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Abstract Cadmium (Cd) pollution is a serious threat to food safety and human health. Minimizing Cd uptake and enhancing Cd tolerance in plants are vital to improve crop yield and reduce hazardous effects to humans. In this study, we designed three Cd concentration stress treatments (Cd1: 0.20 mg·kg−1, Cd2: 0.60 mg·kg−1, and Cd3: 1.60 mg·kg−1) and two foliar silicon (Si) treatments (CK: no spraying of any material, and Si: foliar Si spraying) to conduct pot experiments on soil Cd stress. The results showed that spraying Si on the leaves reduced the Cd content in brown rice by 4.79–42.14%. Si application increased net photosynthetic rate (Pn) by 1.77–4.08%, stomatal conductance (Gs) by 5.27–23.43%, transpiration rate (Tr) by 2.99–20.50% and intercellular carbon dioxide (CO2) concentration (Ci) by 6.55–8.84%. Foliar spraying of Si significantly increased the activities of superoxide dismutase (SOD) and peroxidase (POD) in rice leaves by 9.84–14.09% and 4.69–53.09%, respectively, and reduced the content of malondialdehyde (MDA) by 7.83–48.72%. In summary, foliar Si spraying protects the photosynthesis and antioxidant system of rice canopy leaves, and is an effective method to reduce the Cd content in brown rice.

In order to evaluate the effect of intercropping of the hyperaccumulator, Solanum nigrum L. and wheat on the absorption and accumulation of cadmium (Cd) in wheat. The experiment was conducted with three replicates, which conducted on four Cd concentrations (0, 20, 40, 60 μmol L−1) in the Hoagland solution and using two planting patterns [monoculture wheat (MW), intercropping wheat and Solanum nigrum L. (IW，IS)]. The results showed that the addition of Cd in the solutions reduced the total root length by 19.08–55.98%, total root area by 12.35–44.48%, and total root volume by 16.01–46.00% of wheat plants. Intercropping with Solanum nigrum L. significantly reduced Cd contents by 28.3–47.2% and Cd accumulations by 10.08–32.43% in the roots of wheat. Transmission electron microscope (TEM) revealed that the root-tip cells of the monoculture wheat treated with Cd exhibited swollen spheres of intracellular mitochondria, disorderly arranged inner ridges of mitochondria, some damaged mitochondrial membranes, and deformed nuclear membranes. Many dense electron particles in the form of Cd were deposited in the cell gap, and the cell nucleus became smaller or even disappeared. Under the same Cd concentrations, root-tip cells of intercropped wheat showed much less density of electron particles, starch granules, and the damage to the nucleus and nuclear membrane by Cd.These results indicated that intercropping with Solanum nigrum L. reduced the Cd toxicity to wheat roots and decreased Cd uptake and accumulation in both the shoots and roots of the wheat.

The modulation of numerous signaling pathways is orchestrated by redox regulation of cellular environments. Maintaining dynamic redox homeostasis is of utmost importance for human health, given the common occurrence of altered redox status in various pathological conditions. The cardinal component of the thioredoxin system, mammalian thioredoxin reductase (TrxR) plays a vital role in supporting various physiological functions; however, its malfunction, disrupting redox balance, is intimately associated with the pathogenesis of multiple diseases. Accordingly, the dynamic monitoring of TrxR of live organisms represents a powerful direction to facilitate the comprehensive understanding and exploration of the profound significance of redox biology in cellular processes. A number of classic assays have been developed for the determination of TrxR activity in biological samples, yet their application is constrained when exploring the real-time dynamics of TrxR activity in live organisms. Fluorescent probes offer several advantages for in situ imaging and the quantification of biological targets, such as non-destructiveness, real-time analysis, and high spatiotemporal resolution. These benefits facilitate the transition from a poise to a flux understanding of cellular targets, further advancing scientific studies in related fields. This review aims to introduce the progress in the development and application of TrxR fluorescent probes in the past years, and it mainly focuses on analyzing their reaction mechanisms, construction strategies, and potential drawbacks. Finally, this study discusses the critical challenges and issues encountered during the development of selective TrxR probes and proposes future directions for their advancement. We anticipate the comprehensive analysis of the present TrxR probes will offer some glitters of enlightenment, and we also expect that this review may shed light on the design and development of novel TrxR probes.

To study the synergistic effects of water management and silicon (Si) foliar spraying on the uptake and transport of cadmium (Cd) in rice, we designed four treatments: conventional intermittent flooding + no Si foliar spraying (CK), continuous flooding throughout the growth stage + no Si foliar spraying (W), conventional intermittent flooding + Si foliar spraying (Si) and continuous flooding throughout the growth stage + Si foliar spraying (WSi). The results show that WSi treatment reduced the uptake and translocation of Cd by rice and significantly reduced the brown rice Cd content, with no effect on rice yield. Compared with CK, the Si treatment increased the net photosynthetic rate (Pn), stomatal conductance (Gs) and transpiration rate (Tr) of rice by 6.5–9.4%, 10.0–16.6% and 2.1–16.8%, respectively. The W treatment decreased these parameters by 20.5–27.9%, 8.6–26.8% and 13.3–23.3%, respectively, and the WSi treatment decreased them by 13.1–21.2%, 3.7–22.3% and 2.2–13.7%, respectively. The superoxide dismutase (SOD) and peroxidase (POD) activity decreased by 6.7–20.6% and 6.5–9.5%, respectively, following the W treatment. Following the Si treatment, SOD and POD activity increased by 10.2–41.1% and 9.3–25.1%, respectively, and following the WSi treatment, they increased by 6.5–18.1% and 2.6–22.4%, respectively. Si foliar spraying ameliorated the detrimental effects of continuous flooding throughout the growth stage on photosynthesis and antioxidant enzyme activity. We conclude that synergistic continuous flooding throughout the growth stage, combined with Si foliar spraying, can significantly block Cd uptake and translocation and is therefore an effective means of reducing the accumulation of Cd in brown rice.

The spatiotemporal heterogeneity of rice canopy leaves at different leaf positions is very important for non-destructive monitoring under Cadmium (Cd) stress, and is one of the key problems that need to be solved urgently in modern agriculture. This study aims to explore the temporal and spatial heterogeneity of chlorophyll content and fluorescence in rice canopy leaves under different Cd stress concentrations. The responses of the relative chlorophyll content (SPAD) and Chlorophyll fluorescence (ChlF) parameters to Cd stress in the tillering, heading, and filling stages were analyzed through pot experiments with three Cd concentrations: 0.20 mg·kg−1 (CK), 0.60 mg·kg−1 (Cd1), and 1.60 mg·kg−1 (Cd2). Results showed that the canopy leaf SPAD value increased with increasing Cd concentration. Compared with CK, the leaf SPAD value in the Cd1 and Cd2 treatments were 1.91–3.45% and 5.52–12.01% lower, respectively. The SPAD value of the lower leaves was higher in the tillering stage, while the SPAD value of the upper leaves was higher in the heading and filling stages. The non-photochemical quenching coefficient (NPQ) of the third leaf (D3), the initial fluorescence yield (Fo) of the second leaf (D2), and the maximum photochemical efficiency (Fv/Fm) of photosystem II (PSII) in the tillering stage; the NPQ, Fo, and Fv/Fm of the D3 in the heading stage; and the NPQ, Fo, Fv/Fm, and Fv/Fm of the D2 in the filling stage were more sensitive to Cd stress than the other leaves and ChlF parameters.

In semantic segmentation, the efficient representation of multi-scale context is of paramount importance. Inspired by the remarkable performance of Vision Transformers (ViT) in image classification, subsequent researchers have proposed some Semantic Segmentation ViTs, most of which have achieved impressive results. However, these models often struggle to effectively utilizing multi-scale context, disregarding intra-image semantic context, and neglecting the global context of training data, i.e., the semantic relationships among pixels across different images. In this paper, we introduce the Sliding Window Dilated Attention and combine it with the Spatial Pyramid Pooling (SPP) to form a novel decoder called Sliding window dilated attention spatial pyramid pooling(SwinASPP). By adjusting the sliding window dilation rates, this decoder is capable of capturing multi-scale contextual information from different granularities. Additionally, we propose the Semantic Attention Block, which integrates semantic attention operations into the encoder. And adopt our proposed supervised pixel-wise contrastive learning algorithm, we shift the current training strategy to inter-image for semantic segmentation. Our experiments demonstrate that these methods lead to performance improvements on the SanJiangYuan MouseHole dataset and Cityscapes.

