Uncertainty-aware prototypical learning for anomaly detection in medical images.

Anomalous object detection (AOD) in medical images aims to recognize the anomalous lesions, and is crucial for early clinical diagnosis of various cancers. However, it is a difficult task because of two reasons: (1) the diversity of the anomalous lesions and (2) the ambiguity of the boundary between anomalous lesions and their normal surroundings. Unlike existing single-modality AOD models based on deterministic mapping, we constructed a probabilistic and deterministic AOD model. Specifically, we designed an uncertainty-aware prototype learning framework, which considers the diversity and ambiguity of anomalous lesions. A prototypical learning transformer (Pformer) is established to extract and store the prototype features of different anomalous lesions. Moreover, Bayesian neural uncertainty quantizer, a probabilistic model, is designed to model the distributions over the outputs of the model to measure the uncertainty of the model's detection results for each pixel. Essentially, the uncertainty of the model's anomaly detection result for a pixel can reflect the anomalous ambiguity of this pixel. Furthermore, an uncertainty-guided reasoning transformer (Uformer) is devised to employ the anomalous ambiguity, encouraging the proposed model to focus on pixels with high uncertainty. Notably, prototypical representations stored in Pformer are also utilized in anomaly reasoning that enables the model to perceive diversities of the anomalous objects. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed method. The source code will be available in github.com/umchaohuang/UPformer.

Dynamic radiomics based on contrast-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma.

OBJECTIVE: To exploit the improved prediction performance based on dynamic contrast-enhanced (DCE) MRI by using dynamic radiomics for microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS: We retrospectively included 175 and 75 HCC patients who underwent preoperative DCE-MRI from September 2019 to August 2022 in institution 1 (development cohort) and institution 2 (validation cohort), respectively. Static radiomics features were extracted from the mask, arterial, portal venous, and equilibrium phase images and used to construct dynamic features. The static, dynamic, and dynamic-static radiomics (SR, DR, and DSR) signatures were separately constructed based on the feature selection method of LASSO and classification algorithm of logistic regression. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each signature. RESULTS: In the three radiomics signatures, the DSR signature performed the best. The AUCs of the SR, DR, and DSR signatures in the training set were 0.750, 0.751 and 0.805, respectively, while in the external validation set, the corresponding AUCs were 0.706, 0756 and 0.777. The DSR signature showed significant improvement over the SR signature in predicting MVI status (training cohort: P = 0.019; validation cohort: P = 0.044). After external validation, the AUC value of the SR signature decreased from 0.750 to 0.706, while the AUC value of the DR signature did not show a decline (AUCs: 0.756 vs. 0.751). CONCLUSIONS: The dynamic radiomics had an improved effect on the MVI prediction in HCC, compared with the static DCE MRI-based radiomics models.

Electrochemical/photoelectrochemical dual-mode aptasensor for sensitive aflatoxin B1 assay based on distance-modulation strategy using Au NPs/PCZIF-8-ZnO as sensing substrate.

Aflatoxin B1 (AFB1), a hepatotoxic and carcinogenic food contaminant, is commonly found in agricultural food. Herein, Au NPs anchored ZIF-8-derived porous carbon-ZnO (Au NPs/PCZIF-8-ZnO) was firstly synthesized to act as the sensing substrate. Then, a ratiometric electrochemical (EC) and "off-on" photoelectrochemical (PEC) dual-mode paper-based aptasensor was presented for AFB1 detection based on a distance-modulation sensing strategy. The independent signal transduction mechanisms and output mode not only broaden the dynamic detection range but also provide a self-verification to assay results, improving the sensitivity and reliability. The wide detection ranges of 0.1 pg/mL-100 ng/mL (EC mode) and 0.02 pg/mL-100 ng/mL (PEC mode) were obtained using dual-mode aptasensor, with detection limits of 36.7 and 9.3 fg/mL, respectively. The fabricated aptasensor exhibited excellent selectivity, reproducibility and stability. Furthermore, it exhibited good practicability for AFB1 assays in real samples, demonstrating great potential applications for food safety evaluation.

