Seeing beyond words: Visualizing autism spectrum disorder biomarker insights.

Objective: This study employs bibliometric and visual analysis to elucidate global research trends in Autism Spectrum Disorder (ASD) biomarkers, identify critical research focal points, and discuss the potential integration of diverse biomarker modalities for precise ASD assessment. Methods: A comprehensive bibliometric analysis was conducted using data from the Web of Science Core Collection database until December 31, 2022. Visualization tools, including R, VOSviewer, CiteSpace, and gCLUTO, were utilized to examine collaborative networks, co-citation patterns, and keyword associations among countries, institutions, authors, journals, documents, and keywords. Results: ASD biomarker research emerged in 2004, accumulating a corpus of 4348 documents by December 31, 2022. The United States, with 1574 publications and an H-index of 213, emerged as the most prolific and influential country. The University of California, Davis, contributed significantly with 346 publications and an H-index of 69, making it the leading institution. Concerning journals, the Journal of Autism and Developmental Disorders, Autism Research, and PLOS ONE were the top three publishers of ASD biomarker-related articles among a total of 1140 academic journals. Co-citation and keyword analyses revealed research hotspots in genetics, imaging, oxidative stress, neuroinflammation, gut microbiota, and eye tracking. Emerging topics included "DNA methylation," "eye tracking," "metabolomics," and "resting-state fMRI." Conclusion: The field of ASD biomarker research is dynamically evolving. Future endeavors should prioritize individual stratification, methodological standardization, the harmonious integration of biomarker modalities, and longitudinal studies to advance the precision of ASD diagnosis and treatment.

Sophocarpine alleviates intestinal fibrosis via inhibition of inflammation and fibroblast into myofibroblast transition by targeting the Sirt1/p65 signaling axis.

In this study, we used alkaloids from Sophora flavescens to inhibit the SASP, leading to fibroblast-into-myofibroblast transition (FMT) to maintain intestinal mucosal homeostasis in vitro and in vivo. We used western blotting (WB) and immunofluorescence staining (IF) to assess whether five kinds of alkaloids inhibit the major inflammatory pathways and chose the most effective compound (sophocarpine; SPC) to ameliorate colorectal inflammation in a dextran sulfate sodium (DSS)-induced UC mouse model. IF, Immunohistochemistry staining (IHC), WB, disease activity index (DAI), and enzyme-linked immunosorbent assay (ELISA) were conducted to investigate the mechanism of action of this compound. Next, we detected the pharmacological activity of SPC on the senescence-associated secretory phenotypes (SASP) and FMT in interleukin 6 (IL-6)-induced senescence-like fibroblasts and discussed the mucosal protection ability of SPC on a fibroblast-epithelium/organoid coculture system and organ-on-chip system. Taken together, our results provide evidence that SPC alleviates the inflammatory response, improves intestinal fibrosis and maintains intestinal mucosal homeostasis in vivo. Meanwhile, SPC was able to prevent IL-6-induced SASP and FMT in fibroblasts, maintain the expression of TJ proteins, and inhibit inflammation and genomic stability of colonic mucosal epithelial cells by activating SIRT1 in vitro. In conclusion, SPC treatment attenuates intestinal fibrosis by regulating SIRT1/NF-κB p65 signaling, and it might be a promising therapeutic agent for inflammatory bowel disease.

Metal Clusters Based Multifunctional Materials for Solar Cells.

As a multifunctional material, metal clusters have recently received some attention for their application in solar cells.This review delves into the multifaceted role of metal clusters in advancing solar cell technologies, covering diverse aspects from electron transport and interface modification to serving as molecular precursors for inorganic materials and acting as photosensitizers in metal-cluster sensitized solar cells (MCSSCs). The studies conducted by various researchers illustrate the crucial impact of metal clusters, such as gold nanoclusters (Au NCs), on enhancing solar cell efficiency through size-dependent effects, distinct interface behaviors, and tailored interface engineering. From optimizing charge transfer rates to improving light absorption and reducing carrier recombination, metal clusters prove instrumental in shaping the landscape of solar energy conversion.The promising performance of metal-cluster sensitized solar cells, coupled with their scalability and flexibility, positions them as a exciting avenue for future clean energy applications. The article concludes by emphasizing the need for continued interdisciplinary research and technological innovation to unlock the full potential of metal clusters in contributing to sustainable and high-performance solar cells.

Revisiting multi-view learning: A perspective of implicitly heterogeneous Graph Convolutional Network.

