Search

Abstract Autophagy, a crucial mechanism for cellular degradation, is regulated by conserved autophagy-related (ATG) core proteins across species. Impairments in autophagy result in significant developmental and reproductive aberrations in mammals. However, autophagy is thought to be functionally dispensable in Arabidopsis thaliana since most of the ATG mutants lack severe growth and reproductive defects. Here, we challenge this perception by unveiling a role for autophagy in male gametophyte development and fertility in Arabidopsis. A detailed re-assessment of atg5 and atg7 mutants found that reduced autophagy activity in germinated pollen accompanied by partial aberrations in sperm cell biogenesis and pollen tube growth, leading to compromised seed formation. Furthermore, we revealed autophagy modulates the spatial organization of actin filaments via targeted degradation of actin depolymerization factors ADF7 and Profilin2 in pollen grains and tubes through a key receptor, Neighbor of BRCA1 (NBR1). Our findings advance the understanding of the evolutionary conservation and diversification of autophagy in modulating male fertility in plants contrasting to mammals.

Abstract Thanks to the strong representation capability of the pre-trained language models, supervised grammatical error correction has achieved promising performance. However, traditional model training depends significantly on the large scale of similar distributed samples. The model performance decreases sharply once the distributions of training and testing data are inconsistent. To address this issue, we propose an automatic sampling approach to effectively select high-quality samples from different corpora and filter out irrelevant or harmful ones. Concretely, we first provide a detailed analysis of error type and sentence length distributions on all datasets. Second, our corpus weighting approach is exploited to yield different weights for each sample automatically based on analysis results, thus emphasizing beneficial samples and ignoring the noisy ones. Finally, we enhance typical Seq2Seq and Seq2Edit grammatical error correction models with pre-trained language models and design a model ensemble algorithm for integrating the advantages of heterogeneous models and weighted samples. Experiments on the benchmark datasets demonstrate that the proper utilization of different corpora is extremely helpful in enhancing the accuracy of grammatical error correction. The detailed analysis gains more insights into the effect of different corpus weighting strategies.

Abstract Background Ischemia/reperfusion (I/R) injury is a severe brain disorder with currently limited effective treatments. This study aims to explore the role of N6-methyladenosine (m6A) modification and associated regulatory factors in I/R to identify potential therapeutic targets. Methods We utilized a middle cerebral artery occlusion (MCAO) rat model and SH-SY5Y cells subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) to assess m6A levels and investigate the impact of METTL3 overexpression on long non-coding RNA (lncRNA) CRNDE expression. The effects of silencing lncRNA CRNDE on the interaction between YTHDC1 and ATG10 mRNA, as well as the stability of ATG10 mRNA, were evaluated. Additionally, apoptosis rates, pro-inflammatory and anti-inflammatory factor levels, ATG10 expression, and autophagic activity were analyzed to determine the effects of METTL3. The reverse effects of YTHDC1 overexpression were also examined. Results MCAO rats and OGD/R-treated SH-SY5Y cells exhibited reduced m6A levels. METTL3 overexpression significantly inhibited lncRNA CRNDE expression. Silencing lncRNA CRNDE mitigated OGD/R-induced apoptosis and inflammation in SH-SY5Y cells, while enhancing autophagy and stabilizing ATG10 mRNA. METTL3 overexpression decreased cell apoptosis, reduced the levels of pro-inflammatory cytokines TNF-α, IL-1β, IL-6, and increased IL-10 secretion. Furthermore, METTL3 overexpression upregulated ATG10 expression and promoted autophagy. Conversely, lncRNA CRNDE overexpression negated these effects. Conclusion The inhibition of lncRNA CRNDE affects the interaction between YTHDC1 and ATG10 mRNA and stabilizes ATG10 mRNA, mediated by METTL3 overexpression. These findings suggest that targeting lncRNA CRNDE to reduce apoptosis, inhibit inflammation, increase ATG10 expression, and enhance autophagy could offer new therapeutic strategies for I/R injury.

