The potential of family-free rearrangements towards gene orthology inference

Recently, we proposed an efficient ILP formulation [Rubert DP, Martinez FV, Braga MDV, Natural family-free genomic distance, Algorithms Mol Biol 16:4, 2021] for exactly computing the rearrangement distance of two genomes in a family-free setting. In such a setting, neither prior classification of genes into families, nor further restrictions on the genomes are imposed. Given two genomes, the mentioned ILP computes an optimal matching of the genes taking into account simultaneously local mutations, given by gene similarities, and large-scale genome rearrangements. Here, we explore the potential of using this ILP for inferring groups of orthologs across several species. More precisely, given a set of genomes, our method first computes all pairwise optimal gene matchings, which are then integrated into gene families in the second step. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities. It can be downloaded from gitlab.ub.uni-bielefeld.de/gi/FFGC. We obtained promising results with experiments on both simulated and real data.

To Evaluate Knowledge, Attitude, Practice of Natural Family Planning Method and Associated Factors among Reproductive Age Group of Women’s at Jimma Town, Jimma Zone, South West Ethiopia

Background: The high fertility rate leading to the rapid growth of country’s population is a major hindrance towards the development of a nation. Sub-Saharan Africa has the highest fertility rate in the world, which is further promoted by the low utilization of contraceptive methods. Yet, many communities claim to have natural methods of family planning that pre-date the introduction of modern contraceptives, implying that contraception is a culturally acceptable norm. Objective: To Evaluate the Knowledge, Attitude and Practice towards Natural family planning among reproductive age women of Jimma town, Oromia, Ethiopia. Methods: Across sectional community based quantitative study was done in Jimma town, among reproductive age group women from February to March; 2020. A total of3 99 sample size is calculated using single population proportion formula with a proportion (P) of 50 %.Systematic random sampling technique issued to draw the study participants among the target population. The data was collected using structured questionnaire adapted from similar and the tools modified to fit the local context. The collected data was tallied and analyzed by using scientific calculator. Results: From the total of 399 respondents who responded the questionnaire Majority, 90% of women in Jimma town had a good level of knowledge of natural family planning methods. A positive attitude towards natural family planning methods was seen in (68%) of the respondents. Conclusion: A fair level of knowledge of natural family planning methods is seen among reproductive women in Jimma town, yet differences in knowledge of specific natural family planning methods exist. Therefore, physicians and other health care providers‟ limited knowledge of and experience with Natural family planning methods inhibits broader use as reproductive age women reported getting information about NFFP methods from health providers and friends, therefore, their level of knowledge will depend largely on the information received.

Feynman’s propagator in Schwinger’s picture of Quantum Mechanics

A novel derivation of Feynman’s sum-over-histories construction of the quantum propagator using the groupoidal description of Schwinger picture of Quantum Mechanics is presented. It is shown that such construction corresponds to the GNS representation of a natural family of states called Dirac–Feynman–Schwinger (DFS) states. Such states are obtained from a q-Lagrangian function [Formula: see text] on the groupoid of configurations of the system. The groupoid of histories of the system is constructed and the q-Lagrangian [Formula: see text] allows us to define a DFS state on the algebra of the groupoid. The particular instance of the groupoid of pairs of a Riemannian manifold serves to illustrate Feynman’s original derivation of the propagator for a point particle described by a classical Lagrangian L.

Natural family-free genomic distance

Background A classical problem in comparative genomics is to compute the rearrangement distance, that is the minimum number of large-scale rearrangements required to transform a given genome into another given genome. The traditional approaches in this area are family-based, i.e., require the classification of DNA fragments of both genomes into families. Furthermore, the most elementary family-based models, which are able to compute distances in polynomial time, restrict the families to occur at most once in each genome. In contrast, the distance computation in models that allow multifamilies (i.e., families with multiple occurrences) is NP-hard. Very recently, Bohnenkämper et al. (J Comput Biol 28:410–431, 2021) proposed an ILP formulation for computing the genomic distance of genomes with multifamilies, allowing structural rearrangements, represented by the generic double cut and join (DCJ) operation, and content-modifying insertions and deletions of DNA segments. This ILP is very efficient, but must maximize a matching of the genes in each multifamily, in order to prevent the free lunch artifact that would otherwise let empty or almost empty matchings give smaller distances. Results In this paper, we adopt the alternative family-free setting that, instead of family classification, simply uses the pairwise similarities between DNA fragments of both genomes to compute their rearrangement distance. We adapted the ILP mentioned above and developed a model in which pairwise similarities are used to assign weights to both matched and unmatched genes, so that an optimal solution does not necessarily maximize the matching. Our model then results in a natural family-free genomic distance, that takes into consideration all given genes, without prior classification into families, and has a search space composed of matchings of any size. In spite of its bigger search space, our ILP seems to be boosted by a reduction of the number of co-optimal solutions due to the weights. Indeed, it converged faster than the original one by Bohnenkämper et al. for instances with the same number of multiple connections. We can handle not only bacterial genomes, but also fungi and insects, or sets of chromosomes of mammals and plants. In a comparison study of six fruit fly genomes, we obtained accurate results.