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The effect of point cloud denoising is very important to the subsequent surface fitting and modeling design in 3D scanning process. How to extract feature points quickly and accurately has become a research hotspot.However,the key point of point cloud denoising lies in the detection of singular values and outliers. Therefore,a denoising model with coupled multi-feature point parameters is proposed,and the influence of each feature point parameters on the denoising model are discussed respectively. The swarm intelligence algorithm is used to solve a set of optimal parameter weights to determine the point cloud denoising model,so as to achieve the optimal denoising effect of three-dimensional scattered point clouds. The denoising simulation of Bunny model and the denoising experiment of a certain type of skin are used to verify the denoising model. The results show that the point cloud denoising model proposed in this paper has faster iteration,less time-consuming and better denoising effect than that of radius filter,statistical filter and improved voxel filter combined with Gaussian filtering algorithm.

Abstract Huangjiu, a traditional Chinese wine with low alcoholic strength, can easily develop rancidification upon microbial contamination in the long‐term storage process. In order to analyze the changes in key indexes in the rancidification process during the storage of Huangjiu, a laboratory simulation of microbial contamination changes was carried out. Changes in microbiological indexes, physicochemical indexes, and volatile flavor compounds in the rancidification process of Huangjiu can be divided into two stages. Test results demonstrated that within the early stage of the rancidification process, multiplication of contaminating microorganisms was in the adaptation phase with a slow OD600 growth value of Huangjiu, while physicochemical indexes such as the pH and amino acid nitrogen content changed relatively slowly. The contents of aldehyde compounds in the volatile flavor components of Huangjiu declined quickly to be lower than 1.0 mg/L, while the conductivity index increased by 6%. In the late stage, the OD600 value of Huangjiu increased rapidly and microorganism multiplication entered the logarithmic phase. Furthermore, changes in the physicochemical indexes are accelerated. Specifically, the histamine content increased by 457% and the content of aldehydes remains lower than 1.0 mg/L. The conductivity index continued to rise by 25% in this stage. This indicates that the two rancidification stages have different influences on the quality of Huangjiu. The conductivity value can be used as a staged representative index throughout the rancidification of Huangjiu to distinguish between different batches and evaluate the degree of microbial contamination. Additionally, the conductivity index can be used for long‐term online monitoring of large tank storage of Huangjiu.

Optimal time selection for inverse synthetic aperture radar imaging of ship target is significant for the determination of image project plane, and the performance of it is dependent on the accuracy of ship centerline extraction. In this article, a novel optimal time selection method based on the random sample consensus (RANSAC) technique is proposed, which can extract the ship centerline with high accuracy in the case of ship superstructure and low signal to noise ratio. First, a necessary distance threshold is determined for the RANSAC algorithm combined with the ship body width estimation. Then, a novel cost function is proposed according to the amplitude characteristic of radar image, which can alleviate the influence of randomness. Finally, the robustness of the optimal time selection method is demonstrated by the results of simulated and real measured data.

BACKGROUND: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. METHODS: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice. FINDINGS: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. INTERPRETATION: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC. FUNDING: The U.S. Department of Defense (DoD) Breast Cancer Research Program (BCRP) (BC190820), United States; MCIN/AEI/10.13039/501100011039 (PID2020-118527RB-I00, PDC2021-121735-I00), the European Union Next Generation EU/PRTR, the Regional Government of Castile and León (CSI144P20), European Union.

The insights into interactions between host genetics and gut microbiome (GM) in colorectal tumor susceptibility (CTS) remains lacking. We used Collaborative Cross mouse population model to identify genetic and microbial determinants of Azoxymethane-induced CTS. We identified 4417 CTS-associated single nucleotide polymorphisms (SNPs) containing 334 genes that were transcriptionally altered in human colorectal cancers (CRCs) and consistently clustered independent human CRC cohorts into two subgroups with different prognosis. We discovered a set of genera in early-life associated with CTS and defined a 16-genus signature that accurately predicted CTS, the majority of which were correlated with human CRCs. We identified 547 SNPs associated with abundances of these genera. Mediation analysis revealed GM as mediators partially exerting the effect of SNP UNC3869242 within Duox2 on CTS. Intestine cell-specific depletion of Duox2 altered GM composition and contribution of Duox2 depletion to CTS was significantly influenced by GM. Our findings provide potential novel targets for personalized CRC prevention and treatment.

BACKGROUND: The understanding of bile acid (BA) and unsaturated fatty acid (UFA) profiles, as well as their dysregulation, remains elusive in individuals with type 2 diabetes mellitus (T2DM) coexisting with non-alcoholic fatty liver disease (NAFLD). Investigating these metabolites could offer valuable insights into the pathophy-siology of NAFLD in T2DM. AIM: To identify potential metabolite biomarkers capable of distinguishing between NAFLD and T2DM. METHODS: A training model was developed involving 399 participants, comprising 113 healthy controls (HCs), 134 individuals with T2DM without NAFLD, and 152 individuals with T2DM and NAFLD. External validation encompassed 172 participants. NAFLD patients were divided based on liver fibrosis scores. The analytical approach employed univariate testing, orthogonal partial least squares-discriminant analysis, logistic regression, receiver operating characteristic curve analysis, and decision curve analysis to pinpoint and assess the diagnostic value of serum biomarkers. RESULTS: Compared to HCs, both T2DM and NAFLD groups exhibited diminished levels of specific BAs. In UFAs, particular acids exhibited a positive correlation with NAFLD risk in T2DM, while the ω-6:ω-3 UFA ratio demonstrated a negative correlation. Levels of α-linolenic acid and γ-linolenic acid were linked to significant liver fibrosis in NAFLD. The validation cohort substantiated the predictive efficacy of these biomarkers for assessing NAFLD risk in T2DM patients. CONCLUSION: This study underscores the connection between altered BA and UFA profiles and the presence of NAFLD in individuals with T2DM, proposing their potential as biomarkers in the pathogenesis of NAFLD.

Increasing evidence shows the oncogenic function of FAM83D in human cancer, but how FAM83D exerts its oncogenic function remains largely unclear. Here, we investigated the importance of FAM83D/FBXW7 interaction in breast cancer (BC). We systematically mapped the FBXW7-binding sites on FAM83D through a comprehensive mutational analysis together with co-immunoprecipitation assay. Mutations at the FBXW7-binding sites on FAM83D led to that FAM83D lost its capability to promote the ubiquitination and proteasomal degradation of FBXW7; cell proliferation, migration, and invasion in vitro; and tumor growth and metastasis in vivo, indicating that the FBXW7-binding sites on FAM83D are essential for its oncogenic functions. A meta-evaluation of FAM83D revealed that the prognostic impact of FAM83D was independent on molecular subtypes. The higher expression of FAM83D has poorer prognosis. Moreover, high expression of FAM83D confers resistance to chemotherapy in BCs, which is experimentally validated in vitro. We conclude that identification of FBXW7-binding sites on FAM83D not only reveals the importance for FAM83D oncogenic function, but also provides valuable insights for drug target.

BACKGROUND: Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment. METHODS: We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit Hs (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels. RESULTS: We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression. CONCLUSIONS: The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

High dose rate radiation has gained considerable interest recently as a possible avenue for increasing the therapeutic window in cancer radiation treatment. The sparing of healthy tissue at high dose rates relative to conventional dose rates, while maintaining tumor control, has been termed the FLASH effect. Although the effect has been validated in animal models using multiple radiation sources, it is not yet well understood. Here, we demonstrate a new experimental platform for quantifying oxidative damage to protein sidechains in solution as a function of radiation dose rate and oxygen availability using liquid chromatography mass spectrometry. Using this reductionist approach, we show that for both X-ray and electron sources, isolated peptides in solution are oxidatively modified to different extents as a function of both dose rate and oxygen availability. Our method provides an experimental platform for exploring the parameter space of the dose rate effect on oxidative changes to proteins in solution.

Hematologic toxicity is a common side effect of multimodal cancer therapy. Nearly all animal studies investigating the causes of radiotherapy-induced hematologic toxicity use inbred strains with limited genetic diversity and do not reflect the diverse responses observed in humans. We used the population-based Collaborative Cross (CC) mouse resource to investigate the genetic architecture of the acute and persistent immune response after radiation exposure by measuring 22 immune parameters in 1,720 CC mice representing 35 strains. We determined relative acute and persistent radiation resistance scores at the individual strain level considering contributions from all immune parameters. Genome-wide association analysis identified quantitative trait loci associated with baseline and radiation responses. A cross-species radiation resistance score predicted recurrence-free survival in medulloblastoma patients. We present a community resource of immune parameters and genome-wide association analyses before and after radiation exposure for future investigations of the contributions of host genetics on radiosensitivity.

