A field guide to cultivating computational biology.

Evo:lvPinlegaisnecsoynnfcirwmitthhatthalelhceoamdipnugtleavtieolnsarreevreopluretisoenntoevdecor rtrheectplya:st 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science

SUMOylation does not affect cardiac troponin I stability but alters indirectly the development of force in response to Ca2+

Post-translational modification of the myofilament protein troponin I by phosphorylation is known to trigger functional changes that support enhanced contraction and relaxation of the heart. We report for the first time that human troponin I can also be modified by SUMOylation at lysine 177. Functionally, TnI SUMOylation is not a factor in the development of passive and maximal force generation in response to calcium, however this modification seems to act indirectly by preventing SUMOylation of other myofilament proteins to alter calcium sensitivity and cooperativity of myofilaments. Utilising a novel, custom SUMO site-specific antibody that recognises only the SUMOylated form of troponin I, we verify that this modification occurs in human heart and that it is upregulated during disease

Multipath exploitation radar with adaptive detection in partially homogeneous environments

The authors deal with the problem of detecting point-like targets in the presence of diffuse multipath under the assumption of a partially homogeneous Gaussian disturbance by introducing an unknown scaling factor which represents the mismatch between the noise contribution of the cell under test and the training samples. Also, they model the target echo as a superposition of direct plus multipath components where multipath returns are thought of as scattered signals from a glistening surface. Hence, multipath echoes are represented as a Gaussian distributed random vector with an unknown covariance matrix. Then, the authors derive a constrained generalised likelihood ratio test under the assumption that the primary data covariance structure is similar to the covariance matrix obtained from training samples where the degree of similarity is up to both noise scaling factor and multipath contribution. Besides, they prove that the proposed detector ensures constant false alarm rate (CFAR) property with respect to the unknown parameters. Finally, they compared the devised algorithm with the commonly used CFAR estimators. The results show that the proposed detector copes well with diffuse multipath conditions under partially homogeneous environments