A conserved interdomain microbial network underpins cadaver decomposition despite environmental variables.

Microbial breakdown of organic matter is one of the most important processes on Earth, yet the controls of decomposition are poorly understood. Here we track 36 terrestrial human cadavers in three locations and show that a phylogenetically distinct, interdomain microbial network assembles during decomposition despite selection effects of location, climate and season. We generated a metagenome-assembled genome library from cadaver-associated soils and integrated it with metabolomics data to identify links between taxonomy and function. This universal network of microbial decomposers is characterized by cross-feeding to metabolize labile decomposition products. The key bacterial and fungal decomposers are rare across non-decomposition environments and appear unique to the breakdown of terrestrial decaying flesh, including humans, swine, mice and cattle, with insects as likely important vectors for dispersal. The observed lockstep of microbial interactions further underlies a robust microbial forensic tool with the potential to aid predictions of the time since death.

Soft phenotyping for sepsis via EHR time-aware soft clustering.

OBJECTIVE: Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories, identifying sepsis sub-phenotypes is crucial to advance our understanding of sepsis characterization, to choose targeted treatments and optimal timing of interventions, and to improve prognostication. Prior studies have described different sub-phenotypes of sepsis using organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships among the sub-phenotypes for clustering procedures. METHODS: We developed a time-aware soft clustering algorithm guided by clinical variables to identify sepsis sub-phenotypes using data available in the EHR. RESULTS: We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. CONCLUSION: Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more accurate information on sepsis-related organ dysfunction and sepsis recovery trajectories which can be important to inform management decisions and sepsis prognosis.

Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data.

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.

A Modified Mediterranean Ketogenic Diet mitigates modifiable risk factors of Alzheimer's Disease: a serum and CSF-based metabolic analysis.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Longitudinal changes in neurocognitive performance related to drug use intensity in a sample of persons with and without HIV who use illicit stimulants.

BACKGROUND: Illicit stimulant use remains a public health concern that has been associated with multiple adverse outcomes, including cognitive deficits. The effects of stimulant use on cognition may be particularly deleterious in persons with HIV. Stimulant use intensity may be an important factor in the magnitude of observed deficits over time. METHODS: We completed neurocognitive testing in a sample of people who use stimulants with (n = 84) and without HIV (n = 123) at baseline and up to 4 follow-up time points over approximately 1 year. Participants reported on substance use at each visit, including frequency of use and stimulant dependence. Mixed effects models examined the relationship between stimulant-related factors and neurocognitive function over time. RESULTS: Participants were mostly male (57%), African American (86%), and 47.41 years old on average. All participants actively used stimulants at enrollment and use remained prevalent throughout the follow-up period, with an average of ≥24 days of use in the past 90 days at all time points. Retention was excellent, with 86% completing all 4 follow-up assessments. Mixed effects models showed that stimulant dependence was associated with lower neurocognitive performance independent of HIV status (p = 0.002), whereas frequency of use had a greater negative impact on performance in participants with HIV compared to those without HIV (p = 0.045). CONCLUSIONS: Our key finding is that stimulant-related factors are associated with neurocognitive performance over time, but in complex ways. These findings have important implications for harm reduction approaches, particularly those that target cognitive function.

Spatial mapping of cellular senescence: emerging challenges and opportunities.

Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.

Timeline Registration for Electronic Health Records.

Electronic Health Record (EHR) data are captured over time as patients receive care. Accordingly, variations among patients, such as when a patient presents for care during the course of a disease, introduce bias into standard longitudinal EHR data analysis methods. We, therefore, aim to provide an alignment method that reduces this bias. We structure this task as a registration problem. While limited prior research on longitudinal EHR data considered registration, we propose a robust registration method to provide better data alignment by estimating the optimum time shift at each time point. We validate the proposed method for mortality prediction. We utilize a Recurrent Neural Network (RNN), time-varying Cox regression model, and Logistic Regression (LR) for mortality prediction. Results suggest our proposed registration method enhances mortality prediction with at least a 1-2% increase in major evaluation metrics utilized.

Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma.

