Impact of the Kuroshio intrusion on the nutrient inventory in the upper northern South China Sea: insights from an isopycnal mixing model

Based on four cruises covering a seasonal cycle in 2009-2011, we examined the impact of the Kuroshio intrusion, featured by extremely oligotrophic waters, on the nutrient inventory in the central northern South China Sea (NSCS). The nutrient inventory in the upper 100m of the water column in the study area ranged from similar to 200 to similar to 290 mmol m(-2) for N+N (nitrate plus nitrite), from similar to 13 to similar to 24 mmol m(-2) for soluble reactive phosphate and from similar to 210 to similar to 430 mmol m(-2) for silicic acid. The nutrient inventory showed a clear seasonal pattern with the highest value appearing in summer, while the N+N inventory in spring and winter had a reduction of similar to 13 and similar to 30 %, respectively, relative to that in summer. To quantify the extent of the Kuroshio intrusion, an isopycnal mixing model was adopted to derive the proportional contribution of water masses from the SCS proper and the Kuroshio along individual isopycnal surfaces. The derived mixing ratio along the isopycnal plane was then employed to predict the genuine gradients of nutrients under the assumption of no biogeochemical alteration. These predicted nutrient concentrations, denoted as N-m, are solely determined by water mass mixing. Results showed that the nutrient inventory in the upper 100m of the NSCS was overall negatively correlated to the Kuroshio water fraction, suggesting that the Kuroshio intrusion significantly influenced the nutrient distribution in the SCS and its seasonal variation. The difference between the observed nutrient concentrations and their corresponding Nm allowed us to further quantify the nutrient removal/addition associated with the biogeochemical processes on top of the water mass mixing. We revealed that the nutrients in the upper 100m of the water column had a net consumption in both winter and spring but a net addition in fall.Based on four cruises covering a seasonal cycle in 2009-2011, we examined the impact of the Kuroshio intrusion, featured by extremely oligotrophic waters, on the nutrient inventory in the central northern South China Sea (NSCS). The nutrient inventory in the upper 100m of the water column in the study area ranged from similar to 200 to similar to 290 mmol m(-2) for N+N (nitrate plus nitrite), from similar to 13 to similar to 24 mmol m(-2) for soluble reactive phosphate and from similar to 210 to similar to 430 mmol m(-2) for silicic acid. The nutrient inventory showed a clear seasonal pattern with the highest value appearing in summer, while the N+N inventory in spring and winter had a reduction of similar to 13 and similar to 30 %, respectively, relative to that in summer. To quantify the extent of the Kuroshio intrusion, an isopycnal mixing model was adopted to derive the proportional contribution of water masses from the SCS proper and the Kuroshio along individual isopycnal surfaces. The derived mixing ratio along the isopycnal plane was then employed to predict the genuine gradients of nutrients under the assumption of no biogeochemical alteration. These predicted nutrient concentrations, denoted as N-m, are solely determined by water mass mixing. Results showed that the nutrient inventory in the upper 100m of the NSCS was overall negatively correlated to the Kuroshio water fraction, suggesting that the Kuroshio intrusion significantly influenced the nutrient distribution in the SCS and its seasonal variation. The difference between the observed nutrient concentrations and their corresponding Nm allowed us to further quantify the nutrient removal/addition associated with the biogeochemical processes on top of the water mass mixing. We revealed that the nutrients in the upper 100m of the water column had a net consumption in both winter and spring but a net addition in fall

Differential regulation of morphology and stemness of mouse embryonic stem cells by substrate stiffness and topography

