nils-herrmann/clean_scirepeval huggingface dataset:
Obesity Before, During, and After Pregnancy: A Review and Comparison of Five National Guidelines.

This study aims to compare how national guidelines approach the management of obesity in reproductive age women.



We conducted a search for national guidelines in the English language on the topic of obesity surrounding the time of a pregnancy. We identified six primary source documents and several secondary source documents from five countries. Each document was then reviewed to identify: (1) statements acknowledging increased health risks related to obesity and reproductive outcomes, (2) recommendations for the management of obesity before, during, or after pregnancy.



All guidelines cited an increased risk for miscarriage, birth defects, gestational diabetes, hypertension, fetal growth abnormalities, cesarean sections, difficulty with anesthesia, postpartum hemorrhage, and obesity in offspring. Counseling on the risks of obesity and weight loss before pregnancy were universal recommendations. There were substantial differences in the recommendations pertaining to gestational weight gain goals, nutrient and vitamin supplements, screening for gestational diabetes, and thromboprophylaxis among the guidelines.



Stronger evidence from randomized trials is needed to devise consistent recommendations for obese reproductive age women. This research may also assist clinicians in overcoming one of the many obstacles they encounter when providing care to obese women.
Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Data mining and analytics have played an important role in knowledge discovery and decision making/supports in the process industry over the past several decades. As a computational engine to data mining and analytics, machine learning serves as basic tools for information extraction, data pattern recognition and predictions. From the perspective of machine learning, this paper provides a review on existing data mining and analytics applications in the process industry over the past several decades. The state-of-the-art of data mining and analytics are reviewed through eight unsupervised learning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms. Several perspectives are highlighted and discussed for future researches on data mining and analytics in the process industry.
A three-dimensional kinematic analysis of the long jump take-off.

The long jump has been widely studied in recent years. Two models exist in the literature which define the relationship between selected variables that affect performance. Both models suggest that the critical phase of the long jump event is the touch-down to take-off phase, as it is in this phase that the necessary vertical velocity is generated. Many three dimensional studies of the long jump exist, but the only studies to have reported detailed data on this phase were two-dimensional in nature. In these, the poor relationships obtained between key variables and performance led to the suggestion that there may be some relevant information in data in the third dimension. The aims of this study were to conduct a three-dimensional analysis of the touch-down to take-off phase in the long jump and to explore the interrelationships between key variables. Fourteen male long jumpers were filmed using three-dimensional methods during the finals of the 1994 (n = 8) and 1995 (n = 6) UK National Championships. Various key variables for the long jump were used in a series of correlational and multiple regression analyses. The relationships between key variables when correlated directly one-to-one were generally poor. However, when analysed using a multiple regression approach, a series of variables was identified which supported the general principles outlined in the two models. These variables could be interpreted in terms of speed, technique and strength. We concluded that in the long jump, variables that are important to performance are interdependent and can only be identified by using appropriate statistical techniques. This has implications for a better understanding of the long jump event and it is likely that this finding can be generalized to other technical sports skills.
A parallel spatial data analysis infrastructure for the cloud

Spatial data analysis applications are emerging from a wide range of domains such as building information management, environmental assessments and medical imaging. Time-consuming computational geometry algorithms make these applications slow, even for medium-sized datasets. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends demands effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud.
 We introduce Niharika, a parallel spatial data analysis infrastructure that exploits all available cores in a heterogeneous cluster. Niharika first uses a declustering technique that creates balanced spatial partitions. Then, Niharika adapts to performance heterogeneity and processing skew in the spatial dataset using dynamic load-balancing. We evaluate Niharika with three load-balancing algorithms and two different spatial datasets (both from TIGER) using Amazon EC2 instances. Niharika adapts to the performance heterogeneity in the EC2 nodes, thereby achieving excellent speedups (e.g., 63.6X using 64 cores on 16 4-core EC2 nodes, in the best case) and outperforming an approach that does not adapt.
Listen reader: an electronically augmented paper-based book

While predictions abound that electronic books will supplant traditional paper-based books, many people bemoan the coming loss of the book as cultural artifact. In this project we deliberately keep the affordances of paper books while adding electronic augmentation. The Listen Reader combines the look and feel of a real book - a beautiful binding, paper pages and printed images and text - with the rich, evocative quality of a movie soundtrack. The book's multi-layered interactive soundtrack consists of music and sound effects. Electric field sensors located in the book binding sense the proximity of the reader's hands and control audio parameters, while RFID tags embedded in each page allow fast, robust page identification.
Three different Listen Readers were built as part of a six-month museum exhibit, with more than 350,000 visitors. This paper discusses design, implementation, and lessons learned through the iterative design process, observation, and visitor interviews.
An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations.

Combined chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) has enabled genome-wide epigenetic profiling of numerous cell lines and tissue types. A major limitation of ChIP-seq, however, is the large number of cells required to generate high-quality data sets, precluding the study of rare cell populations. Here, we present an ultra-low-input micrococcal nuclease-based native ChIP (ULI-NChIP) and sequencing method to generate genome-wide histone mark profiles with high resolution from as few as 10(3) cells. We demonstrate that ULI-NChIP-seq generates high-quality maps of covalent histone marks from 10(3) to 10(6) embryonic stem cells. Subsequently, we show that ULI-NChIP-seq H3K27me3 profiles generated from E13.5 primordial germ cells isolated from single male and female embryos show high similarity to recent data sets generated using 50-180 × more material. Finally, we identify sexually dimorphic H3K27me3 enrichment at specific genic promoters, thereby illustrating the utility of this method for generating high-quality and -complexity libraries from rare cell populations.
Deep fusion of visual signatures for client-server facial analysis

Facial analysis is a key technology for enabling human-machine interaction. In this context, we present a client-server framework, where a client transmits the signature of a face to be analyzed to the server, and, in return, the server sends back various information describing the face e.g. is the person male or female, is she/he bald, does he have a mustache, etc. We assume that a client can compute one (or a combination) of visual features; from very simple and efficient features, like Local Binary Patterns, to more complex and computationally heavy, like Fisher Vectors and CNN based, depending on the computing resources available. The challenge addressed in this paper is to design a common universal representation such that a single merged signature is transmitted to the server, whatever be the type and number of features computed by the client, ensuring nonetheless an optimal performance. Our solution is based on learning of a common optimal subspace for aligning the different face features and merging them into a universal signature. We have validated the proposed method on the challenging CelebA dataset, on which our method outperforms existing state-of-art methods when rich representation is available at test time, while giving competitive performance when only simple signatures (like LBP) are available at test time due to resource constraints on the client.
How Does Free Riding on Customer Service Affect Competition ?

T free-riding problem occurs if the presales activities needed to sell a product can be conducted separately from the actual sale of the product. Intuitively, free riding should hurt the retailer that provides that service, but the author shows analytically that free riding benefits not only the free-riding retailer, but also the retailer that provides the service when customers are heterogeneous in terms of their opportunity costs for shopping. The service-providing retailer has a postservice advantage, because customers who have resolved their matching uncertainty through sales service incur zero marginal shopping cost if they purchase from the service-providing retailer rather than the free-riding retailer. Moreover, allowing free riding gives the free rider less incentive to compete with the service provider on price, because many customers eventually will switch to it due to their own free riding. In turn, this induced soft strategic response enables the service provider to charge a higher price and enjoy the strictly positive profit that otherwise would have been wiped away by head-to-head price competition. Therefore, allowing free riding can be regarded as a necessary mechanism that prevents an aggressive response from another retailer and reduces the intensity of price competition.
Automatic Teacher Modeling from Live Classroom Audio

We investigate automatic analysis of teachers' instructional strategies from audio recordings collected in live classrooms. We collected a data set of teacher audio and human-coded instructional activities (e.g., lecture, question and answer, group work) in 76 middle school literature, language arts, and civics classes from eleven teachers across six schools. We automatically segment teacher audio to analyze speech vs. rest patterns, generate automatic transcripts of the teachers' speech to extract natural language features, and compute low-level acoustic features. We train supervised machine learning models to identify occurrences of five key instructional segments (Question & Answer, Procedures and Directions, Supervised Seatwork, Small Group Work, and Lecture) that collectively comprise 76% of the data. Models are validated independently of teacher in order to increase generalizability to new teachers from the same sample. We were able to identify the five instructional segments above chance levels with F1 scores ranging from 0.64 to 0.78. We discuss key findings in the context of teacher modeling for formative assessment and professional development.
Evolving viral marketing strategies

One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determining how many and which consumers within a particular social network should be seeded to maximize adoption is challenging. We define a strategy space for consumer seeding by weighting a combination of network characteristics such as average path length, clustering coefficient, and degree. We measure strategy effectiveness by simulating adoption on a Bass-like agent-based model, with five different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and one empirical (extracted from Twitter friendship data). To discover good seeding strategies, we have developed a new tool, called BehaviorSearch, which uses genetic algorithms to search through the parameter-space of agent-based models. This evolutionary search also provides insight into the interaction between strategies and network structure. Our results show that one simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical Twitter-based network. We also find a correlation between the optimal seeding budget for a network, and the inequality of the degree distribution.
Improving blood pressure control in end stage renal disease through a supportive educative nursing intervention.

Hypertension in patients on hemodialysis (HD) contributes significantly to their morbidity and mortality. This study examined whether a supportive nursing intervention incorporating monitoring, goal setting, and reinforcement can improve blood pressure (BP) control in a chronic HD population. A randomized controlled design was used and 118 participants were recruited from six HD units in the Detroit metro area. The intervention consisted of (1) BP education sessions; (2) a 12-week intervention, including monitoring, goal setting, and reinforcement; and (3) a 30-day post-intervention follow-up period. Participants in the treatment were asked to monitor their BP, sodium, and fluid intake weekly for 12 weeks in weekly logs. BP, fluid and sodium logs were reviewed weekly with the researcher to determine if goals were met or not met. Reinforcement was given for goals met and problem solving offered when goals were not met. The control group received standard care. Both systolic and diastolic BPs were significantly decreased in the treatment group.
Subsea: an efficient heuristic algorithm for subgraph isomorphism

We present a novel approach to the problem of finding all subgraphs and induced subgraphs of a (target) graph which are isomorphic to another (pattern) graph. To attain efficiency we use a special representation of the pattern graph. We also combine our search algorithm with some known bisection algorithms. Experimental comparison with other algorithms was performed on several types of graphs. The comparison results suggest that the approach provided here is most effective when all instances of a subgraph need to be found.
Straddle injuries in female children and adolescents: 10-year accident and management analysis.

To analyze unintentional straddle injuries in girls with regards to epidemiology, etiology and injury management.



The hospital database was retrospectively reviewed (1999-2009) for female patients managed for genital trauma. Patients were evaluated based on age, causative factors, type of injury, area of genitals affected, management and outcomes.



Straddle injuries were documented in 91 girls with age ranging from 1 to 15 y (mean = 6.3 y; median = 6.1 y). The causes of injuries were falls at home (n = 31) or outdoors (n = 27), and sport activities (swimming pool n = 11, skating n = 11, bicycle n = 9 and scooter n = 2). Most of the injuries were lacerations. Injuries involved major labia (n = 56), minor labia (n = 45) and introitus vaginae (n = 15). Twelve children received outpatient treatment. Inspection under anesthesia was performed in 79 patients, with 76 requiring sutures. While hematuria was observed in 18 patients, cystoscopy did not reveal lesions in the urethra or bladder. Associated injuries were femur fracture (n = 1), lower extremity lacerations (n = 4) and anal lesions (n = 2). Follow-up investigations were uneventful; however one patient developed a secondary abscess and another secondary hyperplasia of the labia minor.



