Network Mapping Research Articles

Efficient O-type mapping and routing of large-scale neural networks to torus-based ONoCs

Efficient O-type mapping and routing of large-scale neural networks to torus-based ONoCs

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Volatile organic compounds (VOCs) in urban public transportation: Seasonal influences and variations

The present work aims to analyze the information related to the organic pollutants present inside the public transport units of León Guanajuato, Mexico, along winter, spring and fall seasons. Through the application of different selection criteria, a database was generated that allowed the analysis of the different selected pollutants and determine their behavior throughout the spring, autumn and winter seasons. These analyzes were carried out using different statistical tools like ANOVA, PCA, and correlation network maps. At least 23 compounds were selected as common pollutants along all the seasons, including aromatics, aliphatic, halogenated, polycyclic, and compounds with heteroatoms species. Results showed that the concentrations of Volatile Organic Compounds s such as hexane (1.5–3.0 µg/m³), dichlorobenzene (2.0–4.5 µg m−3), and toluene (5.0–12.0 µg m−3) varied significantly across the seasons, with higher levels observed in winter. The study highlights the influence of external environmental factors on indoor air quality within public transport units proven through statistical analysis. The results allowed to mention that the concentrations of Volatile Organic Compounds can vary significantly in different season along the year, as well as that the main pollutants vary according to the season, of which there are compounds that are only present during these periods.

Network analysis and experimental verification of Salvia miltiorrhiza Bunge-Reynoutria japonica Houtt. drug pair in the treatment of non-alcoholic fatty liver disease

ContextThere are currently no approved specific clinical drugs for non-alcoholic fatty liver disease (NAFLD). Salvia miltiorrhiza Bunge-Reynoutria japonica Houtt. drug pair (SRDP) has been widely used in the treatment of chronic liver diseases. However, the mechanism of SRDP treating NAFLD remains unclear.ObjectiveBased on network analysis and in vitro experimental verification, we investigated the effect of SRDP on lipid deposition and explored its possible mechanism for the treatment of NAFLD.MethodsThe TCMSP platform was used to screen the active metabolites of SRDP and corresponding targets. The GeneCards and OMIM databases were used to screen the NAFLD targets. The drug-disease intersecting targets were extracted to obtain the potential targets. Then the protein–protein interaction (PPI) and drug-active metabolites-target-disease network map was constructed. The DAVID database was performed to GO and KEGG pathway enrichment analysis for the intersecting targets. The core active metabolite and signaling pathway were verified by in vitro experiments.ResultsNetwork analysis predicted 59 active metabolites and 89 targets of SRDP for the treatment of NAFLD. 112 signaling pathways were enriched for KEGG pathways, including PI3K-AKT signaling pathway,etc. It was confirmed that luteolin, the core active metabolite of SRDP, effectively reduced fat accumulation and intracellular triglyceride content in HepG2 fatty liver cell model. Luteolin could inhibit mTOR pathway by inhibiting PI3K-AKT signaling pathway phosphorylation, thereby activating autophagy to alleviate NAFLD.Discussion and conclusionThe results of this study validate and predict the possible role of various active metabolites of SRDP in the treatment of NAFLD through multiple targets and signaling pathways. The core active metabolite of SRDP, luteolin can alleviate NAFLD by acting on the PI3K-AKT-mTOR signaling pathway to induce autophagy.

Screening for Genes Related to Meat Production Traits in Duroc × Bama Xiang Crossbred Pigs by Whole Transcriptome Sequencing.

The meat production traits of pigs are influenced by the expression regulation of multiple gene types, including mRNAs, miRNAs, and lncRNAs. To study the differences in meat production traits at the transcriptional level among individuals with different growth rates, the longissimus dorsi samples from eight Duroc × Bama Xiang F2 crossbred pigs with a fast growth rate (high gTroup) or a slow growth rate (low group) were selected to perform whole transcriptome sequencing and ceRNA regulatory network construction. This study first analyzed the differences in physiological and biochemical indicators, muscle histological characteristics, and muscle fiber types. A total of 248 mRNAs, 25 miRNAs, and 432 lncRNAs were identified as differentially expressed by whole transcriptome sequencing. Key genes that may influence meat production traits include MTMR14, PPP1R3A, PYGM, PGAM2, MYH1, and MYH7. The ceRNA regulatory network map showed that ENSSSCG00000042061-ssc-mir-208b-MYH7, ENSSSCG00000042223-ssc-mir-146a-MTMR14, ENSSSCG00000045539-ssc-mir-9-3-MYH1, and ENSSSCG00000047852-ssc-mir-103-1-PPP1R3A may be the key factors affecting meat production traits through their regulatory relationships. This study provides valuable insights into the molecular mechanisms underlying porcine muscle development and can aid in improving meat production traits.

Well-being in middle-aged and older adults who volunteer: a qualitative network analysis

Objective Social participation is essential for the health of older adults. Formal volunteering can reduce loneliness and is associated with numerous positive health outcomes. Older adults have limited resources to meet their social and well-being needs. We explore factors contributing to the well-being of middle-aged and older adults who engage in formal volunteering using a qualitative social network approach. Methods Between 2022 and 2023, qualitative semi-structured interviews were conducted with 28 members of social clubs in Germany, aged 45–80 (mean age 62 years). Thirteen participants were retired. Eight interviewees were female and twenty were male. Participants created unstructured egocentric network maps of individuals or organisations important to their well-being. Participants reflected on their social networks using these maps, which allowed for deeper insights into changing network dynamics. The data were analysed using thematic analysis. Results We developed six themes: intrapersonal, interpersonal and social network characteristics affecting well-being, self-fulfilment, feeling indispensable and unfulfilled expectations. Formal social participation, particularly through volunteering, contributed significantly to the well-being. Conclusion Formal volunteering can aid successful role substitution and compensation, especially after retirement. This may prevent loneliness and increase well-being in older age.

High-performance deep spiking neural networks with 0.3 spikes per neuron

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical results provide exact mapping algorithms from artificial to spiking neural networks with time-to-first-spike coding. In this paper we analyze in theory and simulation the learning dynamics of time-to-first-spike-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of spiking neural network mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between spiking and artificial neural networks with rectified linear units. For specific image classification architectures comprising feed-forward dense or convolutional layers, we demonstrate that deep spiking neural network models can be effectively trained from scratch on MNIST and Fashion-MNIST datasets, or fine-tuned on large-scale datasets, such as CIFAR10, CIFAR100 and PLACES365, to achieve the exact same performance as that of artificial neural networks, surpassing previous spiking neural networks. Our approach accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation. We also show that fine-tuning spiking neural networks with our robust gradient descent algorithm enables their optimization for hardware implementations with low latency and resilience to noise and quantization.

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

Comprehensive profiling of lncRNAs in the immune response of largemouth bass to Nocardia Seriolae infection

Long noncoding RNAs (lncRNAs) are crucial regulators of various biological processes. However, it remains unclear how lncRNAs can be involved in the immune response to Nocardia seriolae invasion in largemouth bass (Micropterus salmoides). Here we leveraged the whole transcriptome RNA-seq technique to screen lncRNAs associated with the head kidney infected with Nocardia seriolae. A total of 5,661 lncRNAs were identified, comprising 2,427 known lncRNAs and 3,234 novel lncRNAs. Among these, 531 lncRNAs were found to be differentially expressed, with 213 showing increased expression and 318 showing reduced expression. In these differentially expressed lncRNAs, 176 lncRNAs may have cis-regulatory relationships with 243 protein-coding genes. For example, MSTRG.17818.1, MSTRG.10160.1 target mapk1 and rela, respectively, thereby regulating gene expression. We then exploited Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the enrichment of target genes of lncRNAs. These target genes were found to be significantly enriched in immune signaling pathways, including RIG-I-like receptor signaling and Toll-like receptor signaling. Furthermore, we harnessed a total of 12 mRNAs, 33 miRNAs, and 167 lncRNAs to construct two lncRNA-miRNA-mRNA network maps. CeRNA maps revealed that some lncRNAs can competitively bind to the same miRNA and affect the expression of immune-related genes. For instance, MSTRG.13668.1 and MSTRG.5180.1 can simultaneously bind to mir-155–5p to increase irak4 expression. These findings suggest that lncRNAs may participate in the immune response process of infected largemouth bass with Nocardia seriolae. Overall, this work presents a thorough lncRNAs expression profile of Nocardia seriolae-infected largemouth bass and offers insights into the ceRNA regulatory processes that influence fish's immune response to bacterial infections.