The maintenance of stem cell pluripotency or sternness is crucial to embryonic development and differentiation. The mechanical or physical microenvironment of stem cells, which includes extracellular matrix stiffness and topography, regulates cell morphology and stemness. Although a growing body of evidence has shown the importance of these factors in stem cell differentiation, the impact of these biophysical or biomechanical regulators remains insufficiently characterized. In the present study, we applied a micro-fabricated polyacrylamide hydrogel substrate with two elasticities and three topographies to systematically test the morphology, proliferation, and sternness of mESCs. The independent or combined impact of the two factors on specific cell functions was analyzed. Cells are able to grow effectively on both polystyrene and polyacrylamide substrates in the absence of feeder cells. Substrate stiffness is predominant in preserving stemness by enhancing Oct-4 and Nanog expression on a soft polyacrylamide substrate. Topography is also a critical factor for manipulating sternness via the formation of a relatively flattened colony on a groove or pillar substrate and a spheroid colony on a hexagonal substrate. Although topography is less effective on soft substrates, it plays a role in retaining cell sternness on stiff, hexagonal or pillar-shaped substrates. mESCs also form, in a timely manner, a 3D structure on groove or hexagonal substrates. These results further the understanding of stem cell morphology and stemness in a microenvironment that mimics physiological conditions. (C) 2014 Elsevier Ltd. All rights reserved

Bone Marrow-Derived Microglia-Based Neurturin Delivery Protects Against Dopaminergic Neurodegeneration in a Mouse Model of Parkinson\u27s Disease

Although neurotrophic factors have long been recognized as potent agents for protecting against neuronal degeneration, clinical success in treating Parkinson\u27s disease and other neurodegenerative disorders has been hindered by difficulties in delivery of trophic factors across the blood brain barrier (BBB). Bone marrow hematopoietic stem cell-based gene therapy is emerging as a promising tool for overcoming drug delivery problems, as myeloid cells can cross the BBB and are recruited in large numbers to sites of neurodegeneration, where they become activated microglia that can secrete trophic factors. We tested the efficacy of bone marrow-derived microglial delivery of neurturin (NTN) in protecting dopaminergic neurons against neurotoxin-induced death in mice. Bone marrow cells were transduced ex vivo with lentivirus expressing the NTN gene driven by a synthetic macrophage-specific promoter. Infected bone marrow cells were then collected and transplanted into recipient animals. Eight weeks after transplantation, the mice were injected with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropuridine (MPTP) for seven days to induce dopaminergic neurodegeneration. Microglia-mediated NTN delivery dramatically ameliorated MPTP-induced degeneration of tyrosine hydroxylase (TH)-positive neurons of the substantia nigra and their terminals in the striatum. Microglia-mediated NTN delivery also induced significant recovery of synaptic marker staining in the striatum of MPTP-treated animals. Functionally, NTN treatment restored MPTP-induced decline in general activity, rearing behavior, and food intake. Thus, bone marrow-derived microglia can serve as cellular vehicles for sustained delivery of neurotrophic factors capable of mitigating dopaminergic injury

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

A Finite Element Thermomechanical Analysis of Polygonal Wear

Polygonal wear is a common type of damage on the railway wheel tread, which could induce wheel-rail impacts and further components failure. This study presents a finite element (FE) thermomechanical model to investigate the causes of wheel polygonal wear. The FE model is able to cope with three possible causes of polygonal wear: thermal effect, initial defects, and structural dynamics. To analyse the influences of the three causes on wheel-rail contact stress and wear depth, different material properties (i.e., elastic, elasto-plastic, thermo-elasto-plastic with thermal softening), and wheel profiles (i.e., round and polygonal) were used in the FE model. The simulation indicates that a high temperature up to 264.20 ℃ could be induced by full-slip wheel-rail rolling contact when the polygonal profile is used. The thermal effect, similar to that induced by tread brake, may then have a significant influence on wheel-rail contact stress and wear depth. In addition, the involvement of initial defects, i.e., polygonal profile, causes wheel-rail impact contact and remarkably increases the contact stress and wear. By reliably considering all the three possible causes, the proposed FE model is believed promising for further explaining the generation mechanisms of wheel polygonal wear.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Driving risk classification methodology for intelligent drive in real traffic event

