Aggregated tau protein is associated with over 20 neurological disorders, which include Alzheimer's disease. Previous work has shown that tau's sequence segments VQIINK and VQIVYK drive its ...aggregation, but inhibitors based on the structure of the VQIVYK segment only partially inhibit full-length tau aggregation and are ineffective at inhibiting seeding by full-length fibrils. Here we show that the VQIINK segment is the more powerful driver of tau aggregation. Two structures of this segment determined by the cryo-electron microscopy method micro-electron diffraction explain its dominant influence on tau aggregation. Of practical significance, the structures lead to the design of inhibitors that not only inhibit tau aggregation but also inhibit the ability of exogenous full-length tau fibrils to seed intracellular tau in HEK293 biosensor cells into amyloid. We also raise the possibility that the two VQIINK structures represent amyloid polymorphs of tau that may account for a subset of prion-like strains of tau.
Abstract We propose a computer-aided detection (CAD) system which can detect small-sized (from 3 mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, ...round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass–Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.
ePIXfab: the silicon photonics platform
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE,
01/2013
Conference Proceeding
Coeliac disease (CD) is a permanent inflammatory disease of the small intestine characterized by the destruction of the mucous membrane of this intestinal tract. Coeliac disease represents the most ...frequent food intolerance and affects about 1% of the population, but it is severely underdiagnosed. Currently available guidelines require CD-specific serology and atrophic histology in duodenal biopsy samples to diagnose CD in adults. In paediatric CD, but recently in adults also, non-invasive diagnostic strategies have become increasingly popular. In order to increase the rates of correct diagnosis of the disease without the use of biopsy, researchers have recently been using approaches based on artificial intelligence techniques. In this work, we present a Clinical Decision Support System (CDSS)system for supporting CD diagnosis, developed in the context of the Italy-Malta cross-border project ITAMA. The implemented CDSS has been based on a neural-network-based fuzzy classifier. The system was developed and tested using a Virtual Database and a Real Database acquired during the ITAMA project. Analysis on 10,000 virtual patients shows that the system achieved an accuracy of 99% and a sensitivity of 99%. On 19,415 real patients, of which 109 with a confirmed diagnosis of coeliac disease, the system achieved 99.6% accuracy, 85.7% sensitivity, 99.6% specificity and 96% precision. Such results show that the developed system can be used effectively to support the diagnosis of the CD by reducing the appeal to invasive techniques such as biopsy.
A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active ...contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is
88.5
%
with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at
80
%
efficiency.
Carotenoids undergo a wide range of photochemical reactions in animal, plant, and microbial systems. In photosynthetic organisms, in addition to light harvesting, they perform an essential role in ...protecting against light-induced damage by quenching singlet oxygen, superoxide anion radicals, or triplet-state chlorophyll. We have determined the crystal structure of a water-soluble orange carotenoid protein (OCP) isolated from the cyanobacterium
Arthrospira maxima at a resolution of 2.1 Å. OCP forms a homodimer with one carotenoid molecule per monomer. The carotenoid binding site is lined by a striking number of methionine residues. The structure reveals several possible ways in which the protein environment influences the spectral properties of the pigment and provides insight into how the OCP carries out its putative functions in photoprotection.
Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated ...classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist’s diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images 2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be
A
z
=
0.783
±
0.008
for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.
The mammography is the most effective procedure for an early diagnosis of the breast cancer. In this paper, an algorithm for detecting masses in mammographic images will be presented. The database ...consists of 3762 digital images acquired in several hospitals belonging to the MAGIC-5 collaboration (Medical Applications on a Grid Infrastructure Connection). A reduction of the whole image's area under investigation is achieved through a segmentation process, by means of a ROI Hunter algorithm, without loss of meaningful information. In the following classification step, feature extraction plays a fundamental role: some features give geometrical information, other ones provide shape parameters. Once the features are computed for each ROI, they are used as inputs to a supervised neural network with momentum. The output neuron provides the probability that the ROI is pathological or not. Results are provided in terms of ROC and FROC curves: the area under the ROC curve was found to be A Z =0.862plusmn0.007, and we get a 2.8 FP/Image at a sensitivity of 82%. This software is included in the CAD station actually working in the hospitals belonging to the MAGIC-5 Collaboration
The voltage-dependent anion channel (VDAC) constitutes the major pathway for the entry and exit of metabolites across the outer membrane of the mitochondria and can serve as a scaffold for molecules ...that modulate the organelle. We report the crystal structure of a β-barrel eukaryotic membrane protein, the murine VDAC1 (mVDAC1) at 2.3 Å resolution, revealing a high-resolution image of its architecture formed by 19 β-strands. Unlike the recent NMR structure of human VDAC1, the position of the voltage-sensing N-terminal segment is clearly resolved. The α-helix of the N-terminal segment is oriented against the interior wall, causing a partial narrowing at the center of the pore. This segment is ideally positioned to regulate the conductance of ions and metabolites passing through the VDAC pore.
Telomerase helps maintain telomeres by processive synthesis of telomere repeat DNA at their 3'-ends, using an integral telomerase RNA (TER) and telomerase reverse transcriptase (TERT). We report the ...cryo-electron microscopy structure of Tetrahymena telomerase at ~9 angstrom resolution. In addition to seven known holoenzyme proteins, we identify two additional proteins that form a complex (TEB) with single-stranded telomere DNA-binding protein Teb1, paralogous to heterotrimeric replication protein A (RPA). The p75-p45-p19 subcomplex is identified as another RPA-related complex, CST (CTC1-STN1-TEN1). This study reveals the paths of TER in the TERT-TER-p65 catalytic core and single-stranded DNA exit; extensive subunit interactions of the TERT essential N-terminal domain, p50, and TEB; and other subunit identities and structures, including p19 and p45C crystal structures. Our findings provide structural and mechanistic insights into telomerase holoenzyme function.
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