Miracle-dz/UniFAR-FTUs huggingface dataset:
Bevacizumab beyond first progression is associated with prolonged overall survival in metastatic colorectal cancer: results from a large observational cohort study (BRiTE).

PURPOSE Bevacizumab provides a survival benefit in first- and second-line metastatic colorectal cancer (mCRC). In a large, observational, bevacizumab treatment study (Bevacizumab Regimens: Investigation of Treatment Effects and Safety [BRiTE]) in patients who had mCRC, a longer-than-expected overall survival (OS) of 25.1 months was reported. The association between various pre- and post-treatment factors (including the use of bevacizumab beyond first progression [BBP]) and survival was examined. PATIENTS AND METHODS The 1,445 of 1,953 previously untreated patients with mCRC who were enrolled in BRiTE and who experienced disease progression (PD) were classified into three groups: no post-PD treatment (n = 253), post-PD treatment without bevacizumab (no BBP; n = 531), and BBP (n = 642). Relevant baseline and on-study variables, including BBP, were analyzed with a Cox model with respect to their independent effect on survival beyond first PD. RESULTS Median OS was 25.1 months (95% CI, 23.4 to 27.5 months), and median progression-free survival was 10.0 months in the overall BRiTE population. Baseline and postbaseline factors were well balanced between the BBP and no-BBP groups. Median OS rates were 12.6, 19.9, and 31.8 months in the no post-PD treatment, no-BBP, and BBP groups, respectively. In multivariate analyses, compared with no BBP, BBP was strongly and independently associated with improved survival (HR, 0.48; P < .001). Hypertension that required medication was the only bevacizumab-related safety event that occurred more frequently in the BBP group (24.6% v 19.2%). CONCLUSION These results from a large, prospective, observational study suggest that continued vascular endothelial growth factor inhibition with bevacizumab beyond initial PD could play an important role improving the overall success of therapy for patients who have mCRC.
A robust and efficient valve model based on resistive immersed surfaces

A procedure for modeling the heart valves is presented. Instead of modeling complete leaflet motion, leaflets are modeled in open and closed configurations. The geometry of each configuration can be defined, for example, from in vivo image data. This method enables significant computational savings compared with complete fluid–structure interaction and contact modeling, while maintaining realistic three‐dimensional velocity and pressure distributions near the valve, which is not possible from lumped parameter modeling. Leaflets are modeled as immersed, fixed surfaces over which a resistance to flow is assigned. On the basis of local flow conditions, the resistance values assigned for each configuration are changed to switch the valve between open and closed states. This formulation allows for the pressure to be discontinuous across the valve. To illustrate the versatility of the model, realistic and patient‐specific simulations are presented, as well as comparison with complete fluid–structure interaction simulation. Copyright © 2012 John Wiley & Sons, Ltd.
Correlation Tracking via Joint Discrimination and Reliability Learning

For visual tracking, an ideal filter learned by the correlation filter (CF) method should take both discrimination and reliability information. However, existing attempts usually focus on the former one while pay less attention to reliability learning. This may make the learned filter be dominated by the unexpected salient regions on the feature map, thereby resulting in model degradation. To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information. First, we treat the filter as the element-wise product of a base filter and a reliability term. The base filter is aimed to learn the discrimination information between the target and backgrounds, and the reliability term encourages the final filter to focus on more reliable regions. Second, we introduce a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker being dominated by unreliable regions. The proposed optimization problem can be solved using the alternating direction method and speeded up in the Fourier domain. We conduct extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to evaluate the proposed tracker. Experimental results show that our tracker performs favorably against other state-of-the-art trackers.
Factor Structure, Evolution, and Predictive Power of Emotional Competencies on Physical and Emotional Health in the Elderly

Objective: Emotional competence (EC) has been found to be an important predictor of individuals’ health. While it is well known that EC predicts important outcomes in young adults, its importance is less clear in the elderly. We aimed to address this gap: Is the structure of EC the same in older as in younger adults? How do EC evolve between 50 and 80 years old? Does the predictive power of EC, regarding physical and emotional adjustment, increase or decrease with age? Method: A total of 6,688 participants filled subjective health and EC questionnaires. We gathered their medication consumption over the last 11 years, from the database of health insurance. Results: While the structure of ECs remains stable in older adults, it generally declines as people get older, except for emotion regulation, which improves with age. Results also show that EC predicts both physical and emotional health. Discussion: These results suggest that the development of specific interventions to improve EC may be useful for the elderly.
Marker-controlled perception-based mesh segmentation

This paper introduces an interactive marker-controlled mesh segmentation algorithm. In many applications, we need to partition a given mesh according to human perception or particular requirements. The algorithm presented in this paper is based on Fast Marching Watersheds. We refer to the experience of image segmentation area and introduce interactive marker selection. Thus we obviously overcome the difficulty in intuitional segmentation control, and improve the quality of segmentation. The output results show the effectiveness and practicability of the algorithm.
Investigating and Analyzing the Light-weight ciphers for Wireless Sensor Networks

The Wireless Sensor Networks (WSNs) are characterized by the severe constraints in the computational, storage and energy resources. Though there has been significant improvement in the available computational resources due to the proliferation of the next-generation sensor nodes, the energy and storage resources of sensor nodes are still limited. As the sensor nodes are often deployed in ubiquitous and pervasive environments, it is necessary to ensure communications security in a WSN. However, the use of the security protocols adds to the associated overhead. Therefore, ensuring communications security in a WSN is a challenge. We believe that since the core component of any security protocol is the cipher used therein; the overhead due to a security protocol can be largely reduced, by employing essentially a lightweight cipher. But at the same time, the cipher so employed must ensure appropriate levels of security with standard key-sizes. Currently, Skipjack (80-bit key-size) is the cipher of choice, for the software based security protocols like TinySec, SenSec and MiniSec, used in WSNs. However, with the help of exhaustive simulation, experiments and analysis, in this paper, we show that our optimized implementation of the Corrected Block Tiny Encryption Algorithm (XXTEA) (128-bit key-size), can be a good alternative to Skipjack; at least in the resource constrained WSN environments.
Accelerating a C + + Image Processing Library with a GPU

This paper presents work-in-progress towards a C++ source-to-source translator that automatically seeks parallelisable code fragments and replaces them with code for a graphics co-processor. We report on our experience with accelerating an industrial image processing library. To increase the effectiveness of our approach, we exploit some domain-specific knowledge of the library’s semantics. We outline the architecture of our translator and how it uses the ROSE source-to-source transformation library to overcome complexities in the C++ language. Techniques for parallel analysis and source transformation are presented in light of their uses in GPU code generation. We conclude with results from a performance evaluation of two examples, image blending and an erosion filter, hand-translated with our parallelisation techniques. We show that our approach has potential and explain some of the remaining challenges in building an effective tool.
Are we there yet? It graduates - bauhaus style

Monash University’s School of Information Management & Systems has over the last few years taken a novel approach to the teaching of its undergraduate degree program. This approach adopts and extends aspects of the Bauhaus style of teaching with the aim of developing IT graduates that are better prepared in both discipline knowledge and the non academic skills required in the IT workforce. This paper describes the studio-based teaching model adopted, and briefly reports on evaluations that were conducted during 2001. The focus of this paper is on the staff’s perceptions of the model, though student’s responses are drawn upon as an insight or support. Findings highlight the acceptance of the model in principle, but also highlight deficiencies in its implementation. The question “Are we there yet? ” will not be fully answered until the student intake of 2001 graduate, as it is these students who have been immersed in the model from the commencement of their studies within the degree program.
Structured Log Linear Models for Noise Robust Speech Recognition

The use of discriminative models for structured classification tasks, such as speech recognition is becoming increasingly popular. This letter examines the use of structured log-linear models for noise robust speech recognition. An important aspect of log-linear models is the form of the features. By using generative models to derive the features, state-of-the-art model-based compensation schemes can be used to make the system robust to noise. Previous work in this area is extended in two important directions. First, a large margin training of sentence-level log linear models is proposed for automatic speech recognition (ASR). This form of model is shown to be similar to the recently proposed structured Support Vector Machines (SVM). Second, based on the designed joint features, efficient lattice-based training and decoding are performed. This novel model combines generative kernels, discriminative models, efficient lattice-based large margin training and model-based noise compensation. It is evaluated on a noise corrupted continuous digit task: AURORA 2.0.
Multisource Earth Observation Data for Land-Cover Classification Using Random Forest

In this letter, multisource earth observation (EO) data sets, including multitemporal Landsat-8, digital surface model, and spatial information, were integrated for land-cover classification by random forest (RF) and support vector machines (SVMs). We demonstrated in this letter that both RF and SVM are useful tools for classification of land cover in the local climate zones featured with highly heterogeneous landscape. Classification of land cover by RF was with an overall accuracy (OA) of 86.2%, while the OA was 85.5% for SVM. However, we found that RF was more stable than SVM for multisource EO data in classifying land cover without normalizing different feature data sets. Experiments showed that the thermal features were more important than temporal and spatial ones in discriminating impervious objects, while the temporal and spatial features were generally better than thermal ones in separating the distinct vegetation categories. Another finding was that our experiments indicated that spectral features were the most important in classification of land cover, followed by temporal, thermal, and spatial features, respectively. As to the spectral features, red channels were the most important, followed by short-wave infrared, near-infrared, and green channels. Thus, it could be concluded that the combination of spectral, thermal, spatial, and temporal information would be an optimal approach to increase the OA of land-cover classification in the zones featured with highly heterogeneous landscape.
Diagnostic usefulness of bone mineral density and biochemical markers of bone turnover in predicting fracture in CKD stage 5D patients--a single-center cohort study.

BACKGROUND In chronic kidney disease stage 5D, diagnostic usefulness of bone mineral density (BMD) in predicting fracture has not been established because of variable results in previous studies. The reason for this may be the heterogeneity of underlying pathogenesis of the fracture. METHODS BMD was measured annually and serum biochemistry monthly for 485 hemodialyzed patients from April 2003 to March 2008, and all fractures were recorded. RESULTS Forty-six new episodes of any type of fracture and 29 cases of prevalent spine fracture were recorded. Serum bone-specific alkaline phosphatase (b-AP) was a very useful surrogate marker for any type of incident fracture risk [area under curve (AUC) = 0.766, P < 0.0001]. A significantly greater risk of any type of incident fracture was associated with parathyroid hormone (PTH) levels either <150 pg/mL [hazard ratio (HR) = 3.47, P < 0.01] or >300 pg/mL (HR = 5.88, P < 0.0001) compared with 150-300 pg/mL. Receiver-operating characteristic analysis demonstrated a significant predictive power for incident of any type of fracture by BMD at the total hip (AUC = 0.760, P < 0.0001) and other hip regions in females in the lower PTH group (PTH < 204 pg/mL). BMDs at every site but whole body or lumbar spine had significant power to discriminate prevalent spine fracture regardless of gender or PTH. CONCLUSIONS Hemodialyzed patients with low or high PTH or increased b-AP had a high fracture risk. BMD by Dual Energy X-ray Absorptiometry (DEXA), especially at the total hip region, was useful to predict any type of incident of fracture for females with low PTH or to discriminate prevalent spine fracture for every patient.
Science production in Iran: The scenario of Iranian medical journals

In Iran, the number of published articles has increased significantly in the basic and applied sciences including medicine and its subspecialties during the recent years. The present study reviewed Iranian science production in medicine, focusing on Iranian medical journals and assessing the current status of Iranian medical journals in several information databases. The study revealed that only a few number of Iranian biomedical journals were indexed by Web of Science, Medline, Scopus and Biological abstract, but most of them have been covered by Index Copernicus and Index Medicus for Eastern Mediterranean Region. Observing some important factors such as journal's basic publishing standards may increase the number of Iranian medical journals indexed by reputable information databases and improve Iranian contribution to the world science.
Phylogenetic analysis of HCV‐4d in Turkey: the curious case of Kayseri Province

In Turkey, genotype 1, especially type 1b virus, causes approximately 90% of these infections, while types 2, 3, and 4 exist, albeit in low prevalences and are due to relatively recent and limited introductions. Two recent reports from Kayseri, a relatively large city in Central Anatolia, indicated unusually high prevalence for type 4 infections in the province reaching a 35% among patients admitted to hospitals for treatment of chronic hepatitis C. In this study, the origin, the demographic history, and the dynamic of the epidemic of unusual HCV genotype 4d in Turkey by using Bayesian coalescent‐based method were investigated. A gene flow migration approach was also used to describe the synchronous geographical dispersal and genetic diversification of this unusual genotype in Kayseri province. The Turkish clade had a tMRCA of 44 years corresponding to the year 1967 and seems to have a different origin being completely segregated from the European one. Gene flow migration analysis indicated that Kayseri province appeared to be the epicenter of HCV‐4d epidemic, exporting the infections. The demographic history of HCV‐4d showed that the epidemic started in 1970s year then following a slow exponential growth until 1980s. The Turkish monophyletic clade suggests a segregate circulation of the epidemic in this region mostly due to unsafe parenteral medical procedures (with drug addiction playing a relatively negligible role). J. Med. Virol. 86:454–460, 2014. © 2013 Wiley Periodicals, Inc.
Prostaglandins and vascular function

