bluuebunny/crossref_metadata_2025_split huggingface dataset:
A better route to ALPPS: minimally invasive vs open ALPPS
Authors: Michal Kawka , Sau Mak , Tamara Gall M. H. , Long Jiao R. 
Published: April 2020

Abstract
Background
Associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) has gained both interest and controversy, as an alternative to portal vein embolisation (PVE) by inducing future liver remnant hypertrophy in patients at risk of liver failure following major hepatectomy. Open ALPPS induces more extensive hypertrophy in a shorter timespan than PVE; however, it is also associated with higher complication rates and mortality. Minimally invasive surgery (MIS), with its known benefits, has been applied to ALPPS in the hope of reducing the surgical insult and improving functional recovery time while preserving the extensive FLR hypertrophy.

Methods
A search of the PubMed, Medline, EMBASE and Cochrane Library databases was conducted on 10 July 2019. 1231 studies were identified and screened. 19 open ALPPS studies, 3 MIS ALPPS and 1 study reporting on both were included in the analysis.

Results
1088 open and 46 MIS-ALPPS cases were included in the analysis. There were significant differences in the baseline characteristic: open ALPPS patients had a more diverse profile of underlying pathologies (p = 0.028) and comparatively more right extended hepatectomies (p = 0.006) as compared to right hepatectomy and left extended hepatectomy performed. Operative parameters (time and blood loss) did not differ between the two groups. MIS ALPPS had a lower rate of severe Clavien–Dindo complications (≥ IIIa) following stage 1 (p = 0.063) and significantly lower median mortality (0.00% vs 8.45%) (p = 0.007) compared to open ALPPS.

Conclusion
Although MIS ALPPS would seem to be better than open ALPPS with reduced morbidity and mortality rates, there is still limited evidence on MIS ALPPS. There is a need for a higher quality of evidence on MIS ALPPS vs. open ALPPS to answer whether MIS ALPPS can replace open ALPPS.
Diurnal rhythmicity in breast-milk glucocorticoids, and infant behavior and sleep at age 3 months
Authors: Toorop Alyssa A. , van der Voorn Bibian , Hollanders Jonneke J. , Dijkstra Lisette R. , Dolman Koert M. , Heijboer Annemieke C. , Rotteveel Joost , Honig Adriaan , Finken Martijn J. J. 
Published: April 2020

Abstract
Purpose
In previous studies, associations between breast-milk cortisol levels obtained on one occasion and infant neurodevelopment were demonstrated. However, more recent evidence indicates that breast-milk cortisol and cortisone concentrations follow the diurnal rhythm of maternal hypothalamus-pituitary-adrenal axis, peaking in the early morning and with a nadir at midnight. We studied associations between breast-milk glucocorticoid (GC) rhythmicity, and infant behavior and sleep.

Methods
We included 59 mothers, and their infants, of whom 17 had consulted an expert center during pregnancy for an increased risk of psychological distress. At 1 month postpartum, breast milk was sampled (on average six times) over a 24 h period for assessment of cortisol and cortisone using LC-MS/MS, and experienced maternal distress was assessed using the Hospital Anxiety and Depression Scale questionnaire. Three months after birth, infant behavior was assessed with the Infant Behavior Questionnaire, and infant sleep pattern was quantified by questionnaire. Associations between breast-milk GC rhythm parameters (maximum, delta, and Area Under the Curve increase and ground) and infant behavior and sleep were tested with linear regression analyses.

Results
No consistent associations between breast-milk GC rhythm parameters and infant behavior or sleep were found.

Conclusions
Breast-milk GC rhythmicity at 1 month postpartum was not associated with infant behavior or sleep at the age of 3 months. Findings from previous studies linking breast-milk cortisol to infant neurodevelopment might be biased by the lack of GC measurements across the full diurnal cycle, and should therefore be interpreted with caution.
Do postponed dental visits for financial reasons reduce quality of life? Evidence from the Survey of Health, Ageing and Retirement in Europe
Authors: Valdez Richelle , Aarabi Ghazal , Spinler Kristin , Walther Carolin , Kofahl Christopher , Buczak-Stec Elzbieta , Heydecke Guido , König Hans-Helmut , Hajek André 
Published: April 2020

Abstract
Background
There is a lack of studies investigating the impact of postponed dental visits due to financial constraints on quality of life.

