Azrou Klane (région d’Assa-Zag,vallée du bas Drâa,Maroc) : une nouvelle scène rupestre complexe attribuable à la phase « bovidienne » finale

Since 2013, a French-Moroccan team is surveying and recording the Azrou Klane site, in Assa region, valley of the low Drâa. This fieldwork permits us to discover a new phase of engraving in this large slab, dated from “bovidian” period. In particular, in the low part of the slab, close to the wadi ridge and partially filled in by alluvial sediments, we found a bovidian scene very complex and with exceptional concentration of figures. This scene sill be analysed here, in its internal composition and in the perspective of its relations with others rock-art sites of the same region. This “rediscovery” of Azrou Klane site allows to take its importance into account and to consider this site like one of the highlights of Maroccan rock art.     

Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning

Background and objectiveBreast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.Materials and methodsThe digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.ResultsThe proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.     

Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning

PurposeLeukaemia is diagnosed conventionally by observing the peripheral blood and bone marrow smear using a microscope and with the help of advanced laboratory tests. Image processing-based methods, which are simple, fast, and cheap, can be used to detect and classify leukemic cells by processing and analysing images of microscopic smear. The proposed study aims to classify Acute Lymphoblastic Leukaemia (ALL) by Deep Learning (DL) based techniques.ProceduresThe study used Deep Convolutional Neural Networks (DNNs) to classify ALL according to WHO classification scheme without using any image segmentation and feature extraction that involves intense computations. Images from an online image bank of American Society of Haematology (ASH) were used for the classification.FindingsA classification accuracy of 94.12% is achieved by the study in isolating the B-cell and T-cell ALL images using a pretrained CNN AlexNet as well as LeukNet, a custom-made deep learning network designed by the proposed work. The study also compared the classification performances using three different training algorithms.ConclusionsThe paper detailed the use of DNNs to classify ALL, without using any image segmentation and feature extraction techniques. Classification of ALL into subtypes according to the WHO classification scheme using image processing techniques is not available in literature to the best of the knowledge of the authors. The present study considered the classification of ALL only, and detection of other types of leukemic images can be attempted in future research.     

The effect of beam interruption during VMAT delivery on the delivered dose distribution

PurposeThe aim of this study is to investigate the effect of beam interruptions during delivery of volumetric modulated arc therapy (VMAT) on delivered dose distributions.MethodsTen prostate and ten head and neck (H&N) VMAT plans were retrospectively selected. Each VMAT plan was delivered using Trilogy™ without beam interruption, and with 4 and 8 intentional beam interruptions per a single arc. Two-dimensional global and local gamma evaluations with a diode array were performed with gamma criteria of 3%/3 mm, 2%/2 mm, 1%/2 mm and 2%/1 mm for each VMAT plan with and without beam interruptions. The VMAT plans were reconstructed with log files recorded during delivery and the dose-volumetric parameters were calculated for each reconstructed plan. The differences among dose-volumetric parameters due to the beam interruptions were calculated.ResultsThe changes in global gamma passing rates with various gamma criteria were less than 1.6% on average, while the changes in local gamma passing rates were less than 5.3% on average. The dose-volumetric parameter changes for the target volumes of prostate and H&N VMAT plans due to beam interruptions were less than 0.72% and 1.5% on average, respectively.ConclusionThe delivered dose distributions with up to 8 beam interruptions per an arc were clinically acceptable, showing minimal changes in both gamma passing rates and dose-volumetric parameters.     

Visual Aid Reviews

Books Practice in Human Biology — Questions for AS and A-level Jane Vellacott and Sarah Side, 2001. Hodder &; Stoughton, 202pp. £9.99. ISBN 0 340 78958 1 Reviewed by Pauline Lowrie

Books Microbiology and Biotechnology Pauline Lowrie and Susan Wells, 2000. Cambridge University Press, 108pp. £7.95. ISBN 0 52178723 8 Reviewed by Julia Milligan

Books Microbiology: an introduction Gerard J Tortora, Berdell R Funke, and Christine L Case, 2000. Pearson Education, 902pp. £33.00. ISBN 0 8053 7554 6 Reviewed by Steve Hall

Books Encyclopedia of Genetics Eric C R Reeve (Ed.), 2001. Fitzroy Dearborn, 952pp. £105.00. ISBN 1 884964 34 6 Reviewed by Michael Reiss

Books Coral Fish Linda Pitkin, 2001. The Natural History Museum, 112pp. £9.95. ISBN 0 565 09152 2 Reviewed by Julia Glanville

Books Liasons of Life Tom Wakeford, 2001. John Wiley &; Sons Ltd, 212pp. £17.95. ISBN 0 471 39972 8 Reviewed by Roger Delpech     

PGF2alpha increases FGF-2 and FGFR2 trafficking in Py1a rat osteoblasts via clathrin independent and importin beta dependent pathway

