bluuebunny/crossref_metadata_2025_split huggingface dataset:
ANN‐based modelling of peppermint flavour encapsulation process with ultrasound approach
Authors: Potdar Shital B. , Bhanvase Bharat A. , Saudagar Prakash , Potoroko Irina , Sonawane Shirish H. 
Published: August 2021

AbstractEncapsulation has great potential for preserving the flavour and health benefits of bioactive compounds. Hence, in the previous study, an attempt was made to encapsulate peppermint flavour in a gum arabic (GA) shell. To further understand the effect of a wide range of parameters, in the present study, artificial neural networks (ANNs) are developed. To predict the effect of various parameters on the encapsulation process, networks are developed with a back‐propagation algorithm. Input parameters for the ANN are flavour concentration, GA concentration, spray dryer temperature and feed flow rate to the spray dryer. The encapsulation process is evaluated in terms of encapsulation efficiency, product yield, and particle size. To predict all outputs simultaneously, a combined model is developed. The results showed that the combined model has similar accuracy as that of the individual model and also helps to save on processing time. For the combined model, the best prediction performance is obtained with 5‐4‐3 ANN architecture exhibiting an R2 value of 0.9991, and corresponding MSE values of 0.000 54, 0.000 63, and 0.000 61 for encapsulation efficiency, product yield, and particle size, respectively. This indicates that the developed ANN model is capable of predicting the encapsulation process. The interpolation and extrapolation ability of the developed network is also evaluated.
Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio‐Canary comorbidity project
Authors: Berman Adam N. , Biery David W. , Ginder Curtis , Hulme Olivia L. , Marcusa Daniel , Leiva Orly , Wu Wanda Y. , Cardin Nicholas , Hainer Jon , Bhatt Deepak L. , Di Carli Marcelo F. , Turchin Alexander , Blankstein Ron 
Published: August 2021

AbstractObjective: Accurate ascertainment of comorbidities is paramount in clinical research. While manual adjudication is labor‐intensive and expensive, the adoption of electronic health records enables computational analysis of free‐text documentation using natural language processing (NLP) tools.Hypothesis: We sought to develop highly accurate NLP modules to assess for the presence of five key cardiovascular comorbidities in a large electronic health record system.Methods: One‐thousand clinical notes were randomly selected from a cardiovascular registry at Mass General Brigham. Trained physicians manually adjudicated these notes for the following five diagnostic comorbidities: hypertension, dyslipidemia, diabetes, coronary artery disease, and stroke/transient ischemic attack. Using the open‐source Canary NLP system, five separate NLP modules were designed based on 800 “training‐set” notes and validated on 200 “test‐set” notes.Results: Across the five NLP modules, the sentence‐level and note‐level sensitivity, specificity, and positive predictive value was always greater than 85% and was most often greater than 90%. Accuracy tended to be highest for conditions with greater diagnostic clarity (e.g. diabetes and hypertension) and slightly lower for conditions whose greater diagnostic challenges (e.g. myocardial infarction and embolic stroke) may lead to less definitive documentation.Conclusion: We designed five open‐source and highly accurate NLP modules that can be used to assess for the presence of important cardiovascular comorbidities in free‐text health records. These modules have been placed in the public domain and can be used for clinical research, trial recruitment and population management at any institution as well as serve as the basis for further development of cardiovascular NLP tools.
Active Learning Training Strategy for Predicting O Adsorption Free Energy on Perovskite Catalysts using Inexpensive Catalyst Features
Authors: Shambhawi Shambhawi , Csányi Gábor , Lapkin Alexei A. 
Published: August 2021

AbstractMachine learning (ML) based energy prediction models are among the most effective descriptor‐based catalyst screening tools for heterogeneous reaction systems. However, their implementations are limited due to expensive data labelling, ab initio feature evaluation and lack of universal catalyst features, that is, beyond d‐band theory. Herein, we propose an inexpensive geometric feature for application on systems beyond d‐band theory, for example perovskites comprising of s‐, p‐, d‐ and f‐block elements. We outline a workflow that inputs these features into an active learning algorithm that enables effective data labelling, whilst improving prediction accuracies of existing models. We then use batch sampling to define termination criteria and to implement time‐series error forecasting for further reducing the number of expensive data labelling for training. We implement this workflow to train ML models for predicting oxygen adsorption free energy on perovskites and achieve similar, if not better, prediction accuracies as obtained from ab initio features.
Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification
Authors: Zhang Yanan , Zhao Juanjuan , Qiang Yan , Yang Xiaotang , Wu Wei , Jia Liye 
Published: August 2021