Graph Convolutional Network (GCN) has become a hotspot in graph-based machine learning due to its powerful graph processing capability. Most of the existing GCN-based approaches are designed for single-view data. In numerous practical scenarios, data is expressed through multiple views, rather than a single view. The ability of GCN to model homogeneous graphs is indisputable, while it is insufficient in facing the heterophily property of multi-view data. In this paper, we revisit multi-view learning to propose an implicit heterogeneous graph convolutional network that efficiently captures the heterogeneity of multi-view data while exploiting the powerful feature aggregation capability of GCN. We automatically assign optimal importance to each view when constructing the meta-path graph. High-order cross-view meta-paths are explored based on the obtained graph, and a series of graph matrices are generated. Combining graph matrices with learnable global feature representation to obtain heterogeneous graph embeddings at various levels. Finally, in order to effectively utilize both local and global information, we introduce a graph-level attention mechanism at the meta-path level that allocates private information to each node individually. Extensive experimental results convincingly support the superior performance of the proposed method compared to other state-of-the-art approaches.

[Application of "major scientific issues and engineering technology difficulties in traditional Chinese medicine(2019-2021)" in national science and t echnology layout].

In order to judge the future development trend of science and technology, plan ahead and lay out the frontier technology fields and directions, China Association of Chinese Medicine(CACM) has launched consultation projects for collecting "major scienti-fic issues and engineering technology difficulties in traditional Chinese medicine(TCM)" for the industry for three consecutive years since 2019. Up to now, 18 projects have been selected as major issues for research, and some experience and achievements have been made. These projects have been applied in important scientific and technological work such as scientific and technological planning and deployment at all levels of national, local, and scientific research institutions, the selection and cultivation of major national scientific and technological projects, and the construction of innovation bases, giving full play to the role of the think tank advisory committee of CACM. This study reviewed the selection of major issues for the first time, systematically combed its application in the national layout of science and technology, and put forward the existing problems and improvement suggestions, aiming to provide new ideas for further improving the selection of major issues and research direction, providing a theoretical basis and decision support for the national scientific and technological layout in the field of TCM, and promoting scientific and technological innovation to facilitate the high quality development of TCM.

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images-Case Study of the Small Hilly Areas in Jurong City.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance.

Ginseng is an important medicinal plant benefiting human health for thousands of years. Root disease is the main cause of ginseng yield loss. It is difficult to detect ginseng root disease by manual observation on the changes of leaves, as it takes a long time until symptoms appear on leaves after the infection on roots. In order to detect root diseases at early stages and limit their further spread, an efficient and non-destructive testing (NDT) method is urgently needed. Hyperspectral remote sensing technology was performed in this study to discern whether ginseng roots were diseased. Hyperspectral reflectance of leaves at 325-1,075 nm were collected from the ginsengs with no symptoms on leaves at visual. These spectra were divided into healthy and diseased groups according to the symptoms on roots after harvest. The hyperspectral data were used to construct machine learning classification models including random forest, extreme random tree (ET), adaptive boosting and gradient boosting decision tree respectively to identify diseased ginsengs, while calculating the vegetation indices and analyzing the region of specific spectral bands. The precision rates of the ET model preprocessed by savitzky golay method for the identification of healthy and diseased ginsengs reached 99% and 98%, respectively. Combined with the preliminary analysis of band importance, vegetation indices and physiological characteristics, 690-726 nm was screened out as a specific band for early detection of ginseng root diseases. Therefore, underground root diseases can be effectively detected at an early stage by leaf hyperspectral reflectance. The NDT method for early detection of ginsengs root diseases is proposed in this study. The method is helpful in the prevention and control of root diseases of ginsengs to prevent the reduction of ginseng yield.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Exploration and validation of hub genes in lung adenocarcinoma based on bioinformatics analysis.

Background: Genomic abnormality is a crucial factor for lung cancer development. This study used bioinformatics analysis to explore the hub genes involved in lung adenocarcinoma. Methods: The GeneCards, Comparative Toxicogenomics Database (CTD), and DISEASES databases were used to screen the genes associated with lung adenocarcinoma. The hub genes were then identified using WebGestalt. The Cancer Genome Atlas (TCGA), UALCAN, and the Human Protein Atlas (HPA) were used to validate the expression of hub genes. The predictive effects of hub genes on the risk of lung adenocarcinoma were evaluated using receiver operating characteristic (ROC) curve analysis. The Tumor-Immune System Interaction Database (TISIDB) was used to estimate the correlation between hub genes and immune infiltration. Results: A total of 21 genes were defined as common genes associated with lung adenocarcinoma, and from these, AKT1, CD44, and CDKN2A were identified as hub genes. Significant differences in the hub gene mRNA and protein expression were observed between lung adenocarcinoma samples and normal samples derived from the TCGA and UALCAN databases. The area under the ROC curve (AUC) for AKT1, CD44, and CDKN2A in predicting lung adenocarcinoma risk was 0.847, 0.880, and 0.805, respectively, with sensitivity of 89.8%, 93.2%, and 94.9%, respectively. TISIDB analysis indicated that AKT1, CD44, and CDKN2A expression had a strong relationship with immune infiltration in lung adenocarcinoma. Conclusions: These hub genes, AKT1, CD44, and CDKN2A, may represent tumor biomarkers that may contribute to the understanding, diagnosis, and treatment of lung adenocarcinoma.

