nils-herrmann/clean_scirepeval huggingface dataset:
Monitoring of treatment responses and clonal evolution of tumor cells by circulating tumor DNA of heterogeneous mutant EGFR genes in lung cancer.
Venue: Lung cancer

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) have dramatic effects on EGFR mutant non small cell lung cancer (NSCLC) However, most patients experience disease recurrences, approximately half of which are T790M mediated. Monitoring EGFR status with re biopsy has spatiotemporal limitations.  EGFR circulating tumor DNA (ctDNA) in serial plasma samples was amplified and 10(5) of them were sequenced with a next generation sequencer. Plasma mutation (PM) score was defined as the number of reads containing deletions/substitutions in 10(5)EGFR cell free DNA (cfDNA) RESULTS PM scores of various EGFR mutations showed dynamic, case specific changes during EGFR TKI treatments in 52 patients. The effects of the treatment on EGFR ctDNA were evaluated in 38 patients with elevated pre treatment PM scores. The ctDNA responses correlated well with radiologic responses in radiologic good responders, whereas correlation was poor in non responders. In addition to the peaks for the most prevalent ctDNA, small peaks of ctDNA with different types of activating EGFR mutations or the T790M mutation (early T790M ctDNA) appeared transiently in 10.5% and 26.3% respectively. Early T790M ctDNA disappeared in all patients, including 7 who eventually developed acquired resistance accompanied by elevated levels of T790M ctDNA.  Monitoring ctDNA is useful in evaluating treatment responses and monitoring driver oncogene status in NSCLC. ctDNA revealed clonal heterogeneity and genetic processes of cancer evolution in individual patients. The simple presence of the T790M mutation may be insufficient to confer EGFR TKI resistance to tumor cells.
Emergent verbal behavior in preschool children learning a second language.
Venue: Journal of applied behavior analysis

We evaluated the emergence of untaught second language skills following directly taught listener and intraverbal responses. Three preschool children were taught first language (English) listener responses (e.g. "Point to the horse" and second language (Welsh) intraverbal responses (e.g. "What is horse in Welsh?" [ceffyl] After intervention, increases in untaught second language tacts (e.g. "What is this in Welsh?" [ceffyl] and listener responses (e.g. "Point to the ceffyl" were observed for all 3 participants.
Kriging for Hilbert space valued random fields: The operatorial point of view
Venue: J. Multivar. Anal.

We develop a comprehensive framework for linear spatial prediction in Hilbert spaces. We explore the problem of Best Linear Unbiased (BLU) prediction in Hilbert spaces through an original point of view, based on a new Operatorial definition of Kriging. We ground our developments on the theory of Gaussian processes in function spaces and on the associated notion of measurable linear transformation. We prove that our new setting allows (a) to derive an explicit solution to the problem of Operatorial Ordinary Kriging, and (b) to establish the relation of our novel predictor with the key concept of conditional expectation of a Gaussian measure. Our new theory is posed as a unifying theory for Kriging, which is shown to include the Kriging predictors proposed in the literature on Functional Data through the notion of finite dimensional approximations. Our original viewpoint to Kriging offers new relevant insights for the geostatistical analysis of either finite or infinite dimensional georeferenced dataset.
Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization
Venue: IEEE Transactions on Visualization and Computer Graphics

We present Reactive Vega, a system architecture that provides the first robust and comprehensive treatment of declarative visual and interaction design for data visualization. Starting from a single declarative specification, Reactive Vega constructs a dataflow graph in which input data, scene graph elements, and interaction events are all treated as first class streaming data sources. To support expressive interactive visualizations that may involve time varying scalar, relational, or hierarchical data, Reactive Vega's dataflow graph can dynamically re write itself at runtime by extending or pruning branches in a data driven fashion. We discuss both compile and run time optimizations applied within Reactive Vega, and share the results of benchmark studies that indicate superior interactive performance to both D3 and the original, non reactive Vega system.
Clustering with Bregman Divergences: an Asymptotic Analysis
Venue: NIPS

Clustering, in particular $k$ means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of $k$ means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters $k$ is large. We establish quantization rates and describe the limiting distribution of the centers as $k\to \infty$ extending well known results for $k$ means clustering.
Model based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis
Venue: Stat. Comput.

