Fragile X syndrome (FXS) is the most common form of inherited intellectual disability. Previous studies have implicated mGlu5 in the pathogenesis of the disease, and many agents that target the underlying pathophysiology of FXS have focused on mGluR5 modulation. In the present work, a novel pharmacological approach for FXS is investigated. NNZ-2566, a synthetic analog of a naturally occurring neurotrophic peptide derived from insulin-like growth factor-1 (IGF-1), was administered to fmr1 knockout mice correcting learning and memory deficits, abnormal hyperactivity and social interaction, normalizing aberrant dendritic spine density, overactive ERK and Akt signaling, and macroorchidism. Altogether, our results indicate a unique disease-modifying potential for NNZ-2566 in FXS. Most importantly, the present data implicate the IGF-1 molecular pathway in the pathogenesis of FXS. A clinical trial is under way to ascertain whether these findings translate into clinical effects in FXS patients.
The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improve the quality of the solutions and convergence times is of great interest in operations research. In this article, the db-scan unsupervised learning technique is explored with the goal of using it in the binarization process of continuous swarm intelligence metaheuristic algorithms. The contribution of the db-scan operator to the binarization process is analyzed systematically through the design of random operators. Additionally, the behavior of this algorithm is studied and compared with other binarization methods based on clusters and transfer functions (TFs). To verify the results, the well-known set covering problem is addressed, and a real-world problem is solved. The results show that the integration of the db-scan technique produces consistently better results in terms of computation time and quality of the solutions when compared with TFs and random operators. Furthermore, when it is compared with other clustering techniques, we see that it achieves significantly improved convergence times.
Alzheimer's disease (AD) is the most common cause of dementia, affecting more than 36 million people worldwide. Octodon degus, a South American rodent, has been found to spontaneously develop neuropathological signs of AD, including amyloid-β (Aβ) and tau deposits, as well as a decline in cognition with age. Firstly, the present work introduces a novel behavioral assessment for O. degus - the burrowing test - which appears to be a useful tool for detecting neurodegeneration in the O. degus model for AD. Such characterization has potentially wide-ranging implications, because many of these changes in species-typical behaviors are reminiscent of the impairments in activities of daily living (ADL), so characteristic of human AD. Furthermore, the present work characterizes the AD-like neuropathology in O. degus from a gene expression point of view, revealing a number of previously unreported AD biomarkers, which are found in human AD: amyloid precursor protein (APP), apolipoprotein E (ApoE), oxidative stress-related genes from the NFE2L2 and PPAR pathway, as well as pro-inflammatory cytokines and complement proteins, in agreement with the known link between neurodegeneration and neuroinflammation. In summary, the present results confirm a natural neuropathology in O. degus with similar characteristics to AD at behavioral, cellular and molecular levels. These characteristics put O. degus in a singular position as a natural rodent model for research into AD pathogenesis and therapeutics against AD.
The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.
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