Variety AHB 1269Fe (MH 2185)

Pearl millet Varietal Identification Committee in its annual meet on 22nd-24th March, 2018, during the 53rd Annual Pearl Millet Workshop at ARS, Jodhpur, identified MH 2185 as “biofortified pearl millet hybrid AHB 1269Fe” for its high grain Fe combined with high grain and stover yield. MH 2185 is a cross between male-sterile line ICMA1 98222 (female parent) and restorer AUBI 1105 (male parent). The line ICMA1 98222 is based on A1 source of cytoplasmic malesterility developed at ICRISAT, Patancheru. Hybrid MH 2185 was tested in the All India Coordinated Pearl Millet Improvement Project (AICRP-PM) trials during 2015-2017 seasons at 36 locations (12 locations each in 2015, 13 locations in 2016 and 11 locations in 2017) together with 6 controls, 86M86, 86M01, MPMH 17, HHB-67 Improved, Pratap, and Dhanashakti. While the first five controls are commercially released highyielding hybrid cultivars, Dhanashakti is an improved version of open pollinated variety (OPV) ICTP8203 with high grain Fe (71 ppm). AHB 1269Fe hybrid was jointly developed and sponsored to AICRP-PM for evaluation by National Agriculture Research Project Aurangabad, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani and International Crops Research Institute for Semi-Aric Tropics (ICRISAT), Patancheru, India

A quantitative and qualitative evaluation of sentence boundary detection for the clinical domain

Sentence boundary detection (SBD) is a critical preprocessing task for many natural language processing (NLP) applications. However, there has been little work on evaluating how well existing methods for SBD perform in the clinical domain. We evaluate five popular off-the-shelf NLP toolkits on the task of SBD in various kinds of text using a diverse set of corpora, including the GENIA corpus of biomedical abstracts, a corpus of clinical notes used in the 2010 i2b2 shared task, and two general-domain corpora (the British National Corpus and Switchboard). We find that, with the exception of the cTAKES system, the toolkits we evaluate perform noticeably worse on clinical text than on general-domain text. We identify and discuss major classes of errors, and suggest directions for future work to improve SBD methods in the clinical domain. We also make the code used for SBD evaluation in this paper available for download at http://github.com/drgriffis/SBD-Evaluation

Ontology-Guided Data Augmentation for Medical Document Classification

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification