bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interactions between hot and dry fuel conditions and vegetation dynamics in the 2017 fire season in Portugal
Authors: Ermitão Tiago , Gouveia Célia M , Bastos Ana , Russo Ana C 
Published: September 2022

Abstract
Wildfires are a serious threat to ecosystems and human. In Portugal, during 2017, a catastrophic fire season burned more than 500 000 hectares and caused the death of more than 100 people. Previous studies have shown that hot and dry fuel conditions promoted widespread propagation of wildfires. However, burned area (BA) and mega-fires, such as the 2017 ones, depend not just on favourable meteorological conditions, but also on fuel accumulation and dryness. In this study, we will assess the influence of spring meteorological conditions on fire season BA, through their effects on fuel accumulation and dryness. Using satellite-based data, we show that the association of higher temperatures and water availability in spring can increase the risk of summer wildfires propagation, flammability and intensity through their influence on vegetation gross productivity. This study highlights the important role of fuel accumulation during the growing season in fire-prone regions like Portugal. Our results imply that fuel management may be an effective way to mitigate extreme fire seasons associated with warmer and drier conditions in the future.
Exact solution of a family of staggered Heisenberg chains with conclusive pretty good quantum state transfer
Authors: Serra Pablo , Ferrón Alejandro , Osenda Omar 
Published: September 2022

Abstract
We construct the exact solution for a family of one-half spin chains explicitly. The spin chains Hamiltonian corresponds to an isotropic Heisenberg Hamiltonian, with staggered exchange couplings (ECs) that take only two different values. We work out the exact solutions in the one-excitation subspace. Regarding the problem of quantum state transfer, we use the solution and some theorems concerning the approximation of irrational numbers, to show the appearance of conclusive pretty good transmission for chains with particular lengths. We present numerical evidence that pretty good transmission is achieved by chains whose length is not a power of two. The set of spin chains that shows pretty good transmission is a subset of the family with an exact solution. Using perturbation theory, we thoroughly analyze the case when one of the EC strengths is orders of magnitude larger than the other. This strong coupling limit (SCL) allows us to study, in a simple way, the appearance of pretty good transmission. The use of analytical closed expressions for the eigenvalues, eigenvectors, and transmission probabilities allows us to obtain the precise asymptotic behavior of the time where the pretty good transmission is observed. Moreover, we show that this time scales as a power law whose exponent is an increasing function of the chain length. We also discuss the crossover behavior obtained for the pretty good transmission time between the regimes of SCL and the one observed when the ECs are of the same order of magnitude.
Core–shell bioprinting of vascularized in vitro liver sinusoid models
Authors: Taymour Rania , Chicaiza-Cabezas Nathaly Alejandra , Gelinsky Michael , Lode Anja 
Published: September 2022

Abstract

In vitro liver models allow the investigation of the cell behavior in disease conditions or in response to changes in the microenvironment. A major challenge in liver tissue engineering is to mimic the tissue-level complexity: besides the selection of suitable biomaterial(s) replacing the extracellular matrix (ECM) and cell sources, the three-dimensional (3D) microarchitecture defined by the fabrication method is a critical factor to achieve functional constructs. In this study, coaxial extrusion-based 3D bioprinting has been applied to develop a liver sinusoid-like model that consists of a core compartment containing pre-vascular structures and a shell compartment containing hepatocytes. The shell ink was composed of alginate and methylcellulose (algMC), dissolved in human fresh frozen plasma. The algMC blend conferred high printing fidelity and stability to the core–shell constructs and the plasma as biologically active component enhanced viability and supported cluster formation and biomarker expression of HepG2 embedded in the shell. For the core, a natural ECM-like ink based on angiogenesis-supporting collagen-fibrin (CF) matrices was developed; the addition of gelatin (G) enabled 3D printing in combination with the plasma-algMC shell ink. Human endothelial cells, laden in the CFG core ink together with human fibroblasts as supportive cells, formed a pre-vascular network in the core in the absence and presence of HepG2 in the shell. The cellular interactions occurring in the triple culture model enhanced the albumin secretion. In conclusion, core–shell bioprinting was shown to be a valuable tool to study cell–cell-interactions and to develop complex tissue-like models.
Rural electrification in Africa: a case study of Yebu community solar minigrid
Authors: Edomah Norbert 
Published: September 2022

Abstract
Using field observation and focus group semi-structured interviews, we explored rural electrification dynamics within a rural community setting. We investigated the issues surrounding the emergence, deployment, operation and management of solar minigrid technology within Yebu community in the Federal Capital Territory, north-central Nigeria, and the associated issues that emerged over time. The study revealed that: (1) many solar minigrid energy access interventions do not have a viable business and sustainability plan that guarantees long term impact; (2) a lack of participatory decision making by all concerned stakeholders (from the project conception stage) has hindered the potential of many solar minigrid energy access projects to achieve their intended developmental and economic benefits. The paper concludes by highlighting the key recommendations that can help address these challenges.
Multiple similarity drug–target interaction prediction with random walks and matrix factorization
Authors: Liu Bin , Papadopoulos Dimitrios , Malliaros Fragkiskos D , Tsoumakas Grigorios , Papadopoulos Apostolos N 
Published: September 2022

Abstract
The discovery of drug–target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and proteins via computational methods, which typically leverage heterogeneous information retrieved from diverse data sources, can boost the development of effective pharmaceuticals. Although random walk and matrix factorization techniques are widely used in DTI prediction, they have several limitations. Random walk-based embedding generation is usually conducted in an unsupervised manner, while the linear similarity combination in matrix factorization distorts individual insights offered by different views. To tackle these issues, we take a multi-layered network approach to handle diverse drug and target similarities, and propose a novel optimization framework, called Multiple similarity DeepWalk-based Matrix Factorization (MDMF), for DTI prediction. The framework unifies embedding generation and interaction prediction, learning vector representations of drugs and targets that not only retain higher order proximity across all hyper-layers and layer-specific local invariance, but also approximate the interactions with their inner product. Furthermore, we develop an ensemble method (MDMF2A) that integrates two instantiations of the MDMF model, optimizing the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC), respectively. The empirical study on real-world DTI datasets shows that our method achieves statistically significant improvement over current state-of-the-art approaches in four different settings. Moreover, the validation of highly ranked non-interacting pairs also demonstrates the potential of MDMF2A to discover novel DTIs.
Not all T cell epitopes are equally desired: a review ofin silicotools for the prediction of cytokine-inducing potential of T-cell epitopes
Authors: Dhanda Sandeep Kumar , Malviya Jitendra , Gupta Sudheer 
Published: September 2022

AbstractAssessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.
A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction
Authors: Wang Lei , Wong Leon , Li Zhengwei , Huang Yuan , Su Xiaorui , Zhao Bowei , You Zhuhong 
Published: September 2022

Abstract
Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have been designed to predict circRNA-disease associations. However, most of the existing methods rely on single correlation data. Here, we propose a machine learning framework for circRNA-disease association prediction, called MLCDA, which effectively fuses multiple sources of heterogeneous information including circRNA sequences and disease ontology. Comprehensive evaluation in the gold standard dataset showed that MLCDA can successfully capture the complex relationships between circRNAs and diseases and accurately predict their potential associations. In addition, the results of case studies on real data show that MLCDA significantly outperforms other existing methods. MLCDA can serve as a useful tool for circRNA-disease association prediction, providing mechanistic insights for disease research and thus facilitating the progress of disease treatment.
iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism
Authors: Zhang Zhao-Yue , Ning Lin , Ye Xiucai , Yang Yu-He , Futamura Yasunori , Sakurai Tetsuya , Lin Hao 
Published: September 2022

Abstract
The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress. Recently, some machine learning-based algorithms have been designed for miRNA subcellular location prediction, but their performance is still far from satisfactory. Here, we present a new data partitioning strategy that categorizes functionally similar locations for the precise and instructive prediction of miRNA subcellular location in Homo sapiens. To characterize the localization signals, we adopted one-hot encoding with post padding to represent the whole miRNA sequences, and proposed a deep bidirectional long short-term memory with the multi-head self-attention algorithm to model. The algorithm showed high selectivity in distinguishing extracellular miRNAs from intracellular miRNAs. Moreover, a series of motif analyses were performed to explore the mechanism of miRNA subcellular localization. To improve the convenience of the model, a user-friendly web server named iLoc-miRNA was established (http://iLoc-miRNA.lin-group.cn/).
SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising
Authors: Liu Jian , Pan Yichen , Ruan Zhihan , Guo Jun 
Published: September 2022

