Surface meltwater runoff dominates present-day mass loss from the Greenland Ice Sheet. In Greenland’s interior, porous firn can limit runoff by retaining meltwater unless perched low-permeability horizons, such as ice slabs, develop and restrict percolation. Recent observations suggest that such horizons might develop rapidly during extreme melt seasons. Here we present radar sounding evidence that an extensive near surface melt layer formed following the extreme melt season in 2012. This layer was still present in 2017 in regions up to 700 m higher in elevation and 160 km further inland than known ice slabs. We find that melt layer formation is driven by local, short-timescale thermal and hydrologic processes in addition to mean climate state. These melt layers reduce vertical percolation pathways, and, under appropriate firn temperature and surface melt conditions, encourage further ice aggregation at their horizon. Therefore, the frequency of extreme melt seasons relative to the rate at which pore space and cold content regenerates above the most recent melt layer may be a key determinant of the firn’s multi-year response to surface melt. The long-term impact of extreme surface melt on the Greenland Ice Sheet is poorly constrained. Here the authors use airborne radar to characterize a subsurface refrozen melt layer that formed following extreme melt in 2012, showing that it likely reduced drainage pathways for subsequent melt. Runoff also impacts ice discharge as the rate and timing of meltwater delivery to the glacier bed affects basal drag and therefore ice velocity6. However, significant uncertainty remains regarding the connection of meltwater through the supraglacial, englacial, and subglacial systems and how those pathways may evolve in a warming climate.The extent, duration, and magnitude of annual surface melting significantly affect the mass balance of the Greenland Ice Sheet. Roughly 55% of Greenland’s mass loss since 2000 is attributed to decreasing surface mass balance (SMB) driven by increasing meltwater runoff7 or refreeze locally9. Refreezing is known to form multi-meter-thick, horizontally continuous ice slabs, or ice layers (0.1–1 m thick) and ice lenses (<0.1 m thick) with limited horizontal extent9. The impact these features have on Greenland’s ice dynamics and mass balance depends on the rate of water infiltration relative to the rate of refreezing within the firn. If deep, heterogeneous infiltration and localized refreezing dominate11, then all firn pore space must be filled before runoff occurs. In this case, the lag between the onset of surface melting and the start of runoff can be on the order of decades12.In the high-elevation accumulation zone, surface meltwater does not flow directly to the ocean or glacier bed, but instead percolates into the porous near-surface snow or firn. Here meltwater can be stored in perennial firn aquifers13. This scenario includes the growth of near-surface ice slabs that have significantly expanded the runoff zone in the Greenland interior9. These ice slabs are understood to be multi-annual features that initially developed below 2000 m elevation through the aggregation of pre-existing ice lenses driven by multi-year excess meltwater production9. In contrast, extensive near-surface ice layers linked only to extreme surface-melt in 2010 have been observed on a single transect in southwest Greenland at elevations up to 2400 m14, suggesting that perched ice layers can form in a single extreme melt season at elevations well above the upper limit of previously detected ice slabs. Field observations also show that ice layers developed following the 2012 extreme melt season at sites as far inland as Summit Station17. However, the large-scale structural impact of refreezing on the firn is difficult to assess from point measurements alone, and such rapid formation in regions of relatively low melt suggests that the formation conditions, character, and multi-year evolution of these melt layers may differ significantly from those of previously characterized ice slabs.Alternatively, if a perched impermeable horizon develops that subsequently caps otherwise permeable firn, runoff can initiate much more rapidly, enhancing surface-melt driven mass lossIn this work, we investigate the role of extreme melt seasons in melt layer formation by assessing the spatial extent and structural impact of the 2012 melt layer and the climatological conditions under which it formed, as well as its ongoing interactions with surface-melt.18. We initially identified this refrozen melt layer in radargrams collected in southwest Greenland in April 2013 that show an exceptionally bright reflector about 1 m below the surface . This melt layer exists in almost every sector of Greenland, covering ~9000 line-km of radar sounding data collected by the Center for the Remote Sensing of Ice Sheets (CReSIS) Accumulation Radar as part of NASA’s Operation IceBridge (OIB). This system, with ~0.3 m range resolution and ~15 m horizontal posting, is highly sensitive to vertical variations in firn densityace Fig. . This re 17 Fig. . The ref. We quantified the lateral layer connectivity, the range of layer densities consistent with the radar observations, and the probability that these estimates are consistent with a layer whose density exceeds pore close-off , both our depth and density estimates are generally consistent with the few firn core measurements that were collected in 2017 within a few kilometers of the radar lines20 . Field studies have demonstrated that total firn ice content increases significantly as elevation decreases14, although the lateral layer connectivity cannot be assessed from cores. Therefore, it is possible that the melt layer exists in the deep percolation zone but is obscured by overlying infiltration ice, as our detection algorithm relies on a strong density contrast between the melt layer and surrounding firn. Altogether, our estimated extent likely represents a conservative minimum.Mapping reveals that the 2012 melt layer is pervasive in the Greenland interior at the transition between the percolation and dry snow zones, located at 2200–2700 m elevation in the south and 1800 m to 2200 m elevation in the northwest Fig. . The lay21. Further, the quantitative connection between this spatially averaged structure metric and the effective permeability of the firn would require dedicated field validation studies to measure sub-meter variability in density and microstructure, as well as the impact of preferential percolation and thermal state which is beyond of the scope of this study.Next, we introduce a new combined nondimensional metric called layer prominence that describes the degree to which the firn structure and density were altered by the formation of the 2012 melt layer Fig. . This me−3 (μ ± σ), mean layer lateral connectivity is 0.82 ± 0.17 (where a continuous layer scores ≥1), and mean layer prominence is 0.79 ± 0.12. The high layer prominence suggests that 2012 refreezing significantly reshaped the firn structure in these regions.Figure 22 to investigate the spatial correlation between the radar-inferred layer prominence and climate variables. Refrozen ice structure is governed by the balance between initial firn temperatures, meltwater volume, and firn permeability23. Therefore, we analyze the annual accumulation, both the 2012 and mean melt to accumulation ratios, 2012 total melt anomalies relative to 1980–2011, and the ratio of latent heat in the total 2012 summer melt to the energy required to bring the firn to its melting temperature  in June 2012 (see “Methods” section)  and 2012 summer (June/July/August) temperatures  v3.5.224 which were particularly prevalent at and above the elevations where the 2012 melt layer is present. This correlation suggests that the intra-season melt variability on timescales of days to weeks likely aided melt layer formation by producing rapid freeze-thaw cycles that promoted near-surface refreezing before deep percolation could occur14.Of these metrics, we find that only the standard deviation of daily melt production during the 2012 melt season is well-correlated with the radar-inferred layer prominence at the continental scale  would require ~1–35% of the total MAR-modeled surface-melt between April 2013 and April 2017. On Transect C, it would require ~0.5–15% of total melt. This suggests that the changes we observe would not require a consistent wetting front advance to the depth of the 2012 melt layer but could be the integrated result of transient breakthroughs from preferential percolation27 over one or more years. The low spatial and temporal coherence in the stratigraphy of firn cores from this region20 is consistent with this mechanism  is most consistent with additional refreezing within thin (<0.1 m) layers sometime during the 2013 melt season. The greater densification (0.1–0.2 g cm−3) in the southwest may represent infiltration and additional refreezing over multiple years within thicker (up to 0.3 m) layers, consistent with the higher melt production, warmer firn temperatures, and shallower depth of the melt layer in this region. Of particular note, 2016 modeled melt production in the southwest was 2–3 standard deviations above the 1980–2011 mean with the 2012 melt layer buried only ~3.5 meters deep at the time.While the field observations of preferential percolation depths are limited and from transects much lower in the percolation zone14. The multi-year persistency and continued growth of these refrozen features highlight a feedback mechanism where, under appropriate firn temperature and surface-melt conditions, existing melt layers can promote the ongoing development of low-permeability horizons by reducing vertical percolation pathways and concentrating refreezing near the surface, even if the original melt layer is not itself fully impermeable. This is analogous to the ice lens aggregation mechanism proposed for the growth of ice slabs9, but in these interior regions, an extreme melt season can be the key catalyst that initiates this aggregation, rather than a multi-year excess melt. This implies that the frequency of extreme melt seasons relative to the rate at which the long-term melt to accumulation ratio allows new pore space and cold content to regenerate above the most recent melt layer36 can alter the water storage capacity of near-surface firn and its response to ongoing surface melting in the ice sheet interior. This timescale is critical because Greenland has already experienced five recording-breaking melt seasons since 200040, with the 2019 season second only to 2012 in total melt extent40. Therefore, while the 2012 melt layer will eventually be isolated below new accumulation, this trend suggests that new extreme melt season ice layers will continue to form, making these processes of continued relevance.These observations of continued densification or ice accretion imply that, in some parts of southern Greenland, the 2012 melt layer was of sufficiently low permeability to concentrate new refreezing at or above its horizon, leading to the continued strengthening of this aquitard even during the subsequent typical melt seasons. Similarly, in south-central Greenland, we find evidence that the 2012 melt layer developed atop an existing melt layer that initially formed following extreme melt in 2010  variations in the near-surface, and because any correction would increase the apparent density of the melt layer  of the data to detect potential ice layers. We select all radar returns between the surface and the depth where the 20-point running mean of the radar trace is <30 dB above the noise floor or the last sample before the surface multiple, whichever occurs first. We detrend the log power data using a 5th order polynomial fit to the mean of the upper and lower envelope of the trace and take the standard deviation of the deepest two-thirds of the data to be indicative of typical seasonal variability. Any layer peak which exceeds three times this seasonal variability threshold is considered a melt layer detection. This threshold has been shown in simulation to detect 78% of ice layers between 0.02 and 0.3 m thick, a limit set by the physics of the imaging problem rather than an algorithm or threshold selection19. We exclude all measurements where aircraft roll exceeds 0.05 radians, as they are radiometrically unreliable due to power loss from the antenna beam pattern.We apply a threshold detection algorithm18. For layer detection algorithm validation, we use a statistical background density model based on those cores to model realistic seasonal variations in density, set the mean density profile based on low-resolution cores from our regions of interest, and use a density for refrozen ice determined by in-situ measurement19.This simulation method has been shown to effectively reproduce Accumulation Radar data from North Greenland Traverse core sites where sufficiently high-resolution density data has been collectedIn general, we expect that this layer detection method produces a conservative estimate as it relies on a measure of ice layer brightness which may be suppressed by roughness, discontinuities, variable density, or receiver noise in real radar data. Additionally, the density inversion described below allows us to better constrain the physical conditions rather than assume that every detection indicates a layer whose density is that of solid ice.We assess the lateral connectivity of the melt layer using a 3 × 3 moving kernel centered on each melt layer peak detected from the radar data. Every adjacent detection in the far right or left columns of this kernel receives one point and this score is normalized by the expected score for a fully laterally connected layer . We use a statistical density model19 with a variability envelope of 0.04 g cm−3 to simulate the background firn density profile. We then simulate the expected mean layer reflectivity for a given layer density, thickness, and background firn density using a 1D multilayered dielectric model of radar scattering19. We run simulations for a parameter space consisting of layer densities from 0.6 to 0.92 g cm−3 in 0.01 g cm−3 steps, layer thicknesses from 0.01 m to 0.3 m in 0.005 m steps, and background densities from 0.35 to 0.55 g cm−3 in 0.05 g cm−3 steps.50.For each melt layer peak detected by our threshold algorithm, we compare the observed, calibrated radar reflectivity with the simulated reflectivity at the appropriate background density to constrain the possible layer thickness and densities consistent with observation. We derive the expected background firn density from a biexponential fit to the lower bound of the April 2012 firn density as modeled by the Institute for Marine and Atmospheric Research Utrecht (IMAU) Firn Densification Model−3, a mean density at which firn becomes impermeable51. We do this by taking the ratio of the number of consistent solutions which require a density in excess of 0.81 g cm−3 to the total number of consistent solutions within the parameter space.We find that we cannot meaningfully constrain layer thickness, as the range of consistent thicknesses typically spans most of the parameter space. We bound the density in two ways. One is the absolute minimum density that is consistent with the observed reflectivity. This is the maximally conservative metric for the layer density. Second, we calculate the probability that the observed radar reflectivity is consistent with a melt layer whose density exceeds 0.81 g cmSimilar to the layer detection algorithm, inversion by empirical comparison would require a robust collection of spatially and temporally coincident field observations spanning most reasonable configurations of layer thickness, spacing, density, and background density. Inversion by comparison to forward modeling allows for a more robust exploration of the parameter space. However, given the assumptions necessary to this process, we report the most conservative metric of the minimum density consistent with our observations.52, extract all detections within ±4 samples (~1.2 m) of that depth and manually confirm that this region included the distinct melt layer. Frequently, the 2012 melt layer consists of a 1–2 m thick stack of ice layers, so we collapse the layer in the vertical by taking the metric maximum within each trace. We then average over 1 km horizontal bins. If a 1 km bin has <10 valid samples in the horizontal (due to aircraft roll or lack of detections), we set that bin to NaN.To semi-automatically extract only the 2012 melt layer from the processed data, we calculate the approximate depth of the 2012 summer surface with the Herron and Langway modelLp is the layer prominence, Lc is the average layer lateral connectivity  in the 1 km bin, and LI is the average probability that the layer density exceeds 0.81 g cm−3  in the 1 km bin.We finally calculate the layer prominence metric according to Eq. True validation of radar-inferred parameters against in-situ observations would require many firn cores that are spatially and temporally coincident with the radar transects, ideally with multiple measurements within the radar footprint. Existing observations collected for other purposes largely do not meet these requirements. However, given the significant extent of the 2012 melt layer, we can at least qualitatively compare our results to observations within a few kilometers of the radar flight lines. The radar data is primarily responsive to sharp vertical variations in dielectric constant and therefore density but represents a spatial average over the horizontal footprint (about 20 m for this system). By contrast, firn core density measurements are a vertical average over the sampling depth but representative of only a single point in space. As a result, these measurements are not entirely equivalent, but the qualitative comparison does provide a sense for whether our radar-derived parameters are consistent with the physical state of the ice sheet at a small scale. Supplementary Fig. M is the total melt in meters of water equivalent, ρw is the density of water, c is the heat capacity of ice, ρf is the volume-weighted mean firn density, and Tf is the absolute value of the mass-weighted mean firn temperature in degrees Celsius35. M, ρf, and Tf are derived from the MARv3.5.2 3D model outputs. The MAR simulations were forced at the boundaries by NCEP-NCAR reanalysis data, and the output is provided at 20 km spatial resolution. The depth resolution varies from 10 cm in the upper 2 m to 5 m in the deeper portion of the firn. We quantify the variability in 2012 summer melt rates as the standard deviation of the daily melt production time series in each grid cell from the same MAR model run. We quantify the correlation between the radar-inferred layer prominence and each climate variable using the distance correlation coefficient53 since the relationships are non-linear, non-monotonic, and not easily parameterized  We measure the distance from the surface return to the bottom of the perched water table return in Figure 12 from Miège et al.42 in 12 locations and estimate the mean depth of the water table in the radar data to be 7 ± 0.3 m.We estimate the April 2014 depth of the 2012 melt layer near Helheim Glacier using the University of Washington Community Firn ModelSupplementary InformationPeer Review File


Cellular function is based on protein–protein interactions. A large proportion of these interactions involves the binding of short linear motifs (SLiMs) by folded globular domains. These interactions are regulated by post-translational modifications, such as phosphorylation, that create and break motif binding sites or tune the affinity of the interactions. In addition, motif-based interactions are involved in targeting serine/threonine kinases and phosphatases to their substrate and contribute to the specificity of the enzymatic actions regulating which sites are phosphorylated. Here, we review how SLiM-based interactions assist in determining the specificity of serine/threonine kinases and phosphatases, and how phosphorylation, in turn, affects motif-based interactions. We provide examples of SLiM-based interactions that are turned on/off, or are tuned by serine/threonine phosphorylation and exemplify how this affects SLiM-based protein complex formation. Cellular function relies on interactions between globular domains and short linear motifs (SLiMs) that are commonly found in the intrinsically disordered regions of the proteome . SLiMs bPhosphorylation occurring on serine, threonine and tyrosine residues (the O-phospho-proteome) has been most extensively studied. In addition, N-linked , A-linked (aspartic and glutamic acid) and S-linked (cysteine) phosphorylation have also been reported and added to the versatility of this PTM . Mass spIn this review, we highlight the roles of SLiMs in orchestrating serine/threonine phosphorylation and dephosphorylation, and how motif phosphorylation alters interactions between binding partners . We focuWe do not attempt to cover the literature comprehensively but use representative examples and recent studies as illustrations, as well as highlighting the functional consequences on different cellular processes.AGC , CAMK , CK1 (casein kinase 1), CMGC (cyclin-dependent kinases (CDKs); mitogen-activated protein kinases (MAPKs); glycogen synthase kinase 3 (GSK3); CDK2 like kinases (CLKs)), STE , TK (tyrosine kinases), TKL (tyrosine kinase-like kinases) and atypical group of kinases  motif found in the two substrates kinases WNK1 and WNK4 ,69. A prThe CDKs need a separate subunit, called cyclins for targeting substrates to the catalytic units. Due to their cell cycle stage-dependent accumulation and degradation, cyclins emerge as key for controlling cell cycle progression ,71. The As described, targeting of serine/threonine kinases to their substrates does at least for a subset of enzymes involve SLiM-based interactions. This contributes to determining kinase substrate specificity in a time- and space-dependent manner. The enzymes phosphorylate in turn sites in the intrinsically disordered regions of their target proteins, potentially affecting the binding properties of their SLiMs in different ways. Phosphorylation of SLiMs may turn on interactions with obligate phospho-binders, which are commonly referred to as readers of the phosphorylation code . AlternaThe obligate phospho-binders described here require a phosphorylated serine or threonine for binding. They include for example the 14-3-3 proteins, the forkhead-associated (FHA) domains, the BRCA1 C-terminal (BRCT) domains, the peptidyl-prolyl isomerase 1 (PIN1) WW domain, and several WD40 repeat proteins ,81. ThesThe 14-3-3 proteins represent the first discovered, and likely most well-studied, pS/T binding domains. The seven 14-3-3 isoforms  commonly form hetero- and homodimers . The proDue to their abundance and large number interaction partners, 14-3-3 proteins have been implicated in numerous cellular processes, such as signal transduction, cell cycle progression, apoptosis and metabolism ,84,92 F. For insAmong the less recognised phospho-binding domains are the kinase-dead GUK domains of MAGUKs (membrane-associated GUKs). MAGUKs are scaffolding proteins, which are abundantly present at cell–cell junctions and pivotal for biological processes such as cell polarity, intercellular communication and cell adhesion  Figure . They arSome phospho-binding domains distinguish between pS and pT. For example, the BRCT domain specifically bind pS-containing SLiMs. The domain was, as the name suggests, originally identified in the tumour suppressor gene BRCA1 (breast cancer type 1 susceptibility protein). The BRCA1 BRCT domain was found to bind to the S990 phosphorylated transcription regulator protein BACH1 in the context of the pSxxF motif ,101 Fig. The obsThe FHA domains represent a domain family that recognises phospho-peptides with a strict requirement for pT ,111. The30-YLDpSGIHpSGAT-86) and cyclin E (374-LPSGLLpTPPQpSG-385), respectively. Both phospho-peptides pack against the narrow side of the channel formed by the β-propeller of the WD40 domains [8-(pT)PPY(pS)P-86) creating a phosphodegron recognised by FBXW7 [WD40 repeats are β-propeller domains that constitute one of the largest domain families in the human proteome . Several domains ,125  PDZ domain to the C-terminus of the β2-adrenergic receptor, which is enabled by serine phosphorylation at the p − 2 position [PSD-95, Disks large tumour suppressor and Zonula occludens 1 (PDZ) domains represent one of the largest domain families with over 260 PDZ domains found in over human 150 proteins. PDZ proteins are ubiquitous scaffolding modules involved, among many processes, in the organisation of signalling complexes at membranes, and in the trafficking of receptors  Figure . The canposition   and later found in a large number of other proteins [During autophagy, cytoplasmic components are engulfed by autophagosomes. The autophagy-related protein 8 (ATG8) proteins are central components of the autophagosome forming machinery and can act as membrane scaffolds and cargo receptors . The ATGproteins ,160–162.The LC3–LIR interactions have been shown to be augmented by ligand phosphorylation at different positions. Several studies have reported that phosphorylation of the p − 1 position upstream of the motif enhances the affinity of the interactions . Phospho346-DSSMVpSKRFRF-356) [354-ARKRKPSP-361) in IRF2BP2 (interferon regulatory factor 2 binding protein 2) was found to be necessary for its nuclear localisation [Along the lines of regulation of targeting motifs by phosphorylation, there are also ample reports on how nuclear localisation is phospho-regulated . KaryophFRF-356) . In contlisation . No specTaken together, the tuning of affinities of SLiM-based interactions may be an underappreciated aspect of the functional consequences of phosphorylation due to the large number of proteins engaging in phospho-modulated binding events.Finally, we focus on the SLiM-based docking interactions of the protein phosphatases that counteract the actions of kinases and thereby contribute to controlling the specificity and length of phosphorylation events. In contrast with the common structure of the kinases , phosphaIn addition to the serine/threonine phosphatases discussed here, there are also dual-specificity phosphatases (DUSPs), which may also use motifs for substrate targeting as recently convincingly outlined for yeast CDC14 . The phoPP1 is responsible for catalysing a large proportion of eukaryotic protein dephosphorylation events . There aSpecificity for PP1 catalytic units among the regulatory domains can be further obtained by auxiliary motif-based interactions involving motifs in the tail regions flanking the catalytic core of the enzyme. Recently, it was shown that TP53BP2 (apoptosis-stimulating of p53 protein 2) binds to the PP1 RVxF site and uses its SH3 domain to discriminate between different PP1 isoforms based on the presence or absence of a PxxPxR motif in the PP1 C-terminal tail .PP2A is a major phosphatase in human cells. It forms a heterotrimeric holoenzyme complex composed of a catalytic subunit (2 subunits), a scaffolding subunit (2 subunits) and a regulatory subunit (15 different subunits of four different classes)  Figure . The det2+/calmodulin-activated serine/threonine phosphatase that links Ca2+ signalling to the phosphorylation state of a large number of proteins [2+-binding regulatory domain (CNB). Calcineurin binds to two SLiMs, PIxIxIT and LxVP, of which the PIxIxT motif is the main ligand [86-PQIIIT-91 motif was recently found in the protein spermatogenesis-associated protein 33 [+/H+-exchanger 1 [in silico predictions and cell-based approaches [Calcineurin is a ubiquitously occurring Caproteins . It is an ligand . It was n ligand , and hasotein 33 . The PIxotein 33  and targotein 33 . The LxVotein 33 ,203  can be negatively regulated by phosphorylation of the p + 5 position. Modulation of phosphatase docking interactions by phosphorylation may thus be a common theme.PP4 is a nuclear and chromatin-associated phosphatase . SimilarWe have here surveyed motif-based interactions that contribute to the specificity of the writer–reader–eraser system of serine/threonine phosphorylation. It is a daunting topic, given the extent of literature. Nevertheless, we expect that the current literature only covers a fraction of all phospho-regulated SLiM-based interactions, and only part of the docking interactions of kinases and phosphatases. Moreover, as showcased here, we would expect that a large proportion of the SLiM-based interactions can be regulated by phosphorylation within the motif or its flanking regions. Much effort will thus be needed to obtain a better understanding of the functional roles of the hundreds and thousands of phosphosites in the proteome, the enzymes that act on them, and what consequences the modification has on SLiM-based interactions. The effects on PTMs on SLiM-based interactions are of course not restricted to phosphorylation, but can be expanded to other PTMs, such as methylation, acetylation, ubiquitination and sumoylation. Along these lines, in the larger scale analysis performed in the switches.elm compendium, it was suggested that more than half of the known validated SLiMs are pre- or post-translationally modulated and regulated . Hence aSLiM-based interactions have previously mostly been identified in low-throughput experiments, but with the use of different display and array-based systems, SLiM-based interactions are now being uncovered on a larger scale . Such la


Chronic wasting disease (CWD) continues to spread in both wild and captive cervid herds in North America and has now been identified in wild reindeer and moose in Norway, Finland and Sweden. There is limited knowledge about the variety and characteristics of isolates or strains of CWD that exist in the landscape and their implications on wild and captive cervid herds. In this study, we evaluated brain samples from two captive elk herds that had differing prevalence, history and timelines of CWD incidence. Site 1 had a 16-year history of CWD with a consistently low prevalence between 5% and 10%. Twelve of fourteen naïve animals placed on the site remained CWD negative after 5 years of residence. Site 2 herd had a nearly 40-year known history of CWD with long-term environmental accrual of prion leading to nearly 100% of naïve animals developing clinical CWD within two to 12 years. Obex samples of several elk from each site were compared for CWD prion strain deposition, genotype in prion protein gene codon 132, and conformational stability of CWD prions. CWD prions in the obex from site 2 had a lower conformational stability than those from site 1, which was independent of prnp genotype at codon 132. These findings suggest the existence of different CWD isolates between the two sites and suggest potential differential disease attack rates for different CWD strains. Transmissible spongiform encephalopathies (TSEs) are infectious neurodegenerative diseases, also referred to as prion diseases that affect a number of mammals, such as humans, sheep, goats, cattle, cervids, minks, and felines. Chronic wasting disease (CWD) is a prion disease of animals in the family Cervidae. Natural transmission of CWD has been detected thus far in deer, elk, reindeer, and moose, and the disease continues to expand in wild and captive populations throughout the United States and Canada ,2. CWD hRES) in the body, and the stability of the PrPRES protein within the same species and TSE type  from this site were subjected to biochemical analysis .Elks were added to the site over a 60-year time period for various reasons and from multiple sources, including wild-captured adults from within the same state, such as orphaned neonates, 6–8 month old elk calves, or natural additions from the resident population. Naïve animals introduced to this site were not part of the study and are mentioned simply to increase understanding of the location history.E coli. and the plasmid DNA extracted with the PureLink Quick Plasmoid Miniprep Kit . The purified PCR products and the cloned plasmids were subjected to sequencing by MCLAB  using primers DePRP223 and DePrP224. The final PrP genotypes were determined based on a combination of sequencing traces from the PCR products and sequences of multiple clones from each elk DNA sample.Genomic DNA was purified from frozen brain tissues using a standard phenol-chloroform extraction procedure. The purified DNA samples were subjected to two more rounds of phenol/chloroform extraction to remove any residual prions before further manipulations. The PrP-coding region from functional PrP genes was amplified by PCR  using the Phusion high fidelity polymerase  and primers DePrP223 (ACACCCTCTTTATTTTGCAG) and DePrP224 (AGAAGATAATGAAAACAGGAAG) that had been previously phosphorylated by T4 polynucleotide kinase . The PCR products were recovered after gel electrophoresis in 1% agarose gels using the QIAquick gel extraction kit , cloned into the EcoRV site of pstBlue-1, transformed into RES as opposed to the normal endogenous cellular prion protein (PrPC) or unfolded PrP. We used this kit to measure the amount of PrPRES in tissue samples after incubation in GdnHCl as described in Vrentas et al.  to get 10% brain homogenate in 1x lysis buffer, which was then incubated at −80°C for at least 12 hours. After thawing on ice, the 10% brain homogenate in the lysis buffer was briefly sonicated (about 10 s), centrifuged for 8 minutes at 500 g at 4°C, and the supernatant carefully transferred to a fresh tube. Aliquot of the supernatant (1–2.5 ul for elk samples and 15ul for human sCJD type 1 and type 2 controls) was taken, and 10% brain homogenate from FVB/PrPKO mice was added to the elk sample tubes to obtain a total of 15 ul of brain homogenate for each sample tube; this was done to ensure highly similar total protein contents in each sample tube for the subsequent proteinase K (PK) digestion and comparable PrPThe functional PrP gene sequences in the elk samples were determined by sequencing the PCR product of the PrP ORFs and multiple clones of the PrP ORF PCR products for each sample. Site 1 animal A was methionine/leucine (ML), and animal B was methionine/methionine (MM) at codon 132. Site 2 animals A and B were MM, while C and D were ML at codon 132 . IntroduA year after its introduction, the female, MM, direct contact elk (S13) was found to be CWD-positive by rectal biopsy and was euthanized. The ‘native’ 11-year-old female that she was co-housed with was also found to be CWD-positive by rectal biopsy at the same time. Neither was clinical. Three years after the introduction, an asymptomatic, direct contact, MM male (801 T) was found to be CWD positive by rectal biopsy. After 5 years, the introduction aspect of the study at Site 1 was terminated. All-remaining animals, who were in excellent body condition and showed no clinical signs of CWD, were euthanized, necropsied, and tested for CWD by IHC. All the remaining males (indirect group n = 6 and direct group n = 6) were non-detect for CWD by IHC in medial retropharyngeal lymph nodes and obex, the only tissues examined.The fibril stability assay indicated that Site 1 animal samples had a discernibly higher resistance to unfolding by the chemical denaturant GdnHCl when compared with those of Site 2 animal samples, with the mean midpoint of the unfolding curves at 2.53 M and 2.12 M, respectively . The difRES in Western Blot was indistinguishable between the two groups when probed with three different antibodies that target different regions of the PrP protein  have been shown to strongly influence the proliferation and incubation period of CWD in elk ,31. The RES) in the body, and the stability of the PrPRES protein within the same species and TSE type. Despite this, we feel that the data we have strongly suggest the presence of two different CWD strains.In this study, we use the term isolate to describe our findings versus strain, as we were unable to conduct transgenic mouse bioassay, and lacked tissues to compare brain and organ pathology to address all the previously described criteria for a prion strain, such as variations in clinical signs, incubation period, distribution of the misfolded prion  or by directly influencing the de novo development itself (deformed template theory) [It is clear from looking at the literature that the phenomenon of strains or isolates in prion diseases is complex. Studies have shown that multiple prion strains can be present in a single sample and that the ratio of strains presents in the sample may alter which form becomes dominant ,12,33,34 theory) ,40. The  theory) . It is uBecause the sample numbers in this study were low, stronger conclusions could not be drawn. However, our findings suggest that prion isoforms may influence the infectivity of the CWD prion and thus how the disease behaves in the landscape. It is our hope that these findings add another piece to the puzzle of CWD prion diversity and the potential evolution of CWD prions over time. More research is needed to understand the role and significance of CWD isoforms in disease transmission.


Background and Objectives: Different types of food introduced gradually in the diet will expose children to different food allergens, increasing the chance of developing allergic diseases. The aim of our study was to determine if allergen-specific IgE values can influence, depending on the diet, the prediction of remission of urticaria in children. Materials and Methods: This prospective study was conducted in 132 patients diagnosed over two years with urticaria, admitted to “Sf. Maria” Clinical Pediatric Hospital Iaşi. Total IgE assay was performed by ELISA, and determination of specific serum IgE by the CLA System Quanti Scan method . Data were gathered and statistical analysis was performed using statistical software SPSS, using descriptive and inferential statistics. Results: The determination of specific IgE to food allergens was performed on a total of 132 cases. The values of specific IgE were positive for one or more food allergens in 84 patients (63.64%). The most common allergens involved were: cow’s milk in 33.3% cases, egg white in 22.6% cases, and hazelnuts in 11.9% cases. The specific IgE values for the different types of food included in our study had a predictive value for disease remission. Conclusions: The determination of specific IgE confirms the presence of a particular food allergen and may have predictive value for the future development of an allergic manifestation. Lifestyle changes , exposure to irritants and pollutants, lead to changes in the body’s immune reactivity, resulting in an increased prevalence of allergic diseases.Based on current information on molecular and cellular biology, histopathology, symptom score, and impact on quality of life, the current trend is to consider urticaria as a systemic disease and not as an organ disease. This approach to the condition will have an impact on the recommended therapeutic and prophylactic behavior. Awareness precedes or accompanies the early symptoms of allergy, and its identification can be considered a marker for the introduction of prophylactic measures. The identification of the food factors involved as well as the therapeutic possibilities present important research objectives. Studies on urticaria triggered by food allergens place its onset at an early age, when there are difficulties in establishing the diagnosis: atypical clinical forms, variations in age-dependent sensitization markers, reduced compliance for skin tests, or challenge tests. Early identification of allergic sensitization in children with familial atopic terrain is essential in avoiding exposure to allergens and introducing specific drug treatment or immunotherapy. An immune mechanism-specific antigen triggers food allergy after exposure to a food allergen ,2,3. In We conducted a prospective study over a period of two years on a group of 132 patients admitted to the Second Pediatric Clinic Emergency Clinical Hospital for Children Iași. The study aimed to establish correlations between diet, immune response, and urticaria in children.Criteria for inclusion in the study were clinical diagnosis of urticaria, suggestive anamnesis of patients who had one or more episodes of acute urticaria in which a causal link with a dietary factor was suspected, or atopic family background.The study protocol was approved by the Ethics Commission of the University of Medicine and Pharmacy “Grigore T. Popa” Iasi. The patient’s informed consent was made known to the patient and the child’s parents or legal guardians. In carrying out the study, national legislation and European directives on the protection of human rights and human dignity in the application of biology and medicine were complied with . The evaluation of patients was performed by obtaining epidemiological, anamnestic, general clinical examination, laboratory examinations , and skin prick tests. The study protocol followed the clinical, biological, and immunological aspects longitudinally as well as the specific recommended therapeutic conduct. Patients were evaluated at the first hospitalization, then periodically at variable intervals depending on the evolution, when the presence/absence of urticaria and the specific IgE level were assessed. The determination of specific IgE was performed at the beginning as a baseline and after six months. Only foods in which the results showed specifically positive IgE were excluded from the diet. The biological investigations performed consisted of routine analyses and immunological investigations. Total IgE assay was performed by ELISA, and determination of specific serum IgE for 15 of the most common food allergens and aeroallergens  by the CLA System Quanti Scan method .Specific aeroallergen IgE determination was performed for Graminaceous mixture pollen gx6: Cynodondactylon, Loliumperenae, Sorghum halepense, Bromusinermis, Holcuslanata, Paspalum notatum; Dermatophagoides pteronyssinus; Dermatophagoides farina; Ambrosia elatior pollen; dog hair, and cat hair.A total of 53 skin prick tests were performed, of which 52.63% were positive.t-test, Pearson-χ2, Fisher’s test, ANOVA test, linear regression, logistic regression, and Cox model regression analysis were used. The significance threshold was p = 0.05 (defined as sufficient in the literature). For continuous variables, descriptive statistical indicators were calculated . Total IgE values do not show a homogeneous distribution, so the Kruskal–Wallis test specific to this type of data was applied to compare the values according to the age of the patients. Based on IgE values and the value of the disease activity score, ROC (receiver operating characteristic) curves were performed. Based on AUC values (area under the curve), it was possible to evaluate the predictability of IgE specific to each food allergen studied.Statistical analysis was performed using statistical software SPSS v.20 , and data were analyzed using descriptive and inferential statistics. Statistically significant differences were considered for a 95% confidence interval, using parametric or nonparametric tests. Student’s The determination of specific IgE to food allergens was performed on a total of 132 cases.Age distribution is detailed in Gender distribution was almost equal: 70 cases representing 53% were boys, and 62 cases representing 47% were girls.The values of specific IgE were positive for one or more food allergens in 84 cases (63.64%). The determination of specific IgE in food allergens was performed in 132 cases. Specific IgE values were positive for one or more food allergens in 84 cases (63.64%). The most common allergens involved were: cow’s milk (33.3% cases), egg white (22.6% cases), hazelnuts (11.9% cases), fish (9.5% cases), soy (7.1% cases), nuts (7.1% cases), wheat flour (6% cases), strawberries 92.4% cases), and rye flour (1.2% case). Statistical indicators of specific IgE vs. allergen type are shown in Based on specific IgE values, ROC (receiver operating characteristic) curves were performed for each type of food. For specific IgE values to be considered predictable for disease remission, the area under the ROC curve (AUC) must have values greater than 0.6. According to these values, the significance level of the test, for a confidence interval considered to be 95%, must be lower than the critical reference threshold of 0.05.IgEflour-wheat specific = 0.674, p = 0.038, 95% CI: AUC → 0.507–0.840)  .p << 0.01, 95% CI).It is observed that specific IgE-Egg White Values Decreased Significantly   .IgE cow’s milk = 0.528, p = 0.715, 95% CI: AUC → 0.366–0.691)  .The mean values in dynamics for specific IgE of hazelnuts, strawberries, and fish are given in p = 0.022, 95% CI), hazelnuts , and fish  decreased significantly , rye flour , soy , and nuts  decreased significantly   .Polysensitivity to food allergens and aeroallergens was correlated with respiratory allergic manifestations , occurring predominantly in the age group over four years, while atopic dermatitis was correlated with food allergy in children under four years.The specific IgE level varies depending on age, total serum IgE level, amount and duration of allergen exposure. In addition, the specific IgE level can be altered by exposure to cross-reactive allergens and by specific immunotherapy. There are studies that have correlated the level of specific IgE in food allergens with the risk of an allergic reaction in case of re-exposure to the incriminated allergen. The determination of specific IgE confirms the sensitization to a certain allergen and may have a predictive value for the future development of an allergic manifestation. The presence of specific food allergens in the first year of life is associated with an increased risk of pneumalergen sensitization, with the subsequent onset of a respiratory allergic condition. The analysis of IgE levels in different age groups  showed that total IgE levels are lower in older children compared to those in young patients.Sichereret et al. described in their study that food allergies, defined as an immune response to food proteins, affect about 8% of children and 2% of adults , but thePredictive factors regarding the resolution of allergy are: the characteristics of the initial reaction (isolated urticaria/angioedema versus other manifestations), the specific IgE level, the intensity of the skin reaction to the skin prick test, and the severity of atopic dermatitis.A negative skin test result has a high predictive value in children older than one year, while in children less than one year, its negative predictive value is lower due to the particularities of cutaneous mast cells .The clinical tolerance criterion was the absence of symptoms for six months from the time of reintroduction of the food. In the present study, oral tolerance was installed in 38 children, more frequently for cow’s milk and egg white.Dietary treatment in children with food allergies are aimed at eliminating allergenic food. The evaluation of the parameters that can determine the recurrence of urticaria by multivariate analysis showed that specific IgE values, compliance with diet, and treatment can be considered predictive factors for the recurrence of urticaria.Knowing the risk factors for recurrence allows patients to be included in the risk group and in patients with atopic dermatitis, the immune status must be evaluated in order to highlight an immune deficiency.There are a number of determinants involved in the prognosis of food allergies: ethnicity, sex, type of food, innate immune system, challenge dose, sensitization status, composition of the intestinal microbiome, and the presence of comorbidities ,6.Establishing the prognostic factors and the phenotype of food allergies is important in the prevention and management of these diseases. A more rigorous classification of allergic patients will make it possible to determine the severity of the exclusion diet and the timing of the reintroduction of the forbidden food . RegardiInduction of food tolerance is a specific process for each food, which is mediated by immunological changes, decreased specific IgE values, and specific spontaneous activation of basophils.Ruinemas-Koerts et al. and Sicherer et al. conducted studies on egg allergy by measuring the allergen specific component, ovalbumin specific IgE, using two methods: indirect luminescent oxygen channeling immunoassay (LOCI) and enzyme-linked immunosorbent assay (ELISA) . The comSicherer and Leung conducted a study on the diagnosis of food allergies by performing the oral food challenge (OFC) test and showed that 29% of the tests performed were positive, with most food allergens being peanuts, eggs, and milk ,13,14. SRegarding hazelnut allergy, recent research considers it to be useful in determining the diagnosis to determine the epitopes for the response to allergens in hazelnuts, along with the basophil activation test and the oral loading test ,18.Limitations and StrengthsThis study has several limitations. The determination of specific IgE for food allergens and the dynamic tracking of the specific IgE level can be an important landmark regarding the subsequent evolution towards an IgE mediated hypersensitivity reaction. Specific IgE determinations were performed for only 15 more frequently involved allergens and also did not perform the basophil activation test that could have been useful in the diagnosis and prediction of allergy.Through this study, we want to educate and train patients on food allergies, as well as to increase the responsibility of parents, starting with the newborn stage, when natural nutrition must be strongly promoted, and continuing with the subsequent stages in which, for the children with food allergies, the recommendations of a regime of eviction the incriminated allergens must be followed.Dynamic monitoring of specific IgE levels can provide information on the evolution of allergic manifestations. The decrease in the specific IgE level compared to the allergenic proteins precedes the installation of the immunological tolerance, and a sustained high level of specific IgEis associated with the persistence of the allergic phenomena. In the studied group, the dynamic tracking of specific IgE to food allergens showed a progressive decrease after the implementation of a removal program of the suspected allergen.Increasing parental responsibility for children’s nutrition must be emphasized and encouraged, especially for those with a familial atopic background, due to the fact that the prevalence of food allergies is increasing, which has a significant impact on pediatric patients.The diagnosis of food allergy is largely reliant on medical history and tests for sensitization, but allergen specific IgE specific use can improve diagnostic accuracy and predict the response to the different therapeutic measures.


Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment. Cloud computing (CC) has shown its strength for bootstrapping genomic data analysis in life science, and has unquestionably influenced earth science ,2. CloudTo this end, many of the efforts made to speed up processing systems for RSBD are from the information technology community (ITC) and geoscience community. Most researchers from ITC focus on scaling RS algorithms with big data processing frameworks (BDPF) in diversified cloud deployments . For example, Sun et al. proposed an optimized framework for RSBD processing in CC . They inOther than the above methods, more and more researchers from the geoscience community are adopting the Google Earth Engine (GEE) cloud platform to target the volume challenge of RSBD. GEE is a public cloud platform specialized for planetary-scale geospatial analysis, which improves the analysis of massive RS data that was not feasible using a desktop processing machine. The surging publications have testified to the rapid adoption of GEE . RecentlIn this study, we are the first to introduce the K8s operator model that explores the elastic computing paradigm in cloud platforms. We propose a framework that integrates K8s with containerized Spark, with consideration of their advantage and disadvantages. K8s is the standard for deploying containerized applications at scale in a multi-cloud environment. K8s operators can be considered to be a control loop for K8s applications to obtain customizable resources without an advanced declaration. Starting from version 2.4.0, Spark can run on K8s clusters in the client model. However, the inconvenience is that manual configurations are required in order to build networking channels between Spark diver (SD) hosts and Spark executor containers (SEC). Spark critical performance parameters should be manually predefined, which is a restriction to the use of elastic computing resources. The memory space and instance number of SEC are unchangeable once the algorithms are submitted. Our framework requires less parameter configuration. The framework only requires the hints on the memory allocated to the SD, and can update the resource configuration of SEC. Because the containerized Spark can be booted in a few seconds , the numThe remainder of this paper is as follows. 2/day). While the spatial and temporal resolutions are both high, the images are expensive. Therefore, a precision agriculture application would require the fusion of these images. State-of-art engines adopt HDFS to store unprecedented volume of RS images [As shown in S images . Spark-bS images . In this section, we propose a cloud-based processing framework. As illustrated in The cloud resource layer is the infrastructure as a service layer. This layer consists of storage, computing, and networking resources. The computing resources are virtual machine instances managed by hypervisors, bare-metal machines (BM), and cloud providers such as Alibaba, Amazon, and Google Cloud. The top layer is a unified cloud platform portal which supports user authentication, service accounting, and image and configuration management. The user authentication service is responsible for multi-tenancy access to cloud computing resources. The service accounting is responsible for security, pricing, and other aspects. The image management service manages image registration on the cloud platforms. The configuration management service supports all service configurations, such as defining monitoring strategies and updating resource quotas of applications.https://github.com/operator-framework (accessed on 23 March 2021)). These long-running storage services rely on network virtualization for communication with each other. Typically, network virtualization functions have different features. For instance, Weave Net creates a virtual network that connects containers across multiple hosts and enables their automatic discovery [In the service-oriented computing layer, we adopt a container-based microservices architecture for easily updating the lossely-coupled service components. Lightweight working components include RS file storage services, the RS object storage service, RS key-value storage services, the Prometheus service, the network function virtualization service, and K8s API services. K8s API is an open-source toolkit that manages the container-based applications in an effective, automated, and scalable manner  provides application management services for the users. After receiving the application specifications from the cloud portal layer, the AM registers the submissions as application objects (AO) in order to manage their life cycle. Application specifications are programmable with Yet Another Markup Language (YAML) files, which specify the types of applications, the container images used to run the applications, the restart policy when a failure occurs, and the specification of storage volumes and computing resources. The AM manages application dependency, retrying failed applications, updating application resource requirements, and scheduling task executions based on predefined policies. Once the application object has been built and the resource requirements have been mapped to the custom resources definitions (CRDs), the AM will create an application controller (AC) for the AO. The AC uses a working queue (WQ) to manage the instances of AOs. The WQ watches all types of events related to these AOs. Once created, the WQ records all of the events of the instance until its completion. In the framework, the Prometheus service would collect the failed or successful events, and would send the status and failed reasons to the WQ. The AC continually checks whether the K8s cluster can match the CRD, and uses a reconciling mechanism to request the K8s to create, update and delete AOs if the K8s cannot match the CRDs. For convenience, we call the procedure as the ‘K8s operator model’.We implement the application management service based on the K8s operator model. The Spark AM accepts the submissions of the user’s Spark applications. Then, Spark AM creates a Spark controller for the construction of a K8s namespace used by the Spark application. The Spark controller is a long-living micro-service. Once added to the working queue of the Spark controller, the Spark controller will create a Spark operator for the Spark application. The Spark controller manages the lifecycle of the Spark operator. The Spark operator watches the pods of the Spark driver and the executors of the application instance. We extend the original Spark operator to collect metrics such as the running counts, the failed counts, and the total number of pods. These metrics will be exports to the Prometheus monitoring service. The Spark controller can use the metrics to schedule the tasks of Spark applications.In order to improve the efficiency of Spark applications on K8s clusters, we propose a task scheduling mechanism (TSM) in this work. The key idea is to dynamically change the task affinities of the Spark application with the computing nodes. This is the only practical solution for Spark on the K8s model. We use the mutating admission webhook (MAW) of K8s to control the Spark pod assignments. MAW intercepts the resources request of the Spark pods and updates the pod specifications before these pods are scheduled to the computing node. The TSM considers the following constraints and conditions.Each computing node has a region label. Typically, the network communication cost in the same region is smaller than the inter-region communication. The Spark executor replicates the blocks of resilient distributed datasets and transmits them to other Spark executors for fault tolerance. Thus, the TSM preferably assigns Spark executor pods to the nodes in the same region. As we know, running too many pods on the same node would cause intense competition among the pods. Therefore, our scheduling algorithm avoids assigning many pods to the same node in order to mitigate the unbalanced workload. Currently, there are many workload prediction algorithms in the cloud. However, the adaptive workload prediction systems are dependent on sophisticated system architecture. Users may have no right to access these systems. Normally, the ratio of the failed counts of the task pods to the successful counts of task pods is a good indicator to reflect the workload characteristics. The newly added computing resources are typically less busy. As mentioned earlier, the Spark AM can update the resource requirement specification, and the Spark controller can therefore change the task pod assignment if this information is used in this context.ij means that hi has j cores, and Memij means that host hi has j megabytes of memory. Assume that the cores and memory requirements of the Spark driver pods (SDP) of the Spark applications are not changeable, and are defined by users. Spark application objects (SAOs) are added to the working queue (WQ), and the WQ will assign the priority (PRI) for each SAO. The spark executor pods (SEPs) execute parallel tasks during their life cycle. Because the parallel tasks are similar in the transformation chain of Spark applications, we assume that each pod executes only one task for discussion convenience. Here, the Core and Mem represent the number of cores and the amount of memory storage used by each SEP, respectively. AFij represents the degree of affinities of the SEPi with the host hj. AFij = 0 means that the i-th pod should not run on the hj. AFij = 1 means that the j-th pod should run on the hj. SEP has an age attribute in terms of the total running time of the pod in a host. The constraints are formally expressed as follows.Based on the above condition, the TSM for the Spark controller should operate under the constraints of the following equations. Suppose there are H hosts in R regions, where each host has several cores, and memory space. Coreµ, the probability of the number of completed SEPs satisfies the Poisson distribution. The larger the probability is, the more stable the host is. Here, the x is the actual number of completed pods; e is approximately equal to 2.718. The goal is to minimize the time cost, given the dynamic nature of the SEPs.We use CP and FP to represent the number of successes and failures of the executed tasks of a SEP, respectively. Once a SEP has finished a batch of tasks successfully, it would continue processing the next batch. Given a period 2) to a value range from 0 to 1. The TSM will rank the affinities and generate the scheduling policy in order to assign each SEP to a computing host. The TSM also dynamically changes the priority of each SAO in the WQ; if the failure rate of an SAO is too large, it will be rescheduled with a higher priority. Finally, the TSM will notify K8s to update the resource requirement and wait a short period.TSM works as follows. First, TSM intercepts the resource specification of Spark applications. Push them to the WQ and initialize a default priority for each SAO. Then, the TSM checks the total CPU core requirement of the SDPs in order to decide whether to request new computing resources from the cloud platforms. The region of an SDP determines the assignment of SEPs of the same SAO. For each SAO, TSM assigns the SEPs to that region with sufficient memory storage. If the memory storage is insufficient, the TSM assigns the SEPs to other regions with small networking communication latency with the SDP. Otherwise, it would traverse the hosts of the region and choose the least recently used computing host. In the reconciling stage, the TSM dequeues the SAO that has the highest priority. Typically, the number of SEP pods is large, while the number of computing hosts is small. Therefore, dynamically changing the affinities of the SEP pods with the computing hosts is beneficial for the improvement of the efficiency of the SAO. The TSM employs a user-friendly strategy to satisfy their expectation. As shown in Now that the framework has been built for Spark applications on cloud platforms with the scheduling mechanism, another question is how to design efficient parallel fusion algorithms for big RS images. Currently, most Spark-based RS algorithms are implemented with low-level resilient distributed datasets (RDDs) . ConsideIn order to solve the problem, we incorporate the Spark DataFrame abstraction for the mapping of RS big images to the resilient distributed tables. The Spark Dataframe supports the window operation on the big tables with user-defined aggregation functions (UDAF) . The winp, the algorithm searches ‘similar pixels’  with the same land cover in the window from the coarse-resolution image at time T0, and leverages the reflectance changes of all of the similar pixels within the window of the coarse-resolution image from times T0 and Tp. As shown in We present the parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) based on the above model by analyzing the procedure of the ESTRAFM and comparing the novities of the model to the strip-oriented programming model (SPM). ESTARFM is the most widely-used fusion algorithm that assumes the spectral reflectance difference of the RS images derived from different satellite sensors capturing the same area in the same day can be modeled by linear functions . SpecifiFirst, the searching of similar neighbor pixels (SSP) is conducted. The input of the algorithm consists of the following five scenes of RS images: the fine-resolution images at times tm and tn, and the coarse-resolution images at tm, tn, and tp. The output of the algorithm is the estimated value for each pixel of the fine-resolution image at tp. In order to distinguish the pixels of the input images, the algorithm uses the center pixel abstraction to represent the pixel in the output image. The SSP identifies the similar pixels for each pixel within a predefined window of the fine-resolution images at times tm and tn, respectively. The output of the SSP is the intersection of the two results to obtain an accurate set of similar pixels. Supposing that we are searching similar pixels within a 5 × 5 window from the two fine-resolution images, the implementation of PESTARFM using SPM requires fives filtering and mapping operations and four joining operations. Second, ESTARFM needs to predict the reflectance changes by calculating the weighted contribution of similar pixels. The contribution of each similar pixel is weighted by the location of the pixel and the spectral similarity between the fine- and coarse-resolution pixels. Intuitively, the higher similarity and smaller distance of the similar pixel to the central pixel has a higher weight. In order to obtain a good representation of the spectral similarity, ESTARFM combines the spectral reflectance of the coarse and the fine resolution images of the two different dates and calculates the correlation coefficient between each similar pixel and its corresponding coarse-resolution pixel of the two images. Therefore, the second stage will require three joining operations to obtain the reflectance changes. In the third stage, ESTARFM calculates the conversion coefficient in order to determine the changing rate for each similar pixel in a search window. This stage is rather similar to the second stage, which also requires three joining operations with SPM. Finally, in order to calculate the reflectance for each center pixel, the final stage requires two joining operations with the SPM. The first operation is to calculate the temporal weight, and the second operation is to calculate the final reflectance from the two estimations according to the temporal weights.https://github.com/huangwei2913/RsImageFusion (accessed on 23 March 2021)). First, the four joining operations map the five images to a distributed table; second, the window operation calculates the standard deviations of the images at Tm and Tn. Typically, this stage is quite fast. The deviations can be easily broadcast to the distributed table. We implemented a “RSImageFusionUDAF” class to manage the window operation. The processing chain that includes searching similar pixels (SSP), calculating the weighted contribution of the similar pixels (CWSP), calculating the conversion coefficient, and estimating the reflectance, can be executed along with the window moving operation. We will check that in the latter sections. For simplicity, PESTARFM means the PESTARFM based on the WPM, if it is not specified in the later sections.While PESTARFM based on the SPM requires nine joining operations, the PESTARFM based on the WPM requires only four joining and one window operation. The PESTARFM source code based on the WPM refers to ). In order to simulate an elastic K8s computing cluster, we used the OpenStack cloud to add on-demand computing resources. As demonstrated in our earlier work, we can use an auto-scaling group (ASG) to easily add VMs. More specifically, when a VM boots up, the init daemon of the VM instance will automatically join the K8s cluster by issuing a kubeadm join command with a predefined token to the endpoint of the K8s API server. We then manually boot a VM instance  that connects to the K8s master node. We used a Heat template to define the scaling policy, which makes the OpenStack cloud provision VM instances when the CPU utilization rate is above 80%, and lasts about 1 min. The minimum, the desired, and the maximum of VM instances were set to 1, 5, and 10, respectively. The flavor of these VM instances was the same as the manually booted VM instance. There are many integrated solutions for managing different cloud resources [https://github.com/fluxcd/flux2 (accessed on 23 March 2021)). Flux uses the kubectl command line interface to change the versions in the CRDs of applications. We used a script to update the version of the Spark-operator image in a short period (10 s in the experiment). Note that we did not change the specification of the Spark driver pods; we only changed the specification of the Spark executor pods without disturbing the execution of the Spark algorithms. By using the approach, we can easily change the replications of the Spark executor pods and the memory space size. Although this approach depends on the external tool to change the computing resources, the approach reduces the cost concerning the rewritten Spark AM. We set the maximum memory space for each SEP to 10 GB in order to consider the K8s master node resource constraints.Because our earlier work demonstrated the strength of Spark to process a massive volume of RS data, the primary goal of this study was to investigate the efficiency of the PESTARFM. All five RS images had 24,886 × 23,655 pixels (each pixel takes four bytes). In order to save some space, we will not introduce the images in the following experiments. We first set up a K8s cluster on top of the four nodes. Because K8s can run on almost all cloud platforms, we used the available computing nodes to simulate a resource-limited computing environment. We built a private local repository on the master node to store the relevant Docker images. We used an NFS client provisioner to ingress the external storage for the containers. We used Flannel to fabricate the container network. The Docker version was 18.06.1-ce, which was installed on every host. We used the Apache Ambari Blueprint (latest version 2.7.5) to construct a Hadoop cluster to store the RS images. Specifically, the containerized Ambari provided shell commands to easily create a containerized Hadoop cluster. All of the RS images were of Geotiff format, and were stored in the Hadoop cluster. The Spark operator consisted of the Spark AM, controller, and operator, and was installed by using Helm charts  to improve their efficiency. We compare the efficiency of PESTARFM using only ASG, with the efficiency of PESTARFM using ASG and TSM, to quantify the performance of the TSM. Typically, the partition number P determines the task size of Spark executors; we set the parameter P to 40 in the experiment in order to partition the data into smaller RDD blocks. As mentioned earlier, users can express the task scheduling expectation by using the weights. We set all of the weights to 0.33 in the experiment. We used the same operation in the former section to remove and construct the computing resources. The parameters DM and EM were set to 8096 MB and 2048 MB. As shown in 1 expresses the expectation to the TSM to dispatch tasks to newer computing hosts, and the weight W2 expresses the expectation to the TSM to dispatch tasks to the least overwhelmed computing host. The last W3 can be considered as the expectation to choose a computing host that has the maximum probability to finish tasks in a given period. We use  to represent the weight set. Because we cannot elaborate all of the subsets, we chose the following four weight sets as , ,  and . The period for the statistical calculation of the probability of the Spark executor pods was set to 10s in the experiment. The Spark parameters were the same as were used in the second experiment. As shown in We conducted the third experiment in order to investigate the impact of different weights of the TSM on the efficiency of PESTARFM. As discussed earlier, the weight WAs it is known, the Hadoop yet another resource negotiator (YARN) has been widely used for cluster resource management, and can also support on-demand containers for BDPFs. We can use a containerized Ambari shell to build a containerized Hadoop cluster. In this section, we investigate the performance of PESTARFM using the containerized Hadoop cluster, and compare the performance with that using the proposed framework. For a fair performance comparison, we removed all OpenStack cloud resources, disabled the TSM, and reserved the K8s cluster before the fourth experiment. We first constructed a containerized Hadoop cluster and uploaded all RS images to the Hadoop cluster. In order to set the maximum memory for the YARN containers to 60 GB, and to restrict the maximum memory of each YARN container to 10 GB, we built a Docker image that used the Hadoop-3.1.2.tar.gz and jdk-8u202-linux-x64 tar.gz. We also packaged the RS fusion class to the Docker image. We used the containerized Ambari to construct the Hadoop cluster with the Docker image. As shown in The Spark driver memory size and Spark executor size were the same as used in the second experiment. Because Hadoop has no such mechanism to scale containers for Spark-based algorithms, we manually ran the following tests to execute the PESTARFM and calculated the averaged time cost of the algorithm. As shown in High-performance computing (HPC) for RS big data on cloud platforms is still a hot topic in the Geoscience community . State-oThere are too many parameters that influence the performance of the PESTARFM. Recently, the authors conducted a comprehensive survey on automatic parameter tuning for big data processing systems . AccordiIn this study, we proposed a lightweight cloud-native framework of RS big data processing. The framework incorporated the Spark-on-K8s operator to improve the efficiency of a parallel RS image fusion algorithm. In the cloud-native computing layer, we adopted a container-based microservice architecture in order to easily update these loss-coupled service components by using K8s. Not only the IaaS scaling mechanism but also the PaaS scaling policy were used in the framework. Moreover, the parallel RS image fusion algorithms, PESTARFM, using Spark SQL was quite efficient for the fusion of RS big images according to our experiment. Additionally, we proposed a task scheduling mechanism to dispatch Spark executor pods to newer and less-overwhelmed computing hosts, thus reducing the time cost of PESTARFM in the framework. According to the experiments, the fusion of five big RS images completes in less than 4 min, while traditional ESTARFM uses multiple hours or even days. We believe that the computing architecture and the parallel RS image fusion algorithm are useful for the Geoscience community. Our future work will investigate factors such as network virtualization technologies, cloud object storage, and other scaling methods to boost the performance of cloud-native applications. Additionally, we will explore other science cloud computing environments to enhance the usability of the proposed framework.


