Moisture-Assisted Hydroboration of Nitriles and Conversion Thereof to N-Heterocyles and N-Containing Derivatives.

The recent revelation of hidden-borane catalysis has revolutionized the field of catalytic hydroboration in organic synthesis. Many nucleophilic reaction promoters, previously believed to be the catalysts, in fact primarily facilitated the formation of borane (BH3), which subsequently acted as the true catalyst. This revelation prompted us to explore the untapped potential of these unexpected transformations, with a view to simplify hydroboration using more cost-effective and environmentally friendly nucleophilic precatalysts. Via computational studies, we were able to identify that water can actually undertake that role. Herein, we report a study on the simple hydroboration of nitriles, a notoriously challenging yet synthetically valuable class of substrates, using nothing more than moisture as an activating agent. This moisture-assisted nitrile hydroboration process can seamlessly integrate with a range of downstream transformations in a one-pot fashion to produce valuable N-containing products such as symmetrical imines, thioureas, and bis(alcohol)amines as well as N-heterocycles such as pyrroles, pyridines, pyridinium salts, 2-iminothiazolines, and carbazoles.

Retraction: New synthesis of 2-aroylbenzothiazoles via metal-free domino transformations of anilines, acetophenones, and elemental sulfur.

[This retracts the article DOI: 10.1039/D0RA01750G.].

Tropylium-promoted Ritter reactions.

The Ritter reaction used to be one of the most powerful synthetic tools to functionalize alcohols and nitriles, providing valuable N-alkyl amide products. However, this reaction has not been frequently used in modern organic synthesis due to its employment of strongly acidic and harsh reaction conditions, which often lead to complicated side reactions. Herein, we report the development of a new method using salts of the tropylium ion to promote the Ritter reaction. This method works well on a range of alcohol and nitrile substrates, giving the corresponding products in good to excellent yields. This reaction protocol is amenable to microwave and continuous flow reactors, offering an attractive opportunity for further applications in organic synthesis.

New synthesis of 2-aroylbenzothiazoles via metal-free domino transformations of anilines, acetophenones, and elemental sulfur.

A new synthesis of 2-aroylbenzothiazoles via iodine-promoted domino transformations of anilines, acetophenones, and elemental sulfur was demonstrated. The highlights of this tandem synthesis are (1) easily available anilines and acetophenones as feedstock; (2) transition metal-free conditions; (3) inexpensive, nontoxic, easy handling, and abundant elemental sulfur as a building block. This synthetic strategy would complement the existing methods in the synthesis of this important heterocyclic scaffold. To our best knowledge, the formation of 2-aroylbenzothiazoles from simple anilines, acetophenones, and elemental sulfur was not previously reported in the literature.

Alternative pathways to α,ß-unsaturated ketones via direct oxidative coupling transformation using Sr-doped LaCoO3 perovskite catalyst.

A strontium-doped lanthanum cobaltite perovskite material was prepared, and used as a recyclable and effective heterogeneous catalyst for the direct oxidative coupling of alkenes with aromatic aldehydes to produce α,ß-unsaturated ketones. The reaction afforded high yields in the presence of di-tert-butylperoxide as oxidant. Single oxides or salts of strontium, lanthanum and cobalt, and the undoped perovskite offered a lower catalytic activity than the strontium-doped perovskite. Benzaldehyde could be replaced by benzyl alcohol, dibenzyl ether, 2-oxo-2-phenylacetaldehyde, 2-bromoacetophenone or (dimethoxymethyl) benzene in the oxidative coupling reaction with alkenes. To our best knowledge, reactions between these starting materials with alkenes are new and unknown in the literature.

Extracting health-related causality from twitter messages using natural language processing.

BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques. METHODS: Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: "stress", "insomnia", and "headache." A large dataset consisting of 24 million tweets are used. RESULTS: The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations. CONCLUSIONS: Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.

Synthesis of triphenylpyridines via an oxidative cyclization reaction using Sr-doped LaCoO3 perovskite as a recyclable heterogeneous catalyst.

An La0.6Sr0.4CoO3 strontium-doped lanthanum cobaltite perovskite was prepared via a gelation and calcination approach and used as a heterogeneous catalyst for the synthesis of triphenylpyridines via the cyclization reaction between ketoximes and phenylacetic acids. The transformation proceeded via the oxidative functionalization of the sp3 C-H bond in phenylacetic acid. The La0.6Sr0.4CoO3 catalyst demonstrated a superior performance to that of the pristine LaCoCO3 as well as a series of homogeneous and heterogeneous catalysts. Furthermore, the La0.6Sr0.4CoO3 catalyst was facilely recovered and reused without considerable decline in its catalytic efficiency. To the best of our knowledge, the combination of ketoximes with easily available phenylacetic acids is novel.

Cross Disciplinary Consultancy to Bridge Public Health Technical Needs and Analytic Developers: Negation Detection Use Case.

This paper describes a continuing initiative of the International Society for Disease Surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. Funded by the Defense Threat Reduction Agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection) in free-text chief complaints and triage reports.

Cross-dehydrogenative coupling of coumarins with Csp3-H bonds using an iron-organic framework as a productive heterogeneous catalyst.

The iron-organic framework VNU-20 was utilized as an active heterogeneous catalyst for the cross-dehydrogenative coupling of coumarins with Csp3-H bonds in alkylbenzenes, cyclohexanes, ethers, and formamides. The combination of DTBP as the oxidant and DABCO as the additive led to high yields of coumarin derivatives. The VNU-20 was more active towards this reaction than numerous other homogeneous and heterogeneous catalysts. Heterogeneous catalysis was confirmed for the cross-dehydrogenative coupling transformation utilizing the VNU-20 catalyst, and the contribution of active iron species in the liquid phase was insignificant. The iron-based framework was reutilized many times for the functionalization of coumarins without a remarkable decline in catalytic efficiency. To the best of our knowledge, these reactions of coumarins have not previously been conducted using heterogeneous catalysts.

Copper-catalyzed domino sequences: a new route to pyrido-fused quinazolinones from 2'-haloacetophenones and 2-aminopyridines.

A new pathway to access pyrido-fused quinazolinones via a Cu(OAc)2-catalyzed domino sequential transformation between 2'-haloacetophenones and 2-aminopyridines was demonstrated. The solvent and base exhibited a remarkable effect on the transformation, in which the combination of DMSO and NaOAc emerged as the best system. Cu(OAc)2·H2O was more active towards the reaction than numerous other catalysts. This methodology is new and would be complementary to previous protocols for the synthesis of pyrido-fused quinazolinones.

A Preliminary Study of Clinical Concept Detection Using Syntactic Relations.

Concept detection is an integral step in natural language processing (NLP) applications in the clinical domain. Clinical concepts are detailed (e.g., "pain in left/right upper/lower arm/leg") and expressed in diverse phrase types (e.g., noun, verb, adjective, or prepositional phrase). There are rich terminological resources in the clinical domain that include many concept synonyms. Even with these resources, concept detection remains challenging due to discontinuous and/or permuted phrase occurrences. To overcome this challenge, we investigated an approach to exploiting syntactic information. Syntactic patterns of concept phrases were mined from continuous, non-permuted forms of synonyms, and these patterns were used to detect discontinuous and/or permuted concept phrases. Experiments on 790 de-identified clinical notes showed that the proposed approach can potentially boost a recall of concept detection. Meanwhile, challenges and limitations were noticed. In this paper, we report and discuss our preliminary analysis and finding.

How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.

BACKGROUND: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases. Relaxation is often recommended in mental health treatment as a frontline strategy to reduce stress, thereby improving health conditions. Twitter is a microblog platform that allows users to post their own personal messages (tweets), including their expressions about feelings and actions related to stress and stress management (eg, relaxing). While Twitter is increasingly used as a source of data for understanding mental health from a population perspective, the specific issue of stress-as manifested on Twitter-has not yet been the focus of any systematic study. OBJECTIVE: The objective of our study was to understand how people express their feelings of stress and relaxation through Twitter messages. In addition, we aimed at investigating automated natural language processing methods to (1) classify stress versus nonstress and relaxation versus nonrelaxation tweets, and (2) identify first-hand experience-that is, who is the experiencer-in stress and relaxation tweets. METHODS: We first performed a qualitative content analysis of 1326 and 781 tweets containing the keywords "stress" and "relax," respectively. We then investigated the use of machine learning algorithms-in particular naive Bayes and support vector machines-to automatically classify tweets as stress versus nonstress and relaxation versus nonrelaxation. Finally, we applied these classifiers to sample datasets drawn from 4 cities in the United States (Los Angeles, New York, San Diego, and San Francisco) obtained from Twitter's streaming application programming interface, with the goal of evaluating the extent of any correlation between our automatic classification of tweets and results from public stress surveys. RESULTS: Content analysis showed that the most frequent topic of stress tweets was education, followed by work and social relationships. The most frequent topic of relaxation tweets was rest & vacation, followed by nature and water. When we applied the classifiers to the cities dataset, the proportion of stress tweets in New York and San Diego was substantially higher than that in Los Angeles and San Francisco. In addition, we found that characteristic expressions of stress and relaxation varied for each city based on its geolocation. CONCLUSIONS: This content analysis and infodemiology study revealed that Twitter, when used in conjunction with natural language processing techniques, is a useful data source for understanding stress and stress management strategies, and can potentially supplement infrequently collected survey-based stress data.

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes.

