propose a reflective architecture which holds business and adaptation knowledge in the Meta level and implements service behaviours in the Base level. The knowledge is modelled in the form of Meta states and Meta transitions. We benefit from Reflective Visitor pattern to materialize an abstract service in different concrete implementations and manipulate them at runtime. Each service implementation fulfils a specific quality concern, so it is possible to delegate user requests to appropriate implementation instead of reselecting a new service which is a time consuming strategy. We used Jmeter load simulator and real-world Quality of Service (QoS) dataset to measure the architecture efficiency. Also, we characterized our work in comparison with related studies according to the European Software Services and Systems Network (S-CUBE) adaptation taxonomy.
Adaptive Fuzzy Hysteresis Internal Model Tracking Control of Piezoelectric Actuators With Nanoscale Application

In this paper, a novel Takagi-Sugeno (T-S) fuzzy-system-based model is proposed for hysteresis in piezoelectric actuators. The antecedent and consequent structures of the developed fuzzy hysteresis model (FHM) can be identified online through uniform partition approach and recursive least squares (RLS) algorithm, respectively. With respect to the controller design, the inverse of FHM is used to develop a fuzzy internal model (FIM) controller. Decreasing the hysteresis effect, the FIM controller has a good performance of high-speed trajectory tracking. To achieve nanometer-scale tracking precision, the novel fuzzy adaptive internal model (FAIM) controller is uniquely developed. Based on real-time input and output data to update FHM, the FAIM controller is capable of compensating for the hysteresis effect of the piezoelectric actuator in real time. Finally, the experimental results for two cases are shown: the first is with 50 Hz and the other with multiple-frequency (50 + 25 Hz) sinusoidal trajectories tracking that demonstrate the efficiency of the proposed controllers. Especially, being 0.32% of the maximum desired displacement, the maximum error of 50-Hz sinusoidal tracking is greatly reduced to 6 nm. This result clearly indicates the nanometer-scale tracking performance of the novel FAIM controller.
Adaptive machine learning methods for event related potential-based brain computer interfaces. (Méthodes adaptatives d'apprentissage pour des interfaces cerveau-ordinateur basées sur les potentiels évoqués)

Les interfaces cerveau machine (BCI pour Brain Computer Interfaces) non invasives permettent a leur utilisateur de controler une machine par la pensee. Ce dernier doit porter un dispositif d'acquisition de signaux electroencephalographiques (EEG), qui sont dotes d'un rapport signal sur bruit assez faible ; a ceci s'ajoute l’importante variabilite tant a travers les sessions d'utilisation qu’a travers les utilisateurs. Par consequent, la calibration du BCI est souvent necessaire avant son utilisation. Cette these etudie les sources de cette variabilite, dans le but d'explorer, concevoir, et implementer des methodes d'autocalibration. Nous etudions la variabilite des potentiels evoques, particulierement une composante tardive appelee P300. Nous nous penchons sur trois methodes d’apprentissage par transfert : la Geometrie Riemannienne, le Transport Optimal, et l’apprentissage ensembliste. Nous proposons un modele de l'EEG qui tient compte de la variabilite. Les parametres resultants de nos analyses nous servent a calibrer ce modele et a simuler une base de donnees, qui nous sert a evaluer la performance des methodes d’apprentissage par transfert. Puis ces methodes sont combinees et appliquees a des donnees experimentales. Nous proposons une methode de classification basee sur le Transport Optimal dont nous evaluons la performance. Ensuite, nous introduisons un marqueur de separabilite qui nous permet de combiner Geometrie Riemannienne, Transport Optimal et apprentissage ensembliste. La combinaison de plusieurs methodes d’apprentissage par transfert nous permet d’obtenir un classifieur qui s’affranchit des differentes sources de variabilite des signaux EEG.
Evaluating the Impact of Alternative Dependency Graph Encodings on Solving Event Extraction Tasks

In state-of-the-art approaches to information extraction (IE), dependency graphs constitute the fundamental data structure for syntactic structuring and subsequent knowledge elicitation from natural language documents. The top-performing systems in the BioNLP 2009 Shared Task on Event Extraction all shared the idea to use dependency structures generated by a variety of parsers --- either directly or in some converted manner --- and optionally modified their output to fit the special needs of IE. As there are systematic differences between various dependency representations being used in this competition, we scrutinize on different encoding styles for dependency information and their possible impact on solving several IE tasks. After assessing more or less established dependency representations such as the Stanford and CoNLL-X dependencies, we will then focus on trimming operations that pave the way to more effective IE. Our evaluation study covers data from a number of constituency- and dependency-based parsers and provides experimental evidence which dependency representations are particularly beneficial for the event extraction task. Based on empirical findings from our study we were able to achieve the performance of 57.2% F-score on the development data set of the BioNLP Shared Task 2009.
Using a Vibrotactile Biofeedback Device to Augment Foot Pressure During Walking in Healthy Older Adults: A Brief Report

Human movement based on sensory control is significant to motor task performance. Thus, impairments to sensory input significantly limit feedback-type motor control. The present study introduces a vibrotactile biofeedback (BF) system which augments information regarding the user’s foot pressure to enhance gait performance. The effects of the proposed system on the gait patterns of healthy older adults and on the cognitive load during gait were evaluated; these factors are essential to clarify feasibility of the device in real-life settings. The primary task of our study was to evaluate gait along with a cognitively demanding activity in 10 healthy older adults. Regarding kinematic and kinetic data in the BF condition, the subjects had significantly increased ankle dorsiflexion during the heel contact phase in the sagittal plane and marginally increased foot pressure at the toe-off and stride length. However, such kinematic and kinetic changes were not attributed to the increased walking speed. In addition, cognitive performance (i.e., the number of correct answers) was significantly decreased in participants during gait measurements in the BF condition. These data suggest that the system had the potential for modifying the kinematic and kinetic patterns during walking but not the more comprehensive walking performance in older adults. Moreover, the device appears to place a cognitive load on older adults. This short report provides crucial primary data that would help in designing successful sensory augmentation devices and further research on a BF system.
Cellular Aspects of Muscle Specialization Demonstrate Genotype – Phenotype Interaction Effects in Athletes

Introduction Gene polymorphisms are associated with athletic phenotypes relying on maximal or continued power production and affect the specialization of skeletal muscle composition with endurance or strength training of untrained subjects. We tested whether prominent polymorphisms in genes for angiotensin converting enzyme (ACE), tenascin-C (TNC), and actinin-3 (ACTN3) are associated with the differentiation of cellular hallmarks of muscle metabolism and contraction in high level athletes. Methods Muscle biopsies were collected from m. vastus lateralis of three distinct phenotypes; endurance athletes (n = 29), power athletes (n = 17), and untrained non-athletes (n = 63). Metabolism-, and contraction-related cellular parameters (such as capillary-to-fiber ratio, capillary length density, volume densities of mitochondria and intramyocellular lipid, fiber mean cross sectional area (MCSA) and volume densities of myofibrils) and the volume densities of sarcoplasma were analyzed by quantitative electron microscopy of the biopsies. Gene polymorphisms of ACE (I/D (insertion/deletion), rs1799752), TNC (A/T, rs2104772), and ACTN3 (C/T, rs1815739) were determined using high-resolution melting polymerase chain reaction (HRM-PCR). Genotype distribution was assessed using Chi2 tests. Genotype and phenotype effects were analyzed by univariate or multivariate analysis of variance and post hoc test of Fisher. P-values below 0.05 were considered statistically significant. Results The athletes demonstrated the specialization of metabolism- and contraction-related cellular parameters. Differences in cellular parameters could be identified for genotypes rs1799752 and rs2104772, and localized post hoc when taking the interaction with the phenotype into account. Between endurance and power athletes these concerned effects on capillary length density for rs1799752 and rs2104772, fiber type distribution and volume densities of myofibrils (rs1799752), and MSCA (rs2104772). Endurance athletes carrying the I-allele of rs1799752 demonstrated 50%-higher volume densities of mitochondria and sarcoplasma, when power athletes that carried only the D-allele showed the highest fiber MCSAs and a lower percentage of slow type muscle fibers. Discussion ACE and tenascin-C gene polymorphisms are associated with differences in cellular aspects of muscle metabolism and contraction in specifically-trained high level athletes. Quantitative differences in muscle fiber type distribution and composition, and capillarization in knee extensor muscle explain, in part, identified associations of the insertion/deletion genotypes of ACE (rs1799752) with endurance- and power-type Sports.
An Encoding Scheme with Constituent Codes Optimization for Polar Code-Aim to Reduce the Decoding Latency

This paper proposes a polar code construction scheme that reduces constituent-code supplemented decoding latency. Constituent codes are the sub-codewords with specific patterns. They are used to accelerate the successive cancellation decoding process of polar code without any performance degradation. We modify the traditional construction approach to yield increased number of desirable constituent codes that speeds the decoding process. For (n,k) polar code, instead of directly setting the k best and (n-k) worst bits to the information bits and frozen bits, respectively, we swap the locations of some information and frozen bits carefully according to the qualities of their equivalent channels. We conducted the simulation of 1024 and 2048 bits length polar codes with multiple rates and analyzed the decoding latency for various length codes. The numerical results show that the proposed construction scheme generally is able to achieve at least around 20% latency deduction with an negligible loss in gain with carefully selected optimization threshold.
Eurographics/ Ieee-vgtc Symposium on Visualization (2006) Integrating Dynamic Deformations into Interactive Volume Visualization

Non-linear geometric deformation (or warping) is a useful tool for working with volumes. Unfortunately, the computational expense of performing the resampling needed to implement volume deformation has precluded its use in interactive applications. In this paper, we show how non-linear deformations can be integrated into interactive volume visualization allowing for dynamic deformations to be used along with interactive viewing, exploration, and manipulation tools. We describe how hardware assisted volume rendering can be adapted to resample volume deformations, leveraging programmable shaders to compute deformations and the local coordinate transformations required for shading effects. We describe how volume interaction techniques, such as ray picking and plane slicing, can be used in concert with our deformation methods. Our methods extend to simultaneous display of multiple volumes enabling comparisons. We demonstrate dynamic volume deformation at interactive rates on commodity hardware for interactive deformation control, animated deformations, and volume widgets.