bluuebunny/crossref_metadata_2025_split huggingface dataset:
Profile and Prognosis of Spontaneous Lobar Intracerebral Hemorrhage: Comparison of 6-month Survival with STICH II and the MISTIE III Lobar Hemorrhage Subset
Authors: Behle Berthold , Beseoglu Kerim , Beez Thomas , Petridis Athanasios K. , Fischer Igor , Hänggi Daniel , Steiger Hans-Jakob 
Published: May 2021

Abstract
Background Randomized trials on spontaneous lobar intracerebral hemorrhage (ICH) provided no convincing evidence of the superiority of surgical treatment. Since recruitment in the trials was under the premise of equipoise, a selection bias toward patients who did not need surgery or were in hopeless condition must be suspected. The aim of the actual analysis was to compare outcome and patient profile of an unselected hospital series with recent randomized trials and to develop a prognostic model.
Methods Of 821 patients with spontaneous ICH managed at the neurosurgical department of the University Hospital Düsseldorf between 2013 and 2018, 159 had lobar bleedings. Patient characteristics, hematoma volume, treatment modality, and 6-month survival were compared with STICH II and the subset of lobar hemorrhage in the MISTIE III trial. In addition, a prognostic model for 6-month survival in our patients was developed using a random forest classifier.
Results One hundred and seven patients were managed by surgical evacuation of the hematoma and 52 without surgical evacuation. Median hemorrhage volume in our surgical cohort was 66 and 42 mL in the conservative cohort, compared with 38 and 36 mL in the STICH II trial, and 46 and 47 mL in the surgical and conservative MISTIE III lobar hemorrhage subset. Median initial Glasgow Coma Scale (GCS) score was 12 in our surgical group and 11 in the conservative group, compared with 13 in the STICH II cohorts and 12 in the MISTIE III lobar hemorrhage subset. Median age in our surgical and conservative cohorts was 73 and 74 years, respectively, compared with 65 years in both STICH II cohorts and 68 years in the MISTIE II subsets. Twenty-nine percent of our surgical cohort and 55% of our conservatively managed patients deceased within the first 6 months, compared with 18 and 24%, respectively, in STICH II and 17 and 24% in the MISTIE III subset. Our prognostic model identified large hemorrhage volumes and low admission GCS score as main unfavorable prognostic factors for 6-month survival. The random forest classifier achieved a predictive accuracy of 78% and an area under curve (AUC)- value of 88% regarding survival at 6 months, on a test set independent of the training set.
Conclusions In comparison with our surgical group, the STICH II and MISTIE III cohorts, recruited under the premise of physician equipoise, underrepresented patients with large ICHs. The cohorts in the randomized trials were therefore biased toward patients with a favorable perspective under conservative management. Initial hematoma volume and admission GCS were the main prognostic factors in our patients.
Mycobacteria: Selection of Transplant Candidates and Post–lung Transplant Outcomes
Authors: Friedman Daniel Z.P. , Doucette Karen 
Published: May 2021

AbstractMycobacterium is a large, clinically relevant bacterial genus made up of the agents of tuberculosis and leprosy and hundreds of species of saprophytic nontuberculous mycobacteria (NTM). Pathogenicity, clinical presentation, epidemiology, and antimicrobial susceptibilities are exceptionally diverse between species. Patients with end-stage lung disease and recipients of lung transplants are at a higher risk of developing NTM colonization and disease and of severe manifestations and outcomes of tuberculosis. Data from the past three decades have increased our knowledge of these infections in lung transplant recipients. Still, there are knowledge gaps to be addressed to further our understanding of risk factors and optimal treatments for mycobacterial infections in this population.
Comparative Assessment of ADA, IDRS, and FINDRISC in Predicting Prediabetes and Diabetes Mellitus in South Indian Population
Authors: Doddamani Parveen , Ramanathan Nitin , Swetha N. K. , Suma M. N. 
Published: May 2021

Abstract
Introduction Diabetes risk-screening tools are validated and implemented across various countries. There is a need for improvement in these risk scores with suitable modifications so as to make them more sensitive, specific, and suitable to the local population.
Objectives The aim of this study was to evaluate and compare the diagnostic accuracy and clinical utility of the Indian diabetes risk score (IDRS), the American diabetic association (ADA) risk score, and the Finnish Diabetes Risk Score in healthy subjects of South Indian origin in predicting the risk of diabetes and to correlate these risk scores with the blood glucose and hemoglobin A1c (HbA1c) levels in the study population.
Materials and Methods A total of 160 subjects attending the master health checkup/outpatient department of a tertiary care hospital were included in the study. Each subject was asked to fill a questionnaire. Details obtained using the questionnaire were assessed as per the three diabetic risk scores. Fasting blood sugar/random blood sugar and HbA1c were estimated.
Statistical Analysis Used Data analysis was done using SPSS 22/23. Pearson correlation was used to compare continuous variables, with p < 0.05 considered statistically significant. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and Mitchell’s clinical utility indices were calculated for each risk tool.
Results We found the prevalence of diabetes to be 11.9%. ADA risk score was the only risk score that showed a statistically significant difference (p-value = 0.05) between the low- and high-risk subjects.
Conclusions ADA or IDRS risk scores can be used for screening diabetes in the South Indian population. We suggest that inclusion of the history of gestational diabetes and hypertension in the IDRS risk score might improve its sensitivity as a screening tool in our local population.
Evaluation of Factors Affecting Enuresis in 6- to 12-Year-Old Children Referred to the Pediatric Nephrology Clinic
Authors: Azarfar Anoush , Ghodsi Alireza , Faravani Farnoosh , Ghahremani Sara 
Published: May 2021

AbstractEnuresis is one of the most common disorders in children, and if left untreated can cause anxiety, low self-esteem, and family problems. The aim of this study was to investigate the factors associated with enuresis to provide recommendations for more efficient prevention and treatment. A cross-sectional study was performed on children aged 6 to 12 years with enuresis, referred to the pediatric clinic between April 2017 and April 2018. Children were divided into two groups: monosymptomatic enuresis and healthy subjects. Then, a questionnaire was completed by their parents to assess the factors related to enuresis. The data were analyzed using SPSS software. In this study, out of 140 children with an average age of 8.22 ± 2.01 years, 77 cases (55%) had enuresis, of which 57 (40.7%) had primary enuresis, and 20 cases (14.3%) had secondary enuresis. There was a significant difference between the control and the case groups in terms of: father's education, family income, family history, number of people sleeping in the room, family problems, problems at school, history of urinary tract infections, history of pinworms, difficulty falling asleep, difficulty in waking up, feeling tired after waking up, and drowsiness during the day. Enuresis is associated with several physiological, psychological, and genetic factors. Controlling these factors requires paying attention to the elements such as the mental health of the family and child's sleep health through education, family awareness, and counseling.
Candidatus Phytoplasma pini (pine witches'-broom phytoplasma).
Authors: Davis Robert E. , Zhao Yan 
Published: May 2021

Abstract

'Ca. Phytoplasma pini' is a member of phytoplasma 16S rRNA gene RFLP group XXI, subgroup XXI-A. It has been identified in a number of European countries, including Germany, Poland, Lithuania, Spain, Czech Republic and Croatia. Outside of Europe it has been identified in China and Mozambique. A related strain has also been identified in Maryland, USA. In the USA, favourable climatic conditions and wide availability of potential host plants of the phytoplasma, suggest that the potential for spread of 'Ca. Phytoplasma pini' could be significant. Host plants include Pinus sylvestris, P. halepensis, P. mugo, P. banksiana, P. nigra, P. tabuliformis, Abies procera and Tsuga canadensis. Symptoms include the formation of ball-like growths containing dwarfed needles, yellowed or reddish needles and the loss of needles. It is transmitted by insect vectors that are currently unknown.
Phoma tracheiphila (Mal secco).
Authors:   USA, USDA-ARS
Published: May 2021

Abstract

P. tracheiphila is a conidial fungus causing serious damage and death to the host, particularly lemon [Citrus limon], in Citrus and related genera, in Mediterranean countries and the Black Sea region. So far, however, it is unknown in Spain, Portugal and Morocco, as well as other major citrus-growing regions of the world. This fungus is of concern to many international plant protection organizations and must be prevented from being spread in infected propagative material. It was once reported as seedborne in lemon, but there is no further evidence of this means of transmission. Once in the orchard, the fungus can be carried as spores from pycnidia and from hyphae on the plant and on fallen debris by rain, wind and irrigation water, and perhaps by birds and insects.
Principal component analysis of measured data for ultrasound transmission tomography
Authors: Rymarczyk T , Polakowski K , Sikora J 
Published: May 2021

Abstract
In this paper, the new version of imaging algorithm for Ultrasound Transmission Tomography was presented. This algorithm was comprehensively tested with both synthetic and real measurement data. Different configuration of an internal objects was considered. In order to improve the quality of imaging the input data were treated by Principal Component Analysis. The proposed algorithm proved its usefulness and superiority over classical imaging algorithms.
Nonlocal total variation regularization with Shape Adaptive Patches for image denoising via Split Bregman method
Authors: Shuli Ma , Huiqian Du , Wenbo Mei , Luliang Jia 
Published: May 2021

