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

Wide Range of Phenotypic Severity in Individuals With Late Truncations Unique to the Predominant CDKL5 Transcript in the Brain

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD) is caused by heterozygous or hemizygous variants in CDKL5 and is characterized by refractory epilepsy, cognitive and motor impairments, and cerebral visual impairment. CDKL5 has multiple transcripts, of which the longest transcripts, NM_003159 and NM_001037343, have been used historically in clinical laboratory testing. However, the transcript NM_001323289 is the most highly expressed in brain and contains 170 nucleotides at the 3\u27 end of its last exon that are noncoding in other transcripts. Two truncating variants in this region have been reported in association with a CDD phenotype. To clarify the significance and range of phenotypes associated with late truncating variants in this region of the predominant transcript in the brain, we report detailed information on two individuals, updated clinical information on a third individual, and a summary of published and unpublished individuals reported in ClinVar. The two new individuals (one male and one female) each had a relatively mild clinical presentation including periods of pharmaco-responsive epilepsy, independent walking and limited purposeful communication skills. A previously reported male continued to have a severe phenotype. Overall, variants in this region demonstrate a range of clinical severity consistent with reports in CDD but with the potential for milder presentation

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

<p>Abstract</p> <p>Background</p> <p>Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of <it>k</it>-partite graphs. These graphs contain <it>k </it>different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type.</p> <p>Results</p> <p>Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a <it>k</it>-partite graph partitioning algorithm that allows for overlapping (fuzzy) clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted <it>k</it>-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2.</p> <p>Conclusions</p> <p>In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy <it>k</it>-partite graph partitioning algorithm that allows the decomposition of these objects in a comprehensive fashion. We validated our approach both on artificial and real-world data. It is readily applicable to any further problem.</p

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

Células-tronco pluripotentes e doenças neurológicas

Grande parte do conhecimento atual dos fenótipos celulares relacionados a doenças neurológicas foi obtida a partir de estudos de tecidos cerebrais coletados após a morte do indivíduo. Essas amostras geralmente representam os estágios finais da doença e, portanto, não servem como fiel representação de como os sintomas aparecem. Além disso, nessas circunstâncias, a patologia observada pode muito bem ser um efeito secundário do processo patológico ou mesmo da deterioração do tecido em vez de um fenótipo celular autêntico. Da mesma forma, modelos animais nem sempre recapitulam exatamente a patologia das doenças em humanos. Neste artigo, pretendo apresentar uma visão crítica dos recentes avanços obtidos a partir da modelagem de doenças neurológicas humanas, utilizando células-tronco pluripotentes. O foco na reprogramação celular de células somáticas, gerando células-tronco pluripotentes induzidas, justifica-se em razão do grande potencial experimental não só para a modelagem de doenças humanas, mas também como ferramenta biotecnológica para triagem de novas drogas, contribuindo para uma futura medicina personalizada.Most of our current knowledge about cellular phenotypes related to neurological diseases was gathered from studies performed in brain tissue collected postmortem. These samples often represent the end-stage of the disease process and may not represent a fair picture of how the disease developed over time. Futhermore, under these conditions, the pathology may as well be a secundary effect of the disease process or even due to the poor tissue condition and may not represent an authentic cellular phenotype. Likewise, animal models not always recapitulate the pathology from human disorders. In this article, I will present a critical view on the recent advances obtained from disease modeling using human pluripotent stem cells. The focus on cellular reprogramming as tool to generate patient-specific induced pluripotent stem cells is justified by the great experimental potential, not only for disease modeling, but also as a biotecnological tool for future drug-screening platforms and personalized medicine

Role of MeCP2, DNA methylation, and HDACs in regulating synapse function

Over the past several years there has been intense effort to delineate the role of epigenetic factors, including methyl-CpG-binding protein 2, histone deacetylases, and DNA methyltransferases, in synaptic function. Studies from our group as well as others have shown that these key epigenetic mechanisms are critical regulators of synapse formation, maturation, as well as function. Although most studies have identified selective deficits in excitatory neurotransmission, the latest work has also uncovered deficits in inhibitory neurotransmission as well. Despite the rapid pace of advances, the exact synaptic mechanisms and gene targets that mediate these effects on neurotransmission remain unclear. Nevertheless, these findings not only open new avenues for understanding neuronal circuit abnormalities associated with neurodevelopmental disorders but also elucidate potential targets for addressing the pathophysiology of several intractable neuropsychiatric disorders

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

Blood transcriptomics of drug-na\uefve sporadic Parkinson's disease patients

BACKGROUND: Parkinson's disease (PD) is a chronic progressive neurodegenerative disorder that is clinically defined in terms of motor symptoms. These are preceded by prodromal non-motor manifestations that prove the systemic nature of the disease. Identifying genes and pathways altered in living patients provide new information on the diagnosis and pathogenesis of sporadic PD. METHODS: Changes in gene expression in the blood of 40 sporadic PD patients and 20 healthy controls ("Discovery set") were analyzed by taking advantage of the Affymetrix platform. Patients were at the onset of motor symptoms and before initiating any pharmacological treatment. Data analysis was performed by applying Ranking-Principal Component Analysis, PUMA and Significance Analysis of Microarrays. Functional annotations were assigned using GO, DAVID, GSEA to unveil significant enriched biological processes in the differentially expressed genes. The expressions of selected genes were validated using RT-qPCR and samples from an independent cohort of 12 patients and controls ("Validation set"). RESULTS: Gene expression profiling of blood samples discriminates PD patients from healthy controls and identifies differentially expressed genes in blood. The majority of these are also present in dopaminergic neurons of the Substantia Nigra, the key site of neurodegeneration. Together with neuronal apoptosis, lymphocyte activation and mitochondrial dysfunction, already found in previous analysis of PD blood and post-mortem brains, we unveiled transcriptome changes enriched in biological terms related to epigenetic modifications including chromatin remodeling and methylation. Candidate transcripts as CBX5, TCF3, MAN1C1 and DOCK10 were validated by RT-qPCR. CONCLUSIONS: Our data support the use of blood transcriptomics to study neurodegenerative diseases. It identifies changes in crucial components of chromatin remodeling and methylation machineries as early events in sporadic PD suggesting epigenetics as target for therapeutic intervention

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