Cosmic Plasmas and Electromagnetic Phenomena

During the past few decades, plasma science has witnessed a great growth in laboratory studies, in simulations, and in space. Plasma is the most common phase of ordinary matter in the universe. It is a state in which ionized matter (even as low as 1%) becomes highly electrically conductive. As such, long-range electric and magnetic fields dominate its behavior. Cosmic plasmas are mostly associated with stars, supernovae, pulsars and neutron stars, quasars and active galaxies at the vicinities of black holes (i.e., their jets and accretion disks). Cosmic plasma phenomena can be studied with different methods, such as laboratory experiments, astrophysical observations, and theoretical/computational approaches (i.e., MHD, particle-in-cell simulations, etc.). They exhibit a multitude of complex magnetohydrodynamic behaviors, acceleration, radiation, turbulence, and various instability phenomena. This Special Issue addresses the growing need of the plasma science principles in astrophysics and presents our current understanding of the physics of astrophysical plasmas, their electromagnetic behaviors and properties (e.g., shocks, waves, turbulence, instabilities, collimation, acceleration and radiation), both microscopically and macroscopically. This Special Issue provides a series of state-of-the-art reviews from international experts in the field of cosmic plasmas and electromagnetic phenomena using theoretical approaches, astrophysical observations, laboratory experiments, and state-of-the-art simulation studies

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our procedure shows improvements ranging from ~13 to ~26 mIoU points over baselines, so establishing new state-of-the-art results

Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE <0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate

Application of the Wavelet Transform to the Digital Image Processing of Electron Micrographs and of Backreflection Electron Diffraction Patterns

In this work we explore the use of the so-called wavelet transform in the digital image processing of micrographs. The wavelet transform of an image f(x,y) is defined as: Wf(s,u,v) = f(x,y) s Ψ(s(x-u),s(y-v)) dxdy where Ψ is an analyzing function called wavelet and which is in our examples always taken to be the Mexican hat given by Ψ(x)=(2-(x2+y2))exp(-(x2+y2)/2) Some synthetic images are shown in which it can be clearly seen how the wavelet transform can be useful to reveal edges and to emphasize the boundaries of the clusters. The technique is applied in the case of the CoMoS catalysts, in which the wavelet transform can be used to emphasize the hexagonal domains while filtering the noise quite effectively. The technique is next applied to electron backreflection patterns where substantial noise reduction and emphasis of the lines are achieved. Several examples of the application of this processing tool to high resolution images of metallic particles and to quasicrystals are presented

Dynamics and diversity of bacteria associated with the disease vectors Aedes aegypti and Aedes albopictus

Aedes aegypti and Aedes albopictus develop in the same aquatic sites where they encounter microorganisms that infuence their life history and capacity to transmit human arboviruses. Some bacteria such as Wolbachia are currently being considered for the control of Dengue, Chikungunya and Zika. Yet little is known about the dynamics and diversity of Aedes-associated bacteria, including larval habitat features that shape their tempo-spatial distribution. We applied large-scale 16S rRNA amplicon sequencing to 960 adults and larvae of both Ae. aegypti and Ae. albopictus mosquitoes from 59 sampling sites widely distributed across nine provinces of Panama. We fnd both species share a limited, yet highly variable core microbiota, refecting high stochasticity within their oviposition habitats. Despite sharing a large proportion of microbiota, Ae. aegypti harbours higher bacterial diversity than Ae. albopictus, primarily due to rarer bacterial groups at the larval stage. We fnd signifcant diferences between the bacterial communities of larvae and adult mosquitoes, and among samples from metal and ceramic containers. However, we fnd little support for geography, water temperature and pH as predictors of bacterial associates. We report a low incidence of natural Wolbachia infection for both Aedes and its geographical distribution. This baseline information provides a foundation for studies on the functions and interactions of Aedes-associated bacteria with consequences for bio-control within Panama.Aedes aegypti and Aedes albopictus develop in the same aquatic sites where they encounter microorganisms that infuence their life history and capacity to transmit human arboviruses. Some bacteria such as Wolbachia are currently being considered for the control of Dengue, Chikungunya and Zika. Yet little is known about the dynamics and diversity of Aedes-associated bacteria, including larval habitat features that shape their tempo-spatial distribution. We applied large-scale 16S rRNA amplicon sequencing to 960 adults and larvae of both Ae. aegypti and Ae. albopictus mosquitoes from 59 sampling sites widely distributed across nine provinces of Panama. We fnd both species share a limited, yet highly variable core microbiota, refecting high stochasticity within their oviposition habitats. Despite sharing a large proportion of microbiota, Ae. aegypti harbours higher bacterial diversity than Ae. albopictus, primarily due to rarer bacterial groups at the larval stage. We fnd signifcant diferences between the bacterial communities of larvae and adult mosquitoes, and among samples from metal and ceramic containers. However, we fnd little support for geography, water temperature and pH as predictors of bacterial associates. We report a low incidence of natural Wolbachia infection for both Aedes and its geographical distribution. This baseline information provides a foundation for studies on the functions and interactions of Aedes-associated bacteria with consequences for bio-control within Panama

Comparative genomics of the Hedgehog loci in chordates and the origins of Shh regulatory novelties

The origin and evolution of the complex regulatory landscapes of some vertebrate developmental genes, often spanning hundreds of Kbp and including neighboring genes, remain poorly understood. The Sonic Hedgehog (Shh) genomic regulatory block (GRB) is one of the best functionally characterized examples, with several discrete enhancers reported within its introns, vast upstream gene-free region and neighboring genes (Lmbr1 and Rnf32). To investigate the origin and evolution of this GRB, we sequenced and characterized the Hedgehog (Hh) loci from three invertebrate chordate amphioxus species, which share several early expression domains with Shh. Using phylogenetic footprinting within and between chordate lineages, and reporter assays in zebrafish probing >30 Kbp of amphioxus Hh, we report large sequence and functional divergence between both groups. In addition, we show that the linkage of Shh to Lmbr1 and Rnf32, necessary for the unique gnatostomate-specific Shh limb expression, is a vertebrate novelty occurred between the two whole-genome duplications

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements

Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods

Fertility rates have dramatically decreased in the last two decades, especially in men. It has been described that environmental factors as well as life habits may affect semen quality. In this paper we use artificial intelligence techniques in order to predict semen characteristics resulting from environmental factors, life habits, and health status, with these techniques constituting a possible decision support system that can help in the study of male fertility potential. A total of 123 young, healthy volunteers provided a semen sample that was analyzed according to the World Health Organization 2010 criteria. They also were asked to complete a validated questionnaire about life habits and health status. Sperm concentration and percentage of motile sperm were related to sociodemographic data, environmental factors, health status, and life habits in order to determine the predictive accuracy of a multilayer perceptron network, a type of artificial neural network. In conclusion, we have developed an artificial neural network that can predict the results of the semen analysis based on the data collected by the questionnaire. The semen parameter that is best predicted using this methodology is the sperm concentration. Although the accuracy for motility is slightly lower than that for concentration, it is possible to predict it with a significant degree of accuracy. This methodology can be a useful tool in early diagnosis of patients with seminal disorders or in the selection of candidates to become semen donors.This study was partially funded by Vicerrectorado de Investigación, University of Alicante, Alicante, Spain (Vigrob-137)

LTMaker: a tool for semiautomatic reconstruction of the embryonic lineage tree from 4D-microscopy

Studies of animal development using a 4Dmicroscopy system generate an immense amount of image data. In order to properly analyze the recorded embryogenesis, a computer-aided systematic process of categorization of cells from the image data should be accomplished. We present a software tool named LTMaker for the systematic semiautomatic identification of embryonic cells centers and also to determine the underlying linage tree. The program saves the generated data to a file so that further analysis of the embryo can be performed with external tools.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Systematic review of the nature of nursing care described by using the Caring Behaviours Inventory

Aim To describe the nature of care received by patients measured through the Caring Behaviours Inventory. Background Professional nursing practice combines two dimensions of caring: instrumental care and expressive care. Instrumental care focuses on physical health needs, in terms of efficiency and employs interventions based on evidence. Expressive care is patient‐centred and based on the interpersonal relationship. It requires caring attitudes that include respect, kindness, sensitivity and patience. The Caring Behaviours Inventory is a tool designed to assess the care expressed through the behaviours nurses perform, contextualised within the Jean Watson's Theory of Human Caring. Methods A systematic review following PRISMA recommendations. Scopus, PubMed and CINAHL databases were consulted using the keywords “Caring Behaviours Inventory” AND “Nursing”. The Joanna Briggs Institute tool was used for the quality appraisal. A conceptual analysis and a thematic synthesis were performed for data extraction. Results 11 articles were selected. Three categories were identified: nature of caring, congruence between perceived care by patients and nurses, and factors associated with the expression of care. Discussion An emphasis on care of an instrumental nature was identified. The perception of patients differs from that of nurses, patients perceive a lower level of expressive caring than the one nurses believe to deliver. Caring behaviours are affected by the working environment, nurses' emotional intelligence and coping skills, and socio‐demographic characteristics. Conclusion This paper described the findings of previous research regarding the nature of care that is transmitted and received in clinical practice. Results highlight an emphasis on the instrumental aspect of the nursing care according to the patients' perception. Relevance to clinical practice Findings summarised in this review could contribute to a better understanding of the nursing care. Results reported in this paper could also help to improve the quality of care delivered by nurses as well as patient‐centeredness