Results 291 to 300 of about 10,510,472 (349)

Cytokine, Chemokine, and Neurofilament Light Chain Signatures in LGI1 Autoimmune Encephalitis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives To investigate the value of cytokine, chemokine, and neurofilament light chain (NfL) concentrations in predicting relapse risk, chronic epilepsy, and functional impairment in LGI1 autoimmune encephalitis (AE). Methods Cytokines/chemokines (IL‐1‐beta, IL‐2, IL‐4, IL‐5, IL‐6, IL‐8/CXCL8, IL‐10, IL‐12p70, IL‐13, IL‐17A, GM‐CSF, TNF ...
Albert Aboseif, Georgios Mangioris, Binxia Yang, Vanessa K. Pazdernik, Jeffrey W. Britton, Divyanshu Dubey, Eoin P. Flanagan, Sarosh R. Irani, Gregory S. Day, Charles L. Howe, A. Sebastian López‐Chiriboga, Andrew McKeon, John R. Mills, Yahel Segal, Michel Toledano, Ivana Vodopivec, Sean J. Pittock, Anastasia Zekeridou   +17 more
wiley   +1 more source

Bilateral versus Unilateral Training With Rhythmic Auditory Cueing in Stroke Rehabilitation: Effects on Upper Limb Function and Interhemispheric Inhibition

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Stroke is a leading cause of long‐term disability in adults, with upper limb hemiparesis being a common impairment. Traditional training is mostly aimed at paralyzed limbs, but the effect of bilateral training is still unclear.
Fangfang Qian, Yanhong Dai, Guoqiang Zheng, Qi Zhong, Jianliang Lu, Luying Hu, Youhua He, Zhuoming Chen   +7 more
wiley   +1 more source

ICU‐EEG Pattern Detection by a Convolutional Neural Network

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Patients in the intensive care unit (ICU) often require continuous EEG (cEEG) monitoring due to the high risk of seizures and rhythmic and periodic patterns (RPPs). However, interpreting cEEG in real time is resource‐intensive and heavily relies on specialized expertise, which is not always available.
Giulio Degano, Hervé Quintard, Andreas Kleinschmidt, Nikita Francini, Oana E. Sarbu, Pia De Stefano   +5 more
wiley   +1 more source

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Linear programming

, 2017
i=1 ajixi ≤ bj for 1 ≤ j ≤ m We can also minimize instead of maximize. The function ∑n i=1 cixi is known as the objective function, and the m inequalities are known as constraints. A vector ~x = x1, . . . , xn is feasible if it satisfies all constraints.
Lancia G., Serafini P.
semanticscholar   +3 more sources

Linear programming and extensions

, 1965
In real-world problems related to finance, business, and management, mathematicians and economists frequently encounter optimization problems. In this classic book, George Dantzig looks at a wealth of examples and develops linear programming methods for ...
G. Dantzig
semanticscholar   +1 more source

A Comparison of Several Linear Genetic Programming Techniques

Complex Systems
A comparison between four Genetic Programming techniques is presented in this paper. The compared methods are Multi-Expression Programming, Gene Expression Programming, Grammatical Evolution, and Linear Genetic Programming.
Mihai Oltean, C. Grosan
semanticscholar   +1 more source

Linear Optimisation (Linear Programming) [PDF]

, 1990
An optimisation problem is one requiring the determination of the optimal (maximum or minimum) value of a given function, called the objective function, subject to a set of stated restrictions, or constraints, placed on the variables concerned.
openaire   +1 more source

Linear Programming Algorithms [PDF]

, 2000
There are basically three types of algorithms for Linear Programming: the Simplex Algorithm (see Section 3.2), interior point algorithms, and the Ellipsoid Method.
Bernhard Korte, Jens Vygen
openaire   +1 more source

Mixed Integer Linear Programming Formulation Techniques

SIAM Review, 2015
A wide range of problems can be modeled as Mixed Integer Linear Programming (MIP) problems using standard formulation techniques. However, in some cases the resulting MIP can be either too weak or too large to be effectively solved by state of the art ...
J. Vielma
semanticscholar   +1 more source

Multiparametric Linear Programming

Management Science, 1972
The multiparametric linear programming (MLP) problem for the right-hand sides (RHS) is to maximize z = cTx subject to Ax = b(λ), x ≧ 0, where b(λ) be expressed in the form [Formula: see text] where F is a matrix of constant coefficients, and λ is a vector-parameter.
Tomas Gal, Josef Nedoma
openaire   +3 more sources