Breast cancer surgery with augmented reality

Introduction: Innovations in 3D spatial technology and augmented reality imaging driven by digital high-tech industrial science have accelerated experimental advances in breast cancer imaging and the development of medical procedures aimed to reduce invasiveness. Presentation of case: A 57-year-old post-menopausal woman presented with screen-detected left-sided breast cancer. After undergoing all staging and pre-operative studies the patient was proposed for conservative breast surgery with tumor localization. During surgery, an experimental digital and non-invasive intra-operative localization method with augmented reality was compared with the standard pre-operative localization with carbon tattooing (institutional protocol). The breast surgeon wearing an augmented reality headset (Hololens) was able to visualize the tumor location projection inside the patient’s left breast in the usual supine position. Discussion: This work describes, to our knowledge, the first experimental test with a digital non-invasive method for intra-operative breast cancer localization using augmented reality to guide breast conservative surgery. In this case, a successful overlap of the previous standard pre-operative marks with carbon tattooing and tumor visualization inside the patient’s breast with augmented reality was obtained. Conclusion: Breast cancer conservative guided surgery with augmented reality can pave the way for a digital non-invasive method for intra-operative tumor localization.

A novel structure for online surgical undergraduate teaching during the COVID-19 pandemic

BACKGROUND

The Covid-19 pandemic necessitated the delivery of online higher education. Online learning is a novel experience for medical education in Sri Lanka. A novel approach to undergraduate surgical learning was taken up in an attempt to improve the interest amongst the students in clinical practice while maximizing the limited contact time.

METHOD

Online learning activity was designed involving medical students from all stages and multi consultant panel discussions. The discussions were designed to cover each topic from basic sciences to high-level clinical management in an attempt to stimulate the student interest in clinical medicine. Online meeting platform with free to use basic plan and a social media platform were used in combination to communicate with the students. The student feedback was periodically assessed for individual topics as well as for general outcome. Lickert scales and numeric scales were used to acquire student agreement on the desired learning outcomes.

RESULTS

A total of 1047 student responses for 7 questionnaires were analysed. During a 6-week period, 24 surgical topics were discussed with 51 contact hours. Eighty-seven per cent definitely agreed (highest agreement) with the statement 'students benefitted from the discussions'. Over 95% have either participated for all or most sessions. A majority of the respondents (83.4%) 'definitely agreed' that the discussions helped to improve their clinical sense. Of the total respondents, 79.3% definitely agreed that the discussions helped to build an interest in clinical medicine. Around 90% agreed that both exam-oriented and clinical practice-oriented topics were highly important and relevant. Most widely raised concerns were the poor Internet connectivity and limitation of access to the meeting platform.

CONCLUSION

Online teaching with a novel structure is feasible and effective in a resource-limited setting. Students agree that it could improve clinical interest while meeting the expected learning outcomes.

Probabilistic vs deep learning based approaches for narrow domain NER in Spanish

This work presents an experimental study on the task of Named Entity Recognition (NER) for a narrow domain in Spanish language. This study considers two approaches commonly used in this kind of problem, namely, a Conditional Random Fields (CRF) model and Recurrent Neural Network (RNN). For the latter, we employed a bidirectional Long Short-Term Memory with ELMO’s pre-trained word embeddings for Spanish. The comparison between the probabilistic model and the deep learning model was carried out in two collections, the Spanish dataset from CoNLL-2002 considering four classes under the IOB tagging schema, and a Mexican Spanish news dataset with seventeen classes under IOBES schema. The paper presents an analysis about the scalability, robustness, and common errors of both models. This analysis indicates in general that the BiLSTM-ELMo model is more suitable than the CRF model when there is “enough” training data, and also that it is more scalable, as its performance was not significantly affected in the incremental experiments (by adding one class at a time). On the other hand, results indicate that the CRF model is more adequate for scenarios having small training datasets and many classes.

Measuring interdisciplinarity of research articles: An analysis of inter-relatedness of different parameters

With evolution of knowledge disciplines and cross fertilization of ideas, research outputs reported as scientific papers are now becoming more and more interdisciplinary. An interdisciplinary research work usually involves ideas and approaches from multiple disciplines of knowledge applied to solve a specific problem. In many cases the interdisciplinary areas eventually emerge as full-fledged disciplines. In the last two decades, several approaches have been proposed to measure the Interdisciplinarity of a scientific article, such as propositions based on authorship, references, set of keywords etc. Among all these approaches, reference-set based approach is most widely used. The diversity of knowledge in the reference set has been measured with three parameters, namely variety, balance, and disparity. Different studies tried to combine these measures in one way or other to propose an aggregate measure of interdisciplinarity, called integrated diversity. However, there is a lack of understanding on inter-relations between these parameters. This paper tries to look into inter-relatedness between the three parameters by analytical study on an important interdisciplinary research area, Internet of Things (IoT). Research articles in IoT, as obtained from Web of Science for the year 2018 have been analyzed to compute the three measures and understand their inter-relatedness. Results obtained show that variety and balance are negatively correlated, variety and disparity do not show a stable relatedness and balance and disparity are negatively correlated. Further, the integrated diversity measure is negatively correlated with variety and weakly positively correlated with balance and disparity. The results imply that the composite integrated diversity measure may not be a suitably constructed composite measure of interdisciplinarity.

Sentiment analysis in Nepali: Exploring machine learning and lexicon-based approaches

In recent times, sentiment analysis research has achieved tremendous impetus on English textual data, however, a very less amount of research has been focused on Nepali textual data. This work is focused towards Nepali textual data. We have explored machine learning approaches and proposed a lexicon-based approach using linguistic features and lexical resources to perform sentiment analysis for tweets written in Nepali language. This lexicon-based approach, first pre-process the tweet, locate the opinion-oriented features and then compute the sentiment polarity of tweet. We have investigated both conventional machine learning models (Multinomial Naïve Bayes (NB), Decision Tree, Support Vector Machine (SVM) and logistic regression) and deep learning models (Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM) for sentiment analysis of Nepali text. These machine learning models and lexicon-based approach have been evaluated on tweet dataset related to Nepal Earthquake 2015 and Nepal blockade 2015. Lexicon based approach has outperformed than conventional machine learning models. Deep learning models have outperformed than conventional machine learning models and lexicon-based approach. We have also created Nepali SentiWordNet and Nepali SenticNet sentiment lexicon from existing English language resources as by-product.

Measuring semantic similarity of documents with weighted cosine and fuzzy logic

Currently, the semantic analysis is used by different fields, such as information retrieval, the biomedical domain, and natural language processing. The primary focus of this research work is on using semantic methods, the cosine similarity algorithm, and fuzzy logic to improve the matching of documents. The algorithms were applied to plain texts in this case CVs (resumes) and job descriptions. Synsets of WordNet were used to enrich the semantic similarity methods such as the Wu-Palmer Similarity (WUP), Leacock-Chodorow similarity (LCH), and path similarity (hypernym/hyponym). Additionally, keyword extraction was used to create a postings list where keywords were weighted. The task of recruiting new personnel in the companies that publish job descriptions and reciprocally finding a company when workers publish their resumes is discussed in this research work. The creation of a new gold standard was required to achieve a comparison of the proposed methods. A web application was designed to match the documents manually, creating the new gold standard. Thereby the new gold standard confirming benefits of enriching the cosine algorithm semantically. Finally, the results were compared with the new gold standard to check the efficiency of the new methods proposed. The measures used for the analysis were precision, recall, and f-measure, concluding that the cosine similarity weighted semantically can be used to get better similarity scores.

K-means based method for overlapping document clustering

Overlapping clustering algorithms have shown to be effective for clustering documents. However, the current overlapping document clustering algorithms produce a big number of clusters, which make them little useful for the user. Therefore, in this paper, we propose a k-means based method for overlapping document clustering, which allows to specify by the user the number of groups to be built. Our experiments with different corpora show that our proposal allows obtaining better results in terms of FBcubed than other recent works for overlapping document clustering reported in the literature.

Revisiting subject classification in academic databases: A comparison of the classification accuracy of Web of Science, Scopus & Dimensions

Classification of research articles into different subject areas is an extremely important task in bibliometric analysis and information retrieval. There are primarily two kinds of subject classification approaches used in different academic databases: journal-based (aka source-level) and article-based (aka publication-level). The two popular academic databases- Web of Science and Scopus- use journal-based subject classification scheme for articles, which assigns articles into a subject based on the subject category assigned to the journal in which they are published. On the other hand, the recently introduced Dimensions database is the first large academic database that uses article-based subject classification scheme that assigns the article to a subject category based on its contents. Though the subject classification schemes of Web of Science have been compared in several studies, no research studies have been done on comparison of the article-based and journal-based subject classification systems in different academic databases. This paper aims to compare the accuracy of subject classification system of the three popular academic databases: Web of Science, Scopus and Dimensions through a large-scale user-based study. Results show that the commonly held belief of superiority of article-based subject classification over the journal-based subject classification scheme does not hold at least at the moment, as Web of Science appears to have the most accurate subject classification.

Automatic Generation of Dialogues based on Grammatical Inference and the use of a Knowledge Base

In this work, we present a model for the automatic generation of written dialogues, through the use of grammatical inference. This model allows the automatic recognition of grammars from a set dialogues employed as a training set. The inferred grammars are then used to generate templates of responses within the dialogues. The final objective is to apply this model in a specific domain dialogue system that answers questions in Spanish with the use of a knowledge base. The experiments carried out have been performend using the DIHANA project corpus which contains dialogues written in Spanish about schedules and prices of a rail system.

Using automatic constructed thesauri instead of dictionaries in the verbal phraseological units validation task

Automatic validation of compositionality vs non-compositionality is a very challenging problem in NLP. A very small number of papers in literature report results in this particular problem. Recently, some new approaches have arised with respect to this particular linguistic task. One of these approaches that have called our attention is based on what authors call “lexical domain”. In this paper, we analyze the use of Pointwise Mutual Information for constructing thesauri on the fly, which can be further employed instead of dictionaries for determining whether or not a given phraseological unit is compositional or not. The experimental results carried out in this paper show that this dissimilarity measure (PMI), can effectively be used when determining compositionality of a given verbal phraseological unit. Moreover, we show that the use of thesauri improves the results obtained in comparison with those experiments employing dictionaries, highlighting the use of self-constructed lexical resources which are, in fact, taking advantage of the same vocabulary of the target dataset.

Correction to: Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures

The article 'Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures',written by J. A. Kanis, was originally published Online First without Open Access. After publication in volume [#], issue [#] and page [#-#], the author decided to opt for Open Choice and to make the article an Open Access publication.

Automatic detection of perforators for microsurgical reconstruction

The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction. In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task. Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P = 6e-4) but better for the remaining ones (P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2-3 mm do not have impact in the dissection step. Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case). The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story.

The Music of the Hattons

The core of the historical collection of printed and manuscript music at Christ Church, Oxford derives from a generous bequest in the year 1710 by the late dean of the college, Henry Aldrich. Generosity of other donors is not in question, but Aldrich was more than primus inter pares. An almost contemporary bequest by Richard Goodson senior pales in comparison: printed books from before 1670 (for example) that were contributed by him number barely one-fifteenth of those from the Aldrich bequest. The means whereby Aldrich amassed his musical treasures were lost to direct investigation through the destruction of his personal papers at his express request. It is the aim of this essay to show the extent of evidence for a new claim on the origins of a large part of the collection: that Aldrich acquired shortly after the year 1670 a musical library built up by the first Baron Hatton of Kirby on the basis of his own father's collection. By correlating printed and manuscript contents, it will be shown that a further group of more widely scattered manuscripts was copied for use by Hatton's musicians, and can thus be considered as directly or indirectly dependent on his patronage.

Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures

Guidance is provided in an international setting on the assessment and specific treatment of postmenopausal women at low, high and very high risk of fragility fractures.

INTRODUCTION

The International Osteoporosis Foundation and European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis published guidance for the diagnosis and management of osteoporosis in 2019. This manuscript seeks to apply this in an international setting, taking additional account of further categorisation of increased risk of fracture, which may inform choice of therapeutic approach.

METHODS

Clinical perspective and updated literature search.

RESULTS

The following areas are reviewed: categorisation of fracture risk and general pharmacological management of osteoporosis.

CONCLUSIONS

A platform is provided on which specific guidelines can be developed for national use to characterise fracture risk and direct interventions.

Lactic Acid Fermentation of Pomegranate Juice as a Tool to Improve Antioxidant Activity

An increasing consumer demand for pomegranate has been globally observed, mainly thanks to the scientific evidence related to its functional and health-promoting features. Pomegranate fruits from twenty accessions identified in Southeastern Italy were characterized according to morphological and chemical features. Juices extracted from pomegranate fruits were fermented with selected Lactobacillus plantarum PU1 and the antioxidant activity investigated. Whey was added to juices to promote the microbial growth. Fermentation led to the increase of the radical scavenging activity (up to 40%) and significant inhibition of the linoleic acid peroxidation. The three fermented juices showing the highest antioxidant activity, and the corresponding unfermented controls, were further characterized. In detail, the cytotoxicity and the protective role toward artificially induced oxidative stress were determined on murine fibroblasts Balb 3T3 through the determination of the viability and the intracellular ROS (reactive oxygen species) scavenging activity (RSA). RSA reached values of ca. 70% in fermented juices, being ca. 40% higher than the unfermented and control samples. Phenols compounds of the pomegranate juices obtained from accessions "Bitonto Piscina," "Sanrà nero," and "Wonderful (reference cultivar) were analyzed through ultrahigh pressure liquid chromatography coupled with mass spectrometry, showing that a marked increase (up to 60%) of the ellagitannins derivatives occurred during fermentation. Sensory analysis showed suitability of the fermented juices to be used as beverage and food ingredient.