221 research outputs found
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Modelling the air-gap field strength of electric machines to improve performance of haptic mechanisms
The air-gap of electro-magnetic (EM) actuators determines key operating parameters such as their ability to generate force. In haptic devices these parameters are not optimised for the conditions typically seen in operation and include the heat produced in the air-gap, the volume of the air-gap, and the intensity and direction of the magnetic field. The relationship between these parameters is complex thus design decisions are difficult to make. This paper considers the role of the radial magnetic field in cylindrical electric motors, a type often used in haptic devices. Two models are derived and compared with experimental measurements. The first model is a closed form solution, the second is a classic Poisson solution to Ampere's equation. These models are shown to be valid for making more general design decisions in relation to haptic actuators, and in particular allow an evaluation of the trade off between the volume of the air-gap, the resulting radial magnetic field and hence heat generated and the resulting forces
Hes-6, an inhibitor of Hes-1, is regulated by 17β-estradiol and promotes breast cancer cell proliferation
Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design
Numerical models of ocean biogeochemistry are becoming the major tools used to detect
and predict the impact of climate change on marine resources and to monitor
ocean health. However, with the continuous improvement of model structure
and spatial resolution, incorporation of these additional degrees of freedom
into fidelity assessment has become increasingly challenging. Here, we
propose a new method to provide information on the model predictive skill in a concise
way. The method is based on the conjoint use of a k-means clustering
technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means
algorithm and the assessment metrics reduce the number of model data points
to be evaluated. The metrics evaluate either the model state accuracy or the
skill of the model with respect to capturing emergent properties, such as the deep
chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo
observations as the sole evaluation data set ensures the accuracy of the
data, as it is a homogenous data set with strict sampling methodologies and
data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine
Service. The model performance is evaluated using the model efficiency
statistical score, which compares the model–observation misfit with the
variability in the observations and, thus, objectively quantifies whether the
model outperforms the BGC-Argo climatology. We show that, overall, the model
surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic
carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and
the mixed layers as well as silicate in the mesopelagic layer. However,
there are still areas for improvement with respect to reducing the model–data misfit for
certain variables such as silicate, pH, and the partial pressure of CO2
in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed
here can also aid in refining the design of the BGC-Argo network, in
particular regarding the regions in which BGC-Argo observations should be enhanced to
improve the model accuracy via the assimilation of BGC-Argo data or
process-oriented assessment studies. We strongly recommend increasing the
number of observations in the Arctic region while maintaining the existing
high-density of observations in the Southern Oceans. The model error in
these regions is only slightly less than the variability observed in
BGC-Argo measurements. Our study illustrates how the synergic use of
modeling and BGC-Argo data can both provide information about the performance of models
and improve the design of observing systems.</p
2′-Azido RNA, a Versatile Tool for Chemical Biology: Synthesis, X-ray Structure, siRNA Applications, Click Labeling
Unifying view of mechanical and functional hotspots across class A GPCRs
G protein-coupled receptors (GPCRs) are the largest superfamily of signaling proteins. Their activation process is accompanied by conformational changes that have not yet been fully uncovered. Here, we carry out a novel comparative analysis of internal structural fluctuations across a variety of receptors from class A GPCRs, which currently has the richest structural coverage. We infer the local mechanical couplings underpinning the receptors' functional dynamics and finally identify those amino acids whose virtual deletion causes a significant softening of the mechanical network. The relevance of these amino acids is demonstrated by their overlap with those known to be crucial for GPCR function, based on static structural criteria. The differences with the latter set allow us to identify those sites whose functional role is more clearly detected by considering dynamical and mechanical properties. Of these sites with a genuine mechanical/dynamical character, the top ranking is amino acid 7x52, a previously unexplored, and experimentally verifiable key site for GPCR conformational response to ligand binding. \ua9 2017 Ponzoni et al
Activity Dependent Protein Degradation Is Critical for the Formation and Stability of Fear Memory in the Amygdala
Protein degradation through the ubiquitin-proteasome system [UPS] plays a critical role in some forms of synaptic plasticity. However, its role in memory formation in the amygdala, a site critical for the formation of fear memories, currently remains unknown. Here we provide the first evidence that protein degradation through the UPS is critically engaged at amygdala synapses during memory formation and retrieval. Fear conditioning results in NMDA-dependent increases in degradation-specific polyubiquitination in the amygdala, targeting proteins involved in translational control and synaptic structure and blocking the degradation of these proteins significantly impairs long-term memory. Furthermore, retrieval of fear memory results in a second wave of NMDA-dependent polyubiquitination that targets proteins involved in translational silencing and synaptic structure and is critical for memory updating following recall. These results indicate that UPS-mediated protein degradation is a major regulator of synaptic plasticity necessary for the formation and stability of long-term memories at amygdala synapses
Copernicus Marine Service Ocean State Report
This is the final version. Available from Taylor & Francis via the DOI in this record
MicroRNA and Target Protein Patterns Reveal Physiopathological Features of Glioma Subtypes
Gliomas such as oligodendrogliomas (ODG) and glioblastomas (GBM) are brain tumours with different clinical outcomes. Histology-based classification of these tumour types is often difficult. Therefore the first aim of this study was to gain microRNA data that can be used as reliable signatures of oligodendrogliomas and glioblastomas. We investigated the levels of 282 microRNAs using membrane-array hybridisation and real-time PCR in ODG, GBM and control brain tissues. In comparison to these control tissues, 26 deregulated microRNAs were identified in tumours and the tissue levels of seven microRNAs (miR-21, miR-128, miR-132, miR-134, miR-155, miR-210 and miR-409-5p) appropriately discriminated oligodendrogliomas from glioblastomas. Genomic, epigenomic and host gene expression studies were conducted to investigate the mechanisms involved in these deregulations. Another aim of this study was to better understand glioma physiopathology looking for targets of deregulated microRNAs. We discovered that some targets of these microRNAs such as STAT3, PTBP1 or SIRT1 are differentially expressed in gliomas consistent with deregulation of microRNA expression. Moreover, MDH1, the target of several deregulated microRNAs, is repressed in glioblastomas, making an intramitochondrial-NAD reduction mediated by the mitochondrial aspartate-malate shuttle unlikely. Understanding the connections between microRNAs and bioenergetic pathways in gliomas may lead to identification of novel therapeutic targets
Proteomics identifies neddylation as a potential therapy target in small intestinal neuroendocrine tumors.
Patients with small intestinal neuroendocrine tumors (SI-NETs) frequently develop spread disease; however, the underlying molecular mechanisms of disease progression are not known and effective preventive treatment strategies are lacking. Here, protein expression profiling was performed by HiRIEF-LC-MS in 14 primary SI-NETs from patients with and without liver metastases detected at the time of surgery and initial treatment. Among differentially expressed proteins, overexpression of the ubiquitin-like protein NEDD8 was identified in samples from patients with liver metastasis. Further, NEDD8 correlation analysis indicated co-expression with RBX1, a key component in cullin-RING ubiquitin ligases (CRLs). In vitro inhibition of neddylation with the therapeutic agent pevonedistat (MLN4924) resulted in a dramatic decrease of proliferation in SI-NET cell lines. Subsequent mass spectrometry-based proteomics analysis of pevonedistat effects and effects of the proteasome inhibitor bortezomib revealed stabilization of multiple targets of CRLs including p27, an established tumor suppressor in SI-NET. Silencing of NEDD8 and RBX1 using siRNA resulted in a stabilization of p27, suggesting that the cellular levels of NEDD8 and RBX1 affect CRL activity. Inhibition of CRL activity, by either NEDD8/RBX1 silencing or pevonedistat treatment of cells resulted in induction of apoptosis that could be partially rescued by siRNA-based silencing of p27. Differential expression of both p27 and NEDD8 was confirmed in a second cohort of SI-NET using immunohistochemistry. Collectively, these findings suggest a role for CRLs and the ubiquitin proteasome system in suppression of p27 in SI-NET, and inhibition of neddylation as a putative therapeutic strategy in SI-NET
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