In vivo dose measurement using TLDs and MOSFET dosimeters for cardiac radiosurgery.

In vivo measurements were made of the dose delivered to animal models in an effort to develop a method for treating cardiac arrhythmia using radiation. This treatment would replace RF energy (currently used to create cardiac scar) with ionizing radiation. In the current study, the pulmonary vein ostia of animal models were irradiated with 6 MV X-rays in order to produce a scar that would block aberrant signals characteristic of atrial fibrillation. The CyberKnife radiosurgery system was used to deliver planned treatments of 20-35 Gy in a single fraction to four animals. The Synchrony system was used to track respiratory motion of the heart, while the contractile motion of the heart was untracked. The dose was measured on the epicardial surface near the right pulmonary vein and on the esophagus using surgically implanted TLD dosimeters, or in the coronary sinus using a MOSFET dosimeter placed using a catheter. The doses measured on the epicardium with TLDs averaged 5% less than predicted for those locations, while doses measured in the coronary sinus with the MOSFET sensor nearest the target averaged 6% less than the predicted dose. The measurements on the esophagus averaged 25% less than predicted. These results provide an indication of the accuracy with which the treatment planning methods accounted for the motion of the target, with its respiratory and cardiac components. This is the first report on the accuracy of CyberKnife dose delivery to cardiac targets

Cascade Tank Water Quality Management: A Case Study in Thirappane Tank Cascade System, Sri Lanka

Tank cascade system (TCS) is a series of tanks located in a mesocatchment and has been accepted as a Globally Important Agricultural Heritage System found in Sri Lanka. Ecosystem components of the TCS play a major role in purifying water within the system. This study attempted to investigate the water quality status and the farmers’ willingness to rehabilitate the ecosystem components of the Thirappane TCS. Drinking and irrigation water quality parameters were tested in 34 locations and drinking and irrigation water quality indexes were calculated. Participatory rural appraisal and a questioner survey were conducted to gather social data. Water of TCS was observed to be appropriate for irrigation but not for drinking during the Maha cropping season. Based on the results of the Nitrate (as NO3 - ) and Total Phosphate (as PO4 3-), water of TCS can be categorized as eutrophic. Presence of ecosystem features of tank cascade system, annual income of the respondents, satisfaction on the quality of water for drinking, and the awareness about the tank cascade system significantly influenced the participatory decisions of the community on the rehabilitation of TCS. This study shall be an example and an eye opener to formulate sustainable tank cascade management plan

Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs

Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality reduction techniques are in need to disentangle biological variation in them, while accounting for technical and biological confounders. In this work, we extend a popular approach for probabilistic non-linear dimensionality reduction, the Gaussian process latent variable model, to scale to massive single-cell datasets while explicitly accounting for technical and biological confounders. The key idea is to use an augmented kernel which preserves the factorisability of the lower bound allowing for fast stochastic variational inference. We demonstrate its ability to reconstruct latent signatures of innate immunity recovered in Kumasaka et al. (2021) with 9x lower training time. We further analyze a COVID dataset and demonstrate across a cohort of 130 individuals, that this framework enables data integration while capturing interpretable signatures of infection. Specifically, we explore COVID severity as a latent dimension to refine patient stratification and capture disease-specific gene expression.Comment: Machine Learning and Computational Biology Symposium (Oral), 202

A Heterogeneous data ensemble approach for protein function prediction under mitochondrion organization

A heterogeneous data ensemble approach for the classification of Saccharomyces cerevisiae proteins under ‘mitochondrion organization’ Proteins are the real role players in keeping a cell healthy and well functioning. An important group of proteins is the subset of mitochondrial proteins that engage in the assembly, arrangement and disassembly of the mitochondrion. Several of them have been identified to cause human diseases. Hence, annotating proteins under the ‘mitochondrion organization’ Biology process is vital for identifying disease causative factors and for designing therapeutics. As manual annotation requires costly and laborious in vitro methods, in silico function prediction is preferred nowadays. Recent studies identify the importance of incorporating data from various biological aspects, to formulate a strong functional context for classification. In addition, many approaches from literature employ ensemble classifiers to attain a higher prediction accuracy. However, an insightful approach for accurate classification; biological data utilization; and biological data type significance determination; is still in need. This study presents an assessment of a heterogeneous data ensemble to classify Saccharomyces cerevisiae proteins under ‘mitochondrion organization’. The ensemble consists of nine euclidean-distance based nearest neighbour models and three affinity-based neighbourhood models; it utilizes sequences, protein domains, peptide chain properties, gene expression, secondary structure and interactions. The base models were trained upon annotations from the Gene Ontology, as well as from a publicly available benchmark gold dataset. They show a substantial level of disagreement, implying their effectiveness in collective decision making. Six combination schemes were evaluated for fusing the base model outputs. A Genetic Algorithmically weighted ensemble gives the highest improvement to the best performing base classifier, by displaying an average area under the Receiver Operating Characteristic curve of 92.52%. Moreover, it is capable of determining the biological importance of each data type. Overall, the proposed heterogeneous data ensemble is capable of identifying eight disease related proteins and one disease related protein in a strong and moderate sense, respectively

First full-scale trials of pebble matrix filtration

Protecting slow sand filters from high turbidity waters by pre-treatment using Pebble Matrix Filtration (PMF) has been studied in the laboratory at University College London followed by pilot field trials in Papua New Guinea and Serbia. Subsequently, the construction of two full-scale PMF units, one out of concrete (4.8m x4.8m x 3.0m high) and the other using pre-cast Ferro-cement panels (900mm x 1600mm x 20mm thick) with an effective diameter of 4.7m and 3m height, and the combined effective plan area of 40 m2 was completed to protect an existing Slow Sand Filter system at the National Water Supply Drainage Board (NWSDB) in Sri Lanka. Although the plant was completed in April 2008 due to some major repairs to address some leaks and other construction defects in both filters, monitoring was intermittent until November 2008. The results on the plant performance are presented here along with some of the construction problems encountered during the project

Archives management system for National Archives Department of Sri Lanka

Globalization has made the people around the world inter connected. Technology has become an increasing a vital component in the service sector today. The recent developments oftechnology have formed innovative concepts and environment in Sri Lanka. As a result of the development of the technology, one of the latest concepts merges to the National Archives Department of Sri Lanka. This project is about developing an e-commerce website with Content Management System (CMS) and advance forum for National Archives Department of Sri Lanka to trade their archives online and convert their current manual service process to computerized online service. It provides the users with a catalogue of different archives content available for purchase in the store and also users can request and purchase many services. There are many disadvantages of the current paper based system. Some of them are, the customers need to visit at least two times to the premises to get archival, time consuming, very low data share ability and security papers can be misplaced and will not be able to access the data. The key objective of this project is to develop a proper e-commerce system with content manage system to help National Archives Department of Sri Lanka for convert current manual paper based system in to a computerized major proportions of their archives management activities and normal day to day activities. In order to develop content management system with an e-commerce system and advance forum system, a number of technologies must be studies and understood. This proposed system was developed using several free open source technology. Drupal was used to develop the CMS along with PHP and MySQL database management system. The proposed system will be running on an Apache web server. The proposed computer based solution is expected to be highly functional and user friendly enabling the staff ofNational Archives Department of Sri Lanka to perform well. The proposed system should eliminate data redundancy, data inconsistency. The proposed solution also improves employer abilities, employer skills, working efficiency, security and achieves the aspired targets. This website is expected to be attractive and be simple enough to understand by an average use