WHEN PROSTAGLANDINS were first identified 50 years ago as components of seminal fluids, scientists had little inkling of the broad range of biological activities of these derivatives of unsaturated fatty acid. The capacity to contract smooth muscle (uterine) and to lower blood pressure were known characteristics of the first stable prostaglandins identified,' but the existence of the major prostaglandins that affect vascular tissue was not suspected a decade ago. Within the past 10 years, the identification of unstable metabolites of arachidonic acid, first the prostaglandin endoperoxides,2 then thromboxane A2 (TXA2)3 and prostacyclin,4 5has opened up new areas of study in biochemistry, physiology, and therapeutics. Prostaglandins are oxygenated derivatives of polyunsaturated fatty acids. The latter are essential nutrients widely distributed in mammalian tissues among all classes of lipids, and are important components of cell membranes. In mammalian cells, the major precursor of prostaglandins is arachidonic acid, a polyunsaturated fatty acid with a 20-carbon backbone and four double bonds (20:4). Arachidonic acid is esterified in membrane and cellular phospholipids. When released from phospholipids by specific phospholipases, free arachidonic acid immediately enters one of several pathways and undergoes (1) enzymatic conversion by cyclooxygenase to endoperoxide intermediates, which are converted to prostaglandins or thromboxanes, (2) enzymatic conversion by lipoxygenases to hydroperoxy acid intermediates, which form hydroxy acids or leukotrienes, (3) enzymatic reacylation into phospholipids, or (4) nonenzymatic binding to plasma albumin (figure 1). These pathway are of differing relative importance in different tissues. In the blood vessel wall, the cyclooxygenase pathway predominates (figure 2). In the endothelial or smooth muscle layers of the blood vessel wall, arachidonic acid is preferentially converted via cyclooxygenase to prostacyclin (PGI2), a potent vasodilator that inhibits platelet activation.4 6 In contrast, in blood platelets,
Bivariate combined linkage and association mapping of quantitative trait loci

In this paper, bivariate/multivariate variance component models are proposed for high‐resolution combined linkage and association mapping of quantitative trait loci (QTL), based on combinations of pedigree and population data. Suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In the region, multiple markers such as single nucleotide polymorphisms are typed. Two regression models, “genotype effect model” and “additive effect model”, are proposed to model the association between the markers and the trait locus. The linkage information, i.e., recombination fractions between the QTL and the markers, is modeled in the variance and covariance matrix. By analytical formulae, we show that the “genotype effect model” can be used to model the additive and dominant effects simultaneously; the “additive effect model” only takes care of additive effect. Based on the two models, F‐test statistics are proposed to test association between the QTL and markers. By analytical power analysis, we show that bivariate models can be more powerful than univariate models. For moderate‐sized samples, the proposed models lead to correct type I error rates; and so the models are reasonably robust. As a practical example, the method is applied to analyze the genetic inheritance of rheumatoid arthritis for the data of The North American Rheumatoid Arthritis Consortium, Problem 2, Genetic Analysis Workshop 15, which confirms the advantage of the proposed bivariate models. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.
Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we can not get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones, which generate synthetic examples randomly in selected line segments. In our work, we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method. In theory, our method covers a wider range. In experiment with real world data, our method performs better than the SMOTE and its meliorations.
Decomposing tensors with structured matrix factors reduces to rank-1 approximations

Tensor decompositions permit to estimate in a deterministic way the parameters in a multi-linear model. Applications have been already pointed out in antenna array processing and digital communications, among others, and are extremely attractive provided some diversity at the receiver is available. As opposed to the widely used ALS algorithm, non-iterative algorithms are proposed in this paper to compute the required tensor decomposition into a sum of rank-1 terms, when some factor matrices enjoy some structure, such as block-Hankel, triangular, band, etc.
Action and resistance mechanisms of antibiotics: A guide for clinicians

Infections account for a major cause of death throughout the developing world. This is mainly due to the emergence of newer infectious agents and more specifically due to the appearance of antimicrobial resistance. With time, the bacteria have become smarter and along with it, massive imprudent usage of antibiotics in clinical practice has resulted in resistance of bacteria to antimicrobial agents. The antimicrobial resistance is recognized as a major problem in the treatment of microbial infections. The biochemical resistance mechanisms used by bacteria include the following: antibiotic inactivation, target modification, altered permeability, and “bypass” of metabolic pathway. Determination of bacterial resistance to antibiotics of all classes (phenotypes) and mutations that are responsible for bacterial resistance to antibiotics (genetic analysis) are helpful. Better understanding of the mechanisms of antibiotic resistance will help clinicians regarding usage of antibiotics in different situations. This review discusses the mechanism of action and resistance development in commonly used antimicrobials.