Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models

In this work, the authors develop regression approaches based on deep learning to perform thread density estimation for plain weave canvas analysis. Previous approaches were based on Fourier analysis, which is quite robust for some scenarios but fails in some others, in machine learning tools, that involve pre-labeling of the painting at hand, or the segmentation of thread crossing points, that provides good estimations in all scenarios with no need of pre-labeling. The segmentation approach is time-consuming as the estimation of the densities is performed after locating the crossing points. In this novel proposal, we avoid this step by computing the density of threads directly from the image with a regression deep learning model. We also incorporate some improvements in the initial preprocessing of the input image with an impact on the final error. Several models are proposed and analyzed to retain the best one. Furthermore, we further reduce the density estimation error by introducing a semi-supervised approach. The performance of our novel algorithm is analyzed with works by Ribera, Vel\'azquez, and Poussin where we compare our results to the ones of previous approaches. Finally, the method is put into practice to support the change of authorship or a masterpiece at the Museo del Prado.Comment: 21 page

Integrating Blood Collection Within Household Surveys: Lessons Learned From Nesting a Measles and Rubella Serological Survey Within a Post-Campaign Coverage Evaluation Survey in Southern Province, Zambia

Age-specific population immunity to many vaccine-preventable diseases can be measured using serological surveys. However, stand-alone serological surveys are infrequently conducted in low- and middle-income countries because of costs, operational challenges, and potential high refusal rates for blood collection. Nesting a serosurvey within a household cluster survey may overcome some of these challenges. We share lessons learned from nesting a serosurvey within a measles and rubella vaccination post-campaign coverage evaluation survey (PCES). In 15 of the 26 PCES clusters in Southern Province, Zambia, we collected dried blood spots from 581 participants aged 9 months and older. Household participation rates for the main PCES were higher in the serosurvey clusters (86%) than PCES-only clusters (71%), suggesting that a serosurvey can be successfully integrated without adversely affecting PCES participation. Among households that participated in the PCES, 80% also participated in the serosurvey and 86% of individuals available in the household provided a blood sample for the serosurvey. Substantial planning and coordination, additional staff training, and community mobilization were critical to the success of the serosurvey. Most challenges stemmed from using different data collecting tools and teams for the serosurvey and PCES. A more efficient design would be to fully integrate the serosurvey by adding blood collection and additional questions to the PCES

flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic

The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread to strict social distancing policies. In response, members of the Johns Hopkins Infectious Disease Dynamics Group developed flepiMoP (formerly called the COVID Scenario Modeling Pipeline), a comprehensive open-source software pipeline designed for creating and simulating compartmental models of infectious disease transmission and inferring parameters through these models. The framework has been used extensively to produce short-term forecasts and longer-term scenario projections of COVID-19 at the state and county level in the US, for COVID-19 in other countries at various geographic scales, and more recently for seasonal influenza. In this paper, we highlight how the flepiMoP has evolved throughout the COVID-19 pandemic to address changing epidemiological dynamics, new interventions, and shifts in policy-relevant model outputs. As the framework has reached a mature state, we provide a detailed overview of flepiMoP's key features and remaining limitations, thereby distributing flepiMoP and its documentation as a flexible and powerful tool for researchers and public health professionals to rapidly build and deploy large-scale complex infectious disease models for any pathogen and demographic setup

Catch-up growth up to ten years of age in children born very preterm or with very low birth weight

BACKGROUND: Improved survival due to advances in neonatal care has brought issues such as postnatal growth and development more to the focus of our attention. Most studies report stunting in children born very preterm and/or small for gestational age. In this article we study the growth pattern of these children and aim to identify factors associated with postnatal catch-up growth. METHODS: 1338 children born with a gestational age <32 weeks and/or a birth weight of <1500 grams were followed during a Dutch nationwide prospective study (POPS). Subgroups were classified as appropriate for gestational age and <32 weeks (AGA) or small for gestational age (<32 wks SGA and ≥32 wks SGA). Data were collected at different intervals from birth until 10 years for the 962 survivors and compared to reference values. The correlation between several factors and growth was analysed. RESULTS: At 10 years the AGA children had attained normal height, whereas the SGA group demonstrated stunting, even after correction for target height (AGA: 0.0 SDS; SGA <32 wks: -0.29SDS and ≥32 wks: -0.13SDS). Catch-up growth was especially seen in the SGA children with a fast initial weight gain. BMI was approximately 1 SD below the population reference mean. CONCLUSION: At 10 years of age, children born very preterm AGA show no stunting. However, many children born SGA, especially the very preterm, show persistent stunting. Early weight gain seems an important prognostic factor in predicting childhood growth

Epidemiology of Epidemic Ebola Virus Disease in Conakry and Surrounding Prefectures, Guinea, 2014–2015

In 2014, Ebola virus disease (EVD) in West Africa was first reported during March in 3 southeastern prefectures in Guinea; from there, the disease rapidly spread across West Africa. We describe the epidemiology of EVD cases reported in Guinea’s capital, Conakry, and 4 surrounding prefectures (Coyah, Dubreka, Forecariah, and Kindia), encompassing a full year of the epidemic. A total of 1,355 EVD cases, representing ≈40% of cases reported in Guinea, originated from these areas. Overall, Forecariah had the highest cumulative incidence (4× higher than that in Conakry). Case-fatality percentage ranged from 40% in Conakry to 60% in Kindia. Cumulative incidence was slightly higher among male than female residents, although incidences by prefecture and commune differed by sex. Over the course of the year, Conakry and neighboring prefectures became the EVD epicenter in Guinea

Researching COVID to enhance recovery (RECOVER) pregnancy study: Rationale, objectives and design

Importance Pregnancy induces unique physiologic changes to the immune response and hormonal changes leading to plausible differences in the risk of developing post-acute sequelae of SARS-CoV-2 (PASC), or Long COVID. Exposure to SARS-CoV-2 during pregnancy may also have long-term ramifications for exposed offspring, and it is critical to evaluate the health outcomes of exposed children. The National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC aims to evaluate the long-term sequelae of SARS-CoV-2 infection in various populations. RECOVER-Pregnancy was designed specifically to address long-term outcomes in maternal-child dyads. Methods RECOVER-Pregnancy cohort is a combined prospective and retrospective cohort that proposes to enroll 2,300 individuals with a pregnancy during the COVID-19 pandemic and their offspring exposed and unexposed in utero, including single and multiple gestations. Enrollment will occur both in person at 27 sites through the Eunice Kennedy Shriver National Institutes of Health Maternal-Fetal Medicine Units Network and remotely through national recruitment by the study team at the University of California San Francisco (UCSF). Adults with and without SARS-CoV-2 infection during pregnancy are eligible for enrollment in the pregnancy cohort and will follow the protocol for RECOVER-Adult including validated screening tools, laboratory analyses and symptom questionnaires followed by more in-depth phenotyping of PASC on a subset of the overall cohort. Offspring exposed and unexposed in utero to SARS-CoV-2 maternal infection will undergo screening tests for neurodevelopment and other health outcomes at 12, 18, 24, 36 and 48 months of age. Blood specimens will be collected at 24 months of age for SARS-CoV-2 antibody testing, storage and anticipated later analyses proposed by RECOVER and other investigators. Discussion RECOVER-Pregnancy will address whether having SARS-CoV-2 during pregnancy modifies the risk factors, prevalence, and phenotype of PASC. The pregnancy cohort will also establish whether there are increased risks of adverse long-term outcomes among children exposed in utero

Impact of Optimized Breastfeeding on the Costs of Necrotizing Enterocolitis in Extremely Low Birthweight Infants

To estimate risk of NEC for ELBW infants as a function of preterm formula and maternal milk (MM) intake and calculate the impact of suboptimal feeding on NEC incidence and costs

Évaluation chez le poney de la digestibilité des pellicules de soja, du marc de pommes, des caroubes et du tourteau de pépins de raisins

International audienc