Low carbon building: Experimental insight on the use of fly ash and glass fibre for making geopolymer concrete

Due to the environmental impacts resulting from the production of Ordinary Portland cement (OPC), the drive to develop alternative binders that can totally replace OPC is gaining huge consideration in the construction field. In the current study, attempt was made to determine the strength characteristics of glass fibre-reinforced fly ash based geopolymer concrete. Sodium hydroxide (NaOH) and sodium silicate (Na2SiO3) were used as alkaline solutions (for activation of geopolymer reaction) at 12, 16, 20 M. Glass fibres were added to the geopolymer concrete in varying proportions of 0.1e0.5% (in steps of 0.1%) by weight of concrete. A constant weight ratio of alkaline solution to fly ash content of 0.43 was adopted for all mixes. British standard concrete test specimens were cast for measuring compressive strength, splittensile strength, and flexural strength. Concrete specimens were cured by heating in oven at 90 �C for 24 h and natural environment, respectively. From the results, thermally cured concrete samples had better mechanical properties than the ambient (natural) cured samples. Thermally cured concrete specimen, containing 0.3% glass fibre and 16 M NaoH, achieved a maximum compressive strength of 24.8 MPa after 28 d, while naturally cured samples achieved a strength of 22.2 MPa. There was substantial increase in tensile strength of geopolymer concrete due to the addition of glass fibres. Split tensile strength increased by 5e10% in glass fibre-reinforced geopolymer concrete, containing 0.1e0.5% glass fibre and 16 M NaoH when compared to the unreinforced geopolymer concrete (1.15 MPa)

An assessment of plankton population of Cauvery river with reference to pollution.

Abstract: Studies on plankton of river Cauvery water, Mettur, Salem District, Tamil Nadu was made to assess the pollution of water from January 2003 to December 2003. The qualitative and quantitative evaluation of the variation in river water showed high quantity of phytoplankton and zooplankton population throughout the study period and rotifers formed dominated group over other groups of organisms. The present study revealed that the water of river Cauvery is highly polluted by direct contamination of sewage and other industrial effluents

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks

Probabilistic Random Walk Models for Comparative Network Analysis

Graph-based systems and data analysis methods have become critical tools in many fields as they can provide an intuitive way of representing and analyzing interactions between variables. Due to the advances in measurement techniques, a massive amount of labeled data that can be represented as nodes on a graph (or network) have been archived in databases. Additionally, novel data without label information have been gradually generated and archived. Labeling and identifying characteristics of novel data is an important first step in utilizing the valuable data in an effective and meaningful way. Comparative network analysis is an effective computational means to identify and predict the properties of the unlabeled data by comparing the similarities and differences between well-studied and less-studied networks. Comparative network analysis aims to identify the matching nodes and conserved subnetworks across multiple networks to enable a prediction of the properties of the nodes in the less-studied networks based on the properties of the matching nodes in the well-studied networks (i.e., transferring knowledge between networks). One of the fundamental and important questions in comparative network analysis is how to accurately estimate node-to-node correspondence as it can be a critical clue in analyzing the similarities and differences between networks. Node correspondence is a comprehensive similarity that integrates various types of similarity measurements in a balanced manner. However, there are several challenges in accurately estimating the node correspondence for large-scale networks. First, the scale of the networks is a critical issue. As networks generally include a large number of nodes, we have to examine an extremely large space and it can pose a computational challenge due to the combinatorial nature of the problem. Furthermore, although there are matching nodes and conserved subnetworks in different networks, structural variations such as node insertions and deletions make it difficult to integrate a topological similarity. In this dissertation, novel probabilistic random walk models are proposed to accurately estimate node-to-node correspondence between networks. First, we propose a context-sensitive random walk (CSRW) model. In the CSRW model, the random walker analyzes the context of the current position of the random walker and it can switch the random movement to either a simultaneous walk on both networks or an individual walk on one of the networks. The context-sensitive nature of the random walker enables the method to effectively integrate different types of similarities by dealing with structural variations. Second, we propose the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) model. In the CUFID model, we construct an integrated network by inserting pseudo edges between potential matching nodes in different networks. Then, we design the random walk protocol to transit more frequently between potential matching nodes as their node similarity increases and they have more matching neighboring nodes. We apply the proposed random walk models to comparative network analysis problems: global network alignment and network querying. Through extensive performance evaluations, we demonstrate that the proposed random walk models can accurately estimate node correspondence and these can lead to improved and reliable network comparison results

Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE

Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to compute similarity between nodes in different networks and an alignment strategy (AS) to find high-scoring alignments with respect to the total NCF over all aligned nodes (or node conservation). But, they then evaluate quality of their alignments via some other measure that is different than the node conservation measure used to guide the alignment construction process. Typically, one measures the amount of conserved edges, but only after alignments are produced. Hence, a recent attempt aimed to directly maximize the amount of conserved edges while constructing alignments, which improved alignment accuracy. Here, we aim to directly maximize both node and edge conservation during alignment construction to further improve alignment accuracy. For this, we design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs each conserved edge so that edges with highly NCF-similar end nodes are favored. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation, and which can be used with any NCF or combination of multiple NCFs. Using WAVE on top of established state-of-the-art NCFs leads to superior alignments compared to the existing methods that optimize only node conservation or only edge conservation or that treat each conserved edge the same. And while we evaluate WAVE in the computational biology domain, it is easily applicable in any domain.Comment: 12 pages, 4 figure

Novel insights into host-fungal pathogen interactions derived from live-cell imaging

Acknowledgments The authors acknowledge funding from the Wellcome Trust (080088, 086827, 075470 and 099215) including a Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377 and FP7-2007–2013 grant agreement HEALTH-F2-2010-260338–ALLFUN to NARG.Peer reviewedPublisher PD

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty

RNase1 prevents the damaging interplay between extracellular RNA and tumour necrosis factor-α in cardiac ischaemia/reperfusion injury

© Schattauer 2014 Despite optimal therapy, the morbidity and mortality of patients presenting with an acute myocardial infarction (M1) remain significant, and the initial mechanistic trigger of myocardial “ischaemia/reperfusion (1/R) injury” remains greatly unexplained. Here we show that factors released from the damaged cardiac tissue itself, in particular extracellular RNA (eRNA) and tumour-necrosis-factor α (TNF-α), may dictate 1/R injury. In an experimental in vivo mouse model of myocardial 1/R as well as in the isolated 1/R Langendorff-perfused rat heart, cardiomyocyte death was induced by eRNA and TNF-α. Moreover, TNF-α promoted further eRNA release especially under hypoxia, feeding a vicious cell damaging cycle during 1/R with the massive production of oxygen radicals, mitochondrial obstruction, decrease in antioxidant enzymes and decline of cardiomyocyte functions. The administration of RNase1 significantly decreased myocardial infarction in both experimental models. This regimen allowed the reduction in cytokine release, normalisation of antioxidant enzymes as well as preservation of cardiac tissue. Thus, RNase1 administration provides a novel therapeutic regimen to interfere with the adverse eRNA-TNF-α interplay and significantly reduces or prevents the pathological outcome of ischaemic heart disease

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences