nils-herrmann/clean_scirepeval huggingface dataset:
Integrated Use of Lung Ultrasound and Chest X Ray in the Detection of Interstitial Lung Disease
Venue: Respiration

Current data have shown that lung ultrasound (LUS) may be useful in the detection of interstitial lung disease (ILD) by the evaluation of B lines, the sonographic marker of pulmonary interstitial syndrome. Nevertheless, no prospective study has compared LUS to chest X ray (CXR) for ILD assessment, and there is no general agreement on the specific echographic diagnostic criteria for defining ILD.  The aims of this study were (1) to compare the accuracy of LUS and CXR in the detection of ILD using high resolution CT (HRCT) as the gold standard and (2) to compare the accuracy of different echographic diagnostic criteria for ILD diagnosis.  LUS was performed on 104 patients undergoing HRCT for suspected ILD. In 49 patients, a CXR scan performed within 3 months of HRCT was analyzed. ILD was defined as the presence of =5 B lines in =3 chest areas. A total B line score (TBLS) was also calculated, as in previous studies. The observers evaluating LUS and CXR were blinded to the HRCT results and clinical data.  On HRCT, ILD was assessed in 50 patients. CXR was specific (91% 95% CI 80 100) but not sensitive (48% 95% CI 28 67) Conversely, LUS showed high sensitivity (92% 95% CI 84 99) and low specificity (79% 95% CI 69 90) Using a TBLS, sensitivity did not change, while specificity decreased.  LUS could be a sensitive tool for ILD detection. CXR and LUS have different but complementary features, and their combined use could reduce the need for HRCT. The use of different diagnostic criteria for defining ILD does not affect sensitivity but influences specificity.
Speech Act Theory And Pragmatics

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Decoding social media speak: developing a speech act theory research agenda

Drawing on the theoretical domain of speech act theory (SAT) and a discussion of its suitability for setting the agenda for social media research, this study aims to explore a range of research directions that are both relevant and conceptually robust, to stimulate the advancement of knowledge and understanding of online verbatim data.  Examining previously published cross disciplinary research, the study identifies how recent conceptual and empirical advances in SAT may further guide the development of text analytics in a social media context.  Decoding content and function word use in customers' social media communication can enhance the efficiency of determining potential impacts of customer reviews, sentiment strength, the quality of contributions in social media, customers' socialization perceptions in online communities and deceptive messages.  Considering the variety of managerial demand, increasing and diverging social media forma.
Particulate vs Non Particulate Steroids for Transforaminal Epidural Steroid Injections: Systematic Review and Meta analysis of the Current Literature.
Venue: Pain physician

The efficacy of particulate and non particulate steroids in patients receiving epidural steroid injections remains unclear.  The purpose of this meta analysis was to compare the efficacy of particulate and non particulate steroids in patients receiving epidural injections for radicular pain over 3 months.  Systematic review and meta analysis.  We reviewed PubMed, PubMed Central, Scopus, Central Register of Clinical Trials of the Cochrane Collaboration, Google Scholar, and Directory of open access journals for trials that compared efficacy of particulate steroid with non particulate. A meta analysis was performed on treatment related to mean change in visual analogue score (VAS) between the particulate and non particulate steroids. Two authors independently reviewed the data for inclusion.  Seven studies comprising 3,542 patients in the particulate group and 856 patients in the non particulate group were included. Pooled mean maximum change of VAS was higher by 0.53 (95% CI: 0.14 to 0.92; P 0.007; I2 50.2% in the particulate group compared to the non particulate group. The non particulate group had a larger proportion of patients with more than 50% pain relief than the particulate group [OR 0.81 (95% CI: 0.68 to 0.97, P 0.024) LIMITATIONS Limited number of trials that fit the inclusion criteria and were available for analysis.  As the use of particulate steroids seems to be associated with slightly better VAS scores only, clinicians need to weigh their clinical relevance in the light of complications and recent FDA recommendations on the use of particulate steroids.
Genome wide identification and expression analysis of CIPK genes in diploid cottons.
Venue: Genetics and molecular research GMR

Calcineurin B like protein interacting protein kinase (CIPK) plays a key regulatory role in the growth, development, and stress resistance of plants by combining with phosphatase B subunit like protein. In the present study, CIPK genes were identified in the whole genomes of diploid cottons and their sequences were subjected to bioinformatic analyses. The results demonstrated that the CIPK gene family was unevenly distributed in two diploid cotton genomes. Forty one CIPKs were identified in the D genome, mainly located on chromosomes 9 and 10, whereas thirty nine CIPKs were identified in the A genome, mainly located on chromosomes 8 and 11. Based on the gene structures, CIPKs in cotton could be classified into two types: one that is intron rich and the other that has few introns. Phylogenetic analysis revealed that the CIPK gene family members in cotton had close evolutionary relationships with those of the dicotyledonous plants, such as Arabidopsis thaliana and poplar. The analysis of transcriptome sequence data demonstrated that there were differences in gene expression in different tissues, indicating that the expression of the CIPKs in cotton had spatio temporal specificity. The expression analysis of CIPKs under abiotic stresses (drought, salt, and low temperature) in different tissues at trefoil stage demonstrated that these stresses induced the expression of CIPKs.
Making Dependency Labeling Simple, Fast and Accurate
Venue: NAACL

This work addresses the task of dependency labeling assigning labels to an (unlabeled) dependency tree. We employ and extend a feature representation learning approach, optimizing it for both high speed and accuracy. We apply our labeling model on top of state of the art parsers and evaluate its performance on standard benchmarks including the CoNLL 2009 and the English PTB datasets. Our model processes over 1,700 English sentences per second, which is 30 times faster than the sparse feature method. It improves labeling accuracy over the outputs of top parsers, achieving the best LAS on 5 out of 7 datasets1.
Kriging Assisted Multi Objective Design of Permanent Magnet Motor for Position Sensorless Control
Venue: IEEE Transactions on Magnetics

This paper presents a novel multi objective evolutionary algorithm (MOEA) based on surrogate models. Kriging surrogate models are created and updated with the solutions from the MOEA for reducing the number of evaluations of the computationally expensive finite element analysis models. The proposed method is applied to solve a multi objective design problem of a surface mounted permanent magnet motor in order to improve its sensorless control capability without comprising other performances, such as output torque and torque ripple. The Kriging assisted MOEA is proved to have the potential of providing good solutions with a limited computation time budget.
Sensor drift compensation using weighted neural networks
Venue: 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

In gas classification systems with multiple sensors, the individual sensor drift affects the system classification capacity over time. A model created to classify data at certain time, doesn't present the same efficiency to classify a sample in a future time. Depending on the problem, this time interval can be days, weeks or months. Chemical gas sensors suffer from drift problem because of the chemical process employed. In this investigation we developed a model that uses an ensemble of neural networks in a parallel way combining the weighted output of classifiers to compensate the drift. Another approach was to weight input data according to their recentness by repeating newer training values. Results show that performance of correct classifications of the gas samples using both methods improved when compared to classifiers trained with just recent data.
Segmentation of Coronal Holes Using Active Contours Without Edges

An application of active contours without edges is presented as an efficient and effective means of extracting and characterizing coronal holes. Coronal holes are regions of low density plasma on the Sun with open magnetic field lines. The detection and characterization of these regions is important for testing theories of their formation and evolution, and also from a space weather perspective because they are the source of the fast solar wind. Coronal holes are detected in full disk extreme ultraviolet (EUV) images of the corona obtained with the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) The proposed method detects coronal boundaries without determining any fixed intensity value in the data. Instead, the active contour segmentation employs an energy minimization in which coronal holes are assumed to have more homogeneous intensities than the surrounding active regions and quiet Sun. The segmented coronal holes tend to correspond to unipolar magnetic regions, are consistent with concurrent solar wind observations, and qualitatively match the coronal holes segmented by other methods. The means to identify a coronal hole without specifying a final intensity threshold may allow this algorithm to be more robust across multiple datasets, regardless of data type, resolution, and quality.
Changes in the Proteome of Langat Infected Ixodes scapularis ISE6 Cells: Metabolic Pathways Associated with Flavivirus Infection
Venue: PLoS neglected tropical diseases

Ticks (Family Ixodidae) transmit a variety of disease causing agents to humans and animals. The tick borne flaviviruses (TBFs; family Flaviviridae) are a complex of viruses, many of which cause encephalitis and hemorrhagic fever, and represent global threats to human health and biosecurity. Pathogenesis has been well studied in human and animal disease models. Equivalent analyses of tick flavivirus interactions are limited and represent an area of study that could reveal novel approaches for TBF control.  High resolution LC MS/MS was used to analyze the proteome of Ixodes scapularis (Lyme disease tick) embryonic ISE6 cells following infection with Langat virus (LGTV) and identify proteins associated with viral infection and replication. Maximal LGTV infection of cells and determination of peak release of infectious virus, was observed at 36 hours post infection (hpi) Proteins were extracted from ISE6 cells treated with LGTV and non infectious (UV inactivated) LGTV at 36 hpi and analyzed by mass spectrometry. The Omics Discovery Pipeline (ODP) identified thousands of MS peaks. Protein homology searches against the I. scapularis IscaW1 genome assembly identified a total of 486 proteins that were subsequently assigned to putative functional pathways using searches against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 266 proteins were differentially expressed following LGTV infection relative to non infected (mock) cells. Of these, 68 proteins exhibited increased expression and 198 proteins had decreased expression. The majority of the former were classified in the KEGG pathways: "translation" "amino acid metabolism" and "protein folding/sorting/degradation" Finally, Trichostatin A and Oligomycin A increased and decreased LGTV replication in vitro in ISE6 cells, respectively.  Proteomic analyses revealed ISE6 proteins that were differentially expressed at the peak of LGTV replication. Proteins with increased expression following infection were associated with cellular metabolic pathways and glutaminolysis. In vitro assays using small molecules implicate malate dehydrogenase (MDH2) the citrate cycle, cellular acetylation, and electron transport chain processes in viral replication. Proteins were identified that may be required for TBF infection of ISE6 cells. These proteins are candidates for functional studies and targets for the development of transmission blocking vaccines and drugs.
Surface Water Organophosphorus Pesticides Concentration and Distribution in the Langat River, Selangor, Malaysia
Venue: Exposure and Health

Pesticide contamination in the environment is a contemporary global issue. As agricultural production through crop planting in Malaysia now supports both energy and food productions, the concentration and distribution of pesticides in surface water may have changed. Therefore, this study assesses the concentration and distribution of organophosphorus pesticides (quinalphos, diazinon and chlorpyrifos) contamination in the Langat River, Selangor. The Langat River Basin is one of the most populated river basins in Malaysia. As a result, it is surrounded by various environmental stresses of which agricultural activity is a dominant cause. An analytical method has been developed and optimised based on solid phase extraction and high performance liquid chromatography, coupled with a diode array detector (SPE HPLC DAD) This method allowed for the determination and quantification of selected organophosphorus pesticides. The optimised method displays a high mean recovery for both quinalphos and diazinon (100.21 and 100.15 respectively) but relatively low recovery for chlorpyrifos (32.40 The low recovery of chlorpyrifos is due to limitations in the determination of multi residues in a single analytical run, optimisation of chromatographic conditions and the recovery of each compound. The method detection limit was found to be 0.003 mg/L for quinalphos and diazinon, and 0.006 mg/L for chlorpyrifos. Sample analyses revealed the occurrence of quinalphos, diazinon and chlorpyrifos in the Langat River with chlorpyrifos found to have the highest mean concentration of 0.0202 mg/L. The quinalphos and diazinon sample concentrations were 0.0178 mg/L and 0.0094 mg/L, respectively. The concentrations of organophosphorus pesticides in this monitoring study were found to be below the Maximum Contaminant Levels (MCLs) established under the European Union (Drinking Water) Regulation 2014. This study was the first to detect concentrations of quinalphos and diazinon in the Langat River, Selangor.
Capitalism, immigration, language and literacy: Mapping a politicized reading of a policy assemblage

Immigration for Australia and Canada is critical to sustain economic growth. Each country's immigration policy stems from its vision of a nation that includes the role of language and literacy and a program of economic outcomes. While the authors acknowledge that economic integration through employment dominates immigration policies in Canada and Australia, the goal of this article is to critically examine and map how language and literacies in an immigration policy are positioned in relation to economic outcomes in neo liberal times. Questions flowing from the article's objective are: what does immigration produce, and what is its effect on how language and literacies are legitimated? The questions explore how capitalism decodes immigration, language and literacy, and in turn how immigration, language and literacies reterritorialize/reconfigure in the context of human and economic capital. These questions are taken up in an assemblage that includes Deleuze and Guattari's writings on capitalism and deploys multiple literacies theory to read capitalism, immigration, language and literacy in the context of immigration policies prevailing in Australia and Canada. These two countries offer an interesting entry point for rhizomatic analysis since Canada's government has, in recent years, been actively investigating Australia's policies and their effectiveness in the successful integration of newcomers. Mapping a politicized reading of the immigration language literacy policy assemblage and questioning how this assemblage reconfigures is important as global migration intensifies around the world.
Adaptive Behavior and Development of Infants and Toddlers with Williams Syndrome
Venue: Front. Psychol.