Model based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM) (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony based approach and an evaluation graph based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.
Successful Management of Crizotinib Induced Neutropenia in a Patient with Anaplastic Lymphoma Kinase Positive Non Small Cell Lung Cancer: A Case Report
Venue: Case Reports in Oncology

Crizotinib, the first clinically available inhibitor of anaplastic lymphoma kinase (ALK) gene rearrangement, is generally well tolerated. In contrast, neutropenia induced by crizotinib is a commonly reported grade 3 or 4 adverse event. In such cases, interruption and dose reduction of crizotinib might be necessary for some patients with severe neutropenia. However, information concerning clinical experience and management of severe neutropenia is currently limited. In this report, the successful management of crizotinib induced neutropenia by dose reduction of crizotinib in a patient with ALK positive non small cell lung cancer is described.
Informing video topics and content on maternal infant and young child nutrition and handwashing: Situational analysis and formative research in Maradi Niger.

In 2013 the USAID Global Health Bureau asked the SPRING project to collaborate with the Resilience and Economic Growth in the Sahel Enhanced Resilience (REGIS ER) project in providing support for maternal infant and young child nutrition related and hygiene related social and behavior change communication. In light of an earlier successful collaboration between SPRING and Digital Green in India (Odisha) SPRING decided that the community video approach used by Digital Green could be tested for use in the resilience context of the Sahel. USAID emphasized the need to include not only REGIS ER into the proof of concept but also other interested Food for Peace partners such as the Livelihoods Agriculture and Health Interventions in Action project and the Sawki project to strengthen their SBCC work in the field of MIYCN. Before developing the formative research protocol SPRING conducted a situational analysis in September 2014 using available published and unpublished reports and other program documents related to nutrition water sanitation and hygiene livelihoods family planning and gender practices in Niger focusing on the Maradi region. The partners selected four existing community groups in each one of the 20 villages to disseminate videos which were planned for production at a later stage. A situational analysis and formative research was to inform the content of the videos to be filmed in the commune of Guidan Roumdji and disseminated in the communes of Guidan Roumdji and Aguie. The formative research was designed to fill the gaps highlighted in the situation analysis for Maradi. Eight priority topics for videos were highlighted as a result of the formative research: Women's nutrition in the first 1000 days Exclusive breastfeeding for the first 6 months Complementary feeding Continued breastfeeding through at least 24 months Diarrhea and malaria prevention and management Handwashing Use of family planning services Use of maternal health services. For each topic a set of recommended behaviors and their respective micro behaviors were identified. A series of videos are planned on these priority topics and behaviors articulating the barriers and motivators for each recommended practice.
Overview of the CL SciSumm 2016 Shared Task
Venue: BIRNDL@JCDL

The CL SciSumm 2016 Shared Task is the first medium scale shared task on scientific document summarization in the computational linguistics (CL) domain. The task built off of the experience and training data set created in its namesake pilot task, which was conducted in 2014 by the same organizing committee. The track included three tasks involving: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generating the abstractive summary. The dataset comprised 30 annotated sets of citing and reference papers from the open access research papers in the CL domain. This overview paper describes the participation and the official results of the second CL SciSumm Shared Task, organized as a part of the Joint Workshop onBibliometric enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2016) held in New Jersey,USA in June, 2016. The annotated dataset used for this shared task and the scripts used for evaluation can be accessed and used by the community at: https:/github.com/WING NUS/scisumm corpus.
Metadherin peptides containing CD4+ and CD8+ T cell epitopes as a therapeutic vaccine candidate against cancer
Venue: Microbiology and immunology

