nils-herrmann/clean_scirepeval huggingface dataset:
Evidence-based review of lasers, light sources and photodynamic therapy in the treatment of acne vulgaris.

There is a considerable need for effective and safe treatment for acne vulgaris.  In a systematic review with an evidence-based approach to assess the effects of optical treatments for acne vulgaris.  Original publications of controlled clinical trials were identified through searches in PubMed and the Cochrane Library.  A total of 16 randomized controlled trials (RCT) and 3 controlled trials (CT) were identified, involving a total of 587 patients. Interventions included photodynamic therapy (PDT; 5 RCTs), infrared lasers (4 RCTs), broad-spectrum light sources (3 RCTs, 1 CT), pulsed dye lasers (PDL; 2 RCTs, 1 CT), intense pulsed light (IPL; 1 RCTs, 2 CTs), and potassium titanyl phosphate laser (1 RCT). The randomization method was mentioned in 6 of 16 RCTs, and one trial described adequate allocation concealment. Most trials were intraindividual trials (12 of 19), which applied blinded response evaluations (12 of 19) and assessed a short-term efficacy up to 12 weeks after treatment (17 of 19). Based on the present best available evidence, we conclude that optical treatments possess the potential to improve inflammatory acne on a short-term basis with the most consistent outcomes for PDT [up to 68% improvement, aminolevulinic acid (ALA), methyl-aminolevulinic acid (MAL) and red light]. IPL-assisted PDT seems to be superior to IPL alone. Only two trials compare optical vs. conventional treatments, and further studies are needed. Side-effects from optical treatments included pain, erythema, oedema, crusting, hyperpigmentation, pustular eruptions and were more intense for treatments combined with ALA or MAL.  Evidence from controlled clinical trials indicates a short-term efficacy from optical treatments for acne vulgaris with the most consistent outcomes for PDT. We recommend that patients are preoperatively informed of the existing evidence, which indicates that optical treatments today are not included among first line treatments.
Switched-capacitor (SC)/switched inductor (SL) structures for getting hybrid step-down Cuk/Sepic/Zeta converters

Three basic switching structures are defined: one is formed by two capacitors and three diodes; the other two are formed by two inductors and two diodes. They are inserted in either a Cuk converter, or a Sepic, or a Zeta converter. The SC/SL structures are built in such a way as when the active switch of the converter is on, the two inductors are charged in series or the two capacitors are discharged in parallel. When the active switch is off, the two inductors are discharged in parallel or the two capacitors are charged in series. As a result, the line voltage is reduced more times than in classical Cuk/Sepic/Zeta converters. The steady-state analysis of the new converters, a comparison of the DC voltage gain and of the voltage and current stresses of the new hybrid converters with those of the available quadratic converters, and experimental results are given
Mass transfer limitations in embryoid bodies during human embryonic stem cell differentiation.

Due to their ability to differentiate into cell types from all the three germ layers and their potential unlimited capacity for expansion, embryonic stem cells have tremendous potential to treat diseases and injuries. Spontaneous differentiation of human embryonic stem cells (hESCs) is influenced by the size of the differentiating embryoid bodies (EBs). To further understand the dynamics between nutrient mass transfer, EB size, and stem cell differentiation, a transient mass diffusion model of a single hESC EB was constructed. The results revealed that the oxygen concentration at the centers of large EBs (400-μm radius) was 50% lower when compared to that in smaller EBs (200-μm radius). In addition, the concentration profile of cytokines within an EB depended strongly on their depletion rate, with higher depletion rates resulting in cytokine concentrations that varied significantly throughout the EB. A comparison of the results of our model with published experimental data reveals a close correlation between the fraction of cells that differentiate to a given lineage and the fraction of cells exposed to different oxygen or cytokine concentrations. This, along with other data from the literature, suggests that diffusive mass transfer influences the differentiation of hESCs within EBs by controlling the spatial distribution of soluble factors. This has important implications for research involving the differentiation of embryonic stem cells in EBs, as well as for bioprocess design and the development of robust differentiation protocols where mass transfer could be altered to control the cell differentiation trajectory.
Time-parameterized queries in spatio-temporal databases

Time-parameterized queries (TP queries for short) retrieve (i) the actual result at the time that the query is issued, (ii) the validity period of the result given the current motion of the query and the database objects, and (iii) the change that causes the expiration of the result. Due to the highly dynamic nature of several spatio-temporal applications, TP queries are important both as standalone methods, as well as building blocks of more complex operations. However, little work has been done towards their efficient processing. In this paper, we propose a general framework that covers time-parameterized variations of the most common spatial queries, namely window queries, k-nearest neighbors and spatial joins. In particular, each of these TP queries is reduced to nearest neighbor search where the distance functions are defined according to the query type. This reduction allows the application and extension of well-known branch and bound techniques to the current problem. The proposed methods can be applied with mobile queries, mobile objects or both, given a suitable indexing method. Our experimental evaluation is based on R-trees and their extensions for dynamic objects.
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

YES is a simplified stroke-based method for sorting Chinese characters. It is free from stroke counting and grouping, and thus much faster and more accurate than the traditional method. This paper presents a collation element table built in YES for a large joint Chinese character set covering (a) all 20,902 characters of Unicode CJK Unified Ideographs, (b) all 11,408 characters in the Complete List of Chinese Characters Used by the Media in 2013, (c) all 13,000 plus characters in the latest versions of Xinhua Dictionary(v11) and Contemporary Chinese Dictionary(v6). Of the 20,902 Chinese characters in Unicode, 97.23% have one-to-one relationship with their stroke order codes in YES, comparing with 90.69% of the traditional method. Enhanced with the secondary and tertiary sorting levels of stroke layout and Unicode value, there is a guarantee of one-to-one relationship between the characters and collation elements. The collation element table has been successfully applied to sorting CC-CEDICT, a Chinese-English dictionary of over 112,000 word entries.
Defining a Forensic Audit

Disclosures about new financial frauds and scandals are continually appearing in the press. As a consequence, the accounting profession's traditional methods of monitoring corporate financial activities are under intense scrutiny. At the same time, there is recognition that principles-based GAAP from the International Accounting Standards Board will become the recognized standard in the U.S. The authors argue that these two factors will change the practices used to fight corporate malfeasance as investigators adapt the techniques of accounting into a forensic audit engagement model.
Diagnosis and management of Graves' disease.

Graves' disease is characterized by hyperthyroidism, diffuse goitre, ophthalmopathy and, rarely, dermopathy. Although diagnostic testing is straightforward once Graves' disease is suspected, physicians need to be aware of heterogeneous and even atypical presentations of the disease, particularly in elderly patients. Because morbidity may be associated with even subtle forms of hyperthyroidism, treatment promoting long-term euthyroidism is necessary. Although all of the available treatments are effective, compliance is best assured by a full discussion of the risks and benefits of each approach. This review focuses on issues of diagnosis and management that will allow the primary care physician to identify patients with Graves' disease and guide them to recovery.
Detecting and Tracking Disease Outbreaks by Mining Social Media Data

The emergence and ubiquity of online social networks have enriched web data with evolving interactions and communities both at mega-scale and in real-time. This data offers an unprecedented opportunity for studying the interaction between society and disease outbreaks. The challenge we describe in this data paper is how to extract and leverage epidemic outbreak insights from massive amounts of social media data and how this exercise can benefit medical professionals, patients, and policymakers alike. We attempt to prepare the research community for this challenge with four datasets. Publishing the four datasets will commoditize the data infrastructure to allow a higher and more efficient focal point for the research community.
High-functioning autism spectrum disorder as a basic disorder in adult psychiatry and psychotherapy: psychopathological presentation, clinical relevance and therapeutic concepts

Autism spectrum disorder (ASD) is characterized by deficits in social cognition and competence, communication, highly circumscribed interests and a strong desire for routines. Besides, there are specific abnormalities in perception and language. Typical symptoms are already present in early childhood. Traditionally autism has been regarded as a severe form of neurodevelopmental disorder which goes along with overtly abnormal language, learning difficulties and low IQ in the majority of cases. However, over the last decades, it has become clear that there are also many patients with high-functioning variants of ASD. These are patients with normal language at a superficial level of description and normal and sometimes above average intelligence. In high-functioning variants of the disease, they may run unrecognized until late in adult life. High-functioning ASD is associated with a very high prevalence of comorbid classical psychiatric disorders such as depression, anxiety, ADHD, tics, psychotic symptoms or emotionally unstable syndromes. In many such cases, there is a causal relationship between ASD and the comorbid psychiatric conditions in that the specific ASD symptoms result in chronic conflicts, misunderstandings and failure in private and vocational relationships. These problems in turn often lead to depression, anxiety and sometimes psychosis-like stress reactions. In this constellation, ASD has to be regarded as a basic disorder with causal relevance for secondary psychiatric syndromes. In this paper, we summarize the classical presentation of high-functioning ASD in adult psychiatry and psychotherapy and suggest a nosological model to classify different ASD conditions instead. To conclude, we outline first treatment concepts in out- and in-patient settings.
Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning

In many real world applications such as satellite image analysis, gene function prediction, and insider threat detection, the data collected from heterogeneous sources often exhibit multiple types of heterogeneity, such as task heterogeneity, view heterogeneity, and label heterogeneity. To address this problem, we propose a Hierarchical Multi-Latent Space (HiMLS) learning approach to jointly model the triple types of heterogeneity. The basic idea is to learn a hierarchical multi-latent space by which we can simultaneously leverage the task relatedness, view consistency and the label correlations to improve the learning performance. We first propose a multi-latent space framework to model the complex heterogeneity, which is used as a building block to stack up a multi-layer structure so as to learn the hierarchical multi-latent space. In such a way, we can gradually learn the more abstract concepts in the higher level. Then, a deep learning algorithm is proposed to solve the optimization problem. The experimental results on various data sets show the effectiveness of the proposed approach.
A correlated topic model of Science

Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation even though, for example, a document about genetics is more likely to also be about disease than X-ray astronomy. This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions. In this paper we develop the correlated topic model (CTM), where the topic proportions exhibit correlation via the logistic normal distribution . We derive a fast variational inference algorithm for approximate posterior inference in this model, which is complicated by the fact that the logistic normal is not conjugate to the multinomial. We apply the CTM to the articles from Science published from 1990–1999, a data set that comprises 57M words. The CTM gives a better fit of the data than LDA, and we demonstrate its use as an exploratory tool of large document collections.
Influence of a passive sonic irrigation system on the elimination of bacteria from root canal systems: a clinical study.

The present investigation evaluated the ability of a new passive sonic irrigation (sonic group) system (EndoActivator) to eliminate cultivable bacteria from root canals in vivo and compared it with that of standard syringe irrigation (control group).



Data were obtained by using bacteriologic sampling of root canals treated by endodontic residents. Sampling results from 1 session of treatment were then compared with results obtained after intervisit calcium hydroxide disinfection and a second session of treatment.



There was no significant difference in the ability of sonic group and control group to eliminate cultivable bacteria from root canals (P > .05). A second session and intervisit calcium hydroxide disinfection were able to eliminate cultivable bacteria from significantly more teeth than a single session of treatment (P < .05).



These in vivo results strengthen the case for a multi-visit approach to the treatment of apical periodontitis.
Validation of biophysical models: issues and methodologies. A review

The potential of mathematical models is widely acknowledged for examining components and interactions of natural systems, estimating the changes and uncertainties on outcomes, and fostering communication between scientists with different backgrounds and between scientists, managers and the community. For favourable reception of models, a systematic accrual of a good knowledge base is crucial for both science and decision-making. As the roles of models grow in importance, there is an increase in the need for appropriate methods with which to test their quality and performance. For biophysical models, the heterogeneity of data and the range of factors influencing usefulness of their outputs often make it difficult for full analysis and assessment. As a result, modelling studies in the domain of natural sciences often lack elements of good modelling practice related to model validation, that is correspondence of models to its intended purpose. Here we review validation issues and methods currently available for assessing the quality of biophysical models. The review covers issues of validation purpose, the robustness of model results, data quality, model prediction and model complexity. The importance of assessing input data quality and interpretation of phenomena is also addressed. Details are then provided on the range of measures commonly used for validation. Requirements for a methodology for assessment during the entire model-cycle are synthesised. Examples are used from a variety of modelling studies which mainly include agronomic modelling, e.g. crop growth and development, climatic modelling, e.g. climate scenarios, and hydrological modelling, e.g. soil hydrology, but the principles are essentially applicable to any area. It is shown that conducting detailed validation requires multi-faceted knowledge, and poses substantial scientific and technical challenges. Special emphasis is placed on using combined multiple statistics to expand our horizons in validation whilst also tailoring the validation requirements to the specific objectives of the application.
Thrombocytopenia absent radius (TAR) syndrome.

The aim of the work was a presentation of one case with Thrombocytopenia absent radius (TAR) syndrome.



Diagnosis of TAR syndrome has been established on the basis of pedigree, laboratory findings (hemogram, platelet count, peripheral smear), bone marrow biopsy, radiological examination and karyotype.



A patient was a two months old female child, hospitalized due petechial bleeding, upper limb anomalies and diarrhea.



red blood cell count was 2.1 x 1012/L, hemoglobin value was 62 g/L, white blood cell count indicated the existence of leukemoid reaction (40.0 x 109/L), the eosinophyle count at the leukocyte formula was increased (3%), bleeding time was prolonged (10'). The platelets at the peripheral blood smear were rarely present, whereas the megacaryocytes appeared in the bone marrow aspiration in the decreased number, or did not appear at all. At the radiological examination of the upper limbs, radius was absent in both shoulders.



