Arteriopathy diagnosis in childhood arterial ischemic stroke: results of the vascular effects of infection in pediatric stroke study.

Background and purposeAlthough arteriopathies are the most common cause of childhood arterial ischemic stroke, and the strongest predictor of recurrent stroke, they are difficult to diagnose. We studied the role of clinical data and follow-up imaging in diagnosing cerebral and cervical arteriopathy in children with arterial ischemic stroke.MethodsVascular effects of infection in pediatric stroke, an international prospective study, enrolled 355 cases of arterial ischemic stroke (age, 29 days to 18 years) at 39 centers. A neuroradiologist and stroke neurologist independently reviewed vascular imaging of the brain (mandatory for inclusion) and neck to establish a diagnosis of arteriopathy (definite, possible, or absent) in 3 steps: (1) baseline imaging alone; (2) plus clinical data; (3) plus follow-up imaging. A 4-person committee, including a second neuroradiologist and stroke neurologist, adjudicated disagreements. Using the final diagnosis as the gold standard, we calculated the sensitivity and specificity of each step.ResultsCases were aged median 7.6 years (interquartile range, 2.8-14 years); 56% boys. The majority (52%) was previously healthy; 41% had follow-up vascular imaging. Only 56 (16%) required adjudication. The gold standard diagnosis was definite arteriopathy in 127 (36%), possible in 34 (9.6%), and absent in 194 (55%). Sensitivity was 79% at step 1, 90% at step 2, and 94% at step 3; specificity was high throughout (99%, 100%, and 100%), as was agreement between reviewers (κ=0.77, 0.81, and 0.78).ConclusionsClinical data and follow-up imaging help, yet uncertainty in the diagnosis of childhood arteriopathy remains. This presents a challenge to better understanding the mechanisms underlying these arteriopathies and designing strategies for prevention of childhood arterial ischemic stroke

Measurement of B(D_s+ -> mu+ nu_mu)/B(D_s+ -> phi mu+ nu_mu) and Determination of the Decay Constant f_{D_s}

We have observed $23.2 \pm 6.0_{-0.9}^{+1.0}$ purely-leptonic decays of $D_s^+ -> \mu^+ \nu_\mu$ from a sample of muonic one prong decay events detected in the emulsion target of Fermilab experiment E653. Using the $D_s^+ -> \phi \mu^+ \nu_\mu$ yield measured previously in this experiment, we obtain $B(D_s^+ --> \mu^+ \nu_\mu) / B(D_s^+ --> \phi \mu^+ \nu_\mu) =0.16 \pm 0.06 \pm 0.03$. In addition, we extract the decay constant $f_{D_s}=194 \pm 35 \pm 20 \pm 14 MeV$.Comment: 15 pages including one figur

Belief logic programming: Uncertainty reasoning with correlation of evidence

Abstract. Belief Logic Programming (BLP) is a novel form of quantitative logic programming in the presence of uncertain and inconsistent information, which was designed to be able to combine and correlate evidence obtained from non-independent information sources. BLP has non-monotonic semantics based on the concepts of belief combination functions and is inspired by Dempster-Shafer theory of evidence. Most importantly, unlike the previous efforts to integrate uncertainty and logic programming, BLP can correlate structural information contained in rules and provides more accurate certainty estimates. The results are illustrated via simple, yet realistic examples of rule-based Web service integration.

Cognitive rationality and alternative belief measures

International audienc

Unifying logical and probabilistic reasoning

Abstract. Most formal techniques of automated reasoning are either rooted in logic or in probability theory. These areas have a long tradition in science, particularly among philosophers and mathematicians. More recently, computer scientists have discovered logic and probability theory to be the two key techniques for building intelligent systems which rely on reasoning as a central component. Despite numerous attempts to link logical and probabilistic reasoning, a satisfiable unified theory of reasoning is still missing. This paper analyses the connection between logical and probabilistic reasoning, it discusses their respective similarities and differences, and proposes a new unified theory of reasoning in which both logic and probability theory are contained as special cases.