LncRNA PCIF1 promotes aerobic glycolysis in A549/DDP cells by competitively binding miR-326 to regulate PKM expression

ObjectiveUtilizing transcriptome analysis to investigate the mechanisms and therapeutic approaches for cisplatin resistance in non-small cell lung cancer (NSCLC). MethodsFirstly, the biological characters of A549 cells and A549/DDP cells were detected by RNA sequencing, CCK-8 and hippocampal energy analyzer. Then, the differential Genes were functionally enriched by GO and KEGG and the competitive endogenous RNA network map was constructed. Finally, the effects of the predicted biogenesis pathway on the biological functions of A549/DDP cells were verified by in vitro and in vivo experiments. ResultThe differentially transcribed genes of A549 and A549/DDP cells were analyzed by enrichment analysis and cell biological characteristics detection. The results showed that A549/DDP cells showed significantly increased resistance to cisplatin, glucose metabolism signaling pathway and glycolysis levels compared with A549 cells. Among glycolysis-related transcription genes, PKM had the most significant difference Fold Change is 8. LncRNA PCIF1 is a new marker of A549/DDP cells and can be used as a molecular sponge to regulate the expression of PKM. LncRNA PCIF1 targets miR-326 to induce PKM expression, promote glycolysis level, and enhance the resistance of A549/DDP cells to cisplatin. ConclusionLncRNA PCIF1 as biomarkers of A549/DDP cells, higher expression can induce the PKM, promote cell glycolysis, lead to the occurrence of cisplatin resistance. LncRNA PCIF1 can be considered as a potential target for treating cisplatin-resistant NSCLC.

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km−2 to 5.5 km km−2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

Robust differential gene expression patterns in the prefrontal cortex of male mice exposed to an occupationally relevant dose of laboratory-generated wildfire smoke.

Wildfires have become common global phenomena concurrent with warmer and drier climates and are now major contributors to ambient air pollution worldwide. Exposure to wildfire smoke has been classically associated with adverse cardiopulmonary health outcomes, especially in vulnerable populations. Recent work has expanded our understanding of wildfire smoke toxicology to include effects on the central nervous system and reproductive function; however, the neurotoxic profile of this toxicant remains ill-explored in an occupational context. Here, we sought to address this by using RNA sequencing to examine transcriptomic signatures in the prefrontal cortex of male mice modeling career wildland firefighter smoke exposure. We report robust changes in gene expression profiles between smoke-exposed samples and filtered air controls, evidenced by 2,862 differentially expressed genes (51.2% increased). We further characterized the functional relevance of these genes highlighting enriched pathways related to synaptic transmission, neuroplasticity, blood-brain barrier integrity, and neurotransmitter metabolism. Additionally, we identified possible contributors to these alterations through protein-protein interaction network mapping, which revealed a central node at ß-catenin and secondary hubs centered around mitochondrial oxidases, the Wnt signaling pathway, and gene expression machinery. The data reported here will serve as the foundation for future experiments aiming to characterize the phenotypic effects and mechanistic underpinnings of occupational wildfire smoke neurotoxicology.

Research Landscape of Filipino Mathematicians: a Co-authorship Network Analysis

Research collaboration presents solutions to various problems that independent researchers might encounter; however, establishing collaboration also poses challenges for most of these researchers. In this study, we created a co-authorship network map of Filipino mathematicians using Elsevier's Scopus database and identified the top five institutions with the largest number of Filipino mathematicians, each with at least one publication in Scopus. We found that among the top three institutions, collaborations rarely occur, with less than 1% distinct inter-institutional collaborations. Various network analysis metrics were performed on the map to establish a baseline for future co-authorship network research. Mathematicians were ranked using both the PageRank algorithm and betweenness centrality to highlight important and bridging figures, respectively. We found that while Canoy SR of Mindanao State University-Iligan Institute of Technology (k = 61), Corcino RB of Cebu Normal University (k = 39), and Mendoza ER of De La Salle University (k = 27) have notable contributions, the University of the Philippines Diliman (UPD) stands out with the most mathematicians having k ≥ 10, where k is the measure of degree centrality. The Leiden community detection algorithm was employed to cluster the network map, revealing 183 distinct clusters – indicating the number of cliques, which are small, exclusive research groups. This highlights the need to strengthen collaborative efforts among Philippine institutions. Interestingly, we found that UPD encompasses a broad range of mathematics research, indicating the potential to serve as a bridge connecting different subject areas. By illustrating the research landscape, this study sheds light on Filipino mathematicians' research, contributing to the enhancement of mathematics research productivity and collaboration in the country.

A lightweight security defense algorithm for multimodal attack blocking in industrial control networks based on momentum enhanced feature graphs

AbstractAs the infrastructure of modern industry, the safe and stable operation of lightweight industrial control network is crucial for industrial production. Multi‐mode attack is a kind of highly customized and low resource consumption network attack method, which can target the specific vulnerabilities and configuration weaknesses of lightweight industrial control network. In order to improve the defense capability of industrial control network against multimodal attacks, a lightweight security defense algorithm based on momentum‐enhanced feature graph is proposed. The lightweight industrial control network operation mode is determined by abstract relationship, and the ontology structure of industrial control network multi‐mode attack is established; with reference to the ontology structure, the multi‐mode attack map of industrial control network is defined; the normal behavior and lightweight attack behavior are divided, and the convolutional network is used to enhance the characteristics of lightweight attack; and the momentum‐enhanced feature map technology is used to correspond to the enhancement process, and to block and defend against the multi‐mode attack of industrial control network. The results show that the studied algorithm can realize accurate prediction of attack behaviors with different attack behaviors as test objects, and ensure accurate blocking of all kinds of attack behaviors with better security defense effect.

Fluorescent Pdots Facilitate High-Resolution Mapping of the Intact Meningeal Vascular Network and Eye-Brain Connections.

Meningeal vascular network is significant in neurology and neurosurgery. However, high-resolution imaging of intact meningeal vascular network is lacking. In this work, we develop a practical experimental method to ensure that the intact meninges are morphologically unfolded and fixed in an agarose gel. With the help of high-brightness polymer dots (Pdots) as probe, macroscopic and detailed imaging of the vascular network on the intact dorsal meninges can be performed. Meningeal vessels are symmetrically distributed along the superior sagittal sinus, and the distribution of meningeal vessels had a certain degree of hierarchy. The meninges are thicker blood vessels and capillary networks from the outside to the inside. Moreover, the diameter of the capillaries is 3.96 ± 0.89 μm. Interestingly, meningeal primo vessels in the central nervous system of mice is imaged with the diameter of 4.18 ± 1.18 μm, which has not been reported previously. It is worth mentioning that we found that orthotopic xenografts of brain tumors caused the appearance of corneal neovascularization and morphological changes in optic nerve microvessels. In conclusion, our work provides an effective Pdots-based imaging method for follow-up research on meningeal vascular-related diseases, and illustrates that the eye can serve as a window for the prevention and diagnosis of brain diseases.

Automatic road network selection method considering functional semantic features of roads with graph convolutional networks

Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.

Network Pharmacology Combined with GEO Analysis of the Mechanism of Qing-Jin-Hua-Tan Decoction in the Treatment of Non-small Cell Lung Cancer.

Non-small-cell lung cancer (NSCLC) is one of the most prevalent malignancies and poses a significant threat to human health. Qing-Jin-Hua-Tan (QJHT) decoction is a classical herbal remedy that has demonstrated therapeutic effects in various diseases, including NSCLC, and can improve the quality of life of patients with respiratory conditions. However, the mechanism underlying the effect of the QJHT decoction on NSCLC remains unclear and requires further investigation. We collected NSCLC-related gene datasets from the GEO database and performed differential gene analysis, followed by using WGCNA to identify the core set of genes associated with NSCLC development. The TCMSP and HERB databases were searched to identify the active ingredients and drug targets, and the core gene target datasets related to NSCLC were merged to identify the intersecting targets of drugs and diseases for GO and KEGG pathway enrichment analysis. We then constructed a protein-protein interaction (PPI) network map of drug diseases using the MCODE algorithm and identified key genes using topology analysis. The disease-gene matrix underwent immunoinfiltration analysis, and we analyzed the association between intersecting targets and immunoinfiltration. We obtained the GSE33532 dataset that met the screening criteria, and a total of 2211 differential genes were identified using differential gene analysis. We performed GSEA analysis and WGCNA analysis for a crossover with differential genes, resulting in 891 key targets for NSCLC. The drug database was screened to obtain 217 active ingredients and 339 drug targets of QJHT. By constructing a PPI network, the active ingredients of QJHT decoction were intersected with the targets of NSCLC, resulting in 31 intersected genes. Enrichment analysis of the intersection targets showed that 1112 biological processes, 18 molecular functions, and 77 cellular compositions were enriched in GO functions, and 36 signaling pathways were enriched in KEGG pathways. Based on immune-infiltrating cell analysis, we found that the intersection targets were significantly associated with multiple infiltrating immune cells. Our analysis using network pharmacology and mining of the GEO database revealed that QJHT decoction can potentially treat NSCLC through multi-target and multi-signaling pathways, while also regulating multiple immune cells.

