Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie

Neutrophil to Lymphocyte Ratio and Outcomes in Patients with New-Onset or Worsening Heart Failure with Reduced and Preserved Ejection Fraction

Inflammation is thought to play a role in heart failure (HF) pathophysiology. Neutrophil-to-lymphocyte ratio (NLR) is a simple, routinely available measure of inflammation. Its relationship with other inflammatory biomarkers and its association with clinical outcomes in addition to other risk markers have not been comprehensively evaluated in HF patients. Methods We evaluated patients with worsening or new-onset HF from the BIOlogy Study to Tailored Treatment in Chronic Heart Failure (BIOSTAT-CHF) study who had available NLR at baseline. The primary outcome was time to all-cause mortality or HF hospitalization. Outcomes were validated in a separate HF population. Results 1622 patients were evaluated (including 523 ventricular ejection fraction [LVEF] < 40% and 662 LVEF ≥ 40%). NLR was significantly correlated with biomarkers related to inflammation as well as NT-proBNP. NLR was significantly associated with the primary outcome in patients irrespective of LVEF (hazard ratio [HR] 1.18 per standard deviation increase; 95% confidence interval [CI] 1.11–1.26, P < 0.001). Patients with NLR in the highest tertile had significantly worse outcome than those in the lowest independent of LVEF (<40%: HR 2.75; 95% CI 1.84–4.09, P < 0.001; LVEF ≥ 40%: HR 1.51; 95% CI 1.05–2.16, P = 0.026). When NLR was added to the BIOSTAT-CHF risk score, there were improvements in integrated discrimination index (IDI) and net reclassification index (NRI) for occurrence of the primary outcome (IDI + 0.009; 95% CI 0.00–0.019, P = 0.030; continuous NRI + 0.112, 95% CI 0.012–0.176, P = 0.040). Elevated NLR was similarly associated with adverse outcome in the validation cohort. Decrease in NLR at 6 months was associated with reduced incidence of the primary outcome (HR 0.75; 95% CI 0.57–0.98, P = 0.036). Conclusions Elevated NLR is significantly associated with elevated markers of inflammation in HF patients and is associated with worse outcome. Elevated NLR might potentially be useful in identifying high-risk HF patients and may represent a treatment target

A unified data representation theory for network visualization, ordering and coarse-graining

Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable both efficient data compression and hierarchical visualization to achieve high quality representations of larger data sets. Our unified data representation theory will help the analysis of extensive data sets, by revealing the large-scale structure of complex networks in a comprehensible form.Comment: 13 pages, 5 figure

Cardiac Dysfunction, Congestion and Loop Diuretics: their Relationship to Prognosis in Heart Failure

Background: Diuretics are the mainstay of treatment for congestion but concerns exist that they adversely affect prognosis. We explored whether the relationship between loop diuretic use and outcome is explained by the underlying severity of congestion amongst patients referred with suspected heart failure. Method and Results: Of 1190 patients, 712 had a left ventricular ejection fraction (LVEF) ≤50 %, 267 had LVEF >50 % with raised plasma NTproBNP (>400 ng/L) and 211 had LVEF >50 % with NTproBNP ≤400 ng/L; respectively, 72 %, 68 % and 37 % of these groups were treated with loop diuretics including 28 %, 29 % and 10 % in doses ≥80 mg furosemide equivalent/day. Compared to patients with cardiac dysfunction (either LVEF ≤50 % or NT-proBNP >400 ng/L) but not taking a loop diuretic, those taking a loop diuretic were older and had more clinical evidence of congestion, renal dysfunction, anaemia and hyponatraemia. During a median follow-up of 934 (IQR: 513–1425) days, 450 patients were hospitalized for HF or died. Patients prescribed loop diuretics had a worse outcome. However, in multi-variable models, clinical, echocardiographic (inferior vena cava diameter), and biochemical (NTproBNP) measures of congestion were strongly associated with an adverse outcome but not the use, or dose, of loop diuretics. Conclusions: Prescription of loop diuretics identifies patients with more advanced features of heart failure and congestion, which may account for their worse prognosis. Further research is needed to clarify the relationship between loop diuretic agents and outcome; imaging and biochemical measures of congestion might be better guides to diuretic dose than symptoms or clinical signs

T cell cytolytic capacity is independent of initial stimulation strength.

