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Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat ( Triticum aestivum L.), soybean ( Glycine max ), tomato ( Solanum lycopersicum ), barley ( Hordeum vulgare L.), maize ( Zea mays L.), millet ( Setaria italica L.), cotton ( Gossypium hirsutum L.), Medicago truncatula , and rice ( Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the “phenotype to genotype” and “genotype to phenotype” concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

Key messageWe found that overexpression of EgMYB111 and EgMYB157 genes positively regulate the abiotic stress tolerance.MYB family genes are well-known regulators in modulating the abiotic stress-responsive mechanisms in plants. However, lesser is known about the functional roles of oil palm MYB genes. Previously, we found that oil palm MYB genes such as EgMYB111 and EgMYB157 were significantly up-regulated under salinity, cold, and drought stress conditions. In this study, we over-expressed EgMYB111 and EgMYB157 genes separately in Arabidopsis plants. The transgenic Arabidopsis plants expressing EgMYB111 have shown improved tolerance to salinity, cold and drought stress conditions, whereas transgenic Arabidopsis plants expressing EgMYB157 dispalyed improved tolerance to cold and drought stress conditions only. Various biochemical analyses also revealed significant improvement of antioxidant enzyme activities, photosynthetic pigments, net photosynthetic rate, stomatal conductance, and intercellular CO2 concentration in transgenic plants compared to wild-type plants under cold, drought, and salinity stress conditions. Significant up-regulation of various known stress marker genes such as RD22, RD29A, RAB18, COR47, ABA1, ABI1, HAB1 was also noticed in EgMYB111 and EgMYB157 expressing transgenic plants compared to wild-type plants under cold, drought, and salinity stress conditions. Taken together, over-expression of EgMYB111 and/or EgMYB157 significantly improve abiotic tolerance in transgenic Arabidopsis plants, indicating that EgMYB111 and EgMYB157 are the potential candidates for developing abiotic stress-tolerant crops in near future.

Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the combinatorially increased interactions among agents and with the environment. However, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor even datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of MARL. We investigate the generalization of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to online fine tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages the transformer’s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios. On the StarCraft II offline dataset, MADT outperforms the state-of-the-art offline reinforcement learning (RL) baselines, including BCQ and CQL. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot cases. To the best of our knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL.

In this paper, we quantify the non-transitivity in chess using human game data. Specifically, we perform non-transitivity quantification in two ways—Nash clustering and counting the number of rock–paper–scissor cycles—on over one billion matches from the Lichess and FICS databases. Our findings indicate that the strategy space of real-world chess strategies has a spinning top geometry and that there exists a strong connection between the degree of non-transitivity and the progression of a chess player’s rating. Particularly, high degrees of non-transitivity tend to prevent human players from making progress in their Elo ratings. We also investigate the implications of non-transitivity for population-based training methods. By considering fixed-memory fictitious play as a proxy, we conclude that maintaining large and diverse populations of strategies is imperative to training effective AI agents for solving chess.

Background Protein–protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology. Results Recently, graph convolutional networks (GCNs) have been proposed to capture the graph structure information and generate representations for nodes in the graph. In our paper, we use GCNs to learn the position information of proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent. Combining amino acid sequence information and position information makes a stronger representation for protein, which improves the accuracy of PPIs prediction. Conclusion In previous research methods, most of them only used protein amino acid sequence as input information to make predictions, without considering the structural information of PPIs networks graph. We first time combine amino acid sequence information and position information to make representations for proteins. The experimental results indicate that our method has strong competitiveness compared with several sequence-based methods.

Background Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public. Results In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting. Conclusion We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.

Methods for detecting mycotoxins are very important because of the great health hazards of mycotoxins. However, there is a high background and low signal-to-noise ratio in real-time sensing, and therefore it is difficult to meet the fast, accurate, and convenient requirements for control of food quality. Here we constructed a quantitative fluorescence image analysis based on multicolor upconversion nanocrystal (UCN)-encoded microspheres for detection of ochratoxin A and zearalenone. The background-free encoding image signal of UCN-doped microspheres was captured by fluorescence microscopy under near-infrared excitation, whereas the detection image signal of phycoerythrin-labeled secondary antibodies conjugated to the microspheres was captured under blue light excitation. We custom-wrote an algorithm to analyze the two images for the same sample in 10 s, and only the gray value in the red channel of the secondary probe confirmed the quantity. The results showed that this novel detection platform performed feasible and reliable fluorescence image measurements by this method. Additionally, the limit of detection of was 0.34721 ng/mL for ochratoxin A and 0.41162 ng/mL for zearalenone. We envision that this UCN encoding strategy will be usefully applied for fast, accurate, and convenient testing of multiple food contaminants to ensure the safety of the food.

Potassium ions (K + ) are important for plant growth and crop yield. However, the effects of K + deficiency on the biomass of coconut seedlings and the mechanism by which K + deficiency regulates plant growth remain largely unknown. Therefore, in this study, we compared the physiological, transcriptome, and metabolite profiles of coconut seedling leaves under K + -deficient and K + -sufficient conditions using pot hydroponic experiments, RNA-sequencing, and metabolomics technologies. K + deficiency stress significantly reduced the plant height, biomass, and soil and plant analyzer development value, as well as K content, soluble protein, crude fat, and soluble sugar contents of coconut seedlings. Under K + deficiency, the leaf malondialdehyde content of coconut seedlings were significantly increased, whereas the proline (Pro) content was significantly reduced. Superoxide dismutase, peroxidase, and catalase activities were significantly reduced. The contents of endogenous hormones such as auxin, gibberellin, and zeatin were significantly decreased, whereas abscisic acid content was significantly increased. RNA-sequencing revealed that compared to the control, there were 1003 differentially expressed genes (DEGs) in the leaves of coconut seedlings under K + deficiency. Gene Ontology analysis revealed that these DEGs were mainly related to “integral component of membrane,” “plasma membrane,” “nucleus”, “transcription factor activity,” “sequence-specific DNA binding,” and “protein kinase activity.” Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that the DEGs were mainly involved in “MAPK signaling pathway-plant,” “plant hormone signal transduction,” “starch and sucrose metabolism,” “plant-pathogen interaction,” “ABC transporters,” and “glycerophospholipid metabolism.” Metabolomic analysis showed that metabolites related to fatty acids, lipidol, amines, organic acids, amino acids, and flavonoids were generally down-regulated in coconut seedlings under K + deficiency, whereas metabolites related to phenolic acids, nucleic acids, sugars, and alkaloids were mostly up-regulated. Therefore, coconut seedlings respond to K + deficiency stress by regulating signal transduction pathways, primary and secondary metabolism, and plant-pathogen interaction. These results confirm the importance of K + for coconut production, and provide a more in-depth understanding of the response of coconut seedlings to K + deficiency and a basis for improving K + utilization efficiency in coconut trees.

Background Oil palm ( Elaeis guineensis , Jacq.) is an important vegetable oil-yielding plant. Somatic embryogenesis is a promising method to produce large-scale elite clones to meet the demand for palm oil. The epigenetic mechanisms such as histone modifications have emerged as critical factors during somatic embryogenesis. These histone modifications are associated with the regulation of various genes controlling somatic embryogenesis. To date, none of the information is available on the histone modification gene ( HM ) family in oil palm. Results We reported the identification of 109 HM gene family members including 48 HMT s, 27 HDM s, 13 HAT s, and 21 HDAC s in the oil palm genome. Gene structural and motif analysis of EgHM s showed varied exon–intron organization and with conserved motifs among them. The identified 109 EgHM s were distributed unevenly across 16 chromosomes and displayed tandem duplication in oil palm genome. Furthermore, relative expression analysis showed the differential expressional pattern of 99 candidate EgHM genes at different stages (non-embryogenic, embryogenic, somatic embryo) of somatic embryogenesis process in oil palm, suggesting the EgHM s play vital roles in somatic embryogenesis. Our study laid a foundation to understand the regulatory roles of several EgHM genes during somatic embryogenesis. Conclusions A total of 109 histone modification gene family members were identified in the oil palm genome via genome-wide analysis. The present study provides insightful information regarding HM gene’s structure, their distribution, duplication in oil palm genome, and also their evolutionary relationship with other HM gene family members in Arabidopsis and rice. Finally, our study provided an essential role of oil palm HM genes during somatic embryogenesis process.