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Estimating penetration-related X-band InSAR elevation bias: a study over the Greenland ice sheet
(2019)
Accelerating melt on the Greenland ice sheet leads to dramatic changes at a global scale. Especially in the last decades, not only the monitoring, but also the quantification of these changes has gained considerably in importance. In this context, Interferometric Synthetic Aperture Radar (InSAR) systems complement existing data sources by their capability to acquire 3D information at high spatial resolution over large areas independent of weather conditions and illumination. However, penetration of the SAR signals into the snow and ice surface leads to a bias in measured height, which has to be corrected to obtain accurate elevation data. Therefore, this study purposes an easy transferable pixel-based approach for X-band penetration-related elevation bias estimation based on single-pass interferometric coherence and backscatter intensity which was performed at two test sites on the Northern Greenland ice sheet. In particular, the penetration bias was estimated using a multiple linear regression model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements to correct TanDEM-X Digital Elevation Model (DEM) scenes. Validation efforts yielded good agreement between observations and estimations with a coefficient of determination of R\(^2\) = 68% and an RMSE of 0.68 m. Furthermore, the study demonstrates the benefits of X-band penetration bias estimation within the application context of ice sheet elevation change detection.
Background and Objective: Staphylococcus aureus is one of the major pathogens of nosocomial infections as wells as community-acquired (CA) infections worldwide. So far, large-scale comprehensive molecular and epidemiological characterisation of S. aureus from very diverse settings has not been carried out in India. The objective of this study is to evaluate the molecular, epidemiological and virulence characteristics of S. aureus in both community and hospital settings in Chennai, southern India. Methods: S. aureus isolates were obtained from four different groups (a) healthy individuals from closed community settings, (b) inpatients from hospitals, (c) outpatients from hospitals, representing isolates of hospital-community interface and (d) HIV-infected patients to define isolates associated with the immunocompromised. Antibiotic susceptibility testing, multiplex polymerase chain reactions for detection of virulence and resistance determinants, molecular typing including Staphylococcal cassette chromosome mec (SCCmec) and agr typing, were carried out. Sequencing-based typing was done using spa and multilocus sequence typing (MLST) methods. Clonal complexes (CC) of hospital and CA methicillin-resistant S. aureus (MRSA) were identified and compared for virulence and resistance.
Results and Conclusion: A total of 769 isolates of S. aureus isolates were studied. The prevalence of MRSA was found to be 7.17%, 81.67%, 58.33% and 22.85% for groups a, b, c and d, respectively. Of the four SCCmec types (I, III, IV and V) detected, SCCmec V was found to be predominant. Panton-Valentine leucocidin toxin genes were detected among MRSA isolates harbouring SCCmec IV and V. A total of 78 spa types were detected, t657 being the most prevalent. 13 MLST types belonging to 9 CC were detected. CC1 (ST-772, ST-1) and CC8 (ST238, ST368 and ST1208) were found to be predominant among MRSA. CA-MRSA isolates with SCCmec IV and V were isolated from all study groups including hospitalised patients and were found to be similar by molecular tools. This shows that CA MRSA has probably infiltrated into the hospital settings.
We consider the computation of volumes contained in a spatial slice of AdS(3) in terms of observables in a dual CFT. Our main tool is kinematic space, defined either from the bulk perspective as the space of oriented bulk geodesics, or from the CFT perspective as the space of entangling intervals. We give an explicit formula for the volume of a general region in a spatial slice of AdS(3) as an integral over kinematic space. For the region lying below a geodesic, we show how to write this volume purely in terms of entangling entropies in the dual CFT. This expression is perhaps most interesting in light of the complexity = volume proposal, which posits that complexity of holographic quantum states is computed by bulk volumes. An extension of this idea proposes that the holographic subregion complexity of an interval, defined as the volume under its Ryu-Takayanagi surface, is a measure of the complexity of the corresponding reduced density matrix. If this is true, our results give an explicit relationship between entanglement and subregion complexity in CFT, at least in the vacuum. We further extend many of our results to conical defect and BTZ black hole geometries.
Protein-protein interaction (PPI) studies are gaining momentum these days due to the plethora of various high-throughput experimental methods available for detecting PPIs. Proteins create complexes and networks by functioning in harmony with other proteins and here in silico network biology hold the promise to reveal new functionality of genes as it is very difficult and laborious to carry out experimental high-throughput genetic screens in living organisms. We demonstrate this approach by computationally screening C. elegans conserved homologs of already reported human tumor suppressor and aging associated genes. We select by this nhr-6, vab-3 and gst-23 as predicted longevity genes for RNAi screen. The RNAi results demonstrated the pro-longevity effect of these genes. Nuclear hormone receptor nhr-6 RNAi inhibition resulted in a C. elegans phenotype of 23.46% lifespan reduction. Moreover, we show that nhr-6 regulates oxidative stress resistance in worms and does not affect the feeding behavior of worms. These findings imply the potential of nhr-6 as a common therapeutic target for aging and cancer ailments, stressing the power of in silico PPI network analysis coupled with RNAi screens to describe gene function.
Studies of the fragmentation of jets into charged particles in heavy-ion collisions can help in understanding the mechanism of jet quenching by the hot and dense QCD matter created in such collisions, the quark-gluon plasma. These proceedings present a measurement of the angular distribution of charged particles around the jet axis in root s(NN) = 5.02 TeV Pb+Pb and pp collisions, done using the ATLAS detector at the LHC. The measurement is performed inside jets reconstructed with the anti-k(t) algorithm with radius parameter R = 0.4, and is extended to regions outside the jet cone. Results are presented as a function of Pb+Pb collision centrality, and both jet and charged-particle transverse momenta.
The Best for the Most Important: Maintaining a Pristine Proteome in Stem and Progenitor Cells
(2019)
Pluripotent stem cells give rise to reproductively enabled offsprings by generating progressively lineage-restricted multipotent stem cells that would differentiate into lineage-committed stem and progenitor cells. These lineage-committed stem and progenitor cells give rise to all adult tissues and organs. Adult stem and progenitor cells are generated as part of the developmental program and play critical roles in tissue and organ maintenance and/or regeneration. The ability of pluripotent stem cells to self-renew, maintain pluripotency, and differentiate into a multicellular organism is highly dependent on sensing and integrating extracellular and extraorganismal cues. Proteins perform and integrate almost all cellular functions including signal transduction, regulation of gene expression, metabolism, and cell division and death. Therefore, maintenance of an appropriate mix of correctly folded proteins, a pristine proteome, is essential for proper stem cell function. The stem cells' proteome must be pristine because unfolded, misfolded, or otherwise damaged proteins would interfere with unlimited self-renewal, maintenance of pluripotency, differentiation into downstream lineages, and consequently with the development of properly functioning tissue and organs. Understanding how various stem cells generate and maintain a pristine proteome is therefore essential for exploiting their potential in regenerative medicine and possibly for the discovery of novel approaches for maintaining, propagating, and differentiating pluripotent, multipotent, and adult stem cells as well as induced pluripotent stem cells. In this review, we will summarize cellular networks used by various stem cells for generation and maintenance of a pristine proteome. We will also explore the coordination of these networks with one another and their integration with the gene regulatory and signaling networks.
We consider the process of muon-electron elastic scattering, which has been proposed as an ideal framework to measure the running of the electromagnetic coupling constant at space-like momenta and determine the leading-order hadronic contribution to the muon g-2 (MUonE experiment). We compute the next-to-leading (NLO) contributions due to QED and purely weak corrections and implement them into a fully differential Monte Carlo event generator, which is available for first experimental studies. We show representative phenomenological results of interest for the MUonE experiment and examine in detail the impact of the various sources of radiative corrections under different selection criteria, in order to study the dependence of the NLO contributions on the applied cuts. The study represents the first step towards the realisation of a high-precision Monte Carlo code necessary for data analysis.
Social robots are becoming increasingly prevalent in everyday life and sex robots are a sub-category of especially high public interest and controversy. Starting from the concept of the otaku, a term from Japanese youth culture that describes secluded persons with a high affinity for fictional manga characters, we examine individual differences behind sex robot appeal (anime and manga fandom, interest in Japanese culture, preference for indoor activities, shyness). In an online-experiment, 261 participants read one out of three randomly assigned descriptions of future technologies (sex robot, nursing robot, genetically modified organism) and reported on their overall evaluation, eeriness, and contact/purchase intentions. Higher anime and manga fandom was associated with higher appeal for all three future technologies. For our male subsample, sex robots and GMOs stood out as shyness yielded a particularly strong relationship to contact/purchase intentions for these new technologies.
Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.
Sphingolipids are major components of cellular membranes, and at steady-state level, their metabolic fluxes are tightly controlled. On challenge by external signals, they undergo rapid turnover, which substantially affects the biophysical properties of membrane lipid and protein compartments and, consequently, signaling and morphodynamics. In T cells, external cues translate into formation of membrane microdomains where proximal signaling platforms essential for metabolic reprograming and cytoskeletal reorganization are organized. This review will focus on sphingomyelinases, which mediate sphingomyelin breakdown and ensuing ceramide release that have been implicated in T-cell viability and function. Acting at the sphingomyelin pool at the extrafacial or cytosolic leaflet of cellular membranes, acid and neutral sphingomyelinases organize ceramide-enriched membrane microdomains that regulate T-cell homeostatic activity and, upon stimulation, compartmentalize receptors, membrane proximal signaling complexes, and cytoskeletal dynamics as essential for initiating T-cell motility and interaction with endothelia and antigen-presenting cells. Prominent examples to be discussed in this review include death receptor family members, integrins, CD3, and CD28 and their associated signalosomes. Progress made with regard to experimental tools has greatly aided our understanding of the role of bioactive sphingolipids in T-cell biology at a molecular level and of targets explored by a model pathogen (measles virus) to specifically interfere with their physiological activity.