nils-herrmann/clean_scirepeval huggingface dataset:
Noninvasive dissection of mouse sleep using a piezoelectric motion sensor
Venue: Journal of Neuroscience Methods

Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. NEW METHOD Vigilance state was scored manually in 4 s epochs for 24 h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis.  Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51% and high specificity (96% The classifier sometimes confused light NREM sleep characterized by irregular breathing and moderate delta EEG power with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. COMPARISON WITH EXISTING  Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM.  This approach could facilitate large sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.
Cultural Diversity and the Challenges of Teaching Multicultural Classes in the Twenty First Century

This study is a meta analysis of the recent literature on teaching culturally and linguistically diverse (CLD) students. Extant literature on this issue has shown that teaching students coming from different ethnic and cultural backgrounds seems to pose serious threats to teachers at different levels of education. Using a quantitative content analysis, this paper examines twenty studies and book chapters in the field of multicultural education so as to identify the major challenges of the multicultural classroom. The aim is also to identify and describe the most effective competencies that teachers need to be better equipped to survive in today's "fast changing world" The findings of this study show that teaching CLD students requires special training, extra competencies, and "culturally responsive" pedagogies. The findings can also be of help to university teachers as universities today are becoming more and more multicultural all over the world. Finally, the results can advance knowledge about the issue of teaching CLD students both in the university environment and in the field of research.
Comparative Analysis of DNA Barcoding and HPLC Fingerprint to Trace Species of Phellodendri Cortex, an Important Traditional Chinese Medicine from Multiple Sources.
Venue: Biological pharmaceutical bulletin

Phellodendri Cortex is derived from the dried barks of Phellodendron genus species, has been extensively used in traditional Chinese medicine. The cortex is divided into two odorless crude drugs Guanhuangbo and Huangbo. Historically, it has been difficult to distinguish their identities due to a lack of identification methods. This study was executed to confirm the identity and to ensure the species traceability of Phellodendri Cortex. In the current study, analysis is based on the internal transcribed spacer (ITS) and psbA trnH intergenic spacer (psbA trnH) barcodes and HPLC fingerprint was carried out to guarantee the species traceability of Guanhuangbo and Huangbo. DNA barcoding data successfully identified the three plants of the Phellodendron genus species by ITS+psbA trnH, with the ability to distinguish the species origin of Huangbo. Moreover, the psbA trnH data distinguished Guanhuangbo and Huangbo except to trace species. The HPLC fingerprint data showed that Guanhuangbo was clearly different from Huangbo, but there was no difference between the two origins of Huangbo. Additionally, the result of hierarchical clustering analysis, based on chlorogenic acid, phellodendrine, magnoflorine, jatrorrhizine, palmatine and berberine, was consistent with the HPLC fingerprint analysis. These results show that DNA barcoding and HPLC fingerprint can discriminate Guanhuangbo and Huangbo. However, DNA barcoding is more powerful than HPLC fingerprint for species traceability in the identification of related species that are genetically similar. DNA barcoding is a useful scientific tool to accurately confirm the identities of medicinal materials from multiple sources.
Inorganic elemental determinations of marine traditional Chinese Medicine Meretricis concha from Jiaozhou Bay: The construction of inorganic elemental fingerprint based on chemometric analysis
Venue: Journal of Ocean University of China

The goal of this paper is to explore the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, which is a commonly used marine traditional Chinese medicine (TCM) for the treatment of asthma and scald burns. For that, the inorganic elemental contents of Meretricis concha from five sampling points in Jiaozhou Bay have been determined by means of inductively coupled plasma optical emission spectrometry, and the comparative investigations based on the contents of 14 inorganic elements (Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se and Zn) of the samples from Jiaozhou Bay and the previous reported Rushan Bay were performed. It has been found that the samples from the two bays are approximately classified into two kinds using hierarchical cluster analysis, and a four factor model based on principle component analysis could explain approximately 75% of the detection data, also linear discriminant analysis can be used to develop a prediction model to distinguish the samples from Jiaozhou Bay and Rushan Bay with accuracy of about 93% The results of the present investigation suggested that the inorganic elemental fingerprint based on the combination of the measured elemental content and chemometric analysis is a promising approach for verifying the geographical origin of Meretricis concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.
[Physical fingerprint for quality control of traditional Chinese medicine extract powders]
Venue: Zhongguo Zhong yao za zhi Zhongguo zhongyao zazhi China journal of Chinese materia medica

The physical properties of both raw materials and excipients are closely correlated with the quality of traditional Chinese medicine preparations in oral solid dosage forms. In this paper, based on the concept of the chemical fingerprint for quality control of traditional Chinese medicine products, the method of physical fingerprint for quality evaluation of traditional Chinese medicine extract powders was proposed. This novel physical fingerprint was built by the radar map, and consisted of five primary indexes (i.e. stackablity, homogeneity, flowability, compressibility and stability) and 12 secondary indexes (i.e. bulk density, tap density, particle size<50 mm percentage, relative homogeneity index, hausner ratio, angle of repose, powder flow time, inter particle porosity, Carr index, cohesion index, loss on drying, hygroscopicity) Panax notoginseng saponins (PNS) extract was taken for an example. This paper introduced the application of physical fingerprint in the evaluation of source to source and batch to batch quality consistence of PNS extract powders. Moreover, the physical fingerprint of PNS was built by calculating the index of parameters, the index of parametric profile and the index of good compressibility, in order to successfully predict the compressibility of the PNS extract powder and relevant formulations containing PNS extract powder and conventional pharmaceutical excipients. The results demonstrated that the proposed method could not only provide new insights into the development and process control of traditional Chinese medicine solid dosage forms.
Quality Evaluation of Traditional Chinese Medicine Compounds in Xiaoyan Lidan Tablets: Fingerprint and Quantitative Analysis Using UPLC MS
Venue: Molecules

XiaoyanLidan tablets (XYLDTs) are traditional Chinese medicines frequently used for syndromes of the liver and gallbladder, cholecystitis and cholangitis. To evaluate the consistency of the quality of commercial XYLDT preparations, we established a simple and reliable ultra performance liquid chromatography (UPLC) method with a photodiode array (PDA) detector and mass spectrometry (MS) including a fingerprint analysis and quantification of the main pharmacologically active markers. In the UPLC PDA detection based fingerprint analysis of XYLDTs, approximately 39 peaks were found in the XYLDT chromatogram, 26 of which were attributed to Picrasmaquassioides, nine to Andrographis and four to Isodonserra. Subsequently, the structures of these bioactive markers were identified through ESI MS analyses. Using the chemometricmethods of similarity analysis and principal component analysis, the five significant herbal componentswere determined as 4 methoxy 5 hydroxycanthin 6 one, andrographolide, dehydroandrographolide, neoandrographolide and rosmarinic acid, and these components were qualitatively assessed. Our experimental results demonstrated that combining the fingerprint analysis with UPLC MS and multi ingredient determination is useful for rapid pharmaceutical quality evaluation. Moreover, the combined approach can potentially differentiate the origin, determine the authenticity and assess the overall quality of the formulae.
Constrained recommendations for query visualizations
Venue: Knowledge and Information Systems

The improvement of data storage and data acquisition techniques has led to huge accumulated data volumes in a variety of applications. International research enterprises such as the Human Genome and the Digital Sky Survey Projects are generating massive volumes of scientific data. A major challenge with these datasets is to glean insights from them to discover patterns or to originate relationships. The analysis of these massive, typically messy, and inconsistent volumes of data is indeed crucial and challenging in many application domains. Hence, the research community has introduced a number of visualizations tools to guide and help analysts in exploring the data space to extract potentially useful information. However, when working with high dimensional datasets, identifying visualizations that show interesting variations and trends in data is not trivial: the analyst must manually specify a large number of visualizations, explore relationships among various attributes, and examine different subsets of data before discovering visualizations that are interesting or insightful. Though, exploring all possible visualizations involves complex challenges. It is a costly and time consuming process especially when the dimensionality is high. Furthermore, the rapid growth of databases becomes multifaceted in their channels and dimensionality; thus, the transition from static analysis to real time analytics represents a fundamental paradigm shift in the field of Big Data. Motivated by the above challenges, we propose an efficient framework called real time scoring engine (RtSEngine) that assists analysts to limit the exploration of visualizations for a specified number of visualizations and/or certain execution time quote to recommend a set of visualizations that meet analysts' budgets. To achieve that, RtSEngine incorporates our proposed approaches to prioritize and score attributes that form all possible visualizations in a dataset based on their statistical properties such as selectivity, data distribution, and number of distinct values. Then, RtSEngine recommends the visualizations created from the top scored attributes. Moreover, we present visualizations cost aware techniques that estimate the retrieval and computation costs of each visualization so that analysts may discard high cost visualizations. We show and evaluate the effectiveness and efficiency of our proposed approaches, and asses the quality of visualizations and the overhead obtained by applying our techniques on both synthetic and real datasets.
Are natural disasters in early childhood associated with mental health and substance use disorders as an adult?
Venue: Social science medicine

Understanding factors that influence risk for mental health and substance use disorders is critical to improve population health and reduce social costs imposed by these disorders. We examine the impact of experiencing a natural disaster a serious fire, tornado, flood, earthquake, or hurricane by age five on adult mental health and substance use disorders. The analysis uses data from the 2004 to 2005 National Epidemiologic Survey of Alcohol and Related Conditions. The analysis sample includes 27,129 individuals ages 21 64 years. We also exploit information on parenting strategies to study how parents respond to natural disasters encountered by their children. We find that experiencing one or more of these natural disasters by age five increases the risk of mental health disorders in adulthood, particularly anxiety disorders, but not substance use disorders. Parents alter some, but not all, of their parenting strategies following a natural disaster experienced by their children. It is important to provide support, for example through counseling services and financial assistance, to families and children exposed to natural disasters to mitigate future mental health and substance use problems attributable to such exposure.
Handbook of how to have a happy life according to Buddhist Principles for the elders

In the study of the handbook for a happy livelihood for the elders in Buddhist doctrines, it was found that a happy livelihood could be achieved if it comprises of 8 Buddhist principles which could be shortly summarized as GAPPSIDE; it is of: a goal, idea, principle, way, system, method, Buddhist principle and an evaluation, this is said to lead a life to the middle path. Its essential principles are as follows: 1) to get oneself trained in the threefold training, 2) to have the understanding of four noble truths' duty, 3) to have the right understanding of Buddhamdhamma leading to happiness, 4) to have a firm determination of a goal in living life and 5) to have the right knowledge in acting towards happiness. As regards the method, it refers to the following of the noble morality and getting oneself trained in following the path of happiness in accordance with Buddhism. While following this step, one should be mindful of every moment of living life: one should be mindful of one's eyes, ears, nose, tongue, body and mind, that is, they should be utilized in the following four ways: 1) in solving problem, 2) in consuming, 3) in associating with others, 4) in thinking, speaking, and communicating through word 'is' the appropriate manner leading to a goal.
Optimized Regression Test Using Test Case Prioritization

Regression testing ensures that changes made in the fixes or any enhancement changes do not impact the previously working functionality. Whenever software is modified, a set of test cases are run to assure that changes don't affect the other parts of the software. Hence all existing test cases need to be tested as well as new test cases need to be created. It is nonviable to re execute every test case for a given software, because if there are more number of test cases to be tested, the more effort and time is required. This problem can be solved by prioritizing test cases. Test case prioritization techniques reorder the priority of a test case in an attempt to ensure that maximum faults are uncovered by the high prioritized test cases. In this paper we propose an optimized test case prioritization technique using Ant Colony Optimization (ACO) to reduce the cost, effort and time taken to perform regression testing and also uncover maximum faults.
Experimental Analysis of m ACO Technique for Regression Testing

