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Systemin, a peptide plant hormone of 18 amino acids, coordinates local and systemic immune responses. The activation of the canonical systemin-mediated systemic signaling pathway involves systemin release from its precursor prosystemin, systemin binding to its membrane receptor SYSTEMIN RECEPTOR1 (SYR1), and the transport of long-distance signaling molecules, including jasmonic acid, the prosystemin mRNA, volatile organic compounds and possibly systemin itself. Here, we review emerging evidence that the disordered structure and unconventional processing and secretion of systemin contribute to the regulation of systemin-mediated signaling during plant defense. We highlight recent advances in systemin research, which elucidated how cells integrate multiple long-distance signals into the systemic defense response. In addition, we discuss the perception of systemin by SYR1 and its mediation of downstream defense responses.

Recently, increasing evidence has suggested the association between gut dysbiosis and Alzheimer’s disease (AD) progression, yet the role of gut microbiota in AD pathogenesis remains obscure. Herein, we provide a potential mechanistic link between gut microbiota dysbiosis and neuroinflammation in AD progression. Using AD mouse models, we discovered that, during AD progression, the alteration of gut microbiota composition leads to the peripheral accumulation of phenylalanine and isoleucine, which stimulates the differentiation and proliferation of pro-inflammatory T helper 1 (Th1) cells. The brain-infiltrated peripheral Th1 immune cells are associated with the M1 microglia activation, contributing to AD-associated neuroinflammation. Importantly, the elevation of phenylalanine and isoleucine concentrations and the increase of Th1 cell frequency in the blood were also observed in two small independent cohorts of patients with mild cognitive impairment (MCI) due to AD. Furthermore, GV-971, a sodium oligomannate that has demonstrated solid and consistent cognition improvement in a phase 3 clinical trial in China, suppresses gut dysbiosis and the associated phenylalanine/isoleucine accumulation, harnesses neuroinflammation and reverses the cognition impairment. Together, our findings highlight the role of gut dysbiosis-promoted neuroinflammation in AD progression and suggest a novel strategy for AD therapy by remodelling the gut microbiota.

Circular RNAs (circRNAs) have a covalently closed circular conformation and are structurally stable. Those circRNAs with tumor‐suppressive properties play an important role in tumorigenesis and metastasis and thus may be used as therapeutic targets of cancers. Herein, we review the current understanding of the classification of circRNAs and summarize the functions and mechanisms of circRNAs that have tumor‐suppressive roles in various cancers, including liver cancer (circARSP91, circADAMTS13, circADAMTS14, circMTO1, hsa_circ_0079299, and circC3P1), bladder cancer (circFNDC3B, circITCH, circHIPK3, circRNA‐3, cdrlas, and circLPAR1), gastric cancer (circLARP4, circYAP1, hsa_cric_0000096, hsa_circ_0000993, and circPSMC3), breast cancer (circ_000911, hsa_circ_0072309, and circASS1), lung cancer (hsa_circ_0000977, circPTK2, circ_0001649, hsa_circ_100395, and circ_0006916), glioma (circ_0001946, circSHPRH, and circFBXW7), and colorectal cancer (circITGA7 and hsa_circ_0014717). Thanks to their structural stability, these tumor‐suppressive circRNAs may be used as potential and potent therapeutic targets. Moreover, we propose a new method for the classification of circRNAs. Based on whether they can be translated, circRNAs can be divided into noncoding circRNAs and coding circRNAs. Tumor‐suppressive circRNAs may be used as potential and potent therapeutic targets. Moreover, we can propose a new method for the classification of circRNAs: based on whether they can be translated, circRNAs can be divided into noncoding circRNAs and coding circRNAs.

Wildlife is an important part of natural ecosystems and protecting wildlife plays a crucial role in maintaining ecological balance. The wildlife detection method for images and videos based on deep learning can save a lot of labor costs and is of great significance and value for the monitoring and protection of wildlife. However, the complex and changing outdoor environment often leads to less than satisfactory detection results due to insufficient lighting, mutual occlusion, and blurriness. The TMS-YOLO (Takin, Monkey, and Snow Leopard-You Only Look Once) proposed in this paper is a modification of YOLOv7, specifically optimized for wildlife detection. It uses the designed O-ELAN (Optimized Efficient Layer Aggregation Networks) and O-SPPCSPC (Optimized Spatial Pyramid Pooling Combined with Cross Stage Partial Channel) modules and incorporates the CBAM (Convolutional Block Attention Module) to enhance its suitability for this task. In simple terms, O-ELAN can preserve a portion of the original features through residual structures when extracting image features, resulting in more background and animal features. However, O-ELAN may include more background information in the extracted features. Therefore, we use CBAM after the backbone to suppress background features and enhance animal features. Then, when fusing the features, we use O-SPPCSPC with fewer network layers to avoid overfitting. Comparative experiments were conducted on a self-built dataset and a Turkish wildlife dataset. The results demonstrated that the enhanced TMS-YOLO models outperformed YOLOv7 on both datasets. The mAP (mean Average Precision) of YOLOv7 on the two datasets was 90.5% and 94.6%, respectively. In contrast, the mAP of TMS-YOLO in the two datasets was 93.4% and 95%, respectively. These findings indicate that TMS-YOLO can achieve more accurate wildlife detection compared to YOLOv7.

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases. Methods for the wireless, continuous monitoring and analysis of activities directly from the throat skin have not been developed. Here, the authors present a stretchable device platform that provides wireless measurements and machine learning-based analysis of vibrations and muscle electrical activities from the throat.

Hepatocellular carcinoma (HCC) is a lethal malignancy, and predicting patient prognosis remains a significant challenge in clinical treatment. T cells play a crucial role in the tumor microenvironment, influencing tumorigenesis and progression. In this study, we constructed a T cell-related prognostic model for HCC. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, we identified 6,281 T cells from 10 HCC patients and subsequently identified 855 T cell-related genes. Comprehensive analyses were conducted on T cells and their associated genes, including enrichment analysis, cell-cell communication, trajectory analysis, and transcription factor analysis. By integrating scRNA-seq and bulk RNA-seq data with prognostic information from The Cancer Genome Atlas (TCGA), we identified T cell-related prognostic genes and constructed a model using LASSO regression. The model, incorporating PTTG1, LMNB1, SLC38A1, and BATF, was externally validated using the International Cancer Genome Consortium (ICGC) database. It effectively stratified patients into high- and low-risk groups based on risk scores, revealing significant differences in immune cell infiltration between these groups. Differential expression levels of PTTG1 and BATF between HCC and adjacent non-tumor tissues were further validated by immunohistochemistry (IHC) in 25 patient tissue samples. Moreover, a Cox regression analysis was performed to integrate risk scores with clinical features, resulting in a nomogram capable of predicting patient survival probabilities. This study introduces a novel prognostic risk model for HCC patients, aimed at stratifying patients by risk, enhancing personalized treatment strategies, and offering new insights into the role of T cell-related genes in HCC progression.

Background To investigate the role and regulatory mechanisms of fargesin, one of the main components of Magnolia fargesii , in macrophage reprogramming and crosstalk across cartilage and synovium during osteoarthritis (OA) development. Methods Ten-week-old male C57BL/6 mice were randomized and assigned to vehicle, collagenase-induced OA (CIOA), or CIOA with intra-articular fargesin treatment groups. Articular cartilage degeneration was evaluated using the Osteoarthritis Research Society International (OARSI) score. Immunostaining and western blot analyses were conducted to detect relative protein. Raw264.7 cells were treated with LPS or IL-4 to investigate the role of polarized macrophages. ADTC5 cells were treated with IL-1β and conditioned medium was collected to investigate the crosstalk between chondrocytes and macrophages. Results Fargesin attenuated articular cartilage degeneration and synovitis, resulting in substantially lower Osteoarthritis Research Society International (OARSI) and synovitis scores. In particular, significantly increased M2 polarization and decreased M1 polarization in synovial macrophages were found in fargesin-treated CIOA mice compared to controls. This was accompanied by downregulation of IL-6 and IL-1β and upregulation of IL-10 in serum. Conditioned medium (CM) from M1 macrophages treated with fargesin reduced the expression of matrix metalloproteinase-13, RUNX2, and type X collagen and increased Col2a1 and SOX9 in OA chondrocytes, but fargesin alone did not affect chondrocyte catabolic processes. Moreover, fargesin exerted protective effects by suppressing p38/ERK MAPK and p65/NF-κB signaling. Conclusions This study showed that fargesin switched the polarized phenotypes of macrophages from M1 to M2 subtypes and prevented cartilage degeneration partially by downregulating p38/ERK MAPK and p65/NF-κB signaling. Targeting macrophage reprogramming or blocking the crosstalk between macrophages and chondrocytes in early OA may be an effective preventive strategy.

RNA N 6 -methyladenosine (m 6 A) modifications are essential in plants. Here, we show that transgenic expression of the human RNA demethylase FTO in rice caused a more than threefold increase in grain yield under greenhouse conditions. In field trials, transgenic expression of FTO in rice and potato caused ~50% increases in yield and biomass. We demonstrate that the presence of FTO stimulates root meristem cell proliferation and tiller bud formation and promotes photosynthetic efficiency and drought tolerance but has no effect on mature cell size, shoot meristem cell proliferation, root diameter, plant height or ploidy. FTO mediates substantial m 6 A demethylation (around 7% of demethylation in poly(A) RNA and around 35% decrease of m 6 A in non-ribosomal nuclear RNA) in plant RNA, inducing chromatin openness and transcriptional activation. Therefore, modulation of plant RNA m 6 A methylation is a promising strategy to dramatically improve plant growth and crop yield. Rice and potato plants are more productive after epitranscriptome engineering.

Modern people's concept of health is more comprehensive; in addition to physical health, they focus on mental health even more. Computer multimedia group psychological measurement is introduced, and a mental health education model is constructed in this paper. The model includes three interrelated levels: mechanism level, evaluation level, and target level. It emphasizes the key and core role of mental quality in mental health. It advocates incorporating mental quality into the evaluation and diagnosis system of the overall state of individual mental health, combines the classified qualitative assessment and quantitative assessment, and arranges appropriate prevention and intervention measures according to different types of individuals. Practice has proved that this method is effective.

Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.