Photodynamic therapy (PDT) can produce high levels of reactive oxygen species (ROS) to kill tumor cells and induce antitumor immunity. However, intracellular antioxidant systems, including glutathione (GSH) system and thioredoxin (Trx) system, limit the accumulation of ROS, resulting in compromised PDT and insufficient immune stimulation. Herein, we designed a nanomedicine PtHPs co-loading photosensitizer pyropheophorbide a (PPa) and cisplatin prodrug Pt–COOH(IV) (Pt (IV)) based on hydroxyethyl starch (HES) to inhibit both GSH and Trx antioxidant systems and achieve potent PDT as well as antitumor immune responses. Specifically, HES-PPa and HES-Pt were obtained by coupling HES with PPa and Pt (IV), and assembled into nanoparticle PtHPs by emulsification method to achieve the purpose of co-delivery of PPa and Pt (IV). PtHPs improved PPa photostability while retaining PPa photodynamic properties. In vitro experiments showed that PtHPs reduced GSH, inhibited Trx system and had better cell-killing effect and ROS generation ability. Subcutaneous tumor models showed that PtHPs had good safety and tumor inhibition effect. Bilateral tumor models suggested that PtHPs promoted the release of damage-associated molecular patterns and the maturation of dendritic cells, induced T cell-mediated immune responses, and thus suppressed the growth of both primary and distal tumors. This study reports a novel platinum-based nanomedicine and provides a new strategy for boosting PDT therapy-mediated antitumor immunity by overcoming intrinsic antioxidant systems.

ABSTRACT: Phytic acid (PA) is a natural antioxidant with various biological activities, providing protective effects in multiple animals. Ochratoxin A (OTA) is a mold toxin commonly found in feed, which induces multi-organ damage, with kidney being the target organ of its toxicity. This study investigates the protective effects of PA on OTA-induced renal damage and its potential mechanisms in chicks. The results demonstrates that PA treatment restores OTA-induced renal pathological injuries, reverses the diminished activities of antioxidant enzymes, reduces the accumulation of malondialdehyde, and normalizes the expression of pro-inflammatory cytokines, which confirms that PA can alleviate OTA-induced renal damage. Further investigations reveal that OTA-induced renal injury accompanied by an increase in tissue iron content and the transcription levels of ferroptosis-related genes (TFR, ACSL4, and HO-1), and a decrease in the levels of SLC7A11 and GPX4. PA treatment reverses all these effects, indicating that PA mitigates OTA-induced renal ferroptosis. Moreover, PA supplementation improves intestinal morphology and mucosal function, corrects OTA-induced changes in the intestinal microbiota. Besides, PA microbiota transplantation alleviates renal inflammation and oxidative stress caused by OTA. In conclusion, PA plays a protective role against renal damage through the regulation of ferroptosis and the intestinal microbiota, possibly providing novel insights into the control and prevention of OTA-related nephrotoxicity.

Abstract With the booming fields of computer vision and natural language processing, cross‐modal intersections such as visual question answering (VQA) have become very popular. However, several studies have shown that many VQA models suffer from severe language prior problems. After a series of experiments, the authors found that previous VQA models are in an unstable state, that is, when training is repeated several times on the same dataset, there are significant differences between the distributions of the predicted answers given by the models each time, and these models also perform unsatisfactorily in terms of accuracy. The reason for model instability is that some of the difficult samples bring serious interference to model training, so we design a method to measure model stability quantitatively and further propose a method that can alleviate both model imbalance and instability phenomena. Precisely, the question types are classified into simple and difficult ones different weighting measures are applied. By imposing constraints on the training process for both types of questions, the stability and accuracy of the model improve. Experimental results demonstrate the effectiveness of our method, which achieves 63.11% on VQA‐CP v2 and 75.49% with the addition of the pre‐trained model.

Abstract Background Marfan syndrome (MFS) is a hereditary connective tissue disorder involving multiple systems, including ophthalmologic abnormalities. Most cases are due to heterozygous mutations in the fibrillin‐1 gene (FBN1). Other associated genes include LTBP2, MYH11, MYLK, and SLC2A10. There is significant clinical overlap between MFS and other Marfan‐like disorders. Purpose To expand the mutation spectrum of FBN1 gene and validate the pathogenicity of Marfan‐related genes in patients with MFS and ocular manifestations. Methods We recruited 318 participants (195 cases, 123 controls), including 59 sporadic cases and 88 families. All patients had comprehensive ophthalmic examinations showing ocular features of MFS and met Ghent criteria. Additionally, 754 cases with other eye diseases were recruited. Panel‐based next‐generation sequencing (NGS) screened mutations in 792 genes related to inherited eye diseases. Results We detected 181 mutations with an 84.7% detection rate in sporadic cases and 87.5% in familial cases. The overall detection rate was 86.4%, with FBN1 accounting for 74.8%. In cases without FBN1 mutations, 23 mutations from seven Marfan‐related genes were identified, including four pathogenic or likely pathogenic mutations in LTBP2. The 181 mutations included 165 missenses, 10 splicings, three frameshifts, and three nonsenses. FBN1 accounted for 53.0% of mutations. The most prevalent pathogenic mutation was FBN1 c.4096G>A. Additionally, 94 novel mutations were detected, with 13 de novo mutations in 14 families. Conclusion We expanded the mutation spectrum of the FBN1 gene and provided evidence for the pathogenicity of other Marfan‐related genes. Variants in LTBP2 may contribute to the ocular manifestations in MFS, underscoring its role in phenotypic diversity.

ABSTRACT: Gliotoxin (GT) belongs to the epipolythiodioxopiperazine (ETP) family, which is considered a crucial virulence determinant among the secondary metabolites produced by Aspergillus fumigatus. The metabolites are commonly found in food and feed, contributing to the invasion and immune escape of Aspergillus fumigatus, thereby posing a significant threat to the health of livestock, poultry, and humans. Heterophil extracellular traps (HETs), a novel form of innate immune defense, have been documented in the chicken's innate immune systems for capturing and eliminating invading microbes. However, the effects and mechanisms of GT on the production of duck HETs in vitro remain unknown. In this study, we first confirmed the presence of HETs in duck innate immune systems and further investigated the molecular mechanism underlying GT-induced HETs release. Our results demonstrate that GT can trigger typical release of HETs in duck. The structures of GT-induced HETs structures were characterized by DNA decoration, citrullinated histones 3, and elastase. Furthermore, NADPH oxidase, glycolysis, ERK1/2 and p38 signaling pathway were found to regulate GT-induced HETs. In summary, our findings reveal that gliotoxin activates HETs release in the early innate immune system of duck while providing new insights into the immunotoxicity of GT towards ducks.

Rubber tire gantry (RTG) plays a pivotal role in facilitating efficient container handling within port operations. Conventional RTG, highly depending on human operations, is inefficient, labor-intensive, and also poses safety issues in adverse environments. This article introduces a multitarget detection and tracking (MTDT) algorithm specifically tailored for automated port RTG operations. The approach seamlessly integrates enhanced YOLOX for object detection and improved DeepSORT for object tracking to enhance the MTDT performance in the complex port settings. In particular, Light-YOLOX, an upgraded version of YOLOX incorporating separable convolution and attention mechanism, is introduced to improve real-time capability and small target detection. Subsequently, OSNet-DeepSORT, an enhanced version of DeepSORT, is proposed to mitigate ID switching challenges arising from unreliable data communication or occlusion in real port scenarios. The effectiveness of the proposed method is validated in various real-life port operations. Ablation studies and comparative experiments against typical MTDT algorithms demonstrate noteworthy enhancements in key performance metrics, encompassing small target detection, tracking accuracy, ID switching frequency, and real-time performance.

Cross-lingual summarization (CLS) is the task of condensing lengthy source language text into a concise summary in a target language. This presents a dual challenge, demanding both cross-language semantic understanding (i.e., semantic alignment) and effective information compression capabilities. Traditionally, researchers have tackled these challenges using two types of methods: pipeline methods (e.g., translate-then-summarize) and end-to-end methods. The former is intuitive but prone to error propagation, particularly for low-resource languages. The later has shown an impressive performance, due to multilingual pre-trained models (mPTMs). However, mPTMs (e.g., mBART) are primarily trained on resource-rich languages, thereby limiting their semantic alignment capabilities for low-resource languages. To address these issues, this paper integrates the intuitiveness of pipeline methods and the effectiveness of mPTMs, and then proposes a two-stage fine-tuning method for low-resource cross-lingual summarization (TFLCLS). In the first stage, by recognizing the deficiency in the semantic alignment for low-resource languages in mPTMs, a semantic alignment fine-tuning method is employed to enhance the mPTMs' understanding of such languages. In the second stage, while considering that mPTMs are not originally tailored for information compression and CLS demands the model to simultaneously align and compress, an adaptive joint fine-tuning method is introduced. This method further enhances the semantic alignment and information compression abilities of mPTMs that were trained in the first stage. To evaluate the performance of TFLCLS, a low-resource CLS dataset, named Vi2ZhLow, is constructed from scratch; moreover, two additional low-resource CLS datasets, En2ZhLow and Zh2EnLow, are synthesized from widely used large-scale CLS datasets. Experimental results show that TFCLS outperforms state-of-the-art methods by 18.88%, 12.71% and 16.91% in ROUGE-2 on the three datasets, respectively, even when limited with only 5,000 training samples.