Search

Background The Lycophyta species are the extant taxa most similar to early vascular plants that were once abundant on Earth. However, their distribution has greatly diminished. So far, the absence of chromosome-level assembled lycophyte genomes has hindered our understanding of evolution and environmental adaption of lycophytes. Findings We present the reference genome of the tetraploid aquatic quillwort, Isoetes sinensis , a lycophyte. This genome represents the first chromosome-level assembled genome of a tetraploid seed-free plant. Comparison of genomes between I. sinensis and Isoetestaiwanensis revealed conserved and different genomic features between diploid and polyploid lycophytes. Comparison of the I. sinensis genome with those of other species representing the evolutionary lineages of green plants revealed the inherited genetic tools for transcriptional regulation and most phytohormones in I. sinensis. The presence and absence of key genes related to development and stress responses provide insights into environmental adaption of lycophytes. Conclusions The high-quality reference genome and genomic analysis presented in this study are crucial for future genetic and environmental studies of not only I. sinensis but also other lycophytes. [ABSTRACT FROM AUTHOR]

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k -nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS). [ABSTRACT FROM AUTHOR]

Motivation Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to (i) remove systematic biases, (ii) model multiple datasets with ambiguous labels and (iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously and generalizability beyond a single model type. Results We developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets (even from different species) and applied our framework on simulated, pancreas and brain scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent cell subtype labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy simulated 1 datasets: 90%; simulated 2 datasets: 94%; pancreas datasets: 88% and brain datasets: 66%) using LAmbDA Feedforward 1 Layer Neural Network with bagging. This method achieved higher weighted accuracy in labeling cellular subtypes than two other state-of-the-art methods, scmap and CaSTLe in brain (66% versus 60% and 32%). Furthermore, it achieved better performance in correctly predicting ambiguous cellular subtype labels across datasets in 88% of test cases compared with CaSTLe (63%), scmap (50%) and MetaNeighbor (50%). LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing. Availability and implementation github.com/tsteelejohnson91/LAmbDA Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The present study was conducted to evaluate the effects of dietary garcinol supplementation during late gestation (from the 90th day of pregnancy; day 90) and lactation on the acid–base balance of the umbilical cord blood and performance of sows and piglets. Sixty sows (Duroc × Yorkshire × Landrace; second- or third-parity; n = 20) were randomly divided into 3 gestation (day 90 of pregnancy) or lactation treatments, control diet (CON; basal diet), basal diet with 200 mg garcinol, and basal diet with 600 mg garcinol per kg of feed. The body weight (BW); backfat thickness and litter size of the sows; and birth weight, weaning weight, and mortality of piglets were recorded. Sows' blood and piglets' umbilical cord blood were collected for the measurements of hematological parameters and antioxidative and immune indexes, and acid–base balance parameters, respectively. The colostrum and milk and fecal samples of the sows were also collected for analysis of milk composition and apparent total tract nutrient digestibility. Garcinol had no effect on the BW and backfat thickness of the sows but significantly increased the birth weight and weaning weight of piglets (P < 0.05) and decreased the mortality (P < 0.05). Moreover, the white blood cell counts and neutrophil count, mean cell hemoglobin, superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) activity in the plasma of the sows were increased more significantly (P < 0.05) in the garcinol groups than that in the CON group, whereas the malondialdehyde (MDA) content was decreased (P < 0.05). The garcinol treatment significantly increased the pH, HCO3− and base excess values (P < 0.05), whereas it decreased the pCO2 and lactate content (P < 0.05) in the umbilical blood. Dry matter (DM), ash, and ether extract in the colostrum were similar between groups (P > 0.05), whereas the garcinol significantly increased the crude protein (CP) in the milk. In addition, the content of immunoglobulin A (IgA) and immunoglobulin G (IgG) in the plasma of piglets and in colostrum and milk of sows were increased more significantly (P < 0.05) in the garcinol groups than that in the CON group. The apparent total tract nutrient digestibility was similar between treatments. Collectively, this study indicates that sows fed with garcinol in late gestation and lactation showed improved maternal health and antioxidative status, milk protein content, acid–base balance in the umbilical cord blood, and growth performance in piglets, showing promise in natural plant extract nutrition for sows. [ABSTRACT FROM AUTHOR]