OBJECTIVE: Delayed diagnosis of Kawasaki disease (KD) may lead to serious cardiac complications. We sought to create and test the performance of a natural language processing (NLP) tool, the KD-NLP, in the identification of emergency department (ED) patients for whom the diagnosis of KD should be considered. METHODS: We developed an NLP tool that recognizes the KD diagnostic criteria based on standard clinical terms and medical word usage using 22 pediatric ED notes augmented by Unified Medical Language System vocabulary. With high suspicion for KD defined as fever and three or more KD clinical signs, KD-NLP was applied to 253 ED notes from children ultimately diagnosed with either KD or another febrile illness. We evaluated KD-NLP performance against ED notes manually reviewed by clinicians and compared the results to a simple keyword search. RESULTS: KD-NLP identified high-suspicion patients with a sensitivity of 93.6% and specificity of 77.5% compared to notes manually reviewed by clinicians. The tool outperformed a simple keyword search (sensitivity = 41.0%; specificity = 76.3%). CONCLUSIONS: KD-NLP showed comparable performance to clinician manual chart review for identification of pediatric ED patients with a high suspicion for KD. This tool could be incorporated into the ED electronic health record system to alert providers to consider the diagnosis of KD. KD-NLP could serve as a model for decision support for other conditions in the ED.

Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR). To extract numerous and diverse concepts, such as data elements (i.e., important concepts related to a certain medical condition), a plausible solution is to combine various NLP tools into an ensemble to improve extraction performance. However, it is unclear to what extent ensembles of popular NLP tools improve the extraction of numerous and diverse concepts. Therefore, we built an NLP ensemble pipeline to synergize the strength of popular NLP tools using seven ensemble methods, and to quantify the improvement in performance achieved by ensembles in the extraction of data elements for three very different cohorts. Evaluation results show that the pipeline can improve the performance of NLP tools, but there is high variability depending on the cohort.

Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use.

Self-reported patient data has been shown to be a valuable knowledge source for post-market pharmacovigilance. In this paper we propose using the popular micro-blogging service Twitter to gather evidence about adverse drug reactions (ADRs) after firstly having identified micro-blog messages (also know as "tweets") that report first-hand experience. In order to achieve this goal we explore machine learning with data crowdsourced from laymen annotators. With the help of lay annotators recruited from CrowdFlower we manually annotated 1548 tweets containing keywords related to two kinds of drugs: SSRIs (eg. Paroxetine), and cognitive enhancers (eg. Ritalin). Our results show that inter-annotator agreement (Fleiss' kappa) for crowdsourcing ranks in moderate agreement with a pair of experienced annotators (Spearman's Rho=0.471). We utilized the gold standard annotations from CrowdFlower for automatically training a range of supervised machine learning models to recognize first-hand experience. F-Score values are reported for 6 of these techniques with the Bayesian Generalized Linear Model being the best (F-Score=0.64 and Informedness=0.43) when combined with a selected set of features obtained by using information gain criteria.

Natural language processing in biomedicine: a unified system architecture overview.

In contemporary electronic medical records much of the clinically important data-signs and symptoms, symptom severity, disease status, etc.-are not provided in structured data fields but rather are encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of unlocking this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we review briefly. Additionally, the challenge facing current research efforts in biomedical NLP includes the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge.

PhenDisco: phenotype discovery system for the database of genotypes and phenotypes.

The database of genotypes and phenotypes (dbGaP) developed by the National Center for Biotechnology Information (NCBI) is a resource that contains information on various genome-wide association studies (GWAS) and is currently available via NCBI's dbGaP Entrez interface. The database is an important resource, providing GWAS data that can be used for new exploratory research or cross-study validation by authorized users. However, finding studies relevant to a particular phenotype of interest is challenging, as phenotype information is presented in a non-standardized way. To address this issue, we developed PhenDisco (phenotype discoverer), a new information retrieval system for dbGaP. PhenDisco consists of two main components: (1) text processing tools that standardize phenotype variables and study metadata, and (2) information retrieval tools that support queries from users and return ranked results. In a preliminary comparison involving 18 search scenarios, PhenDisco showed promising performance for both unranked and ranked search comparisons with dbGaP's search engine Entrez. The system can be accessed at http://pfindr.net.

Standardizing Phenotype Variables in the Database of Genotypes and phenotypes (dbGaP) based on Information Models.

This paper describes an information model based approach to standardizing phenotype variables in dbGaP. Our attempt to utilize existing information models of Clinical Element Models (CEM) was not successful although CEM provided a robust means of representing clinical data. Thus, we developed information models derived from phenotype variable descriptions and standardized phenotype variables by fitting them into the models using a simple Natural Language Processing (NLP) algorithm. We report the experience of standardizing findings related variables, which tend to be more idiosyncratic thus pose more challenges to standardization, using this approach.

Recent trends in biomedical informatics: a study based on JAMIA articles.

In a growing interdisciplinary field like biomedical informatics, information dissemination and citation trends are changing rapidly due to many factors. To understand these factors better, we analyzed the evolution of the number of articles per major biomedical informatics topic, download/online view frequencies, and citation patterns (using Web of Science) for articles published from 2009 to 2012 in JAMIA. The number of articles published in JAMIA increased significantly from 2009 to 2012, and there were some topic differences in the last 4 years. Medical Record Systems, Algorithms, and Methods are topic categories that are growing fast in several publications. We observed a significant correlation between download frequencies and the number of citations per month since publication for a given article. Earlier free availability of articles to non-subscribers was associated with a higher number of downloads and showed a trend towards a higher number of citations. This trend will need to be verified as more data accumulate in coming years.