Nitric Oxide Releasing Nanoparticles Are Therapeutic for Staphylococcus aureus Abscesses in a Murine Model of Infection

Staphylococcus aureus (SA) is a leading cause of a diverse spectrum of bacterial diseases, including abscesses. Nitric oxide (NO) is a critical component of the natural host defense against pathogens such as SA, but its therapeutic applications have been limited by a lack of effective delivery options. We tested the efficacy of a NO-releasing nanoparticle system (NO-np) in methicillin-resistant SA (MRSA) abscesses in mice. The results show that the NO-np exert antimicrobial activity against MRSA in vitro and in abscesses. Topical or intradermal NO-np treatment of abscesses reduces the involved area and bacterial load while improving skin architecture. Notably, we evaluated pro- and anti-inflammatory cytokines that are involved in immunomodulation and wound healing, revealing that NO-np lead to a reduction in angiogenesis preventing bacterial dissemination from abscesses. These data suggest that NO-np may be useful therapeutics for microbial abscesses

Nitric Oxide Releasing Nanoparticles for Treatment of Candida Albicans Burn Infections

Candida albicans is a leading fungal cause of burn infections in hospital settings, and sepsis is one of the principle causes of death after a severe burn. The prevalence of invasive candidiasis in burn cases varies widely, but it accounts for 3–23% of severe infection with a mortality rate ranging from 14 to 70%. Therefore, it is imperative that we develop innovative therapeutics to which this fungus is unlikely to evolve resistance, thus curtailing the associated morbidity and mortality and ultimately improving our capacity to treat these infections. An inexpensive and stable nitric oxide (NO)-releasing nanoparticle (NO-np) platform has been recently developed. NO is known to have direct antifungal activity, modulate host immune responses and significantly regulate wound healing. In this study, we hypothesized that NO-np would be an effective therapy in the treatment of C. albicans burn infections. Using a murine burn model, NO-np demonstrated antifungal activity against C. albicans in vivo, most likely by arresting its growth and morphogenesis as demonstrated in vitro. NO-np demonstrated effective antimicrobial activity against yeast and filamentous forms of the fungus. Moreover, we showed that NO-np significantly accelerated the rate of wound healing in cutaneous burn infections when compared to controls. The histological evaluation of the affected tissue revealed that NO-np treatment modified leukocyte infiltration, minimized the fungal burden, and reduced collagen degradation, thus providing potential mechanisms for the therapeutics’ biological activity. Together, these data suggest that NO-np have the potential to serve as a novel topical antifungal which can be used for the treatment of cutaneous burn infections and wounds

Population genomic and historical analysis suggests a global invasion by bridgehead processes in Mimulus guttatus

© 2021, The Author(s). Imperfect historical records and complex demographic histories present challenges for reconstructing the history of biological invasions. Here, we combine historical records, extensive worldwide and genome-wide sampling, and demographic analyses to investigate the global invasion of Mimulus guttatus from North America to Europe and the Southwest Pacific. By sampling 521 plants from 158 native and introduced populations genotyped at >44,000 loci, we determined that invasive M. guttatus was first likely introduced to the British Isles from the Aleutian Islands (Alaska), followed by admixture from multiple parts of the native range. We hypothesise that populations in the British Isles then served as a bridgehead for vanguard invasions worldwide. Our results emphasise the highly admixed nature of introduced M. guttatus and demonstrate the potential of introduced populations to serve as sources of secondary admixture, producing novel hybrids. Unravelling the history of biological invasions provides a starting point to understand how invasive populations adapt to novel environments

CfAIR2: Near Infrared Light Curves of 94 Type Ia Supernovae

CfAIR2 is a large homogeneously reduced set of near-infrared (NIR) light curves for Type Ia supernovae (SN Ia) obtained with the 1.3m Peters Automated InfraRed Imaging TELescope (PAIRITEL). This data set includes 4607 measurements of 94 SN Ia and 4 additional SN Iax observed from 2005-2011 at the Fred Lawrence Whipple Observatory on Mount Hopkins, Arizona. CfAIR2 includes JHKs photometric measurements for 88 normal and 6 spectroscopically peculiar SN Ia in the nearby universe, with a median redshift of z~0.021 for the normal SN Ia. CfAIR2 data span the range from -13 days to +127 days from B-band maximum. More than half of the light curves begin before the time of maximum and the coverage typically contains ~13-18 epochs of observation, depending on the filter. We present extensive tests that verify the fidelity of the CfAIR2 data pipeline, including comparison to the excellent data of the Carnegie Supernova Project. CfAIR2 contributes to a firm local anchor for supernova cosmology studies in the NIR. Because SN Ia are more nearly standard candles in the NIR and are less vulnerable to the vexing problems of extinction by dust, CfAIR2 will help the supernova cosmology community develop more precise and accurate extragalactic distance probes to improve our knowledge of cosmological parameters, including dark energy and its potential time variation.Comment: 31 pages, 15 figures, 10 tables. Accepted to ApJS. v2 modified to more closely match journal versio

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics ("feature"), detail methods to robustly estimate periodic light-curve features, introduce tree-ensemble methods for accurate variable star classification, and show how to rigorously evaluate the classification results using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% overall classification error using the random forest classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. We show that the random forest (RF) classifier is superior to other machine-learned methods in terms of accuracy, speed, and relative immunity to features with no useful class information; the RF classifier can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which further reduces the catastrophic error rate to 7.8%. Excluding low-amplitude sources, our overall error rate improves to 14%, with a catastrophic error rate of 3.5%.Comment: 23 pages, 9 figure

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

BACKGROUND: Validation of physiologic miRNA targets has been met with significant challenges. We employed HITS-CLIP to identify which miRNAs participate in liver regeneration, and to identify their target mRNAs. RESULTS: miRNA recruitment to the RISC is highly dynamic, changing more than five-fold for several miRNAs. miRNA recruitment to the RISC did not correlate with changes in overall miRNA expression for these dynamically recruited miRNAs, emphasizing the necessity to determine miRNA recruitment to the RISC in order to fully assess the impact of miRNA regulation. We incorporated RNA-seq quantification of total mRNA to identify expression-weighted Ago footprints, and developed a microRNA regulatory element (MRE) prediction algorithm that represents a greater than 20-fold refinement over computational methods alone. These high confidence MREs were used to generate candidate \u27competing endogenous RNA\u27 (ceRNA) networks. CONCLUSION: HITS-CLIP analysis provide novel insights into global miRNA:mRNA relationships in the regenerating liver

Solidaires, unitaires et démocratiques: social movement unionism and beyond?

A contribution to a Special Issue on trade union renewal that focuses on this topic in relation to the radical French trade union Solidaires, Unitaires et Démocratiques (SUD)

Glucocorticoid Receptor-Dependent Gene Regulatory Networks

While the molecular mechanisms of glucocorticoid regulation of transcription have been studied in detail, the global networks regulated by the glucocorticoid receptor (GR) remain unknown. To address this question, we performed an orthogonal analysis to identify direct targets of the GR. First, we analyzed the expression profile of mouse livers in the presence or absence of exogenous glucocorticoid, resulting in over 1,300 differentially expressed genes. We then executed genome-wide location analysis on chromatin from the same livers, identifying more than 300 promoters that are bound by the GR. Intersecting the two lists yielded 53 genes whose expression is functionally dependent upon the ligand-bound GR. Further network and sequence analysis of the functional targets enabled us to suggest interactions between the GR and other transcription factors at specific target genes. Together, our results further our understanding of the GR and its targets, and provide the basis for more targeted glucocorticoid therapies

The Luminosity Function of Galaxies in SDSS Commissioning Data

During commissioning observations, the Sloan Digital Sky Survey (SDSS) has produced one of the largest existing galaxy redshift samples selected from CCD images. Using 11,275 galaxies complete to r^* = 17.6 over 140 square degrees, we compute the luminosity function of galaxies in the r^* band over a range -23 < M < -16 (for h=1). The result is well-described by a Schechter function with parameters phi_* = 0.0146 +/- 0.0012 h^3 Mpc^{-3}, M_* = -20.83 +/- 0.03, and alpha = -1.20 +/- 0.03. The implied luminosity density in r^* is j = (2.6 +/- 0.3) x 10^8 h L_sun Mpc^{-3}. The surface brightness selection threshold has a negligible impact for M < -18. We measure the luminosity function in the u^*, g^*, i^*, and z^* bands as well; the slope at low luminosities ranges from alpha=-1.35 to alpha=-1.2. We measure the bivariate distribution of r^* luminosity with half-light surface brightness, intrinsic color, and morphology. High surface brightness, red, highly concentrated galaxies are on average more luminous than low surface brightness, blue, less concentrated galaxies. If we synthesize results for R-band or b_j-band using the Petrosian magnitudes with which the SDSS measures galaxy fluxes, we obtain luminosity densities 2.0 times that found by the Las Campanas Redshift Survey in R and 1.4 times that found by the Two-degree Field Galaxy Redshift Survey in b_j. We are able to reproduce the luminosity functions obtained by these surveys if we also mimic their isophotal limits for defining galaxy magnitudes, which are shallower and more redshift dependent than the Petrosian magnitudes used by the SDSS. (Abridged)Comment: 49 pages, including 23 figures, accepted by AJ; some minor textual changes, plus an important change in comparison to LCR

The Sloan Digital Sky Survey Quasar Catalog I. Early Data Release

We present the first edition of the Sloan Digital Sky Survey (SDSS) Quasar Catalog. The catalog consists of the 3814 objects (3000 discovered by the SDSS) in the initial SDSS public data release that have at least one emission line with a full width at half maximum larger than 1000 km/s, luminosities brighter than M_i^* = -23, and highly reliable redshifts. The area covered by the catalog is 494 square degrees; the majority of the objects were found in SDSS commissioning data using a multicolor selection technique. The quasar redshifts range from 0.15 to 5.03. For each object the catalog presents positions accurate to better than 0.2" rms per coordinate, five band (ugriz) CCD-based photometry with typical accuracy of 0.05 mag, radio and X-ray emission properties, and information on the morphology and selection method. Calibrated spectra of all objects in the catalog, covering the wavelength region 3800 to 9200 Angstroms at a spectral resolution of 1800-2100, are also available. Since the quasars were selected during the commissioning period, a time when the quasar selection algorithm was undergoing frequent revisions, the sample is not homogeneous and is not intended for statistical analysis.Comment: 27 pages, 4 figures, 4 tables, accepted by A