Université libre de Bruxelles

About: Université libre de Bruxelles is a education organization based out in Brussels, Belgium. It is known for research contribution in the topics: Population & Breast cancer. The organization has 24974 authors who have published 56969 publications receiving 2084303 citations. The organization is also known as: ULB.

Expression of members of the putative olfactory receptor gene family in mammalian germ cells

Abstract: A series of genomic and complementary DNA clones encoding new putative members of G protein-coupled receptors were isolated using homology cloning and low-stringency polymerase chain reaction. Among the unidentified receptors ('orphan receptors'), a human genomic clone (HGMP07) was characterized by the presence of its transcripts in the testis and by its belonging to a large subfamily of genes sharing extensive sequence similarities. Sequence comparison demonstrated that this gene subfamily is the human counterpart of the putative rat olfactory receptors cloned recently. Another 48 members of the family were cloned. Northern blotting further demonstrated the presence of olfactory receptor transcripts in germ cells. Our finding suggests that a common receptor gene family encodes olfactory receptors and sperm cell receptors that could be involved in chemotaxis during fertilization.

Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics

Abstract: Aim Large trees (d.b.h. 70 cm) store large amounts of biomass. Several studies suggest that large trees may be vulnerable to changing climate, poten- tially leading to declining forest biomass storage. Here we determine the importance of large trees for tropical forest biomass storage and explore which intrinsic (species trait) and extrinsic (environment) variables are associated with the density of large trees and forest biomass at continental and pan-tropical scales. Location Pan-tropical.

Observation of Gravitational Waves from Two Neutron Star–Black Hole Coalescences

Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO’s and Virgo’s third observing run with properties consistent with neutron star–black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo and the second by all three LIGO–Virgo detectors. The source of GW200105 has component masses 8.9−1.5+1.2 and 1.9−0.2+0.3M⊙ , whereas the source of GW200115 has component masses 5.7−2.1+1.8 and 1.5−0.3+0.7M⊙ (all measurements quoted at the 90% credible level). The probability that the secondary’s mass is below the maximal mass of a neutron star is 89%–96% and 87%–98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are 280−110+110 and 300−100+150Mpc , respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain the spin or tidal deformation of the secondary component for either event. We infer an NSBH merger rate density of 45−33+75Gpc−3yr−1 when assuming that GW200105 and GW200115 are representative of the NSBH population or 130−69+112Gpc−3yr−1 under the assumption of a broader distribution of component masses.

Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks

Abstract: Motivation: The rational design of proteins with modified properties, through amino acid substitutions, is of crucial importance in a large variety of applications. Given the huge number of possible substitutions, every protein engineering project would benefit strongly from the guidance of in silico methods able to predict rapidly, and with reasonable accuracy, the stability changes resulting from all possible mutations in a protein. Results: We exploit newly developed statistical potentials, based on a formalism that highlights the coupling between four protein sequence and structure descriptors, and take into account the amino acid volume variation upon mutation. The stability change is expressed as a linear combination of these energy functions, whose proportionality coefficients vary with the solvent accessibility of the mutated residue and are identified with the help of a neural network. A correlation coefficient of R = 0.63 and a root mean square error of σc = 1.15 kcal/mol between measured and predicted stability changes are obtained upon cross-validation. These scores reach R = 0.79, and σc = 0.86 kcal/mol after exclusion of 10% outliers. The predictive power of our method is shown to be significantly higher than that of other programs described in the literature. Availability: http://babylone.ulb.ac.be/popmusic Contact: ydehouck@ulb.ac.be Supplementary information:Supplementary data are available at Bioinformatics online.