Explore the vast range of titles available.

Industrial computed tomography (CT) is widely used in the measurement field owing to its advantages such as non-contact and high precision. To obtain accurate size parameters, fitting parameters can be obtained rapidly by processing volume data in the form of point clouds. However, due to factors such as artifacts in the CT reconstruction process, many abnormal interference points exist in the point clouds obtained after segmentation. The classic least squares algorithm is easily affected by these points, resulting in significant deviation of the solution of linear equations from the normal value and poor robustness, while the random sample consensus (RANSAC) approach has insufficient fitting accuracy within a limited timeframe and the number of iterations. To address these shortcomings, we propose a spherical point cloud fitting algorithm based on projection filtering and K-Means clustering (PK-RANSAC), which strategically integrates and enhances these two methods to achieve excellent accuracy and robustness. The proposed method first uses RANSAC for rough parameter estimation, then corrects the deviation of the spherical center coordinates through two-dimensional projection, and finally obtains the spherical center point set by sampling and performing K-Means clustering. The largest cluster is weighted to obtain accurate fitting parameters. We conducted a comparative experiment using a three-dimensional ball-plate standard. The sphere center fitting deviation of PK-RANSAC was 1.91 μm, which is significantly better than RANSAC’s value of 25.41 μm. The experimental results demonstrate that PK-RANSAC has higher accuracy and stronger robustness for fitting geometric parameters.

Fecal microbiota transplantation (FMT) via colonoscopy or enema has become a commonly used treatment of recurrent C. difficile infection (CDI). To compare the safety and preliminary efficacy of orally administered lyophilized microbiota product compared with frozen product by enema. In a single center, adults with ≥ 3 episodes of recurrent CDI were randomized to receive encapsulated lyophilized fecal microbiota from 100-200 g of donor feces (n = 31) or frozen FMT from 100 g of donor feces (n = 34) by enema. Safety during the three months post FMT was the primary study objective. Prevention of CDI recurrence during the 60 days after FMT was a secondary objective. Fecal microbiome changes were examined in first 39 subjects studied. Adverse experiences were commonly seen in equal frequency in both groups and did not appear to relate to the route of delivery of FMT. CDI recurrence was prevented in 26 of 31 (84%) subjects randomized to capsules and in 30 of 34 (88%) receiving FMT by enema (p = 0.76). Both products normalized fecal microbiota diversity while the lyophilized orally administered product was less effective in repleting Bacteroidia and Verrucomicrobia classes compared to frozen product via enema. The route of delivery, oral or rectal, did not influence adverse experiences in FMT. In preliminary evaluation, both routes appeared to show equivalent efficacy, although the dose may need to be higher for lyophilized product. Spore-forming bacteria appear to be the most important engrafting organisms in FMT by the oral route using lyophilized product. ClinicalTrials.gov NCT02449174.

Radiographic imaging is the standard approach for evaluating the disease involvement of lymph nodes in patients with operable NSCLC although the impact of neoadjuvant immune checkpoint inhibitors (ICIs) on lymph nodes has not yet been characterized. Herein, we present an ad hoc analysis of the NEOSTAR trial (NCT03158129) where we observed a phenomenon we refer to as “nodal immune flare” (NIF) in which patients treated with neoadjuvant ICIs demonstrate radiologically abnormal nodes post-therapy that upon pathological evaluation are devoid of cancer and demonstrate de novo non-caseating granulomas. Abnormal lymph nodes are analyzed by computed tomography and 18 F-fluorodeoxyglucose positron emission tomography/computer tomography to evaluate the size and the maximum standard uptake value post- and pre-therapy in NEOSTAR and an independent neoadjuvant chemotherapy cohort. NIF occurs in 16% (7/44) of patients treated with ICIs but in 0% (0/28) of patients after neoadjuvant chemotherapy. NIF is associated with an inflamed nodal immune microenvironment and with fecal abundance of genera belonging to the family Coriobacteriaceae of phylum Actinobacteria, but not with tumor responses or treatment-related toxicity. Our findings suggest that this apparent radiological cancer progression in lymph nodes may occur due to an inflammatory response after neoadjuvant immunotherapy, and such cases should be evaluated by pathological examination to distinguish NIF from true nodal progression and to ensure appropriate clinical treatment planning. Granulomatous/sarcoid-like lesions have been reported in patients treated with immune checkpoint inhibitors (ICIs). Here the authors report the occurrence of “nodal immune flare”, an apparent radiological cancer progression in the nodes characterized by the absence of cancer and the presence of non-caseating granulomas, in patients with non-small cell lung cancer following neoadjuvant ICI treatment.

