bluuebunny/crossref_metadata_2025_split huggingface dataset:
Establishment and characterization of 38 novel patient-derived primary cancer cell lines using multi-region sampling revealing intra-tumor heterogeneity of gallbladder carcinoma
Authors: Feng Feiling , Cheng Qingbao , Li Bin , Liu Chen , Wang Huizhen , Li Bin , Xu Xiaoya , Yu Yong , Chen Zishuo , Wu Xiaobing , Dong Hua , Chu Kaijian , Xie Zhenghua , Gao Qingxiang , Xiong Lei , Li Fugen , Yi Bin , Zhang Dadong , Jiang Xiaoqing 
Published: April 2021

AbstractGallbladder carcinoma (GBC) is a lethal biliary tract malignant neoplasm. Patient-derived primary cancer cell lines (PDPCs) are appropriate models to explore biological characteristics and potential therapeutics; however, there is a lack of PDPCs in GBC. In this study, we aimed to establish and characterize the GBC PDPCs, and further investigated the intra-tumor heterogeneity (ITH). Multi-region sampling (3–9 regions) of the operable tumor tissue samples was used to establish PDPCs. Short tandem repeat genotyping for cell authentication and karyotyping was performed, followed by whole-exome sequencing and RNA sequencing to assess the ITH at the genetic and transcriptional levels, respectively. Thirty-eight PDPCs were successfully established from seven GBC patients and characterized. ITH was observed with a median of 38.3% mutations being heterogeneous (range, 26.6–59.4%) across all patients. Similar with other tumor types, TP53 mutations were always truncal. In addition, there were three genes, KMT2C, CDKN2A, and ARID1A, with truncal mutations in at least two patients. A median of 370 differentially expressed genes (DEGs) was identified per patient. Distinct expression patterns were observed between major histocompatibility complex (MHC) class I and II genes. We found the expression of MHC class II genes in the PDPC samples was closely regulated by CIITA, while that of MHC class I genes were not correlated with CIITA expression. The PDPCs established from GBC patients can serve as novel in vitro models to identify the ITH, which may pave a crucial molecular foundation for enhanced understanding of tumorigenesis and progression.
De novo ulcerative colitis after kidney transplantation treated with infliximab
Authors: Oki Rikako , Hidaka Sumi , Sasaki Akiko , Teshima Shinichi , Mochida Yasuhiro , Miyake Katsunori , Ishioka Kunihiro , Moriya Hidekazu , Ohtake Takayasu , Kobayashi Shuzo 
Published: April 2021

AbstractDiarrhea is a common complication in kidney transplant recipients. Common causes of diarrhea include infection, side effect from medication, rejection, and malignancy. A less common but important cause of diarrhea is de novo inflammatory bowel disease (IBD). This is unexpected, as these patients are already immunosuppressed. Herein, we present the case of a 45-year-old man with end-stage kidney disease because of focal segmental glomerulosclerosis who underwent preemptive kidney transplantation, with his mother as donor. His immunosuppressive regimen included methylprednisolone, mycophenolate mofetil, and tacrolimus. He had no episodes of graft dysfunction, rejection, or infectious events. Two and a half years post-transplantation, he developed bloody diarrhea. After excluding infections, colonoscopy was performed and revealed edematous mucosa and erythema with pigmentation, which are typical findings in ulcerative colitis. Despite therapy with 5-aminosalicylate and granulocyte monocyte apheresis, he presented with massive bloody diarrhea. We initiated infliximab, an anti-tumor necrosis factor-α (TNF-α) agent. He responded very well and achieved remission within 6 months after initiation of infliximab, while administration of the other immunosuppressants was maintained. His course was uneventful and no complications developed. Management of immunosuppressants for de novo IBD after organ transplantation is complicated, because treatment of IBD, graft function protection, and prevention of infection must be considered. Therefore, cooperation between transplantation physicians and gastroenterologists is essential during therapy.
Multidimensional Multiplicative Combinatorial Properties of Dynamical Syndetic Sets
Authors: Qiu Jiahao , Zhao Jianjie 
Published: April 2021

AbstractIn this paper, it is shown that for a minimal system (X, G), ifHis a normal subgroup ofGwith finite indexn, thenXcan be decomposed intoncomponents of closed sets such that each component is minimal underH-action. Meanwhile, we prove that for a residual set of points in a minimal system with finitely many commuting homeomorphisms, the set of return times to any non-empty open set contains arbitrarily long geometric progressions in multidimension, extending a previous result by Glasscock, Koutsogiannis and Richter.
Deep transfer learning: a novel glucose prediction framework for new subjects with type 2 diabetes
Authors: Yu Xia , Yang Tao , Lu Jingyi , Shen Yun , Lu Wei , Zhu Wei , Bao Yuqian , Li Hongru , Zhou Jian 
Published: April 2021

AbstractBlood glucose (BG) prediction is an effective approach to avoid hyper- and hypoglycemia, and achieve intelligent glucose management for patients with type 1 or serious type 2 diabetes. Recent studies have tended to adopt deep learning networks to obtain improved prediction models and more accurate prediction results, which have often required significant quantities of historical continuous glucose-monitoring (CGM) data. However, for new patients with limited historical dataset, it becomes difficult to establish an acceptable deep learning network for glucose prediction. Consequently, the goal of this study was to design a novel prediction framework with instance-based and network-based deep transfer learning for cross-subject glucose prediction based on segmented CGM time series. Taking the effects of biodiversity into consideration, dynamic time warping (DTW) was applied to determine the proper source domain dataset that shared the greatest degree of similarity for new subjects. After that, a network-based deep transfer learning method was designed with cross-domain dataset to obtain a personalized model combined with improved generalization capability. In a case study, the clinical dataset demonstrated that, with additional segmented dataset from other subjects, the proposed deep transfer learning framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.
Strain Rate Sensitivity of the Additive Manufacturing Material Scalmalloy®
Authors: Jakkula P. , Ganzenmüller G. , Gutmann F. , Pfaff A. , Mermagen J. , Hiermaier S. 
Published: April 2021

AbstractThis work investigates the influence of strain rate on the stress/strain behaviour of Scalmalloy. This material is an aluminium–scandium–magnesium alloy, specifically developed for additive manufacturing. The bulk yield stress of the material processed by Selective Laser Melting is approximately 340 MPa which can be increased by heat-treating to approximately 530 MPa. These numbers, combined with the low mass density of 2.7 g/cm3, make Scalmalloy an interesting candidate for lightweight crash-absorbing structures. As this application is inherently dynamic, it is of interest to study the loading rate sensitivity, which is difficult to predict: Al–Sc alloys exhibit classic strain rate sensitivity with an increased yield stress at elevated strain rates. However, Al–Mg alloys are known to show the contrary effect, they exhibit less strength as strain rate is increased. To answer the question how these effects combine, we study the dynamic behaviour at four different strain rates ranging from 10−3 to 1000 /s using servo-hydraulic and split-Hopkinson testing methods. The resulting data is analysed in terms of strain rate sensitivity of tensile strength and failure strain. A constitutive model based on a simplified Johnson–Cook approach is employed to simulate the tensile tests and provides good agreement with the experimental observations.
Unterschiede in der Wahrnehmung positiven und negativen Feedbacks von Mädchen und Jungen im Leseunterricht der Grundschule
Authors: Hoya Fabian 
Published: April 2021

