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Pig organs are at risk for hyperacute and acute vascular rejection mediated by anti-pig antibodies, mainly binding to the Galalpha(1,3)Gal epitope. Acute cellular rejection is characterized by progressive infiltration of mononuclear cells. There is an ongoing search for immunosuppressive regimens that provide adequate protection against all patterns of xenograft rejection, but have no severe impact on the condition of xenograft recipients. Herein orthotopic heart transplantations were performed from hDAF or hCD46 piglets to nonsplenectomized baboons. Basic immunosuppression consisted of tacrolimus, sirolimus, GAS914, steroids, and ATG. Group 1 received basic immunosuppression. Group 2 was additionally treated with rituximab and group 3 with half-dose cyclophosphamide. Group 4 received cyclophosphamide and an anti-HLA-DR antibody. Three baboons received GAS914 and TPC. Monitoring included the regular assessment of anti-porcine antibodies, blood counts, therapeutic drug monitoring, and graft histology. Two grafts failed due to technical mistakes. In group 1, baboons died after 1 and 9 days. In group 2, maximum survival was 30 hours. In group 3, baboons lived 20 hours, 25 days, and 14 days. Group 4 survival times were 9.5 hours, 5.5 hours, 4 days, 34 hours, and 3 days. An increase of non-Galalpha(1,3)Gal antibodies was observed. Depositions of immunoglobulins and complement revealed a humoral rejection process. No cellular infiltration could be observed. In conclusion, suppressing cellular rejection with half-dose cyclophosphamide together with tacrolimus and sirolimus produced longer graft survival with a good general condition. Prevention of acute xenograft rejection further needs inhibition of non-Galalpha(1,3)Gal cytotoxicity by sufficient depression of B-cell activation.

Purpose We have demonstrated previously that isolated hearts transplanted into class I disparate miniature swine treated with a 12-day course of CyA all developed cardiac allograft vaszculopathy and rejected within 55 days. In contrast, hearts co-transplanted with donor-specific kidneys survived indefinitely without CAV. Here, we asked whether tolerance can be induced in heart/kidney recipients across a full MHC barrier, which is more clinically relevant. Methods and Materials Heart/kidneys (n=5) or hearts alone (n=3) were transplanted heterotopically into the abdomen of MGH inbred miniature swine across a full MHC mismatch. Immunosuppression consisted of a 12-day course of tacrolimus. Open biopsies were taken on days 30, 60, and 100 to rule out rejection. CML and MLR assays were performed to determine responsiveness toward donor antigen. Results All control animals rejected their isolated hearts within 40 days. In contrast, all heart and kidney recipients survived for >100 days with no evidence of rejection on serial cardiac biopsies. Heart/kidney recipients were hyporesponsive or unresponsive toward donor antigen by CML/MLR assays and had no detectable alloantibody by flow cytometry. Conclusions Induction of long-term cardiac allograft tolerance is possible across a full MHC- mismatched barrier in large animals co-transplanted with a kidney from the same donor. Elucidating the renal element responsible for cardiac allograft tolerance could provide mechanistic information that could lead to protocols aimed at inducing tolerance in recipients of isolated heart allografts.

Background: Valvular aortic stenosis is a common disease in the elderly, often in multimorbid patients. It is often associated with coronary artery disease and peripheral artery disease. In this situation, the risk of conventional open-heart surgery is too high, and other treatment strategies have to be evaluated. Case report: A 79-year-old female patient with severe aortic stenosis, coronary artery disease and end-stage chronic obstructive pulmonary disease suffering from dyspnea at rest and permanently dependent on oxygen was treated in three steps. Firstly, her pulmonary infection was treated with antibiotics for 7 days. Then, the left anterior descending artery was stented (bare-metal stent). In the same session, valvuloplasty of the aortic valve was performed. She was sent to rehabilitation to improve her pulmonary condition and took clopidogrel for 4 weeks. Finally, she underwent transapical aortic valve replacement. She was released to rehabilitation on postoperative day 12. Conclusion: A combination of modern interventional and minimally invasive surgical techniques to treat aortic stenosis and coronary heart disease can be a viable option for multimorbid patients with extremely high risk in conventional open-heart surgery.

In an aging population, numerous patients who underwent previous coronary artery bypass grafting (CABG) are presenting with end-stage ischemic cardiomyopathy. Although redo CABG and cardiological interventions are possible treatment options, orthotopic heart transplantation remains an ultimate option for these patients. However, there is high morbidity and mortality on the waiting list, and mechanical circulatory support is a life-saving concept [Hetzer 2006; Taylor 2009].We developed a simplified and safe technique for implantation of a biventricular assist device as a redo in complex patients after previous CABG and end-stage heart failure.

Congenital anomalies of the pericardium are extremely rare and often difficult to diagnose. We describe a 66-year-old woman presenting with multivessel coronary artery disease and right heart insufficiency complicated by an extremely leftrotated heart due to a congenital absence of left pericardium. A multidetector row computerized tomographic study was performed to evaluate the torsion of the heart as well as the physical relationship between the left internal thoracic artery and the left coronary system. Off-pump coronary surgery was scheduled. Both the operative and postoperative courses were uneventful and the patient was discharged after 11 days.

We present Juxtaposed approximate PageRank (JXP), a distributed algorithm for computing PageRank-style authority scores of Web pages on a peer-to-peer (P2P) network. Unlike previous algorithms, JXP allows peers to have overlapping content and requires no a priori knowledge of other peers' content. Our algorithm combines locally computed authority scores with information obtained from other peers by means of random meetings among the peers in the network. This computation is based on a Markov-chain state-lumping technique, and iteratively approximates global authority scores. The algorithm scales with the number of peers in the network and we show that the JXP scores converge to the true PageRank scores that one would obtain with a centralized algorithm. Finally, we show how to deal with misbehaving peers by extending JXP with a reputation model.

Grausgruber, Heinrich/0000-0003-4790-0922; Gungor, Huseyin/0000-0001-6708-6337; Epure, Doru-Gabriel/0000-0002-6209-2794; Buerstmayr, Hermann/0000-0002-0748-2351; Michel, Sebastian/0000-0001-6636-2694 WOS: 000393965900009 PubMed: 27826661 Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently. University of Vienna; EUEuropean Union (EU) Open access funding provided by University of Vienna. We like to thank Maria Burstmayr and her team for the tremendous work when extracting the DNA of several hundred wheat lines each year as well as Herbert Hetzendorfer for managing the collection of the phenotypic data. This research was funded by the EU Eurostars projects "E! 6399 Genomic selection of wheat varieties for robustness, yield and quality" and "E! 8959 Genomic selection for nitrogen use efficiency in wheat". We thank the anonymous reviewers for their valuable comments and suggestions for improving the manuscript.

We address the problem of processing continuous multi-join queries, over distributed data streams, making use of existing work in the field of publish/subscribe systems. We show how these principles can be ported to data streams, by enriching the common query model with location dependent attributes. Users can subscribe to a set of sensor attributes, a service that requires processing multi-join correlation queries. The goal is to decrease the overall network traffic consumption by removing redundant subscriptions and eliminating unrequested events close to the publishing sensors. This is non-trivial, especially in the presence of multi-join queries without any central control mechanism. Our approach is based on the concept of filter-split-forward phases for efficient subscription filtering and placement inside the network. We report on a performance evaluation using a real-world dataset, showing the suitability of our approach to reduce the overall data traffic.

In this paper we consider distributed K-Nearest Neighbor (KNN) search and range query processing in high dimensional data. Our approach is based on Locality Sensitive Hashing (LSH) which has proven very efficient in answering KNN queries in centralized settings. We consider mappings from the multi-dimensional LSH bucket space to the linearly ordered set of peers that jointly maintain the indexed data and derive requirements to achieve high quality search results and limit the number of network accesses. We put forward two such mappings that come with these salient properties: being locality preserving so that buckets likely to hold similar data are stored on the same or neighboring peers and having a predictable output distribution to ensure fair load balancing. We show how to leverage the linearly aligned data for efficient KNN search and how to efficiently process range queries which is, to the best of our knowledge, not possible in existing LSH schemes. We show by comprehensive performance evaluations using real world data that our approach brings major performance and accuracy gains compared to state-of-the-art.

We present HAMLET, a suite of principles, scoring models and algorithms to automatically propagate metadata along edges in a document neighborhood. As a showcase scenario we consider tag prediction in community-based Web 2.0 tagging applications. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce. To the best of our knowledge, HAMLET is the first system to promote an efficient and precise reuse of shared metadata in highly dynamic, large-scale Web 2.0 tagging systems.

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.

Online communities like Flickr, del.icio.us and YouTube have established themselves as very popular and powerful services for publishing and searching contents, but also for identifying other users who share similar interests. In these communities, data are usually annotated with carefully selected and often semantically meaningful tags, collaboratively chosen by the user who uploaded an item and other users who came across the item. Items like urls or videos are typically retrieved by issueing queries that consist of a set of tags, returning items that have been frequently annotated with these tags. However, users often prefer a more personalized way of searching over such a 'global' search, exploiting preferences of and connections between users. The SENSE system presented in this demo supports hybrid personalization along two dimensions: in the social dimension, a search process is focused towards items tagged by users explicitly selected as friends by the querying user, whereas in the spiritual dimension, users that share preferences with the querying user are preferred. Orthorgonal to this, the system additionally integrates semantic expansion of query tags to improve search results. SENSE provides an efficient top-k algorithm that dynamically expands the search to related users and tags. It is based on principles of threshold algorithms, folding related users and tags into the search space in an incremental on-demand manner, thus visiting only a small fraction of the social network when evaluating a query. The demonstration uses three different real-world datasets: a large set of urls from del.icio.us, a large set of pictures from Flickr, and a large set of books from librarything, each together with a large fraction of the corresponding social network of these sites.

We consider the applicability of terms extracted from anchortext as a source of Web page descriptions in the form of tags. With a relatively simple and easy-to-use method, we show that anchortext significantly overlaps with tags obtained from the popular tagging portal del.icio.us. Considering the size and diversity of the user community potentially involved in social tagging, this observation is rather surprising. Furthermore, we show by an evaluation using human-created relevance assessments the general suitability of the anchortext tag generation in terms of user-perceived precision values. The awareness of this easy-to-obtain source of tags could trigger the rise of new tagging portals pushed by this automatic bootstrapping process or be applied in already existing portals to increase the number of tags per page by merely looking at the anchortext which exists anyway.

The study of the genetic basis of grain yield is one of the major challenges of the scientific community because of both its complex genetic control and the strong interaction with environment and management practices. Furthermore, such interactions may affect yield during the entire life cycle of the plant. It is therefore crucial to consider yield as the result of multiple simpler traits and thus study their genetic control separately. In this study, we used the phenotyping platform PhenoArch in order to identify growth and water use related traits that may explain the segregation for yield and plant height observed at the QTL Qyld.idw-3B by Graziani et al. (2010). Four pairs of durum wheat near-isogenic lines (NILs) for the QTL Qyld.idw-3B were grown at three levels of drought stress: no stress (soil water potential > -1 bar), mild stress (soil water potential of -5/-8 bar) and severe stress (soil water potential ≈ -13 bar). The stress was applied at the beginning of stem elongation until the end of the experiment (late milk stage, Zadok 77) on eight replicates per genotype per treatment. We recorded two main types of phenotypic data: (i) canopy images and (ii) weight measurements: every night, digital RGB images were collected. From these images we estimated several growth related phenotypes like biomass, leaf expansion and plant height; every plant was weighted to estimate the evapotranspiration at least once per day. Combining these data, it was possible to evaluate key physiological parameters like water use efficiency (WUE) and leaf transpiration. The QTL seemed to not affect vegetative behaviour and water use of plants during the early vegetative stages while majorly differentiating the NILs couples during the reproductive and earl ripening stages. This explains the segregation for final plant height previously observed by Graziani et al (2010). These results may provide useful information for further phenotypic as well as physiological and genetic characterization of the QTL Qyld.idw-3B with a main focus on mid to late stem elongation and reproductive/maturity stages.