Numerical investigation of two-dimensional light scattering patterns of cervical cell nuclei to map dysplastic changes at different epithelial depths

This essay identifies and analyses the life cycle trend of the department store industry in Hong Kong. The main research question is whether its development follows a pattern that can be explained by retail life cycle theory. The tracing of its life cycle trend line is the main contribution. Methodologically, the trend line is plotted on the basis of triangulating a number of quantitative and qualitative data sets. Quantitative data sets used include time series longitudinal data constructed from government statistics, while qualitative data consist of historical data from archival documents, and semi-structured interviews of retailing professionals. The significant findings are that Hong Kong's department store industry approximates the pattern described in retail life cycle theory and that it has in recent years entered a stage of maturity.

BackgroundCervical cancer (CC) is the fourth most common cancer in women worldwide with an estimated 528,000 new cases in 2012. The same year México had an incidence of 13,960 and a mortality of 4769 cases. There are several diagnosis methods of CC; among the most frequents are the conventional Pap cytology (Pap), colposcopy, and visual inspection with acetic acid (VIA), histopathological examination, tests of imaging and detection of high-risk papilloma virus (HR-HPV) with molecular tests (PCR, hybridization, sequencing). Proteomics is a tool for the detection of new biomarkers that can be associated with clinical stage, histological type, prognosis, and/or response to treatment. In this study we performed a comparative analysis of CC cells with normal cervical cells. The proteomic analysis was carried out with the fluorescent two-dimensional electrophoresis (2D-DIGE) technique to subsequently identify differential protein profiles using Decyder Software, and the selected proteins were identified by Mass Spectrometry (MALDI-TOF).ResultsThe proteins that showed an increased expression in cervical cancer in comparison with normal cervix cells were: Mimecan, Actin from aortic smooth muscle and Lumican. While Keratin, type II cytoskeletal 5, Peroxiredoxin-1 and 14-3-3 protein sigma showed a decrease in their protein expression level in cervical cancer in comparison with normal cervix cells.ConclusionsThus, this study was successful in identifying biomarker signatures for cervical cancer, and might provide new insights into the mechanism of CC progression.

In the current era of systems biology research, there is a need for the integrative analysis of binary and quantitative genomics data sets measured on the same objects. One standard tool of exploring the underlying dependence structure present in multiple quantitative data sets is the simultaneous component analysis (SCA) model. However, it does not have any provisions when a part of the data are binary. To this end, we propose the generalized SCA (GSCA) model, which takes into account the distinct mathematical properties of binary and quantitative measurements in the maximum likelihood framework. Like in the SCA model, a common low‐dimensional subspace is assumed to represent the shared information between these two distinct types of measurements. To achieve a low rank solution, we propose to use a concave variant of the nuclear norm penalty. An efficient majorization algorithm is developed to fit this model with different concave penalties. Realistic simulations (low signal‐to‐noise ratio and highly imbalanced binary data) are used to evaluate the performance of the proposed model in recovering the underlying structure. Also, a missing value based cross‐validation procedure is implemented for model selection. We illustrate the usefulness of the GSCA model for exploratory data analysis of quantitative gene expression and binary copy number aberration measurements obtained from the GDSC1000 data sets.

Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs). Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%.

Cervical cancer is a human papilloma virus (HPV)-related cancer, but most HPV infections are transient or intermittent and resolve spontaneously. Thus, other factors, such as cervical microflora, which are dominated by lactobacilli, must be involved in invasive cervical carcinoma development after HPV infection. Previous studies have demonstrated that lactobacilli have antitumour effects, and it is possible that vaginal lactobacilli prevent cervical cancer. Here we examined the proliferative and apoptotic responses of normal and tumour cervical cells to common vaginal lactobacilli components by investigating human normal fibroblast-like cervical (normal cervical) and HeLa (cervical tumour) cell responses to Lactobacillus gasseri and Lactobacillus crispatus. The effects of different lactobacilli components, such as culture supernatants, cytoplasmic extracts, cell-wall extracts and live cells, were determined by MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay, trypan blue staining, lactate dehydrogenase assay and colorimetric caspase-3 activity assay. Changes in caspase-3 and human chorionic gonadotropin β (hCGβ) expression were analysed by quantitative RT-PCR. Tumour cell growth inhibition by culture supernatants was higher than that by pH- and lactate-adjusted controls. However, the effects of the supernatants on normal cells were similar to those of lactate-adjusted controls. Apoptosis was inhibited by supernatants, which was consistent with higher hCGβ expression since hCG inhibits apoptosis. Our study demonstrated that common vaginal lactobacilli exert cytotoxic effects on cervical tumour cells, but not on normal cells, and that this cytotoxicity is independent of pH and lactate. Our results encourage further studies on the interaction between lactobacilli and cervical cells, and administration of common vaginal lactobacilli as probiotics.

