Sub-20 nm Core-Shell-Shell Nanoparticles for Bright Upconversion and Enhanced Förster Resonant Energy Transfer.

Upconverting nanoparticles provide valuable benefits as optical probes for bioimaging and Förster resonant energy transfer (FRET) due to their high signal-to-noise ratio, photostability, and biocompatibility; yet, making nanoparticles small yields a significant decay in brightness due to increased surface quenching. Approaches to improve the brightness of UCNPs exist but often require increased nanoparticle size. Here we present a unique core-shell-shell nanoparticle architecture for small (sub-20 nm), bright upconversion with several key features: (1) maximal sensitizer concentration in the core for high near-infrared absorption, (2) efficient energy transfer between core and interior shell for strong emission, and (3) emitter localization near the nanoparticle surface for efficient FRET. This architecture consists of β-NaYbF4 (core) @NaY0.8-xErxGd0.2F4 (interior shell) @NaY0.8Gd0.2F4 (exterior shell), where sensitizer and emitter ions are partitioned into core and interior shell, respectively. Emitter concentration is varied (x = 1, 2, 5, 10, 20, 50, and 80%) to investigate influence on single particle brightness, upconversion quantum yield, decay lifetimes, and FRET coupling. We compare these seven samples with the field-standard core-shell architecture of β-NaY0.58Gd0.2Yb0.2Er0.02F4 (core) @NaY0.8Gd0.2F4 (shell), with sensitizer and emitter ions codoped in the core. At a single particle level, the core-shell-shell design was up to 2-fold brighter than the standard core-shell design. Further, by coupling a fluorescent dye to the surface of the two different architectures, we demonstrated up to 8-fold improved emission enhancement with the core-shell-shell compared to the core-shell design. We show how, given proper consideration for emitter concentration, we can design a unique nanoparticle architecture to yield comparable or improved brightness and FRET coupling within a small volume

Biogeographic patterns and drivers of soil viromes

Viruses are crucial in shaping soil microbial functions and ecosystems. However, studies on soil viromes have been limited in both spatial scale and biome coverage. Here we present a comprehensive synthesis of soil virome biogeographic patterns using the Global Soil Virome dataset (GSV) wherein we analysed 1,824 soil metagenomes worldwide, uncovering 80,750 partial genomes of DNA viruses, 96.7% of which are taxonomically unassigned. The biogeography of soil viral diversity and community structure varies across different biomes. Interestingly, the diversity of viruses does not align with microbial diversity and contrasts with it by showing low diversity in forest and shrubland soils. Soil texture and moisture conditions are further corroborated as key factors affecting diversity by our predicted soil viral diversity atlas, revealing higher diversity in humid and subhumid regions. In addition, the binomial degree distribution pattern suggests a random co-occurrence pattern of soil viruses. These findings are essential for elucidating soil viral ecology and for the comprehensive incorporation of viruses into soil ecosystem models

Characterizing and Improving the Data Reduction Pipeline for the Keck OSIRIS Integral Field Spectrograph

OSIRIS is a near-infrared (1.0--2.4 $\mu$m) integral field spectrograph operating behind the adaptive optics system at Keck Observatory, and is one of the first lenslet-based integral field spectrographs. Since its commissioning in 2005, it has been a productive instrument, producing nearly half the laser guide star adaptive optics (LGS AO) papers on Keck. The complexity of its raw data format necessitated a custom data reduction pipeline (DRP) delivered with the instrument in order to iteratively assign flux in overlapping spectra to the proper spatial and spectral locations in a data cube. Other than bug fixes and updates required for hardware upgrades, the bulk of the DRP has not been updated since initial instrument commissioning. We report on the first major comprehensive characterization of the DRP using on-sky and calibration data. We also detail improvements to the DRP including characterization of the flux assignment algorithm; exploration of spatial rippling in the reduced data cubes; and improvements to several calibration files, including the rectification matrix, the bad pixel mask, and the wavelength solution. We present lessons learned from over a decade of OSIRIS data reduction that are relevant to the next generation of integral field spectrograph hardware and data reduction software design.Comment: 18 pages, 16 figures; accepted for publication in A

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder