Analysis of the Plant bos1 Mutant Highlights Necrosis as an Efficient Defence Mechanism during D. dadantii/Arabidospis thaliana Interaction

Dickeya dadantii is a broad host range phytopathogenic bacterium provoking soft rot disease on many plants including Arabidopsis. We showed that, after D. dadantii infection, the expression of the Arabidopsis BOS1 gene was specifically induced by the production of the bacterial PelB/C pectinases able to degrade pectin. This prompted us to analyze the interaction between the bos1 mutant and D. dadantii. The phenotype of the infected bos1 mutant is complex. Indeed, maceration symptoms occurred more rapidly in the bos1 mutant than in the wild type parent but at a later stage of infection, a necrosis developed around the inoculation site that provoked a halt in the progression of the maceration. This necrosis became systemic and spread throughout the whole plant, a phenotype reminiscent of that observed in some lesion mimic mutants. In accordance with the progression of maceration symptoms, bacterial population began to grow more rapidly in the bos1 mutant than in the wild type plant but, when necrosis appeared in the bos1 mutant, a reduction in bacterial population was observed. From the plant side, this complex interaction between D. dadantii and its host includes an early plant defence response that comprises reactive oxygen species (ROS) production accompanied by the reinforcement of the plant cell wall by protein cross-linking. At later timepoints, another plant defence is raised by the death of the plant cells surrounding the inoculation site. This plant cell death appears to constitute an efficient defence mechanism induced by D. dadantii during Arabidopsis infection

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Isolation, culture expansion and characterization of canine bone marrow derived mesenchymal stem cells

The purpose of the present study was to isolate, culture expand and characterize canine bone marrow derived mesenchymal stem cells. Bone marrow aspirates of 15 adult male dogs were collected to this end and their mononuclear cells isolated by centrifugation and cultured in standard media. The adherent cells were isolated and their mesenchymal origin was confirmed at 3rd passage by cellular morphology, expression of surface antigens and differentiation to osteogenic and adipogenic lineage. After 4 days, spindle shaped fibroblast like cells which were apparently bone marrow derived mesenchymal stem cells appeared in culture medium and their numbers increased over time. The cells reached 3rd passage with over 75% confluent after a mean of 22.89±5.75 days. Flow cytometric analysis revealed that the cells negatively expressed CD34 and CD45 antigens while positively expressing CD44 and CD105 antigens. Differentiation into osteogenic and adipogenic lineage had taken place after one month culture in induction medium. VDR, COL1A1, BGLAP and SPARC gene expression indicated that mesenchymal stem cells isolated from canine bone marrow had differentiated into osteogenic lineage. These findings can form the basis of any forthcoming clinical studies involving the use of canine mesenchymal stem cells particularly in the field of bone and cartilage regeneration

Demand response in future power networks: Panorama and state-of-the-art

One of the key features of future power networks, referred to as smart grids, is deploying demand-side resources in order to reduce the stress at the supply side. This implies active participation of electricity customers, as a societal network, in the power networks, as a physical network, which increases the interdependencies of these two networks due to the effect of demand response programs on power systems. Furthermore, in the future smart cities there is a crucial need to take advantage of demand-side resources to supply electricity in a sustainable manner. In this context, demand response programs play a pivotal role in electricity market in order to achieve supply-demand balance by taking advantage of the load flexibility. In this chapter, we provide a thorough review of the state-of-the-art approaches to implement demand response programs in smart grid environment. To this end, we first introduce the available methods to model load participation in terms of demand response programs, such as game theoretic frameworks, price elasticity, and direct load control. We then review the methods for integrating demand-side resources into power systems. Several aspects of demand response programs are reviewed in this chapter. Finally, an overview of the recent advances in demand response literature is presented