Pre-defined Categories Research Articles

Description and Analysis of Characteristics of COVID-19 Clinical Trials Registered in the Clinical Trials Registry-India (CTRI)

Many clinical trials are ongoing in India to evaluate the efficacy and safety of various interventions in COVID-19. It is mandatory that the clinical trials be registered in the Clinical Trials Registry-India (CTRI) before enrollment of study participants. The present study was carried out with the objective of collecting, compiling and analyzing various types of trial data such as study design, interventions, outcomes and study sites. The clinical trial data were collected from the CTRI web portal using the key word “COVID-19” on 14 July 2020. The CTRI data output of every study registered till 14 July 2020 was stored as PDF document and the data were transcribed into a validated excel sheet based on the pre-defined methods and categories and analyzed. A total of 293 clinical studies have been registered in CTRI as on 14 July 2020. Among them, 188 (64.16%) are interventional and 105 (35.83%) are observational studies. The interventions being evaluated are modern medications including drugs and biologicals, AYUSH formulations, Nutraceuticals, Yoga and Naturopathy. Most of the interventions are already in clinical use for non-COVID indications and undergoing repurposing evaluations for COVID-19 in the clinical studies. Trials with AYUSH formulations constitute more than half of the interventional studies (51.06%) while modern medications in 31.09% of the studies. 119 trials (63.3%) of the interventional studies are randomized studies. Large numbers of trials are conducted in the states where the incidence of COVID-19 is high. 146 interventional studies out of 188 are expected to be completed within 6 months and the outcomes of these studies may provide valid information on the potential treatments in COVID-19.

Publishers/sources (Disinformation)

Recent research has mainly used two approaches to identify publishers or sources of disinformation: First, alternative media are identified as potential publishers of disinformation. Second, potential publishers of disinformation are identified via fact-checking websites. Samples created using those approaches can partly overlap. However, the two approaches differ in terms of validity and comprehensiveness of the identified population. Sampling of alternative media outlets is theory-driven and allows for cross-national comparison. However, researchers face the challenge to identify misinforming content published by alternative media outlets. In contrast, fact-checked content facilitates the identification of a given disinformation population; however, fact-checker often have a publication bias focusing on a small range of (elite) actors or sources (e.g. individual blogs, hyper partisan news outlets, or politicians). In both approaches it is important to describe, compare and, if possible, assign the outlets to already existing categories in order to enable a temporal and spatial comparison.

Machine learning for psychological disorder prediction in Indians during COVID-19 nationwide lockdown

As the world combats with the outrageous and perilous novel coronavirus, national lockdown has been enforced in most of the countries. It is necessary for public health but on the flip side it is detrimental for people’s mental health. While the psychological repercussions are predictable during the period of COVID-19 lockdown but this enforcement can lead to long-term behavioral changes post lockdown too. Moreover, the detection of psychological effects may take months or years. This mental health crisis situation requires timely, pro-active intervention to cope and persevere the Coro-anxiety (Corona-related). To address this gap, this research firstly studies the psychological burden among Indians using a COVID-19 Mental Health Questionnaire and then does a predictive analytics using machine learning to identify the likelihood of mental health outcomes using learned features of 395 Indian participants. The proposed Psychological Disorder Prediction (PDP) tool uses a multinomial Naïve Bayes classifier to train the model to detect the onset of specific psychological disorder and classify the participants into two pre-defined categories, namely, anxiety disorder and mood disorder. Experimental evaluation reports a classification accuracy of 92.15%. This automation plays a pivotal role in clinical support as it aims to suggest individuals who may need psychological help.

Fiji plugins for qualitative image annotations: routine analysis and application to image classification.

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Rethinking the course of psychotic disorders: modelling long-term symptom trajectories.

The clinical course of psychotic disorders is highly variable. Typically, researchers have captured different course types using broad pre-defined categories. However, whether these adequately capture symptom trajectories of psychotic disorders has not been fully assessed. Using data from AESOP-10, we sought to identify classes of individuals with specific symptom trajectories over a 10-year follow-up using a data-driven approach. AESOP-10 is a follow-up, at 10 years, of 532 incident cases with a first episode of psychosis initially identified in south-east London and Nottingham, UK. Using extensive information on fluctuations in the presence of psychotic symptoms, we fitted growth mixture models to identify latent trajectory classes that accounted for heterogeneity in the patterns of change in psychotic symptoms over time. We had sufficient data on psychotic symptoms during the follow-up on 326 incident patients. A four-class quadratic growth mixture model identified four trajectories of psychotic symptoms: (1) remitting-improving (58.5%); (2) late decline (5.6%); (3) late improvement (5.4%); (4) persistent (30.6%). A persistent trajectory, compared with remitting-improving, was associated with gender (more men), black Caribbean ethnicity, low baseline education and high disadvantage, low premorbid IQ, a baseline diagnosis of non-affective psychosis and long DUP. Numbers were small, but there were indications that those with a late decline trajectory more closely resembled those with a persistent trajectory. Our current approach to categorising the course of psychotic disorders may misclassify patients. This may confound efforts to elucidate the predictors of long-term course and related biomarkers.

Fiji plugins for qualitative image annotations: routine analysis and application to image classification

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Does mid-regional pro-adrenomedullin (MR-proADM) improve the sequential organ failure assessment-score (SOFA score) for mortality-prediction in patients with acute infections? Results of a prospective observational study.

Risk stratification in patients with infection is usually based on the Sequential Organ Failure Assessment-Score (SOFA score). Our aim was to investigate whether the vasoactive peptide mid-regional pro-adrenomedullin (MR-proADM) improves the predictive value of the SOFA score for 30-day mortality in patients with acute infection presenting to the emergency department (ED). This secondary analysis of the prospective observational TRIAGE study included 657 patients with infection. The SOFA score, MR-proADM, and traditional inflammation markers were all measured at time of admission. Associations of admission parameters and 30-day mortality were investigated by measures of logistic regression, discrimination analyses, net reclassification index (NRI), and integrated discrimination index (IDI). MR-proADM values were higher in non-survivors compared with survivors (4.5±3.5nmol/L vs. 1.7±1.8nmol/L) with an adjusted odds ratio of 26.6 (95% CI 3.92 to 180.61, p=0.001) per 1nmol/L increase in admission MR-proADM levels and an area under the receiver operator curve (AUC) of 0.86. While the SOFA score alone revealed an AUC of 0.81, adding MR-proADM further improved discrimination (AUC 0.87) and classification within predefined risk categories (NRI 0.075, p-value <0.05). An admission MR-proADM threshold of 1.75nmol/L provided the best prognostic accuracy for 30-day mortality; with a sensitivity of 81% and a specificity of 75%, and a negative predictive value of 98%. MR-proADM improved the mortality risk stratification in patients with infection presenting to the ED beyond SOFA score alone and may further improve initial therapeutic site-of-care decisions. ClinicalTrials.gov NCT01768494. Registered January 15, 2013.

Named entity recognition for weather domain text in Hindi

Named entity recognition (NER) is the process of categorisation of a given entity in texts into a corresponding pre-defined category such as PE for name of the person, LOC for the name of location ...

RelightGAN: Instance-level Generative Adversarial Network for Face Illumination Transfer.

Face illumination perception and processing is a significantly difficult issue especially due to asymmetric shadings, local highlights, and local shadows. This study focuses on the face illumination transfer problem, which is to transfer the illumination style from a reference face image to a target face image while preserving other attributes. Such an instance-level transfer task is more challenging than the domain-level one that only considers the pre-defined lighting categories. To tackle this problem, we develop an instance-level conditional Generative Adversarial Networks (GAN). Specifically, face identifier is integrated into GAN learning, which enables an individual-specific low-level visual generation. Moreover, the illumination-inspired attention mechanism is conducted to allow GAN to well handle the local lighting effect. Our method requires neither lighting categorization, 3D information, nor strict face alignment, which are often employed by traditional methods. Experiments demonstrate that our method achieves significantly better results than previous methods.