SummaryThe diagnosis of the disease requires a variety of data and indicators. In order to make the computer perform intelligent computation and diagnosis from a doctor's perspective, complementary information between different modal data needs to be taken into account. Meanwhile, the redundancy features with key information should be selected in order to reduce the complexity of calculation. In this study, an adaptive dynamic loss function is proposed to weight different scales in the multi‐scale expansion network of pathological images according to the doctor's diagnosis process. And an ant colony algorithm based on maximum information coefficient correlation was designed for unsupervised feature selection of fusion features combined image feature and patient differential genes. Experimental results show that the addition of pathological image information and genetic information plays an important role in the classification of lung cancer subtypes. Compared with other feature selection methods, the proposed algorithm can quickly converge. Combining pathological image and gene expression matrix for cancer diagnosis can improve the diagnostic accuracy of specific patients, with an accuracy of 95.62 and AUC achieves 0.897. The proposed method has high effectiveness and superior performance in the classification of lung cancer.
A fuzzy logic‐based approach for fuzzy queries over NoSQL graph database
Authors: Ahmadi Zahra , Parand Fereshteh‐Azadi , Matinfar Farzam 
Published: August 2021

AbstractThe massive production of data in the web environment increases the need for efficient and effective solutions to store and manage data. NoSQL databases with the capability of storing various types of data were introduced to be used in the management of big data. Besides, supporting flexible querying has significant impacts on data management usability and users prefer to ask their requests using fuzzy terms and get crisp results to make efficient decisions. In this paper, a fuzzy‐based approach is presented which adopts fuzzy queries and delivers crisp recommendations to the users. For this purpose, a counselor, which acts as a fuzzy agent and has knowledge about the data, defines fuzzy terms, membership functions and fuzzy rule tables in a way that each user can ask queries through fuzzy terms. The evaluation results show the efficiency of the proposed approach which removes the need for high‐level programming skills for users. As a result, our approach's efficiency and user acceptance are increased as well and made it an appropriate solution for using ambiguous queries in big graph databases.
Band Gap Tuning of Covalent Triazine‐Based Frameworks through Iron Doping for Visible‐Light‐Driven Photocatalytic Hydrogen Evolution
Authors: Gao Shengjie , Zhang Peng , Huang Guocheng , Chen Qiaoshan , Bi Jinhong , Wu Ling 
Published: August 2021

AbstractPhotocatalytic hydrogen energy production through water splitting paves a promising pathway for alleviating the increasingly severe energy crisis. Seeking affordable, highly active, and stable photocatalysts is crucial to access the technology in a sustainable manner. Herein, a trivalent iron‐doped covalent triazine‐based framework (CTF‐1) was elaborately designed in this study to finely tune the band structure and photocatalytic activity of CTF‐1 for H2 production. With optimal doping amount, Fe10/CTF‐1 exhibited a satisfying H2 production activity of 1460 μmol h−1 g−1, corresponding to 28‐fold enhancement compared with pure CTF‐1. The Fe3+ doping is responsible for a remarkedly broadened visible‐light adsorption range, improved reduction ability and inhibited electron–hole recombination of CTF‐1. Specifically, the doped Fe3+ could serve as photocatalytically active center and “electron relay” to accelerate charge separation and transformation. This study offers a feasible strategy to validly design and synthesize CTF‐based photocatalytic materials to efficiently utilize solar energy.
Single‐cell transcriptomes reveal heterogeneity of high‐grade serous ovarian carcinoma
Authors: Hao Qian , Li Jiajia , Zhang Qinghua , Xu Fei , Xie Bangxiang , Lu Hua , Wu Xiaohua , Zhou Xiang 
Published: August 2021

AbstractBackgroundHigh‐grade serous ovarian carcinoma (HGSOC) is the most common and aggressive histotype of epithelial ovarian cancer. The heterogeneity and molecular basis of this disease remain incompletely understood.MethodsTo address this question, we have performed a single‐cell transcriptomics analysis of matched primary and metastatic HGSOC samples.ResultsA total of 13 571 cells are categorized into six distinct cell types, including epithelial cells, fibroblast cells, T cells, B cells, macrophages, and endothelial cells. A subset of aggressive epithelial cells with hyperproliferative and drug‐resistant potentials is identified. Several new markers that are highly expressed in epithelial cells are characterized, and their roles in ovarian cancer cell growth and migration are further confirmed. Dysregulation of multiple signaling pathways, including the translational machinery, is associated with ovarian cancer metastasis through the trajectory analysis. Moreover, single‐cell regulatory network inference and clustering (SCENIC) analysis reveals the gene regulatory networks and suggests the JUN signaling pathway as a potential therapeutic target for treatment of ovarian cancer, which is validated using the JUN/AP‐1 inhibitor T‐5224. Finally, our study depicts the epithelial‐fibroblast cell communication atlas and identifies several important receptor‐ligand complexes in ovarian cancer development.ConclusionsThis study uncovers new molecular features and the potential therapeutic target of HGSOC, which would advance the understanding and treatment of the disease.
Can functional genomic diversity provide novel insights into mechanisms of community assembly? A pilot study from an invaded alpine streambed
Authors: Marx Hannah E. , Carboni Marta , Douzet Rolland , Perrier Christophe , Delbart Franck , Thuiller Wilfried , Lavergne Sébastien , Tank David C. 
Published: August 2021

AbstractAn important focus of community ecology, including invasion biology, is to investigate functional trait diversity patterns to disentangle the effects of environmental and biotic interactions. However, a notable limitation is that studies usually rely on a small and easy‐to‐measure set of functional traits, which might not immediately reflect ongoing ecological responses to changing abiotic or biotic conditions, including those that occur at a molecular or physiological level. We explored the potential of using the diversity of expressed genes—functional genomic diversity (FGD)—to understand ecological dynamics of a recent and ongoing alpine invasion. We quantified FGD based on transcriptomic data measured for 26 plant species occurring along adjacent invaded and pristine streambeds. We used an RNA‐seq approach to summarize the overall number of expressed transcripts and their annotations to functional categories, and contrasted this with functional trait diversity (FTD) measured from a suite of characters that have been traditionally considered in plant ecology. We found greater FGD and FTD in the invaded community, independent of differences in species richness. However, the magnitude of functional dispersion was greater from the perspective of FGD than from FTD. Comparing FGD between congeneric alien–native species pairs, we did not find many significant differences in the proportion of genes whose annotations matched functional categories. Still, native species with a greater relative abundance in the invaded community compared with the pristine tended to express a greater fraction of genes at significant levels in the invaded community, suggesting that changes in FGD may relate to shifts in community composition. Comparisons of diversity patterns from the community to the species level offer complementary insights into processes and mechanisms driving invasion dynamics. FGD has the potential to illuminate cryptic changes in ecological diversity, and we foresee promising avenues for future extensions across taxonomic levels and macro‐ecosystems.
Diversity and habitat association of medium and large mammals in Gibe Sheleko National Park, Southern Ethiopia
Authors: Abie Kassahun , Tilahun Belete , Feyisa Abel , Kumssa Tewodros , Amare Alemneh 
Published: August 2021

AbstractComplete documentation on the status of mammals is indispensable for appropriate conservation measures in protected areas. However, there is inadequate information on mammalian resources in the ecosystem of Gibe Sheleko National Park (GSNP). Thus, the study aimed to assess species diversity, abundance, and habitat association of medium‐ and large‐sized mammals in GSNP. We stratified the study area into five dominant habitat types, namely dense forest, wooded grassland, grassland, riverine forest, and farmland habitat types based on land cover and vegetation structures and further employed stratified random sampling technique across each habitat type. The sample transects covered 20% of the study area. Transect width ranged from 50 m to 400 m based on vegetation cover and visibility of mammals. The main data were collected via direct observation. Data were analyzed via chi‐square test and species diversity indexes. We recorded the total of 20 mammals species' those belong to 10 families of which 8 species were large‐sized and 12 species medium‐sized mammals. There were two IUCN vulnerable species, namelyHippopotamus amphibiousandPanthera pardus, and two globally near‐threatened species, particularlyLitocranius walleriandCaracal caracalin the study area. Dense forest held the highest species diversity of medium‐ and large‐sized mammals (H′ = 2.28) with the highest evenness index (J = 0.84). Riverine forest had the least diversity with uneven population distribution.Papio anubiswas the most abundance species, whereasCaracal caracalwas the least abundant in the study area. GSNP is home for threatened and spectacular mammals species'; hence, an appropriate conservation measure is mandatory to keep existing mammals species'.
Combined evaluation of right ventricular function using echocardiography in non‐ischaemic dilated cardiomyopathy
Authors: Ishiwata Jumpei , Daimon Masao , Nakanishi Koki , Sugimoto Tadafumi , Kawata Takayuki , Shinozaki Tomohiro , Nakao Tomoko , Hirokawa Megumi , Sawada Naoko , Yoshida Yuriko , Amiya Eisuke , Hatano Masaru , Morita Hiroyuki , Yatomi Yutaka , Komuro Issei 
Published: August 2021

