Automatic segmentation of centromeres, foci and delineation of chromosomes

The observation of chromosomes has been crucial for our understanding of their structure, function, organization, and evolution of genes and genomes as well as morphological changes during mitotic and meiotic divisions. In this work, we present an automatic algorithm for the segmentation of centromeres and foci of DNA processing proteins, as well as the delineation of convoluted chromosomes. The algorithm is fully automatic and does not require tuning of parameters. Statistical measurements of numbers, areas distance and lengths are provided by the algorithm. The work is preliminary as this algorithm has not been tested on a large database nor used to differentiate between populations, however, it is considered that given it is fully automatic and fast it should be a useful tool for the analysis of chromosomes

Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models.

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality. Aiming to counter this issue, this research draws on the advancements in machine learning techniques in other fields for the generation of artificial data. The generated methods were tested through the evaluation of their output against that of a real-world labelled MRS brain tumor data-set. Furthermore the resultant output from the generative techniques were each used to train separate traditional classifiers which were tested on a subset of the real MRS brain tumor dataset. The results suggest that there exist methods capable of producing accurate, ground truth based MRS voxels. These findings indicate that through generative techniques, large datasets can be made available for training deep, learning models for the use in brain tumor diagnosis

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%)

Segmentation and modelling of hela nuclear envelope

This paper describes an algorithm to segment the 3D nuclear envelope of HeLa cancer cells from electron microscopy images and model the volumetric shape of the nuclear envelope against an ellipsoid. The algorithm was trained on a single cell and then tested in six separate cells. To assess the algorithm, Jaccard similarity index and Hausdorff distance against a manually-delineated gold standard were calculated on two cells. The mean Jaccard value and Hausdorff distance that the segmentation achieved for central slices were 98% and 4 pixels for the first cell and 94% and 13 pixels for the second cell and outperformed segmentation with active contours. The modelling projects a 3D to a 2D surface that summarises the complexity of the shape in an intuitive result. Measurements extracted from the modelled surface may be useful to correlate shape with biological characteristics. The algorithm is unsupervised, fully automatic, fast and processes one image in less than 10 seconds. Code and data are freely available at https://github.com/reyesaldasoro/Hela-Cell-Segmentation and http://dx.doi.org/10.6019/EMPIAR-10094

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

ISO 14006. Experiencias previas de estudios de arquitectura que han adoptado el estándar de ecodiseño UNE 150301:2003

This paper aims to analyze the pioneer UNE 150301 standard, as well as its adoption process and its practical results in the sector of architecture studios. First, the structure, the aim and the scope of norm UNE 150301 have been analysed. Second, the standard's implementation has been examined, concluding that 73% of the companies that have obtained the certificate are architecture studios. A case study has therefore been carried out with the participation of five architecture studios pioneers. These experiences have let us know the main aspects and difficulties of the process. In conclusion, the adoption of the standard UNE 150301 can be a helpful tool in order to reduce the environmental impact of the products and obtain some competitive advantages such as cost reduction, improvement in energy efficiency of the product and a better adaptation to acts and regulations.En este artículo se analiza la experiencia de implantación de la norma UNE 150301 de ecodiseño en el sector de los estudios de arquitectura. Tras el examen de la estructura de norma UNE 150301, de sus objetivos y de su alcance, se analiza su difusión, destacándose que el 73% de las empresas certificadas en España son estudios de arquitectura. A continuación se estudia el proceso de implantación real, mediante el estudio de caso que lo analiza en cinco estudios de arquitectura. El estudio realizado permite conocer de primera mano las principales claves y dificultades del proceso de implantación del estándar, así como los resultados obtenidos, entre los que destaca una reducción del impacto ambiental. Se constata, en suma, que la norma UNE 150301 es una herramienta que puede proporcionar ventajas competitivas interesantes a las empresas del sector de los estudios de arquitectura

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

In this work, the geometrical characteristics of two different types of cells observed with Electron Microscopy were analysed. The nuclear envelope of Wild-type HeLa cells and Chlamydia trachomatis-infected HeLa cells were automatically segmented and then modelled against a spheroid and converted to a two-dimensional surface. Geometric measurements from this surface and the volumetric nuclear envelope were extracted to compare the two types of cells. The measurements included the nuclear volume, the sphericity of the nucleus, its flatness or spikiness. In total 13 different cells were segmented: 7 Wild-type and 6 Chlamydia trachomatis-infected. The cells were statistically different in the following measurements. Wild-type HeLa cells have greater volumes than that of Chlamydia trachomatis-infected HeLa cells and they are more spherical as Jaccard index suggests. Standard deviation (σ), and range of values for the nuclear envelope, which shows the distance of the highest peaks and deepest valleys from the spheroid, were also extracted from the modelling against a spheroid and these metrics were used to compare two different data sets in order to draw conclusions

Radiography Classification: A comparison between Eleven Convolutional Neural Networks

This paper investigates the classification of normal and abnormal radiographic images. Eleven convolutional neural network architectures (GoogleNet, Vgg-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, Vgg-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2) were used to classify a series of x-ray images from Stanford Musculoskeletal Radiographs (MURA) dataset corresponding to the wrist images of the data base. For each architecture, the results were compared against the known labels (normal / abnormal) and then the following metrics were calculated: accuracy (labels correctly classified) and Cohen's kappa (a measure of agreement) following MURA guidelines. Numerous experiments were conducted by changing classifiers (Adam, Sgdm, RmsProp), the number of epochs, with/without data augmentation. The best results were provided by InceptionResnet-v2 (Mean accuracy = 0.723, Mean Kappa = 0.506). Interestingly, these results lower than those reported in the Leaderboard of MURA. We speculate that to improve the results from basic CNN architectures several options could be tested, for instance: pre-processing, post-processing or domain knowledge, and ensembles

Maternal Hyperleptinemia Increases Arterial Stiffening and Alters Vasodilatoy Responses to Insulin in Adult Male Mice Offspring

Cardiovascular disease (CVD) is the number one cause of death in the U.S., and exposure to adverse maternal environments has been associated with the development of CVD including hypertension. Gestational diabetes mellitus (GDM) is an adverse maternal environment that has been associated with metabolic and CVD outcomes in the offspring. Key features of GDM and CVD are maternal hyperleptinemia and vascular disfunction/remodeling, respectively. Yet, there is limited information on the effects of maternal hyperleptinemia has on the function and structure of the offspring’s resistance vasculature. We hypothesize that alterations in offspring’s resistance artery structure and function underlie programming mechanisms for cardiovascular disease that are associated with maternal hyperleptinemia and GDM. To test this hypothesis, we used Leprdb/+ mice dams, which exhibit maternal hyperleptinemia and wildtype (WT) as controls. Vascular function was assessed in WT male offspring of control and hyperleptinemic dams at 31 weeks of age, after half the offspring had been fed a high fat diet (HFD) for 6 weeks. On a standard diet (SD), offspring of hyperleptinemic dams had mesenteric arteries with larger internal diameters than those of WT dams (258.36±14.99 vs 233.65±9.36 μm, p<0.05) indicative of outwardly remodeled, and enhanced maximal vasodilatory responses to insulin (39.97±6.71 vs 32.23±5.07 %, p<0.05). In offspring of WT, but not hyperleptinemic dams, HFD increased vessel wall cross-sectional area (18590.01±1251.16 vs 12807.20±1060.70 μm2, p<0.05), and enhanced the maximal vasodilatory response to acetylcholine (33.74±4.92 vs 21.86±2.73 %, p<0.05). HFD reduced the maximal response to insulin in offspring of hyperleptinemic dams compared to their WT and lean controls (21.88±3.80 vs 37.42±7.84 and 39.97±6.71 % respectively, p<0.05). Offspring of hyperleptinemic dams fed a HFD had increased elastic moduli normalized as a function of the percolation of the internal elastic lamina compared to their WT and lean controls (0.53±0.038 vs 0.34±0.023 and 0.38±0.032 ×106 dynes/cm2 respectively, p<0.05). Offspring of hyperleptinemic dams also had stiffer arteries at high pressure under both dietary conditions (2.36±0.35 vs 1.45±0.11 ×106 dynes/cm2, p<0.05). We conclude that when mice were fed a SD, maternal hyperleptinemia had beneficial effects to offspring’s vascular health, but did not protect offspring fed a HFD. Furthermore, maternal hyperleptinemia induced arterial stiffness in offspring regardless of diet. These results suggest that GDM programs offspring vascular function and structure through mechanisms that may be in part dependent on circulating maternal leptin levels and are differentially affected by postnatal developmental exposures

β-glucan-dependent shuttling of conidia from neutrophils to macrophages occurs during fungal infection establishment

The initial host response to fungal pathogen invasion is critical to infection establishment and outcome. However, the diversity of leukocyte-pathogen interactions is only recently being appreciated. We describe a new form of interleukocyte conidial exchange called "shuttling." In Talaromyces marneffei and Aspergillus fumigatus zebrafish in vivo infections, live imaging demonstrated conidia initially phagocytosed by neutrophils were transferred to macrophages. Shuttling is unidirectional, not a chance event, and involves alterations of phagocyte mobility, intercellular tethering, and phagosome transfer. Shuttling kinetics were fungal-species-specific, implicating a fungal determinant. β-glucan serves as a fungal-derived signal sufficient for shuttling. Murine phagocytes also shuttled in vitro. The impact of shuttling for microbiological outcomes of in vivo infections is difficult to specifically assess experimentally, but for these two pathogens, shuttling augments initial conidial redistribution away from fungicidal neutrophils into the favorable macrophage intracellular niche. Shuttling is a frequent host-pathogen interaction contributing to fungal infection establishment patterns