135 research outputs found
Accurate 3D Object Detection using Energy-Based Models
Accurate 3D object detection (3DOD) is crucial for safe navigation of complex
environments by autonomous robots. Regressing accurate 3D bounding boxes in
cluttered environments based on sparse LiDAR data is however a highly
challenging problem. We address this task by exploring recent advances in
conditional energy-based models (EBMs) for probabilistic regression. While
methods employing EBMs for regression have demonstrated impressive performance
on 2D object detection in images, these techniques are not directly applicable
to 3D bounding boxes. In this work, we therefore design a differentiable
pooling operator for 3D bounding boxes, serving as the core module of our EBM
network. We further integrate this general approach into the state-of-the-art
3D object detector SA-SSD. On the KITTI dataset, our proposed approach
consistently outperforms the SA-SSD baseline across all 3DOD metrics,
demonstrating the potential of EBM-based regression for highly accurate 3DOD.
Code is available at https://github.com/fregu856/ebms_3dod.Comment: Code is available at https://github.com/fregu856/ebms_3do
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
Objective: Imbalances of the electrolyte concentration levels in the body can
lead to catastrophic consequences, but accurate and accessible measurements
could improve patient outcomes. While blood tests provide accurate
measurements, they are invasive and the laboratory analysis can be slow or
inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool
which is quick and simple to acquire. However, the problem of estimating
continuous electrolyte concentrations directly from ECGs is not well-studied.
We therefore investigate if regression methods can be used for accurate
ECG-based prediction of electrolyte concentrations. Methods: We explore the use
of deep neural networks (DNNs) for this task. We analyze the regression
performance across four electrolytes, utilizing a novel dataset containing over
290000 ECGs. For improved understanding, we also study the full spectrum from
continuous predictions to binary classification of extreme concentration
levels. To enhance clinical usefulness, we finally extend to a probabilistic
regression approach and evaluate different uncertainty estimates. Results: We
find that the performance varies significantly between different electrolytes,
which is clinically justified in the interplay of electrolytes and their
manifestation in the ECG. We also compare the regression accuracy with that of
traditional machine learning models, demonstrating superior performance of
DNNs. Conclusion: Discretization can lead to good classification performance,
but does not help solve the original problem of predicting continuous
concentration levels. While probabilistic regression demonstrates potential
practical usefulness, the uncertainty estimates are not particularly
well-calibrated. Significance: Our study is a first step towards accurate and
reliable ECG-based prediction of electrolyte concentration levels.Comment: Code and trained models are available at
https://github.com/philippvb/ecg-electrolyte-regressio
Genes with Relevance for Early to Late Progression of Colon Carcinoma Based on Combined Genomic and Transcriptomic Information from the Same Patients
Background: Genetic and epigenetic alterations in colorectal cancer are numerous. However, it is difficult to judge whether such changes are primary or secondary to the appearance and progression of tumors. Therefore, the aim of the present study was to identify altered DNA regions with significant covariation to transcription alterations along colon cancer progression. Methods: Tumor and normal colon tissue were obtained at primary operations from 24 patients selected by chance. DNA, RNA and microRNAs were extracted from the same biopsy material in all individuals and analyzed by oligo-nucleotide array-based comparative genomic hybridization (CGH), mRNA- and microRNA oligo-arrays. Statistical analyses were performed to assess statistical interactions (correlations, co-variations) between DNA copy number changes and significant alterations in gene and microRNA expression using appropriate parametric and non-parametric statistics. Results: Main DNA alterations were located on chromosome 7, 8, 13 and 20. Tumor DNA copy number gain increased with tumor progression, significantly related to increased gene expression. Copy number loss was not observed in Dukes A tumors. There was no significant relationship between expressed genes and tumor progression across Dukes AâD tumors; and no relationship between tumor stage and the number of microRNAs with significantly altered expression. Interaction analyses identified overall 41 genes, which discriminated early Dukes A plus B tumors from late Dukes C plus D tumor; 28 of these genes remained with correlations between genomic and transcriptomic alterations in Dukes C plus D tumors and 17 in Dukes D. One microRNA (microR-663) showed interactions with DNA alterations in all Dukes A-D tumors. Conclusions: Our modeling confirms that colon cancer progression is related to genomic instability and altered gene expression. How- ever, early invasive tumor growth seemed rather related to transcriptomic alterations, where changes in microRNA may be an early phenomenon, and less to DNA copy number changes
Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.
The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain âŒ8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD
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