SCG: Saliency and Contour Guided Salient Instance Segmentation.

Different from conventional instance segmentation, salient instance segmentation (SIS) faces two difficulties. The first is that it involves segmenting salient instances only while ignoring background, and the second is that it targets generic object instances without pre-defined object categories. In this paper, based on the state-of-the-art Mask R-CNN model, we propose to leverage complementary saliency and contour information to handle these two challenges. We first improve Mask R-CNN by introducing an interleaved execution strategy and proposing a novel mask head network to incorporate global context within each RoI. Then we add two branches to Mask R-CNN for saliency and contour detection, respectively. We fuse the Mask R-CNN features with the saliency and contour features, where the former supply pixel-wise saliency information to help with identifying salient regions and the latter provide a generic object contour prior to help detect and segment generic objects. We also propose a novel multiscale global attention model to generate attentive global features from multiscale representative features for feature fusion. Experimental results demonstrate that all our proposed model components can improve SIS performance. Finally, our overall model outperforms state-of-the-art SIS methods and Mask R-CNN by more than 6% and 3%, respectively. By using additional multitask training data, we can further improve the model performance on the ILSO dataset.

Comparing the use of direct observation, standardized patients and exit interviews in low- and middle-income countries: a systematic review of methods of assessing quality of primary care.

Clinical records in primary healthcare settings in low- and middle-income countries (LMIC) are often lacking or of too poor quality to accurately assess what happens during the patient consultation. We examined the most common methods for assessing healthcare workers’ clinical behaviour: direct observation, standardized patients and patient/healthcare worker exit interview. The comparative feasibility, acceptability, reliability, validity and practicalities of using these methods in this setting are unclear. We systematically review and synthesize the evidence to compare and contrast the advantages and disadvantages of each method. We include studies in LMICs where methods have been directly compared and systematic and narrative reviews of each method. We searched several electronic databases and focused on real-life (not educational) primary healthcare encounters. The most recent update to the search for direct comparison studies was November 2019. We updated the search for systematic and narrative reviews on the standardized patient method in March 2020 and expanded it to all methods. Search strategies combined indexed terms and keywords. We searched reference lists of eligible articles and sourced additional references from relevant review articles. Titles and abstracts were independently screened by two reviewers and discrepancies resolved through discussion. Data were iteratively coded according to pre-defined categories and synthesized. We included 13 direct comparison studies and eight systematic and narrative reviews. We found that no method was clearly superior to the others—each has pros and cons and may assess different aspects of quality of care provision by healthcare workers. All methods require careful preparation, though the exact domain of quality assessed and ethics and selection and training of personnel are nuanced and the methods were subject to different biases. The differential strengths suggest that individual methods should be used strategically based on the research question or in combination for comprehensive global assessments of quality.

Abstract PR12: Dynamic clustering of cancer genomics datasets beyond pre-defined human categories

Abstract Background: The history of the race concept sheds the light on the lack of scientific basis of racial categories. Early attempts of using race in scientific practice (ca. 17th century) took place during times of colonial expansions and the transatlantic slave trade. The lenses through which scientists approached race categories changed over the next centuries, but legacies of using race categories in genetic studies are still embedded in some of today’s practices. Consequently, this engenders the need for novel approaches that transcend categories of or related to race in genomics to avoid unintended consequences and improve the study of disease. Methods: We introduce an approach that dynamically generates trait-specific cohorts based on genomic clustering at a subset of loci known to contribute to the disease under study. Reflecting the broad diversity of traits, the number of clusters and cluster memberships vary depending on the trait and its implicated genomic regions. Our method hence directs the lens of studying human biodiversity with respect to disease to the trait level rather than “population level.” Results: We test our approach on cancer and control whole genome sequencing datasets to partition each based on predisposition to 8 cancer types. Focusing on COSMIC genes known to have a germline contribution to cancer, we demonstrate that resulting clusterings vary across cancer types and transcend race, ethnicity—and ancestry categories, supporting our call to generate dynamic categorization sets at the trait level. Conclusion: Our suggested method highlights the importance of historical perspectives to co-shape today’s scientific practice. Experimental results resonate with previous joint calls from scholars in genetics and the humanities and social sciences to focus on the study racial and ethnic disparities in health as a result of rampant societal and environmental disparities—rather than genomic differences partly based on historical attempts to impose fixity on fluid social constructs. Citation Format: Hussein Mohsen, Kim Blenman, Lajos Pusztai. Dynamic clustering of cancer genomics datasets beyond pre-defined human categories [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PR12.

Grau de pertencimento como insumo para classificação automática de textos: uma abordagem sintática

Grouping documents into categories is one of the solutions adopted to streamline the information retrieval process, which is increasingly relevant due to the large amount of information available today. The manual localization of documents of a specific theme, available in digital repositories, involves reading the title, abstract and keywords, in addition to further detailed evaluation in order to identify whether the publication belongs to the desired thematic axis. Considering the number of publications in a digital repository, manually locating all the desired texts on a given topic can be laborious and time-consuming. This research proposes an architecture for automatic classification of texts that is based on syntactic questions, that is, it undertakes a comparison of n-grams, which are combinations of n-pairs of words that are identified throughout the text. An exploratory applied research was carried out, which applied a type of supervised learning, fundamentally based on the document representation model called bag-of-words (BoW). The paper’s macro objective was to classify texts in general, according to pre-defined categories, by generating and comparing degrees of belonging between texts, as one of the key criteria. The results of these comparisons, using n-gram = 3, demonstrate that in the use of classifications by n-grams, the greater the number of grams, and with the removal of the stop words, we obtain a reduced degree of belonging, demonstrating greater rigor in identifying the match during the classification. In order to have greater confidence in the results, a larger training corpus is necessary to expand the number of words that characterize the pre-defined categories, to be used in the classification of the texts.

SERVICE CENTERS: ANALYSIS OFCHALLENGES AT DIFFERENT DEVELOPMENTAL STAGES

Purpose – To link the different levels of maturity of Service Centers (SCs) in multinational companies to the challenges faced by this organizational arrangement.Design/methodology/approach – Using a qualitative approach, we conducted a multiple case study with data collection, via semi-structured interviews, with managers of three multinational companies, based in SCs in southern and southeastern Brazil. The interviews were recorded and transcribed, and a content analysis was made based on four pre-defined categories using a theoretical framework.Findings – Results show that challenges arise depending on the Center's level of development and are related to organizational structure; cost reduction; customer relationship management; and processes. Some challenges have always recurred at SCs, others are not relevant initially, and then become more important, while others decrease in complexity over time.Originality/value – As a theoretical contribution, in addition to proposing a three-stage analysis, this study demonstrates the importance of examining each with its specific challenges. The understanding that a SC develops based on tensions and solutions demonstrates the dynamics and complexity of this phenomenon. The study also encourages and guides managers in organizations, who are planning a SC or who already have one set up, to align their resources and work with the measures needed to deal with the challenges of each stage and maximize the use of resources for better results at the SC.Keywords - Service Center. Service Center Stages. Organizational management

A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification

User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification.

What makes a model prescriber? A documentary analysis

Introduction In recent years, the authority to prescribe medications in healthcare has expanded to include pharmacists, nurses and Allied Healthcare Professionals. Subsequently, the quantity of guidelines describing appropriate prescribing practice has increased. Despite this, the literature notes a lack of consensus regarding the overall qualities of a good prescriber. The aim of this study was to attempt to define what makes a model prescriber in practice, regardless of professional background. Methods A documentary analysis of UK-based and international prescribing practice guidelines was performed. Data analysis was conducted through a constructivist grounded theory approach to enable concepts to be identified from the data itself without the use of pre-defined categories. Results A total of 13 guideline documents were analysed. Overall, four core categories of a model prescriber in practice were identified: Knowledgeable: including that of disease and drug properties; Safe: relating to appropriate drug quantities and treatment-monitoring; Good Communicators: with both patients and colleagues; Contemporary: through enhancing knowledge and skills. Conclusions These four categories can serve as a definition of a high-level prescriber and as an additional tool for prescribing educators to evaluate the extent their curriculum develops and assesses the core qualities needed by their students to be high-level prescribers in practice.

Fraud Risk Management Model: A Content Analysis Approach

The objective of this study is to explore the whole process of fraud risk management strategies that should be implemented by the organizations. Secondly, this study discusses the governance issues that arise at each stage of the process. For the purpose of this study, a content analysis of previous literatures is used as a technique for gathering data. This process usually involves codifying qualitative and quantitative information into pre-defined categories in order to derive patterns in the presentation and reporting of information. Based on our content analysis, we found that the fraud risk management process should be made of at least five stages which are inculcating the culture of managing risks in an organization, identifying the risks, evaluating the risks, determining preventive actions and implementing and reviewing stages. Our extended analysis of the fraud risk management process finds that a lot of governance issues arise in the fraud risk management process that should be solved by regulators and companies in order to ensure that fraud risk management process is embedded as corporate culture, not merely as a process. Among them are how to create the risk culture in an organization and whether auditors and risk management committees identify risks from each available source.

Fiji plugins for qualitative image annotations: routine analysis and application to image classification

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To facilitate and spread the usage of analysis tools, we provide examples of such pipelines, including a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Text categorization: past and present

Automatic text categorization is the operation of sorting out the text documents into pre-defined text categories using some machine learning algorithms. Normally, it defines the most important approaches to organizing and making the use of a large volume of information exists in unstructured form. Nowadays, text categorization is becoming an extensively researched field of text mining and processing of languages. Word sense, semantic relationships among terms, text documents and categories are quite essential in order of enhancing the performances of categorization. Various surveys on text categorization have already been available which involve techniques of various text representation schemes to such extent but do not include several approaches that have been explored in text categorization over the standard techniques. Here, an exhaustive analysis of different text categorization approaches over the conventional approaches has been undertaken. This survey paper explores a wide variety of algorithms used for categorizing text documents and tries to assemble the existing works into three basic fields: conventional methods, fuzzy logic-based methods, deep learning-based methods. Further, conventional methods have been categorized into three fields: text categorization using handcrafted features, text categorization using nature-inspired algorithms and text categorization using graph-based methods. Furthermore, this survey provides a clear idea about the available libraries used for different algorithms, availability of datasets, categorization technologies explored in various non-Indian and Indian languages as well.

How do undergraduates cope with anxiety resulting from active learning practices in introductory biology?

Active learning pedagogies decrease failure rates in undergraduate introductory biology courses, but these practices also cause anxiety for some students. Classroom anxiety can impact student learning and has been associated with decreased student retention in the major, but little is known about how students cope with anxiety caused by active learning practices. In this study, we investigated student coping strategies for various types of active learning (clickers, volunteering to answer a question, cold calling, and group work) that were used in 13 introductory Biology courses at a large public university in 2016–2017. A survey asked students to rate their anxiety regarding the four active learning practices and over half of the students explained the coping strategies they used to manage their active learning anxieties. Coping responses from 880 students were sorted into pre-defined categories of coping strategies: problem solving, information seeking, self-reliance, support seeking, accommodation, helplessness, escape, delegation, and isolation. We found that a different category of coping was dominant for each type of active learning. The dominant coping strategies for anxiety associated with clickers, cold calling, and group work were adaptive coping strategies of information seeking, self-reliance, and support-seeking, respectively. The dominant coping strategy for volunteering to answer a question was escape, which is a maladaptive strategy. This study provides a detailed exploration of student self-reported coping in response to active learning practices and suggests several areas that could be foci for future psychosocial interventions to bolster student regulation of their emotions in response to these new classroom practices.