Operational assimilation of ASCAT surface soil wetness at the Met Office

Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010

A model to map levelised cost of energy for wave energy projects

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.An economic model has been developed which allows the spatial dependence of wave energy levelised cost of energy (LCOE) to be calculated and mapped in graphical information system (GIS) software. Calculation is performed across a domain of points which define hindcast wave data; these data are obtained from wave propagation models like Simulating WAves Nearshore (SWAN). Time series of metocean data are interpolated across a device power matrix, obtaining energy production at every location. Spatial costs are calculated using Dijkstra's algorithm, to find distances between points from which costs are inferred. These include the export cable and operations, the latter also calculated by statistically estimating weather window waiting time. A case study is presented, considering the Scottish Western Isles and using real data from a device developer. Results indicate that, for the small scale device examined, the lowest LCOE hotspots occur in the Minches. This area is relatively sheltered, showing that performance is device specific and does not always correspond to the areas of highest energy resource. Sensitivity studies are performed, examining the effects of cut-in and cut-out significant wave height on LCOE, and month on installation cost. The results show that the impact of these parameters is highly location-specific.The authors wish to thank Albatern Ltd, the industrial partner of the research project, for their funding and support in sponsoring the lead author. In addition, the Energy Technology Institute (ETI) and Research Councils UK Energy Programme who have funded this research through the IDCORE programme (grant number: EP/J500847/1)

Acupuncture for chronic neck pain: a pilot for a randomised controlled trial

Background: Acupuncture is increasingly being used for many conditions including chronic neck pain. However the evidence remains inconclusive, indicating the need for further well-designed research. The aim of this study was to conduct a pilot randomised controlled parallel arm trial, to establish key features required for the design and implementation of a large-scale trial on acupuncture for chronic neck pain. Methods: Patients whose GPs had diagnosed neck pain were recruited from one general practice, and randomised to receive usual GP care only, or acupuncture ( up to 10 treatments over 3 months) as an adjunctive treatment to usual GP care. The primary outcome measure was the Northwick Park Neck Pain Questionnaire (NPQ) at 3 months. The primary analysis was to determine the sample size for the full scale study. Results: Of the 227 patients with neck pain identified from the GP database, 28 (12.3%) consenting patients were eligible to participate in the pilot and 24 (10.5%) were recruited to the trial. Ten patients were randomised to acupuncture, receiving an average of eight treatments from one of four acupuncturists, and 14 were randomised to usual GP care alone. The sample size for the full scale trial was calculated from a clinically meaningful difference of 5% on the NPQ and, from this pilot, an adjusted standard deviation of 15.3%. Assuming 90% power at the 5% significance level, a sample size of 229 would be required in each arm in a large-scale trial when allowing for a loss to follow-up rate of 14%. In order to achieve this sample, one would need to identify patients from databases of GP practices with a total population of 230,000 patients, or approximately 15 GP practices roughly equal in size to the one involved in this study (i.e. 15,694 patients). Conclusion: This pilot study has allowed a number of recommendations to be made to facilitate the design of a large-scale trial, which in turn will help to clarify the existing evidence base on acupuncture for neck pain

Tonic inhibition of accumbal spiny neurons by extrasynaptic 4 GABAA receptors modulates the actions of psychostimulants

Within the nucleus accumbens (NAc), synaptic GABAA receptors (GABAARs) mediate phasic inhibition of medium spiny neurons (MSNs) and influence behavioral responses to cocaine. We demonstrate that both dopamine D1- and D2-receptor-expressing MSNs (D-MSNs) additionally harbor extrasynaptic GABAARs incorporating α4, β, and δ subunits that mediate tonic inhibition, thereby influencing neuronal excitability. Both the selective δ-GABAAR agonist THIP and DS2, a selective positive allosteric modulator, greatly increased the tonic current of all MSNs from wild-type (WT), but not from δ−/− or α4−/− mice. Coupling dopamine and tonic inhibition, the acute activation of D1 receptors (by a selective agonist or indirectly by amphetamine) greatly enhanced tonic inhibition in D1-MSNs but not D2-MSNs. In contrast, prolonged D2 receptor activation modestly reduced the tonic conductance of D2-MSNs. Behaviorally, WT and constitutive α4−/− mice did not differ in their expression of cocaine-conditioned place preference (CPP). Importantly, however, mice with the α4 deletion specific to D1-expressing neurons (α4D1−/−) showed increased CPP. Furthermore, THIP administered systemically or directly into the NAc of WT, but not α4−/− or α4D1−/− mice, blocked cocaine enhancement of CPP. In comparison, α4D2−/− mice exhibited normal CPP, but no cocaine enhancement. In conclusion, dopamine modulation of GABAergic tonic inhibition of D1- and D2-MSNs provides an intrinsic mechanism to differentially affect their excitability in response to psychostimulants and thereby influence their ability to potentiate conditioned reward. Therefore, α4βδ GABAARs may represent a viable target for the development of novel therapeutics to better understand and influence addictive behaviors

Keratin 5 overexpression is associated with serous ovarian cancer recurrence and chemotherapy resistance

This study investigated the clinical significance of keratin 5 and 6 expression in serous ovarian cancer progression and chemotherapy resistance. KRT5 and KRT6 (KRT6A, KRT6B & KRT6C) gene expression was assessed in publically available serous ovarian cancer data sets, ovarian cancer cell lines and primary serous ovarian cancer cells. Monoclonal antibodies which detect both K5/6 or only K5 were used to assess protein expression in ovarian cancer cell lines and a cohort of high grade serous ovarian carcinomas at surgery (n = 117) and after neoadjuvant chemotherapy (n = 21). Survival analyses showed that high KRT5 mRNA in stage III/IV serous ovarian cancers was significantly associated with reduced progression-free (HR 1.38, P < 0.0001) and overall survival (HR 1.28, P = 0.013) whilst high KRT6 mRNA was only associated with reduced progression-free survival (HR 1.2, P = 0.031). Both high K5/6 (≥ 10%, HR 1.78 95% CI; 1.03−2.65, P = 0.017) and high K5 (≥ 10%, HR 1.90, 95% CI; 1.12−3.19, P = 0.017) were associated with an increased risk of disease recurrence. KRT5 but not KRT6C mRNA expression was increased in chemotherapy resistant primary serous ovarian cancer cells compared to chemotherapy sensitive cells. The proportion of serous ovarian carcinomas with high K5/6 or high K5 immunostaining was significantly increased following neoadjuvant chemotherapy. K5 can be used to predict serous ovarian cancer prognosis and identify cancer cells that are resistant to chemotherapy. Developing strategies to target K5 may therefore improve serous ovarian cancer survival.Carmela Ricciardelli, Noor A Lokman, Carmen E Pyragius, Miranda P Ween, Anne M Macpherson, Andrew Ruszkiewicz, Peter Hoffmann, Martin K Oehle

Quantitative localized proton-promoted dissolution kinetics of calcite using scanning electrochemical microscopy (SECM)

Scanning electrochemical microscopy (SECM) has been used to determine quantitatively the kinetics of proton-promoted dissolution of the calcite (101̅4) cleavage surface (from natural “Iceland Spar”) at the microscopic scale. By working under conditions where the probe size is much less than the characteristic dislocation spacing (as revealed from etching), it has been possible to measure kinetics mainly in regions of the surface which are free from dislocations, for the first time. To clearly reveal the locations of measurements, studies focused on cleaved “mirror” surfaces, where one of the two faces produced by cleavage was etched freely to reveal defects intersecting the surface, while the other (mirror) face was etched locally (and quantitatively) using SECM to generate high proton fluxes with a 25 μm diameter Pt disk ultramicroelectrode (UME) positioned at a defined (known) distance from a crystal surface. The etch pits formed at various etch times were measured using white light interferometry to ascertain pit dimensions. To determine quantitative dissolution kinetics, a moving boundary finite element model was formulated in which experimental time-dependent pit expansion data formed the input for simulations, from which solution and interfacial concentrations of key chemical species, and interfacial fluxes, could then be determined and visualized. This novel analysis allowed the rate constant for proton attack on calcite, and the order of the reaction with respect to the interfacial proton concentration, to be determined unambiguously. The process was found to be first order in terms of interfacial proton concentration with a rate constant k = 6.3 (± 1.3) × 10–4 m s–1. Significantly, this value is similar to previous macroscopic rate measurements of calcite dissolution which averaged over large areas and many dislocation sites, and where such sites provided a continuous source of steps for dissolution. Since the local measurements reported herein are mainly made in regions without dislocations, this study demonstrates that dislocations and steps that arise from such sites are not needed for fast proton-promoted calcite dissolution. Other sites, such as point defects, which are naturally abundant in calcite, are likely to be key reaction sites

Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients

Background At least a third of dengue patients develop plasma leakage with increased risk of life-threat-ening complications. Predicting plasma leakage using laboratory parameters obtained in early infection as means of triaging patients for hospital admission is important for resource-limited settings. Methods A Sri Lankan cohort including 4,768 instances of clinical data from N = 877 patients (60.3% patients with confirmed dengue infection) recorded in the first 96 hours of fever was consid-ered. After excluding incomplete instances, the dataset was randomly split into a development and a test set with 374 (70%) and 172 (30%) patients, respectively. From the development set, five most informative features were selected using the minimum description length (MDL) algorithm. Random forest and light gradient boosting machine (LightGBM) were used to develop a classification model using the development set based on nested cross validation. An ensemble of the learners via average stacking was used as the final model to predict plasma leakage. Results Lymphocyte count, haemoglobin, haematocrit, age, and aspartate aminotransferase were the most informative features to predict plasma leakage. The final model achieved the area under the receiver operating characteristics curve, AUC = 0.80 with positive predictive value, PPV = 76.9%, negative predictive value, NPV = 72.5%, specificity = 87.9%, and sensitivity = 54.8% on the test set. Conclusion The early predictors of plasma leakage identified in this study are similar to those identified in several prior studies that used non-machine learning based methods. However, our observations strengthen the evidence base for these predictors by showing their relevance even when individual data points, missing data and non-linear associations were consid-ered. Testing the model on different populations using these low-cost observations would identify further strengths and limitations of the presented model