Identification and functional analyses of drought stress resistance genes by transcriptomics of the Mongolian grassland plant Chloris virgata.

Background: Mongolian grasslands, including the Gobi Desert, have been exposed to drought conditions with few rains. In such harsh environments, plants with highly resistant abilities against drought stress survive over long periods. We hypothesized that these plants could harbor novel and valuable genes for enhancing drought stress resistance.

A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability.

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops.

Functional data analysis-based yield modeling in year-round crop cultivation.

Crop yield prediction is essential for effective agricultural management. We introduce a methodology for modeling the relationship between environmental parameters and crop yield in longitudinal crop cultivation, exemplified by strawberry and tomato production based on year-round cultivation. Employing functional data analysis (FDA), we developed a model to assess the impact of these factors on crop yield, particularly in the face of environmental fluctuation.

Background removal for debiasing computer-aided cytological diagnosis.

To address the background-bias problem in computer-aided cytology caused by microscopic slide deterioration, this article proposes a deep learning approach for cell segmentation and background removal without requiring cell annotation. A U-Net-based model was trained to separate cells from the background in an unsupervised manner by leveraging the redundancy of the background and the sparsity of cells in liquid-based cytology (LBC) images. The experimental results demonstrate that the U-Net-based model trained on a small set of cytology images can exclude background features and accurately segment cells.

Transcriptome analysis of the Japanese eel (Anguilla japonica) during larval metamorphosis.

Background: Anguillid eels spend their larval period as leptocephalus larvae that have a unique and specialized body form with leaf-like and transparent features, and they undergo drastic metamorphosis to juvenile glass eels. Less is known about the transition of leptocephali to the glass eel stage, because it is difficult to catch the metamorphosing larvae in the open ocean. However, recent advances in rearing techniques for the Japanese eel have made it possible to study the larval metamorphosis of anguillid eels.

High-efficiency genome editing by Cas12a ribonucleoprotein complex in Euglena gracilis.

Transgene-free genome editing based on clustered regularly interspaced short palindromic repeats (CRISPR) technology is key to achieving genetic engineering in microalgae for basic research and industrial applications. Euglena gracilis, a unicellular phytoflagellate microalga, is a promising biomaterial for foods, feeds, cosmetics and biofuels. However, methods for the genetic manipulation of E.

IL-27 produced during acute malaria infection regulates Plasmodium-specific memory CD4 T cells.

Malaria infection elicits both protective and pathogenic immune responses, and IL-27 is a critical cytokine that regulate effector responses during infection. Here, we identified a critical window of CD4 T cell responses that is targeted by IL-27. Neutralization of IL-27 during acute infection with Plasmodium chabaudi expanded specific CD4 T cells, which were maintained at high levels thereafter.

Illuminating the diversity of carotenoids in microalgal eyespots and phototaxis.

Photosynthetic organisms biosynthesize various carotenoids, a group of light-absorbing isoprenoid pigments that have key functions in photosynthesis, photoprotection, and phototaxis. Microalgae, in particular, contain diverse carotenoids and carotenoid biosynthetic pathways as a consequence of the various endosymbiotic events in their evolutionary history. Carotenoids such as astaxanthin, diadinoxanthin, and fucoxanthin are unique to algae.

A novel lung cancer model based on bioengineered rat lungs.

Two-dimensional cell cultures have contributed substantially to lung cancer research, but 3D cultures are gaining attention as a new, more efficient, and effective research model. A model reproducing the 3D characteristics and tumor microenvironment of the lungs , including the co-existence of healthy alveolar cells with lung cancer cells, is ideal. Here, we describe the creation of a successful lung cancer model based on bioengineered lungs formed by decellularization and recellularization.

An effective salivation-facilitating method for COVID-19 testing: An experimental study.

Background: Simple tools, such as antigen test kits, are readily available for determining coronavirus disease 2019 infection at hospitals and homes. However, it is challenging for elderly people who are prone to dry mouth and other diseases. The main objective of this study was to investigate whether the presence or consumption of a plum pickle can facilitate salivation during coronavirus disease 2019 testing.

A metabolome genome-wide association study implicates histidine -pi-methyltransferase as a key enzyme in -methylhistidine biosynthesis in .

A genome-wide association study (GWAS), which uses information on single nucleotide polymorphisms (SNPs) from many accessions, has become a powerful approach to gene identification. A metabolome GWAS (mGWAS), which relies on phenotypic information based on metabolite accumulation, can identify genes that contribute to primary and secondary metabolite contents. In this study, we carried out a mGWAS using seed metabolomic data from accessions obtained by liquid chromatography-mass spectrometry to identify SNPs highly associated with the contents of metabolites such as glucosinolates.

Association between COVID-19 emergency declarations and physical activity among community-dwelling older adults enrolled in a physical activity measurement program: Evidence from a retrospective observational study using the regression discontinuity design.

Background: The current study examines the negative impact of the coronavirus disease 2019 (COVID-19) emergency declarations on physical activity among the community-dwelling older adults, the participants of a physical activity measurement program, in Japan.

Methods: This retrospective observational study included 1,773 community-dwelling older adults (aged 74.6 ± 6.

Beta-negative binomial nonlinear spatio-temporal random effects modeling of COVID-19 case counts in Japan.

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has spread seriously throughout the world. Predicting the spread, or the number of cases, in the future can facilitate preparation for, and prevention of, a worst-case scenario. To achieve these purposes, statistical modeling using past data is one feasible approach.

Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients.

Objectives: To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes.

Methods: In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features.

A Foreground Prototype-Based One-Shot Segmentation of Brain Tumors.

The potential for enhancing brain tumor segmentation with few-shot learning is enormous. While several deep learning networks (DNNs) show promising segmentation results, they all take a substantial amount of training data in order to yield appropriate results. Moreover, a prominent problem for most of these models is to perform well in unseen classes.

MORE PANICLES 3, a natural allele of OsTB1/FC1, impacts rice yield in paddy fields at elevated CO levels.

Improving crop yield potential through an enhanced response to rising atmospheric CO levels is an effective strategy for sustainable crop production in the face of climate change. Large-sized panicles (containing many spikelets per panicle) have been a recent ideal plant architecture (IPA) for high-yield rice breeding. However, few breeding programs have proposed an IPA under the projected climate change.

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks.

This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification.

Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data.

Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (fMRI). A hierarchical recurrent neural network embodying predictive processing theory was subjected to a facial emotion recognition task.

Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network.

The critical structure and nature of different bone marrow cells which form a base in the diagnosis of haematological ailments requires a high-grade classification which is a very prolonged approach and accounts for human error if performed manually, even by field experts. Therefore, the aim of this research is to automate the process to study and accurately classify the structure of bone marrow cells which will help in the diagnosis of haematological ailments at a much faster and better rate. Various machine learning algorithms and models, such as CNN + SVM, CNN + XGB Boost and Siamese network, were trained and tested across a dataset of 170,000 expert-annotated cell images from 945 patients' bone marrow smears with haematological disorders.

Zeaxanthin is required for eyespot formation and phototaxis in Euglena gracilis.

The eyespot apparatus is an organelle that forms carotenoid-rich globules in diverse flagellated microalgae and functions in phototaxis. The euglenophytes have structurally and functionally distinct eyespot apparatuses from chlorophytes. β-Carotene is the most abundant pigment detected in chlorophytes' eyespots, while xanthophylls such as zeaxanthin and diadinoxanthin have been suggested to function in euglenophytes' eyespots.

Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces.

Barley (Hordeum vulgare ssp. vulgare) is one of the first crops to be domesticated and is adapted to a wide range of environments. Worldwide barley germplasm collections possess valuable allelic variations that could further improve barley productivity.