Evaluation of Bio Briquettes made from Musa acuminata Colla, Musa acuminata and Musa balbisiana Silk, and Citrus reticulata and Citrus sinensis Peels

Accumulation of food waste and the burning of coal emit harmful chemicals which contribute to environmental problems such as climate change and global warming. These also risk the health of people, which causes deaths. Briquettes help improve and preserve the environment by lessening food waste and coal emissions. This study aims to determine the best treatment for briquettes to help disadvantaged communities and alleviate the adverse effects on the environment and health. A combination of banana (Musa acuminata Colla (AA Group) \u27Lakatan\u27 and Musa acuminata × M. balbisiana (AAB Group) \u27Silk\u27, and orange (Citrus × reticulata and Citrus × sinensis) peels were used as bases for the briquettes. Sawdust also served as a controlled treatment, and two different binder treatments were also used, namely paper pulp and cassava starch. The briquettes\u27 quality was tested based on their density, burning rate, ignition time, and efficiency (Water Boiling Test). One-way Multivariate Analysis of Variance (One-way MANOVA), Shapiro-Wilk Normality Test and Levene’s Homogeneity of Variances Test, One-way ANOVA, Post-Hoc Test, specifically Tukey’s LSD were then used to analyze the gathered results. Results revealed that the best briquettes are orange & cassava (density), banana & paper (burning rate), sawdust & cassava (ignition), and sawdust & cassava (efficiency). The findings indicate that the best briquettes were sawdust & cassava (most efficient in Water Boiling Test and fastest to ignite) and banana & paper (lowest burning rate) briquettes. Additionally, the findings suggest different production practices

Expression Profiling of Autism Candidate Genes during Human Brain Development Implicates Central Immune Signaling Pathways

The Autism Spectrum Disorders (ASD) represent a clinically heterogeneous set of conditions with strong hereditary components. Despite substantial efforts to uncover the genetic basis of ASD, the genomic etiology appears complex and a clear understanding of the molecular mechanisms underlying Autism remains elusive. We hypothesized that focusing gene interaction networks on ASD-implicated genes that are highly expressed in the developing brain may reveal core mechanisms that are otherwise obscured by the genomic heterogeneity of the disorder. Here we report an in silico study of the gene expression profile from ASD-implicated genes in the unaffected developing human brain. By implementing a biologically relevant approach, we identified a subset of highly expressed ASD-candidate genes from which interactome networks were derived. Strikingly, immune signaling through NFκB, Tnf, and Jnk was central to ASD networks at multiple levels of our analysis, and cell-type specific expression suggested glia—in addition to neurons—deserve consideration. This work provides integrated genomic evidence that ASD-implicated genes may converge on central cytokine signaling pathways

Human Intelligence and Polymorphisms in the DNA Methyltransferase Genes Involved in Epigenetic Marking

Epigenetic mechanisms have been implicated in syndromes associated with mental impairment but little is known about the role of epigenetics in determining the normal variation in human intelligence. We measured polymorphisms in four DNA methyltransferases (DNMT1, DNMT3A, DNMT3B and DNMT3L) involved in epigenetic marking and related these to childhood and adult general intelligence in a population (n = 1542) consisting of two Scottish cohorts born in 1936 and residing in Lothian (n = 1075) or Aberdeen (n = 467). All subjects had taken the same test of intelligence at age 11yrs. The Lothian cohort took the test again at age 70yrs. The minor T allele of DNMT3L SNP 11330C>T (rs7354779) allele was associated with a higher standardised childhood intelligence score; greatest effect in the dominant analysis but also significant in the additive model (coefficient = 1.40additive; 95%CI 0.22,2.59; p = 0.020 and 1.99dominant; 95%CI 0.55,3.43; p = 0.007). The DNMT3L C allele was associated with an increased risk of being below average intelligence (OR 1.25additive; 95%CI 1.05,1.51; p = 0.011 and OR 1.37dominant; 95%CI 1.11,1.68; p = 0.003), and being in the lowest 40th (padditive = 0.009; pdominant = 0.002) and lowest 30th (padditive = 0.004; pdominant = 0.002) centiles for intelligence. After Bonferroni correction for the number variants tested the link between DNMT3L 11330C>T and childhood intelligence remained significant by linear regression and centile analysis; only the additive regression model was borderline significant. Adult intelligence was similarly linked to the DNMT3L variant but this analysis was limited by the numbers studied and nature of the test and the association was not significant after Bonferroni correction. We believe that the role of epigenetics in the normal variation in human intelligence merits further study and that this novel finding should be tested in other cohorts

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce \u27soft windowing\u27, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Fragile x syndrome and autism: from disease model to therapeutic targets

Autism is an umbrella diagnosis with several different etiologies. Fragile X syndrome (FXS), one of the first identified and leading causes of autism, has been modeled in mice using molecular genetic manipulation. These Fmr1 knockout mice have recently been used to identify a new putative therapeutic target, the metabotropic glutamate receptor 5 (mGluR5), for the treatment of FXS. Moreover, mGluR5 signaling cascades interact with a number of synaptic proteins, many of which have been implicated in autism, raising the possibility that therapeutic targets identified for FXS may have efficacy in treating multiple other causes of autism

Medical conditions in autism spectrum disorders

Autism spectrum disorder (ASD) is a behaviourally defined syndrome where the etiology and pathophysiology is only partially understood. In a small proportion of children with the condition, a specific medical disorder is identified, but the causal significance in many instances is unclear. Currently, the medical conditions that are best established as probable causes of ASD include Fragile X syndrome, Tuberous Sclerosis and abnormalities of chromosome 15 involving the 15q11-13 region. Various other single gene mutations, genetic syndromes, chromosomal abnormalities and rare de novo copy number variants have been reported as being possibly implicated in etiology, as have several ante and post natal exposures and complications. However, in most instances the evidence base for an association with ASD is very limited and largely derives from case reports or findings from small, highly selected and uncontrolled case series. Not only therefore, is there uncertainty over whether the condition is associated, but the potential basis for the association is very poorly understood. In some cases the medical condition may be a consequence of autism or simply represent an associated feature deriving from an underlying shared etiology. Nevertheless, it is clear that in a growing proportion of individuals potentially causal medical conditions are being identified and clarification of their role in etio-pathogenesis is necessary. Indeed, investigations into the causal mechanisms underlying the association between conditions such as tuberous sclerosis, Fragile X and chromosome 15 abnormalities are beginning to cast light on the molecular and neurobiological pathways involved in the pathophysiology of ASD. It is evident therefore, that much can be learnt from the study of probably causal medical disorders as they represent simpler and more tractable model systems in which to investigate causal mechanisms. Recent advances in genetics, molecular and systems biology and neuroscience now mean that there are unparalleled opportunities to test causal hypotheses and gain fundamental insights into the nature of autism and its development

Clinical and biological progress over 50 years in Rett syndrome

In the 50 years since Andreas Rett first described the syndrome that came to bear his name, and is now known to be caused by a mutation in the methyl-CpG-binding protein 2 (MECP2) gene, a compelling blend of astute clinical observations and clinical and laboratory research has substantially enhanced our understanding of this rare disorder. Here, we document the contributions of the early pioneers in Rett syndrome (RTT) research, and describe the evolution of knowledge in terms of diagnostic criteria, clinical variation, and the interplay with other Rett-related disorders. We provide a synthesis of what is known about the neurobiology of MeCP2, considering the lessons learned from both cell and animal models, and how they might inform future clinical trials. With a focus on the core criteria, we examine the relationships between genotype and clinical severity. We review current knowledge about the many comorbidities that occur in RTT, and how genotype may modify their presentation. We also acknowledge the important drivers that are accelerating this research programme, including the roles of research infrastructure, international collaboration and advocacy groups. Finally, we highlight the major milestones since 1966, and what they mean for the day-to-day lives of individuals with RTT and their families