The concept of peptide based vaccines against cancer has made noteworthy progress. Metadherin (MTDH) overexpression and its role in the development of diverse cancers make it an attractive target for cancer immunotherapy. In the current study, six different T cell epitope prediction tools were run to identify MTDH peptides with multiple immunogenic regions. Further, molecular docking was performed to assess HLA peptide binding interactions. Nine and eleven peptides fragments containing multiple CD8 and CD4 T cell epitopes, ranging from 9 to 20 amino acids, respectively, were obtained using a consensus immunoinformatics approach. The three peptides that were finally identified as having overlapping CD4 and CD8 T cell epitopes are ARLREMLSVGLGFLRTELG, FLLGYGWAAACAGAR, YIDDEWSGLNGLSSADP. These peptides were found to not only have multiple T cell epitopes but also to have binding affinity with wide HLA molecules. A molecular docking study revealed that the predicted immunogenic peptides (with single or multiple T cell epitopes) of MTDH have comparable binding energies with naturally bound peptides for both HLA classes I and II. Thus, these peptides have the potential to induce immune responses that could be considered for developing synthetic peptide vaccines against multiple cancers.
Predicting promiscuous antigenic T cell epitopes of Mycobacterium tuberculosis mymA operon proteins binding to MHC Class I and Class II molecules.
Venue: Infection, genetics and evolution journal of molecular epidemiology and evolutionary genetics in infectious diseases

Limited efficacy of Bacillus Calmette Guerin vaccine has raised the need to explore other immunogenic candidates to develop an effective vaccine against Mycobacterium tuberculosis (Mtb) Both CD4+ and CD8+ T cells play a critical role in host immunity to Mtb. Infection of macrophages with Mtb results in upregulation of mymA operon genes thereby suggesting their importance as immune targets. In the present study, after exclusion of self peptides mymA operon proteins of Mtb were analyzed in silico for the presence of Human Leukocyte Antigen (HLA) Class I and Class II binding peptides using Bioinformatics and molecular analysis section, NetMHC 3.4, ProPred and Immune epitope database software. Out of 56 promiscuous epitopes obtained, 41 epitopes were predicted to be antigenic for MHC Class I. In MHC Class II, out of 336 promiscuous epitopes obtained, 142 epitopes were predicted to be antigenic. The comparative bioinformatics analysis of mymA operon proteins found Rv3083 to be the best vaccine candidate. Molecular docking was performed with the most antigenic peptides of Rv3083 (LASGAASVV with alleles HLA B51:01, HAATSGTLI with HLA A02, IVTATGLNI and EKIHYGLKVNTA with HLA DRB1_01:01) to study the structural basis for recognition of peptides by various HLA molecules. The software binding prediction was validated by the obtained molecular docking score of peptide HLA complex. These peptides can be further investigated for their immunological relevance in patients of tuberculosis using major histocompatibility complex tetramer approach.
A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve
Venue: Appl. Soft Comput.

Display  complete flowchart routine of energy curve based multilevel image thresholding. Energy curve based colour multilevel thresholding has been proposed.CS and ELR CS based optimization techniques have been exploited.Different objective functions have been utilized for optimum results.CS Kapur's found giving better results. Amongst all the multilevel thresholding techniques, standard histogram based thresholding approaches are very impressive for bi level thresholding. But, it is not effective to select spatial contextual information of the image for choosing optimal thresholds. In this paper, a new color image thresholding technique is presented by using an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The property of this energy curve is very much similar to histogram of the image. To estimate the spatial contextual information for thresholding practice, in place of histogram, the energy curve function is used as an input. A new energy curve based color image segmentation approach using three well known objective functions named Kapur's entropy, between class variance, and Tsalli's entropy is proposed. In this paper, cuckoo search (CS) and egg lying radius cuckoo search (ELR CS) optimization algorithms with different parameter analysis have been used for solving the color image multilevel thresholding problem. The experimental results demonstrate that the proposed CS Kapur's energy curve based segmentation can powerfully and accurately search the multilevel thresholds.
Pixel classification based color image segmentation using quaternion exponent moments
Venue: Neural Networks

Image segmentation remains an important, but hard to solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we propose a pixel classification based color image segmentation using quaternion exponent moments. Firstly, the pixel level image feature is extracted based on quaternion exponent moments (QEMs) which can capture effectively the image pixel content by considering the correlation between different color channels. Then, the pixel level image feature is used as input of twin support vector machines (TSVM) classifier, and the TSVM model is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained TSVM model. The proposed scheme has the following advantages: (1) the effective QEMs is introduced to describe color image pixel content, which considers the correlation between different color channels, (2) the excellent TSVM classifier is utilized, which has lower computation time and higher classification accuracy. Experimental results show that our proposed method has very promising segmentation performance compared with the state of the art segmentation approaches recently proposed in the literature.
Diabetes self management education for adults with type 2 diabetes mellitus: A systematic review of the effect on glycemic control.
Venue: Patient education and counseling

