Relating Statistical Image Differences and Degradation Features

Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image

Castleman's Disease with Cutaneous Involvement Manifestating as Multiple Violaceous Plaques on Entire Body

Castleman's disease (CD) is an uncommon B-cell lymphoproliferative disorder characterized by lymph node hyperplasia with vascular proliferation. Cutaneous involvement in CD is rare. A 65-year-old man presented with a 7-year history of gradually developing multiple reddish to violaceous indurated plaques on the scalp, trunk, and legs. On physical examination, there were palpable enlarged cervical, axillary, and inguinal lymph nodes. Laboratory examination revealed anemia, thrombocytosis, hyperproteinemia, hypoalbuminemia, and polyclonal hypergammaglobulinemia. An inguinal lymph node biopsy and a skin biopsy were performed and the patient was diagnosed with the plasma cell type of CD. Chemotherapy was started and the lesions have responded to treatment

Structural and Functional Changes of the Human Macula during Acute Exposure to High Altitude

Background: This study aimed to quantify structural and functional changes at the macula during acute exposure to high altitude and to assess their structure/function relationship. This work is related to the Tuebingen High Altitude Ophthalmology (THAO) study. Methodology/Principal Findings: Spectral domain optical coherence tomography and microperimetry were used to quantify changes of central retinal structure and function in 14 healthy subjects during acute exposure to high altitude (4559 m). High-resolution volume scans and fundus-controlled microperimetry of the posterior pole were performed in addition to best-corrected visual acuity (BCVA) measurements and assessment of acute mountain sickness. Analysis of measurements at altitude vs. baseline revealed increased total retinal thickness (TRT) in all four outer ETDRS grid subfields during acute altitude exposure (TRTouter = 2.8061.00 mm; mean change695%CI). This change was inverted towards the inner four subfields (TRT inner = 21.8960.97 mm) with significant reduction of TRT in the fovea (TRT foveal = 26.6260.90 mm) at altitude. BCVA revealed no significant difference compared to baseline (0.0660.08 logMAR). Microperimetry showed stable mean sensitivity in all but the foveal subfield (MSfoveal = 21.1260.68 dB). At baseline recordings before and.2 weeks after high altitude exposure, all subjects showed equal levels with no sign of persisting structural or functional sequels. Conclusions/Significance: During acute exposure to high altitude central retinal thickness is subject to minor, ye

Physical protein–protein interactions predicted from microarrays

Motivation: Microarray expression data reveal functionally associated proteins. However, most proteins that are associated are not actually in direct physical contact. Predicting physical interactions directly from microarrays is both a challenging and important task that we addressed by developing a novel machine learning method optimized for this task

Accurate Prediction of Secreted Substrates and Identification of a Conserved Putative Secretion Signal for Type III Secretion Systems

The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates—effector proteins—are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal

Mechanisms of Autoantibody-Induced Pathology

Autoantibodies are frequently observed in healthy individuals. In a minority of these individuals, they lead to manifestation of autoimmune diseases, such as rheumatoid arthritis or Graves' disease. Overall, more than 2.5% of the population is affected by autoantibody-driven autoimmune disease. Pathways leading to autoantibody-induced pathology greatly differ among different diseases, and autoantibodies directed against the same antigen, depending on the targeted epitope, can have diverse effects. To foster knowledge in autoantibody-induced pathology and to encourage development of urgently needed novel therapeutic strategies, we here categorized autoantibodies according to their effects. According to our algorithm, autoantibodies can be classified into the following categories: (1) mimic receptor stimulation, (2) blocking of neural transmission, (3) induction of altered signaling, triggering uncontrolled (4) microthrombosis, (5) cell lysis, (6) neutrophil activation, and (7) induction of inflammation. These mechanisms in relation to disease, as well as principles of autoantibody generation and detection, are reviewed herein