On the seasonal variations of salinity of the tropical Atlantic mixed layer

The physical processes controlling the mixed layer salinity (MLS) seasonal budget in the tropical Atlantic Ocean are investigated using a regional configuration of an ocean general circulation model. The analysis reveals that the MLS cycle is generally weak in comparison of individual physical processes entering in the budget because of strong compensation. In evaporative regions, around the surface salinity maxima, the ocean acts to freshen the mixed layer against the action of evaporation. Poleward of the southern SSS maxima, the freshening is ensured by geostrophic advection, the vertical salinity diffusion and, during winter, a dominant contribution of the convective entrainment. On the equatorward flanks of the SSS maxima, Ekman transport mainly contributes to supply freshwater from ITCZ regions while vertical salinity diffusion adds on the effect of evaporation. All these terms are phase locked through the effect of the wind. Under the seasonal march of the ITCZ and in coastal areas affected by river (7°S:15°N), the upper ocean freshening by precipitations and/or runoff is attenuated by vertical salinity diffusion. In the eastern equatorial regions, seasonal cycle of wind forced surface currents advect freshwaters, which are mixed with subsurface saline water because of the strong vertical turbulent diffusion. In all these regions, the vertical diffusion presents an important contribution to the MLS budget by providing, in general, an upwelling flux of salinity. It is generally due to vertical salinity gradient and mixing due to winds. Furthermore, in the equator where the vertical shear, associated to surface horizontal currents, is developed, the diffusion depends also on the sheared flow stability.

Percutaneous coronary interventional strategies for treatment of in-stent restenosis: a network meta-analysis.

BACKGROUND Percutaneous coronary intervention (PCI) with drug-eluting stents is the standard of care for treatment of native coronary artery stenoses, but optimum treatment strategies for bare metal stent and drug-eluting stent in-stent restenosis (ISR) have not been established. We aimed to compare and rank percutaneous treatment strategies for ISR. METHODS We did a network meta-analysis to synthesise both direct and indirect evidence from relevant trials. We searched PubMed, the Cochrane Library Central Register of Controlled Trials, and Embase for randomised controlled trials published up to Oct 31, 2014, of different PCI strategies for treatment of any type of coronary ISR. The primary outcome was percent diameter stenosis at angiographic follow-up. This study is registered with PROSPERO, number CRD42014014191. FINDINGS We deemed 27 trials eligible, including 5923 patients, with follow-up ranging from 6 months to 60 months after the index intervention. Angiographic follow-up was available for 4975 (84%) of 5923 patients 6-12 months after the intervention. PCI with everolimus-eluting stents was the most effective treatment for percent diameter stenosis, with a difference of -9·0% (95% CI -15·8 to -2·2) versus drug-coated balloons (DCB), -9·4% (-17·4 to -1·4) versus sirolimus-eluting stents, -10·2% (-18·4 to -2·0) versus paclitaxel-eluting stents, -19·2% (-28·2 to -10·4) versus vascular brachytherapy, -23·4% (-36·2 to -10·8) versus bare metal stents, -24·2% (-32·2 to -16·4) versus balloon angioplasty, and -31·8% (-44·8 to -18·6) versus rotablation. DCB were ranked as the second most effective treatment, but without significant differences from sirolimus-eluting (-0·2% [95% CI -6·2 to 5·6]) or paclitaxel-eluting (-1·2% [-6·4 to 4·2]) stents. INTERPRETATION These findings suggest that two strategies should be considered for treatment of any type of coronary ISR: PCI with everolimus-eluting stents because of the best angiographic and clinical outcomes, and DCB because of its ability to provide favourable results without adding a new stent layer. FUNDING None.

An Application-Layer RESTful Sleepy Nodes Implementation For Internet of Things Systems

Two of the main issues in wireless industrial Internet of Things applications are interoperability and network lifetime. In this work we extend a semantic interoperability platform and introduce an application-layer sleepy nodes protocol that can leverage on information stored in semantic repositories. We propose an integration platform for managing the sleep states and an application layer protocol based upon the Constraint Application Layer protocol. We evaluate our system on windowing based task allocation strategies, aiming for lower overall energy consumption that results in higher network lifetime.

