Plos One
-
Whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) are widely used for measuring DNA methylation levels on a genome-wide scale. Both methods have limitations: WGBS is expensive and prohibitive for most large-scale projects; RRBS only interrogates 6-12% of the CpGs in the human genome. Here, we introduce methylation-sensitive restriction enzyme bisulfite sequencing (MREBS) which has the reduced sequencing requirements of RRBS, but significantly expands the coverage of CpG sites in the genome. ⋯ This combined approach allowed us to estimate differential methylation across 60% of the genome using read count data alone, and where counts were sufficiently high in both samples (about 1.5% of the genome), our estimates were significantly improved by the single CpG conversion information. We show that differential DNA methylation values based on MREBS data correlate well with those based on WGBS and RRBS. This newly developed technique combines the sequencing cost of RRBS and DNA methylation estimates on a portion of the genome similar to WGBS, making it ideal for large-scale projects of mammalian genomes.
-
To investigate the association between ossification of the posterior longitudinal ligament (OPLL) and ossification of the nuchal ligament (ONL) in terms of incidence and size. ⋯ The presence of ONL was associated with the presence of OPLL. The length of OPLL and ONL showed no correlation.
-
Climate change is driving shifts in the abundance and distribution of marine fish and invertebrates and is having direct and indirect impacts on seafood catches and fishing communities, exacerbating the already negative effects of unsustainably high fishing pressure that exist for some stocks. Although the majority of fisheries in the world are managed at the national or local scale, most existing approaches to assessing climate impacts on fisheries have been developed on a global scale. It is often difficult to translate from the global to regional and local settings because of limited relevant data. ⋯ Using the climate impact estimations and model outputs, we identify high priority stocks, fleets, and regions for policy reform in Mexico in the face of climate change. This approach can be applied in other data-poor circumstances to focus future research and policy reform efforts on stocks now subject to additional stress due to climate change. Considering their growing relevance as a critical source of protein and micronutrients to nourish our growing population, it is urgent for regions to develop sound fishery management policies in the short-term as they are the most important intervention to mitigate the adverse effects of climate change on marine fisheries.
-
Value and waste in preclinical and clinical research projects are intensively debated in biomedicine at present. Such different aspects as the need for setting objectives and priorities, improving study design, quality of reporting, and problematic incentives of the academic reward system are addressed. While this debate is also fueled by ethical considerations and thus informed by bioethical research, up to now, the field of bioethics lacks a similar extensive debate. Nonetheless, bioethical research should not go unquestioned regarding its scientific or social value. What exactly constitutes the value of bioethical research, however, remains widely unclear so far. ⋯ Even though limitations arise regarding the sample, the study revealed a plethora of value dimensions that can inform further debates about what makes bioethical research valuable for science and society. Besides theoretical reflections on the value of bioethics more meta-research in bioethics is needed.
-
This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models. ⋯ The results indicated that delta-features could potentially provide better treatment assessment than single-time-point features. The treatment assessment is substantially affected by the time point for computing the delta-features and the combination of machine learning methods for feature selection and classification.