Experimental evaluation of "m ACO" (Modified Ant Colony Optimization) technique for test case prioritization has been performed on two well known software testing problems namely "Triangle Classification Problem" and " Apart from these two problems, m ACO has been experimentally evaluated using open source software JFreeChart.  m ACO finds the optimized solution to test suite prioritization by modifying the phenomenon used by natural ants to reach to its food source and select the food. This paper attempts to experimentally and comparatively evaluate the proposed m ACO technique for test case prioritization against some contemporary meta heuristic techniques using two well known software testing problems and open source problem. Performance evaluation has been measured using two metrics namely APFD (Average Percentage of Faults Detected) and PTR (Percentage of Test Suite Required for Complete Fault Coverage)  The proposed technique m ACO proves its efficiency on both the parameters. m ACO achieves higher fault detection rate with minimized test suite as comparative to other meta heuristic techniques for test case prioritization.  The proposed technique m ACO basically works by modifying the food source searching and selection pattern of the real ants. Real ants grab every type food source it comes across; while modified ants evaluate the food fitness and uniqueness before selection. This phenomenon enhances the quality and diversity of deposited food source.
Frame and Segment Level Features and Candidate Pool Evaluation for Video Caption Generation
Venue: ACM Multimedia

We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. Using these diverse features we train models specializing in two separate input sub domains. We then train an evaluator model which is used to pick the best caption from the pool of candidates generated by these domain expert models. We argue that this approach is better suited for the current video captioning task, compared to using a single model, due to the diversity in the dataset. Efficacy of our method is proven by the fact that it was rated best in MSR Video to Language Challenge, as per human evaluation. Additionally, we were ranked second in the automatic evaluation metrics based table.
An efficient conditional random field approach for automatic and interactive neuron segmentation
Venue: Medical Image Anal.

We present a new graphical model based method for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. For automated reconstruction, our learning based model selects a collection of nodes from a hierarchical merging tree as the proposed segmentation. More specifically, this is achieved by training a conditional random field (CRF) whose underlying graph is the watershed merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our results are comparable to the results of state of the art methods. Furthermore, both the inference and the training are very efficient as the graph is tree structured. The problem of neuron segmentation requires extremely high segmentation quality. Therefore, proofreading, namely, interactively correcting mistakes of the automatic method, is a necessary module in the pipeline. Based on our efficient tree structured inference algorithm, we develop an interactive segmentation framework which only selects locations where the model is uncertain for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Only giving a limited number of choices makes the user interaction very efficient. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.
Approaching ethical, legal and social issues of emerging forensic DNA phenotyping (FDP) technologies comprehensively: Reply to 'Forensic DNA phenotyping: Predicting human appearance from crime scene material for investigative purposes' by Manfred Kayser.
Venue: Forensic science international. Genetics

In a recent special issue of the journal on new trends in forensic genetics, Manfred Kayser contributed a review of developments, opportunities and challenges of forensic DNA phenotyping (FDP) In his article he argues that FDP technologies such as determining eye, hair and skin color should be considered as akin to a "biological witness" with the potential of providing more accurate information than traditional eye witnesses [1].In this letter, we add some of these missing dimensions to the proposals made by Kayser, embedding our response to his paper into a wider discourse of forensic genetics studies, and addressing the wider community of forensic geneticists, practitioners and policy makers.
Manfred Gurlitt and the Japanese Operatic Scene, 1939 1972

Manfred Gurlitt (1890 1972) a gifted composer and conductor, came to feel he had to escape his native Germany in the late 1930s, as did many other musicians. Although he had, in an idiosyncratic fashion, come to terms with the National Socialist regime, he exiled himself to Japan in 1939, hoping to obtain a position that would allow him to maintain or even to burnish his reputation in the world of music. His most brilliant earlier successes had been in opera, and in Japan his career was mainly linked with opera. He worked diligently to affirm the "correct" European way of performing the operatic repertoire, and produced many first time productions of high quality. His competent, rigorous work in the field grounded the whole operatic domain of postwar Japan, and indeed his influence continues to be felt to this day. An article in the Mainichi shin bun in 1956 stated that "no musician has had such a vast and important influence over the Japanese musical world." His years in Japan, however, were spent with an underlying sentiment of displacement. His sense of unease was compounded by dissatisfaction with and unfulfilled desire for Western (particularly German) acknowledgment of the merit of his work. In this article I aim to shed new light on the place of Manfred Gurlitt in twentieth century music history by reexamining his Japanese activity, with some reference to his early oeuvre as a composer and success as a conduc tor in Germany.