Modeling Maintenance of Long-Term Potentiation in Clustered Synapses, Long-Term Memory Without Bistability

Memories are stored, at least partly, as patterns of strong synapses. Given molecular turnover, how can synapses maintain strong for the years that memories can persist? Some models postulate that biochemical bistability maintains strong synapses. However, bistability should give a bimodal distribution of synaptic strength or weight, whereas current data show unimodal distributions for weights and for a correlated variable, dendritic spine volume. Bistability of single synapses has also never been empirically demonstrated. Thus it is important for models to simulate both unimodal distributions and long-term memory persistence. Here a model is developed that connects ongoing, competing processes of synaptic growth and weakening to stochastic processes of receptor insertion and removal in dendritic spines. The model simulates long-term (in excess of 1 yr) persistence of groups of strong synapses. A unimodal weight distribution results. For stability of this distribution it proved essential to incorporate resource competition between synapses organized into small clusters. With competition, these clusters are stable for years. These simulations concur with recent data to support the clustered plasticity hypothesis, which suggests clusters, rather than single synaptic contacts, may be a fundamental unit for storage of long-term memory. The model makes empirical predictions, and may provide a framework to investigate mechanisms maintaining the balance between synaptic plasticity and stability of memory.Comment: 17 pages, 5 figure

Rheumatoid arthritis

Rheumatoid arthritis is a chronic inflammatory joint disease, which can cause cartilage and bone damage as well as disability. Early diagnosis is key to optimal therapeutic success, particularly in patients with well-characterised risk factors for poor outcomes such as high disease activity, presence of autoantibodies, and early joint damage. Treatment algorithms involve measuring disease activity with composite indices, applying a treatment-to-target strategy, and use of conventional, biological, and newz non-biological disease-modifying antirheumatic drugs. After the treatment target of stringent remission (or at least low disease activity) is maintained, dose reduction should be attempted. Although the prospects for most patients are now favourable, many still do not respond to current therapies. Accordingly, new therapies are urgently required. In this Seminar, we describe current insights into genetics and aetiology, pathophysiology, epidemiology, assessment, therapeutic agents, and treatment strategies together with unmet needs of patients with rheumatoid arthritis

A Phase III Study Evaluating Continuation, Tapering, and Withdrawal of Certolizumab Pegol After One Year of Therapy in Patients With Early Rheumatoid Arthritis

Objective: For DMARD-naïve, early rheumatoid arthritis patients who achieved sustained low disease activity (sLDA; DAS28[ESR]≤3.2 at both Weeks 40 and 52) after 1 year of treatment with certolizumab pegol (CZP 200mg Q2W+optimized MTX), we evaluated whether continuation of CZP as a standard (200mg Q2W+MTX) or reduced-frequency (200mg Q4W+MTX) dose was superior to stopping CZP (placebo+MTX) in maintaining LDA for 1 additional year. Methods: 293 patients from C-EARLY Period 1 were re-randomized 2:3:2 in Period 2 to CZP standard (n=84), reduced-frequency (n=127), CZP stopped (n=82). The primary endpoint was the percentage of patients who maintained LDA throughout Weeks 52-104 without flares. Hierarchical testing scheme: CZP standard versus CZP stopped, if p<0.05 achieved, then CZP reduced-frequency versus CZP stopped (non-responder imputation). Results: 36% fewer patients than projected achieved sLDA in Period 1 and were eligible for enrollment in Period 2. A higher proportion of CZP standard and reduced-frequency patients maintained LDA versus CZP stopped (48.8% [p=0.112], 53.2% [p=0.041; nominal p value, first hierarchical test not significant] versus 39.2%). Similar trends were observed for radiographic non-progression (change from baseline mTSS≤0.5; 79.2%, 77.9% versus 70.3%) and normative physical function (HAQ-DI≤0.5; 71.4%, 70.6% versus 57.0%). Safety profiles were similar between all groups, with no new safety signals identified for continuing CZP to Week 104. No deaths were reported. Conclusion: The study failed to meet its primary endpoint. However, there were no clinically meaningful differences between the standard or reduced-frequency doses of CZP+MTX; both more effectively controlled rheumatoid arthritis in comparison to CZP withdrawal

Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm

The official published version can be found at the link below.This paper presents a novel particle swarm optimization (PSO) algorithm based on Markov chains and competitive penalized method. Such an algorithm is developed to solve global optimization problems with applications in identifying unknown parameters of a class of genetic regulatory networks (GRNs). By using an evolutionary factor, a new switching PSO (SPSO) algorithm is first proposed and analyzed, where the velocity updating equation jumps from one mode to another according to a Markov chain, and acceleration coefficients are dependent on mode switching. Furthermore, a leader competitive penalized multi-learning approach (LCPMLA) is introduced to improve the global search ability and refine the convergent solutions. The LCPMLA can automatically choose search strategy using a learning and penalizing mechanism. The presented SPSO algorithm is compared with some well-known PSO algorithms in the experiments. It is shown that the SPSO algorithm has faster local convergence speed, higher accuracy and algorithm reliability, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. Finally, we utilize the presented SPSO algorithm to identify not only the unknown parameters but also the coupling topology and time-delay of a class of GRNs.This research was partially supported by the National Natural Science Foundation of PR China (Grant No. 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No. 200802550007), the Key Creative Project of Shanghai Education Community (Grant No. 09ZZ66), the Key Foundation Project of Shanghai (Grant No. 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant No. GR/S27658/01, the International Science and Technology Cooperation Project of China under Grant No. 2009DFA32050, an International Joint Project sponsored by the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove that the distance between these two descriptions, as measured by expectations of functionals of the processes, converges to zero with increasing system size. Further, we prove that the delay birth-death process converges to the thermodynamic limit as system size tends to infinity. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the spatial and temporal distributions of transition pathways in metastable systems, oscillatory behavior in negative feedback circuits, and cross-correlations between nodes in a network. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay

Monostability and multistability of genetic regulatory networks with different types of regulation functions

The official published version of the article can be found at the link below.Monostability and multistability are proven to be two important topics in synthesis biology and system biology. In this paper, both monostability and multistability are analyzed in a unified framework by applying control theory and mathematical tools. The genetic regulatory networks (GRNs) with multiple time-varying delays and different types of regulation functions are considered. By putting forward a general sector-like regulation function and utilizing up-to-date techniques, a novel Lyapunov–Krasovskii functional is introduced for achieving delay dependence to ensure less conservatism. A new condition is then proposed for the general stability of a GRN in the form of linear matrix inequalities (LMIs) that are dependent on the upper and lower bounds of the delays. Our general stability conditions are applicable to several frequently used regulation functions. It is shown that the existing results for monostability of GRNs are special cases of our main results. Five examples are employed to illustrate the applicability and usefulness of the developed theoretical results.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the U.K. under Grant BB/C506264/1, the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 60504008 and 60804028, the Program for New Century Excellent Talents in Universities of China, and the Alexander von Humboldt Foundation of Germany

Economic Effects of Hazardous Chemical and Proposed Radioactive Waste Landfills on Surrounding Real Estate Values

The results of the study of residential housing prices of homes located in the proximity of a large toxic chemical waste landfill in the Toledo, Ohio, area for 1986-1990, strongly suggest a distinct negative impact on sale prices for homes located within 2.6 miles of the existing site, and a diminishing impact before a distance of 5.75 miles is reached. Within the 0-2.6 mile range of the Envirosafe Landfill, a $14,200 premium was found for each mile a house was located away from the Landfill. The premium is greater than found in other studies. A second proposed site in 1989, for low-level radioactive wastes, showed a clear, initial negative impact on housing sales prices upon announcement, but the negative effect on prices dissipated soon after extensive public resistance became evident and caused the proposal to be canceled.