Assess effect of diabetes self management education and support methods, providers, duration, and contact time on glycemic control in adults with type 2 diabetes.  We searched MEDLINE, CINAHL, EMBASE, ERIC, and PsycINFO to December 2013 for interventions which included elements to improve participants' knowledge, skills, and ability to perform self management activities as well as informed decision making around goal setting.  This review included 118 unique interventions, with 61.9% reporting significant changes in A1C. Overall mean reduction in A1C was 0.74 and 0.17 for intervention and control groups; an average absolute reduction in A1C of 0.57. A combination of group and individual engagement results in the largest decreases in A1C (0.88) Contact hours =10 were associated with a greater proportion of interventions with significant reduction in A1C (70.3% In patients with persistently elevated glycemic values (A1C>9) a greater proportion of studies reported statistically significant reduction in A1C (83.9%  This systematic review found robust data demonstrating that engagement in diabetes self management education results in a statistically significant decrease in A1C levels.  The data suggest mode of delivery, hours of engagement, and baseline A1C can affect the likelihood of achieving statistically significant and clinically meaningful improvement in A1C.
Performance measurement approach for innovation capability in SMEs

The purpose of this paper is to present a framework for improving innovation capability through performance measurement in small and medium sized enterprises (SMEs) The development of an organisation's innovation capability is considered increasingly important in the current literature. Developing such capability is essential, as innovation plays a key role in the survival and growth of organisations. A review of current literature highlights the need for a framework on the development of innovation capability, especially in SMEs.  A literature review was used to form an understanding of previous work in the research area. Previous literature was used to define the key concepts and further to build the conceptual framework.  As a result of the study, a framework for improving innovation capability through performance measurement in SMEs is presented. Key issues that must be addressed are highlighted and discussed.  The paper contai.
Metric learning with geometric mean for similarities measurement
Venue: Soft Comput.

Distance metric learning aims to find an appropriate method to measure similarities between samples. An excellent distance metric can greatly improve the performance of many machine learning algorithms. Most previous methods in this area have focused on finding metrics which utilize large margin criterion to optimize compactness and separability simultaneously. One major shortcoming of these methods is their failure to balance all between class scatters when the distributions of samples are extremely unbalanced. Large margin criterion tends to maintain bigger scatters while abandoning those smaller ones to make the total scatters maximized. In this paper, we introduce a regularized metric learning framework, metric learning with geometric mean which obtains a distance metric using geometric mean. The novel method balances all between class scatters and separates samples from different classes simultaneously. Various experiments on benchmark datasets show the good performance of the novel method.
The most "perfect" voting system in the world

Elections are indispensable for democracy, but their trustworthiness demands transparency and impartiality from governments, even more so for automated elections. This work presents common criticisms to e voting systems, focusing on the Venezuelan case, where there have been around 15 elections of this kind in the last 17 years. The Venezuelan government calls it "the most perfect voting system in the world" but its results have been questioned, studied and contested.
A practical biogas based energy neutral home system for rural communities of Bangladesh

Growing demand of energy consumption, subsequent increase in energy generation costs, and increased greenhouse gas(GHG) emissions, as well as global warming from the conventional energy sources, encourages interest worldwide to bring a higher percentage of renewable energy sources such as biogas into the energy mix to build a climate friendly environment for the future. Moreover, due to high investment and maintenance costs, governments are not providing enough support for grid extension and delivering electricity to remote locations or rural areas, in particular, in under developing countries like Bangladesh. Therefore, this paper presents an Energy Neutral Home System (ENHS) that can meet all its energy requirements from low cost, locally available, nonpolluting biogas generated from animal waste, in particular, chicken and cow manure. The proposed ENHS has been developed for rural community, typically an area of 200 families, and will not only provide cooking gas and sustainable and affordable power supply to the community with low emissions, but will also facilitate high quality fertilizer for agricultural purposes. In depth analysis clearly demonstrates that the proposed ENHS not only offers electricity and cooking gas to the community with the lowest costs, but also reduces the energy crisis and GHG emissions and can play an active role in developing socio economic infrastructure of rural communities in Bangladesh in many ways.