How cells respond to myriad stimuli with finite signaling machinery is central to immunology. In naive T cells, the inherent effect of ligand strength on activation pathways and endpoints has remained controversial, confounded by environmental fluctuations and intercellular variability within populations. Here we studied how ligand potency affected the activation of CD8+ T cells in vitro, through the use of genome-wide RNA, multi-dimensional protein and functional measurements in single cells. Our data revealed that strong ligands drove more efficient and uniform activation than did weak ligands, but all activated cells were fully cytolytic. Notably, activation followed the same transcriptional pathways regardless of ligand potency. Thus, stimulation strength did not intrinsically dictate the T cell-activation route or phenotype; instead, it controlled how rapidly and simultaneously the cells initiated activation, allowing limited machinery to elicit wide-ranging responses

Serum potassium levels and outcome in acute heart failure (Data from the PROTECT and COACH Trials)

Serum potassium is routinely measured at admission for acute heart failure (AHF), but information on association with clinical variables and prognosis is limited. Potassium measurements at admission were available in 1,867 patients with AHF in the original cohort of 2,033 patients included in the Patients Hospitalized with acute heart failure and Volume Overload to Assess Treatment Effect on Congestion and Renal FuncTion trial. Patients were grouped according to low potassium ( 5.0 mEq/l) levels. Results were veri fi ed in a validation cohort of 1,023 patients. Mean age of patients was 71 – 11 years, and 66% were men. Low potassium was present in 115 patients (6%), normal potassium in 1,576 (84%), and high potassium in 176 (9%). Potassium levels increased during hospitalization (0.18 – 0.69 mEq/l). Patients with high potassium more often used angiotensin-converting enzyme inhibitors and mineralo- corticoid receptor antagonists before admission, had impaired baseline renal function and a better diuretic response (p [ 0.005), independent of mineralocorticoid receptor antagonist usage. During 180-day follow-up, a total of 330 patients (18%) died. Potassium levels at admission showed a univariate linear association with mortality (hazard ratio [log] 2.36, 95% con fi dence interval 1.07 to 5.23; p [ 0.034) but not after multivariate adjustment. Changes of potassium levels during hospitalization or potassium levels at discharge were not associated with outcome after multivariate analysis. Results in the validation cohort were similar to the index cohort. In conclusion, high potassium levels at admission are associated with an impaired renal function but a better diuretic response. Changes in po- tassium levels are common, and overall levels increase during hospitalization. In conclu- sion, potassium levels at admission or its change during hospitalization are not associated with mortality after multivariate adjustment

Co-regulation map of the human proteome enables identification of protein functions

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this recordData availability: All mass spectrometry raw files generated in-house have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository36 with the dataset identifier PXD008888. The co-regulation map is hosted on our website at www.proteomeHD.net, and pair-wise co-regulation scores are available through STRING (https://string-db.org). A network of the top 0.5% co-regulated protein pairs can be explored interactively on NDEx (https://doi.org/10.18119/N9N30Q).Code availability: Data analysis was performed in R 3.5.1. R scripts and input files required to reproduce the results of this manuscript are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/ProteomeHD. R scripts related specifically to the benchmarking of the treeClust algorithm using synthetic data are available in the following GitHub repository: https://github.com/Rappsilber-Laboratory/treeClust-benchmarking. The R package data.table was used for fast data processing. Figures were prepared using ggplot2, gridExtra, cowplot and viridis.Note that the title of the AAM is different from the published versionThe annotation of protein function is a longstanding challenge of cell biology that suffers from the sheer magnitude of the task. Here we present ProteomeHD, which documents the response of 10,323 human proteins to 294 biological perturbations, measured by isotope-labelling mass spectrometry. We reveal functional associations between human proteins using the treeClust machine learning algorithm, which we show to improve protein co-regulation analysis due to robust selectivity for close linear relationships. Our co-regulation map identifies a functional context for many uncharacterized proteins, including microproteins that are difficult to study with traditional methods. Co-regulation also captures relationships between proteins which do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover a novel organelle interface between peroxisomes and mitochondria in mammalian cells. The co-regulation map can be explored at www.proteomeHD.net .Biotechnology & Biological Sciences Research Council (BBSRC)European Commissio