Effect of castration and vasectomy on some oxidative stress parameters and blood hormone levels in rats

ABSTRACT Vasectomy and castration are the most preferred surgical methods to control reproduction in males. While sexual functions are terminated reversibly in vasectomy, they are removed irreversibly in castration. After these processes, changes are observed in hormones and oxidative stress parameters. In this study, we investigated the effects of vasectomy and castration operations on blood follicle stimulating hormone (FSH), luteinizing hormone (LH), testosterone, nitric oxide (NO) and malondialdehyde (MDA) levels in rats. As a result of the analysis, it was determined that FSH, LH, NO, and MDA levels increased (p<0.05) and testosterone levels decreased (p<0.05) in the castration group compared to the sham and vasectomy groups. Considering the data obtained from the present study, when the two operations (vasectomy and castration) are compared in rats, which are preferred for the control of reproduction, it is thought that vasectomy is a healthier method because it is reversible, does not affect hormone levels, and does not increase oxidative stress

Fast and parallel decomposition of constraint satisfaction problems

Constraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP instance, and previous algorithms have shown their limitations in doing so. In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster. We thus advance the ability to compute optimal (i.e., minimal-width) GHDs for a significantly wider range of CSP instances on modern machines. This lays the foundation for more systems and applications in evaluating CSPs and related problems (such as Conjunctive Query answering) based on their structural properties

Fast and parallel decomposition of constraint satisfaction problems

Constraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP instance, and previous algorithms have shown their limitations in doing so. In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster. We thus advance the ability to compute optimal (i.e., minimal-width) GHDs for a significantly wider range of CSP instances on modern machines. This lays the foundation for more systems and applications in evaluating CSPs and related problems (such as Conjunctive Query answering) based on their structural properties

Incremental updates of generalized hypertree decompositions

Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition even if the scopes change only slightly. In this paper, we make the first steps toward solving the problem of updating the decomposition of a CSP so that it becomes a valid decomposition of a new CSP ′ produced by some modification of . Even though the problem is hard in theory, we propose and implement a framework for effectively updating GHDs. The experimental evaluation of our algorithm strongly suggests practical applicability

Fast parallel hypertree decompositions in logarithmic recursion depth

Various classic reasoning problems with natural hypergraph representations are known to be tractable when a hypertree decomposition (HD) of low width exists. The resulting algorithms are attractive for practical use in fields like databases and constraint satisfaction. However, algorithmic use of HDs relies on the difficult task of first computing a decomposition of the hypergraph underlying a given problem instance, which is then used to guide the algorithm for this particular instance. The performance of purely sequential methods for computing HDs is inherently limited, yet the problem is, theoretically, amenable to parallelisation. In this paper we propose the first algorithm for computing hypertree decompositions that is well-suited for parallelisation. The newly proposed algorithm log-k-decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to so-called balanced separators. We provide a detailed experimental evaluation over the HyperBench benchmark and demonstrate that log-k-decomp outperforms the current state-of-the-art significantly.</p