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Global mRNA expression analysis is efficient for phenotypic profiling of tumours, and has been used to define molecular subtypes for almost every major tumour type. A key limitation is that most tumours are communities of both tumour and non-tumour cells. This problem is particularly pertinent for analysis of advanced invasive tumours, which are known to induce major changes and responses in both the tumour and the surrounding tissue. To identify bladder cancer tumour-cell phenotypes and compare classification by tumour-cell phenotype with classification by global gene expression analysis, we analysed 307 advanced bladder cancers (cystectomized) both by genome gene expression analysis and by immunohistochemistry with antibodies for 28 proteins. According to systematic analysis of gene and protein expression data, focusing on key molecular processes, we describe five tumour-cell phenotypes of advanced urothelial carcinoma: urothelial-like, genomically unstable, basal/SCC-like, mesenchymal-like, and small-cell/neuroendocrine-like. We provide molecular pathological definitions for each subtype. Tumours expressing urothelial differentiation factors show inconsistent and abnormal protein expression of terminal differentiation markers, suggesting pseudo-differentiation. Cancers with different tumour-cell phenotypes may co-cluster (converge), and cases with identical tumour-cell phenotypes may cluster apart (diverge), in global mRNA analyses. This divergence/convergence suggests that broad global commonalities related to the invasive process may exist between muscle-invasive tumours regardless of specific tumour-cell phenotype. Hence, there is a systematic disagreement in subtype classification determined by global mRNA profiling and by immunohistochemical profiling at the tumour-cell level. We suggest that a combination of molecular pathology (tumour-cell phenotype) and global mRNA profiling (context) is required for adequate subtype classification of muscle-invasive bladder cancer. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
Growth is a crucial inconsistent parameter, which is a primary requisite in diagnosing and planning orthodontic and orthopedic treatment. The use of the epiphyseal development of the middle phalanx of the third finger (MP3) radiograph is advisable instead of hand and wrist radiographs for growth assessment.
Specified Certainty Classification (SCC) is a new paradigm for employing classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified level of certainty, as well as provides insights into classifier behavior by examining all decisions that are possible. Our primary illustration is read classification for reference-guided genome assembly, but we demonstrate the breadth of SCC by also analyzing COVID-19 vaccination data.
The Transporter Classification Database (TCDB; http://www.tcdb.org) serves as a common reference point for transport protein research. The database contains more than 10,000 non-redundant proteins that represent all currently recognized families of transmembrane molecular transport systems. Proteins in TCDB are organized in a five level hierarchical system, where the first two levels are the class and subclass, the second two are the family and subfamily, and the last one is the transport system. Superfamilies that contain multiple families are included as hyperlinks to the five tier TC hierarchy. TCDB includes proteins from all types of living organisms and is the only transporter classification system that is both universal and recognized by the International Union of Biochemistry and Molecular Biology. It has been expanded by manual curation, contains extensive text descriptions providing structural, functional, mechanistic and evolutionary information, is supported by unique software and is interconnected to many other relevant databases. TCDB is of increasing usefulness to the international scientific community and can serve as a model for the expansion of database technologies. This manuscript describes an update of the database descriptions previously featured in NAR database issues.
AJCC TNM stage and WHO grade (G) are two widely used staging systems to guide clinical management for pancreatic neuroendocrine neoplasms (panNENs), based on clinical staging and pathological grading information, respectively. We proposed to integrate TNM stage and G grade into one staging system (TNMG) and to evaluate its clinical application as a prognostic indicator for panNENs. Accordingly, 5254 patients diagnosed with panNENs were used to evaluate and to validate the applicability of TNMG to panNENs. The predictive accuracy of TNMG system was compared with that of each separate staging/grading system. We found that TNM stage and G grade were independent risk factors for survival in both the Surveillance, Epidemiology, and End Result (SEER) and multicenter series. The interaction effect between TNM stage and G grade was significant. Twelve subgroups combining the TNM stage and G grade were proposed in the TNMG stage, which were classified into five stages TNMG. According to the TNMG staging classification in the SEER series, the estimated median survival for stages I, II, III, IV, and V were 203, 174, 112, 61, and 8 months, respectively. The predictive accuracy of TNMG stage was higher than that of TNM stage and G grade used independently. The TNMG stage classification was more accurate in predicting panNEN patient's prognosis than either the TNM stage or G grade.
A web-based image classification tool (DiLearn) was developed to facilitate active learning in the oral health profession. Students engage with oral lesion images using swipe gestures to classify each image into pre-determined categories (e.g., left for refer and right for no intervention). To assemble the training modules and to provide feedback to students, DiLearn requires each oral lesion image to be classified, with various features displayed in the image. The collection of accurate meta-information is a crucial step for enabling the self-directed active learning approach taken in DiLearn. The purpose of this study is to evaluate the classification consistency of features in oral lesion images by experts and students for use in the learning tool. Twenty oral lesion images from DiLearn's image bank were classified by three oral lesion experts and two senior dental hygiene students using the same rubric containing eight features. Classification agreement among and between raters were evaluated using Fleiss' and Cohen's Kappa. Classification agreement among the three experts ranged from identical (Fleiss' Kappa = 1) for "clinical action", to slight agreement for "border regularity" (Fleiss' Kappa = 0.136), with the majority of categories having fair to moderate agreement (Fleiss' Kappa = 0.332-0.545). Inclusion of the two student raters with the experts yielded fair to moderate overall classification agreement (Fleiss' Kappa = 0.224-0.554), with the exception of "morphology". The feature of clinical action could be accurately classified, while other anatomical features indirectly related to diagnosis had a lower classification consistency. The findings suggest that one oral lesion expert or two student raters can provide fairly consistent meta-information for selected categories of features implicated in the creation of image classification tasks in DiLearn.
Multiple-class land-cover classification approaches can be inefficient when the main goal is to classify only one or a few classes. Under this scenario one-class classification algorithms could be a more efficient alternative. Currently there are several algorithms that can fulfil this task, with MaxEnt being one of the most promising. However, there is scarce information regarding parametrization for performing land-cover classification using MaxEnt. In this study we aimed to understand how MaxEnt parameterization affects the classification accuracy of four different land-covers (i.e., built-up, irrigated grass, evergreen trees and deciduous trees) in the city of Santiago de Chile. We also evaluated if MaxEnt manual parameterization outperforms classification results obtained when using MaxEnt default parameters setting. To accomplish our objectives, we generated a set of 25,344 classification maps (i.e., 6,336 for each assessed land-cover), which are based on all the potential combination of 12 different classes of features restrictions, four regularization multipliers, four different sample sizes, three training/testing proportions, and 11 thresholds for generating the binary maps. Our results showed that with a good parameterization, MaxEnt can effectively classify different land covers with kappa values ranging from 0.68 for deciduous trees to 0.89 for irrigated grass. However, the accuracy of classification results is highly influenced by the type of land-cover being classified. Simpler models produced good classification outcomes for homogenous land-covers, but not for heterogeneous covers, where complex models provided better outcomes. In general, manual parameterization improves the accuracy of classification results, but this improvement will depend on the threshold used to generate the binary map. In fact, threshold selection showed to be the most relevant factor impacting the accuracy of the four land-cover classification. The number of sampling points for training the model also has a positive effect on classification results. However, this effect followed a logarithmic distribution, showing an improvement of kappa values when increasing the sampling from 40 to 60 points, but showing only a marginal effect if more than 60 sampling points are used. In light of these results, we suggest testing different parametrization and thresholds until satisfactory kappa or other accuracy metrics values are achieved. Our results highlight the huge potential that MaxEnt has a as a tool for one-class classification, but a good understanding of the software settings and model parameterization is needed to obtain reliable results.
Translational cancer genomics research aims to ensure that experimental knowledge is subject to computational analysis, and integrated with a variety of records from omics and clinical sources. The data retrieval from such sources is not trivial, due to their redundancy and heterogeneity, and the presence of false evidence. In silico marker identification, therefore, remains a complex task that is mainly motivated by the impact that target identification from the elucidation of gene co-expression dynamics and regulation mechanisms, combined with the discovery of genotype-phenotype associations, may have for clinical validation. Based on the reuse of publicly available gene expression data, our aim is to propose cancer marker classification by integrating the prediction power of multiple annotation sources. In particular, with reference to the functional annotation for colorectal markers, we indicate a classification of markers into diagnostic and prognostic classes combined with susceptibility and risk factors.
Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation-called a masked form-can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.
Fish classifications, as those of most other taxonomic groups, are being transformed drastically as new molecular phylogenies provide support for natural groups that were unanticipated by previous studies. A brief review of the main criteria used by ichthyologists to define their classifications during the last 50 years, however, reveals slow progress towards using an explicit phylogenetic framework. Instead, the trend has been to rely, in varying degrees, on deep-rooted anatomical concepts and authority, often mixing taxa with explicit phylogenetic support with arbitrary groupings. Two leading sources in ichthyology frequently used for fish classifications (JS Nelson's volumes of Fishes of the World and W. Eschmeyer's Catalog of Fishes) fail to adopt a global phylogenetic framework despite much recent progress made towards the resolution of the fish Tree of Life. The first explicit phylogenetic classification of bony fishes was published in 2013, based on a comprehensive molecular phylogeny ( www.deepfin.org ). We here update the first version of that classification by incorporating the most recent phylogenetic results.
This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer's disease (long and local projections), and ischemia. These neuron sets were then subjected to supervised classification and the results were compared considering three sets of features: morphological, features obtained from the time series and a combination of both. The best results were obtained using features from the time series, which outperformed the classification using only morphological features, showing higher correct classification rates with differences of 5.15, 3.75, 5.33% for epilepsy and Alzheimer's disease (long and local projections) respectively. The morphological features were better for the ischemia set with a difference of 3.05%. Features like variance, Spearman auto-correlation, partial auto-correlation, mutual information, local minima and maxima, all related to the time series, exhibited the best performance. Also we compared different evaluators, among which ReliefF was the best ranked.
This year, the American College of Rheumatology (ACR) 1982 classification criteria for systemic lupus erythematosus (SLE) celebrate their 40th anniversary. From this start, the quest for optimal SLE criteria has led to the 1997 ACR update, the 2012 publication of the Systemic Lupus International Collaborating Clinics (SLICC) criteria, and, in 2019, the European League Against Rheumatism (EULAR)/ACR classification criteria. The latter have since been externally validated in more than two dozen studies and have become the gold standard inclusion criterion of SLE clinical trials. This comprehensive review attempts to follow the evolving success story of SLE classification, highlighting relevant decisions and their rationale, and discussing consequences for the way SLE is defined and managed.
Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model "sees" as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.
Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.
The success of assisted reproduction techniques is dependent on a sound foundation of understanding sperm characteristics to evaluate so as to improve semen processing. This study offers a descriptive basis for ostrich semen quality in terms of sperm function characteristics (SFC) that include motility, measured by computer assisted sperm analysis CASA (SCA(®)), viability (SYBR14/PI) and membrane integrity (hypo-osmotic swelling test). Relationships among these SFC's were explored and described by correlations and regressions. Certain fixed effects including the dilution of semen, season, year and male associated with semen collection were interpreted for future applications. The seasonal effect on sperm samples collected throughout the year suggested that it is prudent to restrict collections to spring and summer when SFC's and sperm concentration are maximized, compared to winter when these aspects of sperm quality are suppressed. Dilution of ejaculates helped to maintain important SFC's associated with fertilization success. The SFC's and sperm concentration varied among males, with specific males, having greater values for the percentage of motile (MOT) and progressively motile (PMOT) sperm, as well as sperm velocity (VCL, VSL, VAP) and linearity (LIN) variables. Males may thus be screened on these variables for inclusion in an artificial insemination (AI) programme to optimize fertility success rates.
Many bacteria contain plasmids, but separating between contigs that originate on the plasmid and those that are part of the bacterial genome can be difficult. This is especially true in metagenomic assembly, which yields many contigs of unknown origin. Existing tools for classifying sequences of plasmid origin give less reliable results for shorter sequences, are trained using a fraction of the known plasmids, and can be difficult to use in practice. We present PlasClass, a new plasmid classifier. It uses a set of standard classifiers trained on the most current set of known plasmid sequences for different sequence lengths. We tested PlasClass sequence classification on held-out data and simulations, as well as publicly available bacterial isolates and plasmidome samples and plasmids assembled from metagenomic samples. PlasClass outperforms the state-of-the-art plasmid classification tool on shorter sequences, which constitute the majority of assembly contigs, allowing it to achieve higher F1 scores in classifying sequences from a wide range of datasets. PlasClass also uses significantly less time and memory. PlasClass can be used to easily classify plasmid and bacterial genome sequences in metagenomic or isolate assemblies. It is available under the MIT license from: https://github.com/Shamir-Lab/PlasClass.
Alport syndrome affects up to 60,000 people in the United States. The proposed reclassification of thin basement membrane nephropathy and some cases of focal segmental glomerulosclerosis as Alport syndrome could substantially increase the affected population. The reclassification scheme categorizes Alport syndrome as 3 distinct diseases of type IV collagen α3/4/5 based on a genetic evaluation: X-linked, autosomal, and digenic. This approach has the advantage of identifying patients at risk for progressive loss of kidney function. Furthermore, the shared molecular cause of Alport syndrome and thin basement membrane nephropathy arises from mutations in the COL4A3, COL4A4, and COL4A5 genes, which contribute to downstream pathophysiologic consequences, including chronic kidney inflammation. Recent evidence indicates that chronic inflammation and its regulation through anti-inflammatory nuclear factor erythroid 2-related factor 2 (Nrf2) and proinflammatory nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) transcription factors plays a central role in renal tubular and glomerular cell responses to injury. Crosstalk between the Nrf2 and NF-κB pathways is important in the regulation of inflammation in patients with chronic kidney disease; moreover, there is evidence that an insufficient Nrf2 response to inflammation contributes to disease progression. Given the association between type IV collagen abnormalities and chronic inflammation, there is renewed interest in targeted anti-inflammatory therapies in Alport syndrome and other forms of progressive chronic kidney disease.
During the past decades, anticancer immunotherapy has evolved from a promising therapeutic option to a robust clinical reality. Many immunotherapeutic regimens are now approved by the US Food and Drug Administration and the European Medicines Agency for use in cancer patients, and many others are being investigated as standalone therapeutic interventions or combined with conventional treatments in clinical studies. Immunotherapies may be subdivided into "passive" and "active" based on their ability to engage the host immune system against cancer. Since the anticancer activity of most passive immunotherapeutics (including tumor-targeting monoclonal antibodies) also relies on the host immune system, this classification does not properly reflect the complexity of the drug-host-tumor interaction. Alternatively, anticancer immunotherapeutics can be classified according to their antigen specificity. While some immunotherapies specifically target one (or a few) defined tumor-associated antigen(s), others operate in a relatively non-specific manner and boost natural or therapy-elicited anticancer immune responses of unknown and often broad specificity. Here, we propose a critical, integrated classification of anticancer immunotherapies and discuss the clinical relevance of these approaches.
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