nils-herrmann/clean_scirepeval huggingface dataset:
Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks

Automated pancreas segmentation in medical images is a prerequisite for many clinical applications, such as diabetes inspection, pancreatic cancer diagnosis, and surgical planing. In this paper, we formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN). Our approach conducts pancreatic detection and boundary segmentation with two types of CNN models respectively: (1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; (2) the boundary detection step to allocate the semantic boundaries of pancreas. Both detection results of the two networks are fused together as the initialization of a conditional random field (CRF) framework to obtain the final segmentation output. Our approach achieves the mean dice similarity coefficient (DSC) 76.1 % with the standard deviation of 8.7 % in a dataset containing 78 abdominal MRI scans. The proposed algorithm achieves the best results compared with other state of the arts.
Interactive visualization of volumetric data with WebGL in real-time

This article presents and discusses the implementation of a direct volume rendering system for the Web, which articulates a large portion of the rendering task in the client machine. By placing the rendering emphasis in the local client, our system takes advantage of its power, while at the same time eliminates processing from unreliable bottlenecks (e.g. network). The system developed articulates in efficient manner the capabilities of the recently released WebGL standard, which makes available the accelerated graphic pipeline (formerly unusable). The dependency on specially customized hardware is eliminated, and yet efficient rendering rates are achieved. The Web increasingly competes against desktop applications in many scenarios, but the graphical demands of some of the applications (e.g. interactive scientific visualization by volume rendering), have impeded their successful settlement in Web scenarios. Performance, scalability, accuracy, security are some of the many challenges that must be solved before visual Web applications popularize. In this publication we discuss both performance and scalability of the volume rendering by WebGL ray-casting in two different but challenging application domains: medical imaging and radar meteorology.
Provenance from log files: a BigData problem

As new data products of research increasingly become the product or output of complex processes, the lineage of the resulting products takes on greater importance as a description of the processes that contributed to the result. Without adequate description of data products, their reuse is lessened. The act of instrumenting an application for provenance capture is burdensome, however. This paper explores the option of deriving provenance from existing log files, an approach that reduces the instrumentation task substantially but raises questions about sifting through huge amounts of information for what may or may not be complete provenance. In this paper we study the tradeoff of ease of capture and provenance completeness, and show that under some circumstances capture through logs can result in high quality provenance.
Augmented Reality : An Overview and Five Directions for AR in Education

Augmented Reality (AR) is an emerging form of experience in which the Real World (RW) is enhanced by computer-generated content tied to specific locations and/or activities. Over the last several years, AR applications have become portable and widely available on mobile devices. AR is becoming visible in our audio-visual media (e.g., news, entertainment, sports) and is beginning to enter other aspects of our lives (e.g., e-commerce, travel, marketing) in tangible and exciting ways. Facilitating ubiquitous learning, AR will give learners instant access to locationspecific information compiled and provided by numerous sources (2009). Both the 2010 and 2011 Horizon Reports predict that AR will soon see widespread use on US college campuses. In preparation, this paper offers an overview of AR, examines recent AR developments, explores the impact of AR on society, and evaluates the implications of AR for learning and education.
A survey of icon taxonomy used in the interface design

Many icon taxonomy systems have been developed by researchers that organise icons based on their graphic elements. Most of these taxonomies classify icons according to how abstract or concrete they are. Categories however overlap and different researchers use different terminology, sometimes to describe what in essence is the same thing. This paper describes nine taxonomies and compares the terminologies they use. Aware of the lack of icon taxonomy systems in the field of icon design, the authors provide an overview of icon taxonomy and develop an icon taxonomy system that could bring practical benefits to the performance of computer related tasks.
Reducing blood culture contamination by a simple informational intervention.

Compared to truly negative cultures, false-positive blood cultures not only increase laboratory work but also prolong lengths of patient stay and use of broad-spectrum antibiotics, both of which are likely to increase antibiotic resistance and patient morbidity. The increased patient suffering and surplus costs caused by blood culture contamination motivate substantial measures to decrease the rate of contamination, including the use of dedicated phlebotomy teams. The present study evaluated the effect of a simple informational intervention aimed at reducing blood culture contamination at Skåne University Hospital (SUS), Malmö, Sweden, during 3.5 months, focusing on departments collecting many blood cultures. The main examined outcomes of the study were pre- and postintervention contamination rates, analyzed with a multivariate logistic regression model adjusting for relevant determinants of contamination. A total of 51,264 blood culture sets were drawn from 14,826 patients during the study period (January 2006 to December 2009). The blood culture contamination rate preintervention was 2.59% and decreased to 2.23% postintervention (odds ratio, 0.86; 95% confidence interval, 0.76 to 0.98). A similar decrease in relevant bacterial isolates was not found postintervention. Contamination rates at three auxiliary hospitals did not decrease during the same period. The effect of the intervention on phlebotomists' knowledge of blood culture routines was also evaluated, with a clear increase in level of knowledge among interviewed phlebotomists postintervention. The present study shows that a relatively simple informational intervention can have significant effects on the level of contaminated blood cultures, even in a setting with low rates of contamination where nurses and auxiliary nurses conduct phlebotomies.
Bridging Semantic Interoperability gaps with SILF

