Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.

HPV infection, a described cause for human cancer: HPV detection and molecular mechanisms of HPV carcinogenesis (Review).

[en]Human papillomavirus (HPV) belongs to the Papillomaviridae virus family and it is one of the most common sexual transmission infections. HPV genome is composed of eight genes, including two early genes and six late genes. Among these late genes, E6 and E7 code for proteins that trigger cell-cycle re-entry in infected cells, which can lead to cervical cancer development. The IARC (International Agency for Research Cancer) proposed a guideline based on Hill’s criteria to determine whether the relation between HPV infection and cervical cancer is causal or not. Epidemiological studies have demonstrated that HPV infection is a necessary but non-sufficient cause for cervical cancer. Furthermore, HPV infection is considered the first necessary cause described of a human cancer, being HPV16 and 18 carcinogenic to humans and the most studied types. Cervical cancer is the second leading cause of cancer death among women worldwide. Different screening programs are carried out with the aim of preventing cervical cancer; such as cytologies and HPV tests. There are two main methods which are equally usable to detect HPV: the real-time PCR assays and the array assays. Regarding the molecular mechanisms of HPV mediated malignancies, E2, E6 and E7 proteins of HPV16 lead to immune response evasion, inducing IL-10 and TGF-β1 gene expression. Besides, E6 and E7 proteins allow cell-cycle reentry, phosphorylating RB and ubiquitinating p53 respectively. HPV genome integration in host genome leads to the alteration of host and viral genes expression, including oncogenes and tumor suppressor genes. However, the differences of E6 and E7 oncoproteins in different HPV types is poorly known due to the fact that almost the most studied HPV type has been HPV16.

Comparison of media and methods for the detection and enumeration of Clostridium perfringens in seafoods

Three direct plating methods and two most probable number (MPN) procedures were compared for the enumeration of Clostridium perfringens in seafoods the sulfitecycloserine (SC) agar, sulfite-polymyxin-sulfadiazine (SPS) agar, tryptone-sulfite- neomycin (TSN) agar, LS medium MPN procedure and iron milk MPN procedure. Isolates were confirmed as C. perfringens. The two MPN procedures compared very well with the three plating media tested with stock culture of C. perfringens from our laboratory collection and the reference strain NCIB 6125. But in fish samples, the two liquid media were found to be more sensitive and hence the MPN procedure using LS medium for the detection of C. perfringens in seafoods is suggested.

Defects Detection and Assessment in Concrete Surfaces

Aside from cracks, the impact of other surface defects, such as air pockets and discoloration, can be detrimental to the quality of concrete in terms of strength, appearance and durability. For this reason, local and national codes provide standards for quantifying the quality impact of these concrete surface defects and owners plan for regular visual inspections to monitor surface conditions. However, manual visual inspection of concrete surfaces is a qualitative (and subjective) process with often unreliable results due to its reliance on inspectors’ own criteria and experience. Also, it is labor intensive and time-consuming. This paper presents a novel, automated concrete surface defects detection and assessment approach that addresses these issues by automatically quantifying the extent of surface deterioration. According to this approach, images of the surface shot from a certain angle/distance can be used to automatically detect the number and size of surface air pockets, and the degree of surface discoloration. The proposed method uses histogram equalization and filtering to extract such defects and identify their properties (e.g. size, shape, location). These properties are used to quantify the degree of impact on the concrete surface quality and provide a numerical tool to help inspectors accurately evaluate concrete surfaces. The method has been implemented in C++ and results that validate its performance are presented.

Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments

The current procedures in post-earthquake safety and structural assessment are performed manually by a skilled triage team of structural engineers/certified inspectors. These procedures, and particularly the physical measurement of the damage properties, are time-consuming and qualitative in nature. This paper proposes a novel method that automatically detects spalled regions on the surface of reinforced concrete columns and measures their properties in image data. Spalling has been accepted as an important indicator of significant damage to structural elements during an earthquake. According to this method, the region of spalling is first isolated by way of a local entropy-based thresholding algorithm. Following this, the exposure of longitudinal reinforcement (depth of spalling into the column) and length of spalling along the column are measured using a novel global adaptive thresholding algorithm in conjunction with image processing methods in template matching and morphological operations. The method was tested on a database of damaged RC column images collected after the 2010 Haiti earthquake, and comparison of the results with manual measurements indicate the validity of the method.