Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis

Habitual smokers are known to be at higher risk for developing oral cancer, which is increasing at an alarming rate globally. Conventionally, oral cancer is associated with high mortality rates, although recent reports show the improved survival outcomes by early diagnosis of disease. An effective prediction system which will enable to identify the probability of cancer development amongst the habitual smokers, is thus expected to benefit sizable number of populations. Present work describes a non-invasive, integrated method for early detection of cellular abnormalities based on analysis of different cyto-morphological features of exfoliative oral epithelial cells. Differential interference contrast (DIC) microscopy provides a potential optical tool as this mode provides a pseudo three dimensional (3-D) image with detailed morphological and textural features obtained from noninvasive, label free epithelial cells. For segmentation of DIC images, gradient vector flow snake model active contour process has been adopted. To evaluate cellular abnormalities amongst habitual smokers, the selected morphological and textural features of epithelial cells are compared with the non-smoker (?ve control group) group and clinically diagnosed pre-cancer patients (+ve control group) using support vector machine (SVM) classifier. Accuracy of the developed SVM based classification has been found to be 86% with 80% sensitivity and 89% specificity in classifying the features from the volunteers having smoking habit.     

Fish geometry and electric organ discharge determine functional organization of the electrosensory epithelium

Active electroreception in Gymnotus omarorum is a sensory modality that perceives the changes that nearby objects cause in a self generated electric field. The field is emitted as repetitive stereotyped pulses that stimulate skin electroreceptors. Differently from mormyriformes electric fish, gymnotiformes have an electric organ distributed along a large portion of the body, which fires sequentially. As a consequence shape and amplitude of both, the electric field generated and the image of objects, change during the electric pulse. To study how G. omarorum constructs a perceptual representation, we developed a computational model that allows the determination of the self-generated field and the electric image. We verify and use the model as a tool to explore image formation in diverse experimental circumstances. We show how the electric images of objects change in shape as a function of time and position, relative to the fish's body. We propose a theoretical framework about the organization of the different perceptive tasks made by electroreception: 1) At the head region, where the electrosensory mosaic presents an electric fovea, the field polarizing nearby objects is coherent and collimated. This favors the high resolution sampling of images of small objects and perception of electric color. Besides, the high sensitivity of the fovea allows the detection and tracking of large faraway objects in rostral regions. 2) In the trunk and tail region a multiplicity of sources illuminate different regions of the object, allowing the characterization of the shape and position of a large object. In this region, electroreceptors are of a unique type and capacitive detection should be based in the pattern of the afferents response. 3) Far from the fish, active electroreception is not possible but the collimated field is suitable to be used for electrocommunication and detection of large objects at the sides and caudally.     

A multi-step directional generalized gradient vector flow snake for target tumor segmentation in US-guided high-intensity focused ultrasound ablation

Getting precise locations of target tumors can help to ensure ablation of cancerous tissues and avoid unwanted destruction of healthy tissues in high-intensity focused ultrasound (HIFU) treatment system. Because of speckle noise and spurious boundaries in ultrasound images, traditional image segmentation methods are not suitable for achieving the precise locations of target tumors in HIFU ablation. In this paper, a multi-step directional generalized gradient vector flow snake model is introduced for target tumor segmentation. In the first step, the traditional generalized gradient vector flow (GGVF) snake is used to obtain an approximate contour of the tumor. According to the approximate contour, a new distance map is generated. Subsequently, a new directional edge map is created by calculating a scalar product of the gradients of the distance map and the initial image. In this process, the gradient directional information and the magnitude information of the distance map are used to attenuate unwanted edges and highlight the real edges in the new directional edge map. Finally, a refined GGVF field is derived from a diffusion operation of the gradient vectors of the directional edge map. The GGVF field is used to refine the tumor's contour, by directing the approximate contour to edges with the desired gradient directionality. Based on the newly developed snake model, the influences of the spurious boundaries and the speckle noise are significantly reduced in the ultrasound image segmentation. Experimental results indicate that this technique is greatly useful for target tumor segmentation in HIFU treatment system     

Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results

Digital image analysis of cell nuclei is useful to obtain quantitative information for the diagnosis and prognosis of cancer. However, the lack of a reliable automatic nuclear segmentation is a limiting factor for high-throughput nuclear image analysis. We have developed a method for automatic segmentation of nuclei in Feulgen-stained histological sections of prostate cancer. A local adaptive thresholding with an object perimeter gradient verification step detected the nuclei and was combined with an active contour model that featured an optimized initialization and worked within a restricted region to improve convergence of the segmentation of each nucleus. The method was tested on 30 randomly selected image frames from three cases, comparing the results from the automatic algorithm to a manual delineation of 924 nuclei. The automatic method segmented a few more nuclei compared to the manual method, and about 73% of the manually segmented nuclei were also segmented by the automatic method. For each nucleus segmented both manually and automatically, the accuracy (i.e., agreement with manual delineation) was estimated. The mean segmentation sensitivity/specificity were 95%/96%. The results from the automatic method were not significantly different from the ground truth provided by manual segmentation. This opens the possibility for large-scale nuclear analysis based on automatic segmentation of nuclei in Feulgen-stained histological sections.     

Development of recombinant B subunit of Shiga-like Toxin 1 as a probe to detect carbohydrate ligands in immunochemical and flowcytometric application

Measurement of carbohydrate binding activity of Escherichia coli Shiga-like toxin in a simple and quantitative way is an important step for evaluation of antibodies with therapeutic value and of effectiveness of vaccine treatment. We constructed a plasmid vector (pVT1-B5) to express carbohydrate binding (B) subunit of Shiga-like toxin 1 without expression of toxic (A) subunit, and established a simple method to purify the recombinant B subunit, which was then labeled with digoxigenin. The binding specificity of the digoxigenin-labeled B subunit for globotriaosylceramide was established by thin-layer chromatography immunostaining. We developed an enzyme-linked immunosorbent assay using immobilized glycolipids, demonstrating high sensitivity and clear-cut specificity of the assay. The digoxigenin-labeled B subunit was also readily applicable to the detection of cell surface carbohydrate ligands by flowcytometry.     

Automated Detection Framework of the Calcified Plaque with Acoustic Shadowing in IVUS Images

Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.     

Development of a sensitive assay for the detection of Pseudoloma neurophilia in laboratory populations of the zebrafish Danio rerio

The zebrafish Danio rerio is an increasingly important biological model in many areas of research. Due to the potential for non-protocol-induced variation, diseases of zebrafish, especially those resulting in chronic, sub-lethal infections, are of great concern. The microsporidium Pseudoloma neurophilia is a common parasite of laboratory zebrafish. Current methods for detection of this parasite require lethal sampling of fish, which is often undesirable with poorly spawning mutant lines and small populations. We present here an improved molecular-based diagnostic assay using real-time polymerase chain reaction (PCR), and including sonication treatment prior to DNA extraction. Comparisons of several DNA extraction methods were performed to determine the method providing the maximum sensitivity. Sonication was found to be the most effective method for disrupting spores. Compared to previously published data on PCR-based assay using a dilution experiment, sensitivity is increased. This shows that our assay, which includes sonication, is capable of detecting parasite DNA at 1 log higher dilution than the conventional PCR-based assay, which does not include sonication. Furthermore, we demonstrate the application of this method to testing of water, eggs, and sperm, providing a potential non-lethal method for detection of this parasite in zebrafish colonies with a sensitivity of 10 spores 1(-1) of water, 2 spores per spiked egg sample, and 10 spores microl(-1) of spiked sperm sample.     

Prediction of mitochondrial proteins using support vector machine and hidden Markov model

Mitochondria are considered as one of the core organelles of eukaryotic cells hence prediction of mitochondrial proteins is one of the major challenges in the field of genome annotation. This study describes a method, MitPred, developed for predicting mitochondrial proteins with high accuracy. The data set used in this study was obtained from Guda, C., Fahy, E. & Subramaniam, S. (2004) Bioinformatics 20, 1785-1794. First support vector machine-based modules/methods were developed using amino acid and dipeptide composition of proteins and achieved accuracy of 78.37 and 79.38%, respectively. The accuracy of prediction further improved to 83.74% when split amino acid composition (25 N-terminal, 25 C-terminal, and remaining residues) of proteins was used. Then BLAST search and support vector machine-based method were combined to get 88.22% accuracy. Finally we developed a hybrid approach that combined hidden Markov model profiles of domains (exclusively found in mitochondrial proteins) and the support vector machine-based method. We were able to predict mitochondrial protein with 100% specificity at a 56.36% sensitivity rate and with 80.50% specificity at 98.95% sensitivity. The method estimated 9.01, 6.35, 4.84, 3.95, and 4.25% of proteins as mitochondrial in Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, mouse, and human proteomes, respectively. MitPred was developed on the above hybrid approach.     

Analytical method for determination of nitric oxide in zebrafish larvae: toxicological and pharmacological applications

Zebrafish are currently used at various stages of the drug discovery process and can be a useful and cost-effective alternative to some mammalian models. Nitric oxide (NO) plays an important role in physiology of zebrafish. The availability of appropriate analytical techniques to quantify the NO is crucial for studying its role in physiological and pathological conditions. This work aimed at establishing a high-performance liquid chromatography method for determination of NO levels in zebrafish larvae. Attempts were also made to assess the normal levels of NO at the first days postfertilization and the possible changes under pathological conditions. The method validation was quantitatively evaluated in terms of sensitivity, specificity, precision, accuracy, linearity, and recovery. NO levels from zebrafish larvae at the first days postfertilization and larvae challenged to N(G)-nitro-L-arginine methyl ester, sodium nitroprusside, Escherichia coli lipopolysaccharide, and copper sulfate were analyzed. The samples were derivatized with 2,3-diaminonaphthalene, and fluorescence detection was used for the indirect determination of NO. The method showed a good performance for all validation parameters evaluated and was efficient to monitor changes in NO concentration under physiological and pathophysiological conditions. This method might represent a powerful tool to be applied in NO studies with zebrafish larvae.     

Microarray blob-defect removal improves array analysis

MOTIVATION: New generation Affymetrix oligonucleotide microarrays often have blob-like image defects that will require investigators to either repeat their hybridization assays or analyze their data with the defects left in place. We investigated the effect of analyzing a spike-in experiment on Affymetrix ENCODE tiling arrays in the presence of simulated blobs covering between 1 and 9% of the array area. Using two different ChIP-chip tiling array analysis programs (Affymetrix tiling array software, TAS, and model-based analysis of tiling arrays, MAT), we found that even the smallest blob defects significantly decreased the sensitivity and increased the false discovery rate (FDR) of the spike-in target prediction. RESULTS: We introduced a new software tool, the microarray blob remover (MBR), which allows rapid visualization, detection and removal of various blob defects from the .CEL files of different types of Affymetrix microarrays. It is shown that using MBR significantly improves the sensitivity and FDR of a tiling array analysis compared to leaving the affected probes in the analysis. AVAILABILITY: The MBR software and the sample array .CEL files used in this article are available at: http://liulab.dfci.harvard.edu/Software/MBR/MBR.htm     

Pareidolia in Infants

Faces convey primal information for our social life. This information is so primal that we sometimes find faces in non-face objects. Such illusory perception is called pareidolia. In this study, using infants’ orientation behavior toward a sound source, we demonstrated that infants also perceive pareidolic faces. An image formed by four blobs and an outline was shown to infants with or without pure tones, and the time they spent looking at each blob was compared. Since the mouth is the unique sound source in a face and the literature has shown that infants older than 6 months already have sound-mouth association, increased looking time towards the bottom blob (pareidolic mouth area) during sound presentation indicated that they illusorily perceive a face in the image. Infants aged 10 and 12 months looked longer at the bottom blob under the upright-image condition, whereas no differences in looking time were observed for any blob under the inverted-image condition. However, 8-month-olds did not show any difference in looking time under both the upright and inverted conditions, suggesting that the perception of pareidolic faces, through sound association, comes to develop at around 8 to 10 months after birth.     

Multi-Image Colocalization and Its Statistical Significance

Accurately localizing molecules within the cell is one of main tasks of modern biology, and colocalization analysis is one of its principal and most often used tools. Despite this popularity, interpretation is often uncertain because colocalization between two or more images is rarely analyzed to determine whether the observed values could have occurred by chance. To address this, we have developed a robust methodology, based on Monte Carlo randomization, to measure the statistical significance of a colocalization. The method works with voxel-based, intensity-based, object-based, and nearest-neighbor metrics. We extend all of these to measure colocalization in images with three colors. We also introduce three new metrics; blob colocalization, where the blob consists of a local maximum surrounded by a three-dimensional group of voxels; cluster diameter, to measure the clustering of fluorophores in three or more images; and the intercluster distance to measure the distance between these clusters. The robustness of these metrics was tested by varying the image thresholds over a broad range, which produced no change in the statistical significance of the colocalizations. A comparison of blob colocalization with voxel and Manders colocalization metrics shows that the different measures produce consistent results with similar values for significance and nonsignificance. Using our methodology, we are able to determine not only whether the labeled molecules colocalize with a probability greater than chance, but also whether they are sequestrated into different compartments. The program, written in C++, is freely available as source, as well as in a Linux version.     