N = 77 young adults. We focused on brain-signal complexity (BSC), parameterized by multi-scale entropy (MSE), as measured during a verbal DT and a cognitive control task. We demonstrated that MSE is a sensitive neural indicator of originality as well as inhibition. Then, we explore the relationships between MSE and factor scores indicating DT and intelligence. In a series of across-scalp analyses, we show that the overall MSE measured during a DT task, as well as MSE measured in cognitive control states, are associated with fluency and originality at specific scalp locations, but not with fluid and crystallized intelligence. The present explorative study broadens our understanding of the relationship between creative thinking, intelligence, and cognitive control from the perspective of BSC and has the potential to inspire future BSC-related theories of creative thinking.Functional connectivity studies have demonstrated that creative thinking builds upon an interplay of multiple neural networks involving the cognitive control system. Theoretically, cognitive control has generally been discussed as the common basis underlying the positive relationship between creative thinking and intelligence. However, the literature still lacks a detailed investigation of the association patterns between cognitive control, the factors of creative thinking as measured by divergent thinking (DT) tasks, i.e., fluency and originality, and intelligence, both fluid and crystallized. In the present study, we explored these relationships at the behavioral and the neural level, based on  Creative thinking and intelligence are vital cognitive abilities for human endeavor, evolution, and cultural transformation . CurrentThe temporal complexity measures of brain signals yield explicative information about the functional activity of neural networks during resting and task-processing states. Studies have demonstrated brain signal complexity (BSC) to be a sensitive neural marker of creative thinking , healthyA series of behavioral and neurocognitive studies demonstrated a positive relationship between creative thinking and intelligence e.g., , 2015. WNetwork neuroscience has revealed a considerable overlap between brain networks, associated with individual differences in creative thinking and g. The functional network connectivity underlying creative thinking is characterized by interactions between two crucial neural networks: the default-mode network (DMN) and the executive control network (ECN). The former consists of a set of midline and inferior parietal regions considered to facilitate the self-referential thought process, introspection, and imagination, whereas the latter consists of lateral prefrontal and anterior inferior parietal regions underlying externally focused attention and inhibitory control , 2016. TAlthough the DMN and ECN operate in opposition, their coordination during heightened creative thinking and reasoning represents a consistent finding , 2016. ACognitive control is the process by which automatic responses are inhibited and flexibly adapted to produce complex goal-directed thoughts . It encoAnother line of theoretical argument proposes that the cognitive basis of creative thinking relies rather on the joint contributions of associative and executive abilities. For example, a study by To conclude, there is considerable empirical evidence showing that certain executive functions  are indeed critical to creative thinking  and thatAnother line of research reported brain activations during DT tasks to be associated with cognitive control. For example, Brain signal complexity (BSC) arises from the interaction among numerous neuronal circuits that operate over a wide range of temporal and spatial scales. These spatiotemporal fluctuations reveal important underlying dynamic information about the brain. The complexity of the EEG signals contains information about the architecture of the neural networks at multiple spatiotemporal scales. This is to say that the inherent properties of a complex system are manifested in the features of the signals produced by it . TherefoBSC has been previously studied by means of entropy-based methods that quantify the complexity, uncertainty, or irregularity of a complex system’s activity  manifestMulti-scale entropy (MSE) , 2005 prSeveral studies have estimated BSC by means of MSE and demonstrated that it is a neural marker of healthy brain functioning, cognitive performance, typical development, and knowledge representation, among others . Hence, Importantly for the aims of the present study, In line with the evidence outlined above, we assume that the relationship between creative thinking, intelligence, and cognitive control is due to the task-dependent synchronized co-activation of several local and largely distributed brain networks, including the FPN, DMN, ECN, and salience networks. Thus, in line with the literature, we argue that the investigation of the interrelation between these complex mental abilities and their underlying neural mechanisms needs also to rely on methods capturing temporal neural complexities that characterize those brain networks. MSE is thus a candidate measure because it captures neural complexity across local and widespread neural networks. Investigating individual differences in the EEG-derived MSE and behavioral performance outcomes indicating creative thinking, intelligence, and cognitive control, as well as their relationships, will thus provide new and complementary insights into the neural foundations of the creative thinking–intelligence relationship. To date, there is no study that has directly examined the relationship between MSE measured during both creative thinking and cognitive control tasks. Furthermore, given the notion that cognitive control partly or fully explains the relationship between creative thinking and intelligence, it remains to be investigated whether MSE captured during creative thinking and inhibition are differentially associated with the behavioral outcomes of gf, gc, and DT tasks, measuring fluency and originality.Does the ability to inhibit irrelevant information explain the positive association between fluency/originality and intelligence (gf and gc)? The previous research reviewed above suggested that the relationship between originality and gf can be partially or fully explained by inhibition ability. Here we aim to extend these findings by considering multiple facets of intelligence and creative thinking, thus also including fluency and gc.Is MSE higher in low-control states (non-inhibition) compared with high-control states (inhibition)? In line with previous findings that, especially at higher scales, MSE depletes with the increasingly focused demands of the neural system, we expect to affirm this question. Should this be the case, we would conclude that MSE can be considered a neural marker of inhibition. This is a requirement for investigating MSE in inhibitory brain states as a correlate of fluency and originality.Is there a positive association between MSE captured during a verbal DT task and MSE measured during inhibition states? Producing original verbal associations theoretically implies inhibiting the typical associations that occur more or less automatically. We thus expect MSE in creative verbal association production states to be more strongly associated with MSE in inhibition states, compared with MSE measured during the production of typical verbal associations.Is MSE in inhibition states, and MSE measured during the production of original associations, more strongly associated with fluency and originality, compared with gf and gc abilities?For this study, we acquired the behavioral measures of gf, gc, fluency, originality, and inhibition, as well as brain signals derived from an EEG during the performance of a verbal DT (creative thinking measure) task and a cognitive control (inhibition measure) task. The verbal DT task required participants to produce unusual or creative and fluent or typical verbal associations. We also labeled MSE during creative verbal association production as MSE in creative idea generation, and MSE during typical verbal association production as MSE in fluent idea generation. The cognitive control task consisted of inhibition  and non-inhibition  conditions. Generally, in line with n = 159 participants. In the second session, the same participants performed two different tasks while the EEG was recorded. There was a verbal DT task (completed by n = 101) and a numerical Simon task (completed by n = 90), both of which were involved in the MSE analyses. All tasks applied in both sessions were programmed with PsychoPy . Among them, 5 had not obtained high-school qualifications, 58 had high-school or equivalent qualifications, and 14 had academic degrees . The dataset and related results of the current study are available online via OSF (https://osf.io/sg9e2/ Accessed on: 15 November 2021).The measurements applied in the present study were approved by the ethics committee of the Department of Psychology, Humboldt-Universität zu Berlin. Participants signed informed consent before participating in the experiments. Data were assessed in two independent sessions: (1) behavioral tasks session, (2) EEG recording session. In the first session, we acquired behavioral estimates of gf, gc, fluency, and originality from PsychoPy . To testBerliner Intelligenzstruktur-Test für Jugendliche: Begabungs- und Hochbegabungsdiagnostik   and nicknames (NI) . For each of the tasks, participants were instructed to provide a single answer that was unique and original. The CO task was adapted from the kit of factor-referenced cognitive tests  and consIntelligence was assessed within the figural and verbal content domains. We adapted three reasoning tasks from the Berlin Test of Fluid and Crystallized Intelligence (BEFKI) . The verThe recording of the EEG datasets during the performance of a verbal DT and a numerical Simon task took place in a closed, quiet, and well-illuminated room, using the Brain Vision Recorder software . We used BrainAmp DC amplifiers  to amplify the EEG signals, with an amplitude resolution of 0.1 μV at a sampling rate of 250 Hz. Cutoff filters were 0.16 and 1000 Hz at the low and high range, respectively. An EEG cap  was fitted with 30 Ag/AgCl electrodes, placed according to the 10–20 system. Eye movements and blinks were monitored using electrodes positioned at the outer canthi of both eyes and below the right eye. The A1 electrode (left mastoid) was used as a reference, and AFz served as ground. Impedances were kept below 5 kΩ.To assess creative thinking, we used the original verb generation task  which was modified by translating the stimulus material into the German language . Each trial started with a fixation cross presented for 1000 ms, after which the target was added for 200 ms. The fixation cross lasted until a response was given or for a maximum of 1600 ms. For every correct response, a blank screen was shown; for a missed response and for an incorrect response, “falsch ” was shown as feedback. Afterward, the blank screen was presented for a random interval of between 100 and 1000 ms. Participants performed three blocks6 trials.The behavioral DT data analyzed here were collected in a multivariate study and partially analyzed by max = 5 (6.5%), nmean = 2.67 (3.5%)). The ICCs for originality were lower compared to the fluency scores but were acceptable. The ICCs for the task, for CO, and for NI were between 0.56 and 0.90. After estimating the ICCs, a compound score was calculated across all three raters for every item, which served as indicators for the originality latent factor.Originality : Every single response from the originality tasks was independently rated by each rater on a five-point scale, based on proposed scoring guidelines from the literature . A respoVerbal DT Task: For the verbal DT task applied during the EEG recording, three trained native German speakers also rated all responses; for detailed information on the ratings, please see runica). Furthermore, SASICA (an EEGLAB plugin (The EEG data were preprocessed in MATLAB and were filtered offline using IIR (zero phase shift), and Butterworth filters of between 0.1 and 50 Hz  and recalculated to average reference using the Brain Vision Analyzer . Further pre-processing steps were executed in EEGLAB . The artB plugin ) was usem defines the length of the signal patterns that are compared with each other, and r represents the similarity bounds within which the signal patterns are matched. The MSE algorithm consists of two steps: (1) the coarse-graining of the signal time series at multiple time scales, which means averaging the data inside a window of length τ, in order to reduce the high-frequency components. Coarse-graining is implemented by replacing the progressively increasing number of data points in non-overlapping windows by their average values, to form a new time series . Then, the number of similar sequences with i + 1 data points are counted and denoted as N(i + 1). Finally, SampEn is calculated as the negative logarithm of the conditional probability that two similar sequences of i data points will be similar for the next i + 1 points:MSE is an information-theoretical metric that characterizes the variability of temporal signals, i.e., EEGs across multiple temporal scales, from a perspective on signal complexity , 2005. Tr. Traditionally, m is fixed to the value 2, and r as 15% of the SD of the original time series that is being analyzed. Therefore, for the present work, we applied these parameter settings.Therefore, two sequences are considered to be similar if the differences between each of the paired data points of the two sequences  and N(i + 1 )) fall within the range of r . Traditionally, r is not equally liberal across all time scales because it is used as r*SD of the original time series, i.e., r*SD will be used for all time scales. As r is only calculated relative to the SD of the original, unfiltered signal . However, successive coarse-graining reduces the SD of the signal and, as a result, signal variance is normalized; this introduces biased entropy estimation if it were calculated with a constant r. Therefore, the authors suggest computing the similarity bounds for each scale factor. Following these suggestions, we additionally performed global similarity bounds or scale-wise r MSE analysis. The results are provided in r MSE did not considerably differ in this particular application. Therefore, the multivariate analyses reported in the main part of the paper were based upon traditional MSE analyses, using invariant-r , because, as such, the results can be directly compared with the results of the previous study  varied from trial to trial and from person to person . This integration procedure has been previously proposed in the literature because low and high scales are interpreted differently but interpretations do not differ for neighboring scales see . For a dWe applied path modeling by using the R Software for Statistical Computing . For modWe estimated two path models to examine the relationship between creative thinking, intelligence, and inhibition. In Model 1, the behavioral scores of gf, gc, fluency, and originality were used to estimate six correlations between the observed variables, as illustrated in The fluency variable is an average score of the four fluency tasks: SA, IN, FF, and RF. The originality variable is obtained by averaging the scores of the CO and NI tasks. gf is obtained by averaging the scores of gff and gfv, and gc is an average of the scores of gcnature, gchuman, and gcsocial. The inhibition score is the mean reaction time difference between the inhibition and non–inhibition trials of the Simon task.r = 0.446, p < 0.001), and originality and gc . Weak and non-significant associations occurred between gf and originality , and gc and gf in the present small sample  and at the level of observed variables. Finally, gc and fluency showed a weak, statistically non-significant association . In Model 2, gf, gc, fluency, and originality were regressed onto inhibition. The inhibition variable did not predict gf , gc , originality , and fluency . Given the small sample size of this study and, potentially, the single-task approach for measuring inhibition, none of the associations with inhibition were substantial. Some associations between intelligence and creative thinking variables slightly changed when controlling for inhibition . The medium-scale-AUC difference plot shows only a few statistically significant effects at frontal electrode sites, with a considerably low p-value . Panel 2c illustrates the line-plots of the grand-mean MSE in the two conditions, for electrodes FC5, C3, and Pz. These electrodes were selected as examples. The line plots illustrate that MSE in the inhibition condition is reduced, compared with the non-inhibition condition across small time scales at the frontal and central electrode sites. This is in line with the notion that the dynamic neural system becomes more focused locally, with challenging information input  than between inhibition and fluency MSE .The left side of panel 3a shows scalp topographies of Pearson correlations between the respective AUC-MSE scores measured during original association, vs. the inhibition neural states. Similarly, the plot at the left side of panel 3b provides scalp topographies of Pearson correlations between MSE in typical association vs. inhibition neural states. As depicted in the topographies of both panels, moderate correlations are prominent in the frontal and left centroparietal sides of the scalp. Thus, positive and statistically substantial associations were observed at the electrodes F7, F8, Fz, CP5, and PO10. Additionally, the scatter plots on the right side of each panel depict the strongest associations between the neural measures of creative thinking and inhibition which were observed at CP5 and Fz electrodes, respectively. The scatter plots for all other electrodes at which the correlations were statistically significant are provided in the r = 0.95, p < 0.001). After controlling for MSE in inhibition, their association did not drop substantially . For more details, see Furthermore, we examined whether MSE in inhibition would explain the relationship between MSE in a creative vs. typical association. We thus computed partial correlations between the MSE in creative vs. typical association states, controlling for MSE in the inhibition condition. The observed correlation between the two conditions was high  for testing these associations, we first explored the grand-mean MSE scalp distributions in creative thinking and inhibition  and intelligence (gf and gc). The four correlated variables of fluency, originality, gf and gc showed relationship patterns of different magnitude that were mostly in line with the previous literature but were smaller as they were estimated at the level of observed variables. Fluency and originality were most strongly associated among these four ability domains. The positive association between the two different factors of DT is in line with recent literature  in the future, with more powerful studies in terms of sampling tasks and cases.Here, we conclude that MSE in inhibition is positively associated with MSE in creative thinking neural states to some extent, and that this correlation might represent stable individual differences in neural complexity, independently of the cognitive state in which MSE is being measured. Our results revealed an association between MSE at coarser time scales, mostly at the frontal electrodes that were previously shown to be associated with information integration . TherefoThe present study is the first attempt to explore neural complexity estimates, measured in creative thinking and cognitive control neural states as correlates of intelligence and DT. Our exploration showed that individuals with higher BSC in creative thinking and inhibition neural states are better at producing fluent and original verbal associations. Thus, complexity in inhibition-related neural states is relevant for creativity but not for intelligence. We expected that MSE in inhibition is associated with creative thinking and intelligence as well, but only found robust associations with fluency and, at certain scalp locations, with originality.As shown previously, MSE in original association and inhibition states are positively correlated. Furthermore, the spatial distribution of grand-mean MSE across the scalp was similar for both MSE in inhibitory and creative thinking neural states. We show that MSE in inhibition and creative thinking neural states show associations with behavioral fluency at scalp locations that are mostly parietal. Additionally, the magnitude of these correlations was also similar in both neural states. Therefore, these results provide a strong argument—now also from a neural signal complexity perspective—for the relevance of inhibition to creative thinking.At the behavioral level, The current study was mainly limited by its sample size. The association between creative thinking–intelligence–inhibition was lower in this sample, which might be due to a larger measurement bias in a rather small sample size. Furthermore, the assessment of behavioral inhibition was narrow, compared to the originality and fluency measurements. Therefore, future studies will need to employ a broader measurement spectrum of executive functions, including updating, switching, and controlled attention. Additionally, future research might investigate how the differences in the BSC measures of creative vs. fluent thinking and executive control vs. non-control states relate to creative thinking abilities. Response surface analysis  might beOur conclusions are also limited to the human ratings of the DT performance. Such ratings, however, have been previously applied as the gold standard, despite several problems they imply  as being a robust biomarker of creative ideation . Our stu