Classification of CT brain images based on deep learning networks

While Computerised Tomography (CT) may have been the first imag-ing tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimers disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great ex-tent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early di-agnosis of Alzheimers disease. Towards this end, three categories of CT images (N=285) are clustered into three groups, which are AD, Lesion (e.g. tumour) and Normal ageing. In addition, considering the character-istics of this collection with larger thickness along the direction of depth (z) (∼3-5mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification ac-curacy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, Lesion and Normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6+-1:10, 86.3+-1:04, 85.2+-1:60, 83.1+-0:35 for 2D CNN, 2D SIFT, 2DKAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE

Effects of Environmental Exposures on Fetal and Childhood Growth Trajectories

Delayed fetal growth and adverse birth outcomes are some of the greatest public health threats to this generation of children worldwide because these conditions are major determinants of mortality, morbidity, and disability in infancy and childhood and are also associated with diseases in adult life. A number of studies have investigated the impacts of a range of environmental conditions during pregnancy (including air pollution, endocrine disruptors, persistent organic pollutants, heavy metals) on fetal and child development. The results, while provocative, have been largely inconsistent. This review summarizes up to date epidemiologic studies linking major environmental pollutants to fetal and child development and suggested future directions for further investigation

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Organelle-Specific Nitric Oxide Detection in Living Cells via HaloTag Protein Labeling

<div><p>Nitric oxide (NO) is a membrane-permeable signaling molecule that is constantly produced, transferred, and consumed <i>in vivo</i>. NO participates and plays important roles in multiple biological processes. However, spatiotemporal imaging of NO in living cells is challenging. To fill the gap in currently used techniques, we exploited the versatility of HaloTag technology and synthesized a novel organelle-targetable fluorescent probe called HTDAF-2DA. We demonstrate the utility of the probe by monitoring subcellular NO dynamics. The developed strategy enables precise determination of local NO function.</p></div

Properties of NO Sensor HTDAF-2.

<p>(A) Fluorescence spectra of HTDAF-2. Fluorescence excitation and emission spectra of 10 nM HTDAF-2 in PBS (pH 7.4) before (dark red lines) and after (orange lines) the addition of 0.5 mM NO donor (DEA NONOate) at 25°C. Excitation spectrum recorded at an emission wavelength of 525 nm shows a maximum at 488 nm. Emission spectrum recorded at an excitation wavelength of 480 nm shows a maximum at 512 nm. (B) The fluorescence intensities of HTDAF-2 in the presence of different concentrations of NO donor (DEA NONOate) normalized to the initial value. (C) The fluorescence response of HTDAF-2 after the addition of 2 mM xanthine/20 mU xanthine oxidase, 0.5 mM H₂O₂, NO²⁻, NO³⁻, MAHMA NONOate, and DEA NONOate for 30 min in PBS solution. (D) The fluorescence response of HTDAF-2 to NO donor (DEA NONOate) at the indicated pH. Error bars represent the standard deviation (SD).</p

Structures and reaction chemistry of HTDAF-2DA.

<p>Structures and reaction chemistry of HTDAF-2DA.</p

Fluorescence detection of NO in subcellular organelles of HeLa and MCF-7 cells.

<p>Targeted localization of HTDAF-2 by conjugation to HaloTag proteins in living HeLa cells. Images present HeLa cells expressing HaloTag in the cytosol/nucleus (A), nucleus (B), membrane (C), and mitochondria (D), with the red fluorescent protein mCherry fused with the same signal peptides. Scale bar = 10 μm. (E) Direct in-gel fluorescence of control (1), nucleus-HaloTag (2), plasma membrane-HaloTag (3), cytosol-HaloTag (4), and mitochondria-HaloTag (5) in HeLa cells labeled with 5 μM HTDAF-2DA. (F) The fluorescence responses of 5 μM HTDAF-2DA targeted in the plasma membrane to various concentrations of NO donor (DEA NONOate) in HeLa cells. (G and H) Kinetics of fluorescence response of 5 μM HTDAF-2DA in different subcellular compartments of HeLa (G) and MCF-7 (H) cells upon the addition of NO donor (DEA NONOate). Error bars represent SD.</p

Measurement of endogenous NO production in activated macrophages by HTDAF-2DA and DAF-2DA.

<p>(A and B) NO detection in Raw 264.7 macrophages expressing HaloTag in the cytosol/nucleus (A) or nucleus (B) stained by HTDAF-2DA. (C) NO detection in Raw 264.7 macrophages stainedby DAF-2DA. For A-C, cells were prestimulated for 8 h with LPS (0.5 μg/ml) and IFN-γ (250 U/ml) with or without L-NAME (2 mM). Data were measured in pooled cells with microplate reader. Error bars represent SD.</p