Eu ³⁺ Sequestration by Biogenic Nano-Hydroxyapatite Synthesized at Neutral and Alkaline pH

<p>Biogenic hydroxyapatite (bio-HA) has the potential for radionuclide capture and remediation of metal-contaminated environments. Biosynthesis of bio-HA was achieved via the phosphatase activity of a <i>Serratia sp</i>. supplemented with various concentrations of CaCl₂ and glycerol 2-phosphate (G2P) provided at pH 7.0 or 8.6. Presence of hydroxyapatite (HA) was confirmed in the samples by X-ray powder diffraction analysis. When provided with limiting (1 mM) G2P and excess (5 mM) Ca²⁺ at pH 8.6, monohydrocalcite was found. This, and bio-HA with less (1 mM) Ca²⁺ accumulated Eu(III) to ∼31% and 20% of the biomineral mass, respectively, as compared to 50% of the mineral mass accumulated by commercial HA. Optimally, with bio-HA made at initial pH 7.0 from 2 mM Ca²⁺ and 5 mM G2P, Eu(III) accumulated to ∼74% of the weight of bio-HA, which was equal to the mass of the HA mineral component of the biomaterial. The implications with respect to potential bio-HA-barrier development in situ or as a remediation strategy are discussed.</p

Investigation of the biological properties of Kerala red rain cells

This PhD research project was designed to provide further information about the biological properties of red cells found in the rains that fell for three months from July 2001 during the monsoon season in Kerala, Southern India. The studies involved using a variety of staining methodologies as well as spectroscopic and microscopic analyses. The red rain cells display an exceptionally thick, multilayered cell wall and contain high concentrations of UV absorbing components. The cells often live in clumps forming a bio- film rich in silicon, most of which is readily precipitated in the outer layers of their thick cell wall. These properties may suggest that the cells can survive in multiple extreme environments. DAPI staining method demonstrating the presence of DNA in these cells contradicted earlier work by Louis and Kumar (2006) that the red cells were devoid of DNA. The positive detection of DNA was only possible if the red cells were pre-treated with DMSO prior to DAPI staining. The DMSO treated cells showed no structural damage, but instead released the red compounds. This solvent thus seemed to affect the binding of the red compounds to the outer layers, but not the structural integrity of the cells. Additional data indicated that the red rain cells are possible hyperthermophiles. The data provided in the current study tentatively suggests that the red cells are unusual prokaryotes of hyperthermophilic nature.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Morphological and Molecular Analysis Calls for a Reappraisal of the Red Rain Cells of Kerala

Early studies on the coloured particles that fell as red rain over southern India identified them as unicellular eukaryotes such as members of the red algae or fungi; however, the results of the present investigation are not consistent with this designation. Using transmission electron microscopy, we have demonstrated significant differences in the ultrastructure when compared with representative species from these other groups. Most notably, the red rain cells show no evidence of typical eukaryotic internal structures such as mitochondria or endoplasmic reticulum. Furthermore, comparisons based on elemental composition using energy-dispersive X-ray analysis, as well as Raman spectral signatures demonstrate significant dissimilarities in their molecular composition. The identity and origins of the red rain cells remain an enigma; however, our findings are more consistent with an unidentified prokaryote, and thus suggest that previous attempts at their identification should be reappraised

Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds

The tubular baffled reactor and its potential for the biological methanation of carbon dioxide

The biological Power to Methane process (PtM) is gaining ground as an answer to the long-term renewable energy storage problem. Methane is an efficient hydrogen carrier, has an established worldwide transport infrastructure and can serve as a link between renewable power generation and a circular carbon economy. One of the defining factors regarding the scalability of the PtM process is the design of the reactor as it can determine the production rate/energy expenditure ratio. The tubular baffled reactor, a popular reactor design within the chemical industry has been assessed in the present study as a biomethanation reactor for the first time. The experiments were conducted with mixed cultures and the results point to high gas-liquid mass transfer capabilities as indicated by the methanation rates achieved (&gt;90 % CH4 at 270 L/L/d mixed gas input rate). The gas/liquid flow ratio appears to have a stronger effect on methanation than the gas residence time. The working length of the reactor determines the pressure drop experienced by the culture, with higher pressure drops showing a negative correlation to methanogenesis