In this paper, we first propose a dependent Dirichlet process (DDP) model using a mixture of Weibull models with each mixture component resembling a Cox model for survival data. We then build a Dirichlet process mixture model for competing risks data without regression covariates. Next we extend this model to a DDP model for competing risks regression data by using a multiplicative covariate effect on subdistribution hazards in the mixture components. Though built on proportional hazards (or subdistribution hazards) models, the proposed nonparametric Bayesian regression models do not require the assumption of constant hazard (or subdistribution hazard) ratio. An external time-dependent covariate is also considered in the survival model. After describing the model, we discuss how both cause-specific and subdistribution hazard ratios can be estimated from the same nonparametric Bayesian model for competing risks regression. For use with the regression models proposed, we introduce an omnibus prior that is suitable when little external information is available about covariate effects. Finally we compare the models’ performance with existing methods through simulations. We also illustrate the proposed competing risks regression model with data from a breast cancer study. An R package “DPWeibull” implementing all of the proposed methods is available at CRAN.

Background In microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups, as well a clinical dataset with less clear separation between groups. Results Although no single method outperformed the others consistently, we did identify the key scenarios where certain methods can underperform. Specifically, the Bray Curtis (BC) metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac (UU) metric clustered poorly on dataset with a high prevalence of low-abundance OTUs. To explore these hypotheses about BC and UU, we systematically modified the properties of the poorly performing datasets and found that this approach resulted in improved BC and UU performance. Based on these observations, we rationally combined BC and UU to generate a novel metric. We tested its performance while varying the relative contributions of each metric and also compared it with another combined metric, the generalized UniFrac distance. The proposed metric showed high performance across all datasets. Conclusions Our systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of BC and UU that capitalizes on the complementary strengths of the two metrics. syUurMUnqpLuEn-KfNXmw Video abstract

To test causal relationships between complex gut microbiota (GM) and host outcomes, researchers frequently transfer GM between donor and recipient mice via embryo transfer (ET) rederivation, cross-fostering (CF), and co-housing. In this study, we assess the influence of the transfer method and the differences in baseline donor and recipient microbiota richness, on transfer efficiency. Additionally, recipient mice were subjected to DSS-induced chronic colitis to determine whether disease severity was affected by GM transfer efficiency or features within the GM. We found that the recipient’s genetic background, the baseline richness of donor and recipient GM, and the transfer method all influenced the GM transfer efficiency. Recipient genetic background and GM both had significant effects on DSS colitis severity and, unexpectedly, the transfer method was strongly associated with differential disease severity regardless of the other factors.

Background Identification of features is a critical task in microbiome studies that is complicated by the fact that microbial data are high dimensional and heterogeneous. Masked by the complexity of the data, the problem of separating signals (differential features between groups) from noise (features that are not differential between groups) becomes challenging and troublesome. For instance, when performing differential abundance tests, multiple testing adjustments tend to be overconservative, as the probability of a type I error (false positive) increases dramatically with the large numbers of hypotheses. Moreover, the grouping effect of interest can be obscured by heterogeneity. These factors can incorrectly lead to the conclusion that there are no differences in the microbiome compositions. Results We translate and represent the problem of identifying differential features, which are differential in two-group comparisons (e.g., treatment versus control), as a dynamic layout of separating the signal from its random background. More specifically, we progressively permute the grouping factor labels of the microbiome samples and perform multiple differential abundance tests in each scenario. We then compare the signal strength of the most differential features from the original data with their performance in permutations, and will observe a visually apparent decreasing trend if these features are true positives identified from the data. Simulations and applications on real data show that the proposed method creates a U-curve when plotting the number of significant features versus the proportion of mixing. The shape of the U-Curve can convey the strength of the overall association between the microbiome and the grouping factor. We also define a fragility index to measure the robustness of the discoveries. Finally, we recommend the identified features by comparing p -values in the observed data with p -values in the fully mixed data. Conclusions We have developed this into a user-friendly and efficient R-shiny tool with visualizations. By default, we use the Wilcoxon rank sum test to compute the p -values, since it is a robust nonparametric test. Our proposed method can also utilize p -values obtained from other testing methods, such as DESeq. This demonstrates the potential of the progressive permutation method to be extended to new settings.

Carbonized polymer dots (CPDs) have shown exceptional potential across a wide range of applications. However, their practical utilization is significantly greatly impeded by the lack of precise control over their structures and functionalities. Consequently, the development of controlled synthesis strategies for CPDs with well‐defined structures and tailored functionalities remains a critical challenge in the field. Here, the controlled synthesis of functional CPDs with reversible assembly properties via airflow‐assisted melt polymerization, followed by a one‐step post‐synthetic doping strategy, is reported. This synthetic approach achieves high product yield, uniform and tunable structures, as well as customized functionalities including solid‐state emission, enhanced catalytic performance (3.5–45 times higher than conventional methods), and selective gas storage in the resulting CPDs. The ability to tailor the properties of CPDs through controlled synthesis opens up new opportunities for their practical application in photocatalysis and gas storage. Carbonized polymer dots (CPDs) with well‐defined structures and tailored functionalities are achieved by airflow‐assisted melt polymerization, followed by a one‐step post‐synthetic doping strategy. This synthetic approach achieves high product yield, uniform and tunable structures, as well as customized functionalities including solid–state emission, enhanced catalytic performance, and selective gas storage in the resulting CPDs.

Background To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tumor immune microenvironment (TIME) into two types: immune-activated (IA), characterized by a high CD8 + score and high PD-L1 combined positive score (CPS), and non-immune-activated (NIA), encompassing all other conditions. We aimed to develop an MRI-based radiomics model to predict TIME types and validate its predictive capability for ICIs efficacy in HCC patients receiving anti-PD-1/PD-L1 therapy. Methods The study included 200 HCC patients who underwent preoperative/pretreatment multiparametric contrast-enhanced MRI (Cohort 1: 168 HCC patients with hepatectomy from two centres; Cohort 2: 42 advanced HCC patients on anti-PD-1/PD-L1 therapy). In Cohort 1, after feature selection, clinical, intratumoral radiomics, peritumoral radiomics, combined radiomics, and clinical-radiomics models were established using machine learning algorithms. In cohort 2, the clinical-radiomics model’s predictive ability for ICIs efficacy was assessed. Results In Cohort 1, the AUC values for intratumoral, peritumoral, and combined radiomics models were 0.825, 0.809, and 0.868, respectively, in the internal validation set, and 0.73, 0.759, and 0.822 in the external validation set; the clinical-radiomics model incorporating neutrophil-to-lymphocyte ratio, tumor size, and combined radiomics score achieved an AUC of 0.887 in the internal validation set, outperforming clinical model ( P  = 0.049), and an AUC of 0.837 in the external validation set. In cohort 2, the clinical-radiomics model stratified patients into low- and high-score groups, demonstrating a significant difference in objective response rate ( p  = 0.003) and progression-free survival ( p  = 0.031). Conclusions The clinical-radiomics model is effective in predicting TIME types and efficacy of ICIs in HCC, potentially aiding in treatment decision-making.

The authors describe a highly sensitive and selective photoelectrochemical (PEC) assay for mercury(II) ions. It is based on a dual signal amplification strategy. The first enhancement results from the surface plasmon resonance (SPR) of Au@Ag nanoparticles (NPs) absorbed on MoS 2 nanosheets. Here, the injection of hot electrons of Au@Ag NPs into MoS 2 nanosheets produces a strong photocurrent, while background signals are strongly reduced. The second enhancement results from the use of a thymine rich ct-DNA aptamer attached to the Au@Ag-MoS 2 nanohybrid. The DNA specifically binds Hg(II) ions to form thymine-Hg(II)-thymine (T-Hg-T) complexes. This leads to the formation of a hairpin-shaped dsDNA structure. The use of a CdSe quantum dot label at the terminal end of the ct-DNA further facilitates electron–hole separation. The photocurrent of the detector is measured as a function of Hg(II) concentration at a bias voltage of 0.1 V and under irradiation of 430 nm light. Due to the two-fold amplification strategy presented here, the linear range extends from 10 pmol·L −1 to 100 nmol·L −1 , with a detection limit of 5 pmol·L −1 (at S/N = 3). Graphical Abstract The injection of hot electrons of Au@Ag into MoS 2 produces a strong photocurrent, and the formation of thymine-Hg(II)-thymine further facilitates electron–hole separation by CdSe. This dual signal amplification strategy is used to detect Hg(II) ions via a photoelectrochemical assay.