Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions

A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems. We present a comparative analysis of several ensemble methods through two case studies in genomics, namely the prediction of genetic interactions and protein functions, to demonstrate their efficacy on real-world datasets and draw useful conclusions about their behavior. These methods include simple aggregation, meta-learning, cluster-based meta-learning, and ensemble selection using heterogeneous classifiers trained on resampled data to improve the diversity of their predictions. We present a detailed analysis of these methods across 4 genomics datasets and find the best of these methods offer statistically significant improvements over the state of the art in their respective domains. In addition, we establish a novel connection between ensemble selection and meta-learning, demonstrating how both of these disparate methods establish a balance between ensemble diversity and performance.Comment: 10 pages, 3 figures, 8 tables, to appear in Proceedings of the 2013 International Conference on Data Minin

Genetic diversity of the rain tree (Albizia saman) in Colombian seasonally dry tropical forest for informing conservation and restoration interventions

Albizia saman is a multipurpose tree species of seasonally dry tropical forests (SDTFs) of Mesoamerica and northern South America typically cultivated in silvopastoral and other agroforestry systems around the world, a trend that is bound to increase in light of multimillion hectare commitments for forest and landscape restoration. The effective conservation and sustainable use of A. saman requires detailed knowledge of its genetic diversity across its native distribution range of which surprisingly little is known to date. We assessed the genetic diversity and structure of A.saman across twelve representative locations of SDTF in Colombia, and how they may have been shaped by past climatic changes and human influence. We found four different genetic groups which may be the result of differentiation due to isolation of populations in preglacial times. The current distribution and mixture of genetic groups across STDF fragments we observed might be the result of range expansion of SDTFs during the last glacial period followed by range contraction during the Holocene and human‐influenced movement of germplasm associated with cattle ranching. Despite the fragmented state of the presumed natural A. saman stands we sampled, we did not find any signs of inbreeding, suggesting that gene flow is not jeopardized in humanized landscapes. However, further research is needed to assess potential deleterious effects of fragmentation on progeny. Climate change is not expected to seriously threaten the in situ persistence of A. saman populations and might present opportunities for future range expansion. However, the sourcing of germplasm for tree planting activities needs to be aligned with the genetic affinity of reference populations across the distribution of Colombian SDTFs. We identify priority source populations for in situ conservation based on their high genetic diversity, lack or limited signs of admixture, and/or genetic uniqueness

Marine forests of the Mediterranean-Atlantic Cystoseira tamariscifolia complex show a southern Iberian genetic hotspot and no reproductive isolation in parapatry

Climate-driven range-shifts create evolutionary opportunities for allopatric divergence and subsequent contact, leading to genetic structuration and hybrid zones. We investigate how these processes influenced the evolution of a complex of three closely related Cystoseira spp., which are a key component of the Mediterranean-Atlantic seaweed forests that are undergoing population declines. The C. tamariscifolia complex, composed of C. tamariscifolia s.s., C. amentacea and C. mediterranea, have indistinct boundaries and natural hybridization is suspected. Our aims are to (1) infer the genetic structure and diversity of these species throughout their distribution ranges using microsatellite markers to identify ancient versus recent geographical populations, contact zones and reproductive barriers, and (2) hindcast past distributions using niche models to investigate the influence of past range shifts on genetic divergence at multiple spatial scales. Results supported a single, morphologically plastic species the genetic structure of which was incongruent with a priori species assignments. The low diversity and low singularity in northern European populations suggest recent colonization after the LGM. The southern Iberian genetic hotspot most likely results from the role of this area as a climatic refugium or a secondary contact zone between differentiated populations or both. We hypothesize that life-history traits (selfing, low dispersal) and prior colonization effects, rather than reproductive barriers, might explain the observed genetic discontinuities.Pew Charitable Trusts (USA); MARINERA, Spain [CTM2008-04183-E/MAR]; FCT (Portugal) [FCT-BIODIVERSA/004/2015, CCMAR/Multi/04326/2013, SFRH/BPD/107878/2015, SFRH/BPD/85040/2012]; FPU fellowship of the Spanish Ministry of Education; European Community ASSEMBLE visiting grant [00399/2012]; University of Cadi

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949