Convergent and parallel evolution in life habit of the scallops (Bivalvia: Pectinidae)

<p>Abstract</p> <p>Background</p> <p>We employed a phylogenetic framework to identify patterns of life habit evolution in the marine bivalve family Pectinidae. Specifically, we examined the number of independent origins of each life habit and distinguished between convergent and parallel trajectories of life habit evolution using ancestral state estimation. We also investigated whether ancestral character states influence the frequency or type of evolutionary trajectories.</p> <p>Results</p> <p>We determined that temporary attachment to substrata by byssal threads is the most likely ancestral condition for the Pectinidae, with subsequent transitions to the five remaining habit types. Nearly all transitions between life habit classes were repeated in our phylogeny and the majority of these transitions were the result of parallel evolution from byssate ancestors. Convergent evolution also occurred within the Pectinidae and produced two additional gliding clades and two recessing lineages. Furthermore, our analysis indicates that byssal attaching gave rise to significantly more of the transitions than any other life habit and that the cementing and nestling classes are only represented as evolutionary outcomes in our phylogeny, never as progenitor states.</p> <p>Conclusions</p> <p>Collectively, our results illustrate that both convergence and parallelism generated repeated life habit states in the scallops. Bias in the types of habit transitions observed may indicate constraints due to physical or ontogenetic limitations of particular phenotypes.</p

Viral Bcl2s' transmembrane domain interact with host Bcl2 proteins to control cellular apoptosis

Viral control of programmed cell death relies in part on the expression of viral analogs of the B-cell lymphoma 2 (Bcl2) protein known as viral Bcl2s (vBcl2s). vBcl2s control apoptosis by interacting with host pro- and anti-apoptotic members of the Bcl2 family. Here, we show that the carboxyl-terminal hydrophobic region of herpesviral and poxviral vBcl2s can operate as transmembrane domains (TMDs) and participate in their homo-oligomerization. Additionally, we show that the viral TMDs mediate interactions with cellular pro- and anti-apoptotic Bcl2 TMDs within the membrane. Furthermore, these intra-membrane interactions among viral and cellular proteins are necessary to control cell death upon an apoptotic stimulus. Therefore, their inhibition represents a new potential therapy against viral infections, which are characterized by short- and long-term deregulation of programmed cell death

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

THE EASTERN PACIFIC SPECIES OF THE BIVALVE FAMILY SPHENIOPSIDAE

Volume: 33Start Page: 394End Page: 40

RECENT EASTERN PACIFIC SPECIES OF THE BIVALVE GENUS SEMELE

Volume: 31Start Page: 1End Page: 4

RECOGNITION OF CYCLOCARDIA-OVATA (RIABININA, 1952) IN THE EASTERN PACIFIC

Volume: 21Start Page: 488End Page: 48

THE RECENT EASTERN PACIFIC SPECIES OF THE BIVALVE FAMILY THRACIIDAE

Volume: 33Start Page: 20End Page: 5

THE MOLLUSCAN PUBLICATIONS AND TAXA OF YATES,LORENZO,GORDIN (1837-1909)

Volume: 33Start Page: 402End Page: 40