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The World Health Organization (WHO) published a report in 1996 about the definition of a normal birth and recommended certain methods to support the physiological processes of labour. The WHO definition further stated that the aim of care in normal birth is to achieve a healthy mother and child with the least possible level of interventions, and in every case, there should be a valid reason to interfere with the natural process. Since this initial report, childbirthing around the world has become heavily medicalised with high rates of interventions and increasingly high rates of operative births. Over the past few years, there has been a renewed interest worldwide to support normality at childbirth and the term "natural birth" has re-appeared in the literature and is used interchangeably with the term "normal birth". At present, there is a consensus in the literature and the majority of professional societies refer to "natural birth" or "normal birth" as the birth achieved with no medical interventions and with no pharmacological means of pain management. In order to promote "normality" and "naturalness" at childbirth with minimal or no interventions and to enhance the birthing experience of women, it is now accepted that we need to revisit our intrapartum care practices, embrace a respectful and woman-centred birthing philosophy, utilise the appropriate clinical skills and tools, and adopt a new organizational model of midwife led care. [ABSTRACT FROM AUTHOR]

Purpose: Cannabidiol (CBD) products are widely used for pain relief, sleep improvement, management of seizures etc. Although the concentrations of Δ9-tetrahydrocannabinol (Δ9-THC) in these products are low (≤0.3% w/w), it is important to investigate if its presence and/or that of its metabolite 11-nor-carboxy-Δ9-THC, is traceable in plasma and urine samples of individuals who take CBD oil products. Methods: A sensitive GC/MS method for the determination of Δ9-THC, 11-nor-carboxy-Δ9-THC and CBD in plasma and urine samples was developed and validated. The sample preparation procedure included protein precipitation for plasma samples and hydrolysis for urine samples, solid-phase extraction and finally derivatization with N,O-bis(trimethylsilyl)trifluoroacetamide) with 1% trimethylchlorosilane. Results: For all analytes, the LOD and LOQ were 0.06 and 0.20 ng/mL, respectively. The calibration curves were linear (R2 ≥ 0.992), and absolute recoveries were ≥91.7%. Accuracy and precision were within the accepted range. From the analysis of biologic samples of 10 human participants who were taking CBD oil, it was realized that Δ9-THC was not detected in urine, while 11-nor-carboxy-Δ9-THC (0.69–23.06 ng/mL) and CBD (0.29–96.78 ng/mL) were found in all urine samples. Regarding plasma samples, Δ9-THC (0.21–0.62 ng/mL) was detected in 10, 11-nor-carboxy-Δ9-THC (0.20–2.44 ng/mL) in 35, while CBD (0.20–1.58 ng/mL) in 25 out of 38 samples, respectively. Conclusion: The results showed that Δ9-THC is likely to be found in plasma although at low concentrations. In addition, the detection of 11-nor-carboxy-Δ9-THC in both urine and plasma samples raises questions and concerns for the proper interpretation of toxicological results, especially considering Greece's zero tolerance law applied in DUID and workplace cases. [ABSTRACT FROM AUTHOR]

Abstract Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

Abstract Background SARS-CoV-2 has been detected not only in respiratory secretions, but also in stool collections. Here were sought to identify SARS-CoV-2 by enrichment next-generation sequencing (NGS) from fecal samples, and to utilize whole genome analysis to characterize SARS-CoV-2 mutational variations in COVID-19 patients. Results Study participants underwent testing for SARS-CoV-2 from fecal samples by whole genome enrichment NGS (n = 14), and RT-PCR nasopharyngeal swab analysis (n = 12). The concordance of SARS-CoV-2 detection by enrichment NGS from stools with RT-PCR nasopharyngeal analysis was 100%. Unique variants were identified in four patients, with a total of 33 different mutations among those in which SARS-CoV-2 was detected by whole genome enrichment NGS. Conclusion These results highlight the potential viability of SARS-CoV-2 in feces, its ongoing mutational accumulation, and its possible role in fecal–oral transmission. This study also elucidates the advantages of SARS-CoV-2 enrichment NGS, which may be a key methodology to document complete viral eradication. Trial registration ClinicalTrials.gov, NCT04359836, Registered 24 April 2020, https://clinicaltrials.gov/ct2/show/NCT04359836?term=NCT04359836&draw=2&rank=1 ).

