Look who's talking

Major museums worldwide are starting to use social media such as blogs, podcasts and online video to encourage users to participate in their programs. Social media are variously described as "online technologies and practices used to share opinions, insights, perspectives", "software-supported social networking" (Chan) and "many-to-many communication supported by web technology"(Watkins and Russo). This article argues that the social media space can be considered in terms of its effect on participation, communication and visitor incentive. It explores each of these in details using recent examples and findings on social media implementation in museums

Independent audit committee members’ board tenure and audit fees

This study examines whether independent audit committee members’ board tenure affects audit fees. We find that audit fees are lower for firms with high proportion of long board tenure directors on the independent audit committee than for firms with low proportion of long board tenure directors on the independent audit committee. The results may suggest that auditors price the monitoring effectiveness arising from long board tenure. The results may also suggest that long board tenure audit committee members have a lower demand for audit effort

Decreased Innate Migration of Pro-Inflammatory M1 Macrophages through the Mesothelial Membrane Is Affected by Ceramide Kinase and Ceramide 1-P

The retrograde flow of endometrial tissues deposited into the peritoneal cavity occurs in women during menstruation. Classically (M1) or alternatively (M2) activated macrophages partake in the removal of regurgitated menstrual tissue. The failure of macrophage egress from the peritoneal cavity through the mesothelium leads to chronic inflammation in endometriosis. To study the migration differences of macrophage phenotypes across mesothelial cells, an in vitro model of macrophage egress across a peritoneal mesothelial cell monolayer membrane was developed. M1 macrophages were more sessile, emigrating 2.9-fold less than M2 macrophages. The M1 macrophages displayed a pro-inflammatory cytokine signature, including IL-1α, IL-1β, TNF-α, TNF-β, and IL-12p70. Mass spectrometry sphingolipidomics revealed decreased levels of ceramide-1-phosphate (C1P), an inducer of migration in M1 macrophages, which correlated with its poor migration behavior. C1P is generated by ceramide kinase (CERK) from ceramide, and blocking C1P synthesis via the action of NVP231, a specific CERK chemical inhibitor, prohibited the emigration of M1 and M2 macrophages up to 6.7-fold. Incubation with exogenously added C1P rescued this effect. These results suggest that M1 macrophages are less mobile and have higher retention in the peritoneum due to lower C1P levels, which contributes to an altered peritoneal environment in endometriosis by generating a predominant pro-inflammatory cytokine environment

Assessment of continuous fermentative hydrogen and methane co-production using macro- and micro-algae with increasing organic loading rate

A two-stage continuous fermentative hydrogen and methane co-production using macro-algae (Laminaria digitata) and micro-algae (Arthrospira platensis) at a C/N ratio of 20 was established. The hydraulic retention time (HRT) of first-stage H2 reactor was 4 days. The highest specific hydrogen yield of 55.3 mL/g volatile solids (VS) was obtained at an organic loading rate (OLR) of 6.0 gVS/L/d. In the second-stage CH4 reactor at a short HRT of 12 days, a specific methane yield of 245.0 mL/gVS was achieved at a corresponding OLR of 2.0 gVS/L/d. At these loading rates, the two-stage continuous system offered process stability and effected an energy yield of 9.4 kJ/gVS, equivalent to 77.7% of that in an idealised batch system. However, further increases in OLR led to reduced hydrogen and methane yields in both reactors. The process was compared to a one-stage anaerobic co-digestion of algal mixtures at an HRT of 16 days. A remarkably high salinity level of 13.3 g/kg was recorded and volatile fatty acid accumulations were encountered in the one-stage CH4 reactor. The two-stage system offered better performances in both energy return and process stability. The gross energy potential of the advanced gaseous biofuels from this algal mixture may reach 213 GJ/ha/yr

Tuning the Photocycle Kinetics of Bacteriorhodopsin in Lipid Nanodiscs

AbstractMonodisperse lipid nanodiscs are particularly suitable for characterizing membrane protein in near-native environment. To study the lipid-composition dependence of photocycle kinetics of bacteriorhodopsin (bR), transient absorption spectroscopy was utilized to monitor the evolution of the photocycle intermediates of bR reconstituted in nanodiscs composed of different ratios of the zwitterionic lipid (DMPC, dimyristoyl phosphatidylcholine; DOPC, dioleoyl phosphatidylcholine) to the negatively charged lipid (DOPG, dioleoyl phosphatidylglycerol; DMPG, dimyristoyl phosphatidylglycerol). The characterization of ion-exchange chromatography showed that the negative surface charge of nanodiscs increased as the content of DOPG or DMPG was increased. The steady-state absorption contours of the light-adapted monomeric bR in nanodiscs composed of different lipid ratios exhibited highly similar absorption features of the retinal moiety at 560 nm, referring to the conservation of the tertiary structure of bR in nanodiscs of different lipid compositions. In addition, transient absorption contours showed that the photocycle kinetics of bR was significantly retarded and the transient populations of intermediates N and O were decreased as the content of DMPG or DOPG was reduced. This observation could be attributed to the negatively charged lipid heads of DMPG and DOPG, exhibiting similar proton relay capability as the native phosphatidylglycerol (PG) analog lipids in the purple membrane. In this work, we not only demonstrated the usefulness of nanodiscs as a membrane-mimicking system, but also showed that the surrounding lipids play a crucial role in altering the biological functions, e.g., the ion translocation kinetics of the transmembrane proteins

Enhanced ex vivo expansion of adult mesenchymal stem cells by fetal mesenchymal stem cell ECM

Large-scale expansion of highly functional adult human mesenchymal stem cells (aMSCs) remains technologically challenging as aMSCs lose self renewal capacity and multipotency during traditional long-term culture and their quality/quantity declines with donor age and disease. Identification of culture conditions enabling prolonged expansion and rejuvenation would have dramatic impact in regenerative medicine. aMSC-derived decellularized extracellular matrix (ECM) has been shown to provide such microenvironment which promotes MSC self renewal and “stemness”. Since previous studies have demonstrated superior proliferation and osteogenic potential of human fetal MSCs (fMSCs), we hypothesize that their ECM may promote expansion of clinically relevant aMSCs. We demonstrated that aMSCs were more proliferative (∼1.6×) on fMSC-derived ECM than aMSC-derived ECMs and traditional tissue culture wares (TCPS). These aMSCs were smaller and more uniform in size (median ± interquartile range: 15.5 ± 4.1 μm versus 17.2 ± 5.0 μm and 15.5 ± 4.1 μm for aMSC ECM and TCPS respectively), exhibited the necessary biomarker signatures, and stained positive for osteogenic, adipogenic and chondrogenic expressions; indications that they maintained multipotency during culture. Furthermore, fMSC ECM improved the proliferation (∼2.2×), size (19.6 ± 11.9 μm vs 30.2 ± 14.5 μm) and differentiation potential in late-passaged aMSCs compared to TCPS. In conclusion, we have established fMSC ECM as a promising cell culture platform for ex vivo expansion of aMSCs.Singapore-MIT Alliance for Research and Technolog

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes : Prediction Model Development Study

Publisher Copyright: © Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja KarnaniBackground: The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective: In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods: Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P = .02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Peer reviewe