One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the ...learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.
All coronaviruses known to have recently emerged as human pathogens probably originated in bats
. Here we use a single experimental platform based on immunodeficient mice implanted with human lung ...tissue (hereafter, human lung-only mice (LoM)) to demonstrate the efficient in vivo replication of severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as well as two endogenous SARS-like bat coronaviruses that show potential for emergence as human pathogens. Virus replication in this model occurs in bona fide human lung tissue and does not require any type of adaptation of the virus or the host. Our results indicate that bats contain endogenous coronaviruses that are capable of direct transmission to humans. Our detailed analysis of in vivo infection with SARS-CoV-2 in human lung tissue from LoM showed a predominant infection of human lung epithelial cells, including type-2 pneumocytes that are present in alveoli and ciliated airway cells. Acute infection with SARS-CoV-2 was highly cytopathic and induced a robust and sustained type-I interferon and inflammatory cytokine and chemokine response. Finally, we evaluated a therapeutic and pre-exposure prophylaxis strategy for SARS-CoV-2 infection. Our results show that therapeutic and prophylactic administration of EIDD-2801-an oral broad-spectrum antiviral agent that is currently in phase II/III clinical trials-markedly inhibited SARS-CoV-2 replication in vivo, and thus has considerable potential for the prevention and treatment of COVID-19.
Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the ...direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients' temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.
We propose two new algorithms, LOADDx and SCADDx, to combine a patient's gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.
We demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed ...our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
The One Class Classification (OCC) problem is different from the conventional binary/multi-class classification problem in the sense that in OCC, the negative class is either not present or not ...properly sampled. The problem of classifying positive (or target) cases in the absence of appropriately-characterized negative cases (or outliers) has gained increasing attention in recent years. Researchers have addressed the task of OCC by using different methodologies in a variety of application domains. In this paper we formulate a taxonomy with three main categories based on the way OCC has been envisaged, implemented and applied by various researchers in different application domains. We also present a survey of current state-of-the-art OCC algorithms, their importance, applications and limitations.
This paper presents a new approach to classification of high-dimensional spectroscopy data and demonstrates that it outperforms other current state-of-the art approaches. The specific task we ...consider is identifying whether samples contain chlorinated solvents or not, based on their Raman spectra. We also examine robustness to classification of outlier samples that are not represented in the training set (negative outliers). A novel application of a locally connected neural network (NN) for the binary classification of spectroscopy data is proposed and demonstrated to yield improved accuracy over traditionally popular algorithms. Additionally, we present the ability to further increase the accuracy of the locally connected NN algorithm through the use of synthetic training spectra, and we investigate the use of autoencoder based one-class classifiers and outlier detectors. Finally, a two-step classification process is presented as an alternative to the binary and one-class classification paradigms. This process combines the locally connected NN classifier, the use of synthetic training data, and an autoencoder based outlier detector to produce a model which is shown to both produce high classification accuracy and be robust in the presence of negative outliers.
How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on 'subjectification' i.e. 'the coming into presence of unique individual ...beings'? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher education, is a hands-on approach to building an open, collaborative pedagogy of data literacy, based on Bayesian Networks (BNs) (Pearl, J. 1985. Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning. Los Angeles: University of California (Computer Science Department)). BNs can be used to merge subjective views of the learning process with objective data analysis from the learning environment; BNs are visual data constructs and, unlike other Machine Learning approaches that obfuscate and complexify, BNs can be developed to reveal relationships from observations. In this paper, we share ways in which teachers and students can work together in a praxis approach to use data to 'read the world' around them (Freire, P. 1970. Pedagogy of the Oppressed. New York: Continuum. 125).
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To achieve goals, organisms are often faced with complex tasks that require enhanced control of cognitive faculties for optimal performance. However, the neural circuit mechanisms underlying this ...ability are unclear. The claustrum is proposed to mediate a variety of functions ranging from sensory binding to cognitive control of action, but direct functional assessments of this telencephalic nucleus are lacking.
Here, we employed the Gnb4 (guanine nucleotide-binding subunit beta-4) cre driver line in mice to selectively monitor and manipulate claustrum projection neurons during 1-choice versus 5-choice serial reaction time task performance.
Using fiber photometry, we found elevated claustrum activity prior to an expected cue during correct performance on the cognitively demanding 5-choice response assay relative to the less demanding 1-choice version of the task. Claustrum activity during reward acquisition was also enhanced when task demand was higher. Furthermore, optogenetically inhibiting the claustrum prior to the onset of the cue reduced choice accuracy on the 5-choice task but not on the 1-choice task.
These results suggest that the claustrum supports a cognitive control function necessary for optimal behavioral performance under cognitively demanding conditions.
Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome characterized by severe respiratory failure requiring mechanical ventilatory support for which there is no therapy and ...in which mortality remains approximately 40%. A recent important advance has been the subclassification of ARDS into subphenotypes that have potential prognostic and/or therapeutic significance. Calfee and colleagues reported that an approach called latent class analysis (LCA) could identify one-third of patients with ARDS with a "hyperinflammatory" phenotype, with the remainder having a "hypoinflammatory" pattern. The hyperinflammatory phenotype had higher levels of proinflammatory biomarkers and poorer outcomes, including fewer ventilator-free and organ failure -- free days and higher mortality. Biological plausibility to these subphenotypes has been suggested by differential responses to treatment. A recent LCA of a large negative randomized controlled trial of simvastatin in ARDS demonstrated potential benefit in the hyperinflammatory group. Other approaches to ARDS phenotyping exist, including a "physiologic phenotyping" approach (based on the focal versus diffuse distribution of lung infiltrates) and transcriptomics-based approaches.
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