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General General

Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text

Second Edition of Emotion Measurement, 2020

Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.

Saif M. Mohammad


General General

Stability of RNA sequences derived from the coronavirus genome in human cells.

In Biochemical and biophysical research communications

Most viruses inhibit the innate immune system and/or the RNA degradation processes of host cells to construct an advantageous intracellular environment for their survival. Characteristic RNA sequences within RNA virus genomes or RNAs transcribed from DNA virus genomes contribute toward this inhibition. In this study, we developed a method called "Fate-seq" to comprehensively identify the RNA sequences derived from RNA and DNA viruses, contributing RNA stability in the cells. We examined the stabilization activity of 5,924 RNA fragments derived from 26 different viruses (16 RNA viruses and 10 DNA viruses) using next-generation sequencing of these RNAs fused 3' downstream of GFP reporter RNA. With the Fate-seq approach, we detected multiple virus-derived RNA sequences that stabilized GFP reporter RNA, including sequences derived from severe acute respiratory syndrome-related coronavirus (SARS-CoV). Comparative genomic analysis revealed that these RNA sequences and their predicted secondary structures are highly conserved between SARS-CoV and the novel coronavirus, SARS-CoV-2, which is responsible for the global outbreak of the coronavirus-associated disease that emerged in December 2019 (COVID-19). These sequences have the potential to enhance the stability of viral RNA genomes, thereby augmenting viral replication efficiency and virulence.

Wakida Hiroyasu, Kawata Kentaro, Yamaji Yuta, Hattori Emi, Tsuchiya Takaho, Wada Youichiro, Ozaki Haruka, Akimitsu Nobuyoshi


COVID-19, Functional sequence, RNA stability, SARS-CoV, SARS-CoV-2, Virus

Radiology Radiology

The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile

ArXiv Preprint

In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the obtained results, it was concluded that a U-Net neural network allows performing the detection of anatomical reference points more accurately than a fully convolutional neural network. The results of the detection of anatomical reference points by the U-Net neural network are closer to the average results of the detection of reference points by a group of orthodontists.

Konstantin Dobratulin, Andrey Gaidel, Irina Aupova, Anna Ivleva, Aleksandr Kapishnikov, Pavel Zelter


Radiology Radiology

Predictors of Successful First-Pass Thrombectomy with a Balloon Guide Catheter: Results of a Decision Tree Analysis.

In Translational stroke research ; h5-index 39.0

Complete recanalization after a single retrieval maneuver is an interventional goal in acute ischemic stroke and an independent factor for good clinical outcome. Anatomical biomarkers for predicting clot removal difficulties have not been comprehensively analyzed and await unused. We retrospectively evaluated 200 consecutive patients who suffered acute stroke and occlusion of the anterior circulation and were treated with mechanical thrombectomy through a balloon guide catheter (BGC). The primary objective was to evaluate the influence of carotid tortuosity and BGC positioning on the one-pass Modified Thrombolysis in Cerebral Infarction Scale (mTICI) 3 rate, and secondarily, the influence of communicating arteries on the angiographic results. After the first-pass mTICI 3, recanalization fell from 51 to 13%. The regression models and decision tree (supervised machine learning) results concurred: carotid tortuosity was the main constraint on efficacy, reducing the likelihood of mTICI 3 after one pass to 30%. BGC positioning was relevant only in carotid arteries without elongation: BGCs located in the distal internal carotid artery (ICA) had a 70% probability of complete recanalization after one pass, dropping to 43% if located in the proximal ICA. These findings demonstrate that first-pass mTICI 3 is influenced by anatomical and interventional factors capable of being anticipated, enabling the BGC technique to be adapted to patient's anatomy to enhance effectivity.

Velasco Gonzalez Aglaé, Görlich Dennis, Buerke Boris, Münnich Nico, Sauerland Cristina, Rusche Thilo, Faldum Andreas, Heindel Walter


Carotid arteries, Circle of Willis, Stroke, Suction, Thrombectomy

General General

Advancing evidence-based healthcare facility design: a systematic literature review.

In Health care management science

Healthcare facility design is a complex process that brings together diverse stakeholders and ideally aligns operational, environmental, experiential, clinical, and organizational objectives. The challenges inherent in facility design arise from the dynamic and complex nature of healthcare itself, and the growing accountability to the quadruple aims of enhancing patient experience, improving population health, reducing costs, and improving staff work life. Many healthcare systems and design practitioners are adopting an evidence-based approach to facility design, defined broadly as basing decisions about the built environment on credible and rigorous research and linking facility design to quality outcomes. Studies focused on architectural options and concepts in the evidence-based design literature have largely employed observation, surveys, post-occupancy study, space syntax analysis, or have been retrospective in nature. Fewer studies have explored layout optimization frameworks, healthcare layout modeling, applications of artificial intelligence, and layout robustness. These operations research/operations management approaches are highly valuable methods to inform healthcare facility design process in its earliest stages and measure performance in quantitative terms, yet they are currently underutilized. A primary objective of this paper is to begin to bridge this gap. This systematic review summarizes 65 evidence-based research studies related to facility layout and planning concepts published from 2008 through 2018, and categorizes them by methodology, area of focus, typology, and metrics of interest. The review identifies gaps in the existing literature and proposes solutions to advance evidence-based healthcare facility design. This work is the first of its kind to review the facility design literature across the disciplines of evidence-based healthcare design research, healthcare systems engineering, and operations research/operations management. The review suggests areas for future study that will enhance evidence-based healthcare facility designs through the integration of operations research and management science methods.

Halawa Farouq, Madathil Sreenath Chalil, Gittler Alice, Khasawneh Mohammad T


Evidence-based design, Facility layout, Layout optimization, Literature review, Operations research

Radiology Radiology

Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images.

In Diagnostics (Basel, Switzerland)

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

Adachi Mio, Fujioka Tomoyuki, Mori Mio, Kubota Kazunori, Kikuchi Yuka, Xiaotong Wu, Oyama Jun, Kimura Koichiro, Oda Goshi, Nakagawa Tsuyoshi, Uetake Hiroyuki, Tateishi Ukihide


artificial intelligence, breast imaging, convolutional neural network, deep learning, magnetic resonance imaging, object detection