AbstractAimsAlthough comprehensive assessment of right ventricular (RV) function using multiple echocardiographic parameters is recommended for management of patients with non‐ischaemic dilated cardiomyopathy (DCM), it is unclear which RV parameters to combine. Additionally, normalization of RV parameters by estimated pulmonary artery systolic pressure (PASP), in consideration of RV–pulmonary artery coupling, may be clinically significant. The aim of our study was to elucidate the best combination of echocardiographic RV functional parameters, with or without indexing for PASP, to predict outcome in patients with heart failure with reduced ejection fraction secondary to DCM.Methods and resultsWe retrospectively analysed 109 DCM patients with left ventricular ejection fraction <40%. RV size was assessed by RV end‐diastolic area (RVEDA) and RV end‐systolic area (RVESA) from RV‐focused apical four‐chamber view. RV function was assessed by fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE) and by RV longitudinal strain (RVLS) using two‐dimensional speckle‐tracking echocardiography. All functional parameters were also indexed for estimated PASP. Cox analyses were used to evaluate the association of RV morphology and functional parameters with 1 year outcome (composite of left ventricular assist device implantation and all‐cause death). Area under the curve was used to compare prognostic values. Mean age was 44 ± 14 years, and 76 (69.7%) were men. Mean left ventricular ejection fraction was 21.9%, median RVEDA was 22.1 cm2, FAC was 27.0%, TAPSE was 15.0 mm, and RVLS was −12.5%. Forty‐one (37.6%) patients experienced the primary outcome. Multivariate Cox analysis revealed that RVEDA, RVESA, FAC, TAPSE, RVLS, FAC/PASP, and RVLS/PASP were independent predictors for primary outcome (all P < 0.05). However, normalization with PASP did not improve area under the curve for any RV functional parameters. When we evaluate hazard ratios according to the combination of two echocardiographic parameters of RV function, patients with impairment of both FAC (<27%) and RVLS (>−8.6%) had significantly higher hazard ratio than those with either impairment alone (11.3 vs. 3.4, P < 0.001); the other combinations did not improve prognostic value.ConclusionsNormalizing echocardiographic RV parameters for PASP did not improve the prognostic values for our population. Meanwhile, combined evaluation of FAC and RVLS improved risk stratification in patients with heart failure with reduced ejection fraction secondary to DCM.
Granzyme A and CD160 expression delineates ILC1 with graded functions in the mouse liver
Authors: Di Censo Chiara , Marotel Marie , Mattiola Irene , Müller Lena , Scarno Gianluca , Pietropaolo Giuseppe , Peruzzi Giovanna , Laffranchi Mattia , Mazej Julija , Hasim Mohamed Shaad , Asif Sara , Russo Eleonora , Tomaipitinca Luana , Stabile Helena , Lee Seung‐Hwan , Vian Laura , Gadina Massimo , Gismondi Angela , Shih Han‐Yu , Mikami Yohei , Capuano Cristina , Bernardini Giovanni , Bonelli Michael , Sozzani Silvano , Diefenbach Andreas , Ardolino Michele , Santoni Angela , Sciumè Giuseppe 
Published: August 2021