Green, pH-Sensitive, Highly Stretchable, and Hydrogen Bond-Dominated Ionogel for Wound Healing Activity.

Traditional hydrogel dressings generally have poor mechanical properties and stability when subjected to external stress due to the undesirable chain entanglement structure of their single valence bond compositions. Therefore, it is particularly important to develop a type of gel dressing with good mechanical strength, stability, and environment-friendly monitoring. In this work, a transparent, pH-sensitive, highly stretchable, and biocompatible anthocyanidin ionogel dressing was prepared, realizing green and accurate detection. Attributed to the antibacterial activity of the ionic liquid, the biocompatibility of the pectin, and the ability to scavenge free radicals of the anthocyanidin, the ionogel dressing exhibited excellent re-epithelialization in the 14 day wound healing process. Besides, changes in pH values monitoring of the ionogel over 3 days coincided with normal wound exudate. The obtained ionogel also showed good water retention, swelling properties, mechanical stretchability, and 5 week stability, illustrating great potential in wound dressings.

Development and testing of a polygenic risk score for breast cancer aggressiveness.

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r2 in 5-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRS's association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.06-1.21, p = 4.0 × 10-4). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRSER-/ER+). Furthermore, its effect was minimally attenuated when adjusted for PRSER-/ER+, suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention.

Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.

Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.

OBJECTIVES: To establish and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI), and to predict microsatellite instability (MSI) status in rectal cancer patients. METHODS: A total of 199 patients with pathologically confirmed rectal cancer were included. The MSI status was confirmed by immunohistochemistry (IHC) staining. Clinical factors and laboratory data associated with MSI status were analyzed. The imaging data of 100 patients from one of the hospitals were used as the training set. The remaining 99 patients from the other two hospitals were used as the external validation set. The regions of interest (ROIs) were delineated from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) sequence to extract the radiomics features. The Tree-based approach was used for feature selection. The models were constructed based on the four single sequences and a combination of the four sequences using the random forest (RF) algorithm. The external validation set was used to verify the generalization ability of each model. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were plotted to evaluate and compare the predictive performance of each model. RESULTS: In the four single-series models, the CE-T1WI model performed the best. The AUCs of the T1WI, T2WI, DWI, and CE-T1WI prediction models in the training set were 0.74, 0.71, 0.71, and 0.78, respectively, while in the external validation set, the corresponding AUCs were 0.67, 0.66, 0.70, and 0.77. The prediction and generalization performance of the combined model of multi-sequences was comparable to that of the CE-T1WI model and it was better than that of the remaining three single-series models, with AUC values of 0.78 and 0.78 in the training and validation sets, respectively. CONCLUSION: The established radiomics models based on CE-T1WI or multiparametric MRI have similar predictive performance. They have the potential to predict MSI status in rectal cancer patients. KEY POINTS: • A radiomics model for the prediction of MSI status in patients with rectal cancer was established and validated using external validation. • The models based on CE-T1WI or multiparametric MRI have better predictive performance than those based on single unenhanced sequence images. • The radiomics model has the potential to suggest MSI status in rectal cancer patients; however, it is not yet a substitute for histological confirmation.

Diet-derived metabolites and mucus link the gut microbiome to fever after cytotoxic cancer treatment.

Not all patients with cancer and severe neutropenia develop fever, and the fecal microbiome may play a role. In a single-center study of patients undergoing hematopoietic cell transplant (n = 119), the fecal microbiome was characterized at onset of severe neutropenia. A total of 63 patients (53%) developed a subsequent fever, and their fecal microbiome displayed increased relative abundances of Akkermansia muciniphila, a species of mucin-degrading bacteria (P = 0.006, corrected for multiple comparisons). Two therapies that induce neutropenia, irradiation and melphalan, similarly expanded A. muciniphila and additionally thinned the colonic mucus layer in mice. Caloric restriction of unirradiated mice also expanded A. muciniphila and thinned the colonic mucus layer. Antibiotic treatment to eradicate A. muciniphila before caloric restriction preserved colonic mucus, whereas A. muciniphila reintroduction restored mucus thinning. Caloric restriction of unirradiated mice raised colonic luminal pH and reduced acetate, propionate, and butyrate. Culturing A. muciniphila in vitro with propionate reduced utilization of mucin as well as of fucose. Treating irradiated mice with an antibiotic targeting A. muciniphila or propionate preserved the mucus layer, suppressed translocation of flagellin, reduced inflammatory cytokines in the colon, and improved thermoregulation. These results suggest that diet, metabolites, and colonic mucus link the microbiome to neutropenic fever and may guide future microbiome-based preventive strategies.

