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Background: Endometrial cancer is common and usually occurs after menopause, but the number of women diagnosed during reproductive age is increasing. The standard treatment including hysterectomy is effective but causes absolute uterine factor infertility. In order to avoid or postpone surgery, conservative management of endometrial cancer (CMEC) has been proposed for younger women who want to retain their fertility., Objective and Rationale: The main objective of this study was to estimate the chances of pregnancy and live birth for women with early-stage endometrial cancer (EEC) who are managed conservatively for fertility preservation., Search Methods: The PRISMA recommendations for systematic reviews and meta-analyses were followed. Structured searches were performed in PubMed, Embase and the Cochrane Library, from inception until 13 June 2021. Inclusion was based on the following criteria: group or subgroup of women with Clinical Stage IA, well-differentiated, endometrioid endometrial cancer (from now on, EEC); CMEC for fertility preservation; and reported frequencies of women achieving pregnancy and/or live birth after CMEC. The following exclusion criteria applied: impossibility to isolate/extract outcome data of interest; second-line CMEC for persistent/recurrent disease; CMEC in the presence of synchronous tumours; case reports; non-original or duplicated data; and articles not in English. Qualitative synthesis was performed by means of tabulation and narrative review of the study characteristics. Study quality was assessed with an ad hoc instrument and several moderator and sensitivity analyses were performed., Outcomes: Out of 1275 unique records, 133 were assessed in full-text and 46 studies were included in the review. Data from 861 women with EEC undergoing CMEC were available. Progestin-based treatment was reported in all but three studies (93.5%; 836 women). Complete response to treatment was achieved in 79.7% of women, with 35.3% of them having a disease recurrence during follow-up. Of 286 pregnancies obtained after CMEC; 69.4% led to live birth (9% of them multiple births) and 66.7% were achieved through fertility treatment. Based on random-effects meta-analyses, women treated with progestin-based CMEC have a 26.7% chance of achieving pregnancy (95% CI 21.3-32.3; I2 = 53.7%; 42 studies, 826 women) and a 20.5% chance to achieve a live birth (95% CI 15.7-25.8; I2 = 40.2%; 39 studies, 650 women). Sample size, average age, publication year, study design and quality score were not associated with the outcomes of progestin-based CMEC in moderator analyses with meta-regression. However, mean follow-up length (in months) was positively associated with the chances of pregnancy (regression coefficient [B] = 0.003; 95% CI 0.001-0.005; P = 0.006) and live birth (B = 0.005; 95% CI 0.003-0.007; P < 0.001). In sensitivity analyses, the highest chances of live birth were estimated in subsets of studies including only women of age 35 or younger (30.7%), the combination of progestins with hysteroscopic resection (30.7%), or at least 3 years of follow-up (42.4%)., Wider Implications: Progestin-based CMEC is viable for women with well-differentiated, Clinical Stage 1A, endometrioid endometrial cancer who want to preserve their fertility, but there is room for improvement as only one-fifth of them are estimated to achieve live birth according to this meta-analysis. Further investigations on prognosis-driven selection, hysteroscopic resection and long-term surveillance are arguably needed to improve the reproductive outcomes of CMEC., (© The Author(s) 2021. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology.)

Over the last eight years, the volume of whole genome, gene expression, SNP genotyping, and phenotype data generated by the cotton research community has exponentially increased. The efficient utilization/re-utilization of these complex and large datasets for knowledge discovery, translation, and application in crop improvement requires them to be curated, integrated with other types of data, and made available for access and analysis through efficient online search tools. Initiated in 2012, CottonGen is an online community database providing access to integrated peer-reviewed cotton genomic, genetic, and breeding data, and analysis tools. Used by cotton researchers worldwide, and managed by experts with crop-specific knowledge, it continuous to be the logical choice to integrate new data and provide necessary interfaces for information retrieval. The repository in CottonGen contains colleague, gene, genome, genotype, germplasm, map, marker, metabolite, phenotype, publication, QTL, species, transcriptome, and trait data curated by the CottonGen team. The number of data entries housed in CottonGen has increased dramatically, for example, since 2014 there has been an 18-fold increase in genes/mRNAs, a 23-fold increase in whole genomes, and a 372-fold increase in genotype data. New tools include a genetic map viewer, a genome browser, a synteny viewer, a metabolite pathways browser, sequence retrieval, BLAST, and a breeding information management system (BIMS), as well as various search pages for new data types. CottonGen serves as the home to the International Cotton Genome Initiative, managing its elections and serving as a communication and coordination hub for the community. With its extensive curation and integration of data and online tools, CottonGen will continue to facilitate utilization of its critical resources to empower research for cotton crop improvement.