TAR syndrome is a rare hereditary disease. Obligatory clinical manifestations are: thrombocytopenia and bilateral absence of the radius. Prenatal diagnosis can be established during the 16th week of gestation by ultrasound and if it is continued with the pregnancy it is preferred that the platelet transfusion be given intrauterine. The mortality rate depends on the age of the patient and the platelet count.
Doppler waveform indices of fetal middle cerebral artery in normal 20 to 40 weeks pregnancies.

One of the main methods for evaluation of fetal well-being is analysis of Doppler flow velocity waveform of fetal vessels. Evaluation of Doppler wave of the middle cerebral artery can predict most of the at-risk fetuses in high-risk pregnancies. In this study, we tried to determine the normal ranges and their trends during pregnancy of Doppler flow velocity indices (resistive index, pulsatility index, systolic-to-diastolic ratio, and peak systolic velocity) of middle cerebral artery in 20 - 40 weeks normal pregnancies in Iranians.



In this cross-sectional study, 1037 women with normal pregnancy and gestational age of 20 to 40 weeks were investigated for fetal middle cerebral artery Doppler examination.



Resistive index, pulsatility index, and systolic-to-diastolic ratio values of middle cerebral artery decreased in a parabolic pattern while the peak systolic velocity value increased linearly with progression of the gestational age. These changes were statistically significant (P<0.001 for all four variables) and were more characteristic during late weeks of pregnancy. The mean fetal heart rate was also significantly (P<0.001) reduced in correlation with the gestational age.



Doppler waveform indices of fetal middle cerebral artery are useful means for determining fetal well-being. Herewith, the normal ranges of Doppler waveform indices for an Iranian population are presented.
Spatially Perturbed Collision Sounds Attenuate Perceived Causality in 3D Launching Events

When a moving object collides with an object at rest, people immediately perceive a causal event: i.e., the first object has launched the second object forwards. However, when the second object's motion is delayed, or is accompanied by a collision sound, causal impressions attenuate and strengthen. Despite a rich literature on causal perception, researchers have exclusively utilized 2D visual displays to examine the launching effect. It remains unclear whether people are equally sensitive to the spatiotemporal properties of observed collisions in the real world. The present study first examined whether previous findings in causal perception with audiovisual inputs can be extended to immersive 3D virtual environments. We then investigated whether perceived causality is influenced by variations in the spatial position of an auditory collision indicator. We found that people are able to localize sound positions based on auditory inputs in VR environments, and spatial discrepancy between the estimated position of the collision sound and the visually observed impact location attenuates perceived causality.
Transthyretin and familial amyloidotic polyneuropathy. Recent progress in understanding the molecular mechanism of neurodegeneration.

Familial amyloidotic polyneuropathy (FAP) is an inherited autosomal dominant disease that is commonly caused by accumulation of deposits of transthyretin (TTR) amyloid around peripheral nerves. The only effective treatment for FAP is liver transplantation. However, recent studies on TTR aggregation provide clues to the mechanism of the molecular pathogenesis of FAP and suggest new avenues for therapeutic intervention. It is increasingly recognized that there are common features of a number of protein-misfolding diseases that can lead to neurodegeneration. As for other amyloidogenic proteins, the most toxic forms of aggregated TTR are likely to be the low-molecular-mass diffusible species, and there is increasing evidence that this toxicity is mediated by disturbances in calcium homeostasis. This article reviews what is already known about the mechanism of TTR aggregation in FAP and describes how recent discoveries in other areas of amyloid research, particularly Alzheimer's disease, provide clues to the molecular pathogenesis of FAP.
Provable Models for Robust Low-Rank Tensor Completion

In this paper, we rigorously study tractable models for provably recovering low-rank tensors. Unlike their matrix-based predecessors, current convex approaches for recovering low-rank tensors based on incomplete (tensor completion) and/or grossly corrupted (tensor robust principal analysis) observations still suffer from the lack of theoretical guarantees, although they have been used in various recent applications and have exhibited promising empirical performance. In this work, we attempt to fill this gap. Specifically, we propose a class of convex recovery models (including strongly convex programs) that can be proved to guarantee exact recovery under a set of new tensor incoherence conditions which only require the existence of one low-rank mode, and characterize the problems where our models tend to perform well.
Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model

Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.