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Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

Long-term climate change and periodic environmental extremes threaten food and fuel security1 and global crop productivity2-4. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience5, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation6-knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate-gene-biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene-trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

This study investigates the complex interplay between financial and economic development within the Regional Comprehensive Economic Partnership (RCEP) countries—Australia, China, Indonesia, Japan, and South Korea—from 2000 to 2022, with a focus on the impacts of natural resource rents and energy efficiency. Employing moment quantile regression to address non‐normal data distribution identified by the Jarque–Bera (JB) test, we unveil diverse slopes and cross‐sectional interdependencies, necessitating a nuanced examination. Panel unit root tests confirm non‐stationarity, enabling a thorough investigation. Our findings reveal a long‐term cointegration relationship between natural resource rents, energy efficiency, economic development, and foreign direct investment (FDI), providing a holistic understanding of their dynamics. Notably, our analysis exposes the trade‐off between natural resource abundance and financial growth, as evidenced by negative coefficients for natural resource rents, while positive coefficients for economic growth, energy efficiency, and FDI counterbalance this effect, fostering a conducive financial environment. Additionally, our examination of location and scale characteristics unveils vulnerabilities associated with natural resources alongside stability indicators linked to economic growth, energy efficiency, and FDI. By employing quantile regression, our study sheds light on the nuanced impacts of natural resources, offering valuable insights for policymakers and researchers in navigating sustainable financial development within the RCEP framework. [ABSTRACT FROM AUTHOR]

BackgroundSwitchgrass (Panicum virgatum L.) is an important bioenergy crop widely used for lignocellulosic research. While extensive transcriptomic analyses have been conducted on this species using short read-based sequencing techniques, very little has been reliably derived regarding alternatively spliced (AS) transcripts.ResultsWe present an analysis of transcriptomes of six switchgrass tissue types pooled together, sequenced using Pacific Biosciences (PacBio) single-molecular long-read technology. Our analysis identified 105,419 unique transcripts covering 43,570 known genes and 8795 previously unknown genes. 45,168 are novel transcripts of known genes. A total of 60,096 AS transcripts are identified, 45,628 being novel. We have also predicted 1549 transcripts of genes involved in cell wall construction and remodeling, 639 being novel transcripts of known cell wall genes. Most of the predicted transcripts are validated against Illumina-based short reads. Specifically, 96% of the splice junction sites in all the unique transcripts are validated by at least five Illumina reads. Comparisons between genes derived from our identified transcripts and the current genome annotation revealed that among the gene set predicted by both analyses, 16,640 have different exon-intron structures.ConclusionsOverall, substantial amount of new information is derived from the PacBio RNA data regarding both the transcriptome and the genome of switchgrass.

Serendipita vermifera (=Sebacina vermifera) has demonstrated plant growth promoting abilities with little or no host specificity. The full genome sequencing of Serendipita vermifera subsp. bescii, the first North American strain, will provide insight into the mechanisms behind mycorrhizal symbiosis and plant enhancement in a variety of agronomically important crops.

Few-shot learning (FSL) is a challenging problem. Transfer learning methods offer a straightforward and effective solution to FSL by leveraging pre-trained models and generalizing them to new tasks. However, pre-trained models often lack the ability to highlight and emphasize salient features, a gap that attention mechanisms can fill. Unfortunately, existing attention mechanisms encounter issues such as high complexity and incomplete attention information. To address these issues, we propose a dimensionally enhanced attention (DEA) module for FSL. This DEA module introduces minimal additional computational overhead while fully attending to both channel and spatial information. Specifically, the feature map is first decomposed into 1D tensors of varying dimensions using strip pooling. Next, a multi-dimensional collaborative learning strategy is introduced, enabling cross-dimensional information interactions through 1D convolutions with adaptive kernel sizes. Finally, the feature representation is enhanced by calculating attention weights for each dimension using a sigmoid function and weighting the original input accordingly. This approach ensures comprehensive attention to different dimensions of information, effectively characterizing data in various directions. Additionally, we have found that knowledge distillation significantly improves FSL performance. To this end, we implement a logit standardization self-distillation method tailored for FSL. This method addresses the issue of exact logit matching, which arises from the shared temperature in the self-distillation process, by employing logit standardization. We present experimental results on several benchmark datasets where the proposed method yields significant performance improvements. [ABSTRACT FROM AUTHOR]

Background: Accurate recognition of Calot's triangle during cholecystectomy is important in preventing intraoperative and postoperative complications. The use of indocyanine green (ICG) fluorescence imaging has become increasingly prevalent in cholecystectomy procedures. Our study aimed to evaluate the specific effects of ICG-assisted imaging in reducing complications. Materials and Methods: A comprehensive search of databases including PubMed, Web of Science, Europe PMC, and WANFANGH DATA was conducted to identify relevant articles up to July 5, 2023. Review Manager 5.3 software was applied to statistical analysis. Results: Our meta-analysis of 14 studies involving 3576 patients compared the ICG group (1351 patients) to the control group (2225 patients). The ICG group had a lower incidence of postoperative complications (4.78% vs 7.25%; RR.71; 95%CI:.54-.95; P =.02). Bile leakage was significantly reduced in the ICG group (.43% vs 2.02%; RR =.27; 95%CI:.12-.62; I2 = 0; P =.002), and they also had a lower bile duct drainage rate (24.8% vs 31.8% RR =.64, 95% CI:.44-.91, P =.01). Intraoperative complexes showed no statistically significant difference between the 2 groups (1.16% vs 9.24%; RR.17; 95%CI.03-1.02), but the incidence of intraoperative bleeding is lower in the ICG group. Conclusion: ICG fluorescence imaging-assisted cholecystectomy was associated with a range of benefits, including a lower incidence of postoperative complications, decreased rates of bile leakage, reduced bile duct drainage, fewer intraoperative complications, and reduced intraoperative bleeding. [ABSTRACT FROM AUTHOR]