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

The nuclei of epithelial cells of the uterine cervix of normal women and of patients with various degrees of dysplasia, carcinoma in situ, and invasive carcinoma were studied by means of electron microscopy. Nuclear ribonucleoprotein components and chromatin were contrasted using preferential methods for RNA and DNA. Changes in the distribution of the extranucleolar ribonucleoprotein-containing structures were found, ranging from low-grade dysplastic lesions to invasive carcinoma. Compared with normal epithelial cells, dysplastic and neoplastic cells possess more nuclear bodies, as well as deep invaginations of the nuclear envelope and lobulations. Morphometric parameters estimated were nuclear volume, numerical density of perichromatin granules (PCG), and fraction of nuclear volume occupied by compact chromatin. The pattern of values of these parameters in the cell layers of normal cervical epithelium was disrupted in all the lesions. These data suggest that the processes studied induce early alterations in transcription and processing and/or exportation of mRNA to the cytoplasm. Two populations of cells were found in invasive carcinomas, one with large nuclei, sparse compact chromatin, and few PCG, and the other with small nuclei, abundant compact chromatin, and numerous PCG. Their morphologic features indicate that the former population is composed of relatively undifferentiated cells, while the latter is made up of well-differentiated cells which could be neoplastic or entrapped normal cells.

The mechanical properties (e.g. surface tension and bending modulus) of the cell membrane usually change when normal cells become cancerous, which opens up a way to diagnose cancer by detecting the vibration of the cell membrane. In this letter, we theoretically propose a highly sensitive method for the detection of cancer cells in terms of an all-optical domain. In the case of cervical cells, we find that cervical cancer cells have a peak shift of in the absorption spectrum compared with normal cervical cells. The results show that a peak shift can be detected in the absorption spectrum once the cells become cancerous. We expect that the proposed approach could advance the research in cancer detection.

Dysplastic progression is known to be associated with changes in morphology and internal structure of cells. A detailed assessment of the influence of these changes on cellular scattering response is needed to develop and optimize optical diagnostic techniques. In this study, we first analyzed a set of quantitative histopathologic images from cervical biopsies and we obtained detailed information on morphometric and photometric features of segmented epithelial cell nuclei. Morphometric parameters included average size and eccentricity of the best-fit ellipse. Photometric parameters included optical density measures that can be related to dielectric properties and texture characteristics of the nuclei. These features enabled us to construct realistic three-dimensional computational models of basal, parabasal, intermediate, and superficial cell nuclei that were representative of four diagnostic categories, namely normal (or negative for dysplasia), mild dysplasia, moderate dysplasia, and severe dysplasia or carcinoma in situ. We then employed the finite-difference time-domain method, a popular numerical tool in electromagnetics, to compute the angle-resolved light scattering properties of these representative models. Results indicated that a high degree of variability can characterize a given diagnostic category, but scattering from moderately and severely dysplastic or cancerous nuclei was generally observed to be stronger compared to scattering from normal and mildly dysplastic nuclei. Simulation results also pointed to significant intensity level variations among different epithelial depths. This suggests that intensity changes associated with dysplastic progression need to be analyzed in a depth-dependent manner.

