Unsupervised Feature Learning for Writer Identification and Writer Retrieval

Deep Convolutional Neural Networks (CNN) have shown great success in supervised classification tasks such as character classification or dating. Deep learning methods typically need a lot of annotated training data, which is not available in many scenarios. In these cases, traditional methods are often better than or equivalent to deep learning methods. In this paper, we propose a simple, yet effective, way to learn CNN activation features in an unsupervised manner. Therefore, we train a deep residual network using surrogate classes. The surrogate classes are created by clustering the training dataset, where each cluster index represents one surrogate class. The activations from the penultimate CNN layer serve as features for subsequent classification tasks. We evaluate the feature representations on two publicly available datasets. The focus lies on the ICDAR17 competition dataset on historical document writer identification (Historical-WI). We show that the activation features trained without supervision are superior to descriptors of state-of-the-art writer identification methods. Additionally, we achieve comparable results in the case of handwriting classification using the ICFHR16 competition dataset on historical Latin script types (CLaMM16).Comment: ICDAR2017 camera ready (fixed p@2 values, missing table references

Automatic Dating of Historical Documents

With the growing number of digitized documents available to researchers it is becoming possible to answer scientific questions by simply analyzing the image content. In this article, a new approach for the automatic dating of historical documents is proposed. It is based on an approach only recently proposed for scribe identification. It uses local RootSIFT descriptors which are encoded using VLAD. The method is evaluated using a dataset consisting of context areas of medieval papal charters covering around 150 years from 1049 to 1198 AD. Experimental results show very promising mean absolute errors of about 17 years

NOD/Scid IL2Rγnull mice reconstituted with peripheral blood mononuclear cells from patients with atopic dermatitis or psoriasis vulgaris reflect the respective phenotype.

NOD/Scid IL2Rγnull (NSG) mice reconstituted with peripheral blood mononuclear cells (PBMC) donated by patients with ulcerative colitis or Crohn’s disease highly reflect the respective pathological phenotype. To determine if these findings could be applicable to atopic dermatitis (AD) and psoriasis vulgaris (PV), PBMCs isolated from AD and PV patients were first subjected to immunological profiling. Subsequently, NSG mice were reconstituted with these PBMCs. Hierarchical clustering and network analysis revealed a distinct profile of AD and PV patients with activated CD4+ T cells (CD69, CD25) occupying a central position in the AD network and CD4+ CD134+ cells acting as the main hub in the PV network. Following dermal application of DMSO, both NSG-AD and NSG-PV mice exhibited increased clinical, skin and histological scores. Immuno-histochemical analysis, frequencies of splenic human leukocytes, and cytokine expression levels indicated that CD4+ CD69+ cells, M1 and TSLPR-expressing monocytes, switched B cells and MCP-3 were the driving factors of inflammation in NSG-AD mice. In contrast, inflammation in NSG-PV mice was characterized by an increase in fibroblasts in the epidermis, frequencies of CD1a-expressing monocytes and IL-17 levels. Therefore, the pathological phenotypes of NSG-AD and NSG-PV mice differ and partially reflect the respective human disease

Autoantibodies as diagnostic markers and potential drivers of inflammation in ulcerative colitis

To date, no comprehensive analysis of autoantibodies in sera of patients with ulcerative colitis has been conducted. To analyze the spectrum of autoantibodies and to elucidate their role serum-IgG from UC patients (n = 49) and non-UC donors (n = 23) were screened by using a human protein microarray. Screening yielded a remarkable number of 697 differentially-reactive at the nominal 0.01 significance level (FDR<0.1) of the univariate test between the UC and the non-UC group. CD99 emerged as a biomarker to discriminate between both groups (p = 1e-04, AUC = 0.8). In addition, cytokines, chemokines and growth factors were analyzed by Olink's Proseek (R) Multiplex Inflammation-I 96x96 immuno-qPCR assay and 31 genes were significant at the nominal 0.05 level of the univariate test to discriminate between UC and non-UC donors. MCP-3, HGF and CXCL-9 were identified as the most significant markers to discriminate between UC patients with clinically active and inactive disease. Levels of CXCL10 (cor = 0.3;p = 0.02), CCL25 (cor = 0.25;p = 0.04) and CCL28 (cor = 0.3;p = 0.02) correlated positively with levels of anti CD99. To assess whether autoantibodies are detectable prior to diagnosis with UC, sera from nine donors at two different time points (T-early, median 21 months and T-late, median 6 months) were analyzed. 1201 features were identified with higher reactivity in samples at time points closer to clinical UC presentation. In vitro, additional challenge of peripheral mononuclear cells with CD99 did not activate CD4+ T cells but induced the secretion of IL-10 (-CD99: 20.21 +/- 20.25;+CD99: 130.20 +/- 89.55;mean +/- sd;p = 0.015). To examine the effect of CD99 in vivo, inflammation and autoantibody levels were examined in NOD/ScidIL2R gamma(null) mice reconstituted with PBMC from UC donors (NSG-UC). Additional challenge with CD99 aggravated disease symptoms and pathological phenotype as indicated by the elevated clinical score (-CD99: 1.85 +/- 1.94;+CD99: 4.25 +/- 1.48) and histological score (-CD99: 2.16 +/- 0.83;+CD99: 3.15 +/- 1.16, p = 0.01). Furthermore, levels of anti-CD99 antibodies increased (Control: 398 +/- 323;mean MFI +/- sd;Ethanol + PBS: 358 +/- 316;Ethanol + CD99: 1363 +/- 1336;Control versu

NOD-scid IL2R γnull\gamma^{null} mice engrafted with human peripheral blood mononuclear cells as a model to test therapeutics targeting human signaling pathways

Background: Animal models of human inflammatory diseases have limited predictive quality for human clinical trials for various reasons including species specific activation mechanisms and the immunological background of the animals which markedly differs from the genetically heterogeneous and often aged patient population. Objective: Development of an animal model allowing for testing therapeutics targeting pathways involved in the development of Atopic Dermatitis (AD) with better translatability to the patient. Methods: NOD-scid IL2R $\gamma^{null}$ mice engrafted with human peripheral blood mononuclear cells (hPBMC) derived from patients suffering from AD and healthy volunteers were treated with IL-4 and the antagonistic IL-4 variant R121/Y124D (Pitrakinra). Levels of human (h) IgE, amount of B-, T- and plasma-cells and ratio of CD4 : CD8 positive cells served as read out for induction and inhibition of cell proliferation and hIgE secretion. Results were compared to in vitro analysis. Results: hIgE secretion was induced by IL-4 and inhibited by the IL-4 antagonist Pitrakinra in vivo when formulated with methylcellulose. B-cells proliferated in response to IL-4 in vivo; the effect was abrogated by Pitrakinra. IL-4 shifted CD4 : CD8 ratios in vitro and in vivo when hPBMC derived from healthy volunteers were used. Pitrakinra reversed the effect. Human PBMC derived from patients with AD remained inert and engrafted mice reflected the individual responses observed in vitro. Conclusion: NOD-scid IL2R $\gamma^{null}$ mice engrafted with human PBMC reflect the immunological history of the donors and provide a complementary tool to in vitro studies. Thus, studies in this model might provide data with better translatability from bench to bedside