bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gliese 49: activity evolution and detection of a super-Earth
Authors: Perger M. , Scandariato G. , Ribas I. , Morales J. C. , Affer L. , Azzaro M. , Amado P. J. , Anglada-Escudé G. , Baroch D. , Barrado D. , Bauer F. F. , Béjar V. J. S. , Caballero J. A. , Cortés-Contreras M. , Damasso M. , Dreizler S. , González-Cuesta L. , González Hernández J. I. , Guenther E. W. , Henning T. , Herrero E. , Jeffers S. V. , Kaminski A. , Kürster M. , Lafarga M. , Leto G. , López-González M. J. , Maldonado J. , Micela G. , Montes D. , Pinamonti M. , Quirrenbach A. , Rebolo R. , Reiners A. , Rodríguez E. , Rodríguez-López C. , Schmitt J. H. M. M. , Sozzetti A. , Suárez Mascareño A. , Toledo-Padrón B. , Zanmar Sánchez R. , Zapatero Osorio M. R. , Zechmeister M. 
Published: March 2019

Context. Small planets around low-mass stars often show orbital periods in a range that corresponds to the temperate zones of their host stars which are therefore of prime interest for planet searches. Surface phenomena such as spots and faculae create periodic signals in radial velocities and in observational activity tracers in the same range, so they can mimic or hide true planetary signals.
Aims. We aim to detect Doppler signals corresponding to planetary companions, determine their most probable orbital configurations, and understand the stellar activity and its impact on different datasets.
Methods. We analyzed 22 yr of data of the M1.5 V-type star Gl 49 (BD+61 195) including HARPS-N and CARMENES spectrographs, complemented by APT2 and SNO photometry. Activity indices are calculated from the observed spectra, and all datasets are analyzed with periodograms and noise models. We investigated how the variation of stellar activity imprints on our datasets. We further tested the origin of the signals and investigate phase shifts between the different sets. To search for the best-fit model we maximize the likelihood function in a Markov chain Monte Carlo approach.
Results. As a result of this study, we are able to detect the super-Earth Gl 49b with a minimum mass of 5.6 M⊕. It orbits its host star with a period of 13.85 d at a semi-major axis of 0.090 au and we calculate an equilibrium temperature of 350 K and a transit probability of 2.0%. The contribution from the spot-dominated host star to the different datasets is complex, and includes signals from the stellar rotation at 18.86 d, evolutionary timescales of activity phenomena at 40–80 d, and a long-term variation of at least four years.
Formation of Analytical Tools of Anti-Crisis Business Regulation on the Basis of Logit-Models
Authors: Opekunov A.N. , Kamardin I.N. , Nikitina N.V. , Kamardina N.V. 
Published: March 2019

Current trends in economic development emphasize the importance of crisis management at both the macro and microeconomic levels. The analysis of current problems of Russian enterprises has shown that at present some outdated approaches to this problem prevail in domestic science and practice. Therefore, a new scientific direction “Anti-crisis business regulation” is becoming increasingly important.
In the world practice, the term crisis-management is not associated solely with the problems of bankruptcy. On the contrary, it is a broader concept that includes a range of problems. Therefore, the technology of anti-crisis business regulation should be universal enough and allow to solve any socio-economic, financial and organizational problem.
The basic tool of anti-crisis business regulation are methods of diagnostics, monitoring and forecasting of the crisis phenomena at the enterprises which have, first of all, preventive character. One of such approaches is a method of forecasting the probability of insolvency (bankruptcy) on the basis of logit-model. This approach, tested on the sample of enterprises of Penza region, has shown a significant increase in the accuracy of forecasts compared to traditional models, for example: Altman and others.
Simulation as Implementation of Digital Approach to Decision-Making under Uncertainty and Risk
Authors: Tiutyk O.V. , Butakova M.E. 
Published: March 2019

The paper articulates the problem of modeling management decision-making process in new digital reality: decision-making under uncertainty, volatile environment, huge amount of data to be accounted, objective analytical risk attitude. The proposed solution includes critical selection of risk and uncertainty management tools aimed at improvement quality of management decision-making information support in the sustainable development context (on the example of construction projects).
The aim of research is the development of the new “digital” approach to the process of reducing uncertainty when decision making in highly risk projects, including the process model and toolkit.
Methodology is based on logical analysis and synthesis, decomposition, qualitative analysis of the relevant literature and primary data (top management informal interviews, targeted sample), comparative and regression analysis, time series analysis, mathematical statistics and simulation modelling based on nine sets of design estimation paperwork and turnover-balance sheets.
The contribution into the existing knowledge includes substantiating the correlation of the terms of the digital decision-making and simulation modeling tools in high-risk projects management under insufficient statistical data and their mutual interaction. Also, advisability of formalization of the decision-making method based on unprocessed design estimates is justified, and appropriate methods for sustainable decision-making information support are selected. The approbation demonstrated practical significance and economic effectiveness of developed approach; experiment was carried out on the base of «RSU – 6» LLC’s projects (Tchaikovsky, Perm region).
Problems of Improving Performance Budgeting in the Russian Constituent Entities under Digital Economy
Authors: Kovaleva T.M. , Mulendeeva L.N. , Glukhova A.G. 
Published: March 2019