Today, as data sets used in computations grow in size and complexity, the technologies developed over the years to deal with scientific data sets have become less efficient and effective. Many frequently used operations, such as eigenvector computation, could quickly exhaust our desktop workstations once the data size reaches certain limits. On the other hand, the high-dimensional data sets we collect every day don't relieve the problem. Many conventional metric designs that build on quantitative or categorical data sets cannot be applied directly to heterogeneous data sets with multiple data types. While building new machines with more resources might conquer the data size problems, the complexity of today's computations requires a new breed of projection techniques to support analysis of the data and verification of the results. We introduce the concept of a data signature, which captures the essence of a scientific data set in a compact format, and use it to conduct analysis as if using the original. A time-dependent climate simulation data set demonstrates our approach and presents the results.

In this paper, we develop a method, termed the Interaction Distribution (ID) method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1), pi,j: the probability for a visit made by the i’th pollinator species to take place on the j’th plant species; (2), qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The method applies the Dirichlet distribution to estimate these two probabilities, based on a given empirical data set. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.

The study assessed the contribution of District School Quality Assurance Officers (DSQAOs) feedback implementation in improving the teaching and learning environment in public primary schools of Kilimanjaro region, Tanzania. The study was guided by the Total Quality Management Theory by William Deming in the 1920s. The study employed a convergent research design under a mixed methods approach to collect and analyze quantitative and qualitative data sets. Probability and non-probability sampling techniques were used to draw the sample from a target population of 5,788 from which 95 head teachers (HTs) were sampled from 950, likewise 480 of 4831 primary schools’ teachers, 190 of 380 Internal Schools Quality Assurance Officers (ISQAOs), and 5 of 7 District Chief School Quality Assurance Officers (DCQAOs) making a sample size of 770 respondents. The study used questionnaires and interview guides for data collection. The validity of the questionnaires was determined by three research experts from Mwenge Catholic University (MWECAU). The reliability of quantitative instruments for Likert scale items was established through the Cronbach’s Alpha technique with 0.879, 0.941, and 0.889 coefficients for questionnaires for teachers, HTs, and ISQAOs, respectively. Using Statistical Package for Social Sciences (SPSS) version 26, the quantitative data were summarized through frequencies, percentages, and means and presented in tables. The qualitative data were thematically analyzed and presented through quotations and narration. The study found that feedback from District Schools Quality Assurance Officers has a great contribution to the teaching and learning environment in public primary schools. The study concludes that the implementation of district school quality assurance officers’ feedback contributes to the improvement of the quality of teaching and learning environments in public primary schools. The study recommends that the Ministry of Education Science and Technology should stress on mandatory implementation of DSQAOs’ feedback to improve the teaching and learning environment for the pupils.

The surface ultrastructure of normal, malignant and dyskaryotic exfoliated cervical squamous cells was examined with the Scanning Electron Microscope (SEM). Normal and malignant cells were found to have very similar surfaces to those seen on similar cells in a biopsy specimen. Dyskaryotic cells, however, showed a large range of surface features and dyskaryotic cells that looked identical at light microscopy could be subdivided into three groups by examining their ultrastructural features.

We present a numerical method for studying reversible electroporation on normal and cancerous cervical cells. This microdosimetry analysis builds on a unique approach for extracting contours of free and overlapping cervical cells in the cluster from the Extended Depth of Field (EDF) images. The algorithm used for extracting the contours is a joint optimization of multiple-level set function along with the Gaussian mixture model and Maximally Stable Extremal Regions. These contours are then exported to a multi-physics domain solver, where a variable frequency pulsed electric field is applied. The trans-Membrane voltage (TMV) developed across the cell membrane is computed using the Maxwell equation coupled with a statistical approach, employing the asymptotic Smoluchowski equation. The numerical model was validated by successful replication of existing experimental configurations that employed low-frequency uni-polar pulses on the overlapping cells to obtain reversible electroporation, wherein, several overlapping clumps of cervical cells were targeted. For high-frequency calculation, a combination of normal and cancerous cells is introduced to the computational domain. The cells are assumed to be dispersive and the Debye dispersion equation is used for further calculations. We also present the resulting strength-duration relationship for achieving the threshold value of electroporation between the normal and cancerous cervical cells due to their size and conductivity differences. The dye uptake modulation during the high-frequency electric field electroporation is further advocated by a mathematical model.

Application of the CellDetect® staining technique in diagnosis of human cervical cancer