Homogeneously Large Polarons in Aromatic Passivators Improves Charge Transport between Perovskite Grains for >24 % Efficiency in Photovoltaics.

Aromatic passivators, such as porphyrin, with large π-backbones have attracted considerable attention to boost the charge carrier in polycrystalline perovskite films, thus enabling the fabrication of efficient and stable perovskite solar cells (PSCs). However, they often self-assemble into supramolecules that probably influence the charge-transfer process in the perovskite grain boundary. Here, by doping a monoamine Cu porphyrin into perovskite films, two porphyrin-based self-assembled supramolecules were successfully prepared between perovskite grains. Crystal structures and theoretical analyses reveal the presence of a stronger interaction between the amine units and the central Cu ions of neighbouring porphyrins in one of the supramolecules. This has a modified effect on the dipole direction of the porphyrins to be quantized as homogeneously large polarons (HLPs) in a periodic lattice. The porphyrin supramolecules can stabilize perovskite grain boundaries to greatly improve the stability of PSCs, while the HLPs-featured supramolecule facilitates hole transport across perovskite grains to remarkably increase the cell performance to as high as 24.2 %. This work proves that the modulation of the intermolecular interaction of aromatic passivators to yield HLPs is crucial for the cascaded acceleration of charge transport between perovskite grains.

Agrimol B inhibits colon carcinoma progression by blocking mitochondrial function through the PGC-1α/NRF1/TFAM signaling pathway.

Background: The activation of peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α) stimulates the transcription of the downstream target proteins, mitochondrial transcription factor A (TFAM) and nuclear respiratory factor 1 (NRF1), which induces mitochondrial biogenesis and promotes colorectal tumorigenesis. Agrimol B (Agr) is a constituent of Agrimonia pilosa Ledeb. that exerts anticancer effects. Herein, we aimed to investigate the antitumor activity of Agr and its mechanism of action. Methods: The interaction between Agr and PGC-1α was predicted by molecular docking. After the treatment with different concentrations of Agr (0, 144, 288, and 576 nM), the cell viability, migration rate, proliferation rate, and apoptosis rate of human colon cancer HCT116 cells were determined. Mitochondrial activity, cellular reactive oxygen species (ROS), and mitochondrial membrane potential were assessed to measure the regulatory effect of Agr on mitochondrial function. Western blotting (WB) assay was used to examine the expression of PGC-1α, NRF1, and TFAM, as well as of the pro-apoptotic proteins, Bax and Caspase-3, and the antiapoptotic protein (Bcl-2). Finally, subcutaneous tumor xenograft model mice were used to evaluate the effect of Agr on colorectal cancer (CRC) in vivo. Results: The molecular docking results revealed a high likelihood of Agr interacting with PGC-1α. Agr inhibited the proliferation and migration of HCT116 cells, promoted ROS production and mitochondrial oxidative stress, inhibited mitochondrial activity, and decreased mitochondrial membrane potential. Agr induced cell apoptosis and, in combination with PGC-1α, impaired mitochondrial biogenesis and suppressed the expression of NRF1 and TFAM. Agr also suppressed the expression of Bcl-2 and Cleaved-Caspase-3 and increased the expression of Bax and Caspase-3. In addition, the in vivo antitumor effect and mechanism of Agr were confirmed by using a subcutaneous tumor xenograft mouse model. Conclusions: Our findings demonstrated that Agr regulates the expression of PGC-1α, thereby inducing mitochondrial dysfunction and promoting tumor cell apoptosis. This work highlights the potential of Agr as a promising therapeutic candidate in CRC.

Grain Boundary Engineering with Self-Assembled Porphyrin Supramolecules for Highly Efficient Large-Area Perovskite Photovoltaics.

