DNA Sequence Analysis of Freshwater Eustigmatophyceae, a Potential Source of Essential Fatty Acids

Freshwater Eustigmatophyceae are a group of microalgae that are considered rare and of low diversity, with only a few genera and species in a single order. Some Eustigmatophyceae produce fatty acids that are important nutrients for aquaculture, as well as for human food consumption. In addition, some Eustigmatophyceae produce hydrocarbons that may be useful in biofuel production. In our studies of the diversity of coccoid algae from Itasca State Park, Minnesota, we discovered several isolates that we tentatively identified as Eustigmatophyceae. Preliminary molecular characterization indicated that these isolates were highly diverse and probably represented species new to science. In this study, we examined fifteen of the Eustigmatophyceae isolates from Itasca State Park using DNA sequence analysis of the plastid rbcL gene. Phylogenetic analyses of these sequences strongly supported Eustigmatophyceae as a monophyletic group and indicated two distinct lineages among our isolates within Eustigmatophyceae. Our results suggest that many of these isolates represent new genera and species. We can also infer the existence of at least two orders in the Eustigmatophyceae, based on the presence of two distinct lineages in the class. In addition to the taxonomic implications, this study will aid in the selection of isolates for further characterization of fatty acids and hydrocarbons, or as part of a regenerative life support system during extended space missions

Characterization of Bacteriophages of Pseudomonas syringae pv. tomato

Bacteriophages from supernatants of the plant pathogenic bacteria Pseudomonas syringae pv. tomato (P. tomato) were isolated, enriched, and purified by density block centrifugation in cesium chloride (CsCl) step gradients. The DNA from purified phage was isolated and digested with the restriction endonucleases EcoRl or HindIII. Three different DNA fingerprint patterns were determined indicating 3 unique phage isolates. Genome sizes of the phage ranged from 40 to 52 kilobases (kB). Buoyant densities of phage particles in CsCI varied from 1.36 to 1.51 glml. Electron microscopy revealed a single morphological type with an elongated polyhedral head and a long tail indicating the family Siphovirida

A novel splice-affecting HNF1A variant with large population impact on diabetes in Greenland

Background: The genetic disease architecture of Inuit includes a large number of common high-impact variants. Identification of such variants contributes to our understanding of the genetic aetiology of diseases and improves global equity in genomic personalised medicine. We aimed to identify and characterise novel variants in genes associated with Maturity Onset Diabetes of the Young (MODY) in the Greenlandic population. Methods: Using combined data from Greenlandic population cohorts of 4497 individuals, including 448 whole genome sequenced individuals, we screened 14 known MODY genes for previously identified and novel variants. We functionally characterised an identified novel variant and assessed its association with diabetes prevalence and cardiometabolic traits and population impact. Findings: We identified a novel variant in the known MODY gene HNF1A with an allele frequency of 1.9% in the Greenlandic Inuit and absent elsewhere. Functional assays indicate that it prevents normal splicing of the gene. The variant caused lower 30-min insulin (β = −232 pmol/L, βSD = −0.695, P = 4.43 × 10−4) and higher 30-min glucose (β = 1.20 mmol/L, βSD = 0.441, P = 0.0271) during an oral glucose tolerance test. Furthermore, the variant was associated with type 2 diabetes (OR 4.35, P = 7.24 × 10−6) and HbA1c (β = 0.113 HbA1c%, βSD = 0.205, P = 7.84 × 10−3). The variant explained 2.5% of diabetes variance in Greenland. Interpretation: The reported variant has the largest population impact of any previously reported variant within a MODY gene. Together with the recessive TBC1D4 variant, we show that close to 1 in 5 cases of diabetes (18%) in Greenland are associated with high-impact genetic variants compared to 1–3% in large populations.publishedVersio

Simple mindreading abilities predict complex theory of mind: developmental delay in autism spectrum disorders

Theory of Mind (ToM) is impaired in individuals with Autism Spectrum Disorders (ASD). The aims of this study were to: i) examine the developmental trajectories of ToM abilities in two different mentalizing tasks in children with ASD compared to TD children; and ii) to assess if a ToM simple test known as Eyes-test could predict performance on the more advanced ToM task, i.e. Comic Strip test. Based on a sample of 37 children with ASD and 55 TD children, our results revealed slower development at varying rates in all ToM measures in children with ASD, with delayed onset compared to TD children. These results could stimulate new treatments for social abilities, which would lessen the social deficit in ASD

Divorce, divorce rates, and professional care seeking for mental health problems in Europe: a cross-sectional population-based study

Background: Little is known about differences in professional care seeking based on marital status. The few existing studies show more professional care seeking among the divorced or separated compared to the married or cohabiting. The aim of this study is to determine whether, in a sample of the European general population, the divorced or separated seek more professional mental health care than the married or cohabiting, regardless of self-reported mental health problems. Furthermore, we examine whether two country-level features-the supply of mental health professionals and the country-level divorce rates-contribute to marital status differences in professional care-seeking behavior. Methods: We use data from the Eurobarometer 248 on mental well-being that was collected via telephone interviews. The unweighted sample includes 27,146 respondents (11,728 men and 15,418 women). Poisson hierarchical regression models were estimated to examine whether the divorced or separated have higher professional health care use for emotional or psychological problems, after controlling for mental and somatic health, sociodemographic characteristics, support from family and friends, and degree of urbanization. We also considered country-level divorce rates and indicators of the supply of mental health professionals, and applied design and population weights. Results: We find that professional care seeking is strongly need based. Moreover, the divorced or separated consult health professionals for mental health problems more often than people who are married or who cohabit do. In addition, we find that the gap between the divorced or separated and the married or cohabiting is highest in countries with low divorce rates. Conclusions: The higher rates of professional care seeking for mental health problems among the divorced or separated only partially correlates with their more severe mental health problems. In countries where marital dissolution is more common, the marital status gap in professional care seeking is narrower, partially because professional care seeking is more common among the married or cohabiting

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI