842 research outputs found
Application of Protein Membranes with Magnetic Nanoparticles for Co-Cultivation of Cell Cultures by Levitation in a Magnetic Field
Financial support was received from Ural Center for Modern Nanotechnologies of the Ural Federal University (UCMN, Yekaterinburg, Russia). This work was supported by the Russian Foundation for Basic Research, project 19-74-00081
Survival analysis of DNA mutation motifs with penalized proportional hazards
Antibodies, an essential part of our immune system, develop through an
intricate process to bind a wide array of pathogens. This process involves
randomly mutating DNA sequences encoding these antibodies to find variants with
improved binding, though mutations are not distributed uniformly across
sequence sites. Immunologists observe this nonuniformity to be consistent with
"mutation motifs", which are short DNA subsequences that affect how likely a
given site is to experience a mutation. Quantifying the effect of motifs on
mutation rates is challenging: a large number of possible motifs makes this
statistical problem high dimensional, while the unobserved history of the
mutation process leads to a nontrivial missing data problem. We introduce an
-penalized proportional hazards model to infer mutation motifs and
their effects. In order to estimate model parameters, our method uses a Monte
Carlo EM algorithm to marginalize over the unknown ordering of mutations. We
show that our method performs better on simulated data compared to current
methods and leads to more parsimonious models. The application of proportional
hazards to mutation processes is, to our knowledge, novel and formalizes the
current methods in a statistical framework that can be easily extended to
analyze the effect of other biological features on mutation rates
New strategic goals and organizational solutions in large R&D labs: lessons from Centro Ricerche Fiat and Telecom Italia Lab
The issue of corporate R&D management has become particularly relevant during the last decade, since many industrial sectors experienced growing complexity in their research areas and increasing constraints in budgets devoted to R&D activities. This paper discusses the cases of the ICT and automotive sectors, exploring the changes in managerial procedures and strategies that two of the largest corporate research centres in Italy (Telecom Italia Lab and Centro Ricerche Fiat) adopted during a delicate phase of transition. Both cases are characterized by a growing pressure towards the effective integration of short-term and long-term perspectives, i.e. towards a balance between valorization of research results and competencies, and exploration of new technological trajectories. The solutions adopted by the two organizations are explored and discussed. Specifically, while TiLab focused on the promotion of controlled spin-off companies, CRF has been very active in local technology transfer, especially in favour of SMFs
Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data
B cells develop high affinity receptors during the course of affinity
maturation, a cyclic process of mutation and selection. At the end of affinity
maturation, a number of cells sharing the same ancestor (i.e. in the same
"clonal family") are released from the germinal center, their amino acid
frequency profile reflects the allowed and disallowed substitutions at each
position. These clonal-family-specific frequency profiles, called "substitution
profiles", are useful for studying the course of affinity maturation as well as
for antibody engineering purposes. However, most often only a single sequence
is recovered from each clonal family in a sequencing experiment, making it
impossible to construct a clonal-family-specific substitution profile. Given
the public release of many high-quality large B cell receptor datasets, one may
ask whether it is possible to use such data in a prediction model for
clonal-family-specific substitution profiles. In this paper, we present the
method "Substitution Profiles Using Related Families" (SPURF), a penalized
tensor regression framework that integrates information from a rich assemblage
of datasets to predict the clonal-family-specific substitution profile for any
single input sequence. Using this framework, we show that substitution profiles
from similar clonal families can be leveraged together with simulated
substitution profiles and germline gene sequence information to improve
prediction. We fit this model on a large public dataset and validate the
robustness of our approach on an external dataset. Furthermore, we provide a
command-line tool in an open-source software package
(https://github.com/krdav/SPURF) implementing these ideas and providing easy
prediction using our pre-fit models.Comment: 23 page
A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis.
The human body generates a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial "V(D)J" rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process. In particular, standard phylogenetic approaches assume the DNA bases of the progenitor (or "naive") sequence arise independently and according to the same distribution, ignoring the complexities of V(D)J rearrangement. In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM). This technique not only integrates a naive rearrangement model with a phylogenetic model for BCR sequence evolution but also naturally accounts for uncertainty in all unobserved variables, including the phylogenetic tree, via posterior distribution sampling
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