Yale University

About: Yale University is a education organization based out in New Haven, Connecticut, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 89824 authors who have published 220665 publications receiving 12834776 citations. The organization is also known as: Yale & Collegiate School.

Guidelines for investigating causality of sequence variants in human disease

Abstract: The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Quantum computation with trapped polar molecules

Abstract: We propose a novel physical realization of a quantum computer. The qubits are electric dipole moments of ultracold diatomic molecules, oriented along or against an external electric field. Individual molecules are held in a 1D trap array, with an electric field gradient allowing spectroscopic addressing of each site. Bits are coupled via the electric dipole-dipole interaction. Using technologies similar to those already demonstrated, this design can plausibly lead to a quantum computer with $\ensuremath{\gtrsim}{10}^{4}$ qubits, which can perform $\ensuremath{\sim}{10}^{5}$ CNOT gates in the anticipated decoherence time of $\ensuremath{\sim}5\mathrm{s}$.

The menthol receptor TRPM8 is the principal detector of environmental cold

Abstract: Sensory nerve fibres can detect changes in temperature over a remarkably wide range, a process that has been proposed to involve direct activation of thermosensitive excitatory transient receptor potential (TRP) ion channels. One such channel--TRP melastatin 8 (TRPM8) or cold and menthol receptor 1 (CMR1)--is activated by chemical cooling agents (such as menthol) or when ambient temperatures drop below approximately 26 degrees C, suggesting that it mediates the detection of cold thermal stimuli by primary afferent sensory neurons. However, some studies have questioned the contribution of TRPM8 to cold detection or proposed that other excitatory or inhibitory channels are more critical to this sensory modality in vivo. Here we show that cultured sensory neurons and intact sensory nerve fibres from TRPM8-deficient mice exhibit profoundly diminished responses to cold. These animals also show clear behavioural deficits in their ability to discriminate between cold and warm surfaces, or to respond to evaporative cooling. At the same time, TRPM8 mutant mice are not completely insensitive to cold as they avoid contact with surfaces below 10 degrees C, albeit with reduced efficiency. Thus, our findings demonstrate an essential and predominant role for TRPM8 in thermosensation over a wide range of cold temperatures, validating the hypothesis that TRP channels are the principal sensors of thermal stimuli in the peripheral nervous system.