60 research outputs found

    Partial dosage compensation in Strepsiptera, a sister group of beetles.

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    Sex chromosomes have evolved independently in many different taxa, and so have mechanisms to compensate for expression differences on sex chromosomes in males and females. Different clades have evolved vastly different ways to achieve dosage compensation, including hypertranscription of the single X in male Drosophila, downregulation of both Xs in XX Caenorhabditis, or inactivation of one X in female mammals. In the flour beetle Tribolium, the X appears hyperexpressed in both sexes, which might represent the first of two steps to evolve dosage compensation along the paths mammals may have taken (i.e., upregulation of X in both sexes, followed by inactivation of one X in females). Here we test for dosage compensation in Strepsiptera, a sister taxon to beetles. We identify sex-linked chromosomes in Xenos vesparum based on genomic analysis of males and females, and show that its sex chromosome consists of two chromosomal arms in Tribolium: The X chromosome that is shared between Tribolium and Strepsiptera, and another chromosome that is autosomal in Tribolium and another distantly related Strepsiptera species, but sex-linked in X. vesparum. We use RNA-seq (RNA sequencing) to show that dosage compensation along the X of X. vesparum is partial and heterogeneous. In particular, genes that are X-linked in both beetles and Strepsiptera appear fully dosage compensated probably through downregulation in both sexes, whereas genes on the more recently added X segment have evolved only partial dosage compensation. In addition, reanalysis of published RNA-seq data suggests that Tribolium has evolved dosage compensation, without hypertranscribing the X in females. Our results demonstrate that patterns of dosage compensation are highly variable across sex-determination systems and even within species

    A case of multiple fibroid uterus, complete placenta praevia, antepartum haemorrhage, myomectomy and obstetric hysterectomy: a near miss

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    An interesting case of young unbooked, unregistered, primigravida with multiple fibroid uterus, placenta praevia type IV, presented with APH in haemorrhagic Shock, impacted large fibroid in Lower Uterine Segment and Multiple fibroids on Anterior wall, fundus for which myomectomy was performed. Preterm Caesarean Section with extremely low birth weight infant delivered followed by life-saving Emergency Obstetric hysterectomy. She had uneventful recovery without any complications of massive transfusion or surgery. Case is being reported as an obstetric near miss

    The impact of equilibrium assumptions on tests of selection

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    With the increasing availability and quality of whole genome population data, various methodologies of population genetic inference are being utilized in order to identify and quantify recent population-level selective events. Though there has been a great proliferation of such methodology, the type-I and type-II error rates of many proposed statistics have not been well-described. Moreover, the performance of these statistics is often not evaluated for different biologically relevant scenarios (e.g., population size change, population structure), nor for the effect of differing data sizes (i.e., genomic vs. sub-genomic). The absence of the above information makes it difficult to evaluate newly available statistics relative to one another, and thus, difficult to choose the proper toolset for a given empirical analysis. Thus, we here describe and compare the performance of four widely used tests of selection: SweepFinder, SweeD, OmegaPlus, and iHS. In order to consider the above questions, we utilize simulated data spanning a variety of selection coefficients and beneficial mutation rates. We demonstrate that the LD-based OmegaPlus performs best in terms of power to reject the neutral model under both equilibrium and non-equilibrium conditions-an important result regarding the relative effectiveness of linkage disequilibrium relative to site frequency spectrum based statics. The results presented here ought to serve as a useful guide for future empirical studies, and provides a guide for statistical choice depending on the history of the population under consideration. Moreover, the parameter space investigated and the Type-I and Type-II error rates calculated, represent a natural benchmark by which future statistics may be assessed

    Quantifying polymorphism and divergence from epigenetic data: a framework for inferring the action of selection

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    Epigenetic modifications are alterations that regulate gene expression without modifying the underlying DNA sequence. DNA methylation and histone modifications, for example, are capable of spatial and temporal regulation of expression-with several studies demonstrating that these epigenetic marks are heritable. Thus, like DNA sequence, epigenetic marks are capable of storing information and passing it from one generation to the next. Because the epigenome is dynamic and epigenetic modifications can respond to external environmental stimuli, such changes may play an important role in adaptive evolution. While recent studies provide strong evidence for species-specific signatures of epigenetic marks, little is known about the mechanisms by which such modifications evolve. In order to address this question, we analyze the genome wide distribution of an epigenetic histone mark (H3K4me3) in prefrontal cortex neurons of humans, chimps and rhesus macaques. We develop a novel statistical framework to quantify within- and between-species variation in histone methylation patterns, using an ANOVA-based method and defining an FST -like measure for epigenetics (termed epi- FST), in order to develop a deeper understanding of the evolutionary pressures acting on epigenetic variation. Results demonstrate that genes with high epigenetic FST values are indeed significantly overrepresented among genes that are differentially expressed between species, and we observe only a weak correlation with SNP density

    Smart Waste Management System using IoT

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    With rapid increase in population, the issues related to sanitation with respect to garbage management are degrading immensely. It creates unhygienic conditions for the citizens in the nearby surrounding, leading to the spread of infectious diseases and illness. To avoid this problem, IoT based “Smart Waste Management” is the best and trending solution. In the proposed system, public dustbins will be provided with embedded device which helps in real time monitoring of level of garbage in garbage bins. The data regarding the garbage levels will be used to provide optimized route for garbage collecting vans, which will reduce cost associated with fuel. The load sensors will increase efficiency of data related to garbage level and moisture sensors will be used to provide data of waste segregation in a dust bin. The analysis of ceaseless data gathered will help municipality and government authorities to improve plans related to smart waste management with the help of various system generated reports

    Classification of Depression on social media using Distant Supervision

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    Amidst Covid-19, young adults have experienced major symptoms of anxiety and/or depression disorder (56%). Mental health issues have been spiking all over the world rapidly. People have taken up to social media as a platform to vent about their mental breakdowns. Twitter has seen enormous rise in depressive and anxious tweets in these times, but the downside being that majority of the population has neglected the importance of mental health issues and there are not enough resources to liberate people about it. Also, people hesitate to talk about their mental issues and seek help. So, a machine learning model using distant supervision to detect depression on Twitter is curated. Use of Sentiment140 dataset with 1.6 million records of different tweets. Our training data makes use of Twitter tweets included with emojis, which are classified as noisy labels on a dataset. Further, this paper mentions about how to use models like Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, XGBoost to distinguishing tweets between depressive or nondepressive. The purpose behind using multiple models is to achieve highest accuracy when trained with emoticon dataset. The paper’s main contribution is the idea of using tweets with emoticons for distant supervised learning

    Outcome Measures in OBPP

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    Traditional outcome measurement scales, such as the Medical Research Council (MRC) score, the Active Movement Scale (AMS), and Mallet score, are used by surgeons to assess outcomes in patients with obstetric brachial plexus palsy (OBPP). The measurement scales used to evaluate patients fall under the International Classification of Functioning (ICF) domains of Body Function, Body Structure, Activity, Participation, and Environment and are used to assess function and disability of patients. Currently used outcome measures scales for OBPP are also contrasted with those used for another perinatal condition affecting the upper limb, cerebral palsy (CP)
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