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ABSTRACT Objective Super‐Refractory Status Epilepticus (SRSE) is a rare, life‐threatening neurological emergency with unclear etiology in many cases. Mitochondrial dysfunction, often due to disease‐causing genetic variants, is increasingly recognized as a cause, with each gene producing distinct pathophysiological mechanisms.
Pouria Mohammadi +2 more
wiley +1 more source
Double-tube end ileostomy: an alternative to classical defunctioning stoma in rectal surgery. [PDF]
Xia Y +6 more
europepmc +1 more source
A data-driven biology-based network model reproduces C. elegans premotor neural dynamics. [PDF]
Morrison M, Young LS.
europepmc +1 more source
FLP-15 modulates the amplitude of body-bends during locomotion in <i>Caenorhabditis elegans</i>. [PDF]
Bhat US +4 more
europepmc +1 more source
Sex differences in noradrenergic regulation of the medial prefrontal cortex in mice. [PDF]
Scroger MV +12 more
europepmc +1 more source
Thalamic regulation of reinforcement learning strategies across prefrontal-striatal networks. [PDF]
Wang BA +8 more
europepmc +1 more source
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SSRN Electronic Journal, 2020
This article outlines a framework for the analysis of extreme events based on forward-looking reverse stress testing. We perform a portfolio simulation and identify stress scenarios critical to the bank’s solvency. Stress scenarios are determined based on their contribution to the capital cost, as expressed by KVA scenario differentials.
Claudio Albanese +2 more
openaire +1 more source
This article outlines a framework for the analysis of extreme events based on forward-looking reverse stress testing. We perform a portfolio simulation and identify stress scenarios critical to the bank’s solvency. Stress scenarios are determined based on their contribution to the capital cost, as expressed by KVA scenario differentials.
Claudio Albanese +2 more
openaire +1 more source
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006
One of the most important assumptions made by many classification algorithms is that the training and test sets are drawn from the same distribution, i.e., the so-called "stationary distribution assumption" that the future and the past data sets are identical from a probabilistic standpoint. In many domains of real-world applications, such as marketing
Wei Fan, Ian Davidson
openaire +1 more source
One of the most important assumptions made by many classification algorithms is that the training and test sets are drawn from the same distribution, i.e., the so-called "stationary distribution assumption" that the future and the past data sets are identical from a probabilistic standpoint. In many domains of real-world applications, such as marketing
Wei Fan, Ian Davidson
openaire +1 more source

