Pathogenicity of anti-ADAMTS13 autoantibodies in acquired thrombotic thrombocytopenic purpura.

BACKGROUND: Acquired thrombotic thrombocytopenic purpura (TTP) is an autoimmune disease in which anti-ADAMTS13 autoantibodies cause severe enzyme deficiency. ADAMTS13 deficiency causes the loss of regulation of von Willebrand factor multimeric size and platelet-tethering function, which results in the formation of disseminated microvascular platelet microthrombi. Precisely how anti-ADAMTS13 autoantibodies, or antibody subsets, cause ADAMTS13 deficiency (ADAMTS13 activity generally < 10%) has not been formally investigated. METHODS: We analysed 92 acquired TTP episodes at presentation, through treatment and remission/relapse using epitope mapping and functional analyses to understand the pathogenic mechanisms of anti-ADAMTS13 IgG. RESULTS: 89/92 of TTP episodes had IgG recognising the ADAMTS13 N-terminal domains. The central spacer domain was the only N-terminal antigenic target detected. 38/92 TTP episodes had autoantibodies recognising the N-terminal domains alone; 54/92 TTP episodes also had antibodies against the ADAMTS13 C-terminal domains (TSP2-8 and/or CUB domains). Changes in autoantibody specificity were detected in 9/16 patients at relapse, suggesting a continued development of the disease. Functional analyses on IgG from 43 patients revealed inhibitory IgG were limited to anti-spacer domain antibodies. However, 15/43 patients had autoantibodies with no detectable inhibitory action and as many as 32/43 patients had autoantibodies with inhibitory function that was insufficient to account for the severe deficiency state, suggesting that in many patients there is an alternative pathogenic mechanism. We therefore analysed plasma ADAMTS13 antigen levels in 91 acquired TTP presentation samples. We demonstrated markedly reduced ADAMTS13 antigen levels in all presentation samples, median 6% normal (range 0-47%), with 84/91 patients having < 25% ADAMTS13 antigen. ADAMTS13 antigen in the lowest quartile at first presentation was associated with increased mortality (odds ratio 5.7). CONCLUSIONS: Anti-spacer domain autoantibodies are the major inhibitory antibodies in acquired TTP. However, depletion of ADAMTS13 antigen (rather than enzyme inhibition) is a dominant pathogenic mechanism. ADAMTS13 antigen levels at presentation have prognostic significance. Taken together, our results provide new insights into the pathophysiology of acquired TTP

Elm Farm Organic Research Centre Bulletin 83 April 2006

Regular bulleting with technical updates from Organic Advisory Service Issue contains: Testing for Tolerance - a pragmatic view GM Debate Vaccination nation - to jab or not to jab Future shape of OCIS Evolutionary wheat makes the grade? NIAB tracks health of organic cereal seed Stopping erosion of soil quality - the organic way Care needed to halt butterfly collapse Aspects of poultry behaviour: How free range is free range? On choosing an organic wheat A local education challenge New Wakelyns Science Building Organic vegetable market growt

ORNEV: Optimized Recharging of Wireless Sensor Networks using Virtual Base Stations

Several recharging methodologies and frameworks have been proposed for recharging wireless sensor networks. In this paper we study these propositions and device a method that would enhance the recharging capability of the framework and keep the network up and running without the energy getting depleted. We explore a new dimension where we use the SenCars used for charging a node to transmit data just like a node in the WSN. The SenCar acts like a virtual base station for a node that carries high traffic that it will be charging. This cuts down transmission of data through nodes that connects the high traffic node with the base station thereby moving them to sleep mode. This SenCar then directly transmits traffic information from the node it is charging to the base station using cellular technology. DOI: 10.17762/ijritcc2321-8169.150712

A Survey on the Various Frameworks Available for Re-Energizing Wireless Senor Networks

Wireless Sensor Networks (WSNs) are finding its applications in different scenarios in our day to day life. However a major problem that our current technology faces these days is the lack of technical knowledge of how these networks can be kept up and functioning to an efficient level. The power consumption and replenishment of these sensors that are deployed in the environment to be monitored has been a challenging factor since decades. Researches on the improvements in the efficiency in the power consumption of WSNs have been going on for quite a while. Premature energy depletion and outdated recharging strategies are some of the major research areas that require improvement in WSNs. In this paper we enumerate the existing technologies and new proposals on the different frameworks that have been designed to enhance the efficiency in recharging Sensors deployed in WSNs. DOI: 10.17762/ijritcc2321-8169.150311

