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The amyloid precursor protein (APP) was discovered in the 1980s as the precursor protein of the amyloid A4 peptide. The amyloid A4 peptide, also known as A-beta (Aβ), is the main constituent of senile plaques implicated in Alzheimer’s disease (AD). In association with the amyloid deposits, increasing impairments in learning and memory as well as the degeneration of neurons especially in the hippocampus formation are hallmarks of the pathogenesis of AD. Within the last decades much effort has been expended into understanding the pathogenesis of AD. However, little is known about the physiological role of APP within the central nervous system (CNS). Allocating APP to the proteome of the highly dynamic presynaptic active zone (PAZ) identified APP as a novel player within this neuronal communication and signaling network. The analysis of the hippocampal PAZ proteome derived from APP-mutant mice demonstrates that APP is tightly embedded in the underlying protein network. Strikingly, APP deletion accounts for major dysregulation within the PAZ proteome network. Ca2+-homeostasis, neurotransmitter release and mitochondrial function are affected and resemble the outcome during the pathogenesis of AD. The observed changes in protein abundance that occur in the absence of APP as well as in AD suggest that APP is a structural and functional regulator within the hippocampal PAZ proteome. Within this review article, we intend to introduce APP as an important player within the hippocampal PAZ proteome and to outline the impact of APP deletion on individual PAZ proteome subcommunities.
The archaeological data dealt with in our database solution Antike Fundmünzen in Europa (AFE), which records finds of ancient coins, is entered by humans. Based on the Linked Open Data (LOD) approach, we link our data to Nomisma.org concepts, as well as to other resources like Online Coins of the Roman Empire (OCRE). Since information such as denomination, material, etc. is recorded for each single coin, this information should be identical for coins of the same type. Unfortunately, this is not always the case, mostly due to human errors. Based on rules that we implemented, we were able to make use of this redundant information in order to detect possible errors within AFE, and were even able to correct errors in Nomimsa.org. However, the approach had the weakness that it was necessary to transform the data into an internal data model. In a second step, we therefore developed our rules within the Linked Open Data world. The rules can now be applied to datasets following the Nomisma. org modelling approach, as we demonstrated with data held by Corpus Nummorum Thracorum (CNT). We believe that the use of methods like this to increase the data quality of individual databases, as well as across different data sources and up to the higher levels of OCRE and Nomisma.org, is mandatory in order to increase trust in them.
Background: Although mortality after cardiac surgery has significantly decreased in the last decade, patients still experience clinically relevant postoperative complications. Among others, atrial fibrillation (AF) is a common consequence of cardiac surgery, which is associated with prolonged hospitalization and increased mortality.
Methods: We retrospectively analyzed data from patients who underwent coronary artery bypass grafting, valve surgery or a combination of both at the University Hospital Muenster between April 2014 and July 2015. We evaluated the incidence of new onset and intermittent/permanent AF (patients with pre- and postoperative AF). Furthermore, we investigated the impact of postoperative AF on clinical outcomes and evaluated potential risk factors.
Results: In total, 999 patients were included in the analysis. New onset AF occurred in 24.9% of the patients and the incidence of intermittent/permanent AF was 59.5%. Both types of postoperative AF were associated with prolonged ICU length of stay (median increase approx. 2 days) and duration of mechanical ventilation (median increase 1 h). Additionally, new onset AF patients had a higher rate of dialysis and hospital mortality and more positive fluid balance on the day of surgery and postoperative days 1 and 2. In a multiple logistic regression model, advanced age (odds ratio (OR) = 1.448 per decade increase, p < 0.0001), a combination of CABG and valve surgery (OR = 1.711, p = 0.047), higher C-reactive protein (OR = 1.06 per unit increase, p < 0.0001) and creatinine plasma concentration (OR = 1.287 per unit increase, p = 0.032) significantly predicted new onset AF. Higher Horowitz index values were associated with a reduced risk (OR = 0.996 per unit increase, p = 0.012). In a separate model, higher plasma creatinine concentration (OR = 2.125 per unit increase, p = 0.022) was a significant risk factor for intermittent/permanent AF whereas higher plasma phosphate concentration (OR = 0.522 per unit increase, p = 0.003) indicated reduced occurrence of this arrhythmia.
Conclusions: New onset and intermittent/permanent AF are associated with adverse clinical outcomes of elective cardiac surgery patients. Different risk factors implicated in postoperative AF suggest different mechanisms might be involved in its pathogenesis. Customized clinical management protocols seem to be warranted for a higher success rate of prevention and treatment of postoperative AF.
We propose a model for measuring the runtime of concurrent programs by the minimal number of evaluation steps. The focus of this paper are improvements, which are program transformations that improve this number in every context, where we distinguish between sequential and parallel improvements, for one or more processors, respectively. We apply the methods to CHF, a model of Concurrent Haskell extended by futures. The language CHF is a typed higher-order functional language with concurrent threads, monadic IO and MVars as synchronizing variables. We show that all deterministic reduction rules and 15 further program transformations are sequential and parallel improvements. We also show that introduction of deterministic parallelism is a parallel improvement, and its inverse a sequential improvement, provided it is applicable. This is a step towards more automated precomputation of concurrent programs during compile time, which is also formally proven to be correctly optimizing.
We explore space improvements in LRP, a polymorphically typed call-by-need functional core language. A relaxed space measure is chosen for the maximal size usage during an evaluation. It Abstracts from the details of the implementation via abstract machines, but it takes garbage collection into account and thus can be seen as a realistic approximation of space usage. The results are: a context lemma for space improving translations and for space equivalences; all but one reduction rule of the calculus are shown to be space improvements, and the exceptional one, the copy-rule, is shown to increase space only moderately.
Several further program transformations are shown to be space improvements or space equivalences, in particular the translation into machine expressions is a space equivalence. These results are a step Forward in making predictions about the change in runtime space behavior of optimizing transformations in callbyneed functional languages.
We explore space improvements in LRP, a polymorphically typed call-by-need functional core language. A relaxed space measure is chosen for the maximal size usage during an evaluation. It Abstracts from the details of the implementation via abstract machines, but it takes garbage collection into account and thus can be seen as a realistic approximation of space usage. The results are: a context lemma for space improving translations and for space equivalences; all but one reduction rule of the calculus are shown to be space improvements, and the exceptional one, the copy-rule, is shown to increase space only moderately.
Several further program transformations are shown to be space improvements or space equivalences, in particular the translation into machine expressions is a space equivalence. These results are a step Forward in making predictions about the change in runtime space behavior of optimizing transformations in callbyneed functional languages.
Bioinformatics analysis quantifies neighborhood preferences of cancer cells in Hodgkin lymphoma
(2017)
Motivation Hodgkin lymphoma is a tumor of the lymphatic system and represents one of the most frequent lymphoma in the Western world. It is characterized by Hodgkin cells and Reed-Sternberg cells, which exhibit a broad morphological spectrum. The cells are visualized by immunohistochemical staining of tissue sections. In pathology, tissue images are mainly manually evaluated, relying on the expertise and experience of pathologists. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of cancer cells is of great interest.
Results Here, we systematically quantified and investigated cancer cell properties and their spatial neighborhood relations by applying statistical analyses to whole slide images of Hodgkin lymphoma and lymphadenitis, which describes a non-cancerous inflammation of the lymph node. We differentiated cells by their morphology and studied the spatial neighborhood relation of more than 400,000 immunohistochemically stained cells. We found that, according to their morphological features, the cells exhibited significant preferences for and aversions to cells of specific profiles as nearest neighbor. We quantified differences between Hodgkin lymphoma and lymphadenitis concerning the neighborhood relations of cells and the sizes of cells. The approach can easily be applied to other cancer types.
Motivated by tools for automaed deduction on functional programming languages and programs, we propose a formalism to symbolically represent $\alpha$-renamings for meta-expressions. The formalism is an extension of usual higher-order meta-syntax which allows to $\alpha$-rename all valid ground instances of a meta-expression to fulfill the distinct variable convention. The renaming mechanism may be helpful for several reasoning tasks in deduction systems. We present our approach for a meta-language which uses higher-order abstract syntax and a meta-notation for recursive let-bindings, contexts, and environments. It is used in the LRSX Tool -- a tool to reason on the correctness of program transformations in higher-order program calculi with respect to their operational semantics. Besides introducing a formalism to represent symbolic $\alpha$-renamings, we present and analyze algorithms for simplification of $\alpha$-renamings, matching, rewriting, and checking $\alpha$-equivalence of symbolically $\alpha$-renamed meta-expressions.
We introduce rewriting of meta-expressions which stem from a meta-language that uses higher-order abstract syntax augmented by meta-notation for recursive let, contexts, sets of bindings, and chain variables. Additionally, three kinds of constraints can be added to meta-expressions to express usual constraints on evaluation rules and program transformations. Rewriting of meta-expressions is required for automated reasoning on programs and their properties. A concrete application is a procedure to automatically prove correctness of program transformations in higher-order program calculi which may permit recursive let-bindings as they occur in functional programming languages. Rewriting on meta-expressions can be performed by solving the so-called letrec matching problem which we introduce. We provide a matching algorithm to solve it. We show that the letrec matching problem is NP-complete, that our matching algorithm is sound and complete, and that it runs in non-deterministic polynomial time.
Random graph models, originally conceived to study the structure of networks and the emergence of their properties, have become an indispensable tool for experimental algorithmics. Amongst them, hyperbolic random graphs form a well-accepted family, yielding realistic complex networks while being both mathematically and algorithmically tractable. We introduce two generators MemGen and HyperGen for the G_{alpha,C}(n) model, which distributes n random points within a hyperbolic plane and produces m=n*d/2 undirected edges for all point pairs close by; the expected average degree d and exponent 2*alpha+1 of the power-law degree distribution are controlled by alpha>1/2 and C. Both algorithms emit a stream of edges which they do not have to store. MemGen keeps O(n) items in internal memory and has a time complexity of O(n*log(log n) + m), which is optimal for networks with an average degree of d=Omega(log(log n)). For realistic values of d=o(n / log^{1/alpha}(n)), HyperGen reduces the memory footprint to O([n^{1-alpha}*d^alpha + log(n)]*log(n)). In an experimental evaluation, we compare HyperGen with four generators among which it is consistently the fastest. For small d=10 we measure a speed-up of 4.0 compared to the fastest publicly available generator increasing to 29.6 for d=1000. On commodity hardware, HyperGen produces 3.7e8 edges per second for graphs with 1e6 < m < 1e12 and alpha=1, utilising less than 600MB of RAM. We demonstrate nearly linear scalability on an Intel Xeon Phi.