A comparative study of compartmental models for COVID-19 transmission in Ontario, Canada.

The number of confirmed COVID-19 cases reached over 1.3 million in Ontario, Canada by June 4, 2022. The continued spread of the virus underlying COVID-19 has been spurred by the emergence of variants since the initial outbreak in December, 2019. Much attention has thus been devoted to tracking and modelling the transmission of COVID-19. Compartmental models are commonly used to mimic epidemic transmission mechanisms and are easy to understand. Their performance in real-world settings, however, needs to be more thoroughly assessed. In this comparative study, we examine five compartmental models-four existing ones and an extended model that we propose-and analyze their ability to describe COVID-19 transmission in Ontario from January 2022 to June 2022.

Estimating and Assessing Differential Equation Models with Time-Course Data.

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This Article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy, and some components of the system may not be observed. Furthermore, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories, including unobserved components, with appropriate uncertainty quantification. Second, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI's efficient computation of model predictions. Overall, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models, which bypasses the need for any numerical integration.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Conformational variability of loops in the SARS-CoV-2 spike protein.

The SARS-CoV-2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. Its flexible loop regions are known to be involved in protein binding and may adopt multiple conformations. This article identifies the S protein loops and studies their conformational variability based on the available Protein Data Bank structures. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations based on a cluster analysis. Loop modeling methods were then applied to the S protein loop targets, and the prediction accuracies discussed in relation to the characteristics of the conformational clusters identified. Loops with multiple conformations were found to be challenging to model based on a single structural template.

Bayesian analysis of coupled cellular and nuclear trajectories for cell migration.

Cell migration, the process by which cells move from one location to another, plays crucial roles in many biological events. While much research has been devoted to understand the process, most statistical cell migration models rely on using time-lapse microscopy data from cell trajectories alone. However, the cell and its associated nucleus work together to orchestrate cell movement, which motivates a joint analysis of coupled cell-nucleus trajectories. In this paper, we propose a Bayesian hierarchical model for analyzing cell migration. We incorporate a bivariate angular distribution to handle the coupled cell-nucleus trajectories and introduce latent motility status indicators to model a cell's motility as a time-dependent characteristic. A Markov chain Monte Carlo algorithm is provided for practical implementation of our model, which is used on real experimental data from MDA-MB-231 and NIH 3T3 cells. Through the fitted models, deeper insights into the migratory patterns of these experimental cell populations are gained and their differences are quantified.

Decomposition of cell activities revealing the role of the cell cycle in driving biofunctional heterogeneity.

Heterogeneity of cell phenotypes remains a barrier in progressing cell research and a challenge in conquering cancer-related drug resistance. Cell morphology, the most direct property of cell phenotype, evolves along the progression of the cell cycle; meanwhile, cell motility, the dynamic property of cell phenotype, also alters over the cell cycle. However, a quantifiable research understanding the relationship between the cell cycle and cell migration is missing. Herein, we coordinate the migratory behaviours of NIH 3T3 fibroblasts to their corresponding phases of the cell cycle, the G1, the S, and the G2 phases, and explain the relationship through the spatiotemporal arrangements between the Rho GTPases' signals and cyclin-dependent kinase inhibitors, p21Cip1, and p27Kip1. Taken together, we demonstrate that both cell morphology and the dynamic subcellular behaviour are homogenous within each stage of the cell cycle phases but heterogenous between phases through quantitative cell analyses and an interactive molecular mechanism between the cell cycle and cell migration, posing potential implications in countering drug resistance.

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

BACKGROUND: Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis. OBJECTIVE: To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP. METHODS: We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score. RESULTS: Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179. CONCLUSION: An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.

Suppression of ACADM-Mediated Fatty Acid Oxidation Promotes Hepatocellular Carcinoma via Aberrant CAV1/SREBP1 Signaling.

Lipid accumulation exacerbates tumor development, as it fuels the proliferative growth of cancer cells. The role of medium-chain acyl-CoA dehydrogenase (ACADM), an enzyme that catalyzes the first step of mitochondrial fatty acid oxidation, in tumor biology remains elusive. Therefore, investigating its mode of dysregulation can shed light on metabolic dependencies in cancer development. In hepatocellular carcinoma (HCC), ACADM was significantly underexpressed, correlating with several aggressive clinicopathologic features observed in patients. Functionally, suppression of ACADM promoted HCC cell motility with elevated triglyceride, phospholipid, and cellular lipid droplet levels, indicating the tumor suppressive ability of ACADM in HCC. Sterol regulatory element-binding protein-1 (SREBP1) was identified as a negative transcriptional regulator of ACADM. Subsequently, high levels of caveolin-1 (CAV1) were observed to inhibit fatty acid oxidation, which revealed its role in regulating lipid metabolism. CAV1 expression negatively correlated with ACADM and its upregulation enhanced nuclear accumulation of SREBP1, resulting in suppressed ACADM activity and contributing to increased HCC cell aggressiveness. Administration of an SREBP1 inhibitor in combination with sorafenib elicited a synergistic antitumor effect and significantly reduced HCC tumor growth in vivo. These findings indicate that deregulation of fatty acid oxidation mediated by the CAV1/SREBP1/ACADM axis results in HCC progression, which implicates targeting fatty acid metabolism to improve HCC treatment. SIGNIFICANCE: This study identifies tumor suppressive effects of ACADM in hepatocellular carcinoma and suggests promotion of ß-oxidation to diminish fatty acid availability to cancer cells could be used as a therapeutic strategy.

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes.

Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.

Incidence of nonarteritic ischemic optic neuropathy following topical clear corneal cataract surgery: survey and meta-analysis.

OBJECTIVE: We estimate the incidence and characteristics of post-cataract-surgery nonarteritic ischemic optic neuropathy (PCNAION) after topical clear corneal cataract extraction (CCCE) in Canada. DESIGN: Canada-wide internet survey and meta-analysis PARTICIPANTS: All certified Canadian ophthalmologists in the Canadian Ophthalmological Society directory, or belonging to a provincial ophthalmology internet group. METHODS: Identical surveys were distributed to 5 regions in Canada. CCCE surgeons were asked to estimate the number of CCCE they had performed in their career, and the number of PCNAION events that occurred within 1 year after CCCE. The results were analyzed using a random effects meta-analysis of proportions for rare events. RESULTS: The estimated survey response rate was 18%-32%. The 182 survey respondents performed a total of 1 499 694 CCCE with 107 events of PCNAION. Twenty-six percent of surgeons had at least one patient with PCNAION. Meta-analysis revealed a pooled estimate incidence of 2.8 PCNAION events (95% confidence interval 1.6-4.7) per 100 000 cataract procedures during the year after cataract surgery. Seventy-seven percent (82/107) of the PCNAION cases occurred within 3 weeks of surgery, and 7 patients had bilateral PCNAION. CONCLUSIONS: PCNAION is a rare complication after topical CCCE. Its incidence is important to estimate for patient care and epidemiologic reasons.

Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation.

PURPOSE: To develop and validate neural network (NN) vs logistic regression (LR) diagnostic prediction models in patients with suspected giant cell arteritis (GCA). Design: Multicenter retrospective chart review. METHODS: An audit of consecutive patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at 14 international medical centers. The outcome variable was biopsy-proven GCA. The predictor variables were age, gender, headache, clinical temporal artery abnormality, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. The data were divided into three groups to train, validate, and test the models. The NN model with the lowest false-negative rate was chosen. Internal and external validations were performed. RESULTS: Of 1,833 patients who underwent TABx, there was complete information on 1,201 patients, 300 (25%) of whom had a positive TABx. On multivariable LR age, platelets, jaw claudication, vision loss, log C-reactive protein, log erythrocyte sedimentation rate, headache, and clinical temporal artery abnormality were statistically significant predictors of a positive TABx (P≤0.05). The area under the receiver operating characteristic curve/Hosmer-Lemeshow P for LR was 0.867 (95% CI, 0.794, 0.917)/0.119 vs NN 0.860 (95% CI, 0.786, 0.911)/0.805, with no statistically significant difference of the area under the curves (P=0.316). The misclassification rate/false-negative rate of LR was 20.6%/47.5% vs 18.1%/30.5% for NN. Missing data analysis did not change the results. CONCLUSION: Statistical models can aid in the triage of patients with suspected GCA. Misclassification remains a concern, but cutoff values for 95% and 99% sensitivities are provided (https://goo.gl/THCnuU).

Integrating transient cellular and nuclear motions to comprehensively describe cell migration patterns.

Various subcellular activities, such as protrusion and detachment, compose a cell migration process. The molecular mechanisms of these subcellular activities have been elucidated. However, there is no method that can assess the contributions of these subcellular activities to the global cell migration pattern of a given cell type. Hence, we develop a powerful approach based on CN correlations that quantitatively profiles the cell migration pattern of a given cell type in terms of assembled subcellular activities. In this way, we bridge migration data at the cellular level with underlying molecular mechanisms. The CN correlation profile is found to uniquely and consistently represent the cell migration pattern of each cell type probed. It can clearly reveal the effects of molecular perturbations, such as Y27632 and Cdc42 knockdown on each subcellular migratory activity. As a result, the CN correlation approach serves as a cell dynamic descriptor that can extract comprehensive quantitative data from cell migration movies for integrative biological analyses.

Fast de novo discovery of low-energy protein loop conformations.

In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All-atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side-chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near-native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402-1412. © 2017 Wiley Periodicals, Inc.

Conformational sampling and structure prediction of multiple interacting loops in soluble and ß-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method.

MOTIVATION: Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task. RESULTS: In this study, we report a new method called multi-loop Distance-guided Sequential chain-Growth Monte Carlo (M-DiSGro) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in ß-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop). AVAILABILITY AND IMPLEMENTATION: The software is freely available at: tanto.bioe.uic.edu/m-DiSGro. CONTACT: jinfeng@stat.fsu.edu or jliang@uic.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.