Fuentescurrin2467

The Creeper (Cp) chicken is characterized by chondrodystrophy in Cp/+ heterozygotes and embryonic lethality in Cp/Cp homozygotes. However, the genes underlying the phenotypes have not been fully known. Here, we show that a 25 kb deletion on chromosome 7, which contains the Indian hedgehog (IHH) and non-homologous end-joining factor 1 (NHEJ1) genes, is responsible for the Cp trait in Japanese bantam chickens. IHH is essential for chondrocyte maturation and is downregulated in the Cp/+ embryos and completely lost in the Cp/Cp embryos. This indicates that chondrodystrophy is caused by the loss of IHH and that chondrocyte maturation is delayed in Cp/+ heterozygotes. The Cp/Cp homozygotes exhibit impaired DNA double-strand break (DSB) repair due to the loss of NHEJ1, resulting in DSB accumulation in the vascular and nervous systems, which leads to apoptosis and early embryonic death. © The Author(s) 2020.In vitro reconstitution is a powerful tool for investigating ribosome functions and biogenesis, as well as discovering new ribosomal features. RG-7112 In this study, we integrated all of the processes required for Escherichia coli small ribosomal subunit assembly. In our method, termed fully Recombinant-based integrated Synthesis, Assembly, and Translation (R-iSAT), assembly and evaluation of the small ribosomal subunits are coupled with ribosomal RNA (rRNA) synthesis in a reconstituted cell-free protein synthesis system. By changing the components of R-iSAT, including recombinant ribosomal protein composition, we coupled ribosomal assembly with ribosomal protein synthesis, enabling functional synthesis of ribosomal proteins and subsequent subunit assembly. In addition, we assembled and evaluated subunits with mutations in both rRNA and ribosomal proteins. The study demonstrated that our scheme provides new ways to comprehensively analyze any elements of the small ribosomal subunit, with the goal of improving our understanding of ribosomal biogenesis, function, and engineering. © The Author(s) 2020.FGF2 is a tumor cell survival factor that is exported from cells by an ER/Golgi-independent secretory pathway. This unconventional mechanism of protein secretion is based on direct translocation of FGF2 across the plasma membrane. The Na,K-ATPase has previously been shown to play a role in this process, however, the underlying mechanism has remained elusive. Here, we define structural elements that are critical for a direct physical interaction between FGF2 and the α1 subunit of the Na,K-ATPase. In intact cells, corresponding FGF2 mutant forms were impaired regarding both recruitment at the inner plasma membrane leaflet and secretion. Ouabain, a drug that inhibits both the Na,K-ATPase and FGF2 secretion, was found to impair the interaction of FGF2 with the Na,K-ATPase in cells. Our findings reveal the Na,K-ATPase as the initial recruitment factor for FGF2 at the inner plasma membrane leaflet being required for efficient membrane translocation of FGF2 to cell surfaces. © The Author(s) 2020.Measuring the full set of society's assets is critical to the design of policies for a sustainable future. © Springer Nature Limited 2020.Data from networked sensors, such as those in our phones, are increasingly being explored and used to identify behaviors related to health and mental health. While computer scientists have referred to this field as context sensing, personal sensing, or mobile sensing, medicine has more recently adopted the term digital phenotyping. This paper discusses the implications of these labels in light of privacy concerns, arguing language that is transparent and meaningful to the people whose data we are acquiring. © The Author(s) 2020.Many virtual care initiatives focus heavily on video visits, essentially mimicking face-to-face visits. Meanwhile, clinicians in established settings continue to use the oldest modality, phone calls, and some use the most ubiquitous, asynchronous messaging. The latter, along with live chat and chatbots, could be transformative if workflows were redesigned to incorporate it. With multiple modalities now available for use in virtual care, the central problem is to direct patient-provider interactions to the channels generating the most value. Marketers call this channel management and use sophisticated approaches to implement it. We propose an adaptation of channel management to virtual care and discuss anticipated challenges to its implementation. © The Author(s) 2020.Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space. © The Author(s) 2020.In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective.