A facility and community-based assessment of scabies in rural Malawi.

Background Scabies is a neglected tropical disease of the skin, causing severe itching, stigmatizing skin lesions and systemic complications. Since 2015, the DerMalawi project provide an integrated skin diseases clinics and Tele-dermatology care in Malawi. Clinic based data suggested a progressive increase in scabies cases observed. To better identify and treat individuals with scabies in the region, we shifted from a clinic-based model to a community based outreach programme. Methodology/Principal findings From May 2015, DerMalawi project provide integrated skin diseases and Tele-dermatological care in the Nkhotakota and Salima health districts in Malawi. Demographic and clinical data of all patients personally attended are recorded. Due to a progressive increase in the number of cases of scabies the project shifted to a community-based outreach programme. For the community outreach activities, we conducted three visits between 2018 to 2019 and undertook screening in schools and villages of Alinafe Hospital catchment area. Treatment was offered for all the cases and school or household contacts. Scabies increased from 2.9% to 39.2% of all cases seen by the DerMalawi project at clinics between 2015 to 2018. During the community-based activities approximately 50% of the population was assessed in each of three visits. The prevalence of scabies was similar in the first two rounds, 15.4% (2392) at the first visit and 17.2% at the second visit. The prevalence of scabies appeared to be lower (2.4%) at the third visit. The prevalence of impetigo appeared unchanged and was 6.7% at the first visit and 5.2% at the final visit. Conclusions/Significance Prevalence of scabies in our setting was very high suggesting that scabies is a major public health problem in parts of Malawi. Further work is required to more accurately assess the burden of disease and develop appropriate public health strategies for its control.post-print876 K

Phylogenetic distribution of large-scale genome patchiness

[Background] The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness) has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. [Results] The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris), birds (Gallus gallus), fishes (Danio rerio), invertebrates (Drosophila melanogaster and Caenorhabditis elegans), plants (Arabidopsis thaliana) and yeasts (Saccharomyces cerevisiae). We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. [Conclusion] Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level.This work was supported by the Spanish Government (BIO2005-09116-C03-01) and Plan Andaluz de Investigación (CVI-162, P06-FQM-01858, P07-FQM-03163 and TIC-640)

Semantically-based crossover in genetic programming: application to real-valued symbolic regression

We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators—semantics aware crossover (SAC) and semantic similarity-based crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with standard crossover (SC), context aware crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC