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.

Early detection of Lobesia botrana is a primary issue for a proper control of this insect considered as the major pest in grapevine. In this article, we propose a novel method for L. botrana recognition using image data mining based on clustering segmentation with descriptors which consider gray scale values and gradient in each segment. This system allows a 95 percent of L. botrana recognition in non-fully controlled lighting, zoom, and orientation environments. Our image capture application is currently implemented in a mobile application and subsequent segmentation processing is done in the cloud.

Fragile X syndrome (FXS), a disorder of synaptic development and function, is the most prevalent genetic form of intellectual disability and autism spectrum disorder. FXS mouse models display clinically-relevant phenotypes, such as increased anxiety and hyperactivity. Despite their availability, so far advances in drug development have not yielded new treatments. Therefore, testing novel drugs that can ameliorate FXS’ cognitive and behavioral impairments is imperative. ANAVEX2-73 (blarcamesine) is a sigma-1 receptor (S1R) agonist with a strong safety record and preliminary efficacy evidence in patients with Alzheimer’s disease and Rett syndrome, other synaptic neurodegenerative and neurodevelopmental disorders. S1R’s role in calcium homeostasis and mitochondrial function, cellular functions related to synaptic function, makes blarcamesine a potential drug candidate for FXS. Administration of blarcamesine in 2-month-old FXS and wild type mice for 2 weeks led to normalization in two key neurobehavioral phenotypes: open field test (hyperactivity) and contextual fear conditioning (associative learning). Furthermore, there was improvement in marble-burying (anxiety, perseverative behavior). It also restored levels of BDNF, a converging point of many synaptic regulators, in the hippocampus. Positron emission tomography (PET) and ex vivo autoradiographic studies, using the highly selective S1R PET ligand [18F]FTC-146, demonstrated the drug’s dose-dependent receptor occupancy. Subsequent analyses also showed a wide but variable brain regional distribution of S1Rs, which was preserved in FXS mice. Altogether, these neurobehavioral, biochemical, and imaging data demonstrates doses that yield measurable receptor occupancy are effective for improving the synaptic and behavioral phenotype in FXS mice. The present findings support the viability of S1R as a therapeutic target in FXS, and the clinical potential of blarcamesine in FXS and other neurodevelopmental disorders.

The human gut microbiome is the ecosystem of microorganisms that live in the human digestive system. Several studies have related gut microbiome variants to metabolic, immune and nervous system disorders. Fragile X syndrome (FXS) is a neurodevelopmental disorder considered the most common cause of inherited intellectual disability and the leading monogenetic cause of autism. The role of the gut microbiome in FXS remains largely unexplored. Here, we report the results of a gut microbiome analysis using a FXS mouse model and 16S ribosomal RNA gene sequencing. We identified alterations in the fmr1 KO2 gut microbiome associated with different bacterial species, including those in the genera Akkermansia, Sutterella, Allobaculum, Bifidobacterium, Odoribacter, Turicibacter, Flexispira, Bacteroides, and Oscillospira. Several gut bacterial metabolic pathways were significantly altered in fmr1 KO2 mice, including menaquinone degradation, catechol degradation, vitamin B6 biosynthesis, fatty acid biosynthesis, and nucleotide metabolism. Several of these metabolic pathways, including catechol degradation, nucleotide metabolism and fatty acid biosynthesis, were previously reported to be altered in children and adults with autism. The present study reports a potential association of the gut microbiome with FXS, thereby opening new possibilities for exploring reliable treatments and non-invasive biomarkers.

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