Aims
The aim of this study was to identify whether these factors are associated longitudinally.

Methods
Data were derived from waves 5 and 6 of the “Survey of Health Ageing and Retirement in Europe” (SHARE). The analysis focused on Germany (n = 7506). The widely used CASP-12 was used to quantify the quality of life. Postponed dental visits for financial reasons in the preceding 12 months (no, yes) were used as the main explanatory variable. Socioeconomic and health-related covariates were included in regression analysis.

Results
Gender stratified regression analysis showed that quality of life decreased with the presence of postponed dental visits due to financial reasons in men. Furthermore, quality of life decreased with the worsening of self-rated health in both men and women. The outcome measure was not associated with age, marital status, income, and chronic diseases in both sexes.

Discussion
Study findings suggest that postponing dental visits due to financial constraints contributes to a decreased quality of life among older men.

Conclusion
Efforts to avoid these circumstances might help to maintain the quality of life in older men.
Pulmonary Therapy 2020 Update and Podcast: Meet the Journal’s Editors-in-Chief
Authors: Beeh Kai-Michael , Dhand Rajiv 
Published: April 2020

AbstractThe Editors-in-Chief of Pulmonary Therapy have prepared podcasts summarizing their current research, recent highlights from the field, and future predictions. Audio-only versions and the transcripts can be downloaded here: https://doi.org/10.6084/m9.figshare.11938863. Following this is a written summary of the journal’s recent developments and the transcripts from the podcasts.
A radiomics-based comparative study on arterial spin labeling and dynamic susceptibility contrast perfusion-weighted imaging in gliomas
Authors: Hashido Takashi , Saito Shigeyoshi , Ishida Takayuki 
Published: April 2020

AbstractRadiomics has potential for reflecting the differences in glioma perfusion heterogeneity between arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) imaging. The aim of this study was to compare radiomic features of ASL and DSC imaging-derived parameters (cerebral blood flow, CBF) and assess radiomics-based classification models for low-grade gliomas (LGGs) and high-grade gliomas (HGGs) using their parameters. The ASL-CBF and DSC-relative CBF of 46 glioma patients were normalized (ASL-nCBF and DSC-nrCBF) for data analysis. For each map, 91 radiomic features were extracted from the tumor volume. Seventy-five radiomic features were significantly different (P < 0.00055) between ASL-nCBF and DSC-nrCBF. Positive correlations were observed in 75 radiomic features between ASL-nCBF and DSC-nrCBF. Even though ASL imaging underestimated CBF compared with DSC imaging, there were significant correlations (P < 0.00055) in the first-order-based mean, median, 90th percentile, and maximum. Texture analysis showed that ASL-nCBF and DSC-nrCBF characterized similar perfusion patterns, while ASL-nCBF could evaluate perfusion heterogeneity better. The areas under the curve of the ASL-nCBF and DSC-nrCBF radiomics-based classification models for gliomas were 0.888 and 0.962, respectively. Radiomics in ASL and DSC imaging is useful for characterizing glioma perfusion patterns quantitatively and for classifying LGGs and HGGs.
The interrelation of sleep and mental and physical health is anchored in grey-matter neuroanatomy and under genetic control
Authors: Tahmasian Masoud , Samea Fateme , Khazaie Habibolah , Zarei Mojtaba , Kharabian Masouleh Shahrzad , Hoffstaedter Felix , Camilleri Julia , Kochunov Peter , Yeo B. T. Thomas , Eickhoff Simon Bodo , Valk Sofie Louise 
Published: April 2020