Characterizing interactions of endoplasmic reticulum resident proteins in situ through the YST-PPI method.

The mutual interactions of endoplasmic reticulum (ER) resident proteins in the ER maintain its functions, prompting the protein folding, modification, and transportation. Here, a new method, named YST-PPI (YESS-based Split fast TEV protease system for Protein-Protein Interaction) was developed, targeting the characterization of protein interactions in ER. YST-PPI method integrated the YESS system, split-TEV technology, and endoplasmic reticulum retention signal peptide (ERS) to provide an effective strategy for studying ER in situ PPIs in a fast and quantitative manner. The interactions among 15 ER-resident proteins, most being identified molecular chaperones, of S. cerevisiae were explored using the YST-PPI system, and their interaction network map was constructed, in which more than 74 interacting resident protein pairs were identified. Our studies also showed that Lhs1p plays a critical role in regulating the interactions of most of the ER-resident proteins, except the Sil1p, indicating its potential role in controlling the ER molecular chaperones. Moreover, the mutual interaction revealed by our studies further confirmed that the ER-resident proteins perform their functions in a cooperative way and a multimer complex might be formed during the process.

A Study Against Colon Cancer Mechanism of Xanthium sibiricum Herba Based on Computer Simulation and Bioinformatics.

Cancer is one of the leading causes of death worldwide, accounting for nearly one in six deaths in 2020. As a folk medicine, Xanthium sibiricum Herba (XSH) has been used many times in clinical practice for the treatment of various diseases. With the increasing number of cancer patients, there is a clinical need to find effective anti-cancer drugs. This study aims to explores the bioactivity and the anti-cancer mechanism of XSH. In this study, bioinformatics, network pharmacology, molecular docking, molecular dynamics simulation techniques, and apoptosis assay were used to explore the bioactivity and the anti- cancer mechanism of XSH. Finally, seven active ingredients in XSH after the screening were obtained, the two most active compounds were β-sitosterol and aloe-emodin, and good anti-cancer activity of XSH was predicted. Four core targets were obtained from the PPI network map, namely Caspase-3 (CASP3), Transcription factor AP-1 (JUN), Myc proto-oncogene protein (MYC), and cellular tumor antigen p53 (TP53). GO and KEGG analyses showed that the mechanism of XSH anti-cancer is mainly related to the apoptosis process, and the main signaling pathways are enriched in the p53 signaling pathway, Apoptosis, and MAPK signaling. The molecular docking and molecular dynamics simulation results showed that CASP3, JUN, MYC, and TP53 had a high affinity with β- sitosterol and aloe-emodin. Bioinformatics analyses demonstrated the importance of core targets. Apoptosis assay showed that XSH could significantly promote the apoptosis of cancer cells, and inhibit their proliferation and migration, especially colon cancer cells. This study uncovered the main active components, bioactivities, and potential targets of XSH, and further revealed the multi-component, multi-target, and multi-pathway mechanism of XSH for cancer treatment and promoting apoptosis.

Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.

Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically-annotated medical images. Many two-dimensional pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain Positron Emission Transmission (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using two-dimensional randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6,000-20,000 trainable parameters) achieved a test mean-absolute-error of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare "classical" and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The mean-absolute-error fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g. DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest mean-absolute-error at test time (~0.45 - 0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.