Transfer efficiency and impact on disease phenotype of differing methods of gut microbiota transfer.

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.

Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder.

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

Reduced housing density improves statistical power of murine gut microbiota studies.

The gut microbiome of humans and animals is critical to host health. Mice are used to investigate the microbiome and its influences; however, the predictive value of such studies is hindered by cage effects due to coprophagy. Our objectives were to evaluate the influence of cage density on the statistical power to detect treatment-dependent effects of a selective pressure on microbiome composition. C57BL/6 mice were separated into groups of 2 or 4 mice per cage and then assigned to groups receiving enrofloxacin, broad-spectrum antibiotics, or control. Fecal samples were collected at weeks 0, 1, and 4, along with contents of the jejunum and cecum. Bacterial DNA analysis examined microbiome richness, diversity, and variability within and between cages. Statistical analyses reveal that reduced housing density consistently results in comparable susceptibility to antibiotics, reduced cage effects, and increased statistical power to detect treatment-associated effects, justifying the practice of reduced housing density.

Performance determinants of unsupervised clustering methods for microbiome data.

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. Video abstract.

Impaired robust interhemispheric function integration of depressive brain from REST-meta-MDD database in China.

BACKGROUND: Recently, functional homotopy (FH) architecture, defined as robust functional connectivity (FC) between homotopic regions, has been frequently reported to be altered in MDD patients (MDDs) but with divergent locations. METHODS: In this study, we obtained resting-state functional magnetic resonance imaging (R-fMRI) data from 1004 MDDs (mean age, 33.88 years; age range, 18-60 years) and 898 matched healthy controls (HCs) from an aggregated dataset from 20 centers in China. We focused on interhemispheric function integration in MDDs and its correlation with clinical characteristics using voxel-mirrored homotopic connectivity (VMHC) devised to inquire about FH patterns. RESULTS: As compared with HCs, MDDs showed decreased VMHC in visual, motor, somatosensory, limbic, angular gyrus, and cerebellum, particularly in posterior cingulate gyrus/precuneus (PCC/PCu) (false discovery rate [FDR] q < 0.002, z = -7.07). Further analysis observed that the reduction in SMG and insula was more prominent with age, of which SMG reflected such age-related change in males instead of females. Besides, the reduction in MTG was found to be a male-special abnormal pattern in MDDs. VMHC alterations were markedly related to episode type and illness severity. The higher Hamilton Depression Rating Scale score, the more apparent VMHC reduction in the primary visual cortex. First-episode MDDs revealed stronger VMHC reduction in PCu relative to recurrent MDDs. CONCLUSIONS: We confirmed a significant VMHC reduction in MDDs in broad areas, especially in PCC/PCu. This reduction was affected by gender, age, episode type, and illness severity. These findings suggest that the depressive brain tends to disconnect information exchange across hemispheres.

Development of a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository.