AbstractType 1 innate lymphoid cells (ILC1) are tissue‐resident lymphocytes that provide early protection against bacterial and viral infections. Discrete transcriptional states of ILC1 have been identified in homeostatic and pathological contexts. However, whether these states delineate ILC1 with different functional properties is not completely understood. Here, we show that liver ILC1 are heterogeneous for the expression of distinct effector molecules and surface receptors, including granzyme A (GzmA) and CD160, in mice. ILC1 expressing high levels of GzmA are enriched in the liver of adult mice, and represent the main hepatic ILC1 population at birth. However, the heterogeneity of GzmA and CD160 expression in hepatic ILC1 begins perinatally and increases with age. GzmA+ ILC1 differ from NK cells for the limited homeostatic requirements of JAK/STAT signals and the transcription factor Nfil3. Moreover, by employing Rorc(γt)‐fate map (fm) reporter mice, we established that ILC3‐ILC1 plasticity contributes to delineate the heterogeneity of liver ILC1, with RORγt‐fm+ cells skewed toward a GzmA–CD160+ phenotype. Finally, we showed that ILC1 defined by the expression of GzmA and CD160 are characterized by graded cytotoxic potential and ability to produce IFN‐γ. In conclusion, our findings help deconvoluting ILC1 heterogeneity and provide evidence for functional diversification of liver ILC1.
Vegetation disturbances and flood energy during an extreme flood on a sub‐tropical river
Authors: Sharpe Richard , Kemp Justine 
Published: August 2021

AbstractThe advent of 2D hydraulic modelling has improved our understanding of flood hydraulics, thresholds, and dynamic effects on floodplain geomorphology and riparian vegetation at the morphological‐unit scale. Hydraulic concepts of bed shear stress, stream power maxima, and energy (cumulative stream power) have been used to characterize floods and define their geomorphic effectiveness. These hydraulic concepts were developed in the context of reach‐averaged, 1D hydraulic analyses, but their application to 2D model results is problematic due to differences in the treatment of energy losses in 1D and 2D analyses. Here we present methods for estimating total and boundary resistance from 2D modelling of an extreme flood on a subtropical river. Hydraulic model results are correlated with observations of the flood impacts on floodplain geomorphology and the riparian vegetation to identify thresholds and compute variants of flood energy. Comparison of LiDAR data in 2011 and 2014 shows that the 2011 flood produced 2–4 m of erosion on floodplain bars that were previously forested or grass‐covered. Deposition on flood levees, dunes, and chute bars was up to 3.4 m thick. Various hydraulic metrics were trialled as candidates for thresholds of vegetation disturbance. The accuracy of thresholds using metrics extracted at the flood peak (i.e. boundary resistance and stream power maxima) was similar to that using energy as a threshold. Disturbance to forest and grass on vegetated bars was associated with stream powers of >834 W/m2and unit flows of >26 m2/s, respectively. Correlation of the hydraulic metrics with erosion and deposition depths showed no substantial improvement in using flood energy compared to metrics extracted at the flood peak for describing erosion and deposition. The extent of vegetation disturbances and morphological adjustments was limited for this extreme flood, and further 2D studies are needed to compare disturbance thresholds across different environments.
Verdichtungseigenschaften von schiefrig‐phyllitischem Tunnelausbruchmaterial
Authors: Voit Klaus , Depiné Manuel , Hofmann Matthias , Orsi Georg , Murr Roland , Zangerl Christian , Bergmeister Konrad 
Published: August 2021

AbstractLarge quantities of excavated rocks are generated during tunnel driving. Recycling possibilities and total percentage of recycling are depending on the rock conditions (rock type and rock properties), as well as the applied driving method. If recycling of the excavated rock mass is not possible or reasonable from a technical or economical point of view, the excavated material needs to be deposited in landfills. Thereby, the consumed landfill volume depends on the material volume of non‐recyclable rock and its compactability as a function of the loosening of the rock by excavation and landfill installation. Both, material volume and compactability of the excavated material, are difficult to evaluate in advance. Apart from unpredictable lithological changes and variation of the driving parameters (e.g. depth of advance in conventional driving, contact pressure of tunnel boring machine), external influences – like afflux of water or weather conditions during installation of the material – significantly affect the compactability of the excavated aggregates. In this study, the actual example of phyllitic rock material excavated by conventional drilling and blasting from the main tunnel tubes of the Brenner Base Tunnel has been chosen to take a closer look at the research question concerning the compactability of phyllitic rock material. Since excavation material is increasingly used in construction industry, detailed knowledge on this matter is necessary for a successful application.