bluuebunny/crossref_metadata_2025_split huggingface dataset:
Variation of wine preference amongst consumers is influenced by the composition of salivary proteins
Authors: Luo Jiaqiang , Ruan Xinwei , Ang Ching-Seng , Nolvachai Yada , Marriott Philip J. , Zhang Pangzhen , Howell Kate 
Published: September 2023

AbstractThe preferences of consumers for different flavours and aromas in wine are varied and may be explained by inherent factors such as cultural background, wine education and personal taste of the wine consumer. Wine flavour, as perceived in the mouth, includes aroma compounds released through the retronasal pathway, which are shaped by interactions with saliva. Saliva and wine interactions could provide an explanation as to why wine tasters express different preferences for wine. To test this hypothesis, 13 Western and 13 Chinese experienced wine tasters were recruited. Sensory evaluation was performed in formal surroundings to acquire free description-based and perceived sensory intensity data using the Pivot® Profile and continuous scale assessment, respectively. Participants’ saliva samples were collected before the sensory evaluation and spiked into a wine sample to investigate the impact on the wine’s volatile release using comprehensive two-dimensional gas chromatography–mass spectrometry (GC × GC–MS). Saliva samples were subjected to enzyme activity assays and protein composition profiling by Tandem Mass Tag (TMT) quantitative proteomics. The wine tasters showed differences in wine flavour perception, which was supported by the difference in wine volatile release resulting from the addition of saliva. The two groups of participants did not have significant differences in total salivary protein concentrations or the amounts of esterase and α-amylase. However, statistically significant variations in the concentrations of specific proteins (proline-rich proteins (PRPs) and lipocalin-1 (LCN-1); p < 0.01) were found between the two groups. Significant correlations between perceived intensities of wine attributes and concentrations of PRPs and LCN-1 were observed. These results indicate that the composition of proteins in saliva is a factor that influences wine perception and preference. Our results provide a biochemical basis for understanding preference for food based on interactions between aroma compounds and salivary proteins and could be used to suggest foods or beverages to particular cultural groups.
Whole genomes from bacteria collected at diagnostic units around the world 2020
Authors: Nag Sidsel , Larsen Gunhild , Szarvas Judit , Birkedahl Laura Elmlund Kohl , Gulyás Gábor Máté , Ciok Wojchiech Jakub , Lagermann Timmie Mikkel , Tafaj Silva , Bradbury Susan , Collignon Peter , Daley Denise , Dougnon Victorien , Fabiyi Kafayath , Coulibaly Boubacar , Dembélé René , Nikiema Georgette , Magloire Natama , Ouindgueta Isidore Juste , Hossain Zenat Zebin , Begum Anowara , Donchev Deyan , Diggle Mathew , Turnbull LeeAnn , Lévesque Simon , Berlinger Livia , Sogaard Kirstine Kobberoe , Guevara Paula Diaz , Valderrama Carolina Duarte , Maikanti Panagiota , Amlerova Jana , Drevinek Pavel , Tkadlec Jan , Dilas Milica , Kaasch Achim , Westh Henrik Torkil , Bachtarzi Mohamed Azzedine , Amhis Wahiba , Salazar Carolina Elisabeth Satán , Villacis JoséEduardo , Lúzon Mária Angeles Dominguez , Palau Dámaris Berbel , Duployez Claire , Paluche Maxime , Asante-Sefa Solomon , Moller Mie , Ip Margaret , Mareković Ivana , Pál-Sonnevend Agnes , Cocuzza Clementiza Elvezia , Dambrauskiene Asta , Macanze Alexandre , Cossa Anelsio , Mandomando Inácio , Nwajiobi-Princewill Philip , Okeke Iruka N. , Kehinde Aderemi O. , Adebiyi Ini , Akintayo Ifeoluwa , Popoola Oluwafemi , Onipede Anthony , Blomfeldt Anita , Nyquist Nora Elisabeth , Bocker Kiri , Ussher James , Ali Amjad , Ullah Nimat , Khan Habibullah , Gustafson Natalie Weiler , Jarrar Ikhlas , Al-Hamad Arif , Luvira Viravarn , Paveenkittiporn Wantana , Baran Irmak , Mwansa James C. L. , Sikakwa Linda , Yamba Kaunda , Hendriksen Rene Sjogren , Aarestrup Frank Moller 
Published: September 2023

AbstractThe Two Weeks in the World research project has resulted in a dataset of 3087 clinically relevant bacterial genomes with pertaining metadata, collected from 59 diagnostic units in 35 countries around the world during 2020. A relational database is available with metadata and summary data from selected bioinformatic analysis, such as species prediction and identification of acquired resistance genes.
DrugMechDB: A Curated Database of Drug Mechanisms
Authors: Gonzalez-Cavazos Adriana Carolina , Tanska Anna , Mayers Michael , Carvalho-Silva Denise , Sridharan Brindha , Rewers Patrick A. , Sankarlal Umasri , Jagannathan Lakshmanan , Su Andrew I. 
Published: September 2023

AbstractComputational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.
Forest growth in Europe shows diverging large regional trends
Authors: Pretzsch Hans , del Río Miren , Arcangeli Catia , Bielak Kamil , Dudzinska Malgorzata , Forrester David Ian , Klädtke Joachim , Kohnle Ulrich , Ledermann Thomas , Matthews Robert , Nagel Jürgen , Nagel Ralf , Ningre François , Nord-Larsen Thomas , Biber Peter 
Published: September 2023

AbstractForests cover about one-third of Europe’s surface and their growth is essential for climate protection through carbon sequestration and many other economic, environmental, and sociocultural ecosystem services. However, reports on how climate change affects forest growth are contradictory, even for same regions. We used 415 unique long-term experiments including 642 plots across Europe covering seven tree species and surveys from 1878 to 2016, and showed that on average forest growth strongly accelerated since the earliest surveys. Based on a subset of 189 plots in Scots pine (the most widespread tree species in Europe) and high-resolution climate data, we identified clear large-regional differences; growth is strongly increasing in Northern Europe and decreasing in the Southwest. A less pronounced increase, which is probably not mainly driven by climate, prevails on large areas of Western, Central and Eastern Europe. The identified regional growth trends suggest adaptive management on regional level for achieving climate-smart forests.
Validation of the irritation scale on a representative German sample: new normative data
Authors: Gralla Maria S. , Guendel Harald , Mueller Andreas , Braehler Elmar , Häuser Winfried , Kruse Johannes , Muschalla Beate , Rigotti Thomas , Strauss Bernhard , Balint Elisabeth M. 
Published: September 2023

AbstractThe irritation scale is a widely used and reliable self-report scale for measuring cognitive and emotional strain related to the work environment. It extends existing measures by providing a sensitive assessment for pre-clinical stress at work. Existing normative data are based on convenience samples and are therefore not representative. This study provides new normative data for the irritation scale based on a representative German sample (N = 1480). The new normative data indicate that the overall level of irritation in the German workforce is significantly lower compared to previously published data. Convergent and discriminant validity is confirmed by correlations with depression and anxiety (Patient Health Questionnaire-4 for Depression and Anxiety), somatic symptom scales (Bodily Distress Syndrome 25 checklist, Somatic Symptom Scale-8, Giessen Subjective Complaints List-8, comorbidity), psychological functioning (Mini-ICF rating for activity and participation disorders in mental illness), work-related stressors (overcommitment and bullying) and individual resources (self-efficacy). The results confirm the utility of the irritation scale and provide new benchmarks that avoid an underestimation of the levels of irritation in future studies.
Label-free drug response evaluation of human derived tumor spheroids using three-dimensional dynamic optical coherence tomography
Authors: Abd El-Sadek Ibrahim , Shen Larina Tzu-Wei , Mori Tomoko , Makita Shuichi , Mukherjee Pradipta , Lichtenegger Antonia , Matsusaka Satoshi , Yasuno Yoshiaki 
Published: September 2023

AbstractThis study aims at demonstrating label-free drug-response-patterns assessment of different tumor spheroids and drug types by dynamic optical coherence tomography (D-OCT). The study involved human breast cancer (MCF-7) and colon cancer (HT-29) spheroids. The MCF-7 and HT-29 spheroids were treated with paclitaxel (Taxol; PTX) and the active metabolite of irinotecan SN-38, respectively. The drugs were applied with 0 (control), 0.1, 1, and 10 μM concentrations and the treatment durations were 1, 3, and 6 days. A swept-source OCT microscope equipped with a repeated raster scanning protocol was used to scan the spheroids. Logarithmic intensity variance (LIV) and late OCT correlation decay speed (OCDS$$_l$$l) algorithms were used to visualize the tumor spheroid dynamics. LIV and OCDS$$_l$$limages visualized different response patterns of the two types of spheroids. In addition, spheroid morphology, LIV, and OCDS$$_l$$lquantification showed different time-courses among the spheroid and drug types. These results may indicate different action mechanisms of the drugs. The results showed the feasibility of D-OCT for the evaluation of drug response patterns of different cell spheroids and drug types and suggest that D-OCT can perform anti-cancer drug testing.
Epigenomic analysis reveals a unique DNA methylation program of metastasis-competent circulating tumor cells in colorectal cancer
Authors: Bao-Caamano Aida , Costa-Fraga Nicolás , Cayrefourcq Laure , Jácome María Amalia , Rodriguez-Casanova Aitor , Muinelo-Romay Laura , López-López Rafael , Alix-Panabières Catherine , Díaz-Lagares Angel 
Published: September 2023

AbstractCirculating tumor cells (CTCs) and epigenetic alterations are involved in the development of metastasis from solid tumors, such as colorectal cancer (CRC). The aim of this study was to characterize the DNA methylation profile of metastasis-competent CTCs in CRC. The DNA methylome of the human CRC-derived cell line CTC-MCC-41 was analyzed and compared with primary (HT29, Caco2, HCT116, RKO) and metastatic (SW620 and COLO205) CRC cells. The association between methylation and the transcriptional profile of CTC-MCC-41 was also evaluated. Differentially methylated CpGs were validated with pyrosequencing and qMSP. Compared to primary and metastatic CRC cells, the methylation profile of CTC-MCC-41 was globally different and characterized by a slight predominance of hypomethylated CpGs mainly distributed in CpG-poor regions. Promoter CpG islands and shore regions of CTC-MCC-41 displayed a unique methylation profile that was associated with the transcriptional program and relevant cancer pathways, mainly Wnt signaling. The epigenetic regulation of relevant genes in CTC-MCC-41 was validated. This study provides new insights into the epigenomic landscape of metastasis-competent CTCs, revealing biological information for metastasis development, as well as new potential biomarkers and therapeutic targets for CRC patients.
Supervised machine learning to validate a novel scoring system for the prediction of disease remission of functional pituitary adenomas following transsphenoidal surgery
Authors: McKevitt Chase , Gabriel Ellie , Marenco-Hillembrand Lina , Otamendi-Lopez Andrea , Jeevaratnam Suren , Almeida Joao Paulo , Samson Susan , Chaichana Kaisorn L. 
Published: September 2023

AbstractFunctional pituitary adenomas (FPAs) are associated with hormonal hypersecretion resulting in systemic endocrinopathies and increased mortality. The heterogenous composition of the FPA population has made modeling predictive factors of postoperative disease remission a challenge. Here, we aim to define a novel scoring system predictive of disease remission following transsphenoidal surgery (TSS) for FPAs and validate our process using supervised machine learning (SML). 392 patients with FPAs treated at one of the three Mayo Clinic campuses were retrospectively reviewed. Variables found significant on multivariate analysis were incorporated into our novel Pit-SCHEME score. The Pit-SCHEME score with a cut-off value ≥ 6 achieved a sensitivity of 86% and positive likelihood ratio of 2.88. In SML models, without the Pit-SCHEME score, the k-nearest neighbor (KNN) model achieved the highest accuracy at 75.6%. An increase in model sensitivity was achieved with inclusion of the Pit-SCHEME score with the linear discriminant analysis (LDA) model achieving an accuracy of 86.9%, which suggests the Pit-SCHEME score is the variable of most importance for prediction of postoperative disease remission. Ultimately, these results support the potential clinical utility of the Pit-SCHEME score and its prospective future for aiding in the perioperative decision making in patients with FPAs.
Method for efficient calculating earth pressure of retaining wall considering plant transpiration
Authors: Zhang Junhui , Hu Huiren , Fu Wei , Peng Junhui , Li Feng , Ding Le 
Published: September 2023

AbstractAn accurate estimation of earth pressure on retaining walls is imperative to achieving its design. This paper presents an analytical method framework that considers the effect of plant transpiration relative to the traditional calculation approaches. Specifically, a closed-form solution for one-dimensional steady unsaturated flow considering plant transpiration is incorporated into a representation of effective stress to obtain the changes in matric suction, and effective stress. The representations are used to extend Hooke’s law and Rankine’s earth pressure theory to determine at-rest, active, and passive earth pressures. Subsequently, the analytical method is used in a series of analysis case studies on the influence of root architecture types, transpiration rates, and soil types on earth pressure, to reveal that it can rapidly obtain the earth pressure. Notably, the effect of plant transpiration on earth pressure is significant. Furthermore, it is found that soil types and transpiration rates have a larger influence than root architecture types. Collectively, the research not only reveals the effect of plant on earth pressure for retaining wall, but also provides a theoretical basis for further exploration of the contribution of plants to the stability of retaining wall.
Anxiety and depression from age 3 to 8 years in children with and without ADHD symptoms
Authors: Ingeborgrud Christine Baalsrud , Oerbeck Beate , Friis Svein , Zeiner Pål , Pripp Are Hugo , Aase Heidi , Biele Guido , Dalsgaard Søren , Overgaard Kristin Romvig 
Published: September 2023

AbstractChildhood anxiety and depressive symptoms may be influenced by symptoms of attention deficit/hyperactivity disorder (ADHD). We investigated whether parent- and teacher-reported anxiety, depressive and ADHD symptoms at age 3 years predicted anxiety disorders and/or depression in children with and without ADHD at age 8 years. This study is part of the longitudinal, population-based Norwegian Mother, Father and Child Cohort Study. Parents of 3-year-olds were interviewed, and preschool teachers rated symptoms of anxiety disorders, depression and ADHD. At age 8 years (n = 783), Child Symptom Inventory-4 was used to identify children who fulfilled the diagnostic criteria for anxiety disorders and/or depression (hereinafter: Anx/Dep), and ADHD. Univariable and multivariable logistic regression analyses were used. In the univariable analyses, parent-reported anxiety, depressive and ADHD symptoms, and teacher-reported anxiety symptoms at age 3 years all significantly predicted subsequent Anx/Dep. In the multivariable analyses, including co-occurring symptoms at age 3 years and ADHD at 8 years, parent-reported anxiety and depressive symptoms remained significant predictors of subsequent Anx/Dep. At age 3 years, regardless of ADHD symptoms being present, asking parents about anxiety and depressive symptoms, and teachers about anxiety symptoms, may be important to identify children at risk for school-age anxiety disorders and/or depression.
A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing
Authors: He Binsheng , Sun Hongmei , Bao Meihua , Li Haigang , He Jianjun , Tian Geng , Wang Bo 
Published: September 2023

AbstractCarcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients’ prognosis. Thus, it’s critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn.
Usage of extruded diamond multi-injectors for improvement of fuel mixing inside the supersonic combustion chamber
Authors: Seraj Hossein , Hosseinnejad Farhad , Rostamiyan Yasser , Fallah Keivan 
Published: September 2023

AbstractThe main attention of this work is to investigate the usage of diamond multi extruded injectors on the fuel distribution in combustor of scramjet. This study applied the computational technique to simulate the transverse fuel jets released from extruded nozzles. The main focus is to evaluate the role of induced shock waves on the penetration and distribution of fuel jets. The effects of jet space and usage of annular nozzle for the fuel injection system are revealed. Results of this work shows that the gap of jet would be more efficient for mixing when the inner air jet is also used. Also, injection of the air from the core of annular nozzle significantly increase the fuel mixing.
Ferroptosis-related factors in the substantia nigra are associated with Parkinson’s disease
Authors: Liu Lei , Cui Yange , Chang Yan-Zhong , Yu Peng 
Published: September 2023

AbstractFerroptosis is an iron-dependent, lipid peroxidation-driven cell death pathway, while Parkinson’s disease (PD) patients exhibit iron deposition and lipid peroxidation in the brain. Thus, the features of ferroptosis highly overlap with the pathophysiological features of PD. Despite this superficial connection, the possible role(s) of ferroptosis-related (Fr) proteins in dopaminergic neurons and/or glial cells in the substantia nigra (SN) in PD have not been examined in depth. To explore the correlations between the different SN cell types and ferroptosis at the single-cell level in PD patients, and to explore genes that may affect the sensitivity of dopaminergic neurons to ferroptosis, we performed in silico analysis of a single cell RNA sequence (RNA-seq) set (GSE178265) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in the different cell types in the human SN, and proceeded to perform enrichment analysis, constructing a protein–protein interaction network from the DEGs of dopaminergic neurons with the Metascape database. We examined the intersection of Fr genes present in the FerrDb database with DEGs from the GSE178265 set to identify Fr-DEGs in the different brain cells. Further, we identified Fr-DEGs encoding secreted proteins to implicate cell–cell interactions in the potential stimulation of ferroptosis in PD. The Fr-DEGs we identified were verified using the bulk RNA-seq sets (GSE49036 and GSE20164). The number of dopaminergic neurons decreased in the SN of PD patients. Interestingly, non-dopaminergic neurons possessed the fewest DEGs. Enrichment analysis of dopaminergic neurons’ DEGs revealed changes in transmission across chemical synapses and ATP metabolic process in PD. The secreted Fr-DEGs identified were ceruloplasmin (CP), high mobility group box 1 (HMGB1) and transferrin (TF). The bulk RNA-seq set from the GEO database demonstrates that CP expression is increased in the PD brain. In conclusion, our results identify CP as a potential therapeutic target to protect dopaminergic neurons by reducing neurons’ sensitivity to ferroptosis.
Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method
Authors: Pirmoradi Saeed , Hosseiniyan Khatibi Seyed Mahdi , Zununi Vahed Sepideh , Homaei Rad Hamed , Khamaneh Amir Mahdi , Akbarpour Zahra , Seyedrezazadeh Ensiyeh , Teshnehlab Mohammad , Chapman Kenneth R. , Ansarin Khalil 
Published: September 2023

AbstractSevere asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.