AbstractHumans need about seven to nine hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental, and physical health. However, the biological basis of the interplay of sleep and health is incompletely understood. Here we show, by combining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (HCP (n = 1106), age range: 22–37, eNKI (n = 783), age range: 12–85), that sleep, mental, and physical health have a shared neurobiological basis in grey matter anatomy; and that these relationships are driven by shared genetic factors. Though local associations between sleep and cortical thickness were inconsistent across samples, we identified two robust latent components, highlighting the multivariate interdigitation of sleep, intelligence, BMI, depression, and macroscale cortical structure. Our observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.
Enhancing catalytic performance of dilute metal alloy nanomaterials
Authors: Luneau Mathilde , Guan Erjia , Chen Wei , Foucher Alexandre C. , Marcella Nicholas , Shirman Tanya , Verbart David M. A. , Aizenberg Joanna , Aizenberg Michael , Stach Eric A. , Madix Robert J. , Frenkel Anatoly I. , Friend Cynthia M. 
Published: April 2020

AbstractDilute alloys are promising materials for sustainable chemical production; however, their composition and structure affect their performance. Herein, a comprehensive study of the effects of pretreatment conditions on the materials properties of Pd0.04Au0.96 nanoparticles partially embedded in porous silica is related to the activity for catalytic hydrogenation of 1-hexyne to 1-hexene. A combination of in situ characterization and theoretical calculations provide evidence that changes in palladium surface content are induced by treatment in oxygen, hydrogen and carbon monoxide at various temperatures. In turn, there are changes in hydrogenation activity because surface palladium is necessary for H2 dissociation. These Pd0.04Au0.96 nanoparticles in the porous silica remain structurally intact under many cycles of activation and deactivation and are remarkably resistant to sintering, demonstrating that dilute alloy catalysts are highly dynamic systems that can be tuned and maintained in a active state.
Homeopathy Deserves Its Own Scientific Identity
Authors: Rutten Lex 
Published: April 2020

AbstractScientific research, especially the randomised controlled trial, was once expected to provide recognition to homeopathy. However, the opposite happened: despite evidence that is not inferior to conventional evidence, the opposition has never been stronger. The philosopher Kuhn predicted this: scientific authorities cannot accept information that is contrary to their basic beliefs (paradigm) because their authority depends on that very paradigm. On the other hand, many patients discover that the conventional medical paradigm is incomplete and that homeopathy complements it. The problems with the present paradigm are expected to grow, especially with antimicrobial resistance1 with respect to respiratory tract infections. Homeopathy can be of great value in this respect. There is also growing awareness that there is no dichotomy between effective and not-effective medicines. The effect of a medicine is a probability depending or several variables. So far improving the homeopathic method with scientific research has been neglected. The increasing interest of conventional medicine in personalised medicine and prognostic factor research (PFR) is an excellent opportunity to redirect homeopathy research towards PFR.
Role of Silicea in Diabetic Foot Ulcer: A Retrospectively Analysed Case Series
Authors: Ponnam Hima Bindu , Gupta Jaya 
Published: April 2020

Abstract
Background and Objectives Diabetic foot is a condition especially vulnerable to amputation because of frequent complications of peripheral neuropathy, infection and peripheral arterial disease. Diabetic foot problems are a major cause of hospitalisation and prolonged hospital stays, constituting 20% of those entering hospitals. If treatment is delayed or improper treatment is given, it leads to infection, gangrene and amputation. Homoeopathic literature reveals that Silicea is a useful medicine for ulcers, but there is dearth of systematic research in this area. This article is an effort to provide detailed information of potentised Silicea in reference to diabetic foot ulcer (DFU).
Method A prospective observational study was conducted by Central Council for Research in Homoeopathy at its Extension Clinical Research Unit, Hyderabad, from 2005 to 2009. From the study, cases where Silicea (n = 22) was indicated and prescribed were taken for retrospective analysis.
Results The analysis showed usefulness of Silicea in treating DFU. The baseline symptom score of DFU and sugar values were statistically analysed before and after treatment. The mean symptom scores at the baseline (8.41 ± 3.03) and at the end of treatment (0.27 ± 0.70) were found to be statistically significant (p = 0.001).
Conclusion The result of the role of Silicea in ulcer healing was found to be positive and encouraging. Further, to validate the effect of Silicea, randomised controlled trials can be undertaken.
A RDA-Based Deep Reinforcement Learning Approach for Autonomous Motion Planning of UAV in Dynamic Unknown Environments
Authors: WAN Kaifang , GAO Xiaoguang , HU Zijian , ZHANG Wei 
Published: April 2020

Abstract
Autonomous motion planning (AMP) in dynamic unknown environments emerges as an urgent requirement with the prosperity of unmanned aerial vehicle (UAV). In this paper, we present a DRL-based planning framework to address the AMP problem, which is applicable in both military and civilian fields. To maintain learning efficiency, a novel reward difference amplifying (RDA) scheme is proposed to reshape the conventional reward functions and is introduced into state-of-the-art DRLs to constructs novel DRL algorithms for the planner’s learning. Different from conventional motion planning approaches, our DRL-based methods provide an end-to-end control for UAV, which directly maps the raw sensory measurements into high-level control signals. The training and testing experiments demonstrate that our RDA scheme makes great contributions to the performance improvement and provides the UAV good adaptability to dynamic environments.
Optimization of K-medoids Algorithm for Initial Clustering Center
Authors: E Wang Yan , Jian An , Yan Liang , HongGang Wang 
Published: April 2020

Abstract
This paper studies the k-medoids of the partitioning clustering algorithm. A variance-based density optimization algorithm is proposed to solve the problem of random selection of initial clustering centers, slow convergence speed and unstable clustering results in K-medoids algorithm. Based on the mean square deviation and distance mean of the sample set, the density radius of the sample set is calculated according to the size of the sample set. Under the same density radius, the samples in dense regions have high density. By dynamically selecting the samples are selected as initial clustering centers from different dense regions, in the clustering process local optimization is used to accelerate the convergence speed. These operations solve the shortcomings of K-medoids algorithm. In order to test the clustering effect, this algorithm is applied to data set of UCI machine learning. The experimental results show that the initial clustering centers selected by the algorithm are located in the dense area of the sample set, which is more in line with the original distribution of the data set. The algorithm has higher clustering accuracy, more stable clustering results and faster convergence speed on data sets.
An Intelligent Chatbot System Based on Entity Extraction Using RASA NLU and Neural Network
Authors: Jiao Anran 
Published: April 2020

Abstract
Intelligent chatbot systems are popular issues in the application fields of robot system and natural language processing. As the development of natural language processing and neural network algorithms, the application of artificial intelligence is increasing in Chatbot systems, which are typically used in dialog systems for various practical purposes including customer service or information acquisition. This paper designs the functional framework and introduces the principle of RASA NLU for the Chatbot system, then it integrates RASA NLU and neural network (NN) methods and implements the system based on entity extraction after intent recognition. With the experimental comparison and validation, our developed system can realize automatic learning and answering the collected questions about finance. The system analysis of two methods also validate that RASA NLU outperforms NN in accuracy for a single experiment, but NN has better integrity to classify entities from segmented words.
Representation Learning for Electronic Health Records: A Survey
Authors: Chen Peiying 
Published: April 2020

Abstract
With the wide application of Electronic Health Record (EHR) in hospitals in past few decades, researches that employ artificial intelligence (AI) and machine learning methods based on EHR data have been explosive. With such EHR data, one can engage in many tasks such as risk prediction, treatment recommendation, information imputation, etc. The performance of classification or prediction highly depends on the quality of data representation, i.e., representing original records into numerical vectors to facilitate further learning. However, there is little research that focuses on the representation learning techniques for EHR data at present, which makes it hard to understanding the development trend of EHR learning in a global map. In this paper, we bridge this gap by systematically investigating the related research efforts that apply the representation learning on EHR data. We analyze and conclude the techniques used in the typical representation learning approaches as well as the limitations and advantages of them. The survey would provide a comprehensive reference for further analysis and application in EHR research.
A Survey on Application of Knowledge Graph
Authors: Zou Xiaohan 
Published: April 2020

AbstractKnowledge graphs, representation of information as a semantic graph, have caused wide concern in both industrial and academic world. Their property of providing semantically structured information has brought important possible solutions for many tasks including question answering, recommendation and information retrieval, and is considered to offer great promise for building more intelligent machines by many researchers. Although knowledge graphs have already supported multiple “Big Data” applications in all sorts of commercial and scientific domains since Google coined this term in 2012, there was no previous study give a systemically review of the application of knowledge graphs. Therefore, unlike other related work which focuses on the construction techniques of knowledge graphs, this present paper aims at providing a first survey on these applications stemming from different domains. This paper also points out that while important advancements of applying knowledge graphs’ great ability of providing semantically structured information into specific domains have been made in recent years, several aspects still remain to be explored.
A Systematic Study for Learning-Based Software Defect Prediction
Authors: Cao Han 
Published: April 2020

Abstract
Software defect refers to the code error in the process of software development, which could cause execution fault under specific conditions, resulting in failure, collapse, and high cost of the target software. Traditional detection techniques for software defect contain static and dynamic analysis, both of which require a great deal of workforce and time. With the development of machine learning and deep learning, software defect prediction has opened a new avenue to circumvent the drawbacks of traditional analysis approaches. Although various learning-based techniques in the prediction field have been developed, there is a lack of systematic summary and classification from the technical point of view. This paper studies the problem from the three aspects: traditional machine learning, deep learning, and hybrid learning. Moreover, the predicted performance is discussed in detail, especially in cross-project and just-in-time, to understand current research status thoroughly. This paper also provides a useful guide for further research, particularly for the potential usage of deep learning in semantic defect prediction.
Application and Development Trend of Smart Home in Residential Interior Design
Authors: Yang Ziqi , Cho JoungHyung 
Published: April 2020

Abstract
Smart home is the product of artificial intelligence, which can provide people with a safe, comfortable and environmentally friendly home environment, as well asmeets people’s living needs in residences in an all-round and personalized way and has a good development prospect. This paper expounded the development, classification and application of smart home in residential interior design. Taking Bill Gates’ “Future Home” as an example, it analyzed that the introduction of smart home can organically combine residences, users, environment and plants to provide a better living environment for human beings.
Massive Learning Behaviours Influence Educational Sustainability: A Machine Learning Approach
Authors: Muhammad Raza Abidi Syed , Hussain Mushtaq , Ge Sen , Ding Hu , Zhu Wenhao , Zhang Wu 
Published: April 2020

Abstract
Discovering learning behavior patterns while using Massive Open Online Courses (MOOCs) or e-learning platform education is being one of the substantial aspects in the plethora of past studies. In this study, we predict the taxonomy of learner behaviors by using the data of Junyi Academy, an e-learning platform similar to Khan Academy. We evaluate the dataset of first 2,000 unique learners and applying machine learning algorithms, i.e., Logistic Regression, and Neural Network (NNET) to predict the status of learners’ behavior. Our result shows that NNET autotuned is high accuracy champion predictive model as compared to Logistic Regression and NNET manually tuned hyperparameters models. The accuracy we measured based on Kolmogorov-Smirnov (KS) statistic for VALIDATION partition dataset. Furthermore, our model allows teachers to track or predict behavioral changes of the individual student and reveal insights into the content of the course material. It will also assist teachers in getting an early prediction of behaviors before finishing the course or skill, which is constructive and valuable for sustainable education or educational sustainability.
Improvement of Performance of Navigation System for Supporting Independence Rehabilitation of Wheelchair - Bed Transfer
Authors: Kato Jin , Deguchi Genbu , Inoue Jun , Iwase Masami 
Published: April 2020

Abstract
The purpose of this research is to improve the position and angle estimation accuracy of the wheelchair guide system for Hemiplegic patients. The wheelchair guide system aims to assist the patients to transfer between their wheelchair and bed. This system prompts properly to user depending on the situation as same as care worker. Therefore, the system is developed to prompt the measured position and angle of wheelchair. The position estimator uses an AR marker to detect the wheelchair position. However, its estimation is contaminated by measurement noises and sometimes fails because the marker is out of the camera’s field of view. To improve the estimation of position and moving direction, we introduce the kinematic model of the wheelchair and utilize the Extended Kalman Filter with the model. In this paper, we describe the self-position estimation method of the navigation system and AR marker, with the estimation method using Kalman Filter.