Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC

Lung function associated gene Integrator Complex subunit 12 regulates protein synthesis pathways

Background: Genetic studies of human lung function and Chronic Obstructive Pulmonary Disease have identified a highly significant and reproducible signal on 4q24. It remains unclear which of the two candidate genes within this locus may regulate lung function: GSTCD, a gene with unknown function, and/or INTS12, a member of the Integrator Complex which is currently thought to mediate 3'end processing of small nuclear RNAs.Results: We found that, in lung tissue, 4q24 polymorphisms associated with lung function correlate with INTS12 but not neighbouring GSTCD expression. In contrast to the previous reports in other species, we only observed a minor alteration of snRNA processing following INTS12 depletion. RNAseq analysis of knockdown cells instead revealed dysregulation of a core subset of genes relevant to airway biology and a robust downregulation of protein synthesis pathways. Consistent with this, protein translation was decreased in INTS12 knockdown cells. In addition, ChIPseq experiments demonstrated INTS12 binding throughout the genome, which was enriched in transcriptionally active regions. Finally, we defined the INTS12 regulome which includes genes belonging to the protein synthesis pathways.Conclusion: INTS12 has functions beyond the canonical snRNA processing. We show that it regulates translation by regulating the expression of genes belonging to protein synthesis pathways. This study provides a detailed analysis of INTS12 activities on a genome-wide scale and contributes to the biology behind the genetic association for lung function at 4q24.</p

Cross-National Measurement Invariance of the Teacher and Classmate Support Scale

The cross-national measurement invariance of the teacher and classmate support scale was assessed in a study of 23202 Grade 8 and 10 students from Austria, Canada, England, Lithuania, Norway, Poland, and Slovenia, participating in the Health Behaviour in School-aged Children (HBSC) 2001/2002 study. A multi-group means and covariance analysis supported configural and metric invariance across countries, but not full scalar equivalence. The composite reliability was adequate and highly consistent across countries. In all seven countries, teacher support showed stronger associations with school satisfaction than did classmate support, with the results being highly consistent across countries. The results indicate that the teacher and classmate support scale may be used in cross-cultural studies that focus on relationships between teacher and classmate support and other constructs. However, the lack of scalar equivalence indicates that direct comparison of the levels support across countries might not be warranted

The Anglo-Saxon migration and the formation of the early English gene pool

The history of the British Isles and Ireland is characterized by multiple periods of major cultural change, including the influential transformation after the end of Roman rule, which precipitated shifts in language, settlement patterns and material culture1. The extent to which migration from continental Europe mediated these transitions is a matter of long-standing debate2–4. Here we study genome-wide ancient DNA from 460 medieval northwestern Europeans—including 278 individuals from England—alongside archaeological data, to infer contemporary population dynamics. We identify a substantial increase of continental northern European ancestry in early medieval England, which is closely related to the early medieval and present-day inhabitants of Germany and Denmark, implying large-scale substantial migration across the North Sea into Britain during the Early Middle Ages. As a result, the individuals who we analysed from eastern England derived up to 76% of their ancestry from the continental North Sea zone, albeit with substantial regional variation and heterogeneity within sites. We show that women with immigrant ancestry were more often furnished with grave goods than women with local ancestry, whereas men with weapons were as likely not to be of immigrant ancestry. A comparison with present- day Britain indicates that subsequent demographic events reduced the fraction of continental northern European ancestry while introducing further ancestry components into the English gene pool, including substantial southwestern European ancestry most closely related to that seen in Iron Age Franc

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Natural and Synthetic Polymers as Inhibitors of Drug Efflux Pumps

Inhibition of efflux pumps is an emerging approach in cancer therapy and drug delivery. Since it has been discovered that polymeric pharmaceutical excipients such as Tweens® or Pluronics® can inhibit efflux pumps, various other polymers have been investigated regarding their potential efflux pump inhibitory activity. Among them are polysaccharides, polyethylene glycols and derivatives, amphiphilic block copolymers, dendrimers and thiolated polymers. In the current review article, natural and synthetic polymers that are capable of inhibiting efflux pumps as well as their application in cancer therapy and drug delivery are discussed