Conventional Classification Schemes Research Articles

Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics

Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has anon-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.

Uncertainty in the availability of natural resources: Fossil fuels, critical metals and biomass

Energy policies are strongly influenced by resource availability and recoverability estimates. Yet these estimates are often highly uncertain, frequently incommensurable, and regularly contested. This paper explores how the uncertainties surrounding estimates of the availability of fossil fuels, biomass and critical metals are conceptualised and communicated. The contention is that a better understanding of the uncertainties surrounding resource estimates for both conventional and renewable energy resources can contribute to more effective policy decision making in the long term. Two complementary approaches for framing uncertainty are considered in detail: a descriptive typology of uncertainties and a framework that conceptualises uncertainty as alternative states of incomplete knowledge. Both have the potential to be useful analytical and communication tools. For the three resource types considered here we find that data limitations, inconsistent definitions and the use of incommensurable methodologies present a pervasive problem that impedes comparison. Many aspects of resource uncertainty are also not commonly captured in the conventional resource classification schemes. This highlights the need for considerable care when developing and comparing aggregate resource estimates and when using these to inform strategic energy policy decisions.

Neural correlates of target selection for reaching movements in superior colliculus.

We recently demonstrated that inactivation of the primate superior colliculus (SC) causes a deficit in target selection for arm-reaching movements when the reach target is located in the inactivated field (Song JH, Rafal RD, McPeek RM. Proc Natl Acad Sci USA 108: E1433-E1440, 2011). This is consistent with the notion that the SC is part of a general-purpose target selection network beyond eye movements. To understand better the role of SC activity in reach target selection, we examined how individual SC neurons in the intermediate layers discriminate a reach target from distractors. Monkeys reached to touch a color oddball target among distractors while maintaining fixation. We found that many SC neurons robustly discriminate the goal of the reaching movement before the onset of the reach even though no saccade is made. To identify these cells in the context of conventional SC cell classification schemes, we also recorded visual, delay-period, and saccade-related responses in a delayed saccade task. On average, SC cells that discriminated the reach target from distractors showed significantly higher visual and delay-period activity than nondiscriminating cells, but there was no significant difference in saccade-related activity. Whereas a majority of SC neurons that discriminated the reach target showed significant delay-period activity, all nondiscriminating cells lacked such activity. We also found that some cells without delay-period activity did discriminate the reach target from distractors. We conclude that the majority of intermediate-layer SC cells discriminate a reach target from distractors, consistent with the idea that the SC contains a priority map used for effector-independent target selection.

Google Searches for Suicide and Risk of Suicide

Routine surveillance of the incidence of disease comprises a core function of federal and regional public health programs (1). Surveillance systems for mental disorders, however, remain underdeveloped despite strong temporal variation in their incidence. Recently Google, the most commonly used search engine in the world, launched “Google Trends.” This website allows the public to gather statistics on queries to the Google.com search engine. To the extent that depression—a key risk factor for suicide—and suicidal ideation presage an increased risk of suicide completion, surveillance of Google Trends queries may assist with focused bursts of suicide prevention efforts. We tested whether suicide-related Google searches predict the monthly incidence of completed suicides. We examined England and Wales given their publicly available suicide database and their high societal cost of premature death due to suicide (i.e., $9.2 billion in 2009 alone) (2). We retrieved the incidence of suicide in England and Wales for January 2004 to December 2010, the longest series that overlaps with internet-based suicide queries. We defined suicide using conventional ICD-10 classification schemes. We used as independent variables the monthly internet-based queries on a series of search terms within England and Wales, drawn from previous literature, that relate to suicide ideation (3,4). We examined three distinct sets of terms: suicide methodology (“suicide”, “suicide” and “methods”), depressive symptoms (terms: “suicide” and “depression”, “depression”, “depression” and “help”), and suicide help (terms: “suicide” and “help”). To statistically control for temporal patterns in suicide (e.g., seasonality, trend), we applied rigorous time-series routines (see Appendix) (5). We further controlled for the unemployment rate and Google searches in the news, which often peak following suspected suicides of popular personalities (e.g., Amy Winehouse) but may not signal increased help-seeking or depression. In the three searches that included the term “depression,” we find a positive relation with search volume and suicide in that same month (see Appendix). The strongest positive relation occurs between the “depression and help” Google Trends query and suicide incidence in the same month (coef. at lag 0 months = .0045, standard error [SE] = .0014, p=.002). We estimate that Google searches statistically account for a seven percent increase in suicides during months in which search volume for “depression” increased. However, none of the internet searches predict the incidence of suicide in the following month. We, moreover, find no relation between “suicide” or “suicide and methods” searches and suicide incidence We caution the reader against inferring an individual’s internet search behavior, depressive symptoms and their risk of suicide from our population-level tests. Temporal fluctuations in Google searches, rather, gauge a population-level interest which cannot shed light on individual differences in suicide risk. In addition, our findings of a positive relation between internet search volume for “suicide and depression” and completed suicide in the same month cannot definitively establish that suicide-related searches precede suicide completion. Nevertheless, our findings in England and Wales indicate that current surveillance efforts for suicide may benefit from the real-time use of Google-based internet searches for depression.

Multivariate decoding of brain images using ordinal regression

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations — whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds — lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection.

Esophageal hypertensive peristaltic disorders

Esophageal motility abnormalities include a series of manometric findings that differ to a significant degree from findings in normal, asymptomatic volunteers. Current review summarizes conventional and high-resolution esophageal manometry criteria used to define and characterize esophageal hypertensive motility abnormalities. In the conventional esophageal manometry classification scheme hypertensive esophageal motility abnormalities include nutcracker esophagus (average distal contraction amplitude >180 mmHg), hypertensive lower esophageal sphincter (average resting LES pressure >45 mmHg) and poorly relaxing lower esophageal sphincter (average LES residual pressure >8 mmHg). The new, high resolution esophageal manometry scheme includes in the group of hypertensive peristaltic disorders hypertensive peristalsis ("nutcracker esophagus": mean DCI >5000 mmHg*sec*cm) and hypercontractile esophagus ("jackhammer esophagus": at least one contraction with DCI > 8,000 mmHg*sec*cm) and defines a separate group for disorders with impaired esophageal-gastric junction relaxation (mean integrated residual (LES) pressure >15 mmHg). Hypertensive motility disorders represent a heterogeneous condition subdivided into hypercontractile esophagus and hypertensive peristalsis. Further studies are required to determine the clinical relevance of this new classification.

Shock-induced changes in density and porosity in shock-metamorphosed crystalline rocks, Haughton impact structure, Canada

Abstract– Shock metamorphism can occur at transient pressures that reach tens of GPa and well over 1000 °C, altering the target material on both megascopic and microscopic scales. This study explores the effects of shock metamorphism on crystalline, quartzofeldspathic basement material from the Haughton impact structure on Devon Island, Arctic Canada. Shock levels were assigned to samples based on petrographic examination of main mineral phases. Conventional shock classification schemes proved to incompletely describe the Haughton samples so a modified shock classification system is presented. Fifty-two crystalline bedrock samples from the clast-rich impact melt rocks in the crater, and one reference site outside of the crater, were classified using this system. The shock levels range from 0 to 7 (according to the new shock stage classification proposed here, i.e., stages 0–IV after the Stoffler classification), indicating shock pressures ranging from 0 to approximately 80 GPa. The second aspect of this study involved measuring bulk physical characteristics of the shocked samples. The bulk density, grain density, and porosity were determined using a water displacement method, a bead displacement method, and a Hepycnometer. Results suggest a nonlinear, negative correlation between density and shock level such that densities of crystalline rocks with original densities of approximately 3 g cm−3 are reduced to <1.0 g cm−3 at high shock levels. The results also show a positive nonlinear correlation between porosity and shock level. These data illustrate the effect of shock on the bulk physical characteristics of crystalline rocks, and has implications for assessing the habitability of shocked rocks.

Soil classification provides a poor indicator of carbon turnover rates in soil

Most soil surveys are based on soil geomorphic, physical and chemical properties, while many classifications are based on morphological properties in soil profile. Typically, microbial properties of the soil (e.g. biomass and functional diversity) or soil biological quality indicators (SBQIs) are not directly considered in soil taxonomic keys, yet soil classification schemes are often used to infer soil biological function relating to policy (e.g. soil pollution attenuation, climate change mitigation). To critically address this, our aim was to assess whether rates of carbon turnover in a diverse range of UK soils ( n > 500) could effectively be described and sub-divided according to broadly defined soil groups by conventional soil classification schemes. Carbon turnover in each soil over a 90 d period was assessed by monitoring the mineralisation of either a labile ( 14C-labelled artificial root exudates) or more recalcitrant C source ( 14C-labelled plant leaves) in soil held at field capacity at 10 °C. A double exponential first order kinetic model was then fitted to the mineralisation profile for each individual substrate and soil. ANOVA of the modelled rate constants and pool sizes revealed significant differences between soil groups; however, these differences were small regardless of substrate type. Principle component and cluster analysis further separated some soil groups; however, the definition of the class limits remained ambiguous. Exclusive reference values for each soil group could not be established since the model parameter ranges greatly overlapped. We conclude that conventional soil classification provides a poor predictor of C residence time in soil, at least over short time periods. We ascribe this lack of observed difference to the high degree of microbial functional redundancy in soil, the strong influence of environmental factors and the uncertainties inherent in the use of short term biological assays to represent pedogenic processes which have taken ca. 10,000 y to become manifest.

Crop classification in rain-fed and irrigated agricultural areas using Landsat TM and ALOS/PALSAR data

Multispectral optical remote-sensing systems have been the base for crop identification and monitoring for many years. However, cloud cover limits and even prevents the use of optical data for this activity. Synthetic aperture radar (SAR) sensors provide an interesting alternative to conventional multispectral classification schemes. Radar sensors can acquire data regardless of cloud cover, and their observations provide complementary information to that captured by optical sensors. The main objective of this paper is to evaluate whether the incorporation of polarimetric SAR observations to a multispectral classification scheme enhances classification results using the proposed method. With this aim, one Landsat thematic mapper scene and two ALOS/PALSAR quadrature polarization scenes acquired in 2007 were processed. The results demonstrated the ability of SAR data to improve the classification based on optical images. However, improvements were slight (an increase of around 2% in the classification's overall accuracy). The results improved significantly when barley, wheat, and oats were considered a single class, called cereals. The best results were achieved using Landsat and the two ALOS/PALSAR scenes together, obtaining an overall Kappa coefficient and accuracy of 0.67 and 90%, respectively. Probably, scenes acquired on other dates (June and July) would have yielded clearer classification enhancements.

A hybrid classification scheme for mining multisource geospatial data

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of the large number of accurate training samples (10 to 30 × |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of the statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately, there is no convenient multivariate statistical model that can be employed for multisource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on Landsat satellite image datasets, and our new hybrid approach shows over 24% to 36% improvement in overall classification accuracy over conventional classification schemes.

Duration distributions for different softness groups of gamma-ray bursts

Gamma-ray bursts (GRBs) are divided into two classes according to their durations. We investigate if the softness of bursts plays a role in the conventional classification of the objects. We employ the BATSE (Burst and Transient Source Experiment) catalog and analyze the duration distributions of different groups of GRBs associated with distinct softness. Our analysis reveals that the conventional classification of GRBs with the duration of bursts is influenced by the softness of the objects. There exists a bimodality in the duration distribution of GRBs for each group of bursts and the time position of the dip in the bimodality histogram shifts with the softness parameter. Our findings suggest that the conventional classification scheme should be modified by separating the two well-known populations in different softness groups, which would be more reasonable than doing so with a single sample. According to the relation between the dip position and the softness parameter, we get an empirical function that can roughly set apart the short-hard and long-soft bursts: SP = (0.100 ± 0.028)T 90 − (0.85 ± 0.18) , where SP is the softness parameter adopted in this paper.

Using lymph node fine needle aspirates for gene expression profiling of canine lymphoma

Conventional classification schemes for canine lymphomas do not discriminate between phenotypically indistinct tumours that may exhibit differences in behaviour. Transcriptional profiling has the potential to afford objective clinically relevant stratification of canine lymphomas, and its sensitivity means that prognostic assays could be performed on tumour needle aspirates collected without anaesthesia. In this pilot study, we compared the expression profiles derived from surgical biopsies and fine needle aspirates of five lymphomas. The aspirates yielded expression profiles of equivalent complexity and strong similarity (median correlation Coefficient = 0.911) to those generated from corresponding surgical biopsies. Differences in gene expression observed between the two tissue sources suggest that the aspirates represent a purer source of lymphocytes. Despite the absence of a standardized sample collection protocol, the aspirates yielded expression profiles of consistently high quality suggesting that they represent a robust source of tumour tissue for a potential transcriptional profile-based prognostic assay for canine lymphomas.

Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection

During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. Although pitch features have been commonly used to recognize emotions, it is not clear what aspects of the pitch contour are the most emotionally salient. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages. The results show that the recognition accuracy of the system is over 77% just with the pitch features (baseline 50%). When compared to conventional classification schemes, the proposed approach performs better in terms of both accuracy and robustness.

Time synchronization of the data of a training sample and the data of recognized objects in classification problems in the course of interpretation of space images of the Earth’s surface

The classification problem is considered under the conditions when there is temporal non-coordination in the data of a training sample and the data of the recognized object. A modification of the conventional classification scheme is proposed that presumes that an additional stage is involved, whose goal is to temporally match the data of the training sample to the current observation moment. The feature vectors are recalculated at the current observation moment by calculating their conditional expectations connecting the realizations of feature vectors at the time instants taking place in the training sample with their values for the current observation moment. By the example of processing and analyzing space images, it is demonstrated that the use in the course of classification of the proposed procedure of synchronizing the data of the training sample and the recognized object can substantially improve the reliability of classification results.

The Role of Flow Cytometry in Myelodysplastic Syndromes

Flow cytometry quantifies the gene product expression on hematopoietic cells, permitting the identification of all cells within a bone marrow aspirate and classifying them according to lineage and maturational stage. The relationships in the expression of multiple markers on myeloblasts, maturing monocytes, and myeloid cells suggests that development of these cells is a stepwise process in which genes are not only turned on or off, but are up- and down-regulated at the junctions between stages. In neoplastic processes, a loss of coordination of these steps is seen, resulting in the abnormal relationships identified by flow cytometry. In myelodysplastic syndromes, the abnormal patterns can be identified on both the immature myeloblasts and maturing myeloid cells and monocytes. The detection of these abnormalities is useful in diagnosing myelodysplasia but requires a detailed understanding of the expression of these gene products to discriminate neoplastic transformation from a stressed marrow. Although the patterns of antigen expression for normal hematopoiesis are precisely defined, each patient with a neoplastic transformation has a unique repertoire of abnormalities that may evolve as the disease progresses. Therefore, scoring systems, in which the abnormalities are counted, provide a means for determining the extent of dysregulation at all the maturational steps, thereby determining a "distance from normal" for a patient at a specific time in the disease course. This is useful, not only to facilitate diagnosis, but to provide prognostic information that may be complementary to the conventional classification schemes and scoring systems.

Functional characteristics of nematocysts found on the scyphomedusa Cyanea capillata

Although prey capture by cnidarians is mediated through nematocysts, their influence on prey selection by cnidarians remains poorly documented. The difficulty in visualizing nematocyst–prey interactions remains the chief obstacle to understanding how the wide variety of nematocyst types influences the mechanics of prey capture. One solution to this limitation has been to assign functional roles to nematocysts based on morphological characters of discharged cnidae. Here we report results of an alternative approach based upon dynamic traits of nematocyst discharge. We examined tubule lengths, tubule discharge velocities and net-to-gross displacement ratios of tubules of discharging nematocysts possessed by the cosmopolitan scyphomedusa, Cyanea capillata. This nematocyst assemblage consisted of euryteles, birhopaloids and three different isorhizas — a-isorhizas, A-isorhizas and O-isorhizas. Dynamic traits varied little within each nematocyst type but there were significant differences between the different types. Most importantly, dynamic traits varied significantly within a broad category of nematocyst – the isorhizas – indicating that conventional classification schemes that infer function based on broad nematocyst categories may not appropriately describe the functional roles of these nematocysts. The dynamic properties of discharging nematocysts were consistent with physical results described in studies using scanning electron microscopy images of nematocyst–prey interactions. These data suggest that nematocysts vary significantly in their roles during predation, but that inferences relating prey selection with broad nematocyst categories merit careful examination.

Conventional functional classification schemes underestimate the relationship with ecosystem functioning

Studies linking the functional diversity of a biota to ecosystem functioning typically employ a priori classifications of species into hypothetically complementary groups. However, multiple alternate classifications exist in which the number of functional groups, the number of species per functional group, and the grouping of species differ from the a priori scheme. Without assessing the relative precision, or ability of an a priori scheme to accurately predict ecosystem functioning relative to its many alternatives, the validity and utility of analyses based on a single a priori classification scheme remains unclear. We examine the precision of a priori classifications used in 10 experimental grassland systems in Europe and the United States that have found evidence for a significant role of functional plant diversity in governing ecosystem function. The predictive precision of the a priori classifications employed in these studies was seldom significantly higher than the precision of random classifications. Post-hoc classification schemes that performed well in predicting ecosystem function resembled each other more with regard to species composition than average classifications, but there was still considerable variability in the manner in which these classification schemes grouped species. These results suggest that we need a more nuanced understanding of how the diversity of functional traits of species in an assemblage affects ecosystem functioning.

Coronary and myocardial angiography: angiographic assessment of both epicardial and myocardial perfusion.

Angiographic assessment of epicardial coronary artery blood flow has played a pivotal role in our understanding of the “time-dependent open artery hypothesis” and in the evaluation of reperfusion strategies over the past 2 decades.1–8 It has become increasingly apparent, however, that clinical outcomes are not only associated with angiographic flow in the epicardial artery, but also with angiographic flow in the myocardium.9–13 To this end, the goal of reperfusion therapies has shifted to include reperfusion downstream at the level of capillary bed, and it might be more appropriate that the hypothesis now be termed “the time dependent open artery and open microvascular hypothesis.” The goal of this article is to review angiographic methods used to evaluate myocardial ischemia and infarction and to discuss the insights into the pathophysiology of acute coronary syndromes provided by these angiographic indexes of coronary artery blood flow and myocardial perfusion. For nearly 2 decades now, the Thrombolysis In Myocardial Infarction (TIMI) flow grade classification scheme has been successfully used to assess coronary blood flow in acute coronary syndromes1 (Table). It has been a valuable tool to compare angiographic outcomes following reperfusion, and the association of the TFGs with clinical outcomes (including mortality) has been well documented.2–8 The relationship between TFG and mortality does satisfy what some consider to be 3 criteria required to validate a surrogate end point for mortality, as follows: (1) There is an association between TIMI grade 3 flow and mortality, (2) an agent such as recombinant tissue plasminogen activator improves TIMI grade 3 flow by 22% over another agent such as streptokinase, and (3) the agent tissue plasminogen activator improves mortality 1.1% over streptokinase. View this table: Definitions of the TFG and the TMPG Systems On the basis of this relationship between TIMI flow and mortality observed in the GUSTO …

Formula omitted] a new symmetry possibly realizing in hadron spectroscopy

Starting from the multi-local Klein–Gordon equations with Lorentz-scalar squared-mass operator we give a covariant quark representation of the general composite mesons and baryons with definite Lorentz transformation property. The phenomenologically observed hadron mass spectra is pointed out to satisfy possibly the approximate symmetry under the U(12) transformation group of light constituent quarks. Here U(12)⊃ U(4) DS ⊗SU(3) F , and U(4) DS is a homogeneous Lorentz transformation group for Dirac spinors, while SU(3) F is the flavor unitary group. This symmetry predicts the existence of new type of chiral mesons and baryons out of the conventional level classification scheme: typical ones are for light q q ̄ system the scalar σ- and axial-vector a 1-nonets to be assigned as relativistic S-wave states, while for light-quark–baryon-system the Roper resonance N(1440) (J P= 1 2 +) and the Λ(1405) (J P= 1 2 −) with comparatively light mass to be assigned as the ground states.

The cutaneous pathology of lupus erythematosus: a review.

The presentation of lupus erythematosus (LE) ranges from a skin rash unaccompanied by extracutaneous stigmata to a rapidly progressive lethal multiorgan disease. The diagnosis and subclassification is traditionally based on the correlation of serological and clinical findings. The latter include a photoinduced skin rash, arthralgia, arthritis, fever, Raynaud's phenomenon, anemia, leukopenia, serositis, nephritis and central nervous sysdtem disease. The conventional classification scheme includes systemic, subacute cutaneous and discoid LE. Recent advances in our understanding of the cutaneous histopathology which correlates with the traditional forms of LE, along with certain novel LE subtypes, are the focus of this review. In addition to the main subtypes of LE, we will discuss associated vasculopathic lesions and the contribution of immunofluorescence microscopy to the diagnosis of LE and related connective tissue disease syndromes. Consideration will be given to unusual variants of LE such as anti-Ro/SSA-positive systemic lupus erythematosus (SLE), bullous SLE, lymphomatoid LE, lupus erythematosus profundus, drug induced LE, linear cutaneous LE, chiblains LE and parvovirus B19-associated LE.