Identification of the core functional microorganisms in food fermentations is necessary to understand the ecological and functional processes for making those foods. Wheat qu, which provides liquefaction and saccharifying power, and affects the flavor quality, is a key ingredient in ancient alcoholic huangjiu fermentation, while core microbiota of them still remains indistinct. In this study, metagenomics, metabolomics, microbial isolation and co-fermentation were used to investigate huangjiu. Although Aspergillus is usually regarded as core microorganism in wheat qu to initiate huangjiu fermentations, our metagenomic analysis showed that bacteria Saccharopolyspora are predominant in wheat qu and responsible for breakdown of starch and cellulose. Metabolic network and correlation analysis showed that Saccharopolyspora rectivirgula, Saccharopolyspora erythraea, and Saccharopolyspora hirsuta made the greatest contributions to huangjius metabolites, consisting of alcohols (phenylethanol, isoamylol and isobutanol), esters, amino acids (Pro, Arg, Glu and Ala) and organic acids (lactate, tartrate, acetate and citrate). S. hirsuta J2 isolated from wheat qu had the highest amylase, glucoamylase and protease activities. Co-fermentations of S. hirsuta J2 with S. cerevisiae HJ resulted in a higher fermentation rate and alcohol content, and huangjiu flavors were more similar to that of traditional huangjiu compared to co-fermentations of Aspergillus or Lactiplantibacillus with S. cerevisiae HJ. Genome of S. hirsuta J2 contained genes encoding biogenic amine degradation enzymes. By S. hirsuta J2 inoculation, biogenic amine content was reduced by 45%, 43% and 62% in huangjiu, sausage and soy sauce, respectively. These findings show the utility of Saccharopolyspora as a key functional organism in fermented food products.

Microbacterium sp. BDGP8 is a species of facultative anaerobic gram-positive bacterium of the family Microbacteriaceae. The complete genome consists of a single circular chromosome of 3,293,567 bp with a G + C content of 69.84% and two plasmids of 49,365 bp and 32,884 bp.

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

Starting in the 1970s, individuals, businesses and the public have increasingly benefited from policies prohibiting smoking indoors, saving thousands of lives and billions of dollars in healthcare expenditures. Smokefree policies to protect against secondhand smoke exposure, however, do not fully protect the public from the persistent and toxic chemical residues from tobacco smoke (also known as thirdhand smoke) that linger in indoor environments for years after smoking stops. Nor do these policies address the economic costs that individuals, businesses and the public bear in their attempts to remediate this toxic residue. We discuss policy-relevant differences between secondhand smoke and thirdhand smoke exposure: persistent pollutant reservoirs, pollutant transport, routes of exposure, the time gap between initial cause and effect, and remediation and disposal. We examine four policy considerations to better protect the public from involuntary exposure to tobacco smoke pollutants from all sources. We call for (a) redefining smokefree as free of tobacco smoke pollutants from secondhand and thirdhand smoke; (b) eliminating exemptions to comprehensive smoking bans; (c) identifying indoor environments with significant thirdhand smoke reservoirs; and (d) remediating thirdhand smoke. We use the case of California as an example of how secondhand smoke-protective laws may be strengthened to encompass thirdhand smoke protections. The health risks and economic costs of thirdhand smoke require that smokefree policies, environmental protections, real estate and rental disclosure policies, tenant protections, and consumer protection laws be strengthened to ensure that the public is fully protected from and informed about the risks of thirdhand smoke exposure.

IntroductionGut microbiome (GM) deregulation has been implicated in major conditions such as obesity and type 2 diabetes (T2DM). Our previous prospective study indicated that fecal microbiota transplantation (FMT) successfully improved patients with T2DM. We hypothesized that FMT may be a potential therapeutic method for T2DM, but its precise mechanisms in T2DM remains to be elucidated.Research design and methodsEight db/m mice were FMT donors and control mice, and 16 genetically diabetic db/db mice were equally divided into two groups (db/db+phosphate-buffered saline (PBS) group, db/db+FMT group). The db/db+FMT group was administered fresh fecal suspension (0.2 mL/mice) daily for 4 weeks. Analysis of the GM and serum metabolome was carried out by 16S ribosomal RNA sequencing and liquid chromatogram-mass spectrometry, respectively. Effects of FMT on the gut barrier and pancreas were assessed using protein assays, messenger RNA, immunohistology and clinical indicators testing.ResultsOur results showed that FMT treatment of db/db mice relieves a series of clinical indicators, including fasting plasma glucose, serum insulin and oral glucose tolerance test among others. Compared with non-diabetic control mice, db/db+PBS mice exhibited decreased abundance of Ruminococaceae, Porphyromonadaceae and increased abundance of Rikenellaceae and Lactobacillaceae. FMT treatment reversed this effect on the microbiome. Eleven metabolites were changed between the db/db+PBS and db/db+FMT groups. Correlation analysis showed that the structural changes of the GM were correlated with host metabolite levels. We further showed that FMT treatment of db/db mice improved intestinal barrier function, reduced inflammation and caused an alteration in the number of circulating immune cells.ConclusionsFMT-mediated changes in the GM, serum metabolites, intestinal epithelial barrier, inflammation and circulating immune cells play an important role in the efficacy of FMT on T2DM disease progression.