A thresholding method for automatic cell image segmentation.

An algorithm for automatic segmentation of PAP-stained cell images and its digital implementation is described. First, the image is filtered in order to eliminate the granularily and small objects in the image which may upset the segmentation procedure. In a second step, information on gradient and compactness is extracted from the filtered image and stored in three histograms as functions of the extinction. From these histograms, two extinction thresholds are computed. These thresholds are suitable to separate the nucleus from the cytoplasm, and the cytoplasm from the background in the filtered image. Masks are determined in this way, and finally used to analyse the nucleus and the cytoplasm in the original image.     

Clinical aspects for the use of DNA image cytometry in detection of bladder cancer: a valuable tool?

The employment of DNA flow or image cytometry for oncological diagnostic procedures is favored because of its high correlation to tumor biological behavior. Prognostic statements and therapeutic strategies therefore are based on the high validity of DNA cytometric measurements. Using 151 bladder washings from patients suspected of bladder cancer for this study, we examined the clinical value of various common methods of DNA single cell (SCI) and stemline interpretations (SLI). Comparing the specificity and sensitivity of DNA image cytometry in detection of bladder tumors, we found 81 and 52%, respectively, for SCI of Boecking, 84 and 45% for SLI of Boecking, 61 and 58% for SLI of Fu, and 82 and 40% for conventional stemline interpretation. To improve diagnostic and prognostic validity of DNA image cytometry, we designed our own method of interpretation. In consequence, we identified six single DNA parameters out of all recorded measurements that correlated most to histopathological grading (G1-G3). Creating reference values at random and rating by points, we used a cytometric grading system for ranking. In detection of bladder cancer specificity and sensitivity ultimately arrived at almost 70% in application of our method. Thus, by this study, we were able to show that sensitivity of DNA examination can be increased by combining various DNA parameters. Apart from our own scheme, the discrepancy in interpretation of DNA image cytometry does not allow us to recommend this procedure as the only diagnostic in detection of bladder cancer. However, in regard to prognostic statements, particularly tumor biological behavior, DNA image cytometry appears to be useful.     

Morphological Characterization of Mycobacterium tuberculosis in a MODS Culture for an Automatic Diagnostics through Pattern Recognition

Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts.The Microscopic Observation Drug Susceptibility (MODS) assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB) to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7–10 days with a low cost.An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS.We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity) and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii). The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity.This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.     

果蝇心脏发育标记基因lbe的多克隆抗体制备

lbe是已经证明的心脏标记基因。为了深入研究lbe在心脏发育中的功能,需要获得lbe蛋白并制备其抗体。首先提取野生型成体果蝇的总RNA,反转录获得其cDNA文库,通过PCR克隆出lbe编码区序列,将其连接到pET-28a原核表达载体上。经酶切及测序鉴定后,质粒构建成功。将重组质粒（pET-28a-lbe）转化大肠杆菌菌株Rosseta,用IPTG诱导表达出融合蛋白,经Ni-IDA凝胶柱纯化后,最后将纯化的融合蛋白免疫新西兰大白兔制备lbe多克隆抗体,并用Western blotting检测抗体的效价和特异性。结果显示获得了lbe原核表达重组融合蛋白及高效价的特异性兔抗lbe多克隆抗体,为lbe功能的进一步研究奠定了基础。     

Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features

DNA replication is a fundamental task that plays a crucial role in the propagation of all living things on earth. Hence, the accurate identification of its origin could be the key to giving an insightful understanding of the regulatory mechanism of gene expression. Indeed, with the robust development of computational techniques and the abundant biological sequencing data, it has become possible for scientists to identify the origin of replication accurately and promptly. This growing concern has drawn a lot of attention among experts in this field. However, to gain better outcomes, more work is required. Therefore, this study is designed to explore the combination of state-of-the-art features and extreme gradient boosting learning system in classifying DNA sequences. Our hybrid approach is able to identify the origin of DNA replication with achieved sensitivity of 85.19%, specificity of 93.83%, accuracy of 89.51%, and MCC of 0.7931. Evidence is presented to show that our proposed method is superior to the state-of-the-art methods on the same benchmark dataset. Moreover, the research results represent a further step towards developing the prediction models for DNA replication in particular and DNA sequences in general.