Purpose: To establish a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository. Methods: This study was approved by the Institutional Review Board. The steps of establishing the database are as follows: 1) According to the standards required for the intelligent diagnosis of HCC, it was attempted to design the corresponding functional modules after analyzing the requirements; 2) Based on client/server (C/S) mode, 3-tier architecture model was adopted. A user interface (UI) could receive data entered by users and show handled data. Business logic layer (BLL) could process the business logic of the data, and data access layer (DAL) could save the data in the database. The storage and management of HCC imaging data could be realized by the SQLSERVER database management software, and Delphi and VC++ programming languages were used. Results: The test results showed that the proposed database could swiftly obtain the pathological, clinical, and imaging data of HCC from the picture archiving and communication system (PACS) and hospital information system (HIS), and perform data storage and visualization of structured imaging reports. According to the HCC imaging data, liver imaging reporting and data system (LI-RADS) assessment, standardized staging, and intelligent imaging analysis were carried out on the high-risk population to establish a one-stop imaging evaluation platform for HCC, strongly supporting clinicians in the diagnosis and treatment of HCC. Conclusions: The establishment of a HCC imaging database can not only provide a huge amount of imaging data for the basic and clinical research on HCC, but also facilitate the scientific management and quantitative assessment of HCC. Besides, a HCC imaging database is advantageous for personalized treatment and follow-up of HCC patients.

Disrupted intrinsic functional brain topology in patients with major depressive disorder.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Nodal immune flare mimics nodal disease progression following neoadjuvant immune checkpoint inhibitors in non-small cell lung cancer.

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 18F-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.

Brain structural alterations in MDD patients with gastrointestinal symptoms: Evidence from the REST-meta-MDD project.

OBJECTIVE: While gastrointestinal (GI) symptoms are very common in patients with major depressive disorder (MDD), few studies have investigated the neural basis behind these symptoms. In this study, we sought to elucidate the neural basis of GI symptoms in MDD patients by analyzing the changes in regional gray matter volume (GMV) and gray matter density (GMD) in brain structure. METHOD: Subjects were recruited from 13 clinical centers and categorized into three groups, each of which is based on the presence or absence of GI symptoms: the GI symptoms group (MDD patients with at least one GI symptom), the non-GI symptoms group (MDD patients without any GI symptoms), and the healthy control group (HCs). Structural magnetic resonance images (MRI) were collected of 335 patients in the GI symptoms group, 149 patients in the non-GI symptoms group, and 446 patients in the healthy control group. The 17-item Hamilton Depression Rating Scale (HAMD-17) was administered to all patients. Correlation analysis and logistic regression analysis were used to determine if there was a correlation between the altered brain regions and the clinical symptoms. RESULTS: There were significantly higher HAMD-17 scores in the GI symptoms group than that of the non-GI symptoms group (P < 0.001). Both GMV and GMD were significant different among the three groups for the bilateral superior temporal gyrus, bilateral middle temporal gyrus, left lingual gyrus, bilateral caudate nucleus, right Fusiform gyrus and bilateral Thalamus (GRF correction, cluster-P < 0.01, voxel-P < 0.001). Compared to the HC group, the GI symptoms group demonstrated increased GMV and GMD in the bilateral superior temporal gyrus, and the non-GI symptoms group demonstrated an increased GMV and GMD in the right superior temporal gyrus, right fusiform gyrus and decreased GMV in the right Caudate nucleus (GRF correction, cluster-P < 0.01, voxel-P < 0.001). Compared to the non-GI symptoms group, the GI symptoms group demonstrated significantly increased GMV and GMD in the bilateral thalamus, as well as decreased GMV in the bilateral superior temporal gyrus and bilateral insula lobe (GRF correction, cluster-P < 0.01, voxel-P < 0.001). While these changed brain areas had significantly association with GI symptoms (P < 0.001), they were not correlated with depressive symptoms (P > 0.05). Risk factors for gastrointestinal symptoms in MDD patients (p < 0.05) included age, increased GMD in the right thalamus, and decreased GMV in the bilateral superior temporal gyrus and left Insula lobe. CONCLUSION: MDD patients with GI symptoms have more severe depressive symptoms. MDD patients with GI symptoms exhibited larger GMV and GMD in the bilateral thalamus, and smaller GMV in the bilateral superior temporal gyrus and bilateral insula lobe that were correlated with GI symptoms, and some of them and age may contribute to the presence of GI symptoms in MDD patients.

ProgPerm: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries.

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.

Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project.

BACKGROUND: Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied. METHODS: Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes). RESULTS: Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities. LIMITATIONS: Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results. CONCLUSIONS: Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.

A dependent Dirichlet process model for survival data with competing risks.

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.