To solve the problem that existing driving data cannot correlate to the large number of vehicles in terms of driving risks, is the functionality of intelligent driving algorithm should be improved. This paper deeply explores driving data to build a link between massive driving data and a large number of sample vehicles for driving risk analysis. It sorted out certain driving behavior parameters in the driving data, and extracted some parameters closely related to the driving risk; it further utilized the principal component analysis and factor analysis in spatio-temporal data to integrate certain extracted parameters into factors that are clearly related to the specific driving risks; then, it selected factor scores of driving behaviors as indexes for hierarchical clustering, and obtained multi-level clustering results of the driving risks of corresponding vehicles; in the end, it interpreted the clustering results of the vehicle driving risks. According to the results, it is found that cluster for different risks proposed in this paper for driving behaviors is effective in the hierarchical cluster for typical driving behaviors and it also offers a solution for risk analyses between driving data and large sample vehicles. The results provide the basis for training on safe driving for the key vehicles, and the improvement of advanced driver assistance system, which shows a wide application prospect in the field of intelligent drive.Intelligent Vehicle

Fast and robust identification of railway track stiffness from simple field measurement

We propose to combine a physics-based finite element (FE) track model and a data-driven Gaussian process regression (GPR) model to directly infer railpad and ballast stiffness from measured frequency response functions (FRF) by field hammer tests. Conventionally, only the rail resonance and full track resonance are used as the FRF features to identify track stiffness. In this paper, eleven features, including sleeper resonances, from a single FRF curve are selected as the predictors of the GPR. To deal with incomplete measurements and uncertainties in the FRF features, we train multiple candidate GPR models with different features, kernels and training sets. Predictions by the candidate models are fused using a weighted Product of Experts method that automatically filters out unreliable predictions. We compare the performance of the proposed method with a model updating method using the particle swam optimization (PSO) on two synthesis datasets in a wide range of scenarios. The results show that the enriched features and the proposed fusion strategy can effectively reduce prediction errors. In the worst-case scenario with only three features and 5% injected noise, the average prediction errors for the railpad and ballast stiffness are approximately 12% and 6%, outperforming the PSO by about 6% and 3%, respectively. Moreover, the method enables fast predictions for large datasets. The predictions for 400 samples takes only approximately 10 s compared with 40 min using the PSO. Finally, a field application example shows that the proposed method is capable of extracting the stiffness values using a simple setup, i.e., with only one accelerometer and one impact location.Railway Engineerin

Comparison of vehicle-track interaction models to simulate vertical wheel/rail impact contact

This study compares various assumptions in different models to assess their capabilities to model vehicle-track interactions up to 2 kHz at a single rail-top defect. Field measurement data are used to evaluate discrepancies. The characteristics of contact force and axle-box acceleration (ABA) are first identified and qualitatively correlated with track, wheelset and contact models. Subsequently, the results from different models are quantitatively compared in terms of their capabilities to reproduce those characteristics. It is found that the differences in sleeper and wheel-rail contact models lead to the most significant discrepancies. The causes and physical implications of the quantified discrepancies are also discussed.Railway Engineerin

Thermal annealing of C ion irradiation defects in nuclear graphite studied by positron annihilation

In order to investigate the thermal behaviour of radiation induced point defects in nuclear graphite, ETU10 graphite was implanted with 350 keV C+ ion to doses of 1015 and 1016 cm-2. The point defects introduced by the implantation were characterized by Positron Annihilation Doppler Broadening (PADB) and their thermal behaviour was studied during "in situ" annealing at Delft Variable Energy Positron beam (VEP). The annealing was performed for 5 minutes at temperatures ranging from 300 K (as implanted) to 1500 K in steps of 100 K. For both doses, an annealing stage at around 450 K is observed followed by a second stage around 700 K. For the high dose implantation vacancy complexes are found which are stable up to a temperature around 1400K.RST/Neutron and Positron Methods in Material