Over the last decade, women have made decisive advances in increasing equality in science, technology, engineering, and medicine (STEM), but they still do not rival that of men. Many mechanisms to reduce gender discrimination have been addressed; however, little to nothing has been done to tackle the differences in the amount of time women spend on responsibilities at home. This has never been more apparent than during the COVID-19 pandemic. After a decade of advances promoting women, the last two years have seen these advances halted, and the long-term implications for women in STEM will be substantial. Moving forward, career advancement and funding mechanisms need to be adjusted to not just help women catch up, but to become a permanent support mechanism for women in the workplace. The higher amount of responsibilities at home and lack of support for women is not reserved for times of international upheaval: it has just become more apparent.

BACKGROUND: Manchurian walnut (Juglans mandshurica Maxim.) is a tree with multiple industrial uses and medicinal properties in the Juglandaceae family (walnuts and hickories). J. mandshurica produces juglone, which is a toxic allelopathic agent and has potential utilization value. Furthermore, the seed of J. mandshurica is rich in various unsaturated fatty acids and has high nutritive value. FINDINGS: Here, we present a high-quality chromosome-scale reference genome assembly and annotation for J. mandshurica (n = 16) with a contig N50 of 21.4 Mb by combining PacBio high-fidelity reads with high-throughput chromosome conformation capture data. The assembled genome has an estimated sequence size of 548.7 Mb and consists of 657 contigs, 623 scaffolds, and 40,453 protein-coding genes. In total, 60.99% of the assembled genome consists of repetitive sequences. Sixteen super-scaffolds corresponding to the 16 chromosomes were assembled, with a scaffold N50 length of 33.7 Mb and a BUSCO complete gene percentage of 98.3%. J. mandshurica displays a close sequence relationship with Juglans cathayensis, with a divergence time of 13.8 million years ago. Combining the high-quality genome, transcriptome, and metabolomics data, we constructed a gene-to-metabolite network and identified 566 core and conserved differentially expressed genes, which may be involved in juglone biosynthesis. Five CYP450 genes were found that may contribute to juglone accumulation. NAC, bZip, NF-YA, and NF-YC are positively correlated with the juglone content. Some candidate regulators (e.g., FUS3, ABI3, LEC2, and WRI1 transcription factors) involved in the regulation of lipid biosynthesis were also identified. CONCLUSIONS: Our genomic data provide new insights into the evolution of the walnut genome and create a new platform for accelerating molecular breeding and improving the comprehensive utilization of these economically important tree species.

Lignocellulose, as the key structural component of plant biomass, is a recalcitrant structure, difficult to degrade. The traditional management of plant waste, including landfill and incineration, usually causes serious environmental pollution and health problems. Interestingly, the xylophagous beetle, Trypoxylus dichotomus, can decompose lignocellulosic biomass. However, the genomics around the digestion mechanism of this beetle remain to be elucidated. Here, we assembled the genome of T. dichotomus, showing that the draft genome size of T. dichotomus is 636.27 Mb, with 95.37% scaffolds anchored onto 10 chromosomes. Phylogenetic results indicated that a divergent evolution between the ancestors of T. dichotomus and the closely related scarabaeid species Onthophagus taurus occurred in the early Cretaceous (120 million years ago). Through gene family evolution analysis, we found 67 rapidly evolving gene families, within which there were 2 digestive gene families (encoding Trypsin and Enoyl-(Acyl carrier protein) reductase) that have experienced significant expansion, indicating that they may contribute to the high degradation efficiency of lignocellulose in T. dichotomus. Additionally, events of chromosome breakage and rearrangement were observed by synteny analysis during the evolution of T. dichotomus due to chromosomes 6 and 8 of T. dichotomus being intersected with chromosomes 2 and 10 of Tribolium castaneum, respectively. Furthermore, the comparative transcriptome analyses of larval guts showed that the digestion-related genes were more commonly expressed in the midgut or mushroom residue group than the hindgut or sawdust group. This study reports the well-assembled and annotated genome of T. dichotomus, providing genomic and transcriptomic bases for further understanding the functional and evolutionary mechanisms of lignocellulose digestion in T. dichotomus.

