Lymphocyte-Tropic Simian Immunodeficiency Virus Causes Persistent Infection in the Brains of Rhesus Monkeys

AbstractMolecularly cloned SIVmac239 is the prototypical SIVmaclymphocyte-tropic virus that replicates productively in lymphocytes but poorly in macrophages. In macaques, the virus causes activation and productive infection of T lymphocytes which invade the central nervous system (CNS) early after infection in the animal. However, infected animals develop immunosuppression and AIDS but rarely overt neurological disease. In this study, we examined multiple regions of the brain and spinal cord for the presence of SIVenvsequences and histological lesions in five macaques that had been infected with SIVmac239 for 1.7 to 2.25 years. Histopathological examination of the brain revealed no lesions consistent with encephalitis; however, viral DNA was found in all five brains. In one animal the virus caused infection in a widely disseminated pattern from the frontal cortex to the distal end of the spinal cord, whereas in the other four animals infection in the CNS occurred in a nonspecific, focal pattern. Sequence analyses were performed on gp120 sequences isolated from selected regions of the CNS and compared to gp120 sequences isolated from corresponding lymph nodes, a tissue known to support productive replication of SIVmac239. Examination of the viral sequences from the CNS tissue from two animals (macaques 10F and 14F) revealed a low mutation rate when compared to the sequences isolated from the lymph node tissues. The percentage change in the amino acid sequence was approximately 1% for CNS clones versus ≥3% for clones isolated from the lymph node. The majority of the CNS viral sequences of macaques 10F and 14F had none of the genetic markers shown in a previous study to be associated with macrophage-tropic variants and indeed retained a nucleotide sequence of similar to the original lymphocyte-tropic virus used for inoculation despite almost 2 years of persistent infection in the animals. Construction of chimeric viruses with V1–V5 regions of selected macaque 10F and macaque 14F CNS-gp120 clones confirmed the predicted lymphocyte-tropic nature of theseenvgenes. In contrast, the gp120 sequences isolated from the CNS tissue of one of the other three animals (macaque 13F) had a mutation rate comparable to that observed for the lymph node clones. The CNS clones from this animal had amino acid substitutions that were previously shown to be associated with macrophage tropism. Compared to the chimeric viruses constructed with V1–V5 sequences from macaques 10F and 14F, viruses constructed with the V1–V5 sequences of several macaque 13F brain clones did not yield infectious virus. These data suggest that following entry into the CSF early during infection in the animals, SIVmac239 caused infection in the CNS. In some animals, the viralenvsequences recovered by the PCR suggested that minimal replication had occurred, whereas in another macaque virus replication had progressed with gradual selection of a more macrophage-tropic genotype

AI-defined cardiac anatomy Improves Risk Stratification of Hybrid Perfusion Imaging

Background: CTAC improves perfusion quantification of hybrid myocardial perfusion imaging (MPI) by correcting for attenuation artifacts. AI can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features. Objectives: We evaluated artificial intelligence (AI)-based derivation of cardiac anatomy from CT attenuation correction (CTAC) and assessed its added prognostic utility.Methods: We considered consecutive patients without known coronary artery disease who underwent SPECT/CT MPI at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium[LA], left ventricle[LV], right atrium[RA], and right ventricle[RV] volume and LV mass) from CTAC. We evaluated associations with major adverse cardiovascular events (MACE), which included death, myocardial infarction, unstable angina, or revascularization.Results: In total, 7,613 patients were included with median age 64. During median follow-up of 2.4 (interquartile range 1.3 – 3.4) years, MACE occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and LA, LV, RA, and RV volume were at significantly increased risk of MACE compared to patients in the lowest quartile, with a hazard ratio ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved continuous net reclassification index by 23.1%.Conclusions: AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.<br/

Approaching the Problem of Time with a Combined Semiclassical-Records-Histories Scheme

I approach the Problem of Time and other foundations of Quantum Cosmology using a combined histories, timeless and semiclassical approach. This approach is along the lines pursued by Halliwell. It involves the timeless probabilities for dynamical trajectories entering regions of configuration space, which are computed within the semiclassical regime. Moreover, the objects that Halliwell uses in this approach commute with the Hamiltonian constraint, H. This approach has not hitherto been considered for models that also possess nontrivial linear constraints, Lin. This paper carries this out for some concrete relational particle models (RPM's). If there is also commutation with Lin - the Kuchar observables condition - the constructed objects are Dirac observables. Moreover, this paper shows that the problem of Kuchar observables is explicitly resolved for 1- and 2-d RPM's. Then as a first route to Halliwell's approach for nontrivial linear constraints that is also a construction of Dirac observables, I consider theories for which Kuchar observables are formally known, giving the relational triangle as an example. As a second route, I apply an indirect method that generalizes both group-averaging and Barbour's best matching. For conceptual clarity, my study involves the simpler case of Halliwell 2003 sharp-edged window function. I leave the elsewise-improved softened case of Halliwell 2009 for a subsequent Paper II. Finally, I provide comments on Halliwell's approach and how well it fares as regards the various facets of the Problem of Time and as an implementation of QM propositions.Comment: An improved version of the text, and with various further references. 25 pages, 4 figure

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Computational Structural Analysis: Multiple Proteins Bound to DNA

BACKGROUND: With increasing numbers of crystal structures of proteinratioDNA and proteinratioproteinratioDNA complexes publically available, it is now possible to extract sufficient structural, physical-chemical and thermodynamic parameters to make general observations and predictions about their interactions. In particular, the properties of macromolecular assemblies of multiple proteins bound to DNA have not previously been investigated in detail. METHODOLOGY/PRINCIPAL FINDINGS: We have performed computational structural analyses on macromolecular assemblies of multiple proteins bound to DNA using a variety of different computational tools: PISA; PROMOTIF; X3DNA; ReadOut; DDNA and DCOMPLEX. Additionally, we have developed and employed an algorithm for approximate collision detection and overlapping volume estimation of two macromolecules. An implementation of this algorithm is available at http://promoterplot.fmi.ch/Collision1/. The results obtained are compared with structural, physical-chemical and thermodynamic parameters from proteinratioprotein and single proteinratioDNA complexes. Many of interface properties of multiple proteinratioDNA complexes were found to be very similar to those observed in binary proteinratioDNA and proteinratioprotein complexes. However, the conformational change of the DNA upon protein binding is significantly higher when multiple proteins bind to it than is observed when single proteins bind. The water mediated contacts are less important (found in less quantity) between the interfaces of components in ternary (proteinratioproteinratioDNA) complexes than in those of binary complexes (proteinratioprotein and proteinratioDNA).The thermodynamic stability of ternary complexes is also higher than in the binary interactions. Greater specificity and affinity of multiple proteins binding to DNA in comparison with binary protein-DNA interactions were observed. However, protein-protein binding affinities are stronger in complexes without the presence of DNA. CONCLUSIONS/SIGNIFICANCE: Our results indicate that the interface properties: interface area; number of interface residues/atoms and hydrogen bonds; and the distribution of interface residues, hydrogen bonds, van der Walls contacts and secondary structure motifs are independent of whether or not a protein is in a binary or ternary complex with DNA. However, changes in the shape of the DNA reduce the off-rate of the proteins which greatly enhances the stability and specificity of ternary complexes compared to binary ones

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning:a retrospective observational study

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].</p

ViPR: an open bioinformatics database and analysis resource for virology research

The Virus Pathogen Database and Analysis Resource (ViPR, www.ViPRbrc.org) is an integrated repository of data and analysis tools for multiple virus families, supported by the National Institute of Allergy and Infectious Diseases (NIAID) Bioinformatics Resource Centers (BRC) program. ViPR contains information for human pathogenic viruses belonging to the Arenaviridae, Bunyaviridae, Caliciviridae, Coronaviridae, Flaviviridae, Filoviridae, Hepeviridae, Herpesviridae, Paramyxoviridae, Picornaviridae, Poxviridae, Reoviridae, Rhabdoviridae and Togaviridae families, with plans to support additional virus families in the future. ViPR captures various types of information, including sequence records, gene and protein annotations, 3D protein structures, immune epitope locations, clinical and surveillance metadata and novel data derived from comparative genomics analysis. Analytical and visualization tools for metadata-driven statistical sequence analysis, multiple sequence alignment, phylogenetic tree construction, BLAST comparison and sequence variation determination are also provided. Data filtering and analysis workflows can be combined and the results saved in personal ‘Workbenches’ for future use. ViPR tools and data are available without charge as a service to the virology research community to help facilitate the development of diagnostics, prophylactics and therapeutics for priority pathogens and other viruses