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Cyclic dinucleotide (CDN) agonists of stimulator of interferon genes (STING) are a promising class of immunotherapeutics that activate innate immunity to increase tumour immunogenicity. However, the efficacy of CDNs is limited by drug delivery barriers, including poor cellular targeting, rapid clearance and inefficient transport to the cytosol where STING is localized. Here, we describe STING-activating nanoparticles (STING-NPs)—rationally designed polymersomes for enhanced cytosolic delivery of the endogenous CDN ligand for STING, 2′3′ cyclic guanosine monophosphate–adenosine monophosphate (cGAMP). STING-NPs increase the biological potency of cGAMP, enhance STING signalling in the tumour microenvironment and sentinel lymph node, and convert immunosuppressive tumours to immunogenic, tumoricidal microenvironments. This leads to enhanced therapeutic efficacy of cGAMP, inhibition of tumour growth, increased rates of long-term survival, improved response to immune checkpoint blockade and induction of immunological memory that protects against tumour rechallenge. We validate STING-NPs in freshly isolated human melanoma tissue, highlighting their potential to improve clinical outcomes of immunotherapy.Rationally designed polymer vesicles increase cytosolic delivery of cGAMP, activating the STING pathway and increasing the immunogenicity of the tumour microenvironment in in vivo murine models of melanoma and in human metastatic melanoma tissues.

Pharmacological activation of the retinoic acid-inducible gene I (RIG-I) pathway holds promise for increasing tumor immunogenicity and improving response to immune checkpoint inhibitors (ICI). However, the potency and clinical efficacy of 5’-triphosphate RNA (3pRNA) agonists of RIG-I is hindered by multiple pharmacological barriers, including poor pharmacokinetics, nuclease degradation, and inefficient delivery to the cytosol where RIG-I is localized. Here, we address these challenges through the design and evaluation of ionizable lipid nanoparticles (LNPs) for the delivery of 3p-modified stem-loop RNAs (SLRs). Packaging of SLRs into LNPs (SLR-LNPs) yielded surface charge-neutral nanoparticles with a size of ∼100 nm that activated RIG-I signaling in vitro and in vivo. SLR-LNPs were safely administered to mice via both intratumoral and intravenous routes, resulting in RIG-I activation in the tumor microenvironment (TME) and inhibition of tumor growth in mouse models of poorly immunogenic melanoma and breast cancer. Significantly, we found that systemic administration of SLR-LNPs reprogrammed the breast TME to enhance the infiltration of CD8+ and CD4+ T cells with antitumor function, resulting in enhanced response to αPD-1 ICI in an orthotopic EO771 model of triple negative breast cancer. Therapeutic efficacy was further demonstrated in a metastatic B16.F10 melanoma model, with systemically administered SLR-LNPs significantly reducing lung metastatic burden compared to combined αPD-1 + αCTLA-4 ICI. Collectively, these studies have established SLR-LNPs as a translationally promising immunotherapeutic nanomedicine for potent and selective activation of RIG-I with potential to enhance response to ICIs and other immunotherapeutic modalities.

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.The 2018 Data Science Bowl challenged competitors to develop an accurate tool for segmenting stained nuclei from diverse light microscopy images. The winners deployed innovative deep-learning strategies to realize configuration-free segmentation.

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg2 area along the celestial equator. This data release is comprised of all transient sources brighter than r 22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically, using host galaxy redshift information when available. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1364 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 624 purely photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat ΛCDM cosmology, we determine M = 0.315 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 .

Eighty years ago, it was proposed that solid hydrogen would become metallic at sufficiently high density. Despite numerous investigations, this transition has not yet been experimentally observed. More recently, there has been much interest in the analog of this predicted metallic transition in the dense liquid, due to its relevance to planetary science. Here, we show direct observation of an abrupt insulator-to-metal transition in dense liquid deuterium. Experimental determination of the location of this transition provides a much-needed benchmark for theory and may constrain the region of hydrogen-helium immiscibility and the boundary-layer pressure in standard models of the internal structure of gas-giant planets.

BackgroundObesity often originates in early life, and is linked to excess sugar intake. Nonnutritive sweeteners (NNS) are widely consumed as “healthier” alternatives to sugar, yet recent evidence suggests NNS may adversely influence weight gain and metabolic health. The impact of NNS during critical periods of early development has rarely been studied. We investigated the effect of prenatal NNS exposure on postnatal adiposity and adipocyte development.MethodsIn the CHILD birth cohort (N = 2298), we assessed maternal NNS beverage intake during pregnancy and child body composition at 3 years, controlling for maternal BMI and other potential confounders. To investigate causal mechanisms, we fed NNS to pregnant C57BL6J mice at doses relevant to human consumption (42 mg/kg/day aspartame or 6.3 mg/kg/day sucralose), and assessed offspring until 12 weeks of age for: body weight, adiposity, adipose tissue morphology and gene expression, glucose and insulin tolerance. We also studied the effect of sucralose on lipid accumulation and gene expression in cultured 3T3-L1 pre-adipocyte cells.ResultsIn the CHILD cohort, children born to mothers who regularly consumed NNS beverages had elevated body mass index (mean z-score difference +0.23, 95% CI 0.05–0.42 for daily vs. no consumption, adjusted for maternal BMI). In mice, maternal NNS caused elevated body weight, adiposity, and insulin resistance in offspring, especially in males (e.g., 47% and 15% increase in body fat for aspartame and sucralose vs. controls, p < 0.001). In cultured adipocytes, sucralose exposure at early stages of differentiation caused increased lipid accumulation and expression of adipocyte differentiation genes (e.g., C/EBP-α, FABP4, and FASN). These genes were also upregulated in adipose tissue of male mouse offspring born to sucralose-fed dams.ConclusionBy triangulating evidence from humans, mice, and cultured adipocytes, this study provides new evidence that maternal NNS consumption during pregnancy may program obesity risk in offspring through effects on adiposity and adipocyte differentiation.

Valproate (VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targets of VPA have remained elusive. Here we employ a preclinical model to identify the therapeutic targets of VPA. We find compounds that inhibit histone deacetylase proteins (HDACs) are effective in normalizing manic-like behavior, and that class I HDACs (e.g., HDAC1 and HDAC2) are most important in this response. Using an RNAi approach, we find that HDAC2, but not HDAC1, inhibition in the ventral tegmental area (VTA) is sufficient to normalize behavior. Furthermore, HDAC2 overexpression in the VTA prevents the actions of VPA. We used RNA sequencing in both mice and human induced pluripotent stem cells (iPSCs) derived from bipolar patients to further identify important molecular targets. Together, these studies identify HDAC2 and downstream targets for the development of novel therapeutics for bipolar mania.

Pyrolysis Gas Chromatography/Mass Spectrometry (Py-GC/MS) has become the method of choice for determining microplastics in various matrices, largely because of its ability to analyze small sample quantities. Identification and subsequent determination of polyethylene (PE) microparticles in complex matrices by Py-GC/MS requires significant technical expertise and experimental care to avoid false positives. For example, many biological matrices contain lipids that produce some of the same pyrolysis products (pyrolyzates) as PE during pyrolysis. Other matrices (e.g., air, sediment, and wastewater) may present similar challenges. If these lipidic components are not or cannot be removed during sample preparation, or via the double shot pyrolysis approach, false positive identification of PE is likely without appropriate supplementary verification protocols. Saturated very-long-chain fatty acids and wax, analyzed by Py-GC/MS in this study, were shown to produce some of the same pyrolyzates as PE. These materials were falsely identified as PE when a software algorithm relying on a few non-unique qualifier ions was used alone for data interpretation. In this study, we also analyzed known standards to define and emphasize the necessary chromatographic and spectral features one should observe for positive identification of PE from Py-GC/MS analysis. Suggestions for improving data interpretation strategies for PE and an evaluation of the double shot approach along with a common KOH digestion procedure are also discussed. Notably, when KOH base digestion was used without salt removal, the double shot approach lost its effectiveness for removing fatty acids. Furthermore, pyrolysates of the KOH-neutralized acids were more abundant and resembled those of PE more than the pure acids. All learnings suggest that a dedicated study is required to evaluate matrix effects and validate matrix removal procedures for any meaningful Py-GC/MS microparticle analysis in biological samples.

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10-8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.