Miracle-dz/UniFAR-FTUs huggingface dataset:
Urinary Elimination of Bile Acid Glucuronides under Severe Cholestatic Situations: Contribution of Hepatic and Renal Glucuronidation Reactions

Biliary obstruction, a severe cholestatic complication, causes accumulation of toxic bile acids (BAs) in liver cells. Glucuronidation, catalyzed by UDP-glucuronosyltransferase (UGT) enzymes, detoxifies cholestatic BAs. Using liquid chromatography coupled to tandem mass spectrometry, 11 BA glucuronide (-G) species were quantified in prebiliary and postbiliary stenting serum and urine samples from 17 patients with biliary obstruction. Stenting caused glucuronide- and fluid-specific changes in BA-G levels and BA-G/BA metabolic ratios. In vitro glucuronidation assays with human liver and kidney microsomes revealed that even if renal enzymes generally displayed lower KM values, the two tissues shared similar glucuronidation capacities for BAs. By contrast, major differences between the two tissues were observed when four human BA-conjugating UGTs 1A3, 1A4, 2B4, and 2B7 were analyzed for mRNA and protein levels. Notably, the BA-24G producing UGT1A3 enzyme, abundant in the liver, was not detected in kidney microsomes. In conclusion, the circulating and urinary BA-G profiles are hugely impacted under severe cholestasis. The similar BA-glucuronidating abilities of hepatic and renal extracts suggest that both the liver and kidney may contribute to the urine BA-G pool.
Similar Epitope Specificities of IgG and IgA Antibodies Elicited by Ad26 Vector Prime, Env Protein Boost Immunizations in Rhesus Monkeys

ABSTRACT Vaccine-elicited immunoglobulin G (IgG) has been shown to be important for protection against simian-human immunodeficiency virus (SHIV) infection in rhesus monkeys. However, it remains unclear whether vaccine-elicited IgA responses are beneficial or detrimental for protection. In this study, we evaluated the kinetics, magnitude, breadth, and linear epitope specificities of vaccine-elicited IgG and IgA responses in serum and mucosal secretions following intramuscular immunization with adenovirus 26 (Ad26) prime, Env protein boost vaccination regimens. The systemic and mucosal antibody responses exhibited kinetics similar to those of the serum antibody responses but lower titers than the serum antibody responses. Moreover, the IgG and IgA responses were correlated, both in terms of the magnitude of the responses and in terms of the antibody specificities against linear human immunodeficiency virus type 1 (HIV-1) Env, Gag, and Pol epitopes. These data suggest that IgG and IgA responses are highly coordinated in both peripheral blood and mucosal compartments following Ad26/Env vaccination in rhesus monkeys. IMPORTANCE Vaccine-elicited IgG responses are important for protection against simian-human immunodeficiency virus (SHIV) infection in nonhuman primates. However, much less is known about the role and function of IgA, despite it being the predominant antibody in mucosal sites. There is debate as to whether HIV-1-specific IgA responses are beneficial or detrimental, since serum anti-Env IgA titers were shown to be inversely correlated with protection in the RV144 clinical trial. We thus assessed vaccine-elicited IgG and IgA antibody responses in peripheral blood and mucosal secretions following vaccination with the Ad26/Env vaccine.
Image resolution impact analysis on pedestrian detection in smart cities surveillance

In the paradigm of smart cities1, video surveillance is becoming a widely-applied technology to help improve the quality of human life in the era of digital living, where pedestrian detection is one of the key components for people-centered smart cities applications including wellbeing, security, traffic guiding, and unmanned vehicles, etc. While so far most surveillance cameras are of low quality resolution for cost-saving reasons, the impact of the image resolution on detection accuracy becomes a concerned issue. Though lower resolution can save the cost as well as the processing time, it has not been clearly reported how the resolution can impact on the detection accuracy. In this paper, we investigate the limit of low-resolution cameras with regards to the accuracy of the pedestrian detection, and experimentally demonstrate its impact on the most widely-applied HOG-SVM pedestrian detector, which is a combination of Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for pedestrian detection. From our experiments, it is found that there is an optimal resolution to balance between speed and accuracy, while we show in our experiments that the resolution has apparent influence on both the accuracy and the computing time.
Pseudonym-Varying Scheduling Scheme Protection Interval Setup Prediction Table Construction Entropy-based Commitment Generation STM Broadcast STM Verification Fast Broadcast Authentication Protocol

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatialtemporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatialtemporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonymvarying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.
Sugar Intake and Expectation Effects on Cognition and Mood

Glucose intake has been found to improve some aspects of cognitive performance; however, results are often inconsistent. This inconsistency may be related to expectations surrounding glucose, which can have strong effects on performance outcomes. The present study evaluated the independent and interactive effects of acute sugar intake, in the form of high-fructose corn syrup (HFCS), and sugar expectancies on cognitive performance and mood. One hundred five healthy young adults were randomized according to sugar intake and expectation: consumed-sugar/told-sugar, consumed-sugar/told-no-sugar, consumed-no-sugar/told-sugar, and consumed-no-sugar/told-no-sugar. Thirty minutes after sugar or no-sugar intake, participants completed the Profile of Mood States and a battery of cognitive tests, including immediate and delayed recall, the Stroop test, n-back task, and continuous performance task. Tension increased following the expectation of consuming sugar, regardless of sugar consumption (p < .05). On the continuous performance task, accuracy and sensitivity were higher (ps < .05) and false alarm rate was lower (p < .05) following sugar than no sugar intake. No effects of sugar intake or expectation were found for any other mood or cognitive measure (ps > .05). The findings suggest that sugar intake in the form of HFCS may benefit certain cognitive processes, such as those that require sustained attention, but that the expectation of sugar intake is not sufficient to produce such benefits.
Workload Generators for Web-Based Systems: Characteristics, Current Status, and Challenges

The growth and evolution of the World Wide Web (WWW) has been rapid over the last ten years and this has been caused mainly by factors such as the social Web and mobile technology. This growth, which presupposes the satisfaction of millions of users accessing Web applications with an adequate quality of service, requires continuous changes in the infrastructure to improve user experience or to handle new demands. Therefore, studies of Web-based systems aimed at comparing different hardware infrastructures, detecting system bottlenecks, provisioning hardware resources, making capacity planning tests, or software testability, are a matter of huge interest. However, the new trends in the WWW have brought new types of user demands and interactions that produce complex workload patterns. These patterns must be exhaustively studied and considered when designing helpful workload generators able to produce representative traces of the current reality. This survey is aimed at providing a useful guide for researchers of the Web, social networking, and other Internet related issues, regarding the main points and concerns about workload generation for Web-based systems. This paper reviews the predominant characteristics and attributes that define Web workloads, including the special cases of other types of Web applications (e.g., blogs, online social network platforms, and video-sharing services). It also identifies the main challenges for the next generation of Web workload generators, and explores current approaches and solutions suggested in recent works.
Combining Explicit and Implicit Semantic Similarity Information for Word Embeddings

In this paper, we propose a new framework that combines both explicit and implicit semantic similarity information for training word embeddings. While the former determines the similarity degree between two words explicitly, the latter reflects word similarities implicitly through contextual and relational similarity. We also propose a novel concept called relative similarity in vocabulary, which deliberately utilizes explicit semantic similarity information (word's definition in particular) for word embeddings. We conduct experimental studies on various word similarity and word categorization datasets. The results show that our framework compares favorably to a number of state-of-the-art approaches for word embeddings.
An efficient multi-frame dynamic search range motion estimation for H.264

H.264/AVC achieves higher compression efficiency than previous video coding standards. However, this comes at the cost of increased complexity due to the use of variable block size motion estimation and long-term memory motion compensated prediction (LTMCP). In this paper, an efficient multi-frame dynamic search range motion estimation algorithm is proposed. This algorithm can adjust the spatial search range and temporal search range according to the video content dynamically. This algorithm can be on the top of many other fast motion estimation (Fast ME) algorithms. Compared with the constant search range scheme used by multi-frame UMHexagonS algorithm, the proposed algorithm can be 4.86 time faster, with negligible degradation of video quality.
Fischer-Tropsch Synthesis of the Promoted Co/ZSM-5 Hybrid Catalysts for the Production of Gasoline Range Hydrocarbons

Fischer-Tropsch synthesis (FTS) reaction for the direct production of gasoline range hydrocarbons (C5-C9) from syngas was investigated on Ru, Pt, and La promoted Co/ZSM-5 (Si/Al = 25) catalysts. The hybrid catalysts were characterized by BET surface area, XRD, H2-TPR, NH3-TPD and XPS analyses. These physico-chemical properties were correlated with activity and selectivity of the catalysts. The promoted Co/ZSM-5 hybrid catalysts were found to be superior to the unpromoted Co/ZSM-5 catalyst in terms of better C5-C9 selectivity. Pt-Co/ZSM-5 exhibited the highest catalytic activity because of the small cobalt particle size.
Noisy Power Method with Grassmann Average

The power method is a simple and efficient algorithm for finding the top k singular vectors of any input matrix. In practice, noise matrices could be added to the input matrix at each iteration of the power method, and the convergence behavior of the algorithm is hard to guarantee. The convergence behavior of the noisy power method is understood only for the cases when the noise level (the spectral norm of noise matrices) is bellow a threshold and the noisy power method cannot extract the exact top k singular vectors because of the noise matrices. We propose a Grassmann average function which can make the noisy power method converge to the exact top k singular vectors and an efficient algorithm that can approximate the Grassmann average with a much less computational cost.
The Role of Organisational Phenomena in Software Cost Estimation: A Case Study of Supporting and Hindering Factors

Despite the fact that many researchers and practitioners agree that organisational issues are equally important as technical issues from the software cost estimation (SCE) success point of view, most of the research focus has been put on the development of methods, whereas organisational factors have received surprisingly little academic scrutiny. This study aims to identify organisational factors that either support or hinder meaningful SCE, identifying their impact on estimation success. Top management’s role is specifically addressed. The study takes a qualitative and explorative case study based approach. In total, 18 semi-structured interviews aided the study of three projects in three organisations. Hence, the transferability of the results is limited. The results suggest that the role of the top management is important in creating prerequisites for meaningful estimation, but their day-to-day participation is not required for successful estimation. Top management may also induce undesired distortion in estimation. Estimation maturity and estimation success seem to have an interrelationship with software process maturity, but there seem to be no significant individual organisational factors, which alone would make estimation successful. Our results validate several distortions and biases reported in the previous studies, and show the SCE research focus has remained on methodologies and technical issues.
Genetic algorithms and the vVariance of fitness

This paper presents a method for calculating the variance of schema fitness using Walsh transforms. The computation is important for understanding the performance of genetic a1gorithms (GAS), because most GAS depend on the sampling of schema fitness in populations of modest size, and the variance of schema fitness is a primary source of noise that can prevent proper evaluation of building blocks, thereby causing convergence to other-than-global optima. The paper also applies these calculations to the sizing of GA populations and to the adjustment of the schema theorem to account for fitness variance; the extension of the variance computation to nonuniform populations is also considered. Taken together, this paper may be viewed as a step along the road to rigorous convergence proofs for recombinative genetic algorithms.
Contemporary applications of intra-aortic balloon counterpulsation for cardiogenic shock: a "real world" experience.

Background Intra-aortic balloon pump (IABP) had a pivotal role in the therapy of cardiogenic shock (CS), but recent studies have questioned its effects on patients' outcome. Aim of this study is the description of a "real world" series of patients in which IABP was used as a primary mechanical circulatory support (MCS). Methods All patients who received IABP in our institution during 1 year were prospectively enrolled in our study. The outcomes were: ICU mortality, length of ICU stay, factors associated with mortality and complications of IABP. A logistic regression model was developed to estimate the effect of several risk factors on mortality. Results A total of 119 patients were enrolled, 54 patients underwent IABP placement for CS unrelated to cardiac surgery (medical CS) and 65 after open-heart surgery. There was no significant difference for mortality between medical CS [12/54 (22.2%)] and cardiac surgery [7/65 (10.8%)] (P=0.09). The morbidity rate related to IABP was 11.3%. Multivariable analysis identified AKI (OR =9.3; 95% CI, 2.0-40.0; P=0.004), inotropic score at the time of IABP implantation (OR =1.06; 95% CI, 1.01-1.11; P=0.009) and history of myocardial revascularization (OR =4.7; 95% CI, 1.1-20.2; P=0.036) as independent predictors for early death (P<0.05). A ROC curve analysis for inotropic score at time of implantation and mortality was performed in the overall population [AUC=0.78 (95% CI, 0.66-0.90)]. A cutoff of 20 has a specificity =72% and sensitivity=74% in this population. Conclusions Mortality is similar in medical and postcardiotomy CS. The benefits of IABP are limited if the amount of inotropes and the severity of shock are too high.
Course tracking and contour extraction of retinal vessels from color fundus photographs: most efficient use of steerable filters for model-based image analysis

To support ophthalmologists in their daily routine and enable the quantitative assessment of vascular changes in color fundus photographs vessel extraction techniques have been improved. Within a model based approach steerable filters have been tested for efficient and precise segmentation of the vessel tree. The global model comprises the detection of the optic disc, the finding of starting points close to the optic disc, the tracking of the vessel course, the extraction of the vessel contour and the identification of branching points. This helps evaluating image quality and pathological changes to the retina and thus, improves diagnosis and therapy. The optic disc location is estimated and then more precisely extracted with the help of a hierarchical filter scheme based on first- order gaussian kernels at varying orientations. Vessel points are automatically identified around the optic disc and the vessel course is tracked in the actual direction by second-order gaussian kernels at varying orientations and scales. Using this backbone, differently oriented first- order gaussian kernels approximate the vessel contour. Thus, the direction and diameter of each vessel segment are determined. Steerable filters enable most efficient implementation. The developed methods have been applied to color fundus photographs showing different levels of diabetic retinopathy.
Speaker-Aware MultiTask Learning for Automatic Speech Recognition

Overfitting is a commonly met issue in automatic speech recognition and is especially impacting when the amount of training data is limited. In order to address this problem, this article investigates acoustic modeling through Multi-Task Learning, with two speaker-related auxiliary tasks. Multi-Task Learning is a regularization method which aims at improving the network’s generalization ability, by training a unique model to solve several different, but related tasks. In this article, two auxiliary tasks are jointly examined. On the one hand, we consider speaker classification as an auxiliary task by training the acoustic model to recognize the speaker, or find the closest one inside the training set. On the other hand, the acoustic model is also trained to extract i-vectors from the standard acoustic features. I-Vectors are efficiently applied in the speaker identification community in order to characterize a speaker and its acoustic environment. The core idea of using these auxiliary tasks is to give the network an additional inter-speaker awareness, and thus, reduce overfitting. We investigate this MultiTask Learning setup on the TIMIT database, while the acoustic modeling is performed using a Recurrent Neural Network with Long Short-Term Memory cells.
Risk of Microvascular Complications and Macrovascular Risk Factors in Early-Onset Type 1 Diabetes after at Least 10 Years Duration: An Analysis of Three Population-Based Cross-Sectional Surveys in Germany between 2009 and 2016

Aims To estimate the risk of microvascular complications and macrovascular risk factors among persons with early-onset (diagnosed at ages 0 to <5 years) and long-duration type 1 diabetes and determine temporal trends and associations with potential predictors. Methods We conducted three population-based cross-sectional surveys in Germany (N = 1789) to obtain information on exposures and five outcomes (retinopathy, nephropathy, dyslipidemia, hypertension, and a composite endpoint combining all four outcomes). For each outcome, log-binomial spline regression was applied to estimate the risk and dose-response relationship with diabetes duration and exposures. Results The risk for microvascular complications increased after 14 years since diabetes diagnosis whereas dyslipidemia and hypertension were already prevalent at 10 years. The 15-year risk (95% confidence interval) of the composite endpoint for female and male patients was 22.9% (18.8%–27.9%) and 19.2% (15.5%–23.8%), respectively. Temporal trends suggested a decreasing risk between 2009 and 2016. Glycemic control, lifestyle-related factors, and SES, but not health care-related factors, were associated with the risk of the composite endpoint. Conclusions In early-onset type 1 diabetes, there exists a considerable risk of complications and comorbidities already in young ages. Future research should focus on prevention of diabetic complications in young patients and clarification of pathways of the associations found.
Draft Genome Sequence of Bacillus velezensis PEBA20, a Strain with a Plant Growth-Promoting Effect and Biocontrol Potential

ABSTRACT Bacillus velezensis PEBA20 is a poplar endophyte with biocontrol activities and plant growth-promoting effects. The genome of B. velezensis PEBA20 was sequenced and the draft genome assembled, with a length of 4,249,176 bp and 4,487 genes.
Word Level Language Identification system for Konkani-English Code-Mixed Social Media Text (CMST)

In this paper, we present an pure logic study on problem of word- level language identification for Konkani- English Code-Mixed Social Media Text (CMST). we describe a new dataset which contains of more than thousands posts from Facebook posts that exhibit code mixing between Konkani-English. To the best of our knowledge, our work is the first attempt at the creation of a linguistic resource for this language pair which will be made public and developed a language identification System for Konkani-English language pair. Using this Konkani-English tagged dataset we have carried out experiment on language detection at word level. We have used Different ways to solve language detection task, unsupervised dictionary-based detection technique, supervised Language identification of word level using sequence labelling using Conditional Random Fields based models, SVM, Random Forest.
Application of image processing techniques for contrast enhancement in dense breast digital mammograms

Dense breasts, that usually are characteristic of women less than 40 years old, difficult many times early detection of breast cancer. In this work we present the application of some image processing techniques intended to enhance the contrast in dense breast images, regarding the detection of clustered microcalcifications. The procedure was, firstly, determining in the literature the main techniques used for mammographic images contrast enhancement. The results indicate that, in general: (1) as expected, the overall performance of the CAD scheme for clusters detection decreased when applied exclusively to dense breast images, compared to the application to a set of images without this characteristic; (2) most of the techniques for contrast enhancement used successfully in generic mammography images databases are not able to enhance structures of athirst in databases formed only by dense breasts images, due to the very poor contrast between microcalcifications, for example, and other tissues. These features should stress, therefore, the need of developing a methodology specifically for this type of images in order to provide better conditions to the detection of breast suspicious structures in these group of women.