Filipe Maximiano Sousa

Bio: Filipe Maximiano Sousa is an academic researcher from University of Bern. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 4, co-authored 11 publications receiving 48 citations.

Estimation of free-roaming domestic dog population size: Investigation of three methods including an Unmanned Aerial Vehicle (UAV) based approach

Abstract: Population size estimation is performed for several reasons including disease surveillance and control, for example to design adequate control strategies such as vaccination programs or to estimate a vaccination campaign coverage. In this study, we aimed at investigating the possibility of using Unmanned Aerial Vehicles (UAV) to estimate the size of free-roaming domestic dog (FRDD) populations and compare the results with two regularly used methods for population estimations: foot-patrol transect survey and the human: dog ratio estimation. Three studies sites of one square kilometer were selected in Peten department, Guatemala. A door-to-door survey was conducted in which all available dogs were marked with a collar and owner were interviewed. The day after, UAV flight were performed twice during two consecutive days per study site. The UAV's camera was set to regularly take pictures and cover the entire surface of the selected areas. Simultaneously to the UAV's flight, a foot-patrol transect survey was performed and the number of collared and non-collared dogs were recorded. Data collected during the interviews and the number of dogs counted during the foot-patrol transects informed a capture-recapture (CR) model fit into a Bayesian inferential framework to estimate the dog population size, which was found to be 78, 259, and 413 in the three study sites. The difference of the CR model estimates compared to previously available dog census count (110 and 289) can be explained by the fact that the study population addressed by the different methods differs. The human: dog ratio covered the same study population as the dog census and tended to underestimate the FRDD population size (97 and 161). Under the conditions within this study, the total number of dogs identified on the UAV pictures was 11, 96, and 71 for the three regions (compared to the total number of dogs counted during the foot-patrol transects of 112, 354 and 211). In addition, the quality of the UAV pictures was not sufficient to assess the presence of a mark on the spotted dogs. Therefore, no CR model could be implemented to estimate the size of the FRDD using UAV. We discussed ways for improving the use of UAV for this purpose, such as flying at a lower altitude in study area wisely chosen. We also suggest to investigate the possibility of using infrared camera and automatic detection of the dogs to increase visibility of the dogs in the pictures and limit workload of finding them. Finally, we discuss the need of using models, such as spatial capture-recapture models to obtain reliable estimates of the FRDD population. This publication may provide helpful directions to design dog population size estimation methods using UAV.

Comparative Study of Free-Roaming Domestic Dog Management and Roaming Behavior Across Four Countries: Chad, Guatemala, Indonesia, and Uganda.

Abstract: Dogs play a major role in public health because of potential transmission of zoonotic diseases, such as rabies. Dog roaming behavior has been studied worldwide, including countries in Asia, Latin America, and Oceania, while studies on dog roaming behavior are lacking in Africa. Many of those studies investigated potential drivers for roaming, which could be used to refine disease control measures. However, it appears that results are often contradictory between countries, which could be caused by differences in study design or the influence of context-specific factors. Comparative studies on dog roaming behavior are needed to better understand domestic dog roaming behavior and address these discrepancies. The aim of this study was to investigate dog demography, management, and roaming behavior across four countries: Chad, Guatemala, Indonesia, and Uganda. We equipped 773 dogs with georeferenced contact sensors (106 in Chad, 303 in Guatemala, 217 in Indonesia, and 149 in Uganda) and interviewed the owners to collect information about the dog [e.g., sex, age, body condition score (BCS)] and its management (e.g., role of the dog, origin of the dog, owner-mediated transportation, confinement, vaccination, and feeding practices). Dog home range was computed using the biased random bridge method, and the core and extended home range sizes were considered. Using an AIC-based approach to select variables, country-specific linear models were developed to identify potential predictors for roaming. We highlighted similarities and differences in term of demography, dog management, and roaming behavior between countries. The median of the core home range size was 0.30 ha (95% range: 0.17-0.92 ha) in Chad, 0.33 ha (0.17-1.1 ha) in Guatemala, 0.30 ha (0.20-0.61 ha) in Indonesia, and 0.25 ha (0.15-0.72 ha) in Uganda. The median of the extended home range size was 7.7 ha (95% range: 1.1-103 ha) in Chad, 5.7 ha (1.5-27.5 ha) in Guatemala, 5.6 ha (1.6-26.5 ha) in Indonesia, and 5.7 ha (1.3-19.1 ha) in Uganda. Factors having a significant impact on the home range size in some of the countries included being male dog (positively), being younger than one year (negatively), being older than 6 years (negatively), having a low or a high BCS (negatively), being a hunting dog (positively), being a shepherd dog (positively), and time when the dog was not supervised or restricted (positively). However, the same outcome could have an impact in a country and no impact in another. We suggest that dog roaming behavior is complex and is closely related to the owner's socioeconomic context and transportation habits and the local environment. Free-roaming domestic dogs are not completely under human control but, contrary to wildlife, they strongly depend upon humans. This particular dog-human bound has to be better understood to explain their behavior and deal with free-roaming domestic dogs related issues.

Evolution of COVID-19 mortality over time: results from the Swiss hospital surveillance system (CH-SUR)

Abstract: BackgroundWhen comparing the periods of time during and after the first wave of the ongoing SARS-CoV-2/COVID-19 pandemic in Europe, the associated COVID-19 mortality seems to have decreased substantially. Various factors could explain this trend, including changes in demographic characteristics of infected persons, and the improvement of case management. To date, no study has been performed to investigate the evolution of COVID-19 in-hospital mortality in Switzerland, while also accounting for risk factors. MethodsWe investigated the trends in COVID-19 related mortality (in-hospital and in-intermediate/intensive-care) over time in Switzerland, from February 2020 to May 2021, comparing in particular the first and the second wave. We used data from the COVID-19 Hospital-based Surveillance (CH-SUR) database. We performed survival analyses adjusting for well-known risk factors of COVID-19 mortality (age, sex and comorbidities) and accounting for competing risk. ResultsOur analysis included 16,030 episodes recorded in CH-SUR, with 2,320 reported deaths due to COVID-19 (13.0% of included episodes). We found that overall in-hospital mortality was lower during the second wave of COVID-19 compared to the first wave (HR 0.71, 95% CI 0.69 - 0.72, p-value < 0.001), a decrease apparently not explained by changes in demographic characteristics of patients. In contrast, mortality in intermediate and intensive care significantly increased in the second wave compared to the first wave (HR 1.48, 95% CI 1.42 - 1.55, p-value < 0.001), with significant changes in the course of hospitalisation between the first and the second wave. ConclusionWe found that, in Switzerland, COVID-19 mortality decreased among hospitalised persons, whereas it increased among patients admitted to intermediate or intensive care, when comparing the second wave to the first wave. We put our findings in perspective with changes over time in case management, treatment strategy, hospital burden and non-pharmaceutical interventions. Further analyses of the potential effect of virus variants and of vaccination on mortality would be crucial to have a complete overview of COVID-19 mortality trends throughout the different phases of the pandemic.

A comprehensive biomedical variant catalogue based on whole genome sequences of 582 dogs and eight wolves.

Abstract: The domestic dog serves as an excellent model to investigate the genetic basis of disease. More than 400 heritable traits analogous to human diseases have been described in dogs. To further canine medical genetics research, we established the Dog Biomedical Variant Database Consortium (DBVDC) and present a comprehensive list of functionally annotated genome variants that were identified with whole genome sequencing of 582 dogs from 126 breeds and eight wolves. The genomes used in the study have a minimum coverage of 10× and an average coverage of ~24×. In total, we identified 23 133 692 single-nucleotide variants (SNVs) and 10 048 038 short indels, including 93% undescribed variants. On average, each individual dog genome carried ∼4.1 million single-nucleotide and ~1.4 million short-indel variants with respect to the reference genome assembly. About 2% of the variants were located in coding regions of annotated genes and loci. Variant effect classification showed 247 141 SNVs and 99 562 short indels having moderate or high impact on 11 267 protein-coding genes. On average, each genome contained heterozygous loss-of-function variants in 30 potentially embryonic lethal genes and 97 genes associated with developmental disorders. More than 50 inherited disorders and traits have been unravelled using the DBVDC variant catalogue, enabling genetic testing for breeding and diagnostics. This resource of annotated variants and their corresponding genotype frequencies constitutes a highly useful tool for the identification of potential variants causative for rare inherited disorders in dogs.

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Abstract: Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β-oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. Significance Statement This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

Keel bone fractures in laying hens: a systematic review of prevalence across age, housing systems, and strains

Abstract: S36 © The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com ICPD PROCEEDINGS Keel bone fractures in laying hens: a systematic review of prevalence across age, housing systems, and strains Christina Rufener and Maja M. Makagon1

Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking

Abstract: Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of “-omics” approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.