A complex process of performance budgeting transition requires restructuring the system of state administration and organization of the budget process, strengthening the autonomy and responsibility of ministries and departments involved in the implementation of government programs.
This paper aims at further development of scientific provisions of performance budgeting in the constituent entities of the Russian Federation, analysis of problems and development prospects.
Improving performance budgeting in the digital economy implies the development of an information infrastructure, namely, an effective system of Internet resources in order to provide citizens with relevant information on the state programs implementation and their efficiency evaluation.
Methodological guidelines for evaluation of performance budgeting efficiency in the RF constituents were developed in the course of this study.
The results of the study could be applied to regional performance budgeting improvement. The findings could also allow government bodies to increase the efficiency of budgeting.
Gastric cancer and paraneoplastic dermatomyositis as complications of an unrecognized juvenile polyposis syndrome
Authors: Schiemer Moritz , Schmitt-Graeff Annette , Brass Volker , Hasselblatt Peter 
Published: March 2019

AbstractJuvenile polyposis syndrome is a rare autosomal-dominant disorder characterized by multiple hamartomatous polyps in the gastrointestinal tract. It is associated with an increased risk of gastrointestinal cancer. We report the case of a 49-year-old woman presenting with proximal muscle weakness, weight loss, severe anemia, and melena. One year before, the diagnosis of a “fundic gland polyposis” was presumed after endoscopic evaluation for iron deficiency anemia had shown numerous polyps limited to the gastric mucosa. On admission, the diagnosis of dermatomyositis was made based on laboratory results with a marked elevated creatine kinase as well as the presence of characteristic clinical findings and muscle histology. Upper endoscopy revealed multiple pedunculated, edematous polyps in the stomach without apparent cancerous lesions intraluminally. Infiltration of the muscular layer was not detectable on endoscopic ultrasound. Histopathological examination of the polyps showed smooth outer surfaces and multiple dilated cystic glands, consistent with hamartomatous juvenile-type polyps. Magnetic resonance imaging revealed a peritoneal mass close to the greater curvature of the stomach, which was identified as a poorly differentiated adenocarcinoma by laparoscopic sampling. Immunohistochemical analysis of resected polyps was remarkable for a loss of SMAD4 expression, a finding that is very commonly observed in patients with gastric juvenile polyposis syndrome. Despite initial treatment response to glucocorticoids and chemotherapy, the patient died 5 months later due to progressive illness. Patients with gastric juvenile polyposis and SMAD4 mutations are at a high risk of developing gastric cancer; hence, early gastrectomy should be considered.
Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives
Authors: Rabhi Sara , Jakubowicz Jérémie , Metzger Marie-Helene 
Published: March 2019

Objective The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports.
Methods The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals. Each of them was labeled as presence (1) or absence (0) of HAI. We started by normalizing the records using a list of preprocessing techniques. We calculated an overall performance metric, the F1 Score, to compare a deep learning method (convolutional neural network [CNN]) with the most popular conventional ML models (Bernoulli and multi-naïve Bayes, k-nearest neighbors, logistic regression, random forests, extra-trees, gradient boosting, support vector machines). We applied the hyperparameter Bayesian optimization for each model based on its HAI identification performances. We included the set of text representation as an additional hyperparameter for each model, using four different text representations (bag of words, term frequency–inverse document frequency, word2vec, and Glove).
Results CNN outperforms all other conventional ML algorithms for HAI classification. The best F1 Score of 97.7% ± 3.6% and best area under the curve score of 99.8% ± 0.41% were achieved when CNN was directly applied to the processed clinical notes without a pretrained word2vec embedding. Through receiver operating characteristic curve analysis, we could achieve a good balance between false notifications (with a specificity equal to 0.937) and system detection capability (with a sensitivity equal to 0.962) using the Youden's index reference.
Conclusions The main drawback of CNNs is their opacity. To address this issue, we investigated CNN inner layers' activation values to visualize the most meaningful phrases in a document. This method could be used to build a phrase-based medical assistant algorithm to help the infection control practitioner to select relevant medical records. Our study demonstrated that deep learning approach outperforms other classification learning algorithms for automatically identifying HAIs in medical reports.
Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases
Authors: Cardoso Isadora , Almeida Eliana , Allende-Cid Hector , Frery Alejandro , Rangayyan Rangaraj , Azevedo-Marques Paulo , Ramos Heitor 
Published: March 2019

Computational Intelligence Re-meets Medical Image Processing

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

Background Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods.
Objectives Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods.
Methods A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features were extracted per ROI. We used Principal Component Analysis, Linear Discriminant Analysis, and Stepwise Selection – Forward, Backward, and Forward-Backward to reduce feature dimensionality. The feature subsets obtained were used as input to the following ML methods: Support Vector Machine, Gaussian Mixture Model, k-Nearest Neighbor, and Deep Feedforward Neural Network. We also applied a Deep Convolutional Neural Network directly to the ROIs.
Results We achieved the maximum reduction from 28 to 5 dimensions using LDA. The best classification results were obtained by DFNN, with 99.60% of overall accuracy.
Conclusions This work contributes to the analysis and selection of features that can efficiently characterize the DLDs studied.
Bipotent stem cells support the cyclical regeneration of endometrial epithelium of the murine uterus
Authors: Jin Shiying 
Published: March 2019

Significance
Disorders of endometrial epithelial regeneration result in severe pathological conditions, including uterine cancer, endometriosis, and infertility. The lack of a detailed understanding of the cellular mechanisms underlying endometrial epithelial regeneration during the mouse estrous cycle or human menstrual cycle hinders the effective treatment of such diseases. Here, I demonstrate that the mouse uterine endometrial epithelium is maintained by a resident tissue stem cell population. These stem cells survive cyclical uterine tissue loss and persistently generate all endometrial epithelial lineages during a female’s reproductive lifespan. Such findings in mice should facilitate the study of human uterine stem cells and regeneration, which will further the understanding of uterine physiology and pathology.
Gold-implanted plasmonic quartz plate as a launch pad for laser-driven photoacoustic microfluidic pumps
Authors: Yue Shuai , Lin Feng , Zhang Qiuhui , Epie Njumbe , Dong Suchuan , Shan Xiaonan , Liu Dong , Chu Wei-Kan , Wang Zhiming , Bao Jiming 
Published: March 2019

Significance
A revolutionary microfluidic pump is demonstrated; it has no moving parts and no electrical contacts. It consists of a quartz plate implanted by Au particles where every point on the plate can function as a micropump. The pump is driven by a laser beam and is based on the discovered principle of photoacoustic laser streaming. When a pulsed laser hits the plate, it is absorbed by Au nanoparticles that generate an ultrasound wave, which then drives the fluid via acoustic streaming. Because laser beams can be arbitrarily patterned and timed, the fluid can be controlled by laser in a fashion similar to musical water fountains. Such a laser-driven photoacoustic micropump will find wide applications in microfluidics and optofluidics.
Tracking the rotation of single CdS nanorods during photocatalysis with surface plasmon resonance microscopy
Authors: Jiang Yingyan , Su Hua , Wei Wei , Wang Yongjie , Chen Hong-Yuan , Wang Wei 
Published: March 2019

Significance
Rotational dynamics of anisotropic nanomaterials reveals and regulates their behaviors and functions in diverse fields ranging from nanomotors, biomechanics, and enzymatic catalysis to microrheology. An optical imaging technique that is suitable for all kinds of anisotropic nanoobjects, regardless of its inherent optical property, is thus highly desirable and it is yet to be demonstrated. In the present work, by taking a nonfluorescent and nonplasmonic CdS nanorod as an example, we demonstrate the capability of a recently developed surface plasmon resonance microscopy for determining the orientation of single anisotropic nanomaterials with arbitrary chemical composition and morphology.
Evidence-based management of injurious pecking.
Authors: Niekerk T. van 
Published: March 2019