Williams syndrome (WS) is a neurodevelopmental disorder that causes deficits in adaptive behavior, difficulties eating and sleeping, cognitive delays, and delayed development. Although researchers have conducted characterizations of children and adults with WS, less is known about young children with this disorder. This study characterizes the developmental and adaptive behavior features of 16 infants and toddlers with WS aged 3 months 5 years. Data for this project was obtained from 2007 to 2014, and includes parent report data and standardized developmental testing. Thirty one percent (31.3% of parents reported that their infant/toddler with WS had sleeping problems and 58.3% reported feeding difficulties. Levels of adaptive behavior were in the Mildly Delayed range as measured by the Adaptive Behavior Assessment System, Second Edition. Self care skills such as feeding or dressing oneself were significantly weaker than skills needed to function in the community, such as recognizing his/her home or throwing away trash. The difficulty with self care skills is hypothesized to be related to the reported difficulties with eating and sleeping. Motor skills were significantly lower than both cognitive and language skills on the Bayley Scales of Infant and Toddler Development, Third Edition. The current study highlights the need for early intervention in these young children across all areas of development, particularly in self care skills.
Contributions of self report and performance based individual differences measures of social cognitive ability to large scale neural network functioning
Venue: Brain Imaging and Behavior

Adaptive social behavior appears to require flexible interaction between multiple large scale brain networks, including the executive control network (ECN) the default mode network (DMN) and the salience network (SN) as well as interactions with the perceptual processing systems these networks function to modulate. Highly connected cortical "hub" regions are also thought to facilitate interactions between these networks, including the dorsolateral prefrontal cortex (DLPFC) dorsomedial prefrontal cortex (DMPFC) anterior cingulate cortex (ACC) and anterior insula (AI) However, less is presently known about the relationship between these network functions and individual differences in social cognitive abilities. In the present study, 23 healthy adults (12 female) underwent functional magnetic resonance imaging (fMRI) while performing a visually based social judgment task (requiring the evaluation of social dominance in faces) Participants also completed both self report and performance based measures of emotional intelligence (EI) as well as measures of personality and facial perception ability. During scanning, social judgment, relative to a control condition involving simple perceptual judgment of facial features in the same stimuli, activated hub regions associated with each of the networks mentioned above (observed clusters included: bilateral DLPFC, DMPFC/ACC, AI, and ventral visual cortex) Interestingly, self reported and performance based measures of social cognitive ability showed opposing associations with these patterns of activation. Specifically, lower self reported EI and lower openness in personality both independently predicted greater activation within hub regions of the SN, DMN, and ECN (i.e. the DLPFC, DMPFC/ACC, and AI clusters) in contrast, in the same analyses greater scores on performance based EI measures and on facial perception tasks independently predicted greater activation within hub regions of the SN and ECN (the DLPFC and AI clusters) and also in the ventral visual cortex. These findings suggest that lower confidence in one's own social cognitive abilities may promote the allocation of greater cognitive resources to, and improve the performance of, social cognitive functions.
Understanding the Relationship between Quality of Life, Adaptive Behavior and Support Needs

Psychometric tools providing quantitative measures of the constructs of adaptive behavior, support needs, and quality of life (QOL) have received considerable attention within the field of intellectual disability (ID) The relationship between the three constructs was investigated by examining scores on the Adaptive Behavior Scale (ABS) Supports Intensity Scale (SIS) and Personal Outcomes Scale (POS; a QOL scale) Data from 146 Portuguese adults with ID revealed that: (a) the ABS domains showed a moderate negative relationship with the SIS subscales; (b) the absolute value of correlations between SIS/ABS domains were greater than either the ABS/POS or SIS/POS correlations; and (c) people with relatively stronger adaptive skills and less intense support needs experience a higher QOL. Additionally, adaptive behavior scores were a stronger predictor of personal outcomes than the support needs scores. Personal outcomes associated with QOL were similar when assessed by the POS through self report and report of others measures. Implications for future research and practice are discussed.
Crowd Based Personalized Natural Language Explanations for Recommendations
Venue: RecSys

Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word of mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state of the art content based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.