Use of Image Processing Techniques to Automatically Diagnose Sickle-Cell Anemia Present in Red Blood Cells Smear

Sickle Cell Anemia is a blood disorder which results from the abnormalities of red blood cells and shortens the life expectancy to 42 and 48 years for males and females respectively. It also causes pain, jaundice, shortness of breath, etc. Sickle Cell Anemia is characterized by the presence of abnormal cells like sickle cell, ovalocyte, anisopoikilocyte. Sickle cell disease usually presenting in childhood, occurs more commonly in people from parts of tropical and subtropical regions where malaria is or was very common. A healthy RBC is usually round in shape. But sometimes it changes its shape to form a sickle cell structure; this is called as sickling of RBC. Majority of the sickle cells (whose shape is like crescent moon) found are due to low haemoglobin content. An image processing algorithm to automate the diagnosis of sickle-cells present in thin blood smears is developed. Images are acquired using a charge-coupled device camera connected to a light microscope. Clustering based segmentation techniques are used to identify erythrocytes (red blood cells) and Sickle-cells present on microscopic slides. Image features based on colour, texture and the geometry of the cells are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. The red blood cell smears were obtained from IG Hospital, Rourkela. The proposed image processing based identification of sickle-cells in anemic patient will be very helpful for automatic, sleek and effective diagnosis of the disease

Modelling and analysis of the control mechanisms of bacterial growth

This thesis concerns the control mechanisms of bacterial growth. Mathematical and experimental work has shown the control of bacterial growth behaves like a switch. It is known that the vitamin B12 riboswitch plays a role in this switching mechanism. These facts motivate considering variable structure control techniques to investigate the control mechanism and the robustness of the riboswitch. Whilst the existence and importance of switches are widely acknowledged within the biological literature, many life scientists do not deal explicitly with the switching behaviour. Frequently, steady-state behaviour before and after switching is the primary focus. The main objective of this thesis is to study the control mechanisms of the vitamin B12 riboswitch on bacterial growth at both a cellular and population level. The results using different bacterial strains show that changing the concentration of vitamin B12 affects growth until the saturation level is reached. The thesis then studies the control mechanism in algal and bacterial co-culture. A model has been developed using data from an in vivo experimental two-species system where the bacterium Mesorhizobium loti (M. loti) supplies the vitamin B12 required for growth to the freshwater green alga Lobomonas rostrata (L. rostrata) and where the action of the B12 riboswitch is known to be a determinant of system behaviour. The reachability analysis from sliding mode control is used to find the algal and bacterial saturation level and study the robustness of the system. Using the validated riboswitch model, an observer design method from the domain of control engineering is used to estimate the vitamin B12 transporter BtuB given measurements of the concentration of vitamin B12. Validation of the estimates of BtuB has been undertaken by comparing the relationship between the BtuB and vitamin B12 concentrations estimated from the observer with the relationship between green fluorescent protein production and the concentration of vitamin B12 obtained experimentally

An Investigation of Argumentation Theory for the Prediction of Survival in Elderly Using Biomarkers

Research on the discovery, classification and validation of biological markers, or biomarkers, have grown extensively in the last decades. Newfound and correctly validated biomarkers have great potential as prognostic and diagnostic indicators, but present a complex relationship with pertinent endpoints such as survival or other diseases manifestations. This research proposes the use of computational argumentation theory as a starting point for the resolution of this problem for cases in which a large amount of data is unavailable. A knowledge-base containing 51 different biomarkers and their association with mortality risks in elderly was provided by a clinician. It was applied for the construction of several argument-based models capable of inferring survival or not. The prediction accuracy and sensitivity of these models were investigated, showing how these are in line with inductive classification using decision trees with limited data

Leukocyte nucleus segmentation and nucleus lobe counting

<p>Abstract</p> <p>Background</p> <p>Leukocytes play an important role in the human immune system. The family of leukocytes is comprised of lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Any infection or acute stress may increase or decrease the number of leukocytes. An increased percentage of neutrophils may be caused by an acute infection, while an increased percentage of lymphocytes can be caused by a chronic bacterial infection. It is important to realize an abnormal variation in the leukocytes. The five types of leukocytes can be distinguished by their cytoplasmic granules, staining properties of the granules, size of cell, the proportion of the nuclear to the cytoplasmic material, and the type of nucleolar lobes. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.Biomedical technologists can currently recognize abnormal leukocytes using human eyes. However, the quality and efficiency of diagnosis may be compromised due to the limitations of the biomedical technologists' eyesight, strength, and medical knowledge. Therefore, the development of an automatic leukocyte recognition system is feasible and necessary. It is essential to extract the leukocyte region from a blood smear image in order to develop an automatic leukocyte recognition system. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.</p> <p>Results</p> <p>The purpose of this paper is to contribute an automatic leukocyte nuclei image segmentation method for such recognition technology. The other goal of this paper is to develop the method of counting the number of lobes in a cell nucleus. The experimental results demonstrated impressive segmentation accuracy.</p> <p>Conclusions</p> <p>Insensitive to the variance of images, the LNS (Leukocyte Nuclei Segmentation) method functioned well to isolate the leukocyte nuclei from a blood smear image with much better UR (Under Segmentation Rate), ER (Overall Error Rate), and RDE (Relative Distance Error). The presented LC (Lobe Counting) method is capable of splitting leukocyte nuclei into lobes. The experimental results illuminated that both methods can give expressive performances. In addition, three advanced image processing techniques were proposed as weighted Sobel operator, GDW (Gradient Direction Weight), and GBPD (Genetic-based Parameter Detector).</p

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

A SCALE DEVELOPMENT STUDY: BRAIN FOG SCALE

Background: This research was conducted to objectively evaluate the level of brain fog that may develop due to many reasons. Subjects and methods: This was a methodological study. This study was conducted in Turkey. Content validity ratio, EFA, CFA, Kaiser-Meyer-Olkin analysis and Bartlett’s test of sphericity, Item analysis, Cronbach’s alpha coefficient, Spearman-Brown, Guttman Analysis and test-retest correlations validity-reliability analysis were performed. The statistical meaningfulness level in all tests was determined as p<0.05. Results: As a result of context validity, factor analysis and item analysis, a 30 item scale with 3 subscale was obtained. The variance amount explained by the 3 subscale was on a very good level (77.43%). The fact that all of the Cronbach alpha, Spearman- Brown and Guttman internal consistency coefficients of the scale and all of its subscale are above 0.70. When the test retest reliability coefficients of the scale was examined, the scale was found to present consistent results in different applications and the scale was found to be reliable with regard to the constancy coefficient. Conclusion: The Brain Fog Scale consists of 30 items and 3 subscales. It is a valid and reliable instrument

Four ways blue foods can help achieve food system ambitions across nations

Blue foods, sourced in aquatic environments, are important for the economies, livelihoods, nutritional security and cultures of people in many nations. They are often nutrient rich, generate lower emissions and impacts on land and water than many terrestrial meats, and contribute to the health, wellbeing and livelihoods of many rural communities4. The Blue Food Assessment recently evaluated nutritional, environmental, economic and justice dimensions of blue foods globally. Here we integrate these findings and translate them into four policy objectives to help realize the contributions that blue foods can make to national food systems around the world: ensuring supplies of critical nutrients, providing healthy alternatives to terrestrial meat, reducing dietary environmental footprints and safeguarding blue food contributions to nutrition, just economies and livelihoods under a changing climate. To account for how context-specific environmental, socio-economic and cultural aspects affect this contribution, we assess the relevance of each policy objective for individual countries, and examine associated co-benefits and trade-offs at national and international scales. We find that in many African and South American nations, facilitating consumption of culturally relevant blue food, especially among nutritionally vulnerable population segments, could address vitamin B12 and omega-3 deficiencies. Meanwhile, in many global North nations, cardiovascular disease rates and large greenhouse gas footprints from ruminant meat intake could be lowered through moderate consumption of seafood with low environmental impact. The analytical framework we provide also identifies countries with high future risk, for whom climate adaptation of blue food systems will be particularly important. Overall the framework helps decision makers to assess the blue food policy objectives most relevant to their geographies, and to compare and contrast the benefits and trade-offs associated with pursuing these objectives

Detection of HIV by using Rough Set and Homotopy Analysis Method

The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV &amp; AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV &amp; AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST &amp; HAM over other soft computing techniques and Mathematical Modelling as both RST and Homotopy Analysis (HAM) because RST can handle vague and imprecise data efficiently, and HAM is a suitable technique for finding analytical solutions. We have used the chi-squared test to validate our claim.&nbsp