OBJECTIVE: To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: Consecutive patients with PDAC who underwent surgery after neoadjuvant therapy from two academic hospitals between December 2012 and June 2018 were retrospectively included. Two radiologists performed a volumetric segmentation of PDAC and mesenteric-portal axis (MPA) using a segmentation software on CT scans before (CTtp0) and after (CTtp1) neoadjuvant therapy. Segmentation masks were resampled into uniform 0.625-mm voxels to develop task-based morphologic features (n = 57). These features aimed to assess MPA shape, MPA narrowing, changes in shape and diameter between CTtp0 and CTtp1, and length of MPA segment affected by the tumor. A Kaplan-Meier curve was generated to estimate the survival function. To identify reliable radiomic features associated with survival, a Cox proportional hazards model was used. Features with an ICC ≥ 0.80 were used as candidate variables, with clinical features included a priori. RESULTS: In total, 107 patients (60 men) were included. The median survival time was 895 days (95% CI: 717, 1061). Three task-based shape radiomic features (Eccentricity mean tp0, Area minimum value tp1, and Ratio 2 minor tp1) were selected. The model showed an integrated AUC of 0.72 for prediction of survival. The hazard ratio for the Area minimum value tp1 feature was 1.78 (p = 0.02) and 0.48 for the Ratio 2 minor tp1 feature (p = 0.002). CONCLUSION: Preliminary results suggest that task-based shape radiomic features can predict survival in PDAC patients. KEY POINTS: • In a retrospective study of 107 patients who underwent neoadjuvant therapy followed by surgery for PDAC, task-based shape radiomic features were extracted and analyzed from the mesenteric-portal axis. • A Cox proportional hazards model that included three selected radiomic features plus clinical information showed an integrated AUC of 0.72 for prediction of survival, and a better fit compared to the model with only clinical information.

Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration.

This paper studies a general framework for high-order tensor SVD. We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estimate the low tensor-train rank structure from the noisy high-order tensor observation. The proposed TTOI consists of initialization via TT-SVD [1] and new iterative backward/forward updates. We develop the general upper bound on estimation error for TTOI with the support of several new representation lemmas on tensor matricizations. By developing a matching information-theoretic lower bound, we also prove that TTOI achieves the minimax optimality under the spiked tensor model. The merits of the proposed TTOI are illustrated through applications to estimation and dimension reduction of high-order Markov processes, numerical studies, and a real data example on New York City taxi travel records. The software of the proposed algorithm is available online (https://github.com/Lili-Zheng-stat/TTOI).

Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.

We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model - an actively studied topic in statistics and machine learning. In the noiseless case, matching upper and lower bounds on sample complexity are established for the exact recovery of sparse vectors and for stable estimation of approximately sparse vectors, respectively. In the noisy case, upper and matching minimax lower bounds for estimation error are obtained. We also consider the debiased sparse group Lasso and investigate its asymptotic property for the purpose of statistical inference. Finally, numerical studies are provided to support the theoretical results.

Ventriculomegaly and postoperative lateral/third ventricular blood as predictors of cerebrospinal fluid diversion following posterior fossa tumor resection.

OBJECTIVE: Postoperative hydrocephalus occurs in one-third of children after posterior fossa tumor resection. Although models to predict the need for CSF diversion after resection exist for preoperative variables, it is unknown which postoperative variables predict the need for CSF diversion. In this study, the authors sought to determine the clinical and radiographic predictors for CSF diversion in children following posterior fossa tumor resection. METHODS: This was a retrospective cohort study involving patients ≤ 18 years of age who underwent resection of a primary posterior fossa tumor between 2000 and 2018. The primary outcome was the need for CSF diversion 6 months after surgery. Candidate predictors for CSF diversion including age, race, sex, frontal occipital horn ratio (FOHR), tumor type, tumor volume and location, transependymal edema, papilledema, presence of postoperative intraventricular blood, and residual tumor were evaluated using a best subset selection method with logistic regression. RESULTS: Of the 63 included patients, 26 (41.3%) had CSF diversion at 6 months. Patients who required CSF diversion had a higher median FOHR (0.5 vs 0.4) and a higher percentage of postoperative intraventricular blood (30.8% vs 2.7%) compared with those who did not. A 0.1-unit increase in FOHR or intraventricular blood was associated with increased odds of CSF diversion (OR 2.9 [95% CI 1.3-7.8], p = 0.02 and OR 20.2 [95% CI 2.9-423.1], p = 0.01, respectively) with an overfitting-corrected concordance index of 0.68 (95% CI 0.56-0.80). CONCLUSIONS: The preoperative FOHR and postoperative intraventricular blood were significant predictors of the need for permanent CSF diversion within 6 months after posterior fossa tumor resection in children.

Denoising atomic resolution 4D scanning transmission electron microscopy data with tensor singular value decomposition.

Tensor singular value decomposition (SVD) is a method to find a low-dimensional representation of data with meaningful structure in three or more dimensions. Tensor SVD has been applied to denoise atomic-resolution 4D scanning transmission electron microscopy (4D STEM) data. On data simulated from a SrTiO3 [100] perfect crystal and a Si [110] edge dislocation, tensor SVD achieved an average peak signal-to-noise ratio (PSNR) of ~40 dB, which matches or exceeds the performance of other denoising methods, with processing times at least 100 times shorter. On experimental data from SrTiO3 [100] and LiZnSb [112¯0]/GaSb [110] samples, tensor SVD denoises multiple GB 4D STEM data sets in ten minutes on a typical personal computer. Denoising with tensor SVD improves both convergent beam electron diffraction patterns and virtual-aperture annular dark field images.