Search

Item does not contain fulltext, Objective: Levels of chitotriosidase (CHIT1) are increased in the cerebrospinal fluid (CSF) of amyotrophic lateral sclerosis (ALS) patients reflecting microglial activation. Here, we determine the diagnostic and prognostic potential of CHIT1 for early symptomatic ALS. Methods: Overall, 275 patients from 8 European neurological centers were examined. We included ALS with <6 and >6 months from symptom onset, other motoneuron diseases (oMND), ALS mimics (DCon) and non-neurodegenerative controls (Con). CSF CHIT1 levels were analyzed for diagnostic power and association with progression and survival in comparison to the benchmark neurofilament. The 24-bp duplication polymorphism of CHIT1 was analyzed in a subset of patients (N = 65). Results: Homozygous CHIT1 duplication mutation carriers (9%) invariably had undetectable CSF CHIT1 levels, while heterozygous carriers had similar levels as patients with wildtype CHIT1 (p = 0.414). In both early and late symptomatic ALS CHIT1 levels was increased, did not correlate with patients' progression rates, and was higher in patients diagnosed with higher diagnostic certainty. Neurofilament levels correlated with CHIT1 levels and prevailed over CHIT1 regarding diagnostic performance. Both CHIT1 and neurofilaments were identified as independent predictors of survival in late but not early symptomatic ALS. Evidence is provided that CHIT1 predicts progression in El Escorial diagnostic category in the group of ALS cases with a short duration. Conclusions: CSF CHIT1 level may have additional value in the prognostication of ALS patients with a short history of symptoms classified in diagnostic categories of lower clinical certainty. To fully interpret apparently low CHIT1 levels knowledge of CHIT1 genotype is needed.

Motivated by distributed implementations of game-theoretical algorithms, we study symmetric process systems and the problem of attaining common knowledge between processes. We formalize our setting by defining a notion of peer-to-peer networks and appropriate symmetry concepts in the context of Communicating Sequential Processes (CSP) [1]. We then prove that CSP with input and output guards makes common knowledge in symmetric peer-to-peer networks possible, but not the restricted version which disallows output statements in guards and is commonly implemented. Our results extend [2].

Motivated by distributed implementations of game-theoretical algorithms, we study symmetric process systems and the problem of attaining common knowledge between processes. We formalize our setting by defining a notion of peer-to-peer networks(*) and appropriate symmetry concepts in the context of Communicating Sequential Processes (CSP), due to the common knowledge creating effects of its synchronous communication primitives. We then prove that CSP with input and output guards makes common knowledge in symmetric peer-to-peer networks possible, but not the restricted version which disallows output statements in guards and is commonly implemented.

Knowledge based artificial networks networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes it is not obvious at all how neural symbolic systems should look like such that they are truly connectionist and allow for a declarative reading at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. After an introduction to the core method, this paper will focus on possible connectionist representations of structured objects and their use in structure-sensitive reasoning tasks.

We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged or noisy data is handled gracefully