Speech recognition technology has many applications in our daily life. However, for many low-resource languages without written forms, acquiring sufficient training data remains a significant challenge for building accurate ASR models. The Tu language, spoken by an ethnic minority group in Qinghai Province in China, is one such example. Due to the lack of written records and the great diversity in regional pronunciations, there has been little previous research on Tu-language speech recognition. This work seeks to address this research gap by creating the first speech dataset for the Tu language spoken in Huzhu County, Qinghai. We first formulated the relevant pronunciation rules for the Tu language based on linguistic analysis. Then, we constructed a new speech corpus, named HZ-TuDs, through targeted data collection and annotation. Based on the HZ-TuDs dataset, we designed several baseline sequence-to-sequence deep neural models for end-to-end Tu-language speech recognition. Additionally, we proposed a novel SA-conformer model, which combines convolutional and channel attention modules to better extract speech features. Experiments showed that our proposed SA-conformer model can significantly reduce the character error rate from 23% to 12%, effectively improving the accuracy of Tu language recognition compared to previous approaches. This demonstrates the effectiveness of our dataset construction and model design efforts in advancing speech recognition technology for this low-resource minority language.

Maintaining a harmonious balance between grassland ecology and local economic development necessitates effective management of livestock resources. Traditional approaches have proven inefficient, highlighting an urgent need for intelligent solutions. Accurate identification of livestock targets is pivotal for precise livestock farming management. However, the You Only Look Once version 8 (YOLOv8) model exhibits limitations in accuracy when confronted with complex backgrounds and densely clustered targets. To address these challenges, this study proposes an optimized CCS-YOLOv8 (Comprehensive Contextual Sensing YOLOv8) model. First, we curated a comprehensive livestock detection dataset encompassing the Qinghai region. Second, the YOLOv8n model underwent three key enhancements: (1) incorporating a Convolutional Block Attention Module (CBAM) to accentuate salient image information, thereby boosting feature representational power; (2) integrating a Content-Aware ReAssembly of FEatures (CARAFE) operator to mitigate irrelevant interference, improving the integrity and accuracy of feature extraction; and (3) introducing a dedicated small object detection layer to capture finer livestock details, enhancing the recognition of smaller targets. Experimental results on our dataset demonstrate the CCS-YOLOv8 model’s superior performance, achieving 84.1% precision, 82.2% recall, 84.4% mAP@0.5, 60.3% mAP@0.75, 53.6% mAP@0.5:0.95, and 83.1% F1-score. These metrics reflect substantial improvements of 1.1%, 7.9%, 5.8%, 6.6%, 4.8%, and 4.7%, respectively, over the baseline model. Compared to mainstream object detection models, CCS-YOLOv8 strikes an optimal balance between accuracy and real-time processing capability. Its robustness is further validated on the VisDrone2019 dataset. The CCS-YOLOv8 model enables rapid and accurate identification of livestock age groups and species, effectively overcoming the challenges posed by complex grassland backgrounds and densely clustered targets. It offers a novel strategy for precise livestock population management and overgrazing prevention, aligning seamlessly with the demands of modern precision livestock farming. Moreover, it promotes local environmental conservation and fosters sustainable development within the livestock industry.

Mume Fructus (MF) contains a variety of organic acids, free amino acids, and nucleoside components, and studies have not yet analyzed the relationship between the components of free amino acids and nucleosides with the varieties of MF. A rapid and sensitive method was established for simultaneous determination of 21 free amino acids and 9 nucleosides in MF by ultrafast liquid chromatography-mass spectrometry. The analysis was carried out on a Waters XBridge Amide column (100 mm × 2.1 mm, 3.5 μm) with elution by the mobile phase of 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a flow rate of 0.2 mL/min with 1 μL per injection. The column temperature was maintained at 30°C. The target compounds were analyzed by the positive ion multiple reaction monitoring (MRM) mode. The comprehensive evaluation of the samples was carried out by principal component analysis (PCA) and technique for order preference by similarity to an ideal solution (TOPSIS) analysis. Results showed the method could simultaneously determine 30 components in MF. The content of total analytes in six mainstream varieties was different, exhibited the order Nangao > Daqingmei > Zhaoshuimei > Yanmei > Shishengme > Baimei, and aspartic acid and adenosine were the most abundant amino acid and nucleoside. PCA and OPLS-DA could easily distinguish the samples, and 11 components could be chemical markers of sample classification. TOPSIS implied that the quality of Nangao and Daqingmei was superior to the other varieties. The results could provide a reliable basis for quality evaluation and utilisation of medicinal and edible MF.