The Chronological Advancement of Soil Organic Carbon Sequestration Research: A Review

According to Kyoto protocol carbon sequestration in terrestrial ecosystem is a low cost option to mitigate the increasing atmospheric CO₂ concentrations. It has been understood that the world’s forests and their soils have a high potential to sequester the atmospheric carbon. For the last two decades there has been increasing interest among scientists in terrestrial soil carbon storage processes. This review made an attempt to summarize the major chronological advancement of soil organic carbon (SOC) sequestration research and also to underpin the problems yet to be focused on this research at global level. SOC sequestration research began in the early seventies, where most of the studies were estimating the size/stock of the global SOC. In the early eighties, most of the researchers have started to focus on the factors involved in the storage of organic carbon in different ecosystems. Subsequently, the researchers started to work on different types of SOC pools, their size, turnover and chemical characterization in different types of ecosystems. Recently the researcher’s main focus has been the temperature sensitivity of organic carbon in different types of soil and their mechanism of stabilization. There has been interest among researchers on the contribution of microbial derived (microbial necromass) carbon in recalcitrant pool of SOC and their sequestration process in different types of ecosystems. Arguably, the contribution in SOC sequestration research on the fate of sequestered carbon in different types of soil and their stabilization mechanisms is insufficient. India is the country spread over regions from temperate to dry desert zones with different types of fragile ecosystems and its vulnerable nature to the global climate change. Therefore, the future research must focus on SOC sensitivity to temperature and SOC stabilization mechanisms in different ecosystems for better understanding of the soil carbon cycle.     

Outcomes of Donation After Circulatory Death Heart Transplantation in Australia

Background

Transplantation of hearts retrieved from donation after circulatory death (DCD) donors is an evolving clinical practice.

Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses

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First report of Mortierella wolfii causing fungal keratitis from a tertiary eye hospital in India

A young 33 year old male presented with non-resolving corneal infiltrate for 2 month duration in the right eye. KOH/ Calcoflour wet mount revealed sparsely septate fungal hyphae. Post therapeutic penetrating keratoplasty 3 doses of intracameral voriconazole(100μg/0.1ml) was administered suspecting recurrence. Fungal culture revealed non sporulating mould on SDA. PCR based DNA sequencing targeting the ITS region identified the fungal isolate as Mortierella wolfii (M. wolfii) belonging to zygomycetes. To the best of our knowledge, this is the first report of human fungal keratitis caused by M. wolfii.     

Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications.Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks.Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets.Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.