Abstract Objectives The increasing number of hip arthroplasties (HA), due to the growing elderly population, is associated with the risk of femoral periprosthetic fractures (FPFs). The purpose of this study was to identify potential risk factors for the development of FPFs after HA. Methods A systematic review was conducted in five data bases (Medline, Embase, Cochrane, Cinahl, ICTRP) according to the Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) guidelines up to May 2019, using the key words “risk factor,” “periprosthetic fracture,” and “hip replacement or arthroplasty.” Meta-analysis of the clinical outcomes of HA and subgroup analysis based on the factors that were implicated in FPFs was performed. Results Sixteen studies were included (sample size: 599,551 HA patients, 4253 FPFs, incidence 0.71%). Risk factors statistically associated with increased incidence of FPFs were female gender (+ 40%), previous revision arthroplasty surgery (× 3 times), and the presence of rheumatoid arthritis (× 2.1 times), while osteoarthritis (− 57%), cement application (− 59%), and insertion of Biomet (− 68%) or Thompson’s prosthesis (− 75%) were correlated with low prevalence of FPFs. Obesity, cardiac diseases, advanced age, bad general health (ASA grade ≥ 3), and use of Exeter or Lubinus prosthesis were not linked to the appearance of FPFs. Conclusion This meta-analysis suggested that female gender, rheumatoid arthritis, and revision arthroplasty are major risk factors for the development of FPFs after a HA. In those patients, frequent follow-ups should be planned. Further prospective studies are necessary to clarify all the risk factors contributing to the appearance of FPFs after HA.

Rodent studies have shown that legumes can reduce chemical induced colonic inflammation, but the role of faba bean fractions for colon health has not been described. We have investigated the role of protein and fiber fractions of faba beans for colonic health and microbiota composition in a low-grade inflammation mice-model when incorporated in a Western diet (WD). The diet of sixty C57BL/6JRj male mice was standardized to a WD (41% fat, 43% carbohydrates) before were randomly assigned to four groups (n = 12) receiving either 1) WD with 30% of the protein replaced with faba-bean proteins, 2) WD with 7% of the fiber replaced with faba-bean fibers, 3) WD with protein and fiber fractions or 4) plain WD (n = 24). Low-grade inflammation was induced by 1% dextran sodium sulfate (DSS) given to mice for the last six days of the trial. Half (n = 12) in group 4) were given only water (controls). Prior to DSS, body weight, energy intake, glucose and insulin tolerance assays were performed. Inflammatory status in the colon was assessed by biomarkers of inflammation and qRT-PCR analyses of inflammatory related genes. Fecal microbiota composition was assessed by 16S rRNA gene sequencing. 1% DSS treatment increased levels in fecal lipocalin-2 and induced disease activity index score, but the presence of faba bean fractions in WD did not influence these indicators nor the expression level of inflammatory associated genes. However, the mice that had faba-bean proteins had a lower amount of Proteobacteria compared the group on plain WD. The Actinobacteria abundance was also lower in the group that had fiber fraction from faba-beans. Overall, outcomes indicated that in a low-grade inflammation model, replacement of protein and or fiber in a WD with faba bean fractions had marginal effects on inflammatory parameters and colonic microbiota.

In today’s era, humanity has been overwhelmed by technological revolutions that have changed and will continue to change how business operations are performed, directly or indirectly. At the same time, the processes within the supply chain are quite complex, and as technology and processes evolve, they become more and more challenging. Traceability has become a critical issue in the food industry to ensure safety, quality, and compliance with regulations. The adoption of blockchain technology in the food supply chain has gained significant attention as a potential solution to improve traceability. This paper presents the development of a distributed application for table olives’ traceability on the Ethereum network. The paper also presents a methodological framework, which can help anyone aiming to implement an Ethereum decentralized application and demonstrates the practical use of the developed application by a Greek table olives producer. The application significantly improved the producer’s product traceability by providing a secure, transparent, and efficient solution for tracking and tracing the products in the supply chain. The app reduced the time, increased the accuracy and reliability of data, improved supply chain efficiency, and helped the producer comply with international regulations and standards.

The impact of climate change on the biosphere and atmosphere is well documented but its impact on the anthroposphere needs to be better understood. Indeed, divergent views remain both at the regional level -as shown by (i) the EU case-by-case approach (ii) the African Kampala Convention (2009) and (iii) the Latin-American Lineamientos regionales (2018)- and finally the international one. As the Paris Agreement (2015) urges States to use best available scientific knowledge to counter the negative effects of climate change, we argue that Earth Observation (EO) could be potentially exploited by policy-makers as an efficient platform for modeling, forecasting, characterizing and understanding the severity of migration flows, contributing to evidence-based Anticipatory Action (A-A) for the effective management of climate migration, and setting the direction for future policies in this field that would be equitable on both regional and international scales. Hence, in implementing the Paris Agreement, States should increase the funding and use of EO and Causality as policy-supporting tools, to reduce the need for reactive and costly responses to displacement, and promote the safety, dignity, and well-being of people affected by climate change. However, based on said input, a well-adapted framework for the legal protection of climate migrants should be adopted.

We develop a “dual-cone” model for millimeter wavelength emission near a spinning black hole. The model consists of optically thin, luminous cones of emission, centered on the spin axis, which are meant to represent jet walls. The resulting image is dominated by a thin ring. We first consider the effect of the black hole’s spin on the image and show that the dominant effect is to displace the ring perpendicular to the projection of the spin axis on the sky by $2{a}_{* }\sin i+{ \mathcal O }({a}_{* }^{3})$ . This effect is lower order in a _* than changes in the shape and size of the photon ring itself but is undetectable without a positional reference. We then show that the centerline of the jet can provide a suitable reference: its location is exactly independent of spin if the observer is outside the cone and nearly independent of spin if the observer is inside the cone. If astrophysical uncertainties can be controlled, then spin displacement is large enough to be detectable by future space very long baseline interferometry missions. Finally, we consider ring substructure in the dual-cone model and show that features in total intensity are not universal and depend on the cone-opening angle.

Crop classification is an important remote sensing task with many applications, e.g., food security monitoring, ecosystem service mapping, climate change impact assessment, etc. This work focuses on mapping 10 crop types at the field level in an agricultural region located in the Spanish province of Navarre. For this, multi-temporal Synthetic Aperture Radar Polarimetric (PolSAR) Sentinel-1 imagery and multi-spectral Sentinel-2 data were jointly used. We applied the Cloude–Pottier polarimetric decomposition on PolSAR data to compute 23 polarimetric indicators and extracted vegetation indices from Sentinel-2 time-series to generate a big feature space of 818 features. In order to assess the relevance of the different features for the crop mapping task, we run a number of scenarios using a Support Vector Machines (SVM) classifier. The model that was trained using only the polarimetric data demonstrates a very promising performance, achieving an overall accuracy over 82%. A genetic algorithm was also implemented as a feature selection method for deriving an optimal feature subset. To showcase the positive effect of using polarimetric data over areas suffering from cloud coverage, we contaminated the original Sentinel-2 time-series with simulated cloud masks. By incorporating the genetic algorithm, we derived a high informative feature subset of 120 optical and polarimetric features, as the corresponding classification model increased the overall accuracy by 5% compared to the model trained only with Sentinel-2 features. The feature importance analysis indicated that apart from the Sentinel-2 spectral bands and vegetation indices, several polarimetric parameters, such as Shannon entropy, second eigenvalue and normalised Shannon entropy are of high value in identifying crops. In summary, the findings of our study highlight the significant contribution of Sentinel-1 PolSAR data in crop classification in areas with frequent cloud coverage and the effectiveness of the genetic algorithm in discovering the most informative features.

DeepCube is a 3-year Horizon 2020 project that leverages advances in the fields of Artificial Intelligence and Semantic Web to unlock the potential of big Copernicus data. Its goal is to address problems of high socio-environmental impact and enhance our understanding of Earth's processes correlated with Climate Change. To achieve this, the project employs mature ICT technologies, integrating them into a scalable, open and interoperable platform that provides solutions for all phases of an Earth Observation based AI pipeline. The Deep- Cube technologies will be demonstrated in five Use Cases.