ZusammenfassungIn zahlreichen Untersuchungen konnten geschlechtsspezifische Unterschiede im Lesen bei Kindern im Grundschulalter belegt werden. Im Detail weisen Mädchen im Vergleich zu Jungen sowohl bessere Leistungen als auch eine höhere Motivation und stärker ausgeprägte Selbstkonzepte auf. Diese leistungsbezogenen Unterschiede wie auch differente Erwartungen, die Lehrerinnen und Lehrer an Mädchen und Jungen im Leseunterricht stellen, können zu einem unterschiedlichen Feedbackverhalten führen, das sie gegenüber Schülerinnen und Schülern zeigen. In der vorliegenden Untersuchung wird der Frage nachgegangen, ob Mädchen und Jungen sich vor diesem Hintergrund in der Wahrnehmung des erteilten positiven und negativen Feedbacks ihrer Lehrkräfte im Leseunterricht unterscheiden. Zusätzlich wird untersucht, ob und inwiefern Unterschiede in dem von Grundschulkindern perzipierten Feedback ihrer Lehrkräfte über ihr Geschlecht, ihre Leistungen, ihre Motivation, sowie ihre Selbstkonzepte erklärt werden können. Dabei wurden N = 665 Schülerinnen und Schüler der dritten und vierten Jahrgangsstufe zu dem von ihnen perzipierten positiven und negativen Feedback ihrer Lehrkräfte im Leseunterricht der Grundschule befragt. Darüber hinaus wurden Instrumente zum lesebezogenen Selbstkonzept, zur intrinsischen Lesemotivation und zum Leseverständnis der Kinder eingesetzt. Die Untersuchungsbefunde belegen, dass Mädchen im Vergleich zu Jungen signifikant häufiger positives Feedback und signifikant seltener negatives Feedback ihrer Lehrkräfte im Leseunterricht der Grundschule wahrnehmen. Insgesamt zeigt sich, dass sich geschlechtsspezifische Unterschiede im wahrgenommenen positiven und negativen Feedback von Grundschulkindern stärker über leistungsbezogene Variablen als über ihr Geschlecht erklären lassen.
Gradient local auto-correlation features for depth human action recognition
Authors: Bulbul Mohammad Farhad , Ali Hazrat 
Published: April 2021

AbstractHuman action classification is a dynamic research topic in computer vision and has applications in video surveillance, human–computer interaction, and sign-language recognition. This paper aims to present an approach for the categorization of depth video oriented human action. In the approach, the enhanced motion and static history images are computed and a set of 2D auto-correlation gradient feature vectors is obtained from them to describe an action. Kernel-based Extreme Learning Machine is used with the extracted features to distinguish the diverse action types promisingly. The proposed approach is thoroughly assessed for the action datasets namely MSRAction3D, DHA, and UTD-MHAD. The approach achieves an accuracy of 97.44% for MSRAction3D, 99.13% for DHA, and 88.37% for UTD-MHAD. The experimental results and analysis demonstrate that the classification performance of the proposed method is considerable and surpasses the state-of-the-art human action classification methods. Besides, from the complexity analysis of the approach, it is turn out that our method is consistent for the real-time operation with low computational complexity.
Identifying maize yield and precipitation gaps in Uganda
Authors: Epule Terence Epule , Dhiba Driss , Etongo Daniel , Peng Changhui , Lepage Laurent 
Published: April 2021

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.
A Bayesian fusion technique for maximum power point tracking under partial shading condition
Authors: Gugulothu Ramesh , Nagu Bhookya 
Published: April 2021

AbstractIn this paper, a Bayesian fusion technique (BFT) based on maximum power point tracking (MPPT) is developed for the photovoltaic (PV) system that can exhibit faster and accurate tracking under partially shaded conditions (PSCs). Although the conventional hill-climbing algorithms have fast tracking capabilities, they are prone to steady-state oscillations and may not guarantee global peak under partially shaded conditions. Contrarily, the meta-heuristic-based techniques may promise a global peak solution, but they are computationally inefficient and take significant time for tracking. To address this problem, a BFT is proposed which combines the solutions obtained from conventional incremental conductance algorithm and Jaya optimization algorithm to produce better responses under various PSCs. The effectiveness of the proposed BFT-based MPPT is evaluated by comparing it with various MPPT methods, viz. incremental conductance, particle swarm optimization (PSO), and Jaya optimization algorithms in MATLAB/Simulink environment. From the various case studies carried, the overall average tracking speed with more than 99% accuracy is less than 0.25 s and having minimum steady-state oscillations. Even under the wide range of partially shaded conditions, the proposed method exhibited superior MPPT compared to the existing methods with tracking speed less than 0.1 s to achieve 99.8% tracking efficiency. A detailed comparison table is provided by comparing with other popular existing MPPT methodologies.
Effect of dietary graded levels of Imbrasia belina on the chemical composition and fatty acid profile of meat from broiler chickens
Authors: Moyo Sipho , Jaja Ishmael Festus , Mopipi Keletso , Hugo Arno , Masika Patrick , Muchenje Voster 
Published: April 2021

AbstractThe effect of dietary graded inclusion levels of Imbrasia belina worm meal on meat chemical composition and fatty acid profile of broiler chicken meat was evaluated. A total of 360 chicks were randomly allocated to four dietary treatments, with each treatment group replicated 6 times (n = 15/replicate). The inclusion levels of the I. belina worm meal in the treatments were 0% control = T1, T2 = 4%, T3 = 8% and 12%. A three-phase feeding program of starter (0-14d), grower (15-28d), and finisher (29-35d) was employed for the study. The results showed that crude protein content of breast meat was higher (P < 0.05) in dietary treatments than control, whereas crude fat content of thigh meat was higher (P < 0.05) than in breast meat. The fatty acid composition of breast meat myristic and myristoleic acid levels were significantly reduced (P < 0.05) in treatment groups than the control, whereas the levels of linoleic acid, ⅀PUFA, ⅀PUFA/SFA, ⅀PUFA: MUFA, ⅀(n-6) in thigh meat decreased significantly (P < 0.05) in treatment groups than control. The ⅀(n-3) and atherogenic index in the thigh meat increased significantly (P < 0.05) with incremental levels of I. belina meal. In conclusion, I. belina meal improved the protein content of breast meat and reduced levels of some fatty acids.
An investigation of the effects of hydrochar application rate on soil amelioration and plant growth in three diverse soils
Authors: de Jager Megan , Giani Luise 
Published: April 2021

AbstractThe hydrothermal carbonization (HTC) of biogas digestate alters the raw materials inherent characteristics to produce a carbon (C)-rich hydrochar (HC), with an improved suitability for soil amelioration. Numerous studies report conflicting impacts of various HC application rates on soil properties and plant growth. In this study, the influence of HC application rate on soil improvement and plant growth aspects was investigated in three diverse soils (Chernozem, Podzol, and Gleysol). Pot trials were conducted in which all soils were amended with 5, 10, 20 and 30% (w/w) HC in quintuplicate, with two controls of pure soil (with and without plants, respectively) also included. Prior to potting, soil samples were collected from all HC-amended soils and controls and analyzed for soil pH, plant available nutrients (PO4-P and K), and microbial activity using standard laboratory and statistical methods. Immediately after potting, a 6-week seed germination experiment using Chinese cabbage was conducted to determine germination success, followed by a plant growth experiment of equal duration and plant species to determine biomass success. At the end of the study (after a total plant growth period of 12 weeks), each pot was sampled and comparatively analyzed for the same soil properties as at the beginning of the study. Soil pH shifted toward the pH of the HC (6.6) in all soils over the course of the study, but was most expressed in the 20% and 30% application rates, confirming the well-documented liming effect of HC. The addition of HC increased the PO4-P and K contents, particularly with 20% and 30% HC amendments. These results are proposedly due to the large labile C fraction of the HC, which is easily degradable by microorganisms. The rapid decomposition of this C fraction prompted the quick release of the HCs inherently high PO4-P and K content into the soil, and in turn, further stimulated microbial activity, until this fraction was essentially depleted. HC addition did not inhibit seed germination at any rate, presumably due to a lack of phytotoxic compounds in the HC from aging and microbial processes, and furthermore, showed no significant impact (positive or negative) on plant growth in any soil, despite improved soil conditions. In conclusion, although less pronounced, soil improvements were still achievable and maintainable at lower application rates (5% and 10%), whereas higher rates did not ensure greater benefits for plant growth. While the addition of high rates of HC did not detrimentally effect soil quality or plant growth, it could lead to leaching if the nutrient supply exceeds plant requirements and the soil’s nutrient retention capacity. Therefore, this study validates the previous study in the effectiveness of the biogas digestate HC for soil amelioration and suggests that smaller regularly repeated HC applications may be recommendable for soil improvement.
The predictive capacity of the high resolution weather research and forecasting model: a year-long verification over Italy
Authors: Apicella L. , Puca S. , Lagasio M. , Meroni A. N. , Milelli M. , Vela N. , Garbero V. , Ferraris L. , Parodi A. 
Published: April 2021

AbstractNumerical models are operationally used for weather forecasting activities to reduce the risks of several hydro-meteorological disasters. The overarching goal of this work is to evaluate the Weather Research and Forecasting (WRF) model predictive capabilities over the Italian national territory in the year 2018, in two specific cloud resolving configurations. The validation is carried out with a fuzzy logic approach, by comparing the precipitation predicted by the WRF model, and the precipitation observed by the national network. The fuzzy logic technique, by considering different intensity thresholds, allows to identify the reliable spatial scales of the forecasts. The same approach is applied to evaluate the performances of COSMO-2I model, a state-of-the-art numerical model configuration used for operational activities. For the entire year, except for summer, the model predictive capabilities are high, with useful forecasts for structures of medium intensities down toO(10 km) length scales. In summer the skills decrease mainly because of localization errors. The work aims to provide a robust evaluation of the forecast performances of another convection permitting operational meteorological models currently available in Italy.
Mathematical analysis of laboratory microbial experiments demonstrating deterministic chaotic dynamics
Authors: Molz Fred , Faybishenko Boris 
Published: April 2021

AbstractPresented is a system of four ordinary differential equations and a mathematical analysis of microbiological experiments in a four-component chemostat—nutrient n, rods r, cocci c, and predators p. The analysis is consistent with the conclusion that previous experiments produced features of deterministic chaotic and classical dynamics depending on dilution rate. The surrogate model incorporates as much experimental detail as possible, but necessarily contains unmeasured parameters. The objective is to understand better the differences between model simulations and experimental results in complex microbial populations. The key methodology for simulation of chaotic dynamics, consistent with the measured dilution rate and microbial volume averages, was to cause the preference of p for r vs. c to vary with the r and c concentrations, to make r more competitive for nutrient than c, and to recycle some dying p biomass, leading to a modified version of the Monod kinetics model. Our mathematical model demonstrated that the occurrence of chaotic dynamics requires a predator, p, preference for r versus c to increase significantly with increases in r and c populations. Also included is a discussion of several generalizations of the existing model and a possible involvement of the minimum energy dissipation principle. This principle appears fundamental to thermodynamic systems including living systems. Several new experiments are suggested.
Cultural dimensions in online purchase behavior: Evidence from a cross-cultural study
Authors: Pratesi Francesca , Hu Lala , Rialti Riccardo , Zollo Lamberto , Faraoni Monica 
Published: April 2021

AbstractThe objective of this research is to investigate how cultural differences affect consumers’ online purchase behavior. We reviewed the recent literature on cross-cultural studies on online behavior and building on Hofstede’s theory of cultural dimensions and the theory of planned behavior (TPB), we developed a conceptual model exploring how the dimensions of national culture influence perceptions of website usability, trust, and perceived risk, which in turn impact on intention to use and online purchase behavior. A web-based questionnaire was distributed to a sample of 350 European and Asian consumers actively using Alibaba e-commerce platforms. The conceptual model was validated through a confirmatory factor analysis (CFA), while structural equation modelling (SEM) was used to empirically test the hypothesized relationships among variables. Results showed how culture significantly influenced website usability and perceived risk in European consumers and, in turn, their intention and behavior. Differently, culture significantly influenced trust of Asian consumers, as well as their intention and online behavior. With this study, we contribute to the literature on consumer online purchase behavior from a cross-cultural perspective. As culture emerged among the significant antecedents of mechanisms explaining online purchase behavior, e-tailers should tailor digital marketing strategies according to consumer cultural differences.