39 research outputs found
Differences in Education in Urban and Rural Areas
This research project examines the educational differences and disparities of urban and rural schools in Ohio. The focus school districts are Switzerland of Ohio Local and Akron Public Schools. Demographic information from each of the focus school districts is examined and compared to the outcomes of the schools to determine if factors such as poverty inhibit student learning. Data and statistics, published research, and personal experience will be used to make connections between the school districts, their students, their environment, and their learning. Differences in educational quality, opportunities, and funding will also be discussed
The Evolving Landscape of Myelodysplastic Syndrome Prognostication
Myelodysplastic syndromes (MDSs) are potentially devastating monoclonal deviations of hematopoiesis that lead to bone marrow dysplasia and variable cytopenias. Predicting severity of disease progression and likelihood to undergo acute myeloid leukemia transformation is the basis of treatment strategy. Some patients belong to a low-risk cohort best managed with conservative supportive care, whereas others are included in a high-risk cohort that requires decisive therapy with hematopoietic cell transplantation or hypomethylating agent administration. Risk scoring systems for MDS prognostication were traditionally based on karyotype characteristics and clinical factors readily available from chart review, and validation was typically conducted on de novo MDS patients. However, retrospective analysis found a large subset of patients incorrectly risk-stratified. In this review, the most commonly used scoring systems are evaluated, and pitfalls therein are identified. Emerging technologies such as personal genomics and machine learning are then explored for efficacy in MDS risk modeling. Barriers to clinical adoption of artificial intelligence-derived models are discussed, with focus on approaches meant to increase model interpretability and clinical relevance. Finally, a guiding set of recommendations is proposed for best designing an accurate and universally applicable prognostic model for MDS, which is supported by more than 20 years of observation of traditional scoring system performance, as well as modern efforts in creating hybrid genomic-clinical scoring systems
Metatranscriptomic profiles of Eastern subterranean termites, Reticulitermes flavipes (Kollar) fed on second generation feedstocks
Background: Second generation lignocellulosic feedstocks are being considered as an alternative to first generation biofuels that are derived from grain starches and sugars. However, the current pre-treatment methods for second generation biofuel production are inefficient and expensive due to the recalcitrant nature of lignocellulose. In this study, we used the lower termite Reticulitermes flavipes (Kollar), as a model to identify potential pretreatment genes/enzymes specifically adapted for use against agricultural feedstocks. Results: Metatranscriptomic profiling was performed on worker termite guts after feeding on corn stover (CS), soybean residue (SR), or 98% pure cellulose (paper) to identify (i) microbial community, (ii) pathway level and (iii) gene-level responses. Microbial community profiles after CS and SR feeding were different from the paper feeding profile, and protist symbiont abundance decreased significantly in termites feeding on SR and CS relative to paper. Functional profiles after CS feeding were similar to paper and SR; whereas paper and SR showed different profiles. Amino acid and carbohydrate metabolism pathways were downregulated in termites feeding on SR relative to paper and CS. Gene expression analyses showed more significant down regulation of genes after SR feeding relative to paper and CS. Stereotypical lignocellulase genes/enzymes were not differentially expressed, but rather were among the most abundant/constitutively-expressed genes. Conclusions: These results suggest that the effect of CS and SR feeding on termite gut lignocellulase composition is minimal and thus, the most abundantly expressed enzymes appear to encode the best candidate catalysts for use in saccharification of these and related second-generation feedstocks. Further, based on these findings we hypothesize that the most abundantly expressed lignocellulases, rather than those that are differentially expressed have the best potential as pretreatment enzymes for CS and SR feedstocks. © 2015 Rajarapu et al
Viral forensic genomics reveals the relatedness of classic herpes simplex virus strains KOS, KOS63, and KOS79
Herpes simplex virus 1 (HSV-1) is a widespread global pathogen, of which the strain KOS is one of the most extensively studied. Previous sequence studies revealed that KOS does not cluster with other strains of North American geographic origin, but instead clustered with Asian strains. We sequenced a historical isolate of the original KOS strain, called KOS63, along with a separately isolated strain attributed to the same source individual, termed KOS79. Genomic analyses revealed that KOS63 closely resembled other recently sequenced isolates of KOS and was of Asian origin, but that KOS79 was a genetically unrelated strain that clustered in genetic distance analyses with HSV-1 strains of North American/European origin. These data suggest that the human source of KOS63 and KOS79 could have been infected with two genetically unrelated strains of disparate geographic origins. A PCR RFLP test was developed for rapid identification of these strains
Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia
Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical subdivision of primary/de novo AML and secondary AML has shown to variably correlate with genetic patterns. The combinatorial complexity and heterogeneity of AML genomic architecture may have thus far precluded genomic-based subclassification to identify distinct molecularly defined subtypes more reflective of shared pathogenesis. We integrated cytogenetic and gene sequencing data from a multicenter cohort of 6788 AML patients that were analyzed using standard and machine learning methods to generate a novel AML molecular subclassification with biologic correlates corresponding to underlying pathogenesis. Standard supervised analyses resulted in modest cross-validation accuracy when attempting to use molecular patterns to predict traditional pathomorphologic AML classifications. We performed unsupervised analysis by applying the Bayesian latent class method that identified 4 unique genomic clusters of distinct prognoses. Invariant genomic features driving each cluster were extracted and resulted in 97% cross-validation accuracy when used for genomic subclassification. Subclasses of AML defined by molecular signatures overlapped current pathomorphologic and clinically defined AML subtypes. We internally and externally validated our results and share an open-access molecular classification scheme for AML patients. Although the heterogeneity inherent in the genomic changes across nearly 7000 AML patients was too vast for traditional prediction methods, machine learning methods allowed for the definition of novel genomic AML subclasses, indicating that traditional pathomorphologic definitions may be less reflective of overlapping pathogenesis
A Machine Learning Model of Response to Hypomethylating Agents in Myelodysplastic Syndromes
Hypomethylating agents (HMA) prolong survival and improve cytopenias in individuals with higher-risk myelodysplastic syndrome (MDS). Only 30-40% of patients, however, respond to HMAs, and responses may not occur for more than 6 months after HMA initiation. We developed a model to more rapidly assess HMA response by analyzing early changes in patients’ blood counts. Three institutions’ data were used to develop a model that assessed patients’ response to therapy 90 days after the initiation using serial blood counts. The model was developed with a training cohort of 424 patients from2 institutions and validated on an independent cohort of 90 patients. The final model achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 in the train/test group and 0.84 in the validation group. The model provides cohort-wide and individual- level explanations for model predictions, and model certainty can be interrogated to gauge the reliability of a given prediction
Genetic Determinants for Enzymatic Digestion of Lignocellulosic Biomass Are Independent of Those for Lignin Abundance in a Maize Recombinant Inbred Population
Dendritic Subglacial Drainage Systems in Cold Glaciers Formed by Cut-and-Closure Processes
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead
Bioinformatics of the Hessian fly
The Hessian fly, Mayetiola destructor (Say), is a major pest affecting wheat-growing regions worldwide, and is annually responsible for significant financial loss. All deleterious effects of the insect on wheat are due to a biological reprogramming of the infested plant that allows the insect\u27s survival. Artificially disrupting this interaction would protect wheat from pest damage and provide a new form of resistance to combat the diminishing effectiveness of currently deployed resistance (R) genes. RNA interference (RNAi) is a useful reverse genetics tool for studying such insect virulence pathways, but requires a systemic phenotype, which is not found in all species. In an effort to correlate the systemic RNAi phenotype with a genetic basis, we have aggregated and compared RNAi related genes across five species. While most of the micro RNA (miRNA) and transcriptional silencing pathway genes were highly conserved across species, the small interfering RNA (siRNA) pathway genes showed increased relative variability. In particular, the Piwi/Argonaute/Zwille (PAZ) domain of Dcr2 had the least amount of sequence similarity of any domain between species surveyed, with a trend of increased conservation by those species with amenable systemic RNAi. Furthermore, the Dicer dsRNA-binding fold domain of Dcr2 was absent from M. destructor, possibly indicating functional incompetence. M. destructor also had the highest degree of RNAi gene expansion of all insect species surveyed, as measured by number of gene duplications observed, suggesting that such pathway expansions do not correlate with efficacy of systemic RNAi. Pathways peripheral to dsRNA uptake and RNAi, including endocytosis, phagocytosis, tunneling nanotubes (TNTs), microvesicles (MVs), and secretory exosomes, were uniformly intact across all insects considered, implying their conservation. Finally, over 100 novel miRNA precursors were identified in the M. destructor genome through highly stringent computational means, which may serve as a reference for future M. destructor small RNAseq research. Taken together, the annotations and associated trends reported here may help describe the disparity of systemic RNAi phenotypes between insect species