Increasing evidence has shown that thirdhand smoke (THS) exposure is likely to induce adverse health effects. An important knowledge gap remains in our understanding of THS exposure related to cancer risk in the human population. Population-based animal models are useful and powerful in investigating the interplay between host genetics and THS exposure on cancer risk. Here, we used the Collaborative Cross (CC) mouse population-based model system, which recapitulates the genetic and phenotypic diversity observed in the human population, to assess cancer risk after a short period of exposure, between 4 and 9 weeks of age. Eight CC strains (CC001, CC019, CC026, CC036, CC037, CC041, CC042 and CC051) were included in our study. We quantified pan-tumor incidence, tumor burden per mouse, organ tumor spectrum and tumor-free survival until 18 months of age. At the population level, we observed a significantly increased pan-tumor incidence and tumor burden per mouse in THS-treated mice as compared to the control (p = 3.04E-06). Lung and liver tissues exhibited the largest risk of undergoing tumorigenesis after THS exposure. Tumor-free survival was significantly reduced in THS-treated mice compared to control (p = 0.044). At the individual strain level, we observed a large variation in tumor incidence across the 8 CC strains. CC036 and CC041 exhibited a significant increase in pan-tumor incidence (p = 0.0084 and p = 0.000066, respectively) after THS exposure compared to control. We conclude that early-life THS exposure increases tumor development in CC mice and that host genetic background plays an important role in individual susceptibility to THS-induced tumorigenesis. Genetic background is an important factor that should be taken into account when determining human cancer risk of THS exposure.

Aiming at the poor noise robustness of traditional Canny algorithm and the defect of false edge or edge loss, an edge detection algorithm using statistical algorithm for filtering denoising and using genetic algorithm to determine the optimal high and low threshold of image segmentation is proposed. Firstly, statistical filtering uses mean and variance to denoise, avoiding the problem of Gaussian denoising susceptible to interference in the traditional Canny algorithm, and ensuring the integrity of image edge information. Secondly, this article uses the genetic algorithm, design the crossover operator and genetic operator to modify the evolution of the population, and determine the optimal height threshold of the image edge connection to make the threshold more accurate. Finally, using MATLAB software to simulate, the results show that the improved Canny edge detection algorithm can further improve the anti-noise ability and robustness, and the edge location is more accurate.

Using the traditional Ant Colony Algorithm for AGV path planning is easy to fall into the local optimal solution and lacking the capability of obstacle avoidance in the complex storage environment. In this paper, by constructing the MAKLINK undirected network routes and the heuristic function is optimized in the Ant Colony Algorithm, then the AGV path reaches the global optimal path and has the ability to avoid obstacles. According to research, the improved Ant Colony Algorithm proposed in this paper is superior to the traditional Ant Colony Algorithm in terms of convergence speed and the distance of optimal path planning.

Aiming at the target detection of remote sensing rice field of uav, the image of large-size uav is firstly segmented, and the type of each image is manually identified, and the image training set and verification set are made. Then, the training model of convolutional neural network is realized by python programming. The advantage and disadvantage of the two-layer convolutional neural network and ResNet50 are compared, and it is found that the training set is less and the picture feature complexity is not high in practical application. In the end, the feature recognition of rice field is realized, which has certain application value.

