Pittshammond2542

021 g/l in comparison with reported biosensors in the literature for detection of Hb concentration. Thus, based on the obtained results one can say that the proposed work unlocks a reliable sensing in the field of medical science to detect hemoglobin-related diseases in human being.Vitamins play an important role in many processes in the human organism. The detection of insufficient supply of vitamins is therefore of particular importance to avoid significant effects for human health. An increasing number of tests is only possible with suitable automated procedures. For the determination of vitamin D3 and vitamin D2 in serum samples, three methods were automated and compared with regard to their performance. All three methods enable reliable detection of 25(OH)D2 and 25(OH)D3 in serum in the ng/ml range.The field of synthetic glycobiotechnology encompasses the synthesis and modification of free carbohydrates and carbohydrates linked to biomolecules. Our group develops bio-catalytic processes for the synthesis of carbohydrate building blocks, so-called sugar nucleotides, and cell-free multi-enzyme cascades to tailor carbohydrates linked to proteins. The technology can eventually help to advance our understanding of the roles of specific carbohydrates in nutrition and medicine and contribute to human health and well-being.Campylobacter jejuni represents an important zoonotic pathogen that is causing foodborne enteric infections. In the human gut, C. jejuni bacteria induce intestinal campylobacteriosis which can develop into systemic post-infectious sequelae such as Guillain-Barré syndrome or rheumatoid arthritis. Here, we review the pathobiology and molecular mechanisms of C. jejuni infections as well as promising strategies to combat campylobacteriosis within the "One World - One Health" approach.The COVID-19 pandemic has disrupted the economy and businesses and impacted all facets of people's lives. It is critical to forecast the number of infected cases to make accurate decisions on the necessary measures to control the outbreak. While deep learning models have proved to be effective in this context, time series augmentation can improve their performance. In this paper, we use time series augmentation techniques to create new time series that take into account the characteristics of the original series, which we then use to generate enough samples to fit deep learning models properly. The proposed method is applied in the context of COVID-19 time series forecasting using three deep learning techniques, (1) the long short-term memory, (2) gated recurrent units, and (3) convolutional neural network. In terms of symmetric mean absolute percentage error and root mean square error measures, the proposed method significantly improves the performance of long short-term memory and convolutional neural networks. Also, the improvement is average for the gated recurrent units. Finally, we present a summary of the top augmentation model as well as a visual representation of the actual and forecasted data for each country.COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (R t) estimated against time, a more realistic than the static R 0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations.The Management International Review (MIR) celebrated its 60th anniversary in 2020. In commemoration of this event, we use a bibliometric analysis to present a retrospective on the journal by analyzing its content for the years between 2006 and 2020. We find that the collaboration culture in MIR has risen over time with the increase in the median size of author teams. Moreover, the collaboration network has become more global over time. The methodology used in the journal is predominantly empirical and quantitative with archival data sources most commonly used. The bibliographic coupling of the MIR corpus reveals that the major themes in the journal revolve around "culture," "emerging economies," "innovation, knowledge transfer, and absorptive capacity," "internationalization process," "culture and entry modes," and "internationalization and performance." A comparison with other leading international business journals provides distinct pathways in which MIR may continue to grow. Finally, it is important to note that while the share of conceptual studies has decreased significantly in the last 15 years, the MIR editors want to see more novel and theoretically grounded conceptual articles in the journal.Systemic oppression includes inequitable education that historically does not fully prepare students for comprehensive participation in society. The tools of science education, however, uniquely enable students to explore social inequities as well as the natural world. Thus, a role of education can be to embed social justice in science curricula. Presented here are three case studies that investigate pedagogical methods used by experienced teachers to integrate social justice into upper level high-school biology curricula. Two separate semi-constructed interviews were conducted with participants, along with an analysis of their pedagogical materials. Two main themes are identified and explored (1) delivery methods (pedagogy) and (2) biological science content. Storytelling and culturally responsive pedagogy were reported to be highly effective in engaging students; using these vehicles for delivery, social justice content can be seamlessly introduced alongside organic evolution. This embedded exploratory multiple-case study serves as an example of how science education can become a tool for student empowerment.

The online version contains supplementary material available at 10.1007/s11191-021-00287-y.

The online version contains supplementary material available at 10.1007/s11191-021-00287-y.In this article, we analyze images from the book "Our Friend the Atom," written by the astrophysicist Heinz Haber in 1957 and developed in the Disney Science Department. In addition to analyzing the work, we investigate its relevance for science education. After the US attack on two Japanese cities with atomic bombs, there was a severance of opinions on nuclear technologies. On one side, it had an association with the destruction arising from the war. On the other, a narrative highlighted the advantages of using nuclear power for developments that would benefit humanity. Haber and Disney's book aim to explain how such power works and supports its use for the good, despite the danger of destruction. Our goal is to contextualize the book's content and its visual imagery and identify aspects to contribute to the science curriculum. We summarized the historical elements of the post-war period and Walt Disney's entertainment approach and political stance. We discuss how it made a dialogue between a scientific concept and the general public through the book. For the matter of this article, we chose to examine one of the figures in the book, which represents through an illustration how chain reactions work to generate atomic power. For such, we followed a four-step methodology proposed by Silva and Neves Em Aberto, 31(103). (2018) to achieve an imagery analysis, giving us an understanding of the visual language contained in the book. It considers its visual choices, as shapes and colors, content, relations that involve the image, and interpretation of the picture as a whole by the reader. We came to understand the book's importance as scientific literacy was achieved through its illustrations, text, and popularity.The most common reaction to suggesting that we could learn valuable lessons from the way the current pandemic has been/ is being handled, is to discourage the attempt; as it is suggested that it can all be done more accurately and authoritatively after the inevitable Public Inquiry (Slater, 2019). PF-04965842 chemical structure On the other hand, a more constructive approach, is to capture and understand the work that was actually done.This would include normal activities, as well as positive adaptations to challenges and failures that may have occurred. Such an approach aimed at improving what worked, rather than blaming people for what went wrong, has the potential to contribute more successfully to controlling the consequences of the current crisis. Such an approach should thus be aimed at detecting and feeding back lessons from emerging and probably unexpected behaviours and helping to design the system to adapt better to counter the effects. The science and discipline of Human Factors (HF) promotes system resilience. This can be definfrom key aspects of the COVID-19 pandemic response in the United Kingdom.The rapid and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the early stage of virus infection can effectively prevent the spread of the virus and control the epidemic. Here, a colorimetric and fluorescent dual-functional lateral flow immunoassay (LFIA) biosensor was developed for the rapid and sensitive detection of spike 1 (S1) protein of SARS-CoV-2. A novel dual-functional immune label was fabricated by coating a single-layer shell formed by mixing 20 nm Au nanoparticles (Au NPs) and quantum dots (QDs) on SiO2 core to produce strong colorimetric and fluorescence signals and ensure good monodispersity and high stability. The colorimetric signal was used for visual detection and rapid screening of suspected SARS-CoV-2 infection on sites. The fluorescence signal was utilized for sensitive and quantitative detection of virus infection at the early stage. The detection limits of detecting S1 protein via colorimetric and fluorescence functions of the biosensor were 1 and 0.033 ng/mL, respectively. Furthermore, we evaluated the performance of the biosensor for analyzing real samples. The novel biosensor developed herein had good repeatability, specificity and accuracy, which showed great potential as a tool for rapidly detecting SARS-CoV-2.This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the Variance of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI).