Oxygen adsorption on Au clusters and a rough Au(111) surface: The role of surface flatness, electron confinement, excess electrons, and band gap

It has been shown recently that while bulk gold is chemically inert, small Au clusters are catalytically active. The reasons for this activity and its dramatic dependence on cluster size are not understood. We use density functional theory to study O2 binding to Au clusters and to a Au(111) surface modified by adsorption of Au clusters on it. We find that O2 does not bind to a flat face of a planar Au cluster, even though it binds well to its edge. Moreover, O2 binds to Au clusters deposited on a Au(111) surface, even though it does not bind to Au(111). This indicates that a band gap is not an essential factor in binding O2, but surface roughness is. Adding electrons to the surface of a Au(111) slab, on which one has deposited a Au cluster, increases the binding energy of O2. However, adding electrons to a flat Ausurface has no effect on O2binding energy. These observations have a simple explanation: in clusters and in the rough surface, the highest occupied molecular orbital (HOMO) is localized and its charge density sticks out in the vacuum. This facilitates charge transfer into the π* orbital of O2, which induces the molecule to bind to gold. A flat face of a cluster or a flat bulk surface tends to delocalize the HOMO, diminishing the ability of the surface to bind O2. The same statements are true for the LUMO orbital, which is occupied by the additional electron given to the system to charge the system negatively

LMW-E/CDK2 Deregulates Acinar Morphogenesis, Induces Tumorigenesis, and Associates with the Activated b-Raf-ERK1/2-mTOR Pathway in Breast Cancer Patients

Elastase-mediated cleavage of cyclin E generates low molecular weight cyclin E (LMW-E) isoforms exhibiting enhanced CDK2–associated kinase activity and resistance to inhibition by CDK inhibitors p21 and p27. Approximately 27% of breast cancers express high LMW-E protein levels, which significantly correlates with poor survival. The objective of this study was to identify the signaling pathway(s) deregulated by LMW-E expression in breast cancer patients and to identify pharmaceutical agents to effectively target this pathway. Ectopic LMW-E expression in nontumorigenic human mammary epithelial cells (hMECs) was sufficient to generate xenografts with greater tumorigenic potential than full-length cyclin E, and the tumorigenicity was augmented by in vivo passaging. However, cyclin E mutants unable to interact with CDK2 protected hMECs from tumor development. When hMECs were cultured on Matrigel, LMW-E mediated aberrant acinar morphogenesis, including enlargement of acinar structures and formation of multi-acinar complexes, as denoted by reduced BIM and elevated Ki67 expression. Similarly, inducible expression of LMW-E in transgenic mice generated hyper-proliferative terminal end buds resulting in enhanced mammary tumor development. Reverse-phase protein array assay of 276 breast tumor patient samples and cells cultured on monolayer and in three-dimensional Matrigel demonstrated that, in terms of protein expression profile, hMECs cultured in Matrigel more closely resembled patient tissues than did cells cultured on monolayer. Additionally, the b-Raf-ERK1/2-mTOR pathway was activated in LMW-E–expressing patient samples, and activation of this pathway was associated with poor disease-specific survival. Combination treatment using roscovitine (CDK inhibitor) plus either rapamycin (mTOR inhibitor) or sorafenib (a pan kinase inhibitor targeting b-Raf) effectively prevented aberrant acinar formation in LMW-E–expressing cells by inducing G1/S cell cycle arrest. LMW-E requires CDK2–associated kinase activity to induce mammary tumor formation by disrupting acinar development. The b-Raf-ERK1/2-mTOR signaling pathway is aberrantly activated in breast cancer and can be suppressed by combination treatment with roscovitine plus either rapamycin or sorafenib

OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets

Background: Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade. METHODS: We have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples. RESULTS: To demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10-6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12. CONCLUSIONS/IMPACT: OvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://glados.ucd.ie/OvMark/index.html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities

Derivation and validation of a prognostic model for postoperative risk stratification of critically ill patients with faecal peritonitis

Background Prognostic scores and models of illness severity are useful both clinically and for research. The aim of this study was to develop two prognostic models for the prediction of long-term (6 months) and 28-day mortality of postoperative critically ill patients with faecal peritonitis (FP). Methods Patients admitted to intensive care units with faecal peritonitis and recruited to the European GenOSept study were divided into a derivation and a geographical validation subset; patients subsequently recruited to the UK GAinS study were used for temporal validation. Using all 50 clinical and laboratory variables available on day 1 of critical care admission, Cox proportional hazards regression was fitted to select variables for inclusion in two prognostic models, using stepwise selection and nonparametric bootstrapping sampling techniques. Using Area under the receiver operating characteristic curve (AuROC) analysis, the performance of the models was compared to SOFA and APACHE II. Results Five variables (age, SOFA score, lowest temperature, highest heart rate, haematocrit) were entered into the prognostic models. The discriminatory performance of the 6-month prognostic model yielded an AuROC 0.81 (95% CI 0.76–0.86), 0.73 (95% CI 0.69–0.78) and 0.76 (95% CI 0.69–0.83) for the derivation, geographic and temporal external validation cohorts, respectively. The 28-day prognostic tool yielded an AuROC 0.82 (95% CI 0.77–0.88), 0.75 (95% CI 0.69–0.80) and 0.79 (95% CI 0.71–0.87) for the same cohorts. These AuROCs appeared consistently superior to those obtained with the SOFA and APACHE II scores alone. Conclusions The two prognostic models developed for 6-month and 28-day mortality prediction in critically ill septic patients with FP, in the postoperative phase, enhanced the day one SOFA score’s predictive utility by adding a few key variables: age, lowest recorded temperature, highest recorded heart rate and haematocrit. External validation of their predictive capability in larger cohorts is needed, before introduction of the proposed scores into clinical practice to inform decision making and the design of clinical trials

The Color of Childhood: The Role of the Child/Human Binary in the Production of Anti-Black Racism

The binary between the figure of the child and the fully human being is invoked with regularity in analyses of race, yet its centrality to the conception of race has never been fully explored. For most commentators, the figure of the child operates as a metaphoric or rhetorical trope, a non-essential strategic tool in the perpetuation of White supremacy. As I show in the following, the child/human binary does not present a contingent or merely rhetorical construction but, rather, a central feature of racialization. Where Black peoples are situated as objects of violence it is often precisely because Blackness has been identified with childhood and childhood is historically identified as the archetypal site of naturalized violence and servitude. I proceed by offering a historical account of how Black peoples came to inherit the subordination and dehumanization of European childhood and how White youth were subsequently spared through their partial categorization as adults

Biomolecular Condensation: A New Phase in Cancer Research

UNLABELLED: Multicellularity was a watershed development in evolution. However, it also meant that individual cells could escape regulatory mechanisms that restrict proliferation at a severe cost to the organism: cancer. From the standpoint of cellular organization, evolutionary complexity scales to organize different molecules within the intracellular milieu. The recent realization that many biomolecules can phase-separate into membraneless organelles, reorganizing cellular biochemistry in space and time, has led to an explosion of research activity in this area. In this review, we explore mechanistic connections between phase separation and cancer-associated processes and emerging examples of how these become deranged in malignancy. SIGNIFICANCE: One of the fundamental functions of phase separation is to rapidly and dynamically respond to environmental perturbations. Importantly, these changes often lead to alterations in cancer-relevant pathways and processes. This review covers recent advances in the field, including emerging principles and mechanisms of phase separation in cancer

RPPAML/RIMS: A metadata format and an information management system for reverse phase protein arrays

<p>Abstract</p> <p>Background</p> <p>Reverse Phase Protein Arrays (RPPA) are convenient assay platforms to investigate the presence of biomarkers in tissue lysates. As with other high-throughput technologies, substantial amounts of analytical data are generated. Over 1000 samples may be printed on a single nitrocellulose slide. Up to 100 different proteins may be assessed using immunoperoxidase or immunoflorescence techniques in order to determine relative amounts of protein expression in the samples of interest.</p> <p>Results</p> <p>In this report an RPPA Information Management System (RIMS) is described and made available with open source software. In order to implement the proposed system, we propose a metadata format known as reverse phase protein array markup language (RPPAML). RPPAML would enable researchers to describe, document and disseminate RPPA data. The complexity of the data structure needed to describe the results and the graphic tools necessary to visualize them require a software deployment distributed between a client and a server application. This was achieved without sacrificing interoperability between individual deployments through the use of an open source semantic database, S3DB. This data service backbone is available to multiple client side applications that can also access other server side deployments. The RIMS platform was designed to interoperate with other data analysis and data visualization tools such as Cytoscape.</p> <p>Conclusion</p> <p>The proposed RPPAML data format hopes to standardize RPPA data. Standardization of data would result in diverse client applications being able to operate on the same set of data. Additionally, having data in a standard format would enable data dissemination and data analysis.</p

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019