BACKGROUND: Spatial organization plays an important role in the function of many biological systems, from cell fate specification in animal development to multistep metabolic conversions in microbial communities. The study of such systems benefits from the use of spatially explicit computational models that combine a discrete description of cells with a continuum description of one or more chemicals diffusing within a surrounding bulk medium. These models allow the in silico testing and refinement of mechanistic hypotheses. However, most existing models of this type do not account for concurrent bulk and intracellular biochemical reactions and their possible coupling. CONCLUSIONS: Here, we describe ChemChaste, an extension for the open-source C++ computational biology library Chaste. ChemChaste enables the spatial simulation of both multicellular and bulk biochemistry by expanding on Chaste's existing capabilities. In particular, ChemChaste enables (i) simulation of an arbitrary number of spatially diffusing chemicals, (ii) spatially heterogeneous chemical diffusion coefficients, and (iii) inclusion of both bulk and intracellular biochemical reactions and their coupling. ChemChaste also introduces a file-based interface that allows users to define the parameters relating to these functional features without the need to interact directly with Chaste's core C++ code. We describe ChemChaste and demonstrate its functionality using a selection of chemical and biochemical exemplars, with a focus on demonstrating increased ability in modeling bulk chemical reactions and their coupling with intracellular reactions. AVAILABILITY AND IMPLEMENTATION: ChemChaste version 1.0 is a free, open-source C++ library, available via GitHub at https://github.com/OSS-Lab/ChemChaste under the BSD license, on the Zenodo archive at zendodo doi, as well as on BioTools (biotools:chemchaste) and SciCrunch (RRID:SCR022208) databases.

BACKGROUND: Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. RESULTS: In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as "cataloging" based on comprehensible computer vision methods. We evaluate the workflow on 2 real-world datasets. One dataset is recorded for observation of Cercospora leaf spot-a fungal disease-in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers a large-scale spatiotemporal image dataset that in turn can be applied to train further machine learning models including various data layers. CONCLUSION: The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.

The increasingly multidisciplinary nature of scientific research necessitates a need for Open Data repositories that can archive data in support of publications in scientific journals. Recognising this need, even before GigaScience launched in 2012, GigaDB was already in place and taking data for a year before (making it 11 this year). Since GigaDB launched, there has been a consistent growth in this resource in terms of data volume, data discoverability and data re-use. In this commentary, we provide a retrospective of key changes over the last decade, and the role of Data Curation in enhancing the user experience. Furthermore we explore a much needed emphasis on enabling researchers to interact with and explore datasets prior to data download.

In the last decade, the field of computational pathology has advanced at a rapid pace because of the availability of deep neural networks, which achieved their first successes in computer vision tasks in 2012. An important driver for the progress of the field were public competitions, so called 'Grand Challenges', in which increasingly large data sets were offered to the public to solve clinically relevant tasks. Going from the first Pathology challenges, which had data obtained from 23 patients, to current challenges sharing data of thousands of patients, performance of developed deep learning solutions has reached (and sometimes surpassed) the level of experienced pathologists for specific tasks. We expect future challenges to broaden the horizon, for instance by combining data from radiology, pathology and tumor genetics, and to extract prognostic and predictive information independent of currently used grading schemes.

Wide interest in species conservation is young. To many it began early in 1903 when Teddy Roosevelt and John Muir set up a camp under the Grizzly Giant in the Mariposa Grove of California's Yosemite Valley. Over three days they decided to broaden the US National Park footprint across the USA. Conservationists were inspired in the coming decades by the writings of wildlife conservation pioneers-Osa Johnson (I Married Adventure), Karen Blixen (Out of Africa) and Rachel Carson (The Silent Spring). Countless crusaders developed a passion for preserving dwindling species in those early days, yet none of these conservation advocates mentioned the word genetics, let alone genomics. The genome sequencing projects that have followed on from these have brought in an enormous amount of data, including whole genome sequences for thousands of non-human species, both individual and population wide. This huge resource has revolutionized conservation genetics, bringing in ways to assess the health of at-risk populations, devise genetic-driven breeding strategies, and other means to attempt to preserve the over 1 million species (and growing) under threat today.

Research resource identifiers (RRIDs) are persistent unique identifiers for scientific resources used to conduct studies such as reagents and tools. Inclusion of these identifiers into the scientific literature has been demonstrated to improve the reproducibility of papers because resources, like antibodies, are easier to find, making methods easier to reproduce. RRIDs also dramatically reduce the use of problematic resources, such as contaminated cell lines. The addition of RRIDs into a manuscript means that authors have to look up information that they may have previously omitted or confront information about problems that may have been reported about their resources. The use of RRIDs is primarily driven by champion journals, such as GigaScience and others. Although still nascent, this practice lays important groundwork for citation types that can cover non-traditional scholarly output, such as software tools and key reagents; giving authors of various types of tools scholarly credit for their contributions.

Neuroscience has undergone a significant transformation over the past decade, becoming an increasingly open and FAIR discipline. I provide personal perspectives on the importance of two community organizations, FORCE11: The Future of Research Communications and e-Scholarship and INCF: The International Neuroinformatics Coordinating Facility in providing the intellectual and community environment where ideas and open sharing of data and code were incubated and tried.