Most traditional methods for detecting the quality of tiny particles are manually detected by measurement tools. Manual detection has the limitations of low efficiency, high false detection rate and low detection accuracy, and is gradually replaced by non-contact measurement. In this paper, a new denoising algorithm based on median and mean filtering is proposed in diamond particle image analysis. Dynamic threshold segmentation is combined with the Canny algorithm for edge extraction. In the edge extraction process of the particle edge, it will lead to non-closed problems. A contour edge location method based on Hough transform is proposed. Thereby, parameters such as particle size, roundness and ellipticity of the diamond particles are measured.

Aiming at the low degree of automation in production enterprises, real-time tracking and automatic access to warehouses are realized by developing warehouse management software. The paper first analyzes the system requirements, and then gives the overall design plan, through C#, MySQL and TCP. IP communication protocol, compiled a set of warehouse management system software. The actual application shows that the automation level and management efficiency of production enterprises are improved.

The traditional filtering methods such as median filter and mean filter always blurrs image features, resulting in poor noise reduction effect. Wavelet transform has unique adaptability due to its variable resolution, which can better implement wavelet denoising on the basis of image feature. Aiming at the shortcoming of traditional wavelet transform threshold denoising, based on the hard threshold and soft threshold function, this paper proposes improved adaptive thresholding function. By comparing and validating, this method obtains the smaller mean square error (MSE) and higher peak signal to noise ratio. Meanwhile, this method improves the quality of detection images, and reduces the impact on images brought by noise from external enviroment and internal system. So, this can be applied to image noise reduction of the detection system.

With the rapid development of the logistics industry, high-speed, high-efficiency and accurate delivery have become the new pursuit goals of the logistics industry, and intelligent logistics has been favored by enterprises. Aiming at the current situation that the offline products must be manually moved, sorted and discharged, the scheme of fully automated three-dimensional warehouse is proposed. Through the design of the whole three-dimensional warehouse, the whole process of the offline products can be realized by AGV, AGV management system, warehouse management system and roller. Lines, palletizers, etc. are completed. As the core part of the whole solution, the warehouse management system plays a vital role in achieving point-to-point tracking of offline products, and can perform regular statistical analysis on the inbound and outbound products to achieve digital management, improve the competitiveness of enterprises and bring considerable economic benefits.

BackgroundLower-grade gliomas (LGG) are heterogeneous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.MethodsCellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5-year overall survival (OS) of LGG patients. Tumor mutational burden (TMB) and immune cell infiltration between subtypes were analyzed using the Mann-Whitney U test. The double-blinded validation for important immunotherapy-related biomarkers was executed using immunohistochemistry (IHC).ResultsWe developed a machine learning (ML) pipeline to extract CMBs from whole-slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multicenter cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by IHC staining. In addition, the subtypes learned from LGG demonstrate translational impact on glioblastoma (GBM).ConclusionsWe developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune microenvironment, prognosis, and treatment response.

AIM: We aimed to identify the ability of serum bile acids (BAs) and unsaturated fatty acids (UFAs) profiles to predict the development of diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) patients. METHODS: We first used univariate and multivariate analysis to compare 15 serum BA and 11 UFA levels in healthy control (HC) group (n = 82), T2DM patients with DR (n = 58) and T2DM patients without DR (n = 60). Forty T2DM patients were considered for validation. Then, the receiver operating characteristic curve (ROC) and decision curve analysis were used to assess the diagnostic value and clinical benefit of serum biomarkers alone, clinical variables alone or in combination, and the area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used to further assess whether the addition of biomarkers significantly improved the predictive ability of the model. RESULTS: Orthogonal partial least squares-discriminant analysis (OPLS-DA) of serum BAs and UFAs separated the three cohorts including HC, T2DM patients with or without DR. The difference in serum BA and UFA profiles of T2DM patients with or without DR was mainly manifested in the three metabolites of taurolithocholic acid (TLCA), tauroursodeoxycholic acid (TUDCA) and arachidonic acid (AA). Together, they had an AUC of 0.785 (0.918 for validation cohort) for predicting DR in T2DM patients. After adjusting for numerous confounding factors, TLCA, TUDCA, and AA were independent predictors that differentiated T2DM with or without DR. The results of AUC, IDI, and NRI demonstrated that adding these three biomarkers to a model with clinical variables statistically increased their predictive value and were replicated in our independent validation cohort. CONCLUSION: These findings highlight the association of three metabolites, TLCA, TUDCA and AA, with DR and may indicate their potential value in the pathogenesis of DR.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival, and other prognostic endpoints in multiple cancer types, including lower-grade gliomas (LGG), breast invasive carcinoma (BRCA), as well as many others. We also showed significant association between MNAI and genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Our association analysis revealed that patients with high MNAI benefitted more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated that multimodal integration of MNAI and the AI-empowered Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to using MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

Evidence showing a relationship between the mouse gut microbiome and properties such as phenotype and reaction to therapeutic agents and other treatments has increased significantly over the past 20 to 30 y. Recent concerns regarding the reproducibility of animal experiments have underscored the importance of understanding this relationship and how differences in husbandry practices can affect the gut microbiome. The current study focuses on effects of different barrier practices in 2 barrier facilities at the same institution on the fecal microbiome of breeding C57Bl/6J mice. Ten female and 10 male C57Bl/6J mice were obtained in one shipment from Jackson Laboratories and were housed under different barrier conditions upon arrival. Fecal samples were collected on arrival and periodically thereafter and were sent to TransnetYX for microbiome analysis. Mice used for collection of feces were housed as breeding pairs, with a total of 5 breeding pairs per barrier. An additional fecal sample was collected from these mice at 8 wk after arrival. One F1 female and one F1 male from each breeding cage were housed as brother-sister breeding pairs and a fecal sample was collected from them at 8 wk of age. Brother-sister breeding colonies were continued through F3, with fecal samples for microbiome analysis were collected from each generation at 8 wk of age. Breeding colonies in the 2 barriers showed differences in relative abundance, α -diversity, and β -diversity. Our data indicate that differences in barrier husbandry practices, including the use of autoclaved cages, the degree of restricted access, feed treatment practices, and water provision practices, can affect fecal microbiome divergence in both the parental and filial generations of different breeding colonies. To our knowledge, this is the first study to examine the effect of barrier husbandry practices on the microbiome of breeding colonies through the F3 generation.

Abstract.: The purpose of the current study is to evaluate the cardioprotective effects of purified Salvia miltiorrhiza extract (PSME) on myocardial ischemia/reperfusion injury in isolated rat hearts. Hearts were excised and perfused at constant flow (7 – 9 ml min−1) via the aorta. Non-recirculating perfusion with Krebs-Henseleit (KH) solution was maintained at 37°C and continuously gassed with 95% O2 and 5% CO2. KH solution with or without PSME (100 mg per liter solution) was used after 30-min zero-flow ischemia for the PSME and control group, respectively. Left ventricular (LV) developed pressure; its derivatives, diastolic pressure, and so on were continuously recorded via a pressure transducer attached to a polyvinylchloride balloon that was placed in the left ventricle through an incision in the left atrium. PSME treated hearts showed significant postischemic contractile function recovery (developed pressure recovered to 44.2 ± 4.9% versus 17.1 ± 5.7%, P

BackgroundThe effect of chemical exposure on obesity has raised great concerns. Real-world chemical exposure always imposes mixture impacts, however their exposure patterns and the corresponding associations with obesity have not been fully evaluated.ObjectivesTo discover obesity-related mixed chemical exposure patterns in the general U.S.PopulationMethodsSparse Decompositional Regression (SDR), a model adapted from sparse representation learning technique, was developed to identify exposure patterns of chemical mixtures with exclusion (non-targeted model) and inclusion (targeted model) of health outcomes. We assessed the relationships between the identified chemical mixture patterns and obesity-related indexes. We also conducted a comprehensive evaluation of this SDR model by comparing to the existing models, including generalized linear regression model (GLM), principal component analysis (PCA), and Bayesian kernel machine regression (BKMR).ResultsEight core exposure patterns were identified using the non-targeted SDR model. Patterns of high levels of MEP, high levels of naphthalene metabolites (ΣOH-Nap), and a pattern of high exposure levels of MCOP, MCNP, and MCPP were positively associated with obesity. Patterns of high levels of BP3, and a pattern of higher mixed levels of MPB, PPB, and MEP were found to have negative associations. Associations were strengthened using the targeted SDR model. In the single chemical analysis by GLM, BP3, MBP, PPB, MCOP, and MCNP showed significant associations with obesity or body indexes. The SDR model exceeded the performance of PCA in pattern identification. Both SDR and BKMR identified a positive contribution of ΣOH-Nap and MCOP, as well as a negative contribution of BP3 and PPB to obesity.ConclusionOur study identified five core exposure patterns of chemical mixtures significantly associated with obesity using the newly developed SDR model. The SDR model could open a new avenue for assessing health effects of environmental mixture contaminants.