Abstract

Injurious pecking in laying hen flocks comprises feather pecking and tissue pecking, the latter often referred to as cannibalism. Although some gentle feather pecking belongs to the natural repertoire of laying hens, the more vigorous form, severe feather pecking, is considered an abnormal behaviour. Various theories have been developed to explain the onset of injurious pecking. All point to suboptimal circumstances leading to abnormal or redirected behaviour. A wide range of husbandry and management factors have been identified. They affect either the onset of injurious pecking (prevention) or its reduction. Prevention is most important, because once started the behaviour is very hard to stop. Therefore the first focus should be on optimizing rearing conditions to prevent injurious pecking. The most important management strategy in rear is a continuous presence of high-quality substrate to stimulate foraging behaviour and to allow the pullets to develop a habit of directing their pecking towards the litter. Any stressor can be a trigger for injurious pecking. This means management should also focus on the prevention of stressful events. Such stressful events may be changes in housing conditions (e.g. transition from rear to lay, climate) and management (e.g. light, feed, access to range), but also suboptimal health, especially parasites and compromised intestinal health. Finally some husbandry conditions seem to increase the propensity to develop injurious pecking, such as large flock sizes and a higher bird density. Management to prevent injurious pecking can only be successful if it aims at optimizing all factors involved.
High-Grade Squamous Intraepithelial Lesion of the Gastroesophageal Junction Secondary to High-Risk Human Papillomavirus
Authors: Stelow Edward B , Dill Erik A , Davick Jonathan J , McCabe Michael B , Shami Vanessa M 
Published: March 2019

ABSTRACT

Objectives
Although the role of human papillomavirus (HPV) in the development of some carcinomas (eg, anogenital and oropharyngeal squamous cell carcinomas) is nondebatable, there is still significant controversy regarding the relationship of HPV and esophageal squamous cell carcinomas (SCCs).

Methods
All cases were sampled at or near the gastroesophageal junctions in patients with reflux and/or known Barrett esophagus and appear to have been initially sampled “incidentally.” Patients were all men, aged 56 to 80 years. None had a known history of other HPV-related disease.

Results
We present four cases of high-grade squamous intraepithelial lesion of the gastroesophageal junction secondary to high-risk HPV that have identical histologic features to similar lesions of the anogenital tract.

Conclusions
Whether such lesions are at risk for developing into invasive SCC remains unclear.
Are We Measuring What Really Counts?
Authors: Thoma Achilleas , Hassan Yusuf , Santos Jenny 
Published: March 2019

Abstract
Most published clinical research is faulty because of many reasons, one being faulty design. A remedy to this problem is the correct utilization of the PICOT (population, intervention, comparative intervention, outcome and time horizon) format in the design of a clinical research question. One element of the PICOT format, “outcome,” has not been assessed adequately in aesthetic surgery. In this review, we found that in the last decade of all randomized controlled trials and comparative studies published in Aesthetic Surgery Journal, only about half specified a primary outcome. Regrettably, only 40% reported both a primary outcome and justification for choosing this outcome. This poses a credibility issue with the conclusions of the majority of published studies. There is an urgent need to develop critical outcome sets for aesthetic procedures to be utilized by future investigators. With such a critical outcome set, we will be able to pool the results of multiple studies on the same subject and reach conclusive results.
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Authors: Chen Zhen , Zhao Pei , Li Fuyi , Marquez-Lago Tatiana T , Leier André , Revote Jerico , Zhu Yan , Powell David R , Akutsu Tatsuya , Webb Geoffrey I , Chou Kuo-Chen , Smith A Ian , Daly Roger J , Li Jian , Song Jiangning 
Published: March 2019

AbstractWith the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.
Freshwater-to-marine transitions may explain the evolution of herbivory in the subgenus Mollienesia (genus Poecilia, mollies and guppies)
Authors: Sanchez Jessica L , Bracken-Grissom Heather D , Trexler Joel C 
Published: March 2019

Abstract
The ability of organisms to cross ecosystem boundaries is an important catalyst of evolutionary diversification. The genus Poecilia (mollies and guppies) is an excellent system for studying ecosystem transitions because species display a range of salinity and dietary preferences, with herbivory concentrated in the subgenus Mollienesia. We reconstructed ancestral habitats and diets across a phylogeny of the genus Poecilia, evaluated diversification rates and used phylogenetically independent contrasts to determine whether diet evolved in response to habitat transition in this group. The results suggest that ancestors of subgenus Mollienesia were exclusively herbivorous, whereas ancestral diets of other Poecilia included animals. We found that transitions across euryhaline boundaries occurred at least once in this group, probably after the divergence of the subgenus Mollienesia. Furthermore, increased salinity affiliation explained 24% of the decrease in animals in the gut, and jaw morphology was associated with the percentage of animals in the gut, but not with the percentage of species occupying saline habitats. These findings suggest that in the genus Poecilia, herbivory evolved in association with transitions from fresh to euryhaline habitats, and jaw morphology evolved in response to the appearance of herbivory. These results provide a rare example of increased diet diversification associated with the transition from freshwater to euryhaline habitats.