ON SOLVING CONSTRAINED OPTIMIZATION PROBLEMS WITH NEURAL NETWORKS : A PENALTY FUNCTION METHOD APPROACH

This paper is concerned with utilizing analog circuits to solve various linear and nonlinear programming problems. The dynamics of these circuits are analyzed. Then, the previously proposed circuit implementations for solving optimization problems are examined. A new nonlinear programming network and its circuit implementation is then introduced which utilizes the nonlinearities to eliminate the problems encountered in previous circuit implementations

Activation of an NLRP3 Inflammasome Restricts Mycobacterium kansasii Infection

Mycobacterium kansasii has emerged as an important nontuberculous mycobacterium pathogen, whose incidence and prevalence have been increasing in the last decade. M. kansasii can cause pulmonary tuberculosis clinically and radiographically indistinguishable from that caused by Mycobacterium tuberculosis infection. Unlike the widely-studied M. tuberculosis, little is known about the innate immune response against M. kansasii infection. Although inflammasome activation plays an important role in host defense against bacterial infection, its role against atypical mycobacteria remains poorly understood. In this report, the role of inflammasome activity in THP-1 macrophages against M. kansasii infection was studied. Results indicated that viable, but not heat-killed, M. kansasii induced caspase-1-dependent IL-1β secretion in macrophages. The underlying mechanism was found to be through activation of an inflammasome containing the NLR (Nod-like receptor) family member NLRP3 and the adaptor protein ASC (apoptosis-associated speck-like protein containing a CARD). Further, potassium efflux, lysosomal acidification, ROS production and cathepsin B release played a role in M. kansasii-induced inflammasome activation. Finally, the secreted IL-1β derived from caspase-1 activation was shown to restrict intracellular M. kansasii. These findings demonstrate a biological role for the NLRP3 inflammasome in host defense against M. kansasii

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

NEURAL NETWORKS FOR CONSTRAINED OPTIMIZATION PROBLEMS

This paper is concerned with utilizing neural networks and analog circuits to solve constrained optimization problems. A novel neural network architecture is proposed for solving a class of nonlinear programming problems. The proposed neural network, or more precisely a physically realizable approximation, is then used to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. The applicability of the proposed neural network is demonstrated on numerical examples

Minimizing Quotient Space Norms Using Penalty Functions

A penalty function method approach is proposed to solve the general problem of quotient space norms minimization. A new class of penalty functions is introduced which allows one to transform constrained optimization problems of quotient space norms minimization by unconstrained optimization problems. The sharp bound on the weight parameter is given for which constrained and unconstrained problems are equivalent. Also a computationally efficient bound on the weight parameter is given. Numerical examples and computer simulations illustrate the results obtained