Interlacing Self-Localization, Moving Object Tracking and Mapping for 3D Range Sensors

This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects

A randomized, phase III trial of capecitabine plus bevacizumab (Cape-Bev) versus capecitabine plus irinotecan plus bevacizumab (CAPIRI-Bev) in first-line treatment of metastatic colorectal cancer: The AIO KRK 0110 Trial/ML22011 Trial

<p>Abstract</p> <p>Background</p> <p>Several randomized trials have indicated that combination chemotherapy applied in metastatic colorectal cancer (mCRC) does not significantly improve overall survival when compared to the sequential use of cytotoxic agents (CAIRO, MRC Focus, FFCD 2000-05). The present study investigates the question whether this statement holds true also for bevacizumab-based first-line treatment including escalation- and de-escalation strategies.</p> <p>Methods/Design</p> <p>The AIO KRK 0110/ML22011 trial is a two-arm, multicenter, open-label randomized phase III trial comparing the efficacy and safety of capecitabine plus bevacizumab (Cape-Bev) versus capecitabine plus irinotecan plus bevacizumab (CAPIRI-Bev) in the first-line treatment of metastatic colorectal cancer. Patients with unresectable metastatic colorectal cancer, Eastern Cooperative Oncology Group (ECOG) performance status 0-1, will be assigned in a 1:1 ratio to receive either capecitabine 1250 mg/m²bid for 14d (d1-14) plus bevacizumab 7.5 mg/kg (d1) q3w (Arm A) or capecitabine 800 mg/m²BID for 14d (d1-14), irinotecan 200 mg/m²(d1) and bevacizumab 7.5 mg/kg (d1) q3w (Arm B). Patients included into this trial are required to consent to the analysis of tumour tissue and blood for translational investigations. In Arm A, treatment escalation from Cape-Bev to CAPIRI-Bev is recommended in case of progressive disease (PD). In Arm B, de-escalation from CAPIRI-Bev to Cape-Bev is possible after 6 months of treatment or in case of irinotecan-associated toxicity. Re-escalation to CAPIRI-Bev after PD is possible. The primary endpoint is time to failure of strategy (TFS). Secondary endpoints are overall response rate (ORR), overall survival, progression-free survival, safety and quality of life.</p> <p>Conclusion</p> <p>The AIO KRK 0110 trial is designed for patients with disseminated, but asymptomatic mCRC who are not potential candidates for surgical resection of metastasis. Two bevacizumab-based strategies are compared: one starting as single-agent chemotherapy (Cape-Bev) allowing escalation to CAPIRI-Bev and another starting with combination chemotherapy (CAPIRI-Bev) and allowing de-escalation to Cape-Bev and subsequent re-escalation if necessary.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov Identifier <a href="http://www.clinicaltrials.gov/ct2/show/NCT01249638">NCT01249638</a></p> <p>EudraCT-No.: 2009-013099-38</p

Common Genetic Variants Contribute to Risk of Transposition of the Great Arteries

RATIONALE: Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored. OBJECTIVE: We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA. METHODS AND RESULTS: We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10-10, OR=0.69 per C allele). SNP-based heritability analysis showed that 25% of variance in susceptibility to D-TGA may be explained by common variants. A genome-wide polygenic risk score derived from the discovery set was significantly associated to D-TGA in the replication set (P=4x10-5). The genome-wide significant locus (3p14.3) co-localizes with a putative regulatory element that interacts with the promoter of WNT5A, which encodes the Wnt Family Member 5A protein known for its role in cardiac development in mice. We show that this element drives reporter gene activity in the developing heart of mice and zebrafish and is bound by the developmental transcription factor TBX20. We further demonstrate that TBX20 attenuates Wnt5a expression levels in the developing mouse heart. CONCLUSIONS: This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 near WNT5A. Genomic and functional data support a causal role of WNT5A at the locus

Contributions and complexities from the use of in-vivo animal models to improve understanding of human neuroimaging signals.

Many of the major advances in our understanding of how functional brain imaging signals relate to neuronal activity over the previous two decades have arisen from physiological research studies involving experimental animal models. This approach has been successful partly because it provides opportunities to measure both the hemodynamic changes that underpin many human functional brain imaging techniques and the neuronal activity about which we wish to make inferences. Although research into the coupling of neuronal and hemodynamic responses using animal models has provided a general validation of the correspondence of neuroimaging signals to specific types of neuronal activity, it is also highlighting the key complexities and uncertainties in estimating neural signals from hemodynamic markers. This review will detail how research in animal models is contributing to our rapidly evolving understanding of what human neuroimaging techniques tell us about neuronal activity. It will highlight emerging issues in the interpretation of neuroimaging data that arise from in-vivo research studies, for example spatial and temporal constraints to neuroimaging signal interpretation, or the effects of disease and modulatory neurotransmitters upon neurovascular coupling. We will also give critical consideration to the limitations and possible complexities of translating data acquired in the typical animals models used in this area to the arena of human fMRI. These include the commonplace use of anaesthesia in animal research studies and the fact that many neuropsychological questions that are being actively explored in humans have limited homologues within current animal models for neuroimaging research. Finally we will highlighting approaches, both in experimental animals models (e.g. imaging in conscious, behaving animals) and human studies (e.g. combined fMRI-EEG), that mitigate against these challenges

Common Genetic Variants Contribute to Risk of Transposition of the Great Arteries.

RATIONALE: Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored. OBJECTIVE: We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA. METHODS AND RESULTS: We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10 CONCLUSIONS: This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 nea

Interlacing Self-Localization, Moving Object Tracking and Mapping for 3D Range Sensors

This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects

Unsupervised discovery of object classes in 3D outdoor scenarios

Designing object models for a robot’s detection-system can be very time-consuming since many object classes exist. This paper presents an approach that automatically infers object classes from recorded 3D data and collects training examples. A special focus is put on difficult unstructured outdoor scenarios with object classes ranging from cars over trees to buildings. In contrast to many existing works, it is not assumed that perfect segmentation of the scene is possible. Instead, a novel hierarchical segmentation method is proposed that works together with a novel inference strategy to infer object classes

Fast discriminative visual codebooks using randomized clustering forests

Some of the most effective recent methods for content-based image classification work by extracting dense or sparse local image descriptors, quantizing them according to a coding rule such as k-means vector quantization, accumulating histograms of the resulting “visual word ” codes over the image, and classifying these with a conventional classifier such as an SVM. Large numbers of descriptors and large codebooks are needed for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests – ensembles of randomly created clustering trees – and show that these provide more accurate results, much faster training and testing and good resistance to background clutter in several state-of-the-art image classification tasks.

Learning saliency maps for object categorization

Abstract. We present a novel approach for object category recognition that can find objects in challenging conditions using visual attention technique. It combines saliency maps very closely with the extraction of random subwindows for classification purposes. The maps are built online by the classifier while being used by it to classify the image. Saliency is therefore automatically suited to the abilities of the classifier and not an additional concept that is tried to fit into another method. Our results show that we can obtain state of the art results on commonly used datasets with using only little information and thus achieve high efficiency and short processing times.