The Role of Computational Chemistry in Discovering and Understanding Organic Photocatalysts for Renewable Fuel Synthesis

In this review the role computational chemistry plays in helping to rationalize the ability of organic materials, such as conjugated polymers, to drive photocatalytic water splitting and carbon dioxide reduction, and the discovery of new organic photocatalysts, is reviewed. The ways in which organic photocatalysts differ from their inorganic counterparts, the mechanism by which such materials, when illuminated, reduce protons or CO2 and oxidize water or sacrificial donors, and how this can be studied using computational methods, as well as the high-throughput virtual screening of organic materials as photocatalysts, are discussed. Finally, the current opportunities and challenges associated with studying photocatalysts computationally, are examined

Directional memory arises from long-lived cytoskeletal asymmetries in polarized chemotactic cells

Chemotaxis, the directional migration of cells in a chemical gradient, is robust to fluctuations associated with low chemical concentrations and dynamically changing gradients as well as high saturating chemical concentrations. Although a number of reports have identified cellular behavior consistent with a directional memory that could account for behavior in these complex environments, the quantitative and molecular details of such a memory process remain unknown. Using microfluidics to confine cellular motion to a 1D channel and control chemoattractant exposure, we observed directional memory in chemotactic neutrophil-like cells. We modeled this directional memory as a long-lived intracellular asymmetry that decays slower than observed membrane phospholipid signaling. Measurements of intracellular dynamics revealed that moesin at the cell rear is a long-lived element that when inhibited, results in a reduction of memory. Inhibition of ROCK (Rho-associated protein kinase), downstream of RhoA (Ras homolog gene family, member A), stabilized moesin and directional memory while depolymerization of microtubules (MTs) disoriented moesin deposition and also reduced directional memory. Our study reveals that long-lived polarized cytoskeletal structures, specifically moesin, actomyosin, and MTs, provide a directional memory in neutrophil-like cells even as they respond on short time scales to external chemical cues

Increasing dominance of large lianas in Amazonian forests

Ecological orthodoxy suggests that old-growth forests should be close to dynamic equilibrium, but this view has been challenged by recent findings that neotropical forests are accumulating carbon and biomass, possibly in response to the increasing atmospheric concentrations of carbon dioxide. However, it is unclear whether the recent increase in tree biomass has been accompanied by a shift in community composition. Such changes could reduce or enhance the carbon storage potential of old-growth forests in the long term. Here we show that non-fragmented Amazon forests are experiencing a concerted increase in the density, basal area and mean size of woody climbing plants (lianas). Over the last two decades of the twentieth century the dominance of large lianas relative to trees has increased by 1.7–4.6% a year. Lianas enhance tree mortality and suppress tree growth, so their rapid increase implies that the tropical terrestrial carbon sink may shut down sooner than current models suggest. Predictions of future tropical carbon fluxes will need to account for the changing composition and dynamics of supposedly undisturbed forests

Macromolecular Rate Theory (MMRT) Provides a Thermodynamics Rationale to Underpin the Convergent Temperature Response in Plant Leaf Respiration

Temperature is a crucial factor in determining the rates of ecosystem processes, for example, leaf respiration (R) – the flux of plant respired CO₂ from leaves to the atmosphere. Generally, R increases exponentially with temperature and formulations such as the Arrhenius equation are widely used in earth system models. However, experimental observations have shown a consequential and consistent departure from an exponential increase in R. What are the principles that underlie these observed patterns? Here, we demonstrate that macromolecular rate theory (MMRT), based on transition state theory (TST) for enzyme-catalyzed kinetics, provides a thermodynamic explanation for the observed departure and the convergent temperature response of R using a global database. Three meaningful parameters emerge from MMRT analysis: the temperature at which the rate of respiration would theoretically reach a maximum (the optimum temperature, Tₒₚₜ), the temperature at which the respiration rate is most sensitive to changes in temperature (the inflection temperature, Tᵢₙf) and the overall curvature of the log(rate) versus temperature plot (the change in heat capacity for the system, ΔCǂₚ). On average, the highest potential enzyme-catalyzed rates of respiratory enzymes for R are predicted to occur at 67.0 ± 1.2°C and the maximum temperature sensitivity at 41.4 ± 0.7°C from MMRT. The average curvature (average negative ΔCǂₚ) was −1.2 ± 0.1 kJ mol⁻¹ K⁻¹. Interestingly, Topt, Tᵢₙf and ΔCǂₚ appear insignificantly different across biomes and plant functional types, suggesting that thermal response of respiratory enzymes in leaves could be conserved. The derived parameters from MMRT can serve as thermal traits for plant leaves that represent the collective temperature response of metabolic respiratory enzymes and could be useful to understand regulations of R under a warmer climate. MMRT extends the classic TST to enzyme-catalyzed reactions and provides an accurate and mechanistic model for the short-term temperature response of R around the globe

Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit

<p>Abstract</p> <p>background</p> <p>The pace of novel medical treatments and approaches to therapy has accelerated in recent years. Unfortunately, many potential therapeutic advances do not fulfil their promise when subjected to randomized controlled trials. It is therefore highly desirable to speed up the process of evaluating new treatment options, particularly in phase II and phase III trials. To help realize such an aim, in 2003, Royston and colleagues proposed a class of multi-arm, two-stage trial designs intended to eliminate poorly performing contenders at a first stage (point in time). Only treatments showing a predefined degree of advantage against a control treatment were allowed through to a second stage. Arms that survived the first-stage comparison on an intermediate outcome measure entered a second stage of patient accrual, culminating in comparisons against control on the definitive outcome measure. The intermediate outcome is typically on the causal pathway to the definitive outcome (i.e. the features that cause an intermediate event also tend to cause a definitive event), an example in cancer being progression-free and overall survival. Although the 2003 paper alluded to multi-arm trials, most of the essential design features concerned only two-arm trials. Here, we extend the two-arm designs to allow an arbitrary number of stages, thereby increasing flexibility by building in several 'looks' at the accumulating data. Such trials can terminate at any of the intermediate stages or the final stage.</p> <p>Methods</p> <p>We describe the trial design and the mathematics required to obtain the timing of the 'looks' and the overall significance level and power of the design. We support our results by extensive simulation studies. As an example, we discuss the design of the STAMPEDE trial in prostate cancer.</p> <p>Results</p> <p>The mathematical results on significance level and power are confirmed by the computer simulations. Our approach compares favourably with methodology based on beta spending functions and on monitoring only a primary outcome measure for lack of benefit of the new treatment.</p> <p>Conclusions</p> <p>The new designs are practical and are supported by theory. They hold considerable promise for speeding up the evaluation of new treatments in phase II and III trials.</p

Lower Trabecular Volumetric BMD at Metaphyseal Regions of Weight-Bearing Bones is Associated With Prior Fracture in Young Girls

Understanding the etiology of skeletal fragility during growth is critical for the development of treatments and prevention strategies aimed at reducing the burden of childhood fractures. Thus we evaluated the relationship between prior fracture and bone parameters in young girls. Data from 465 girls aged 8 to 13 years from the Jump-In: Building Better Bones study were analyzed. Bone parameters were assessed at metaphyseal and diaphyseal sites of the nondominant femur and tibia using peripheral quantitative computed tomography (pQCT). Dual-energy X-ray absorptiometry (DXA) was used to assess femur, tibia, lumbar spine, and total body less head bone mineral content. Binary logistic regression was used to evaluate the relationship between prior fracture and bone parameters, controlling for maturity, body mass, leg length, ethnicity, and physical activity. Associations between prior fracture and all DXA and pQCT bone parameters at diaphyseal sites were nonsignificant. In contrast, lower trabecular volumetric BMD (vBMD) at distal metaphyseal sites of the femur and tibia was significantly associated with prior fracture. After adjustment for covariates, every SD decrease in trabecular vBMD at metaphyseal sites of the distal femur and tibia was associated with 1.4 (1.1–1.9) and 1.3 (1.0–1.7) times higher fracture prevalence, respectively. Prior fracture was not associated with metaphyseal bone size (ie, periosteal circumference). In conclusion, fractures in girls are associated with lower trabecular vBMD, but not bone size, at metaphyseal sites of the femur and tibia. Lower trabecular vBMD at metaphyseal sites of long bones may be an early marker of skeletal fragility in girls. © 2011 American Society for Bone and Mineral Research

HbA_1c variability is associated with increased mortality and earlier hospital admission in people with Type 1 diabetes

Aim: Despite evidence of morbidity, no evidence exists on the relationship between HbA1c variability and mortality in Type 1 diabetes. We performed an observational study to investigate whether the association between HbA1c variability and mortality exists in a population of people with Type 1 diabetes. As a secondary outcome, we compared onset of first hospital admission between groups. Methods: People with Type 1 diabetes were identified for inclusion from the Scottish Care Information – Diabetes data set. This database includes data of all people known to have diabetes who live within Scotland. A survival analysis was carried out over a 47‐month period comparing two groups; group 1 with a HbA1c coefficient of variation (CV) above the median CV value, and group 2 with a CV below the median value. Time to death or first admission was also analysed. A Cox proportional hazard model was used to compare time to death, adjusting for appropriate covariables. Results: Some 6048 individuals with Type 1 diabetes were included in the analysis. Median HbA1c CV was 7.9. The hazard ratio (HR) for mortality for those with an HbA1c CV above the median value is 1.5 over 47 months of follow‐up (P &lt; 0.001). HR for survival to either the first admission to hospital or death for those with an HbA1c CV above the median value was 1.35 (95% confidence interval 1.25–1.45) over 730 days of follow‐up (P &lt; 0.001). Conclusion: Our results show that people with greater HbA1c variability have a higher rate of mortality and earlier hospital admission in Type 1 diabetes

Not Perfect, but Better: Primary Care Providers’ Experiences with Electronic Referrals in a Safety Net Health System

BackgroundElectronic referrals can improve access to subspecialty care in safety net settings. In January 2007, San Francisco General Hospital (SFGH) launched an electronic referral portal that incorporated subspecialist triage, iterative communication with referring providers, and existing electronic health record data to improve access to subspecialty care.ObjectiveWe surveyed primary care providers (PCPs) to assess the impact of electronic referrals on workflow and clinical care.DesignWe administered an 18-item, web-based questionnaire to all 368 PCPs who had the option of referring to SFGH.MeasurementsWe asked participants to rate time spent submitting a referral, guidance of workup, wait times, and change in overall clinical care compared to prior referral methods using 5-point Likert scales. We used multivariate logistic regression to identify variables associated with perceived improvement in overall clinical care.ResultsTwo hundred ninety-eight PCPs (81.0%) from 24 clinics participated. Over half (55.4%) worked at hospital-based clinics, 27.9% at county-funded community clinics, and 17.1% at non-county-funded community clinics. Most (71.9%) reported that electronic referrals had improved overall clinical care. Providers from non-county-funded clinics (AOR 0.40, 95% CI 0.14-0.79) and those who spent &gt; or =6 min submitting an electronic referral (AOR 0.33, 95%CI 0.18-0.61) were significantly less likely than other participants to report that electronic referrals had improved clinical care.ConclusionsPCPs felt electronic referrals improved health-care access and quality; those who reported a negative impact on workflow were less likely to agree. While electronic referrals hold promise as a tool to improve clinical care, their impact on workflow should be considered