The Feasibility, Practicality and Uses of Detecting Crop Water Stress in Southern Ontario Apple Orchards with a UAS

UAS (Unmanned Aerial Systems) are becoming more common place in agricultural sites around the world. While the accuracy of achieving NDVI (Normalized Difference Vegetation Index) from a UAS is well understood, few studies have attempted to acquire other plant health attributes such as CWSI (Crop Water Stress Index), particularly in horticulture such as apple orchards. In addition, no academic studies up to the time of this writing have explored the perceived usefulness of data obtained from a UAS for the average farmer. This study explored the practicality and feasibility of using UAS for apple orchards in Southern Ontario. This study sought to find out if NDVI and CWSI can be accurately obtained from a UAS for apple orchards and if this data can be feasibly obtained and is practical for the average Ontario apple farmer. By flying a UAS over a volunteer orchard and conducting charrette style interviews with orchard owners with the obtained data, the results showed that data is indeed useful to the farmers, despite improvements needed for CWSI accuracy. However, this data is only useful during key times of the growing season and obtaining this data, while feasible, requires planning and logistics around weather and government red tape. This study has laid the ground work for future studies to use as a staging point to improve CWSI estimate accuracy, create new methods of observing health attributes or diseases in apple orchards, and obtain more information on the usefulness of UAS data for Ontario farmers

Reducing the Risk of Suicide on an Inpatient Acute Behavioral Health Unit

Evidence suggest for those who have successfully committed suicide in the form of asphyxiation inside locked facilities are more than likely due to patient bathroom doors. The current unit consists of 30 inpatient medical-psychiatric beds serving the adult and geriatric populations. The goal is to ensure patient safety by removing patient bathroom doors and replacing them with breakaway shower curtains for those exhibiting direct signs and symptoms of suicidal ideations with a plan. The specific aim is to improve patient outcomes by decreasing the number of deaths or attempted suicides. With great results, this type of project has been implemented within other acute psychiatric settings, which is considered best practice in acute mental health. Suicide has ranked in the top five most frequently reported events to the Joint Commission since 1995 (Joint Commission, 2010). A random sample questionnaire was distributed to 20 fulltime behavioral health registered nurses, which yielded16 completed questionnaires. Of the 16 completed questionnaires, 13 of the nurses (81%) were in favor of this project. Nurses play a crucial role in making recommendations for the design of psychiatric units, not only in securing the units but also in creating a culture of safety. References American Nurses Association. (2015). Nursing world. Florence Nightingale Pledge. Retrieved on October 28, 2015, from: http://www.nursingworld.org/FlorenceNightingalePledge Bowers, L., Dack, C., Gul, N., Thomas, B., & James, K. (2011). Learning from prevented suicide in psychiatric inpatient care: An analysis of data from the National Patient Safety Agency. International Journal Of Nursing Studies, 481459-1465. Cardell, R., Bratcher, K., & Quinnett, P. (2009). Revisiting \u27suicide proofing\u27 an inpatient unit through environmental safeguards: a review. Perspectives In Psychiatric Care, 45(1), 36-44. Hunt, I. M., Bickley, H., Windfuhr, K., Shaw, J., Appleby, L., & Kapur, N. (2013). Suicide in recently admitted psychiatric in-patients: A case-control study. Journal Of Affective Disorders, (1-2), 123. Joint Commission Resources. (2007). Suicide prevention: Toolkit for implementing national patient safety goal 15A. Oakbrook Terrace, IL: Author. Tishler, C., & Reiss, N. (2009). Inpatient suicide: preventing a common sentinel event. General Hospital Psychiatry, 31(2), 103-10

Punitive Warfare: Measuring The Effects of a Punitive Disposition On Public Support For War

Recent research has posited that retributiveness is an individual level disposition that can help us understand foreign policy preferences (e.g. Liberman 2006, Liberman 2007, Liberman in press, Stein n.d.). However, previous research is limited in two related respects. First, previous research relies on correlational data, blunting our ability to make clear causal inferences. Also, retributiveness is not made theoretically distinct from general hawkishness. In this paper, I present results from two experiments to refine our understanding of how retributiveness can affect support for use of the military. In the first experiment, I examine how retributiveness affects support for greater military commitment across a number of potential missions. In the second experiment, I examine how retributiveness interacts with different rhetorical justifications for military endeavors (e.g. punishing transgressors versus eliminating a foreign policy threat)

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning