First-Class Subtypes

First class type equalities, in the form of generalized algebraic data types (GADTs), are commonly found in functional programs. However, first-class representations of other relations between types, such as subtyping, are not yet directly supported in most functional programming languages. We present several encodings of first-class subtypes using existing features of the OCaml language (made more convenient by the proposed modular implicits extension), show that any such encodings are interconvertible, and illustrate the utility of the encodings with several examples.Comment: In Proceedings ML 2017, arXiv:1905.0590

First-class subtypes

First class type equalities, in the form of generalized algebraic data types (GADTs), are commonly found in functional programs. However, first-class representations of other relations between types, such as subtyping, are not yet directly supported in most functional programming languages. We present several encodings of first-class subtypes using existing features of the OCaml language (made more convenient by the proposed modular implicits extension), show that any such encodings are interconvertible, and illustrate the utility of the encodings with several examples

Increased complexity of mushroom body Kenyon cell subtypes in the brain is associated with behavioral evolution in hymenopteran insects

In insect brains, the mushroom bodies (MBs) are a higher-order center for sensory integration and memory. Honeybee (Apis mellifera L.) MBs comprise four Kenyon cell (KC) subtypes: class I large-, middle-, and small-type, and class II KCs, which are distinguished by the size and location of somata, and gene expression profiles. Although these subtypes have only been reported in the honeybee, the time of their acquisition during evolution remains unknown. Here we performed in situ hybridization of tachykinin-related peptide, which is differentially expressed among KC subtypes in the honeybee MBs, in four hymenopteran species to analyze whether the complexity of KC subtypes is associated with their behavioral traits. Three class I KC subtypes were detected in the MBs of the eusocial hornet Vespa mandarinia and the nidificating scoliid wasp Campsomeris prismatica, like in A. mellifera, whereas only two class I KC subtypes were detected in the parasitic wasp Ascogaster reticulata. In contrast, we were unable to detect class I KC subtype in the primitive and phytophagous sawfly Arge similis. Our findings suggest that the number of class I KC subtypes increased at least twice – first with the evolution of the parasitic lifestyle and then with the evolution of nidification

Towards a clinical staging for bipolar disorder: defining patient subtypes based on functional outcome.

BACKGROUND: The functional outcome of Bipolar Disorder (BD) is highly variable. This variability has been attributed to multiple demographic, clinical and cognitive factors. The critical next step is to identify combinations of predictors that can be used to specify prognostic subtypes, thus providing a basis for a staging classification in BD. METHODS: Latent Class Analysis was applied to multiple predictors of functional outcome in a sample of 106 remitted adults with BD. RESULTS: We identified two subtypes of patients presenting "good" (n=50; 47.6%) and "poor" (n=56; 52.4%) outcome. Episode density, level of residual depressive symptoms, estimated verbal intelligence and inhibitory control emerged as the most significant predictors of subtype membership at the p<0.05 level. Their odds ratio (OR) and confidence interval (CI) with reference to the "good" outcome group were: episode density (OR=4.622, CI 1.592-13.418), level of residual depressive symptoms (OR=1.543, CI 1.210-1.969), estimated verbal intelligence (OR=0.969; CI 0.945-0.995), and inhibitory control (OR=0.771, CI 0.656-0.907). Age, age of onset and duration of illness were comparable between prognostic groups. LIMITATIONS: The longitudinal stability or evolution of the subtypes was not tested. CONCLUSIONS: Our findings provide the first empirically derived staging classification of BD based on two underlying dimensions, one for illness severity and another for cognitive function. This approach can be further developed by expanding the dimensions included and testing the reproducibility and prospective prognostic value of the emerging classes. Developing a disease staging system for BD will allow individualised treatment planning for patients and selection of more homogeneous patient groups for research purposes

GABAA receptor subtype involvement in addictive behaviour

GABAA receptors form the major class of inhibitory neurotransmitter receptors in the mammalian brain. This review sets out to summarise the evidence that variations in genes encoding GABAA receptor isoforms are associated with aspects of addictive behaviour in humans, while animal models of addictive behaviour also implicate certain subtypes of GABAA receptor. In addition to outlining the evidence for the involvement of specific subtypes in addiction, we summarise the particular contributions of these isoforms in control over the functioning of brain circuits, especially the mesolimbic system, and make a first attempt to bring together evidence from several fields to understanding potential involvement of GABAA Receptor Subtypes in addictive behaviour. While the weight of the published literature is on alcohol dependency, the underlying principles outlined are relevant across a number of different aspects of addictive behaviour

Network-based stratification of tumor mutations.

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN

Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.Peer ReviewedPostprint (author's final draft