Information exchange among coalition command and control (C2) systems in network-enabled environments requires ensuring that each recipient system understands and interprets messages exactly as the source system intended. The Semantic Interoperability Logical Framework (SILF) aims at meeting NATO's needs for semantically correct interoperability between C2 systems, as well as the need to adapt quickly to new missions and new combinations of coalition partners and systems. This paper presents an overview of the SILF framework and performs a detailed analysis of a case study for implementing SILF in a real-world military scenario.
Cellular and Molecular Aspects of Parkinson Treatment: Future Therapeutic Perspectives

Parkinson’s disease is a neurodegenerative disorder accompanied by depletion of dopamine and loss of dopaminergic neurons in the brain that is believed to be responsible for the motor and non-motor symptoms in this disease. The main drug prescribed for Parkinsonian patients is l-dopa, which can be converted to dopamine by passing through the blood-brain barrier. Although l-dopa is able to improve motor function and improve the quality of life in the patients, there is inter-individual variability and some patients do not achieve the therapeutic effect. Variations in treatment response and side effects of current drugs have convinced scientists to think of treating Parkinson’s disease at the cellular and molecular level. Molecular and cellular therapy for Parkinson’s disease include (i) cell transplantation therapy with human embryonic stem (ES) cells, human induced pluripotent stem (iPS) cells and human fetal mesencephalic tissue, (ii) immunological and inflammatory therapy which is done using antibodies, and (iii) gene therapy with AADC-TH-GCH gene therapy, viral vector-mediated gene delivery, RNA interference-based therapy, CRISPR-Cas9 gene editing system, and alternative methods such as optogenetics and chemogenetics. Although these methods currently have a series of challenges, they seem to be promising techniques for Parkinson’s treatment in future. In this study, these prospective therapeutic approaches are reviewed.
BRAF mutation and CDKN2A deletion define a clinically distinct subgroup of childhood secondary high-grade glioma.

To uncover the genetic events leading to transformation of pediatric low-grade glioma (PLGG) to secondary high-grade glioma (sHGG).



We retrospectively identified patients with sHGG from a population-based cohort of 886 patients with PLGG with long clinical follow-up. Exome sequencing and array CGH were performed on available samples followed by detailed genetic analysis of the entire sHGG cohort. Clinical and outcome data of genetically distinct subgroups were obtained.



sHGG was observed in 2.9% of PLGGs (26 of 886 patients). Patients with sHGG had a high frequency of nonsilent somatic mutations compared with patients with primary pediatric high-grade glioma (HGG; median, 25 mutations per exome; P = .0042). Alterations in chromatin-modifying genes and telomere-maintenance pathways were commonly observed, whereas no sHGG harbored the BRAF-KIAA1549 fusion. The most recurrent alterations were BRAF V600E and CDKN2A deletion in 39% and 57% of sHGGs, respectively. Importantly, all BRAF V600E and 80% of CDKN2A alterations could be traced back to their PLGG counterparts. BRAF V600E distinguished sHGG from primary HGG (P = .0023), whereas BRAF and CDKN2A alterations were less commonly observed in PLGG that did not transform (P < .001 and P < .001 respectively). PLGGs with BRAF mutations had longer latency to transformation than wild-type PLGG (median, 6.65 years [range, 3.5 to 20.3 years] v 1.59 years [range, 0.32 to 15.9 years], respectively; P = .0389). Furthermore, 5-year overall survival was 75% ± 15% and 29% ± 12% for children with BRAF mutant and wild-type tumors, respectively (P = .024).



BRAF V600E mutations and CDKN2A deletions constitute a clinically distinct subtype of sHGG. The prolonged course to transformation for BRAF V600E PLGGs provides an opportunity for surgical interventions, surveillance, and targeted therapies to mitigate the outcome of sHGG.
Securing Virtual Machines from Anomalies Using Program-Behavior Analysis in Cloud Environment

Cloud Computing is the key technology of today's cyber world which provides online provisioning of resources on demand and pay per use basis. Malware attacks such as virus, worm and rootkits etc. are some threats to virtual machines (VMs) in cloud environment. In this paper, we present a system call analysis approach to detect malware attacks which maliciously affect the legitimate programs running in Virtual Machines (VMs) and modify their behavior. Our approach is named as 'Malicious System Call Sequence Detection (MSCSD)' which is based on analysis of short sequence of system calls (n-grams). MSCSD employs an efficient feature representation method for system call patterns to improve the accuracy of attack detection and reduce the cost of storage with reduced false positives. MSCSD applies Machine Learning (Decision Tree C 4.5) over the collected n-gram patterns for learning the behavior of monitored programs and detecting malicious system call patterns in future. We have analyzed the performance of some other classifiers and compared our work with the existing work for securing virtual machine in cloud. A prototype implementation of the approach is carried out over UNM dataset and results seem to be promising.
Cerebral palsy

Cerebral palsy is the most common cause of childhood-onset, lifelong physical disability in most countries, affecting about 1 in 500 neonates with an estimated prevalence of 17 million people worldwide. Cerebral palsy is not a disease entity in the traditional sense but a clinical description of children who share features of a non-progressive brain injury or lesion acquired during the antenatal, perinatal or early postnatal period. The clinical manifestations of cerebral palsy vary greatly in the type of movement disorder, the degree of functional ability and limitation and the affected parts of the body. There is currently no cure, but progress is being made in both the prevention and the amelioration of the brain injury. For example, administration of magnesium sulfate during premature labour and cooling of high-risk infants can reduce the rate and severity of cerebral palsy. Although the disorder affects individuals throughout their lifetime, most cerebral palsy research efforts and management strategies currently focus on the needs of children. Clinical management of children with cerebral palsy is directed towards maximizing function and participation in activities and minimizing the effects of the factors that can make the condition worse, such as epilepsy, feeding challenges, hip dislocation and scoliosis. These management strategies include enhancing neurological function during early development; managing medical co-morbidities, weakness and hypertonia; using rehabilitation technologies to enhance motor function; and preventing secondary musculoskeletal problems. Meeting the needs of people with cerebral palsy in resource-poor settings is particularly challenging.
A CAD system for B-mode fatty liver ultrasound images using texture features.

The present study proposes a computer-aided diagnosis (CAD) system for the diagnosis of grades of fatty liver disease, namely mild, moderate and severe fatty liver along with normal liver tissue. Fifty-three B-mode ultrasound images consisting of 12 normal, 14 mild, 14 moderate and 13 severe fatty liver images are used. Based on the visual interpretations by the radiologists, region of interests (ROIs) from within the liver and one ROI from the diaphragm region are considered from each image. The texture features of these ROIs are combined in three ways to form ratio features, inverse ratio features and additive features. The sub-sets of optimal features are obtained by a differential evolution feature selection (DEFS) algorithm and a support vector machine (SVM) has been used for the classification task. The Laws ratio features have shown better performance with an average accuracy and standard deviation of 84.9±3.2. Hence, the CAD system could be useful to the radiologists in diagnosing grades of fatty liver disease.
Relationship of corporal punishment and antisocial behavior by neighborhood.

To examine the relationship of corporal punishment with children's behavior problems while accounting for neighborhood context and while using stronger statistical methods than previous literature in this area, and to examine whether different levels of corporal punishment have different effects in different neighborhood contexts.



Longitudinal cohort study.



General community.



1943 mother-child pairs from the National Longitudinal Survey of Youth.



Internalizing and externalizing behavior problem scales of the Behavior Problems Index.



Parental use of corporal punishment was associated with a 0.71 increase (P<.05) in children's externalizing behavior problems even when several parenting behaviors, neighborhood quality, and all time-invariant variables were accounted for. The association of corporal punishment and children's externalizing behavior problems was not dependent on neighborhood context. The research found no discernible relationship between corporal punishment and internalizing behavior problems.
Purification and characterisation of lactate dehydrogenase : an undergraduate biochemistry laboratory experiment