In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/)., (© The Author(s) 2021. Published by Oxford University Press.)

Monitoring for errors and behavioral adjustments after errors are essential for daily life. A question that has not been addressed systematically yet, is whether consciously perceived errors lead to different behavioral adjustments compared to unperceived errors. Our goal was to develop a task that would enable us to study different commonly observed neural correlates of error processing and post-error adjustments in their relation to error awareness and accuracy confidence in a single experiment. We assessed performance in a new number judgement error awareness task in 70 participants. We used multiple, robust, single-trial EEG regressions to investigate the link between neural correlates of error processing (e.g., error-related negativity (ERN) and error positivity (Pe)) and error awareness. We found that only aware errors had a slowing effect on reaction times in consecutive trials, but this slowing was not accompanied by post-error increases in accuracy. On a neural level, error awareness and confidence had a modulating effect on both the ERN and Pe, whereby the Pe was most predictive of participants' error awareness. Additionally, we found partial support for a mediating role of error awareness on the coupling between the ERN and behavioral adjustments in the following trial. Our results corroborate previous findings that show both an ERN/Pe and a post-error behavioral adaptation modulation by error awareness. This suggests that conscious error perception can support meta-control processes balancing the recruitment of proactive and reactive control. Furthermore, this study strengthens the role of the Pe as a robust neural index of error awareness.

Tripal MegaSearch is a Tripal module for querying and downloading biological data stored in Chado. This module allows site users to select data types, restrict the dataset by applying various filters and then customizing fields to view and download through a single interface. Set by site administrators, example data types include gene, germplasm, marker, map, QTL, genotype, phenotype and expression data. When querying for genes, users can restrict the gene dataset using various filters such as name, chromosome position and functional annotation. They can then customize fields to download, such as name, organism, type, chromosome position, various functional annotations such as BLAST, KEGG, InterPro and GO term. FASTA files can also be downloaded for the sequence data. Site administrators can choose from two different data sources to serve data: Tripal MegaSearch materialized views or Chado tables. If neither data source is desired, administrators may also create their own materialized views and serve them through the flexible dynamic Tripal MegaSearch query form. Tripal MegaSearch is currently implemented in several databases including the Genome Database for Rosaceae www.rosaceae.org and TreeGenes www.https://treegenesdb.org/., (© The Author(s) 2021. Published by Oxford University Press.)

The present investigation was conducted to evaluate the effects of testosterone on ethanol-induced alterations of µ-opioid receptor binding kinetics in specific brain regions of castrated rats. Male Sprague Dawley rats (100-124 g) adapted to a 12-h light/dark cycle were used. Animals were castrated under pentobarbital anesthesia. After a recovery period of 14 days, ethanol [3 g/kg as 22.5% solution in saline via intraperitoneal injection (i.p.)], testosterone [2.5 mg in 0.2 ml of olive oil via subcutaneous injection (s.c.) in the dorsal neck region] or the combination of ethanol and testosterone were administered to rats at 9:00 a.m. The control group was injected i.p. with 2 ml saline and s.c. with 0.2 ml olive oil for 7 days. Animals were sacrificed by decapitation at 2 h after the final injection. The brains were immediately removed, and the cortex, hippocampus, hypothalamus and midbrain were dissected. In an attempt to elucidate the mechanism involved in the hormonal modulation of the effects of ethanol and testosterone on the endogenous opioid system, the binding kinetics of the µ-opioid receptors were determined. The results obtained in the present study assisted in identifying the regulatory role of testosterone on ethanol-induced changes on µ-opioid receptor binding kinetics.

The Genome Database for Rosaceae (GDR, https://www.rosaceae.org) is an integrated web-based community database resource providing access to publicly available genomics, genetics and breeding data and data-mining tools to facilitate basic, translational and applied research in Rosaceae. The volume of data in GDR has increased greatly over the last 5 years. The GDR now houses multiple versions of whole genome assembly and annotation data from 14 species, made available by recent advances in sequencing technology. Annotated and searchable reference transcriptomes, RefTrans, combining peer-reviewed published RNA-Seq as well as EST datasets, are newly available for major crop species. Significantly more quantitative trait loci, genetic maps and markers are available in MapViewer, a new visualization tool that better integrates with other pages in GDR. Pathways can be accessed through the new GDR Cyc Pathways databases, and synteny among the newest genome assemblies from eight species can be viewed through the new synteny browser, SynView. Collated single-nucleotide polymorphism diversity data and phenotypic data from publicly available breeding datasets are integrated with other relevant data. Also, the new Breeding Information Management System allows breeders to upload, manage and analyze their private breeding data within the secure GDR server with an option to release data publicly.

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

Tripal is an open-source database platform primarily used for development of genomic, genetic and breeding databases. We report here on the release of the Chado Loader, Chado Data Display and Chado Search modules to extend the functionality of the core Tripal modules. These new extension modules provide additional tools for (1) data loading, (2) customized visualization and (3) advanced search functions for supported data types such as organism, marker, QTL/Mendelian Trait Loci, germplasm, map, project, phenotype, genotype and their respective metadata. The Chado Loader module provides data collection templates in Excel with defined metadata and data loaders with front end forms. The Chado Data Display module contains tools to visualize each data type and the metadata which can be used as is or customized as desired. The Chado Search module provides search and download functionality for the supported data types. Also included are the tools to visualize map and species summary. The use of materialized views in the Chado Search module enables better performance as well as flexibility of data modeling in Chado, allowing existing Tripal databases with different metadata types to utilize the module. These Tripal Extension modules are implemented in the Genome Database for Rosaceae (rosaceae.org), CottonGen (cottongen.org), Citrus Genome Database (citrusgenomedb.org), Genome Database for Vaccinium (vaccinium.org) and the Cool Season Food Legume Database (coolseasonfoodlegume.org). Database URL: https://www.citrusgenomedb.org/, https://www.coolseasonfoodlegume.org/, https://www.cottongen.org/, https://www.rosaceae.org/, https://www.vaccinium.org/., (© The Author(s) 2017. Published by Oxford University Press.)

Aim: This study evaluates a porcine collagen matrix (CM) for soft tissue thickening in comparison to the subepithelial connective tissue graft (SCTG)., Material and Methods: In eight beagle dogs, soft tissue thickening was performed at the buccal aspects of the upper canines (SCTG and CM). Impressions were taken before augmentation (i1), after surgery (i2), after one (i3), three (i4) and ten month (i5). Casts were optically scanned with a 3D scanner and each augmented region (unit of analysis) evaluated (primary outcome variable: volume increase in mm(3) ; secondary outcome variables: volume increase in percent, mean and maximum thickness increases in mm)., Results: 3D tissue measurements after surgery revealed a significant higher volume increase in the CM (86.37 mm(3)  ± 35.16 mm(3) ) than in the SCTG group (47.65 mm(3)  ± 17.90 mm(3) ). After 10 months, volume increase was non-significant between groups (SCTG:11.36 mm(3)  ± 9.26 mm(3) ; CM: 8.67 mm(3)  ± 13.67 mm(3) ). Maximum soft tissue thickness increase (i1-i5) was 0.66 mm ± 0.29 mm (SCTG) and 0.79 mm ± 0.37 mm (CM) with no significant difference., Conclusions: Ten months after soft tissue thickening, the CM is statistically non-inferior to the SCTG in terms of soft tissue volume and thickness increase. Further 3D studies are needed to confirm the data., (© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)