Synthetic carbohydrate-based cell wall components from Staphylococcus aureus

Glycopolymers are found surrounding the outer layer of many bacterial species. The first uses as immunogenic component in vaccines are reported since the beginning of the XX century, but it is only in the last decades that glycoconjugate based vaccines have been effectively applied for controlling and preventing several infectious diseases, such as H. influenzae type b (Hib), N. meningitidis, S. pneumoniae or group B Streptococcus. Methicillin resistant S. aureus (MRSA) strains has been appointed by the WHO as one of those pathogens, for which new treatments are urgently needed. Herein we present an overview of the carbohydrate-based cell wall polymers associated with different S. aureus strains and the related affords to deliver well-defined fragments through synthetic chemistry.Horizon 2020(H2020)No. 675671Bio-organic Synthesi

Samen voor God afscheid vieren. Vrijwilligers binnen de uitvaartliturgie.

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'Een vrijwilliger kan meer aandacht geven'. Een model voor vrijwilligers bij uitvaartliturgie.

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EffiCuts: optimizing packet classification for memory and throughput

ABSTRACT Packet Classification is a key functionality provided by modern routers. Previous decision-tree algorithms, HiCuts and HyperCuts, cut the multi-dimensional rule space to separate a classifier's rules. Despite their optimizations, the algorithms incur considerable memory overhead due to two issues: (1) Many rules in a classifier overlap and the overlapping rules vary vastly in size, causing the algorithms' fine cuts for separating the small rules to replicate the large rules. (2) Because a classifier's rule-space density varies significantly, the algorithms' equi-sized cuts for separating the dense parts needlessly partition the sparse parts, resulting in many ineffectual nodes that hold only a few rules. We propose EffiCuts which employs four novel ideas: (1) Separable trees: To eliminate overlap among small and large rules, we separate all small and large rules. We define a subset of rules to be separable if all the rules are either small or large in each dimension. We build a distinct tree for each such subset where each dimension can be cut coarsely to separate the large rules, or finely to separate the small rules without incurring replication. (2) Selective tree merging: To reduce the multiple trees' extra accesses which degrade throughput, we selectively merge separable trees mixing rules that may be small or large in at most one dimension. (3) Equi-dense cuts: We employ unequal cuts which distribute a node's rules evenly among the children, avoiding ineffectual nodes at the cost of a small processing overhead in the tree traversal. (4) Node Co-location: To achieve fewer accesses per node than HiCuts and HyperCuts, we co-locate parts of a node and its children. Using ClassBench, we show that for similar throughput EffiCuts needs factors of 57 less memory than HyperCuts and of 4-8 less power than TCAM

Evaluation of the traumatic coma bank computed tomography classification for severe head injury.

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