The practical work described here was designed in the aim of combining several periods that were previously carried-out independently during the academic year and to more appropriately mimic a "research" environment. It illustrates several fundamental biochemical principles as well as experimental aspects and important techniques including spectrophotometry, chromatography, centrifugation, and electrophoresis. Lactate dehydrogenase (LDH) is an enzyme of choice for a student laboratory experiment. This enzyme has many advantages, namely its relative high abundance, high specific activity and high stability. In the first part, the purification scheme starting from pig heart includes ammonium sulphate fractionation, desalting by size exclusion chromatography, anion exchange chromatography and pseudo-affinity chromatography. In the second part of the work the obtained fractions are accessed for protein and activity content in order to evaluate the efficiency of the different purification steps, and are also characterised by electrophoresis using non-denaturing and denaturing conditions. Finally, in the third part, the purified enzyme is subjected to comprehensive analysis of its kinetic properties and compared to those of a commercial skeletal muscle LDH preparation. The results presented thereafter are representative of the data-sets obtained by the student-pairs and are comparable to those obtained by the instructors and the reference publications. This multistep purification of an enzyme from its source material, where students perform different purification techniques over successive laboratory days, the characterisation of the purified enzyme, and the extensive approach of enzyme kinetics, naturally fits into a project-based biochemistry learning process.
Applications of Robot Assisted Language Learning (RALL) in Language Learning and Teaching

This study covers a complete overview of the theore tical rationale of application of robots, other ins tructional inter faces like CALL, MALL, m-learning, r-learning , different types of robots, their instructional ro es , their educational activities, the related researches, findings, and c hallenges of robotic assisted language learning . S ince robotic revolution, many investigators in different countries have atte mpted to utilize robots to enhance education. As ma ny people in the world have personal computers (PCs), in the followi ng years, Personal Robots (PR) may become the next tool for every one’s life. Robots not only have the attributes of CALL/MALL, but also are able for independent moveme nts, voice/visual recognition and environmental interactions, non-ver bal communication, collaboration with native speake rs, diagnosing pronunciation, video conferencing with native speak ers, native speaker tutoring, adaptability, sensing , repeatability, intelligence, mobility and human appearance. Robotaided learning (rlearning) services can be descr ibed as interactive and instructional activities which can be interacte d and performed between robots and learners in both virtual and real worlds.
Multistage Object Detection With Group Recursive Learning

Most existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately. However, the important semantic and spatial layout correlations among proposals are often ignored, which are actually useful for more accurate object detection. In this paper, we propose a new EM-like group recursive learning approach to iteratively refine object proposals by incorporating such context of surrounding proposals and provide an optimal spatial configuration of object detections. In addition, we propose to incorporate the weakly supervised object segmentation cues and region-based object detection into a multistage architecture in order to fully exploit the learned segmentation features for better object detection in an end-to-end way. The proposed architecture consists of three cascaded networks that, respectively, learn to perform weakly supervised object segmentation, object proposal generation, and recursive detection refinement. Combining the group recursive learning and the multistage architecture provides competitive mAPs of <inline-formula> <tex-math notation="LaTeX">$78.7\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$74.9\%$ </tex-math></inline-formula> on the PASCAL VOC2007 and VOC2012 datasets, respectively, which outperform many well-established baselines significantly.
A novel zero-current-detector for DCM operation in synchronous converter

Discontinuous-conduction mode (DCM) operation is usually employed in DC-DC converters for small inductor on printed circuit board (PCB) and high efficiency at light load. However, it is normally difficult for synchronous converter to realize the DCM operation, especially in high frequency applications, which requires a high speed and high precision comparator to detect the zero crossing point at cost of extra power losses. In this paper, a novel zero current detector (ZCD) circuit with an adaptive delay control loop for high frequency synchronous buck converter is presented. Compared to the conventional ZCD, proposed technique is proven to offer 8.5% efficiency enhancement when performed in a buck converter at the switching frequency of 4MHz and showed less sensitivity to the transistor mismatch of the sensor circuit.
Accuracy of Plate Counts

“Microbiology Topics” discusses various topics in microbiology of practical use in validation and compliance. We intend this column to be a useful resource for daily work applications. Reader comments, questions, and suggestions are needed to help us fulfill our objective for this column. Please send your comments and suggestions to column coordinator Scott Sutton at scott. sutton@microbiol.org or journal managing editor Susan Haigney at shaigney@advanstar.com.
Taxonomy Embedding in Poincaré Space : A General Exploration

Graph embedding is an important branch in Data Mining and Machine Learning, and most of recent studies are focused on preserving the hierarchical structure with less dimensions. One of such models, called Poincare Embedding, achieves the goal by using Poincare Ball model to embed hierarchical structure in hyperbolic space instead of traditionally used Euclidean space. However, Poincare Embedding suffers from two major problems: (1) performance drops as depth of nodes increases since nodes tend to lay at the boundary; (2) the embedding model is constrained with pre-constructed structures and cannot be easily extended. In this paper, we first raise several techniques to overcome the problem of low performance for deep nodes, such as using partial structure, adding regularization, and exploring sibling relations in the structure. Then we also extend the Poincare Embedding model by extracting information from text corpus and propose a joint embedding model with Poincare Embedding and Word2vec.