The Shape Of Diversity: A Morphometric Analysis Of Late Archaic Bifaces From Lamoka Lake

The general assumption of Late Archaic peoples in the Northeast is that they were one homogeneous culture group, but through the study of Lamoka Lake bifaces found at the Lamoka Lake Site, as well as applying the concepts of community of practice, I have shown that tool shape variation could indicate distinct social groups. Using computer software to digitally outline bifaces I compared the shape of over 400 bifaces from Lamoka Lake and statistically analyzed their morphologies in order to provide material correlates of social diversity. Whether this morphological variation is representative of the conscious or unconscious design choices made by these peoples remains to be seen, however there exists significant statistical difference in biface morphology at this site suggesting distinct social groups. Such a development is significant for Late Archaic research in New York since it directly contradicts the idea that Late Archaic people were one culturally homogenous group

A new pear scab resistance gene Rvp1 from the European pear cultivar ‘Navara’ maps in a genomic region syntenic to an apple scab resistance gene cluster on linkage group 2

Scab, caused by the ascomycete fungus Venturia pirina, leads to severe damage on European pear varieties resulting in a loss of commercial value and requiring frequent use of fungicides. Identifying scab resistance genes, developing molecular markers linked to these genes and establishing marker-assisted selection would be an effective way to improve European pear breeding for scab resistance. Most of the European pear cultivars (Pyrus communis) are currently reported to be sensitive. The pear cultivar ‘Navara’ was shown to carry a major scab resistance gene whose phenotypic expression in seedling progenies was a typical stellate necrosis symptom. The resistance gene was called Rvp1, for resistance to V. pirina, and was mapped on linkage group 2 of the pear genome close to microsatellite marker CH02b10. This genomic region is known to carry a cluster of scab resistance genes in apple indicating a first functional synteny for scab resistance between apple and pear

Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens.

The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions.IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children's hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed

Interactive analysis and quality assessment of single-cell copy-number variations

Single-cell sequencing is emerging as a critical technology for understanding the biology of cancer, neurons, and other complex systems. Here we introduce Ginkgo, a web platform for the interactive analysis and quality assessment of single-cell copy-number alterations. Ginkgo fully automates the process of binning, normalizing, and segmenting mapped reads to infer copy number profiles of individual cells, as well as constructing phylogenetic trees of how those cells are related. We validate Ginkgo by reproducing the results of five major single-cell studies, and discuss how it addresses the wide array of biases that affect single-cell analysis. We also examine the data characteristics of three commonly used single-cell amplification techniques: MDA, MALBAC, and DOP-PCR/WGA4 through comparative analysis of 9 different single-cell datasets. We conclude that DOP-PCR provides the most uniform amplification, while MDA introduces substantial biases into the analysis. Furthermore, given the same level of coverage, our results indicate that data prepared using DOP-PCR can reliably call CNVs at higher resolution than data prepared using either MALBAC or MDA. Ginkgo is freely available at http://qb.cshl.edu/ginkgo.Received November 11, 2014.Accepted November 12, 2014.© 2014, Published by Cold Spring Harbor Laboratory PressThis pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0