Seawater reverse osmosis desalination and (harmful) algal blooms

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Desalination 360 (2015): 61-80, doi:10.1016/j.desal.2015.01.007.This article reviews the occurrence of HABs in seawater, their effects on the operation of seawater reverse osmosis (SWRO) plants, the indicators for quantifying/predicting these effects, and the pretreatment strategies for mitigating operational issues during algal blooms. The potential issues in SWRO plants during HABs are particulate/organic fouling of pretreatment systems and biological fouling of RO membranes, mainly due to accumulation of algal organic matter (AOM). The presence of HAB toxins in desalinated water is also a potential concern but only at very low concentrations. Monitoring algal cell density, AOM concentrations and membrane fouling indices is a promising approach to assess the quality of SWRO feedwater and performance of the pretreatment system. When geological condition is favourable, subsurface intake can be a robust pretreatment for SWRO during HABs. Existing SWRO plants with open intake and are fitted with granular media filtration can improve performance in terms of capacity and product water quality, if preceded by dissolved air flotation or sedimentation. However, the application of advanced pretreatment using ultrafiltration membrane with in‐line coagulation is often a better option as it is capable of maintaining stable operation and better RO feed water quality during algal bloom periods with significantly lower chemical consumption.This study was conducted with the financial support of UNESCO‐IHE Institute for Water Education, Wetsus Centre of Excellence for Sustainable Water Technology and Water Desalination and Reuse Center (KAUST, Saudi Arabia). Support for D. M. Anderson was provided through the Woods Hole Center for Oceans and Human Health, National Science Foundation Grant OCE‐1314642 and National Institute of Environmental Health Sciences Grant 1‐P01‐ES021923‐01

The Use of Novel Oral Anti-Coagulant's (NOAC) compared to Vitamin K Antagonists (Warfarin) in patients with Left Ventricular thrombus after Acute Myocardial Infarction (AMI).

This is a pre-copyedited, author-produced version of an article accepted for publication in European Heart Journal - Cardiovascular Pharmacotherapy following peer review. The version of record: Daniel A Jones, Paul Wright, Momin A Alizadeh, Sadeer Fhadil, Krishnaraj S Rathod, Oliver Guttmann, Charles Knight, Adam Timmis, Andreas Baumbach, Andrew Wragg, Anthony Mathur, Sotiris Antoniou, The Use of Novel Oral Anti-Coagulant’s (NOAC) compared to Vitamin K Antagonists (Warfarin) in patients with Left Ventricular thrombus after Acute Myocardial Infarction (AMI), European Heart Journal - Cardiovascular Pharmacotherapy, pvaa096, https://doi.org/10.1093/ehjcvp/pvaa096AIM: Current guidelines recommend the use of Vitamin K Antagonist (VKA) for up to 3 - 6 months for treatment of LV thrombus post-acute myocardial infarction (AMI). However, based on evidence supporting non-inferiority of Novel Oral Anti-Coagulant's (NOAC) compared to VKA for other indications such as DVT, PE and thrombo-embolic prevention in atrial fibrillation, NOACs are being increasingly used off licence for the treatment of LV thrombus post AMI. In this study we investigated the safety and effect of NOACs compared to VKA on LV thrombus resolution in patients presenting with AMI. METHODS AND RESULTS: This was an observational study of 2,328 consecutive patients undergoing Coronary Angiography +/- Percutaneous Coronary Intervention (PCI) for AMI between May 2015- December 2018, at a UK cardiac centre. Patients' details were collected from the hospital electronic database. The primary end-point was rate of LV thrombus resolution with bleeding rates a secondary outcome.Left ventricular (LV) thrombus was diagnosed in 101 (4.3%) patients. Sixty patients (59.4%) were started on VKA and 41 patients (40.6%) on NOAC therapy (rivaroxaban: 58.5%, apixaban, 36.5% and edoxaban: 5.0%). Both groups were well matched in terms of baseline characteristics including age, previous cardiac history (Previous MI, PCI, CABG), and cardiovascular risk factors (Hypertension, Diabetes, Hypercholesterolaemia).Over the follow up period (median 2.2 years), overall rates of LV thrombus resolution were 86.1%. There was greater and earlier LV thrombus resolution in the NOAC group compared to patients treated with warfarin (82% vs 64.4%, p = 0.0018, at 1 year), which persisted after adjusting for baseline variables (OR 1.8 95% CI 1.2-2.9). Major bleeding events during the f/u period were lower in the NOAC group, compared with VKA group (0% vs 6.7%, p = 0.030) with no difference in rates of systemic thromboembolism (5% vs 2.4%, p = 0.388). CONCLUSION: This data suggests improved thrombus resolution in post ACS LV thrombosis in patients treated with NOACs compared to vitamin K antagonists. This improvement in thrombus resolution was accompanied with a better safety profile for NOAC patients' vs VKA treated patients. Thus, provides data to support a randomised trial to answer this question

The potential of harnessing real-time occupancy data for improving energy performance of activity-based workplaces

Currently, the available studies on the prediction of building energy performance and real occupancy data are typically characterized by aggregated and averaged occupancy patterns or large thermal zones of reference. Despite the increasing diffusion of smart energy management systems and the growing availability of longitudinal data regarding occupancy, these two domains rarely inform each other. This research aims at understanding the potential of employing real-time occupancy data to identify better cooling strategies for activity-based-working (ABW)-supportive offices and reduce the overall energy consumption. It presents a case study comparing the energy performance of the office when different resolutions of occupancy and thermal zoning are applied, ranging from the standard energy certification approach to real-time occupancy patterns. For the first time, one year of real-time occupancy data at the desk resolution, captured through computer logs and Bluetooth devices, is used to investigate this issue. Results show that the actual cooling demand is 9% lower than predicted, unveiling the energy-saving potential to be achieved from HVAC systems for non-assigned seating environments. This research demonstrates that harnessing real-time occupancy data for demand-supply cooling management at a fine-grid resolution is an efficient strategy to reduce cooling consumption and increase workers’ comfort. It also emphasizes the need for more data and monitoring campaigns for the definition of more accurate and robust energy management strategies

PHASED VARIANTS IMPROVE DLBCL MINIMAL RESIDUAL DISEASE DETECTION AT THE END OF THERAPY

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Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation.</p> <p>Results</p> <p>We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at <url>http://gepat.sourceforge.net</url>.</p> <p>Conclusion</p> <p>GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at <url>http://gepat.bioapps.biozentrum.uni-wuerzburg.de</url>.</p

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.</p> <p>Results</p> <p>In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say <it>C</it>₁and <it>C</it>₂). We model the expression at <it>C</it>₁using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from <it>C</it>₂is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.</p> <p>Conclusion</p> <p>In both cases, the proposed method identified biologically significant genes.</p

Toward understanding and exploiting tumor heterogeneity

The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here