Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d-1·L -1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L-1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.

Data extraction (DE) is a challenging step in systematic reviews (SRs). Complex SRs can involve multiple interventions and/or outcomes and encompass multiple research questions. Attempts have been made to clarify DE aspects focusing on the subsequent meta-analysis; there are, however, no guidelines for DE in complex SRs. Comparing datasets extracted independently by pairs of reviewers to detect discrepancies is also cumbersome, especially when the number of extracted variables and/or studies is colossal. This work aims to provide a set of practical steps to help SR teams design and build DE tools and compare extracted data for complex SRs.
We provided a 10-step guideline, from determining data items and structure to data comparison, to help identify discrepancies and solve data disagreements between reviewers. The steps were organised into three phases: planning and building the database and data manipulation. Each step was described and illustrated with examples, and relevant references were provided for further guidance. A demonstration example was presented to illustrate the application of Epi Info and R in the database building and data manipulation phases. The proposed guideline was also summarised and compared with previous DE guidelines.
The steps of this guideline are described generally without focusing on a particular software application or meta-analysis technique. We emphasised determining the organisational data structure and highlighted its role in the subsequent steps of database building. In addition to the minimal programming skills needed, creating relational databases and data validation features of Epi info can be utilised to build DE tools for complex SRs. However, two R libraries are needed to facilitate data comparison and solve discrepancies.
We hope adopting this guideline can help review teams construct DE tools that suit their complex review projects. Although Epi Info depends on proprietary software for data storage, it can still be a potential alternative to other commercial DE software for completing complex reviews.

Standardized nomenclature for genes, gene products, and isoforms is crucial to prevent ambiguity and enable clear communication of scientific data, facilitating efficient biocuration and data sharing. Standardized genotype nomenclature, which describes alleles present in a specific strain that differ from those in the wild-type reference strain, is equally essential to maximize research impact and ensure that results linking genotypes to phenotypes are Findable, Accessible, Interoperable, and Reusable (FAIR). In this publication, we extend the fission yeast clade gene nomenclature guidelines to support the curation efforts at PomBase (www.pombase.org), the Schizosaccharomyces pombe Model Organism Database. This update introduces nomenclature guidelines for noncoding RNA genes, following those set forth by the Human Genome Organisation Gene Nomenclature Committee. Additionally, we provide a significant update to the allele and genotype nomenclature guidelines originally published in 1987, to standardize the diverse range of genetic modifications enabled by the fission yeast genetic toolbox. These updated guidelines reflect a community consensus between numerous fission yeast researchers. Adoption of these rules will improve consistency in gene and genotype nomenclature, and facilitate machine-readability and automated entity recognition of fission yeast genes and alleles in publications or datasets. In conclusion, our updated guidelines provide a valuable resource for the fission yeast research community, promoting consistency, clarity, and FAIRness in genetic data sharing and interpretation.

We sought to describe the processes undertaken for the systematic selection and consensus determination of the common data elements for inclusion in a national pediatric critical care database in Canada.
We conducted a multicentre Delphi consensus study of Canadian pediatric intensive care units (PICUs) participating in the creation of a national database. Participants were PICU health care professionals, allied health professionals, caregivers, and other stakeholders. A dedicated panel group created a baseline survey of data elements based on literature, current PICU databases, and expertise in the field. The survey was then used for a Delphi iterative consensus process over three rounds, conducted from March to June 2021.
Of 86 invited participants, 68 (79%) engaged and agreed to participate as part of an expert panel. Panel participants were sent three rounds of the survey with response rates of 62 (91%), 61 (90%) and 55 (81%), respectively. After three rounds, 72 data elements were included from six domains, mostly reflecting clinical status and complex medical interventions received in the PICU. While race, gender, and home region were included by consensus, variables such as minority status, indigenous status, primary language, and ethnicity were not.
We present the methodological framework used to select data elements by consensus for a national pediatric critical care database, with participation from a diverse stakeholder group of experts and caregivers from all PICUs in Canada. The selected core data elements will provide standardized and synthesized data for research, benchmarking, and quality improvement initiatives of critically ill children.
RéSUMé: OBJECTIF: Nous avons cherché à décrire les processus entrepris pour la sélection systématique et la détermination consensuelle des éléments de données communs à inclure dans une base de données nationale sur les soins intensifs pédiatriques au Canada. MéTHODE: Nous avons mené une étude multicentrique de consensus selon la méthode Delphi sur les unités de soins intensifs pédiatriques (USIP) canadiennes participant à la création d’une base de données nationale. Les personnes participant à l’étude étaient des professionnel·les de la santé de l’USIP, du personnel paramédical, des soignant·es et d’autres intervenant·es. Un groupe de travail spécialisé a créé une enquête de base des éléments de données sur la littérature, les bases de données actuelles portant sur les USIP et l’expertise dans le domaine. L’enquête a ensuite été utilisée pour créer un processus de consensus itératif Delphi sur trois cycles, mené de mars à juin 2021. RéSULTATS: Sur les 86 personnes invitées à participer, 68 (79 %) se sont engagées et ont accepté de participer à un groupe d’experts. Les membres du panel ont reçu trois rondes du sondage, avec des taux de réponse de 62 (91 %), 61 (90 %) et 55 (81 %), respectivement. Après trois cycles, 72 éléments de données provenant de six domaines ont été inclus, reflétant principalement l’état clinique et les interventions médicales complexes reçues à l’USIP. Alors que la race, le genre et la région d’origine ont été inclus par consensus, des variables telles que le statut de minorité, le statut d’autochtone, la langue principale parlée et l’origine ethnique ne l’ont pas été. CONCLUSION: Nous présentons le cadre méthodologique utilisé pour sélectionner des éléments de données consensuels destinés à une base de données nationale sur les soins intensifs pédiatriques, avec la participation d’un groupe diversifié d’expert·es et de soignant·es de toutes les USIP au Canada. Les éléments de données de base sélectionnés fourniront des données normalisées et synthétisées pour la recherche, l’analyse comparative et les initiatives d’amélioration de la qualité pour les enfants gravement malades.

BACKGROUND: Despite the many opportunities data reuse offers, its implementation presents many difficulties, and raw data cannot be reused directly. Information is not always directly available in the source database and needs to be computed afterwards with raw data for defining an algorithm.
OBJECTIVE: The main purpose of this article is to present a standardized description of the steps and transformations required during the feature extraction process when conducting retrospective observational studies. A secondary objective is to identify how the features could be stored in the schema of a data warehouse.
METHODS: This study involved the following 3 main steps: (1) the collection of relevant study cases related to feature extraction and based on the automatic and secondary use of data; (2) the standardized description of raw data, steps, and transformations, which were common to the study cases; and (3) the identification of an appropriate table to store the features in the Observation Medical Outcomes Partnership (OMOP) common data model (CDM).
RESULTS: We interviewed 10 researchers from 3 French university hospitals and a national institution, who were involved in 8 retrospective and observational studies. Based on these studies, 2 states (track and feature) and 2 transformations (track definition and track aggregation) emerged. \"Track\" is a time-dependent signal or period of interest, defined by a statistical unit, a value, and 2 milestones (a start event and an end event). \"Feature\" is time-independent high-level information with dimensionality identical to the statistical unit of the study, defined by a label and a value. The time dimension has become implicit in the value or name of the variable. We propose the 2 tables \"TRACK\" and \"FEATURE\" to store variables obtained in feature extraction and extend the OMOP CDM.
CONCLUSIONS: We propose a standardized description of the feature extraction process. The process combined the 2 steps of track definition and track aggregation. By dividing the feature extraction into these 2 steps, difficulty was managed during track definition. The standardization of tracks requires great expertise with regard to the data, but allows the application of an infinite number of complex transformations. On the contrary, track aggregation is a very simple operation with a finite number of possibilities. A complete description of these steps could enhance the reproducibility of retrospective studies.

We assessed blood pressure (BP) changes during fiscal years (April to March of the following year) 2015-2020 to clarify the effect of the state of emergency due to the coronavirus disease 2019 (COVID-19) pandemic in 2020. We then considered BP in 2019 separately, as the Japanese hypertension guidelines were updated in 2019. The present retrospective cohort study extracted data from 157,510 Japanese individuals aged <75 years (mean age: 50.3 years, men: 67.5%) from the annual health check-up data of the DeSC database. The trends in BP were assessed using a repeated measures linear mixed model. After adjusting for the month of health check-ups to exclude seasonal BP variation, systolic BP linearly increased during fiscal years 2015-2018. From the value estimated by the trend in 2015-2018, systolic BP was lower by ≤1 mmHg in fiscal year 2019 among the treated participants. Meanwhile, systolic/diastolic BP (95% confidence interval) increased by 2.11 (1.97-2.24)/1.05 (0.96-1.14) mmHg for untreated women (n = 43,292), 1.60 (1.51-1.70)/1.17 (1.11-1.24) mmHg for untreated men (n = 88,479), 1.92 (1.60-2.23)/0.46 (0.25-0.67) mmHg for treated women (n = 7855), and 1.00 (0.79-1.21)/0.39 (0.25-0.53) mmHg for treated men (n = 17,884) in fiscal year 2020. These increases remained time-dependent covariates after adjustments for age, body mass index, alcohol consumption, smoking, physical activity, and blood sampling indices. Social change due to the pandemic might have increased BP by approximately 1-2/0.5-1 mmHg. Meanwhile, only a slight decrease in BP was observed immediately after the guideline update in Japan.

Clinic-based headache registries collect data for a wide variety of purposes including delineating disease characteristics, longitudinal natural disease courses, headache management approaches, quality of care, treatment safety and effectiveness, factors that predict treatment response, health care resource utilization, clinician adherence to guidelines, and cost-effectiveness. Registry data are valuable for numerous stakeholders, including individuals with headache disorders and their caregivers, healthcare providers, scientists, healthcare systems, regulatory authorities, pharmaceutical companies, employers, and policymakers. This International Headache Society document may serve as guidance for developing clinic-based headache registries. Use of registry data requires a formal research protocol that includes: 1) research aims; 2) methods for data collection, harmonization, analysis, privacy, and protection; 3) methods for human subject protection; and 4) publication and dissemination plans. Depending upon their objectives, headache registries should include validated headache-specific questionnaires, patient reported outcome measures, data elements that are used consistently across studies (i.e., \"common data elements\"), and medical record data. Amongst other data types, registries may be linked to healthcare and pharmacy claims data, biospecimens, and neuroimaging data. Headache diagnoses should be made according to the International Classification of Headache Disorders diagnostic criteria. The data from well-designed headache registries can provide wide-ranging and novel insights into the characteristics, burden, and treatment of headache disorders and ultimately lead to improvements in the management of patients with headache.

Amyotrophic lateral sclerosis (ALS) is the most common type of motor neuron disease whose causes are unclear. The first ALS gene associated with the autosomal dominant form of the disease was SOD1. This gene has a high rate of rare variants, and an appropriate classification is essential for a correct ALS diagnosis. In this study, we re-evaluated the classification of all previously reported SOD1 variants (n = 202) from ALSoD, project MinE, and in-house databases by applying the ACMG-AMP criteria to ALS. New bioinformatics analysis, frequency rating, and a thorough search for functional studies were performed. We also proposed adjusting criteria strength describing how to apply them to SOD1 variants. Most of the previously reported variants have been reclassified as likely pathogenic and pathogenic based on the modified weight of the PS3 criterion, highlighting how in vivo or in vitro functional studies are determining their interpretation and classification. Furthermore, this study reveals the concordance and discordance of annotations between open databases, indicating the need for expert review to adapt the study of variants to a specific disease. Indeed, in complex diseases, such as ALS, the oligogenic inheritance, the presence of genes that act as risk factors and the reduced penetration must be considered. Overall, the diagnosis of ALS remains clinical, and improving variant classification could support genetic data as diagnostic criteria.

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

Institutional arthroplasty registries are very popular nowadays; however, very few efforts have been made in order to standardize the information to be collected, thus limiting the possibility of inter-institutional data interpretation. This manuscript reports the results of a single-country consensus designed to define the minimum standardized dataset to be recorded within an institutional arthroplasty registry.
A national consensus was carried out among all members of the Colombian Society of Hip and Knee Surgeons using the Delphi method. Eleven questions and answers comprising every potential domain of an institutional registry of hip and knee arthroplasty were defined. According to the methodology, anonymous voting and multiple discussion rounds were performed. Three levels of agreement were defined: Strong consensus: equal to or greater than 80%, weak consensus between 70 and 79.9%, and no consensus below 70%.
All of the questions reached consensus level. The minimum dataset was defined to include demographic and clinical information, intraoperative and implant details, follow-up and early complications, implant survival, and functional outcome scores, as well as the validation model to assess information quality within the database. Currently, this dataset is being implemented voluntarily by the members of our national society.
A national consensus is a feasible method to build homogeneous arthroplasty registries. We recommend such an exercise since it establishes the basis to compare and add data between institutions and the joint analysis of said information in a national registry.