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Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the \"neoplasia\" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

This paper demonstrates that the mechanism of break-induced replication (BIR) is significantly different from S-phase replication, as it proceeds via a migrating bubble driven by Pif1 helicase, results in conservative inheritance of newly synthesized DNA, and is inherently mutagenic. Pif1 helicase promotes BIR-specific DNA synthesis When DNA is repaired by homologous recombination, DNA synthesis is involved in the latter stages. Two papers published in this issue of Nature now define a role for the DNA helicase Pif1 in this reaction. They show that although the initial stages of break-induced replication (BIR) can occur normally in the absence of Pif1, synthesis from a migrating D-loop intermediate is compromised. The mechanism of replication during BIR involves a unique bubble-like replication fork that results in conservative inheritance of the new genetic material, in contrast to the S-phase replication that duplicates the genome before cell division, and is inherently mutagenic. The repair of chromosomal double strand breaks (DSBs) is crucial for the maintenance of genomic integrity. However, the repair of DSBs can also destabilize the genome by causing mutations and chromosomal rearrangements, the driving forces for carcinogenesis and hereditary diseases. Break-induced replication (BIR) is one of the DSB repair pathways that is highly prone to genetic instability 1 , 2 , 3 . BIR proceeds by invasion of one broken end into a homologous DNA sequence followed by replication that can copy hundreds of kilobases of DNA from a donor molecule all the way through its telomere 4 , 5 . The resulting repaired chromosome comes at a great cost to the cell, as BIR promotes mutagenesis, loss of heterozygosity, translocations, and copy number variations, all hallmarks of carcinogenesis 4 , 5 , 6 , 7 , 8 , 9 . BIR uses most known replication proteins to copy large portions of DNA, similar to S-phase replication 10 , 11 . It has therefore been suggested that BIR proceeds by semiconservative replication; however, the model of a bona fide, stable replication fork contradicts the known instabilities associated with BIR such as a 1,000-fold increase in mutation rate compared to normal replication 9 . Here we demonstrate that in budding yeast the mechanism of replication during BIR is significantly different from S-phase replication, as it proceeds via an unusual bubble-like replication fork that results in conservative inheritance of the new genetic material. We provide evidence that this atypical mode of DNA replication, dependent on Pif1 helicase, is responsible for the marked increase in BIR-associated mutations. We propose that the BIR mode of synthesis presents a powerful mechanism that can initiate bursts of genetic instability in eukaryotes, including humans.

DNA double strand break (DSB) is one of the most threatening of all types of DNA damages as it leads to a complete breakage of the chromosome. The cell has evolved several mechanisms to repair DSBs, one of which is break-induced replication (BIR). BIR repair of DSBs occurs through invasion of one end of the broken chromosome into a homologous template followed by processive replication of DNA from the donor molecule. BIR is a key cellular process and is implicated in the restart of collapsed replication forks and several chromosomal instabilities. Recently, our lab demonstrated that the fidelity of DNA synthesis associated with BIR in yeast Saccharomyces Cerevisiae is extremely low. The level of frameshift mutations associated with BIR is 1000-fold higher as compared to normal DNA replication. This work demonstrates that BIR stimulates base substitution mutations, which comprise 90% of all point mutations, making them 400-1400 times more frequent than during S-phase DNA replication. We show that DNA Polymerase ζ proofreading corrects many of the base substitutions in BIR. Further, we demonstrate that Pif1, a 5'-3' DNA helicase, is responsible for making BIR efficient and also highly mutagenic. Pif1p is responsible for the majority of BIR mutagenesis not only close to the DSB site, where BIR is less stable but also at chromosomal regions far away from the DSB break site, where BIR is fast, processive and stable. This work further reveals that, at positions close to the DSB, BIR mutagenesis in the absence of Pif1 depends on Rev3, the catalytic subunit of translesion DNA Polymerase ζ. We observe that mutations promoted by Pol ζ are often complex and propose that they are generated by a Pol ζ- led template switching mechanism. These complex mutations were also found to be frequently associated with gross chromosomal rearrangements. Finally we demonstrate that BIR is carried out by unusual conservative mode of DNA synthesis. Based on this study, we speculate that the unusual mode of DNA synthesis associated with BIR leads to various kinds of genomic instability including mutations and chromosomal rearrangements.

