Age discrimination in modern global society

Human rights discrimination has a destructive impact on legal reality. It demonstrates in different ways. On the modern stage of society development, one of the most common form of discrimination is age discrimination since it concerns all society groups in any country. In science literature, there is no common view on the understanding of age discrimination, that is why its interpretation (as a category that is demonstrated through stereotype, prejudice and age discrimination in its broad sense i.e., all individuals because of age that cause harmful consequences for society and particular individuals) is justified. Monitoring reports, analytical reviews and notes of professional international institutions at the level of UN bodies demonstrate the variable determinants of modern society in the context of aging trends and other related population and legal trends. Comprehensive approach of the survey is conditioned by need in methodical representation of declared problems through definition of economical, cultural, political, legal, social, middle-aged, labor, medical, gender and other agents that are directly related to the guarantee of equality and discrimination issues. Regulatory legal method helps to interpret rules of law in broader context in national legal systems of Albania, Great Britain, Norway, Ukraine, the Czech Republic, Lithuania.

Detecting adaptive introgression in human evolution using convolutional neural networks

Studies in a variety of species have shown evidence for positively selected variants introduced into a population via introgression from another, distantly related population - a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics, and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets - both phased and unphased - to detect candidates for adaptive introgression that shaped our evolutionary history.

Strategy to strengthen the traditional milpa family production systems

Objective: To characterize family production units (FPUs) to identify critical points for their activities and propose intervention strategies for them.Design/methodology/approach: The research took place at Yaxcabá municipality, state of Yucatán, Mexico. It is descriptive and its information obtained through 1) a questionnaire in a mobile application compatible with the Android operating system, structured by modules: producer data, FPU characteristics, crops, infrastructure, machinery, equipment, and marketing. The sample size was randomized with replacement, under the maximum variance condition, 2) assessment visits to the farmer’s plots and 3) participatory community diagnosis workshops.Results: The traditional milpa system was oriented to the cultivation of corn, beans and squash of creole origin, for consumption by the FPUs with minimum technologies usage. Through apiculture, producers obtain an economic resource to finance other activities, including those of the milpa. It is, therefore, necessary to strengthen their productivecapacities of this activity with a chain approach, for the diversification of their products and derivatives of their hives that allow their income to increase.Limitations on study/implications: The proposals and intervention strategies may only be applied to the production system in the evaluated area.Findings/conclusions: The strategies for the traditional milpa production should be oriented to food security, biodiversity preservation and the nutritional health of their related population. Apuculture strategies should aim to include producers in the value chain.

Automated Online Flow Cytometry Advances Microalgal Ecosystem Management as in situ, High-Temporal Resolution Monitoring Tool

Microalgae are emerging as a next-generation biotechnological production system in the pharmaceutical, biofuel, and food domain. The economization of microalgal biorefineries remains a main target, where culture contamination and prokaryotic upsurge are main bottlenecks to impair culture stability, reproducibility, and consequently productivity. Automated online flow cytometry (FCM) is gaining momentum as bioprocess optimization tool, as it allows for spatial and temporal landscaping, real-time investigations of rapid microbial processes, and the assessment of intrinsic cell features. So far, automated online FCM has not been applied to microalgal ecosystems but poses a powerful technology for improving the feasibility of microalgal feedstock production through in situ, real-time, high-temporal resolution monitoring. The study lays the foundations for an application of automated online FCM implying far-reaching applications to impel and facilitate the implementation of innovations targeting at microalgal bioprocesses optimization. It shows that emissions collected on the FL1/FL3 fluorescent channels, harnessing nucleic acid staining and chlorophyll autofluorescence, enable a simultaneous assessment (quantitative and diversity-related) of prokaryotes and industrially relevant phototrophic Chlorella vulgaris in mixed ecosystems of different complexity over a broad concentration range (2.2–1,002.4 cells ⋅μL–1). Automated online FCM combined with data analysis relying on phenotypic fingerprinting poses a powerful tool for quantitative and diversity-related population dynamics monitoring. Quantitative data assessment showed that prokaryotic growth phases in engineered and natural ecosystems were characterized by different growth speeds and distinct peaks. Diversity-related population monitoring based on phenotypic fingerprinting indicated that prokaryotic upsurge in mixed cultures was governed by the dominance of single prokaryotic species. Automated online FCM is a powerful tool for microalgal bioprocess optimization owing to its adaptability to myriad phenotypic assays and its compatibility with various cultivation systems. This allows advancing bioprocesses associated with both microalgal biomass and compound production. Hence, automated online FCM poses a viable tool with applications across multiple domains within the biobased sector relying on single cell–based value chains.

Ten-year trends in epidemiology and outcomes of pediatric kidney replacement therapy in Europe: data from the ESPN/ERA-EDTA Registry

Background For 10 consecutive years, the ESPN/ERA-EDTA Registry has included data on children with stage 5 chronic kidney disease (CKD 5) receiving kidney replacement therapy (KRT) in Europe. We examined trends in incidence and prevalence of KRT and patient survival. Methods We included all children aged <15 years starting KRT 2007–2016 in 22 European countries participating in the ESPN/ERA-EDTA Registry since 2007. General population statistics were derived from Eurostat. Incidence and prevalence were expressed per million age-related population (pmarp) and time trends studied with JoinPoint regression. We analyzed survival trends using Cox regression. Results Incidence of children commencing KRT <15 years remained stable over the study period, varying between 5.5 and 6.6 pmarp. Incidence by treatment modality was unchanged over time: 2.0 for hemodialysis (HD) and peritoneal dialysis (PD) and 1.0 for transplantation. Prevalence increased in all age categories and overall rose 2% annually from 26.4 pmarp in 2007 to 32.1 pmarp in 2016. Kidney transplantation prevalence increased 5.1% annually 2007–2009, followed by 1.5% increase/year until 2016. Prevalence of PD steadily increased 1.4% per year over the entire period, and HD prevalence started increasing 6.1% per year from 2011 onwards. Five-year unadjusted patient survival on KRT was around 94% and similar for those initiating KRT 2007–2009 or 2010–2012 (adjusted HR: 0.98, 95% CI:0.71–1.35). Conclusions We found a stable incidence and increasing prevalence of European children on KRT 2007–2016. Five-year patient survival was good and was unchanged over time. These data can inform patients and healthcare providers and aid health policy makers on future resource planning of pediatric KRT in Europe.

Detecting adaptive introgression in human evolution using convolutional neural networks

Studies in a variety of species have shown evidence for positively selected variants introduced into one population via introgression from another, distantly related population—a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics, and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets—both phased and unphased—to detect candidates for adaptive introgression that shaped our evolutionary history.

Public Involvement & Engagement in the work of a data safe haven:

Background: The SAIL Databank is a data safe haven established in 2007 at Swansea University (Wales). It was set up to create new opportunities for research using routinely-collected health and other public service datasets in linkable anonymised form. SAIL forms the bedrock of other Population Data Science initiatives made possible by the data and safe haven environment. Aim: The aim of this paper is to provide an overview of public involvement and engagement in connection with the SAIL Databank and related Population Data Science initiatives. Approach: We have a public involvement and engagement policy for SAIL in the context of Population Data Science. We established a Consumer Panel to provide advice on the work of SAIL and associated initiatives, including on proposed uses of SAIL data. We reviewed the topics discussed and provide examples of advice to researchers. We carried out a survey with members on their experiences of being on the Panel and their perceptions of the work of SAIL. We have a programme of wider public engagement and provide illustrations of this work. Discussion: We summarise what this paper adds and some lessons learned. In the rapidly developing area of Population Data Science it is important that people feel welcome, that they are encouraged to ask questions and are provided with digestible information and adequate consideration time. Citizens have provided us with valuable anticipated and unanticipated opinions and novel viewpoints. We seek to take a pragmatic approach, prioritising the communication modes that allow maximum public input commensurate with the purpose of the activity. Conclusion: This paper has set out our policy, rationale, scope and practical approaches to public involvement and engagement for SAIL and our related Population Data Science initiatives. Although there will be jurisdictional, cultural and organizational differences, we believe that the material covered in this paper will be of interest to other data focused enterprises across the world.

Lessons learned from India’s Green Revolution

Partition of British India in 1947 triggered a huge refugee crisis in India. In addition, low agricultural yield and high population growth fueled food insecurity. The fear of the Bengal Famine of 1943 was still fresh and the Indian Government wanted to prevent further famines. The philanthropic organizations of the USA (Rockefeller and Ford Foundation) collaborated with Indian policymakers and scientists that helped in the groundwork of the Green Revolution. Jack Loveridge explains how technology and international cooperation contributed to India's Green Revolution and what lessons can be learned for the future. The challenges related population control, environment, social and economic inequality in the Green Revolution were highlighted. Interview by Somidh Saha (ITAS-KIT).