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Relation extraction, as a key task in natural language processing, plays a significant role in deepening language understanding, constructing knowledge graphs, and optimizing information retrieval systems. However, traditional supervised learning methods are not well-suited for real-world scenarios due to the continuous emergence of new relations and the lack of large annotated datasets. Although the advent of large language models has significantly improved the performance of many natural language processing tasks, they still cannot effectively address the challenges of few-shot continual relation extraction. To fully leverage the semantic knowledge of large language models to mitigate catastrophic forgetting and overfitting issues, a novel few-shot continual relation extraction method, LAFA (large language model augmentation and feature alignment), is proposed. This method enhances representation alignment through various strategies such as relation instance rewriting, semantic expansion, and enhanced relation representation. It effectively improves the model adaptability to new relations and the retention of old knowledge while maintaining low data and computational costs. Experimental validation on two relation extraction datasets, FewRel and TACRED, demonstrates that LAFA outperforms existing methods in few-shot continual relation extraction tasks, particularly achieving the best results in incremental stages. Ablation experiments further reveal the significant contributions of each module to overall performance. Moreover, the inference efficiency and cost of LAFA are substantially lower than those of existing large language model-based methods, and it boasts strong scalability, being able to adapt to various language models.

Diverse splicing types in nuclear and chloroplast genes of protist Euglena gracilis have been recognized for decades. However, the splicing machinery responsible for processing nuclear precursor messenger RNA introns, including trans-splicing of the 5′ terminal outron and spliced leader (SL) RNA, remains elusive. Here, we identify 166 spliceosomal protein genes and two snRNA genes from E. gracilis by performing bioinformatics analysis from a combination of next-generation and full-length transcriptomic RNA sequencing (RNAseq) data as well as draft genomic data. With the spliceosomal proteins we identified in hand, the insensitivity of E. gracilis to some splicing modulators is revealed at the sequence level. The prevalence of SL RNA-mediated trans-splicing is estimated to be more than 70% from our full-length RNAseq data. Finally, the splicing proteomes between E. gracilis and its three evolutionary cousins within the same Excavata group are compared. In conclusion, our study characterizes the spliceosomal components in E. gracilis and provides the molecular basis for further exploration of underlying splicing mechanisms.

Metamaterials based on quasi‐bound states in the continuum (qBICs) with manipulable resonance quality (Q) factors have provided a standout platform for cutting‐edge terahertz (THz) sensing applications. However, most so far have been implemented as conventional metal patch structures with adjacent substrate layers, incurring the limitation of insufficient light‐matter interaction due to substrate effects. Here, qBIC‐driven metamaterials with substrate‐free metallic aperture structures for tailoring light‐matter interactions and exhibiting near‐ideal sensing performance is introduced. Specifically, it is incorporated ultrafast femtosecond laser processing technology to fabricate H‐type metallic aperture metamaterials with accessible high‐contrast Q factor resonances allowed by in‐plane symmetry breaking. Correspondingly, stronger light field energies are applied to the interactions due to completely eliminating the confinement of the substrate effect, enabling experimental sensitivity of up to 0.86 THz RIU−1 for the qBIC resonance, 1.9 times that of the conventional dipole resonance. Moreover, a high Q qBIC resonance achieved by optimized asymmetry parameter is exploited for detecting ultrathin layers of L‐proline molecules as low as 0.87 nmol. It is envisioned that this approach will deliver insights for real‐time, precise, and high‐performance detection of trace biomolecules, and open new perspectives for realizing ideal performance metadevices. [ABSTRACT FROM AUTHOR]

\nHIGLIGHTSThe imbalance of gut microbiota can significantly exacerbate or even directly contribute to the development of colorectal cancer (CRC). Modulating the gut microbiota through dietary interventions, such as supplementation with probiotics, prebiotics, synbiotics, and postbiotics presents a promising strategy to enhance treatment efficacy and mitigate the adverse effects of CRC therapies. This review outlines the significant potential of dietary interventions (supplementation with probiotics, prebiotics, synbiotics, and postbiotics) in preventing or ameliorating CRC by modulating the gut microbiota. On the one hand, this review illustrates the intricate relationship between the gut microbiota and CRC, as well as the carcinogenic mechanisms mediated by these microbial communities. On the other hand, the application prospects and strengths, weaknesses, opportunities, and threats (SWOT) analyses of dietary interventions in preventing CRC are reviewed in detail. In conclusion, this review provides new preventive and mitigative strategies for improving the safety and efficiency of CRC therapy/prevention through dietary interventions. It also expands the use of probiotics, prebiotics, synbiotics, or postbiotics in dietary nutritional interventions. Gut microbiota dysbiosis is one of the important causes of colorectal cancer (CRC).Dietary interventions are highly associated with the modulation of gut microbiota.Probiotics, prebiotics, synbiotics, and postbiotics can prevent/ameliorate CRC.The use of dietary interventions to prevent/mitigate CRC has both pros and cons.Gut microbiota dysbiosis is one of the important causes of colorectal cancer (CRC).Dietary interventions are highly associated with the modulation of gut microbiota.Probiotics, prebiotics, synbiotics, and postbiotics can prevent/ameliorate CRC.The use of dietary interventions to prevent/mitigate CRC has both pros and cons. [ABSTRACT FROM AUTHOR]

The exploration and development of conventional oil and gas resources are becoming more difficult, and the proportion of low-permeability reservoirs in newly discovered reservoir resources has expanded to 45%. As the main focus of the oil industry, the global average recovery rate of low-permeability reservoir resources is only 20%, and most crude oil is still unavailable, so our understanding of such reservoirs needs to be deepened. The microscopic pore structure of low-permeability reservoir rocks exhibits significant complexity and variability; reservoir evaluation is more difficult. For elucidating the internal distribution of storage space and the mechanisms influencing seepage, we focus on the low-permeability sandstone reservoir of the Shahejie Formation, located on the northern slope of the Chenjiazhuang uplift, Bohai Bay. Employing a suite of advanced analytical techniques, including helium expansion, pressure pulse, high-pressure mercury intrusion (HPMI), and micro-computed tomography (micro-CT) scanning, we examined the main pore–throat size affecting reservoir storage and seepage in the reservoir at both the micrometer and nanometer scales. The results reveal that pores with diameters exceeding 40 μm are sparsely developed within the low-permeability reservoir rocks of the study area. However, pores ranging from 0 to 20 μm predominate, exhibiting an uneven distribution and a clustered structure in the three-dimensional pore structure model. The pore volume showed a unimodal and bimodal distribution, thus significantly contributing to the storage space. The main sizes of the reservoir in this study area are 40–80 μm and 200–400 μm. Micron-sized pores, while present, are not the primary determinants of the reservoir's seepage capacity. Instead, coarser submicron and nano-pores exert a more substantial influence on the permeability of the rock. Additionally, the presence of micro-fractures is found to enhance the reservoir's seepage capacity markedly. The critical pore–throat size range impacting the permeability of the reservoir in the study area is identified to be between 0.025 and 0.4 μm. [ABSTRACT FROM AUTHOR]

Postbiotics are defined as "preparation of inanimate microorganisms and/or their components that confers a health benefit on the host". Postbiotics have unique advantages over probiotics, such as stability, safety, and wide application. Although postbiotics are research hotspots, the research on them is still very limited. This review provides comprehensive information on the scope of postbiotics, the preparation methods of inanimate microorganisms, and the application and mechanisms of postbiotics in metabolic syndrome (MetS). Furthermore, the application trends of postbiotics in the food industry are reviewed. It was found that postbiotics mainly include inactivated microorganisms, microbial lysates, cell components, and metabolites. Thermal treatments are the main methods to prepare inanimate microorganisms as postbiotics, while non-thermal treatments, such as ionizing radiation, ultraviolet light, ultrasound, and supercritical CO2, show great potential in postbiotic preparation. Postbiotics could ameliorate MetS through multiple pathways including the modulation of gut microbiota, the enhancement of intestinal barrier, the regulation of inflammation and immunity, and the modulation of hormone homeostasis. Additionally, postbiotics have great potential in the food industry as functional food supplements, food quality improvers, and food preservatives. In addition, the SWOT analyses showed that the development of postbiotics in the food industry exists both opportunities and challenges. [ABSTRACT FROM AUTHOR]

Extant scholarship increasingly attends to the mixed—and paradoxical—incorporation of information and communication technologies in social lives. Building on existing research, this study further explicates how digitally mediated (dis)empowerment paradoxes stem from the interactions among unevenly transformed structural affordances and constraints in the political, market, socio-cultural, and technological realms under digitalized contexts. Drawing on ethnographic data on women-led digital group-buying during the coronavirus disease 2019 lockdown in Shanghai, China, this study illustrates that the pandemic-inaugurated digitalization paradoxically transformed existing technological constraints into digital resources and techno-skills for women to lead digital group-buying and empower them socio-technologically. Yet these empowerments paradoxically turned into new gender inequalities, as entrenched state and market inequalities compelled female organizers to meet added or dueling burdens. This study, therefore, contributes to understanding the re/configuration and varied interaction patterns of different facets of structural affordances and constraints that condition digitally mediated (dis)empowerment paradoxes under digitalized contexts. [ABSTRACT FROM AUTHOR]

In the manufacturing process of power amplifier (PA), various defects on the circuit surface will seriously affect the circuit performance and its operation. To solve the above problems, this article proposes a circuit defect detection method based on improved patch‐based support vector data description algorithm (Patch SVDD), which uses contrastive learning to enhance the feature extraction ability of neural network. An image calibration algorithm is used for calibration preprocessing to enhance the robustness of the model. The Euclidean distance between a query image patch and its nearest normal patch is defined to be an anomaly score. For verification, three defects in the PA circuit, including components missing, solder contamination and component orientation dislocation, were detected. The experimental results show that, compared with conventional method, the area under receiver operating characteristic (AUROC) of the improved anomaly detection model increased from 90.1% to 95.1%, which improves the detection accuracy effectively. [ABSTRACT FROM AUTHOR]