Grain boundary management is critical to the performance and stability of polycrystalline perovskite solar cells (PSCs), especially large-area devices. However, typical passivators are insulating in nature and limit carrier transport. Here, we design a supramolecular binder for grain boundaries to simultaneously passivate defects and promote hole transport across perovskite grain boundaries. By doping the monoamine porphyrins (MPs, M = Co, Ni, Cu, Zn, or H) into perovskite films, MPs self-assemble into supramolecules at grain boundaries. Organic cations in perovskites protonate MPs in supramolecules to form ammonium porphyrins bound on the perovskite grain surface, to passivate defects and extract holes from the perovskite lattice. Periodic polarons in supramolecules (especially NiP-supramolecule) promote the transport of extracted holes across boundaries, reducing nonradiative carrier recombination. The NiP-doped PSCs reveal a certified efficiency of 22.1% for an active area of 1.0 cm2 with the remarkably improved open-circuit voltage and fill factor. The unencapsulated device retained over 80% initial performance under AM 1.5G solar light continuous illumination or heating at 85 °C over 3000 h.

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification.

The occlusion problem is very common in pedestrian retrieval scenarios. When persons are occluded by various obstacles, the noise caused by the occluded area greatly affects the retrieval results. However, many previous pedestrian re-identification (Re-ID) methods ignore this problem. To solve it, we propose a semantic-guided alignment model that uses image semantic information to separate useful information from occlusion noise. In the image preprocessing phase, we use a human semantic parsing network to generate probability maps. These maps show which regions of images are occluded, and the model automatically crops images to preserve the visible parts. In the construction phase, we fuse the probability maps with the global features of the image, and semantic information guides the model to focus on visible human regions and extract local features. During the matching process, we propose a measurement strategy that only calculates the distance of public areas (visible human areas on both images) between images, thereby suppressing the spatial misalignment caused by non-public areas. Experimental results on a series of public datasets confirm that our method outperforms previous occluded Re-ID methods, and it achieves top performance in the holistic Re-ID problem.

Bushen Kangshuai tablet inhibits progression of atherosclerosis by intervening in macrophage autophagy and polarization.

OBJECTIVE: To investigate the efficacy of Bushen Kangshuai (BS-KS) tablet on autophagy and polarization in mouse macrophage RAW 264.7. MEYHODS: Macrophage autophagy was induced by oxidized low-density lipoprotein (100 µg/mL). To detect the levels of autophagy, macrophage were transfected with double fluorescence LC3 autophagy adenovirus, then the numbers of autophagosomes and autophagic lysosomes were asessed by confocal microscopy. The autophagy related proteins expression of PI3K, Akt, phospho-mAkt (p-Akt) and mTOR, phospho-mTOR ([p-TOR), p62, microtubule-associated protein 1 (LC3-Ⅱ)were determined by western blotting. The macrophage polarization model was induced by lipopolysaccharide (1 µg/mL). The mRNA levels of iNOS, CD86 (M1 macrophages marker molecules), and CD206, Arg-1 (M2 macrophages marker molecules) were detected by real-time quantitative PCR. The concentration of cytokines TNF-α and IL-10 was determined by enzyme-linked immunosorbent assay. The protein expression of nuclear proteins PPAR-γ, NF-κB, and cytoplasmic protein IKB α was determined by western blotting. RESULTS: The expression of the autophagy-related protein LC3-Ⅱ was increased and the expression of p62 was decreased in the BS-KS intervention group. The protein expression of PI3K, p-Akt, and p-mTOR was also reduced. BS-KS also inhibited the mRNA expression of iNOS and CD86 on M2 macrophage, but promoted the expression of CD206 and Arg-1 on M2 macrophage. With respect to the regulation of inflammatory factors, BS-KS could inhibit the secretion of pro-inflammatory TNF-α and promote the secretion of anti-inflammatory IL-10. It also inhibited the protein expression of IKB-α and NF-κB, and promoted the expression of nuclear protein PPAR-γ. CONCLUSION: We believe that BS-KS promotes macrophage autophagy by increasing the level of autophagy protein and inhibiting the PI3K/Akt/mTOR signaling pathway. Furthermore, BS-KS seems to inhibit macrophage M1 polarization and promote M2 polarization via the PPAR gamma /NF-κB signaling pathway, thus playing an inhibitory role in atherosclerosis.

Exosomes: An emerging factor in atherosclerosis.

Atherosclerosis is the main reason for morbidity and death caused by cardiovascular disease which leads to approximately 20% of total death around the world. Exosomes secreted by the cells is a kind of extracellular vesicles with lipid bilayer structure, containing a variety of cell specific lipid, nucleic acid and protein, involved in intercellular communication, plays an important role in different physiological and pathological process. In recent years, with the deepening of research, the role of exosomes in cardiovascular diseases has received extensive attention. This review summarizes the roles of exosomes and exosome-derived from microRNAs, proteins and DNA as biomarkers in the development of atherosclerosis, and explores the mechanism of exosome-mediated intercellular crosstalk in atherosclerosis, providing potential roles for diagnosis and treatment.