We present release 2.0 of the ALPS (Algorithms and Libraries for Physics Simulations) project, an open source software project to develop libraries and application programs for the simulation of strongly correlated quantum lattice models such as quantum magnets, lattice bosons, and strongly correlated fermion systems. The code development is centered on common XML and HDF5 data formats, libraries to simplify and speed up code development, common evaluation and plotting tools, and simulation programs. The programs enable non-experts to start carrying out serial or parallel numerical simulations by providing basic implementations of the important algorithms for quantum lattice models: classical and quantum Monte Carlo (QMC) using non-local updates, extended ensemble simulations, exact and full diagonalization (ED), the density matrix renormalization group (DMRG) both in a static version and a dynamic time-evolving block decimation (TEBD) code, and quantum Monte Carlo solvers for dynamical mean field theory (DMFT). The ALPS libraries provide a powerful framework for programers to develop their own applications, which, for instance, greatly simplify the steps of porting a serial code onto a parallel, distributed memory machine. Major changes in release 2.0 include the use of HDF5 for binary data, evaluation tools in Python, support for the Windows operating system, the use of CMake as build system and binary installation packages for Mac OS X and Windows, and integration with the VisTrails workflow provenance tool. The software is available from our web server at http://alps.comp-phys.org/.

FD by suturing eyelids is an effective technique to induce a significant myopic shift, vitreous chamber and axial elongation in rabbits as a model of myopia development. These changes associated with FD were retarded by intravitreal injections of DA.

Osteoarthritis (OA) is a common age‐related disorder. Chondrocytes in joint tissue play a critical role in normal articular cartilage function and tissue homeostasis. Local inflammatory cytokine‐induced chondrocyte senescence contributes to the development and progression of OA. Various dipeptidyl peptidase‐4 (DPP‐4) inhibitors have been widely used to treat type 2 diabetes. Here, we report a novel pharmacological role of the DPP‐4 inhibitor vildagliptin in chondrocyte senescence. Our data indicate that DPP‐4 is an inducible factor responsive to tumor necrosis factor‐α (TNF‐α) treatment in chondrocytes. The inhibition of DPP‐4 by vildagliptin ameliorates TNF‐α‐induced chondrocyte senescence as determined by cellular senescence‐associated β‐galactosidase (SA‐β‐Gal) activity. Vildagliptin displayed protective capabilities against TNF‐α‐induced chondrocyte cell cycle arrest in the G1 phase. Moreover, vildagliptin suppresses the three major TNF‐α‐induced chondrocyte senescence proteins including p53, p21, and plasminogen activator inhibitor‐1 (PAI‐1). Vildagliptin also suppresses TNF‐α‐induced p53 acetylation at K382. Consistently, our findings demonstrate the inhibitory effect of vildagliptin on p53 acetylation, which is mediated by sirtuin 1 (SIRT1) as the inhibition of SIRT1 negated the inhibitory action of vildagliptin on p53 acetylation. Furthermore, we found that the effect of vildagliptin on SIRT1 protection is adenosine 5′‐monophosphate (AMP)‐activated protein kinase (AMPK) dependent, and the inhibition of AMPK activity negated the protection of vildagliptin against SIRT1 and chondrocytes senescence. In conclusion, our study explored the molecular mechanism and protective effect of the antidiabetic drug vildagliptin against chondrocyte senescence, and our findings imply that vildagliptin has a therapeutic potential in OA. © 2019 IUBMB Life, 1–2, 2019

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 Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton's law of gravity  and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational  constant attenuation factor alpha (α) plays a vital role in convergence and the balance between exploration and  exploitation. However, in GSA and most of its variants, all agents share the same α value without considering  their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and  exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability  constrained adaptive alpha for GSA (SCAA). In SCAA, each agent's evolutionary state is estimated, which is then  combined with the variation of the agent's position and fitness feedback to adaptively adjust the value of α.  Moreover, to preserve agents' stable trajectories and improve convergence precision, a boundary constraint is  derived from the stability conditions of GSA to restrict the value of α in each iteration. The performance of  SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13    conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that  SCAA has significantly better searching performance than its peers do.