It is essential for the advancement of science that scientists and researchers share, reuse and reproduce workflows and protocols used by others. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize a number of important points regarding the means by which digital objects are found and reused by others. The question of how to apply these principles not just to the static input and output data but also to the dynamic workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe our inclusive and overarching approach to apply the FAIR principles to workflows and protocols and demonstrate its benefits. We apply and evaluate our approach on a case study that consists of making the PREDICT workflow, a highly cited drug repurposing workflow, open and FAIR. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. A semantic model was proposed to better address these specific requirements and were evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

Genetic instabilities, including mutations and chromosomal rearrangements, lead to cancer and other diseases in humans and play an important role in evolution. A frequent cause of genetic instabilities is double-strand DNA breaks (DSBs), which may arise from a wide range of exogeneous and endogeneous cellular factors. Although the repair of DSBs is required, some repair pathways are dangerous because they may destabilize the genome. One such pathway, break-induced replication (BIR), is the mechanism for repairing DSBs that possesses only one repairable end. This situation commonly arises as a result of eroded telomeres or collapsed replication forks. Although BIR plays a positive role in repairing DSBs, it can alternatively be a dangerous source of several types of genetic instabilities, including loss of heterozygosity, telomere maintenance in the absence of telomerase, and non-reciprocal translocations. Also, mutation rates in BIR are about 1000 times higher as compared to normal DNA replication. In addition, micro-homology-mediated BIR (MMBIR), which is a mechanism related to BIR, can generate copy-number variations (CNVs) as well as various complex chromosomal rearrangements. Overall, activation of BIR may contribute to genomic destabilization resulting in substantial biological consequences including those affecting human health.

Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classification of heart sound recordings but rely on expert features that have not been thoroughly evaluated on noisy recordings. Here we propose a segmental convolutional neural network architecture that achieves automatic feature learning from noisy heart sound recordings. Our experiments show that our best model, trained on noisy recording segments acquired with an existing hidden semi-markov model-based approach, attains a classification accuracy of 87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6% accuracy of the state-of-the-art statistical classifier trained and evaluated on the same dataset. Our results indicate the potential of using neural network-based methods to increase the accuracy of automated classification of heart sound recordings for improved screening of heart diseases.

Objective: Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes; despite these efforts, label availability and accuracy are both suboptimal. Materials and Methods: In this retrospective study, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baselines by training Decision Trees (DT) and Random Forests (RF), and using MetaMap Lite, a clinical natural language processing tool. Results: We show that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the \"neoplasia\" category, the model built with veterinary data has a high accuracy in veterinary data, and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. Discussion: The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Conclusion: Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from, and inform, one another. Footnotes * This revised version has updated descriptions and focuses on comparative oncology.

The co-precipitation method was used for the synthesis of CdS quantum dots doped with Mn (1%, 2%, and 3%) and Mn (1%)/Co(2%) and Mn(2%)/Co(4%). Powder X-ray diffraction (XRD), transmission electron microscopy (TEM), UV–Vis absorption spectroscopy and photoluminescence (PL) spectroscopy analysis was carried out and evaluated their structural, morphological and optical properties. The quantum dot sensitized solar cell with the incorporation of the samples in photoanode is subjected for J-V characteristics to determine the solar cell parameters. Cubic structure of Mn and Mn/Co co-doped CdS quantum dots was obtained with the grain size of 10 nm confirmed by TEM images. The energy bandgap (E g ) values are varying between 2.98 and 2.89 eV for Mn/CdS and Mn/Co co-doped CdS, which was confirmed from Tauc plot. The maximum power conversion efficiency (1.67%) was obtained for the solar cells Mn (1%) and Co (2%) co-doped CdS with fill factor ( ff ), open circuit voltage and short circuit current density of 0.67, 0.3703 V and 6.7365 mA/cm 2 , respectively.