Study of mineral dust entrainment in the planetary boundary layer by lidar depolarisation technique

Measurements on 27 June 2011 were performed over the Southern Iberian Peninsula at Granada EARLINET station, using active and passive remote sensing and airborne and surface in-situ data in order to study the entrainment processes between aerosols in the free troposphere and those in the planetary boundary layer (PBL). To this aim the temporal evolution of the lidar depolarisation, backscatter-related Angström exponent and potential temperature profiles were used in combination with the PBL contribution to the aerosol optical depth (AOD). Our results show that the mineral dust entrainment in the PBL was caused by the convective processes which ‘trapped’ the lofted mineral dust layer, distributing the mineral dust particles within the PBL. The temporal evolution of ground-based in-situ data evidenced the impact of this process at surface level. Finally, the amount of mineral dust in the atmospheric column available to be dispersed into the PBL was estimated by means of POLIPHON (Polarizing Lidar Photometer Networking). The dust mass concentration derived from POLIPHON was compared with the coarse-mode mass concentration retrieved with airborne in-situ measurements. Comparison shows differences below 50 µg/m³ (30% relative difference) indicating a relative good agreement between both techniques.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Effectiveness of non-steroidal anti-inflammatory drugs for the treatment of pain in knee and hip osteoarthritis: a network meta-analysis.

BACKGROUND Non-steroidal anti-inflammatory drugs (NSAIDs) are the backbone of osteoarthritis pain management. We aimed to assess the effectiveness of different preparations and doses of NSAIDs on osteoarthritis pain in a network meta-analysis. METHODS For this network meta-analysis, we considered randomised trials comparing any of the following interventions: NSAIDs, paracetamol, or placebo, for the treatment of osteoarthritis pain. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) and the reference lists of relevant articles for trials published between Jan 1, 1980, and Feb 24, 2015, with at least 100 patients per group. The prespecified primary and secondary outcomes were pain and physical function, and were extracted in duplicate for up to seven timepoints after the start of treatment. We used an extension of multivariable Bayesian random effects models for mixed multiple treatment comparisons with a random effect at the level of trials. For the primary analysis, a random walk of first order was used to account for multiple follow-up outcome data within a trial. Preparations that used different total daily dose were considered separately in the analysis. To assess a potential dose-response relation, we used preparation-specific covariates assuming linearity on log relative dose. FINDINGS We identified 8973 manuscripts from our search, of which 74 randomised trials with a total of 58 556 patients were included in this analysis. 23 nodes concerning seven different NSAIDs or paracetamol with specific daily dose of administration or placebo were considered. All preparations, irrespective of dose, improved point estimates of pain symptoms when compared with placebo. For six interventions (diclofenac 150 mg/day, etoricoxib 30 mg/day, 60 mg/day, and 90 mg/day, and rofecoxib 25 mg/day and 50 mg/day), the probability that the difference to placebo is at or below a prespecified minimum clinically important effect for pain reduction (effect size [ES] -0·37) was at least 95%. Among maximally approved daily doses, diclofenac 150 mg/day (ES -0·57, 95% credibility interval [CrI] -0·69 to -0·46) and etoricoxib 60 mg/day (ES -0·58, -0·73 to -0·43) had the highest probability to be the best intervention, both with 100% probability to reach the minimum clinically important difference. Treatment effects increased as drug dose increased, but corresponding tests for a linear dose effect were significant only for celecoxib (p=0·030), diclofenac (p=0·031), and naproxen (p=0·026). We found no evidence that treatment effects varied over the duration of treatment. Model fit was good, and between-trial heterogeneity and inconsistency were low in all analyses. All trials were deemed to have a low risk of bias for blinding of patients. Effect estimates did not change in sensitivity analyses with two additional statistical models and accounting for methodological quality criteria in meta-regression analysis. INTERPRETATION On the basis of the available data, we see no role for single-agent paracetamol for the treatment of patients with osteoarthritis irrespective of dose. We provide sound evidence that diclofenac 150 mg/day is the most effective NSAID available at present, in terms of improving both pain and function. Nevertheless, in view of the safety profile of these drugs, physicians need to consider our results together with all known safety information when selecting the preparation and dose for individual patients. FUNDING Swiss National Science Foundation (grant number 405340-104762) and Arco Foundation, Switzerland.