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Underutilized crops are, by definition, under-researched compared to staple crops yet come with traits that may be especially important given climate change and the need to feed a globally increasing population. These crops are often stress-tolerant, and this combined with unique and beneficial nutritional profiles. Whilst progress is being made by generating reference genome sequences, in this Tansley Review, we show how this is only the very first step. We advocate that going ‘beyond a reference genome’ should be a priority, as it is only at this stage one can identify the specific genes and the adaptive alleles that underpin the valuable traits. We sum up how population genomic and pangenomic approaches have led to the identification of stress- and disease-tolerant alleles in staple crops and compare this to the small number of examples from underutilized crops. We also demonstrate how previously underutilized crops have benefitted from genomic advances and that many breeding targets in underutilized crops are often well studied in staple crops. This cross-crop population-level resequencing could lead to an understanding of the genetic basis of adaptive traits in underutilized crops. This level of investment may be crucial for fully understanding the value of these crops before they are lost.

Structural characterization of natural products in complex herbal extracts remains a major challenge in phytochemical analysis. In this study, we present a novel post-acquisition data-processing strategy—key ion diagnostics–neutral loss filtering (KID-NLF)—combined with ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) for systematic profiling of the medicinal plant Terminalia chebula. The strategy consists of four main steps. First, untargeted data are acquired in negative electrospray ionization (ESI−) mode. Second, a genus-specific diagnostic ion database is constructed by leveraging characteristic fragment ions (e.g., gallic acid, chebuloyl, and HHDP groups) and conserved substructures. Third, MS/MS data are high-resolution filtered using key ion diagnostics and neutral loss patterns (302 Da for HHDP; 320 Da for chebuloyl). Finally, structures are elucidated via detailed spectral analysis. The methanol extract of T. chebula was separated on a C18 column using a gradient of acetonitrile and 0.1% aqueous formic acid within 33 min. This separation enabled detection of 164 compounds, of which 47 were reported for the first time. Based on fragmentation pathways and diagnostic ions (e.g., m/z 169 for gallic acid, m/z 301 for ellagic acid, and neutral losses of 152, 302, and 320 Da), the compounds were classified into three major groups: gallic acid derivatives, ellagitannins (containing HHDP, chebuloyl, or neochebuloyl moieties), and triterpenoid glycosides. KID-NLF overcomes key limitations of conventional workflows—namely, isomer discrimination and detection of low-abundance compounds—by exploiting genus-specific structural signatures. This strategy demonstrates high efficiency in resolving complex polyphenolic and triterpenoid profiles and enables rapid annotation of both known and novel metabolites. This study highlights KID-NLF as a robust framework for phytochemical analysis in species with high chemical complexity. It also paves the way for applications in quality control, drug discovery, and mechanistic studies of medicinal plants.

The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN–Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model’s effectiveness and robustness in the WTI crude oil market via Diebold–Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market.

Background: microRNAs (miRNAs) are non-coding RNAs with important roles in the progression of human cancers, including gastric cancer. Exosomes are extracellular vesicles, which could transfer numerous noncoding RNAs, such as miRNAs. Here, in our study, we intended to investigate the role of exosomal miR-122-5p in gastric cancer progression. Methods: Exosomes were isolated utilizing commercial kit or ultracentrifugation. Biomarkers of exosomes or epithelia-mesenchymal transition (EMT) were monitored by western blot. Expression levels of miR-122-5p and G-protein-coupled receptor kinase interacting protein-1 (GIT1) were evaluated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) or western blot. Cell proliferation and apoptosis were assessed by colony formation assay, methyl thiazolyl tetrazolium assay and flow cytometry. Cell metastasis was evaluated via Transwell assay. The interaction between miR-122-5p and GIT1 was validated by dual-luciferase reporter assay. Furthermore, tumor growth in vivo was detected by xenograft assay. Results: Exosomes were successfully isolated. MiR-122-5p was downregulated in exosomes derived from the serum of gastric cancer patients. Exosomal miR-122-5p could hinder gastric cancer cell proliferation and metastasis in vitro and tumor growth in vivo. Knockdown of GIT1 also inhibited gastric cancer cell proliferation and metastasis. Exosomal miR-122-5p targeted GIT1 to alter cellular behaviors of gastric cancer cells. Conclusion: Exosomal miR-122-5p suppressed gastric cancer progression by targeting GIT1.

Background: Research data services (RDS) have expanded in academic libraries but can be challenging to develop, particularly in teaching-intensive and less-resourced institutions. Learning communities offer a promising model for building skills, fostering collaboration, and aligning services with local needs. Case Presentation: This case report describes the development and implementation of three learning communities—a library group, a faculty group, and a student group—at a teaching-focused institution. These communities brought together library professionals, faculty, and students from diverse disciplines—including health sciences, education, data science, and engineering—to collaboratively explore the All of Us dataset. By working with the same dataset, participants were able to move quickly from abstract concepts to hands-on practice, while developing a shared understanding of tools, workflows, and challenges. The learning communities also served as platforms for building institutional capacity in data-intensive research. Conclusions: The learning communities model proved to be an effective strategy for fostering cross-disciplinary collaboration, promoting data literacy, and building institutional readiness to support research using the All of Us dataset. By centering on local expertise, learning communities provide a sustainable, resource-conscious framework for developing RDS. This approach also demonstrates how academic libraries can act as conveners and catalysts for equitable data engagement. Lessons learned from this case may inform similar efforts at other institutions seeking to build collaborative, inclusive models for engaging with various data resources.

Gut microbial dysbiosis contributes to the development of colorectal cancer (CRC). Here we catalogue the microbial communities in human gut mucosae at different stages of colorectal tumorigenesis. We analyse the gut mucosal microbiome of 47 paired samples of adenoma and adenoma-adjacent mucosae, 52 paired samples of carcinoma and carcinoma-adjacent mucosae and 61 healthy controls. Probabilistic partitioning of relative abundance profiles reveals that a metacommunity predominated by members of the oral microbiome is primarily associated with CRC. Analysis of paired samples shows differences in community configurations between lesions and the adjacent mucosae. Correlations of bacterial taxa indicate early signs of dysbiosis in adenoma, and co-exclusive relationships are subsequently more common in cancer. We validate these alterations in CRC-associated microbiome by comparison with two previously published data sets. Our results suggest that a taxonomically defined microbial consortium is implicated in the development of CRC. Changes in gut microbial communities contribute to the development of colorectal cancer. Here, the authors analyse the gut mucosal microbiome of patients and healthy subjects and identify distinct microbial consortia associated with different stages of colorectal cancer tumorigenesis.

Bile acids are acknowledged as signaling molecules involved in metabolic syndrome. The Takeda G protein-coupled receptor 5 (TGR5) functions as a significant bile acid receptor. The accumulated evidence suggests that TGR5 involves lipid homeostasis, glucose metabolism, and inflammation regulation. In line with this, recent preclinical studies also demonstrate that TGR5 plays a significant role in the generation and progression of metabolic syndrome, encompassing type 2 diabetes mellitus, obesity, atherosclerosis, and non-alcoholic fatty liver disease (NAFLD). In this review, we discuss the role of TGR5 in metabolic syndrome, illustrating the underlying mechanisms and therapeutic targets.

Discovering targets in natural products is a critical and challenging task in new drug development. Rapid and efficient screening of active ingredients from complex systems like traditional Chinese medicine (TCM) is now crucial in drug research. Affinity ultrafiltration (AUF) technology is widely used to screen active ingredients in natural medicines. AUF-liquid chromatography–mass spectrometry (AUF-LC-MS) leverages the affinity between natural medicine extracts and targets to isolate active ingredients from complex matrices, employing LC-MS for detection and activity assessment. This review discusses the developments in employing AUF-LC-MS to analyze TCM and TCM compound preparations over the last decade. This review succinctly presents the advantages and limitations of AUF-LC-MS, illustrating its benefits through the example of screening for active ingredients in natural pharmaceuticals.

The liver is the largest internal organ of the human body. It has a complex structure and function and plays a vital role in drug metabolism. In recent decades, extensive research has aimed to develop in vitro models that can simulate liver function to demonstrate changes in the physiological and pathological environment of the liver. Animal models and in vitro cell models are common, but the data obtained from animal models lack relevance when applied to humans, while cell models have limited predictive ability for metabolism and toxicity in humans. Recent advancements in tissue engineering, biomaterials, chip technology, and 3D bioprinting have provided opportunities for further research in in vitro models. Among them, liver-on-a-Chip (LOC) technology has made significant achievements in reproducing the in vivo behavior, physiological microenvironment, and metabolism of cells and organs. In this review, we discuss the development of LOC and its research progress in liver diseases, hepatotoxicity tests, and drug screening, as well as chip combinations. First, we review the structure and the physiological function of the liver. Then, we introduce the LOC technology, including general concepts, preparation materials, and methods. Finally, we review the application of LOC in disease modeling, hepatotoxicity tests, drug screening, and chip combinations, as well as the future challenges and directions of LOC.

In recent 10 years, ferroptosis has become a hot research direction in the scientific research community as a new way of cell death. Iron toxicity accumulation and lipotoxicity are unique features. Several studies have found that ferroptosis is involved in the regulation of the hepatic microenvironment and various hepatic metabolisms, thereby mediating the progression of related liver diseases. For example, NRF2 and FSP1, as important regulatory proteins of ferroptosis, are involved in the development of liver tumors and liver failure. In this manuscript, we present the mechanisms involved in ferroptosis, the concern of ferroptosis with the liver microenvironment and the progression of ferroptosis in various liver diseases. In addition, we summarize recent clinical advances in targeted ferroptosis therapy for related diseases. We expect that this manuscript can provide a new perspective for clinical treatment of related diseases.