AbstractSingle-cell sequencing technologies are widely used to discover the evolutionary relationships and the differences in cells. Since dropout events may frustrate the analysis, many imputation approaches for single-cell RNA-seq data have appeared in previous attempts. However, previous imputation attempts usually suffer from the over-smooth problem, which may bring limited improvement or negative effect for the downstream analysis of single-cell RNA-seq data. To solve this difficulty, we propose a novel two-stage diffusion-denoising method called SCDD for large-scale single-cell RNA-seq imputation in this paper. We introduce the diffusion i.e. a direct imputation strategy using the expression of similar cells for potential dropout sites, to perform the initial imputation at first. After the diffusion, a joint model integrated with graph convolutional neural network and contractive autoencoder is developed to generate superposition states of similar cells, from which we restore the original states and remove the noise introduced by the diffusion. The final experimental results indicate that SCDD could effectively suppress the over-smooth problem and remarkably improve the effect of single-cell RNA-seq downstream analysis, including clustering and trajectory analysis.
Fluid management in acute kidney injury: from evaluating fluid responsiveness towards assessment of fluid tolerance
Authors: Argaiz Eduardo R , Rola Philippe , Haycock Korbin H , Verbrugge Frederik H 
Published: September 2022

Abstract
Despite the widespread use of intravenous fluids in acute kidney injury (AKI), solid evidence is lacking. Intravenous fluids mainly improve AKI due to true hypovolaemia, which is difficult to discern at the bedside unless it is very pronounced. Empiric fluid resuscitation triggered only by elevated serum creatinine levels or oliguria is frequently misguided, especially in the presence of fluid intolerance syndromes such as increased extravascular lung water, capillary leak, intra-abdominal hypertension, and systemic venous congestion. While fluid responsiveness tests clearly identify patients who will not benefit from fluid administration (i.e. those without an increase in cardiac output), the presence of fluid responsiveness does not guarantee that fluid therapy is indicated or even safe. This review calls for more attention to the concept of fluid tolerance, incorporating it into a practical algorithm with systematic venous Doppler ultrasonography assessment to use at the bedside, thereby lowering the risk of detrimental kidney congestion in AKI.
Pericoronary fat attenuation index—a new imaging biomarker and its diagnostic and prognostic utility: a systematic review and meta-analysis
Authors: Sagris Marios , Antonopoulos Alexios S , Simantiris Spiridon , Oikonomou Evangelos , Siasos Gerasimos , Tsioufis Konstantinos , Tousoulis Dimitris 
Published: September 2022

Abstract
Pericoronary fat attenuation index (FAI) on coronary computed tomography angiography imaging has been proposed as a novel marker of coronary vascular inflammation with prognostic value for major cardiovascular events. To date, there is no systematic review of the published literature and no meta-analysed data of previously published results. We performed a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. We systematically explored published literature in MEDLINE (PubMed) before 20 January 2022 for studies assessing FAI in both diagnostic and prognostic clinical settings in patients with or without cardiovascular disease. The primary outcome was the mean difference in FAI attenuation between stable and unstable coronary plaques. The secondary outcome was the hazard ratio (HR) of high FAI values for future cardiovascular events. We calculated I2 to test heterogeneity. We used random-effects modelling for the meta-analyses to assess the primary and secondary outcomes. This study is registered with PROSPERO (CRD42021229491). In total, 20 studies referred in a total of 7797 patients were included in this systematic review, while nine studies were used for the meta-analysis. FAI was significantly higher in unstable compared with stable plaques with a mean difference of 4.50 Hounsfield units [95% confidence interval (CI): 1.10–7.89, I2 = 88%] among 902 patients. Higher pericoronary FAI values offered incremental prognostic value for major adverse cardiovascular events (MACEs) in studies with prospective follow-up (HR = 3.29, 95% CI: 1.88–5.76, I2 = 75%) among 6335 patients. Pericoronary FAI seems to be a promising imaging biomarker that can be used for the detection of coronary inflammation, possibly to discriminate between stable and unstable plaques, and inform on the prognosis for future MACE. Further validation of these findings and exploration of the cost-effectiveness of the method before implementation in clinical practice are needed.
Atrial fibrillation and chronic obstructive pulmonary disease: diagnostic sequence and mortality risk
Authors: Warming Peder E , Garcia Rodrigue , Hansen Carl J , Simons Sami O , Torp-Pedersen Christian , Linz Dominik , Tfelt-Hansen Jacob 
Published: September 2022

AbstractBackground and aimsChronic obstructive pulmonary disease (COPD) is present in 13% of atrial fibrillation (AF) patients. In patients diagnosed with both AF and COPD, we aimed to assess overall mortality risk and its association with temporal sequence in AF and COPD diagnosis.MethodsThis nationwide study assessed all patients aged 18–85 years diagnosed with both COPD and AF between 1999 and 2018 in Denmark. Three groups were defined according to the temporal sequence of diagnosis: COPD diagnosed at least 6 months before AF (COPD-First), AF diagnosed at least 6 months before COPD (AF-First) and COPD, and AF diagnosed within a 6-months’ time frame (AF∼COPD).ResultsWe included 62 806 patients (75.0 years; 56.5% males). After 5 years of follow-up, 31 494 (50.1%) died. Mortality was highest in the COPD-First group (COPD-First: 52.8%; AF-First: 46.0%; AF∼COPD 50.6%). In a multivariable Cox-regression model adjusted for age, sex, type 2 diabetes, history of acute myocardial infarction, hypertension, heart failure, dyslipidemia, cancer, chronic kidney disease, and stroke, the AF∼COPD group (HR 1.19, 95% CI 1.16–1.23; P &lt; 0.001) and COPD-First group (HR 1.30, 95% CI 1.27–1.33; P &lt; 0.001) had a higher risk of death compared with the AF-First group. A restricted cubic spline analysis showed that the earlier the COPD was diagnosed, the worse is the prognosis.ConclusionPatients with concomitant AF and COPD had a very poor prognosis and the temporal sequence in diagnosis was differentially associated with prognosis, where a COPD diagnosis preceding an AF diagnosis was accompanied with a higher mortality risk compared with a COPD diagnosis following an AF diagnosis.
Retroaortic left renal vein in open thoracoabdominal aneurysm repair: A modified approach
Authors: Idhrees Mohammed , Velayudhan Bashi 
Published: September 2022

Abstract
The incidence of retroaortic left renal vein (RLRV) is less than 6%. This anatomical variation hinders the exposure and anastomosis of visceral arteries during open thoracoabdominal aneurysm repair. This situation may warrant division and ligation of the RLRV using the conventional retroperitoneal approach. This report describes a modified approach wherein the vein is not divided, thereby improving its surgical exposure.
Modified biatrial heart transplantation after bilateral bidirectional cavo-pulmonary shunts
Authors: Yoneyama Fumiya , Imamura Michiaki 
Published: September 2022

Abstract
We describe a patient who underwent modified biatrial anastomosis heart transplantation after the bilateral Glenn procedure. We introduced a new surgical technique to use the native central pulmonary artery as systemic venous return, which was anastomosed to right atrium, and then, biatrial anastomosis was performed.
Exploratory action research: experiences of Nepalese EFL teachers
Authors: Shrestha Sagun , Laudari Suman , Gnawali Laxman 
Published: September 2022

Abstract
The number of reports and publications about exploratory action research (EAR) has been increasing in recent years; however, there is still a dearth of studies that examine its effectiveness for improving teaching and learning. This study explores Nepalese EFL teachers’ perceptions of EAR, the ways they explored their classroom issues, and how they acted to improve their classroom practices through involvement in an online EAR training course for a year. The data collected from interviews and the teachers’ written reports show that these teachers found EAR to be an effective approach to explore their classroom puzzles or problems and develop informed, context-sensitive strategies to solve these. The findings of this study are relevant for teachers, teacher educators, and education policymakers in providing insights into the value of EAR for bringing about changes in teaching and learning situations.
The First Chromosome-level Genome Assembly ofCheumatopsyche charitesMalicky and Chantaramongkol, 1997 (Trichoptera: Hydropsychidae) Reveals How It Responds to Pollution
Authors: Ge Xinyu , Jin Jianfeng , Peng Lang , Zang Haoming , Wang Beixin , Sun Changhai 
Published: September 2022

AbstractTrichoptera is a highly adapted group of freshwater insects. They are generally more sensitive to dissolved oxygen and water quality than most freshwater organisms, and this sensitivity allows them to be used as reliable biological indicators of water quality. At present, there exists no chromosome-level genome of a hydropsychid species. Cheumatopsyche charites Malicky & Chantaramongkol, 1997 can successfully survive and thrive in polluted streams where other caddisflies are infrequent, suggesting that they are tolerant to latent contamination. Here we report a high-quality chromosome-level genome assembly of C. charites generated combining PacBio long reads and Hi-C reads. We obtained a genome assembly of 223.23 Mb, containing 68 scaffolds with an N50 length of 13.97 Mb, and 155 contigs (99.67%) anchored into 16 pseudochromosomes. We identified 36.12 Mb (16.18%) of the genome as being composed of repetitive elements, identified 369 noncoding RNAs, and predicted 8,772 protein-coding genes (96.80% BUSCO completeness). Gene family evolution analyses identified 7,148 gene families, of which 41 experienced rapid evolution. The expanded gene families were shown to be involved in detoxification metabolism, digestive absorption, and resistance to viruses or bacteria. This high-quality genome provides a valuable genomic basis for the study of trichopteran evolution.