The Container Selection Problem

We introduce and study a network resource management problem that is a special case of non-metric k-median, naturally arising in cross platform scheduling and cloud computing. In the continuous d-dimensional container selection problem, we are given a set C of input points in d-dimensional Euclidean space, for some d >= 2, and a budget k. An input point p can be assigned to a "container point" c only if c dominates p in every dimension. The assignment cost is then equal to the L1-norm of the container point. The goal is to find k container points in the d-dimensional space, such that the total assignment cost for all input points is minimized. The discrete variant of the problem has one key distinction, namely, the container points must be chosen from a given set F of points. For the continuous version, we obtain a polynomial time approximation scheme for any fixed dimension d>= 2. On the negative side, we show that the problem is NP-hard for any d>=3. We further show that the discrete version is significantly harder, as it is NP-hard to approximate without violating the budget k in any dimension d>=3. Thus, we focus on obtaining bi-approximation algorithms. For d=2, the bi-approximation guarantee is (1+epsilon,3), i.e., for any epsilon>0, our scheme outputs a solution of size 3k and cost at most (1+epsilon) times the optimum. For fixed d>2, we present a (1+epsilon,O((1/epsilon)log k)) bi-approximation algorithm

Selective vulnerability to atrophy in sporadic Creutzfeldt-Jakob disease

Identification of brain regions susceptible to quantifiable atrophy in sporadic Creutzfeldt-Jakob disease (sCJD) should allow for improved understanding of disease pathophysiology and development of structural biomarkers that might be useful in future treatment trials. Although brain atrophy is not usually present by visual assessment of MRIs in sCJD, we assessed whether using voxel-based morphometry (VBM) can detect group-wise brain atrophy in sCJD. 3T brain MRI data were analyzed with VBM in 22 sCJD participants and 26 age-matched controls. Analyses included relationships of regional brain volumes with major clinical variables and dichotomization of the cohort according to expected disease duration based on prion molecular classification (i.e., short-duration/Fast-progressors (MM1, MV1, and VV2) vs. long-duration/Slow-progressors (MV2, VV1, and MM2)). Structural equation modeling (SEM) was used to assess network-level interactions of atrophy between specific brain regions. sCJD showed selective atrophy in cortical and subcortical regions overlapping with all but one region of the default mode network (DMN) and the insulae, thalami, and right occipital lobe. SEM showed that the effective connectivity model fit in sCJD but not controls. The presence of visual hallucinations correlated with right fusiform, bilateral thalami, and medial orbitofrontal atrophy. Interestingly, brain atrophy was present in both Fast- and Slow-progressors. Worse cognition was associated with bilateral mesial frontal, insular, temporal pole, thalamus, and cerebellum atrophy. Brain atrophy in sCJD preferentially affects specific cortical and subcortical regions, with an effective connectivity model showing strength and directionality between regions. Brain atrophy is present in Fast- and Slow-progressors, correlates with clinical findings, and is a potential biomarker in sCJD

Impact of cortical and subcortical atrophy in the diagnosis and prognosis of bvFTD: A multicenter longitudinal study

AbstractBackgroundThe behavioral variant of frontotemporal dementia (bvFTD) presents with variable patterns of cortical and subcortical atrophy on Magnetic Resonance Imaging (MRI). We aimed to assess the clinical utility of two reproducible measurements of cerebral atrophy (Harper's visual atrophy scale [HVAS], and the Magnetic Resonance Parkinsonism Index [MRPI]) in a large multicenter sample of bvFTD with longitudinal follow‐up.MethodsWe included 466 participants from three centers: 241 bvFTD (according to the International bvFTD Criteria Consortium), and 225 healthy controls (HC). Clinical deterioration was assessed with Mini‐Mental State Examination (MMSE) and the Clinical Deterioration Scale Sum‐of‐boxes (CDR‐sb). bvFTD participants were considered to have an increased certainty of underlying Frontotemporal Lobar Degeneration (+FTLD) when: (i) FTLD was confirmed at autopsy (n=72); (ii) a secondary FTLD‐related phenotype was identified during follow‐up (n=47) or (iii) a FTLD‐related mutation was found (n=49). Six raters blinded to clinical data were first asked to dichotomize participants according to the presence of "a clear pattern of atrophy suggestive of probable bvFTD", and then applied the HVAS (ICC(2,k)=.86 to .96). The MRPI was calculated with a fully automated algorithm.ResultsMean age of bvFTD participants was 63.3 ± 10, 68% were male (MMSE=23 ± 7 and CDR‐sb=6.7 ± 3.5). Blinded raters had 52% sensitivity and 97% specificity for the identification of bvFTD participants (AUC=.74, p=.001). All HVAS measures with the exception of posterior atrophy discriminated between bvFTD and HC (AUC=.77 to .83, p<.001). The composite bvFTD score (average score of orbitofrontal, anterior cingulate, anterior temporal, medial temporal lobe and frontal insula regions) showed the best diagnostic accuracy for the differentiation of bvFTD from HC (AUC=.91, p<.001 in +FTLD). This composite score also differentiated between bvFTD participants that were not considered to have a clear pattern of atrophy suggestive of probable bvFTD (blinded raters) and HC (p<.001). We hypothesized that HVAS and MRPI scores may be independent predictors of clinical deterioration and survival in bvFTD (definitive results pending).ConclusionThe combination of HVAS and MRPI may provide valuable diagnostic and prognostic information in the behavioral syndromes verifying bvFTD criteria. These measures represent reliable, reproducible and affordable imaging biomarkers

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Suboptimal Activation of Antigen-Specific CD4+ Effector Cells Enables Persistence of M. tuberculosis In Vivo

Adaptive immunity to Mycobacterium tuberculosis controls progressive bacterial growth and disease but does not eradicate infection. Among CD4+ T cells in the lungs of M. tuberculosis-infected mice, we observed that few produced IFN-γ without ex vivo restimulation. Therefore, we hypothesized that one mechanism whereby M. tuberculosis avoids elimination is by limiting activation of CD4+ effector T cells at the site of infection in the lungs. To test this hypothesis, we adoptively transferred Th1-polarized CD4+ effector T cells specific for M. tuberculosis Ag85B peptide 25 (P25TCRTh1 cells), which trafficked to the lungs of infected mice and exhibited antigen-dependent IFN-γ production. During the early phase of infection, ∼10% of P25TCRTh1 cells produced IFN-γ in vivo; this declined to <1% as infection progressed to chronic phase. Bacterial downregulation of fbpB (encoding Ag85B) contributed to the decrease in effector T cell activation in the lungs, as a strain of M. tuberculosis engineered to express fbpB in the chronic phase stimulated P25TCRTh1 effector cells at higher frequencies in vivo, and this resulted in CD4+ T cell-dependent reduction of lung bacterial burdens and prolonged survival of mice. Administration of synthetic peptide 25 alone also increased activation of endogenous antigen-specific effector cells and reduced the bacterial burden in the lungs without apparent host toxicity. These results indicate that CD4+ effector T cells are activated at suboptimal frequencies in tuberculosis, and that increasing effector T cell activation in the lungs by providing one or more epitope peptides may be a successful strategy for TB therapy

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Diagnostic Utility of Measuring Cerebral Atrophy in the Behavioral Variant of Frontotemporal Dementia and Association With Clinical Deterioration

Can widely available measures of atrophy on magnetic resonance imaging increase diagnostic certainty of underlying frontotemporal lobar degeneration (FTLD) and estimate clinical deterioration in the behavioral variant of frontotemporal dementia (bvFTD)? This diagnostic/prognostic study investigated the clinical utility of 5 validated visual atrophy scales (VAS) and the Magnetic Resonance Parkinsonism Index. When combined, VAS showed excellent diagnostic performance for differentiating between bvFTD with high and low confidence of FTLD and for the estimation of longitudinal clinical deterioration, whereas the Magnetic Resonance Parkinsonism Index was increased in bvFTD with underlying 4-repeat tauopathies. These findings suggest that, in bvFTD, VAS can be used to increase diagnostic certainty of underlying FTLD and estimate longitudinal clinical deterioration. This diagnostic/prognostic study assesses the utility of 6 visual atrophy scales and the Magnetic Resonance Parkinsonism Index in patients with behavioral variant frontotemporal dementia to distinguish those with high vs low confidence of frontotemporal lobar degeneration. The presence of atrophy on magnetic resonance imaging can support the diagnosis of the behavioral variant of frontotemporal dementia (bvFTD), but reproducible measurements are lacking. To assess the diagnostic and prognostic utility of 6 visual atrophy scales (VAS) and the Magnetic Resonance Parkinsonism Index (MRPI). In this diagnostic/prognostic study, data from 235 patients with bvFTD and 225 age- and magnetic resonance imaging-matched control individuals from 3 centers were collected from December 1, 1998, to September 30, 2019. One hundred twenty-one participants with bvFTD had high confidence of frontotemporal lobar degeneration (FTLD) (bvFTD-HC), and 19 had low confidence of FTLD (bvFTD-LC). Blinded clinicians applied 6 previously validated VAS, and the MRPI was calculated with a fully automated approach. Cortical thickness and subcortical volumes were also measured for comparison. Data were analyzed from February 1 to June 30, 2020. The main outcomes of this study were bvFTD-HC or a neuropathological diagnosis of 4-repeat (4R) tauopathy and the clinical deterioration rate (assessed by longitudinal measurements of Clinical Dementia Rating Sum of Boxes). Measures of cerebral atrophy included VAS scores, the bvFTD atrophy score (sum of VAS scores in orbitofrontal, anterior cingulate, anterior temporal, medial temporal lobe, and frontal insula regions), the MRPI, and other computerized quantifications of cortical and subcortical volumes. The areas under the receiver operating characteristic curve (AUROC) were calculated for the differentiation of participants with bvFTD-HC and bvFTD-LC and controls. Linear mixed models were used to evaluate the ability of atrophy measures to estimate longitudinal clinical deterioration. Of the 460 included participants, 296 (64.3%) were men, and the mean (SD) age was 62.6 (11.4) years. The accuracy of the bvFTD atrophy score for the differentiation of bvFTD-HC from controls (AUROC, 0.930; 95% CI, 0.903-0.957) and bvFTD-HC from bvFTD-LC (AUROC, 0.880; 95% CI, 0.787-0.972) was comparable to computerized measures (AUROC, 0.973 [95% CI, 0.954-0.993] and 0.898 [95% CI, 0.834-0.962], respectively). The MRPI was increased in patients with bvFTD and underlying 4R tauopathies compared with other FTLD subtypes (14.1 [2.0] vs 11.2 [2.6] points; P < .001). Higher bvFTD atrophy scores were associated with faster clinical deterioration in bvFTD (1.86-point change in Clinical Dementia Rating Sum of Boxes score per bvFTD atrophy score increase per year; 95% CI, 0.99-2.73; P < .001). Based on these study findings, in bvFTD, VAS increased the diagnostic certainty of underlying FTLD, and the MRPI showed potential for the detection of participants with underlying 4R tauopathies. These widely available measures of atrophy can also be useful to estimate longitudinal clinical deterioration