Falls and sports are major causes of straddle injuries with a peak at the age of six years. Lacerations are the most common injuries and often require surgical management. Urinary tract injuries and other associated injuries are relatively uncommon in girls with straddle injuries.
Sensing Movement: Microsensors for Body Motion Measurement

Recognition of body posture and motion is an important physiological function that can keep the body in balance. Man-made motion sensors have also been widely applied for a broad array of biomedical applications including diagnosis of balance disorders and evaluation of energy expenditure. This paper reviews the state-of-the-art sensing components utilized for body motion measurement. The anatomy and working principles of a natural body motion sensor, the human vestibular system, are first described. Various man-made inertial sensors are then elaborated based on their distinctive sensing mechanisms. In particular, both the conventional solid-state motion sensors and the emerging non solid-state motion sensors are depicted. With their lower cost and increased intelligence, man-made motion sensors are expected to play an increasingly important role in biomedical systems for basic research as well as clinical diagnostics.
Deep learning models for bacteria taxonomic classification of metagenomic data

An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them. To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data. In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.
Tumors of the nail unit. A review. Part I: acquired localized longitudinal melanonychia and erythronychia.

The article aims to be a guide to the interpretation of tumors specific to the nail, that is, tumors presenting peculiar histological features linked specifically to the nail unit. Therefore, the classical epithelial, fibroepithelial, and fibrous skin tumors occurring in the nail region are not analyzed. The interpretation of nail biopsies requires the identification and integration of the 2 main clinical modes of presentation of nail tumors, the acquired localized (monodactylous) longitudinal (ALL) band pattern, and the "masked" nail tumor. The ALL band pattern often allows the recognition of a nail tumor in its early phase of progression, with a limited differential diagnosis. The masked nail tumor mimics an inflammatory nail process, as a clinically misleading reactive benign lesion, which delays diagnosis with the subsequent development of partial nail loss and a locally destructive evolution. ALL band pattern appears as a longitudinal band starting at the matrix and extending to the tip of the nail plate. The band is usually single, rarely bifid. This clinical pattern can divided into 2 presentations. The generic term of ALL maculonychia could be proposed to define the macular aspect of the colored band of the nail plate. It encompasses 3 syndromes: longitudinal melanonychia, longitudinal erythronychia, and longitudinal leukonychia. ALL pachyonychia is a rare presentation. Pachyonychia indicates a localized thickening of the nail plate specific to the matrical nail tumor. In this group, there is differentiation toward cells of the nail matrix. The prototype tumor is the onychomatricoma, which present classically with a yellow (xantholeukonychia) band pattern. Recently, a new clinical band pattern has been described as longitudinal pachymelanonychia with 2 etiologies: pigmented onychomatricoma and onychocytic matricoma. The first part of this review delineate, in the first section, the distinctive microanatomical features of the nail unit and the second is dedicated to the most important pitfalls in pathological diagnosis of nail tumors because of nail surgery techniques. In the third section, the histopathology of ALL melanonychia and ALL erythronychia is discussed in a detailed description.
Dermoscopy Findings of Pseudolymphomatous Folliculitis

Pseudolymphomatous folliculitis (PLF), which clinically mimicks cutaneous lymphoma, is a rare manifestation of cutaneous pseudolymphoma and cutaneous lymphoid hyperplasia. Here, we report on a 45-year-old Japanese woman with PLF. Dermoscopy findings revealed prominent arborizing vessels with small perifollicular and follicular yellowish spots and follicular red dots. A biopsy specimen also revealed dense lymphocytes, especially CD1a+ cells, infiltrated around the hair follicles. Without any additional treatment, the patient's nodule rapidly decreased. The presented case suggests that typical dermoscopy findings could be a possible supportive tool for the diagnosis of PLF.