Previous studies showed that prostaglandin F2alpha (PGF2alpha) stimulated fibroblast growth factor-2 (FGF-2) and fibroblast growth factor receptor 2 (FGFR2) cytosolic and nuclear accumulation, however, the endocytic pathway has not been elucidated. This study demonstrates that although PGF2alpha increased the formation of clathrin-coated structures in Py1a rat osteoblasts, they were not involved in FGF-2 and FGFR2 trafficking. PGF2alpha increased binding of FGF-2 and FGFR2 and co-localization of reactive sites in addition to nuclear translocation at the nuclear pore complex level. FGF-2 and FGFR2 were in close spatial correlation with importin beta, further supporting nuclear import of the FGF-2/FGFR2 complex. Immunogold and immunofluorescence techniques as well as Western blotting demonstrated increased importin beta protein labeling in response to PGF2alpha. Similar to PGF2alpha, phorbol 12-myristate 13-acetate (PMA) also increased importin beta protein. These data strongly suggest that prostaglandins may regulate osteoblast metabolism via FGF-2/FGFR2/importin beta nuclear trafficking.     

Cervical smear adequacy: cellularity references were found to increase both interobserver agreement and unsatisfactory rate

Objectives:  To determine the degree of interobserver variation in the assessment of conventional cervical smear adequacy as defined by The Bethesda System (TBS) 2001, and to determine the effect of using reference images of known squamous cellularity when performing squamous adequacy assessments.
Methods:  Experimental pre-test/post-test design utilizing 70 conventionally prepared cervical smears. Sample smears containing scant squamous cellularity were independently rated on two occasions by six cytotechnologists. Time 1 was without the use of reference images, and Time 2 was aided by cellularity reference images. The κ statistic was used to compare rater agreement.
Results:  The level of agreement increased from an average κ of 0.26 (SD 0.10) for Time 1, to an average κ of 0.40 (SD 0.15) for Time 2. The difference in mean κ values at the two assessments was statistically significant ( t  = 3.71; P  = 0.002). Unanimous agreement among the raters was observed for 15 samples (21.42%) at Time 1 (only one of which was classified as unsatisfactory) and 21 samples (30.00%) at Time 2 (12 of which were classified as unsatisfactory).
Conclusion:  Interobserver agreement increased after cellularity reference images were implemented. Using TBS 2001 squamous adequacy criteria and images of known squamous cellularity as references resulted in a decreased number of smears reported as satisfactory.     

3D finite element model of biofilm detachment using real biofilm structures from CLSM data

In this work, a three‐dimensional model of fluid–structure interactions (FSI) in biofilm systems is developed in order to simulate biofilm detachment as a result of mechanical processes. Therein, fluid flow past the biofilm surface results in a mechanical load on the structure which in turn causes internal stresses in the biofilm matrix. When the strength of the matrix is exceeded parts of the structure are detached. The model is used to investigate the influence of several parameters related to the mechanical strength of the biofilm matrix, Young's modulus, Reynolds number, and biofilm structure on biofilm detachment. Variations in biofilm strength and flow conditions significantly influence the simulation outcome. With respect to structural properties the model is widely independent from a change of Young's modulus. A further result of this work indicates that the change of biofilm structure due to growth or other processes will significantly change the stress distribution in the biofilm and thereby the detachment rate. An increase of the mechanical load by increasing fluid flow results in a flat surface of the remaining biofilm structure. It is concluded that the change of structure during biofilm development is the key determinant in terms of the detachment behavior. Biotechnol. Bioeng. 2009;103: 177–186. © 2008 Wiley Periodicals, Inc.     

Electron transfer protein complexes in the thylakoid membranes of heterocysts from the cyanobacterium Nostoc punctiforme

Filamentous, heterocystous cyanobacteria are capable of nitrogen fixation and photoautotrophic growth. Nitrogen fixation takes place in heterocysts that differentiate as a result of nitrogen starvation. Heterocysts uphold a microoxic environment to avoid inactivation of nitrogenase, e.g. by downregulation of oxygenic photosynthesis. The ATP and reductant requirement for the nitrogenase reaction is considered to depend on Photosystem I, but little is known about the organization of energy converting membrane proteins in heterocysts. We have investigated the membrane proteome of heterocysts from nitrogen fixing filaments of Nostoc punctiforme sp. PCC 73102, by 2D gel electrophoresis and mass spectrometry. The membrane proteome was found to be dominated by the Photosystem I and ATP-synthase complexes. We could identify a significant amount of assembled Photosystem II complexes containing the D1, D2, CP43, CP47 and PsbO proteins from these complexes. We could also measure light-driven in vitro electron transfer from Photosystem II in heterocyst thylakoid membranes. We did not find any partially disassembled Photosystem II complexes lacking the CP43 protein. Several subunits of the NDH-1 complex were also identified. The relative amount of NDH-1M complexes was found to be higher than NDH-1L complexes, which might suggest a role for this complex in cyclic electron transfer in the heterocysts of Nostoc punctiforme.     

Improving 2‐DE gel image denoising using contourlets

One of the most commonly used methods for protein separation is 2‐DE. After 2‐DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false‐spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2‐DE gel images denoising. We compare its effectiveness with state‐of‐the‐art methods such as wavelets‐based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average S/N performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.