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Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of ‘indications’, ‘contradictions’, and ‘off-label use’ drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG’s graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available. Measurement(s) knowledge graph • Relation Code • textual entity Technology Type(s) machine learning • computational modeling technique

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation. The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large‐scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields—Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence‐aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy—are discussed. The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. The review focuses on the recent progress and breakthroughs in the application of big data within precision medicine.

Background Caleosins are lipid-associated proteins that exist in plants and fungi. Its molecules and biological functions have been extensively characterized, particularly in some economic crops. Different caleosins have various physiological roles in plant growth, development, and plant-environment interactions. However, a comprehensive investigation into their evolutionary history and patterns has yet to be undertaken. Results Here, we identified 922 caleosins from 203 species comprising green algae and other plant taxa, followed by large-scale phylogenetic analysis. Phylogenetic analysis indicates that the plant caleosin family gave rise to the H and L branches after the emergence of aquatic algae and before the appearance of land plants. Hornworts and liverworts lost the L- caleosin during the evolutionary process. Caleosins from Araucariaceae, Podocarpaceae, Sciadopityaceae, and Stangeriaceae are absent in the H clade, and those from Ginkgoaceae, Gnetaceae, Pinaceae, and Zamiaceae are missing in the L clade. This suggests that the H and L clades were lost at the family level. In addition, we present a more comprehensive phylogenetic structure of angiosperm caleosin . The H and L branches of angiosperm caleosin expanded once each, generating two branches, respectively. We also explored the diversification of caleosin in Brassicaceae and Poaceae, respectively. Conclusion Our study offers a comprehensive understanding of the evolutionary trajectory of the caleosin gene family in green plants at a genome-wide level. These findings establish a crucial groundwork for future research to conduct thorough functional characterization.

Background Trehalose-6-phosphate synthase (TPS) is an essential enzyme involved in the production of trehalose, and the genes associated with TPS are crucial for various processes such as growth, development, defense mechanisms, and resistance to stress. However, there has been no documentation regarding the evolution and functional roles of the TPS gene family within Theaceae. Results Here, we uncovered the lineage-specific evolution of TPS genes in Theaceae. A total of 102 TPS genes were discovered across ten Theaceae species with sequenced genomes. Consistent with the previous classification, our phylogenetic analysis indicated that the TPS genes in Theaceae can be categorized into two primary subfamilies and six distinct clades (I, II-1, II-2, II-3, II-4, II-5), with clade I containing a greater number of introns compared to those found in clade II. Segmental duplication served as the main catalyst for the evolution of TPS genes within Theaceae, and numerous TPS genes exhibited inter-species synteny among various Theaceae species. Most of the TPS genes were ubiquitously expressed, and expression divergence of TPS paralogous pairs was observed. The cis -acting elements found in TPS genes indicated their involvement in responses to phytohormones and stress. Conclusion This research enhanced our understanding of the lineage-specific evolution of the TPS gene family in Theaceae and offered important insights for future functional analyses.

Ecosystem services (ESs) are an essential link between ecosystems and human well-being, and trade-offs/synergies happen in ESs at different temporal and spatial scales. It is crucial to explore patterns of trade-offs/synergies among ESs, and their nonlinear relationships with changes in ESs. The primary objective of this study was to evaluate five ESs in 2000 and 2018: namely, water yield, food production, carbon sequestration, soil conservation, and habitat quality in mountainous regions of Southwest China. The mean values of the five ESs increased by 365.8 m3/ha, 13.92 t/hm2, 497.09 TgC/yr2, 138.48 t/km2, and 0.002, respectively. Using spatial statistics and analysis, an ES trade-off synergy model (ESTD) was constructed for the five ESs change values. Overall, soil conservation has a trade-off with all five ESs, except habitat quality; this trade-off is increasing slightly. Water yield is in synergy with all ESs except soil conservation, with decreasing synergy; habitat quality is in synergy with all ESs except food production, with increasing synergy. Finally, the nonlinear relationship between the value of the change in the ES and ESTD was analyzed using a generalized additive model. Changes in water yield showed the greatest impact on ESTD except for food production, wherein changes in all three ESs had minimal impacts on ESTD. Food production dominates its trade-offs/synergies relationship with soil conservation; carbon sequestration is the dominant player in its trade-offs/synergies relationship with soil conservation. Habitat quality has a secondary position of influence, except in the trade-offs/synergies involving food production. By exploring the drivers of trade-offs/synergies among ESs, this study can provide guidance for the effective implementation of policies related to ecological protection and restoration.

Molecular ions are relatively cold in the E region ionosphere; however, they can upwell to the magnetosphere during geomagnetically active times. Resonance between electrostatic ion cyclotron (EIC) waves is a potential pathway to energize molecular ions. In this work, the E region ionospheric plasma was modeled in the laboratory, and EIC waves were excited by a nonuniform field‐aligned current. The EIC wave was excited even when the ion neutral collision frequency is much higher than the ion cyclotron frequency, and the fundamental frequency was observed to be below the ion cyclotron frequency. In addition, the wave dispersion of the collisional EIC wave was calculated, which shows a consistent trend with experimental results as the collisions increasing. Therefore, this work suggests that EIC waves can be excited in the E region ionospheric‐like plasma, which can support the explanation of the energization of molecular ions in the E region ionosphere. Plain Language Summary Spacecraft observed the presence of molecular ions in the topside ionosphere (600–1,000 km) and the magnetosphere during geomagnetically active times, and the potential mechanisms responsible for the acceleration of these molecular ions are still not fully understood. The resonance between electrostatic ion cyclotron (EIC) waves and molecular ions is a potential pathway to energize the molecular ions. However, most previous works on EIC waves were studied in collisionless plasma similar to the magnetospheric plasma, which is different from the E region ionospheric plasma. A typical characteristic of E region ionospheric plasma is the partially ionized effect, which introduces new physical processes that do not occur in collisionless plasmas. In this work, strong collisions that occurred in the E region ionosphere were simulated in the laboratory, and the current‐driven electrostatic ion cyclotron waves were observed in the modeled ionosphere. It is found that electrostatic ion cyclotron waves can be generated in the E region ionospheric‐like plasma. This work provides solid experimental evidence that electrostatic ion cyclotron instability can exist in weakly ionized plasmas of the E region ionosphere, and can be applied to explain the transverse ion heating of bulk ions in the bottomside ionosphere. Key Points The E region ionospheric‐like collisional plasma was modeled in the laboratory EIC waves were excited by field aligned current in the ionospheric‐like plasma This work can be applied to explain the energization of molecular ions in the E region ionosphere

Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.

Surface coal mining procedures include piercing—blasting—mining and loading—transportation—discharging, blasting link exists due to the poor blasting effect leads to low loading efficiency, blasting dust caused by environmental pollution and other problems. In this paper, from the mechanical characteristics of the water medium, we analyze in detail the transferring effect, transducing effect and bubble pulsation phenomenon of the water medium in the blasting process. The results show that when the blasting medium is water medium, the maximum principal stress is 1.53 times that of air medium; the peak energy transfer can be up to 2.73 times that of air medium. With the help of TrueGrid/LS-DYNA finite element analysis software to simulate the dynamic process of blasting, the study of the maximum principal stresses around the hole, the top of the slope, the foot of the slope on the maximum principal stress changes, the results show that the maximum principal stresses around the hole, the top of the slope, the foot of the slope unit with the increase in the water content is gradually increasing trend. Finally, combined with the actual mine production conditions for blasting field test, water-mediated blasting dust reduction rate of 75%, the use of AHP—fuzzy comprehensive evaluation method of two groups of traditional dry hole blasting and three groups of water-mediated blasting comprehensive evaluation, the results show that the water-mediated blasting scores are higher than the traditional dry hole blasting, proving that the water-mediated blasting has a certain prospect of engineering applications.

This study investigated dynamic brain network changes and their genetic correlations in children with idiopathic generalized epilepsy (IGE). We included 26 children with IGE and 35 healthy controls, all participants underwent resting-state functional magnetic resonance imaging and cognitive assessments. Modular variability (MV) in time-varying networks was compared, and correlations with cognition and clinical variables were analyzed, we also explored classification problems using machine learning. Gene sets associated with IGE-related network remodeling were identified using the Allen Human Brain Atlas and gene enrichment analysis tools. The results showed that children with IGE exhibited reduced MV in sensorimotor and frontoparietal networks and increased MV in the default mode network (DMN). MV changes in the left prefrontal and right orbitofrontal cortices correlated with verbal and full-scale IQ scores, respectively. MV changes in the left precuneus/posterior cingulate cortex correlated with performance IQ scores. Transcriptomic analysis revealed 985 genes (FDR < 0.05) whose spatial expression patterns covaried with network alterations, prominently enriched for synaptic signaling and neuroactive ligand-receptor interactions, including GABA receptor subunits ( GABRE ) and neurodevelopmental regulators ( BCL11A ). Machine learning confirmed MV as a significant predictor of verbal IQ (permutation P  = 0.041), with DMN and frontoparietal regions contributing most to prediction. Dynamic brain network abnormalities in children with IGE were significantly associated with cognitive function and gene expression, providing new insights into the neural mechanisms underlying network dysfunction and cognitive impairment in epilepsy.