BACKGROUND: Bretschneidera sinensis is an endangered relic tree species in the Akaniaceae family and is sporadically distributed in eastern Asia. As opposed to its current narrow and rare distribution, the fossil pollen of B. sinensis has been found to be frequent and widespread in the Northern Hemisphere during the Late Miocene. B. sinensis is also a typical mycorrhizal plant, and its annual seedlings exhibit high mortality rates in absence of mycorrhizal development. The chromosome-level high-quality genome of B. sinensis will help us to more deeply understand the survival and demographic histories of this relic species. RESULTS: A total of 25.39 Gb HiFi reads and 109.17 Gb Hi-C reads were used to construct the chromosome-level genome of B. sinensis, which is 1.21 Gb in length with the contig N50 of 64.13 Mb and chromosome N50 of 146.54 Mb. The identified transposable elements account for 55.21% of the genome. A total of 45,839 protein-coding genes were predicted in B. sinensis. A lineage-specific whole-genome duplication was detected, and 7,283 lineage-specific expanded gene families with functions related to the specialized endotrophic mycorrhizal adaptation were identified. The historical effective population size (Ne) of B. sinensis was found to oscillate greatly in response to Quaternary climatic changes. The Ne of B. sinensis has decreased rapidly in the recent past, making its extant Ne extremely lower. Our additional evolutionary genomic analyses suggested that the developed mycorrhizal adaption might have been repeatedly disrupted by environmental changes caused by Quaternary climatic oscillations. The environmental changes and an already decreased population size during the Holocene may have led to the current rarity of B. sinensis. CONCLUSION: This is a detailed report of the genome sequences for the family Akaniaceae distributed in evergreen forests in eastern Asia. Such a high-quality genomic resource may provide critical clues for comparative genomics studies of this family in the future.

Artocarpus nanchuanensis (Moraceae), which is naturally distributed in China, is a representative and extremely endangered tree species. In this study, we obtained a high-quality chromosome-scale genome assembly and annotation information for A. nanchuanensis using integrated approaches, including Illumina, Nanopore sequencing platform, and Hi-C. A total of 128.71 Gb of raw Nanopore reads were generated from 20-kb libraries, and 123.38 Gb of clean reads were obtained after filtration with 160.34× coverage depth and a 17.48-kb average read length. The final assembled A. nanchuanensis genome was 769.44 Mb with a 2.09 Mb contig N50, and 99.62% (766.50 Mb) of the assembled data was assigned to 28 pseudochromosomes. In total, 39,596 genes (95.10%, 39,596/41,636) were successfully annotated, and 129 metabolic pathways were detected. Plants disease resistance/insect resistance genes, plant-pathogen interaction metabolic pathways, and abundant biosynthesis pathways of vitamins, flavonoid, and gingerol were detected. Unigene reveals the basis of species-specific functions, and gene family in contraction and expansion generally implies strong functional differences in the evolution. Compared with other related species, a total of 512 unigenes, 309 gene families in contraction, and 559 gene families in expansion were detected in A. nanchuanensis. This A. nanchuanensis genome information provides an important resource to expand our understanding of the unique biological processes, nutritional and medicinal benefits, and evolutionary relationship of this species. The study of gene function and metabolic pathway in A. nanchuanensis may reveal the theoretical basis of a special trait in A. nanchuanensis and promote the study and utilization of its rare medicinal value.

BACKGROUND: The combination of computer vision devices such as multispectral cameras coupled with artificial intelligence has provided a major leap forward in image-based analysis of biological processes. Supervised artificial intelligence algorithms require large ground truth image datasets for model training, which allows to validate or refute research hypotheses and to carry out comparisons between models. However, public datasets of images are scarce and ground truth images are surprisingly few considering the numbers required for training algorithms. RESULTS: We created a dataset of 1,283 multidimensional arrays, using berries from five different grape varieties. Each array has 37 images of wavelengths between 488.38 and 952.76 nm obtained from single berries. Coupled to each multispectral image, we added a dataset with measurements including, weight, anthocyanin content, and Brix index for each independent grape. Thus, the images have paired measures, creating a ground truth dataset. We tested the dataset with 2 neural network algorithms: multilayer perceptron (MLP) and 3-dimensional convolutional neural network (3D-CNN). A perfect (100% accuracy) classification model was fit with either the MLP or 3D-CNN algorithms. CONCLUSIONS: This is the first public dataset of grape ground truth multispectral images. Associated with each multispectral image, there are measures of the weight, anthocyanins, and Brix index. The dataset should be useful to develop deep learning algorithms for classification, dimensionality reduction, regression, and prediction analysis.