Abstract
Non-Local or patch-based approaches are used in most of the state-of-the-art methods for image denoising. Denoising with regular square patches may cause noise halos around edges, particularly high contrasted edges. In order to overcome this drawback, this work presents an extension of the Non-Local TV framework that effectively exploits the potential local geometric features of the image by replacing the simple square patches with different oriented shapes. We first solve the denoised models of ROF-NLTV with different oriented patch shapes by Split Bregman algorithm. Then, use exponentially weighted aggregation method based on Stein’s unbiased risk estimate to combine the estimators obtained in the first step. The numerical results show our method outperforms some previous ones. Moreover, common noise halos around edges usually observed by denoising with Non-Local TV method are reduced thanks our approach.
Operation practice and regulatory framework of offshore wind grid connection system
Authors: Zhao Feifei , Han Xingning , Ma Jinlong , Sun Yong , Ye Xueshun 
Published: May 2021

Abstract
Offshore wind is experiencing rapid growth around the world, and is moving towards large-scale and far-reaching sea. Grid connection has become an important challenge for large-scale development of offshore wind power. In this paper, technology and regulatory framework of offshore wind grid connection are systematically summarized and complied. Europe has accumulated rich experience in this field, therefore taking UK, Denmark and Germany as examples, the practical experience of offshore wind grid integration is analyzed in terms of technology choice, framework, operation and maintenance, and recovery of connection costs. which is a good reference for China when dealing with the coming challenges.
Transient Voltage Stability Assessment Method based on gcForest
Authors: Zhiyuan Sun , Mingpo LI , Jie Zhang , Binjiang Hu , Guo Qi , Yihua Zhu 
Published: May 2021

Abstract
Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi Power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid.
Tumor Type Prediction based on Residual Attention Model
Authors: Wang Y H , Tie Y , Qi L , Wang F , Wang L 
Published: May 2021

AbstractEarly detection of tumors is an important part of cancer treatment. In view of the existing algorithms: single data types, low feature extraction efficiency, and low classification network accuracy. A tumor types prediction model based on deep learning is proposed. The network model uses a Variational auto-encoder (VAE) to fuse the RNA expression and DNA methylation data of 32 tumor types, then uses the Hilbert curve to visualize it. Finally fuse data is sent to classification module: embed the attention module in the backbone network ResNet18 framework, convolutional layer instead of fully connected layer. The new sample is used to predict the tumor type. The experimental results show that this network model has excellent performance in tumor type classification and has important guiding significance for the early diagnosis of tumor patients.
Reactive power optimization system of distribution network based on edge calculation
Authors: Huang Jinbo , Fang Jiangxiao , Hu Liexiang , Shi Bolong , Li Suirong , Tu Yiyan 
Published: May 2021

Abstract
To deal with the adjustment difficulties caused by the access of photovoltaic (PV) in distribution network, this paper introduces a reactive power optimization system of distribution network based on edge calculation. The proposed system includes two parts. Firstly, the distribution PV edge nodes based on edge calculation are carefully designed. Secondly, based on the edge nodes, the PV station, the grid reactive power optimization, the Automatic Voltage Control (AVC) centre, and the distribution photovoltaic power reactive voltage are controlled co-ordinately. The simulation results prove that the proposed system can effectively improve the voltage quality and the voltage regulation ability of distribution network.
DCSNet: A Surface Defect Classification and Segmentation Model by One-Class Learning
Authors: Yuxiang Wang , Shiyi Mu , Xiang Xie , Shanshan Huang 
Published: May 2021

Abstract
Researches in surface defect classification and segmentation technology have been seen significant progress in recent years. However, there are few works on One-Class learning in this direction by a single model. In previous researches, some problems remain unsolved in the surface defect detection methods, e.g. the training needs a large number of samples and these models cannot classify and locate the surface defect accurately, etc. The main contribution in this work is that we summarize the overall ideas of previous research in network design and propose a multi-task model which could be trained only using a few of positive samples. Meanwhile, the experiments on AITEX detection datasets[1] which get 84.4% DR, 4.4% FAR and 34.2% MIOU, and conduct an ablation experiment in real industrial product dataset to validate the effect of different backbones on DCSNet. It’s worth mentioning that DCSNet provides a solution to the task of surface defect classification and segmentation based on One-Class learning. The code will be open source in ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://agit.ai/wyxxx/zhengtu">https://agit.ai/wyxxx/zhengtu.
Optimized alginate-based 3D printed scaffolds as a model of patient derived breast cancer microenvironments in drug discovery
Authors: Svanström Andreas , Rosendahl Jennifer , Salerno Simona , Leiva Maria Carmen , Gregersson Pernilla , Berglin Mattias , Bogestål Yalda , Lausmaa Jukka , Oko Asaf , Chinga-Carrasco Gary , Petronis Sarunas , Standoft Simon , Ståhlberg Anders , Håkansson Joakim , Landberg Göran 
Published: May 2021

Abstract
The cancer microenvironment influences tumor progression and metastasis and is pivotal to consider when designing in vivo-like cancer models. Current preclinical testing platforms for cancer drug development are mainly limited to 2D cell culture systems that poorly mimic physiological environments and traditional, low throughput animal models. The aim of this work was to produce a tunable testing platform based on 3D printed scaffolds (3DPS) with a simple geometry that, by extracellular components and response of breast cancer reporter cells, mimics patient-derived scaffolds (PDS) of breast cancer. Here, the biocompatible polysaccharide alginate was used as base material to generate scaffolds consisting of a 3D grid containing periostin and hydroxyapatite. Breast cancer cell lines (MCF7 and MDA-MB-231) produced similar phenotypes and gene expression levels of cancer stem cell, epithelial–mesenchymal transition, differentiation and proliferation markers when cultured on 3DPS and PDS, contrasting conventional 2D cultures. Importantly, cells cultured on 3DPS and PDS showed scaffold-specific responses to cytotoxic drugs (doxorubicin and 5-fluorouracil) that were different from 2D cultured cells. In conclusion, the data presented support the use of a tunable alginate-based 3DPS as a tumor model in breast cancer drug discovery.
Plasma-enhanced protein patterning in a microfluidic compartmentalized platform for multi-organs-on-chip: a liver-tumor model
Authors: Ferrari Erika , Ugolini Giovanni Stefano , Piutti Claudia , Marzorati Simona , Rasponi Marco 
Published: May 2021

Abstract
A microfluidic technique is presented for micropatterning protein domains and cell cultures within permanently bonded organs-on-chip devices. This method is based on the use of polydimethylsiloxane layers coupled with the plasma ablation technique for selective protein removal. We show how this technique can be employed to generate a multi-organ in vitro model directly within a microscale platform suitable for pharmacokinetic-based drug screening. We miniaturized a liver model based on micropatterned co-cultures in dual-compartment microfluidic devices. The cytotoxic effect of liver-metabolized Tegafur on colon cancer cell line was assessed using two microfluidic devices where microgrooves and valves systems are used to model drug diffusion between culture compartments. The platforms can reproduce the metabolism of Tegafur in the liver, thus killing colon cancer cells. The proposed plasma-enhanced microfluidic protein patterning method thus successfully combines the ability to generate precise cell micropatterning with the intrinsic advantages of microfluidics in cell biology.
Range Sensor Overview and Blind-Zone Reduction of Autonomous Vehicle Shuttles
Authors: Gu Junyi , Chhetri Tek Raj 
Published: May 2021

Abstract
In recent years, with the advancement in sensor technologies, computing technologies and artificial intelligence, the long-sought autonomous vehicles (AVs) have become a reality. Many AVs today are already driving on the roads. Still, we have not reached full autonomy. Sensors which allow AVs to perceive the surroundings are keys to the success of AVs to reach full autonomy. However, this requires an understanding of sensor configurations, performance and sensor placements. In this paper, we present our experience on sensors obtained from AV shuttle ise Auto. An AV shuttle ise Auto designed and developed in Tallinn University of Technology is used as an experimental platform for sensor configuration and set-up.
Green eco-friendly acoustic materials
Authors: Lavrentjev J 
Published: May 2021

Abstract
Green soundproofing acoustic porous materials are a valid alternative to traditional synthetic materials. Green eco-friendly acoustic panels and other soundproofing solutions are made at least in part recycled or natural materials that are safer for the environment. They do not contribute to the environmental and toxic waste problem since at the end of their useful life, green materials can be disposed of without polluting the environment. They are increasingly being used in the automotive industries and also in construction improving the acoustic comfort inside buildings as well as mitigate reverberation effects and reduce the transmission of noise between rooms. In this paper, the acoustic properties of different but typical green acoustic materials are analysed and their acoustic characteristics experimentally determined. The problems of designing of green acoustic materials have been analyzed and the main solutions to the design and use in acoustics have been proposed. The effect of the structure and arrangement of materials on the acoustic properties of materials has been analyzed.
Acoustic study of multi-layered microperforated elements for fibreless noise control applications
Authors: Villau M. , Rämmal H. , Lavrentjev J. 
Published: May 2021

Abstract
Recently fibreless solutions have become a trend in the design of sustainable and environmentally friendly duct silencers. Hereby microperforated panels have been proven to provide adequate performance for the substitution of unfavourable fibrous material layers commonly used in mass-produced silencers. This paper presents an acoustic study of microperforated elements aimed for an effective and eco-friendly heating, ventilation, and air conditioning (HVAC) duct silencer. Several microperforated sheet metal panels have been experimentally tested in a variety of layered configurations in order to maximize the sound absorption coefficient in operational frequency range. The optimal solution is implemented in the design concept of a novel fibreless HVAC silencer presented in this research. The results of the study demonstrate the appropriateness of the double-layered microperforates for a duct silencer as well as for a variety of Noise, vibration and harshness (NVH) implementations were potent noise absorption is aimed.