International comparisons of neurodevelopmental outcomes in infants born very preterm

We summarise rates of survival and neurodevelopmental impairment in very (<32 weeks' gestation) and extremely (<28 weeks' gestation) preterm infants using data from recent meta-analyses. Methodological issues that require consideration when comparing international data are highlighted using examples of population-based or multi-centre cohorts of children born extremely preterm. The impact of baseline population, outcome definition, gestational age assessment, age at neurodevelopmental assessment, year of birth and follow-up rates are discussed. The impact of the intensity of perinatal care and of post-discharge management on survival and neurodevelopmental outcomes is also discussed. There is a future need for harmonisation of data collection and for more accurate and standardised reporting of neurodevelopmental outcomes in very preterm children

Chlamydia trachomatis infection and the risk of perinatal mortality in Hungary

Introduction: Chlamydial infections of the genital tract are thought to often lead to preterm birth, which is the most important perinatal problem in Hungary. Aim of study: A multicenter study was carried out to determine the prevalence of Chlamydia trachomatis infection, risk factors for the infection and to relate the infection to perinatal mortality, accounting for potential confounding effects. Methods: The nucleic acid hybridization method (PACE2 Gen-Probe) was applied for the examination of Chlamydia trachomatis. Logistic regression analysis was used to assess risk. Results: A total of 6156 pregnant women were examined for the occurrence of Chlamydia trachomatis. The observed overall rate of chlamydial infection was 5.9%. Young age (less than 24 years old) (OR and 95% CI:1.6 (1.3-2.0)), unmarried status (1.5 (1.2-1.9)) and the high unemployment rate (2.1 (1.6-2.7)) were statistically significant predictors of the infection. In logistic regression analysis, chlamydial infection (1.9 (1.1-3.3)). high unemployment rate (1.5 (1.2-2.2)) and low birth weight (1.7 (1.1-2.7) were significant predictors of perinatal mortality. Conclusions: Testing pregnant women for diseases that can be transmitted perinatally is an important part of obstetric cart. Screening for C. trachomatis of unmarried women under 24 years of age is suggested and need increased observation during labor

An Epidemiological Network Model for Disease Outbreak Detection

Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts

Emperor penguin body surfaces cool below air temperature

Emperor penguins Aptenodytes forsteri are able to survive the harsh Antarctic climate because of specialized anatomical, physiological and behavioural adaptations for minimizing heat loss. Heat transfer theory predicts that metabolic heat loss in this species will mostly depend on radiative and convective cooling. To examine this, thermal imaging of emperor penguins was undertaken at the breeding colony of Pointe Géologie in Terre Adélie (668400 S 1408 010 E), Antarctica in June 2008. During clear sky conditions, most outer surfaces of the body were colder than surrounding sub-zero air owing to radiative cooling. In these conditions, the feather surface will paradoxically gain heat by convection from surrounding air. However, owing to the low thermal conductivity of plumage any heat transfer to the skin surface will be negligible. Future thermal imaging studies are likely to yield further insights into the adaptations of this species to the Antarctic climate

The interrelatedness of cognitive abilities in very preterm and full‐term born children at 5.5 years of age : a psychometric network analysis approach

Background Very preterm (VP) birth is associated with a considerable risk for cognitive impairment, putting children at a disadvantage in academic and everyday life. Despite lower cognitive ability on the group level, there are large individual differences among VP born children. Contemporary theories define intelligence as a network of reciprocally connected cognitive abilities. Therefore, intelligence was studied as a network of interrelated abilities to provide insight into interindividual differences. We described and compared the network of cognitive abilities, including strength of interrelations between and the relative importance of abilities, of VP and full-term (FT) born children and VP children with below-average and average-high intelligence at 5.5 years. Methods A total of 2,253 VP children from the EPIPAGE-2 cohort and 578 FT controls who participated in the 5.5-year-follow-up were eligible for inclusion. The WPPSI-IV was used to measure verbal comprehension, visuospatial abilities, fluid reasoning, working memory, and processing speed. Psychometric network analysis was applied to analyse the data. Results Cognitive abilities were densely and positively interconnected in all networks, but the strength of connections differed between networks. The cognitive network of VP children was more strongly interconnected than that of FT children. Furthermore, VP children with below average IQ had a more strongly connected network than VP children with average-high IQ. Contrary to our expectations, working memory had the least central role in all networks. Conclusions In line with the ability differentiation hypothesis, children with higher levels of cognitive ability had a less interconnected and more specialised cognitive structure. Composite intelligence scores may therefore mask domain-specific deficits, particularly in children at risk for cognitive impairments (e.g., VP born children), even when general intelligence is unimpaired. In children with strongly and densely connected networks, domain-specific deficits may have a larger overall impact, resulting in lower intelligence levels

Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model

Food webs, networks of feeding relationships among organisms, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. Despite long-standing interest in the compartmental structure of food webs, past network analyses of food webs have been constrained by a standard definition of compartments, or modules, that requires many links within compartments and few links between them. Empirical analyses have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure in food webs using a flexible definition of a group that can describe both functional roles and standard compartments. The Serengeti ecosystem provides an opportunity to examine structure in a newly compiled food web that includes species-level resolution among plants, allowing us to address whether groups in the food web correspond to tightly-connected compartments or functional groups, and whether network structure reflects spatial or trophic organization, or a combination of the two. We have compiled the major mammalian and plant components of the Serengeti food web from published literature, and we infer its group structure using our method. We find that network structure corresponds to spatially distinct plant groups coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial patterns, in contrast to the standard compartments typically identified in ecological networks. From data consisting only of nodes and links, the group structure that emerges supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence.Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting

Evolving Clustered Random Networks

We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering. The networks generated by our algorithm are random in all other respects and can thus serve as generic models for studying the impacts of degree distributions and clustering on dynamical processes as well as null models for detecting other structural properties in empirical networks

Pandemic fatigue impedes mitigation of COVID-19 in Hong Kong

Hong Kong has implemented stringent public health and social measures (PHSMs) to curb each of the four COVID-19 epidemic waves since January 2020. The third wave between July and September 2020 was brought under control within 2 m, while the fourth wave starting from the end of October 2020 has taken longer to bring under control and lasted at least 5 mo. Here, we report the pandemic fatigue as one of the potential reasons for the reduced impact of PHSMs on transmission in the fourth wave. We contacted either 500 or 1,000 local residents through weekly random-digit dialing of landlines and mobile telephones from May 2020 to February 2021. We analyze the epidemiological impact of pandemic fatigue by using the large and detailed cross-sectional telephone surveys to quantify risk perception and self-reported protective behaviors and mathematical models to incorporate population protective behaviors. Our retrospective prediction suggests that an increase of 100 daily new reported cases would lead to 6.60% (95% CI: 4.03, 9.17) more people worrying about being infected, increase 3.77% (95% CI: 2.46, 5.09) more people to avoid social gatherings, and reduce the weekly mean reproduction number by 0.32 (95% CI: 0.20, 0.44). Accordingly, the fourth wave would have been 14% (95% CI%: −53%, 81%) smaller if not for pandemic fatigue. This indicates the important role of mitigating pandemic fatigue in maintaining population protective behaviors for controlling COVID-19