Trichodesmium invaded the beaches and coastal waters of the Canary Islands, causing great social alarm. Some local media and public sectors ascribed, without any strong scientific evidence, the origin and reactivation of these blooms to untreated sewage outfalls distributed along the coasts. In order to test whether sewage outfalls could have any influence on the metabolic activity of Trichodesmium, we performed 13C and 15N2 uptake experiments with colonies experiencing three different bloom development stages, incubated both with clear seawater and sewage water from an outfall south of Gran Canaria island. Our results showed that sewage outfalls did not promote any increase in dinitrogen (N2) fixation in Trichodesmium, supporting the hypothesis that decaying blooms were generated offshore and transported shoreward by local currents and winds, accumulating mostly leeward of the islands. The combination of unusually warm seawater temperatures, enhanced and sustained stratification of the upper water column and recurrent dust deposition events would have favored the development of the Trichodesmium blooms, which lasted for at least four months.During the summer of 2017, recurrent extensive blooms of the diazotrophic cyanobacterium  Trichodesmium is a colonial filamentous cyanobacterium capable of atmospheric dinitrogen (N2) fixation. It abounds in subtropical and tropical oceanic waters, often forming blooms large enough to be detected by satellite images1, rendering a source of fixed nitrogen that fuels primary production significantly2. Its growth may be limited however by the availability of iron3 and phosphorus6. In the open waters of the subtropical North Atlantic Ocean, the seasonal meridional shift of the North African dipole controls the deposition of iron-rich Saharan dust on surface waters7. Dust events are known to enhance the proliferation and diazotrophic activity of Trichodesmium leading to the formation of massive blooms11, with colonies adopting three-dimensional shapes known as "puffs" or “tufts”. Their organic matter coating as well as the microbial epibiont community provide Trichodesmium colonies the ability to degrade atmospheric dust particles to obtain the iron they contain13, which is likely not possible for free single Trichodesmium filaments14.Trichodesmium blooms, warm (> 20 °C) and stratified waters as well as the availability of phosphorus and iron, are essential requirements to sustain extensive blooms15. Under such conditions, Trichodesmium develops swiftly, fixing high amounts of carbon and nitrogen16. This intense activity may exhaust limiting nutrients, forcing Trichodesmium filaments to release exopolymeric substances while aggregating and forming dense colonies17. This strategy optimizes nutrient uptake and repartition of resources between filaments of a same colony18. When all limiting resources are depleted, Trichodesmium cells induce apoptosis, also referred to as programmed cell death (PCD)19, a process that can make Trichodesmium blooms almost disappear in a matter of hours to days20.Although it is not clear what mechanism, or combination of mechanisms, triggers the development of 16, Trichodesmium is now known to use dissolved organic matter (DOM) molecules which may provide it with alternative nutrient resources when their inorganic forms are unavailable or their use becomes too energetically demanding. Trichodesmium is able to use dissolved organic phosphorus (DOP) forms including phosphoesters and phosphonates when inorganic phosphate availability is low6. The RuBisCo enzyme of Trichodesmium has a low affinity for CO221, which is thought to explain its enhanced carbon fixation when exposed to high CO2 levels and/or lower pH23. The inefficiency of RuBisCo may drive Trichodesmium to use DOM to meet its carbon needs. For example, natural Trichodesmium colonies from the Southwest Pacific Ocean have been observed to obtain as much carbon via amino acids as via CO224. Cultures of Trichodesmium have been reported to grow faster on combined nitrogen forms such as urea than on N225. More recent laboratory experiments have shown that when grown under limiting concentrations of iron and/or phosphorus, Trichodesmium downregulates N2 fixation and obtains nitrogen from trimethylamine or ammonium26. Collectively, these studies suggest that Trichodesmium adopts a mixotrophic nutrition mode when its basic metabolic activities are reduced due to environmental stress or a limitation of resources. Mixotrophy thus confers Trichodesmium metabolic plasticity and adaptation to dynamic environmental nutrient scenarios.Traditionally considered an autotrophTrichodesmium were observed in coastal and offshore waters around the Canary Islands27. These blooms appeared first in the western side of the archipelago, coinciding with dense Saharan dust deposition events, enhanced water column stratification and seawater temperatures > 23 °C, later spreading to the eastern side of the archipelago, when those conditions extended eastwards27. The massive accumulations of Trichodesmium along the coasts of the Canary Islands  and nitrogen (DON) in sewage waters were double than in clear waters , enhanced stratification of the water column and several dust deposition peaks superimposed on a lower but steady dust supply throughout the summer months27. There was no evidence that Trichodesmium consumed DOC and DON despite their higher concentrations in sewage waters than in clear waters . This sequential decrease in N2 fixation between bloom development stages agrees well with those observed in culture and field experiments38, and is clearly depicted by an increase in the release of PO43−, NH4+ and DOC as cells die and the bloom decays . Contrary to Nely Fig. c,d. HoweTrichodesmium accumulations observed along the Canary Islands’ coasts in the summer of 2017 , potentially leading to differences in their impact on bacteria, non N2 fixing phytoplankton and diazotrophs.The recurrent PCD-like status of the Trichodesmium, other results (which will form the basis of a complementary paper) provide evidence that the 2017 bloom originated in open ocean waters of a large part of the Canary Current region, and the colonies were transported towards the coasts by the regional circulation and local winds. The progression from sparse colonies in open ocean waters, followed by surface slicks nearer the islands to finally decaying blooms accumulated in the lee of the islands strongly supports this hypothesis27.In conclusion, our results support the hypothesis that sewage outfalls south of the islands did not trigger the formation of new blooms, nor sustain or enhance blooms originated previously offshore. Although this study examines only the potential effects of sewage waters on the growth of 39 indicate that the surface waters of the Canary Current are warming unabatedly, presumably favoring the more common appearance of Trichodesmium blooms in the near future. This has unknown consequences for the Canary region, as blooms have also occurred in the years 2018–2020, although with less intensity than in 2017. In order to predict the development of these blooms it is therefore necessary to determine which factors (or combination of factors) trigger and maintain the blooms from their origin to their collapse. The results of this study shed some light on this issue by clearly showing that coastal sewage outfalls do not enhance or maintain these blooms near the coast.Trends in ocean temperature in the past two decades2 and carbon fixation activity of Trichodesmium, we sampled colonies from near surface waters south of Gran Canaria Island in September 2017  and 13C-labeled bicarbonate  as previously described40. The bottles were incubated in shaded incubators with surface seawater for 24 h. At the end of incubations, the content of the bottles was filtered through pre-combusted  GF/F filters (Whatman) and stored at – 20 °C until analysis. The concentration of particulate nitrogen and carbon as well as the isotopic ratio of samples (15N/14N and 13C/12C) were obtained by means of a Thermo Flash 1112 elemental analyzer interfaced by a Conflo III with a Thermo Delta V Advantage isotope ratio mass spectrometer. To ensure an accurate calculation of N2 fixation rates, background dissolved 15N atom % enrichments were determined in all incubations by membrane inlet mass spectrometry as previously described41.Sparse, slick and PCD-like colonies were concentrated using a 20 µm mesh sieve. Subsequently, 5 mL of the concentrate was distributed in triplicate acid-washed 2.3 L polycarbonate bottles  containing either clear seawater or sewage water from separate coastal areas Table , previouTrichodesmium blooms . Samples for the determination of dissolved organic carbon (DOC) concentrations were collected from within Trichodesmium blooms as above, but also at the start and end of incubations for carbon and N2 fixation measurements. All samples were filtered through pre-combusted GF/F filters (Whatman) before storage at – 20 °C.Seawater samples for the analysis of inorganic nutrients and dissolved organic nitrogen and phosphorus  were collected from clear waters, sewage-affected waters and within the 3− + NO2−), phosphate (PO43−) and ammonium (NH4+) were collected in 15 mL polyethylene tubes and stored at − 20 °C. Nutrient concentrations were determined using a Technicon II segmented-flow autoanalyzer. Samples for DOC analyses were collected in HCl-washed 20 mL polycarbonate tubes , stored at – 20 °C, and DOC concentrations determined with a TOC-V Shimadzu as detailed in Santana-Falcón et al.42. DON and DOP concentrations were collected in 50 mL polyethylene tubes and analyzed by the wet oxidation of total dissolved nitrogen and phosphorus , with subsequent subtraction of NO3− + NO2− and PO43− concentrations, respectively using standard wet oxidation methods43.Samples for the analysis of nitrate and nitrite (NOProchlorococcus and Synechococcus type cyanobacteria and pigmented picoeukaryotes) and heterotrophic prokaryote assemblages were determined by flow cytometry. Samples (1.6 mL) were preserved with paraformaldehyde , left 15 min at 4 °C in the dark to fix, deep frozen in liquid nitrogen and stored at – 80 °C until analyzed. Fixed samples were thawed, stained in the dark for a few minutes with a DMS-diluted SYTO-13 (Molecular Probes Inc.) stock (10:1) at 2.5 µM final concentration, and run through a BD FACSCalibur cytometer with a laser emitting at 488 nm. High and Low Nucleic Acid content prokaryotes  were identified in bivariate scatter plots of side scatter (SSC-H) versus green fluorescence (FL1-H). Autotrophic picoplankton were discriminated in plots of orange fluorescence (FL2) versus red fluorescence (FL3) and picocyanobacteria (Prochlorococcus and Synechococcus) were subtracted from HNA prokaryote counts. Samples were run at low or medium speed until 10.000 events were captured. A suspension of yellow–green 1 µm latex beads (105–106 beads ml−1) was added as an internal standard .Abundances of autotrophic (


The cells were treated with sub-toxic doses of CdS QDs; 3 µg ml−1 in HepG2 and 6.4 µg ml−1 and 50 µg ml−1 in THP-1, as well as equivalent cadmium doses as Cd(II). In this dataset, changes in expression levels of miRNAs are reported. In addition, GO enrichment analyses of target genes of miRNAs modulated by Cd stress, network analysis of the microRNome and an in silico pathway analysis are also reported. These data enhance and also summarize much of the data independently presented in the research article and therefore, must be considered as supplementary.The data included in this paper are associated with a research article entitled 'Differences in toxicity, mitochondrial function and miRNome in human cells exposed  Specifications TableValue of the DataThe dataset provides a picture of microRNome changes useful for comparing effects of nanomaterials in other cell systemsThe data as graph and heatmaps may be useful for other researchers to identify potential hazards of nanomaterials and for ensuring the appropriate selection of biological endpointsThe data reported may be useful for planning additional experiments on risk assessment of Cadmium-based quantum dots122.1−1), penicillin (100 U ml−1) and 4 mM of glutamine. THP-1 monocytes were cultured in same medium but with supplement of 2 mM of glutamine. Cells were routinely cultured in 10 cm dishes under humidified air in the presence of 5% CO2. Before treatment, the suspension THP-1 cells were differentiated into macrophages using 0.1 µM of PMA for 3 days and incubated at 37°C in 5% CO2 in air. Cells were seeded in complete growth medium in 10-cm dishes at a density of 3 × 106 cells/dish. The cell growth medium was replaced 24 h after cell seeding with fresh medium. The cells were treated with different doses of Cd as CdS QDs or Cd(II).HepG2 cells were cultured in DMEM supplemented with FBS 10%, streptomycin  following the manufacturer's instructions; this is a column-based kit, which has been significantly improved the profiling of circulating miRNAs TM RT Primers (Applied Biosystems) that contain a pool of miRNA-specific primers present in each card and TaqMan microRNA Reverse Transcription kit (Applied Biosystems). The PCR array was run in an ABI 7900HT Fast Real-Time PCR system (Applied Biosystems) according to MegaPlexTM Pool Protocol (PN 4399721 RevC). Each sample was performed in duplicate. Fold transcription changes (RQ) were calculated from the expression 2−ΔΔCtDetails on miRNAs isolation and quantification of HepG2 and THP-1 cells exposed to Cd are reported in Paesano Considering the different values obtained and the discrimination window , the first step was to compare the miRNAs response in the different conditions and in the different cell-types.To visualize transcriptomic data, hierarchical clustering was performed using the heatmap.2 routine implemented in the R software package


Here, the chemical status of the Northeast Atlantic is examined by means of a high-quality database of carbon variables based on the GO-SHIP A25 section (1997–2018). The increase of atmospheric CO2 leads to an increase in ocean anthropogenic carbon (Cant) and a decrease in carbonate that is unequivocal in the upper and mid-layers . In the mid-layer, the carbonate content in the Northeast Atlantic is maintained by the interplay between the northward spreading of recently conveyed Mediterranean Water with excess of carbonate and the arrival of subpolar-origin waters close to carbonate undersaturation. In this study we show a progression to undersaturation with respect to aragonite that could compromise the conservation of the habitats and ecosystem services developed by benthic marine calcifiers inhabiting that depth-range, such as the cold-water corals (CWC) communities. For each additional ppm in atmospheric pCO2 the waters surrounding CWC communities lose carbonate at a rate of − 0.17 ± 0.02 μmol kg−1 ppm−1. The accomplishment of global climate policies to limit global warming below 1.5–2 ℃ will avoid the exhaustion of excess carbonate in the Northeast Atlantic.The oceanic uptake of atmospheric carbon dioxide (CO The North Atlantic stores the largest amount of Cant in the ocean3, with the Atlantic Meridional Overturning Circulation (AMOC) conveying and exporting acidified Cant-loaded waters to the deep ocean4. At basin-scale, North Atlantic Ocean acidification is a well-known process5, usually reported with pH decrease rates of ~ 1–2 × 10–3 pH units yr−19. The reduction in the concentration of carbonate ions ([CO32−]) is also a consequence of ocean acidification10, usually expressed as the change in the calcium carbonate (CaCO3) saturation state. The availability of carbonate connects the chemistry of the seawater with the biological activity, since carbonate is used by calcifying organisms to create the different forms of biogenic CaCO3: aragonite  or calcite . The difference between the in situ [CO32−] and the concentration at saturation is the excess of carbonate (xc[CO32−])11. Positive values of xc[CO32−] indicate supersaturated waters, while negative values indicate undersaturation and the tendency for the biogenic mineral to dissolve13. Ocean acidification decreases xc[CO32−], compromising the fitness of marine calcifiers and even their survival when waters reach negative values of xc[CO32−].The uptake of anthropogenic CO3 skeletons made of aragonite14 are important ecosystem engineers of deep-sea habitats16. Ocean acidification is recognized as one of the most challenging threats that CWC will face with global change17. At global scale, large CWC reefs are more abundant in the North Atlantic, since the depth at which aragonite becomes susceptible of dissolution is deeper than elsewhere in the world ocean19. In the Northeast Atlantic, the relationship between reefs and hydrography is such that living CWC reefs are located in the potential density range 27.35 > σ0 > 27.65 kg m−320. Recently, several Marine Protected Areas (MPA) have been proposed in European waters based on the presence of Lophelia pertusa sp. communities22. An assessment of the long-term viability of these hotspots of biodiversity and ecosystem services14 in the context of a changing ocean is therefore necessary.Cold-water corals (CWC) with biogenic CaCOhttps://www.go-ship.org) to explore the implications of ocean acidification for marine calcifiers from a biogeochemical perspective  surface waters with more than 100 μmol kg−1 of xc[CO32−], towards abyssal values of 50 μmol kg−1 below saturation (xc[CO32−] < 0). At mid-depths there are larger xc[CO32−] values close to the Iberian Peninsula, creating a gradual eastward upward tilt of the isolines. From 3,000 m depth to the bottom, the isolines are horizontal. The isoline of 0 μmol kg−1 of xc[CO32−], known as the Aragonite Saturation Horizon  and the ASH are pretty similar. The limit between the upper and lower NADW is around 3,800 m depth, where waters are already undersaturated (xc[CO32−] < 0).The vertical distribution of xcCO3− along txcCO3− along t2 for the period 1997–2018 increased from 364 to 409 ppm  mean concentration by water mass Fig. a shows tnat) and the anthropogenic (Cant) components. DICnat , with concentrations exceeding 60 μmol kg−1 by the end of the observational period. MW and LSW mid-layers show rather similar increase in Cant concentration rates . Note that when time (in years) is used as dependent variable  and TA normalized by salinity (S = 35) along the OVIDE cruise section for the year 2018 are shown in Figure ion Fig. a. The vaion Fig. b shows axc[CO32−] by water mass and associated uncertainties with respect to the atmospheric CO2 concentration for 1997–2018 are represented in Fig. xc[CO32−] values between water masses follows a vertical distribution, with positive xc[CO32−] values at the surface and negative values in deep waters. The xc[CO32−] decreases with the excess of atmospheric CO2 in the upper  and mid-layers  of the Northeast Atlantic. Such decrease in xc[CO32−] does not exist in the two layers of NADW . The rate of decrease in xc[CO32−] is almost three times larger at surface than in LSW, with its physicochemical properties and its position in the water column making LSW to be close to the limit of no excess. If these trends persist in time, LSW will become undersatured in the Northeast Atlantic when a concentration of 514 ± 31 ppm of atmospheric CO2 is reached.The trends of the mean xc[CO32−] with increasing atmospheric CO2 shows a decrease from 56 to less than 50 μmol kg−1 during the observing time frame . Conserving this linear trend, the current layer of living CWC in the Northeast Atlantic would then be in undersaturated waters at a concentration of 702 ± 53 ppm of atmospheric CO2 , so the linear trend is a valid approach for the study purposes.In the depth range of living CWC, the change in 2. Current anthropogenic perturbation in NACW layer  is large enough to be detected in the DIC pool within a 21-year timespan (1997–2018)  of the Iberian Abyssal Plain, a longer monitoring period is required to identify it. In summary, the combination of the natural component of DIC in steady-state with the unequivocal trends for Cant in NACW, MW and LSW, makes it reasonable to assume that the existence of perturbations in other marine carbonate system variables are also produced by human influence, as seen for pH, CO3 18) Fig. a. The deity Fig. . And eveyer Fig.  suggestiCO3 Fig.  and xc[C%18) Fig.xc[CO32−] decreases with depth due to solubility (high pressure and low temperature increases aragonite solubility). The anthropogenic decline of xc[CO32−] exists in all the supersaturated water masses, that is, NACW, MW and LSW. The decline is happening three times faster in NACW than in LSW, as expected from the atmospheric source of the driver for ocean acidification (atmospheric CO2) and the age of these water masses9. The relatively young and recently formed NACW has the largest trend, whereas LSW, ventilated in western subpolar latitudes, reaches the Northeast Atlantic basin with a greater mean age and hence has the lowest trend among the acidified water masses. MW, formed at the southeast of the Iberian Peninsula  in the composition of MW32. Due its intrinsic properties of high salinity and alkalinity35, MW is therefore crucial to keep the Northeast Atlantic CWC in chemically optimal waters. In the Gulf of Cadiz, the ocean acidification rates in MW are relatively high36. Currently, living CWC are located between the fast-acidifying surface waters from above and the rising ASH from below. A potential reduction in MOW by global change37 would involve a greater influence of subpolar origin waters that could accelerate the exhaustion of excess carbonate in the Northeast Atlantic.xcCO3− decreas38. The scenarios that best represent the limitation of global warming to 1.5 or 2 °C of the Paris Agreement are the SSP1-19 and the SSP1-26 respectively39. For both scenarios, the decline of xc[CO32−] will not reach undersaturation values not even in LSW layer  of dead corals framework45. In contrast to the living CWC, the dissolution of the dead skeletons is a predicted chemical reaction46 because the skeletons have no capacity to cope with dissolution. The net dissolution will be directly proportional to the time CWC remain in undersaturated waters and that could happen within a century if the Paris Agreement is not accomplished. To the best of our knowledge, there are no long-term in situ experiments that confirm net dissolution of dead CWC framework available at this time. However, the combination of bioerosion proceeding faster in substrates weakened by ocean acidification47 with the close relationship between carbonate availability and the location of healthy CWC communities19 suggests that CWC development might be compromised. Furthermore, the decrease in available carbonate saturation level may combine with, and potentially exacerbated by, other climate change pressures such as warming and deoxygenation49.Many species of benthic calcifiers, even CWC other than 2 concentration reaches 702 ± 57 ppm. If greenhouse gas emissions maintain the path of fossil-fuel development, the running out of excess carbonate in the Northeast Atlantic will take place during this century. The efficiency of high seas Marine Protected Areas (MPA) created for the long-term conservation of CWC and their associated ecosystems is ultimately associated with the accomplishment of the Paris Agreement and the limitation of global warming below 2 ℃.The uptake of anthropogenic carbon from the atmosphere is unequivocally decreasing the amount of carbonate available for marine calcifiers in the upper (NACW) and intermediate water masses (MW and LSW) of the Northeast Atlantic Ocean. The northward spreading of MW plays a key role in limiting the arrival of subpolar-origin waters with low excess of carbonate available. The chemical conditions that made the Northeast Atlantic a region favorable for CWC development in preindustrial time are changing fast due to ocean acidification. Currently, provided the water masses contribution remain the same, living CWC would inhabit undersaturated waters when the atmospheric CO2 measurements. The observations came from ten hydrographic cruises across the Iberian Abyssal Plain between 1997 and 2018  and 9 repeats of the OVIDE section . They are almost evenly spaced in time since all cruises except the first one belong to the OVIDE sampling program, a high-resolution hydrographic survey that has been carried out every other year during spring–summer since 2002 (https://www.umr-lops.fr/en/Projets/Projets-actifs/OVIDE). The assembled high-quality CO2 system database between the Azores Biscay Ridge  and the Iberian Peninsula therefore spans 21 years  exclusively.Two-decades of ocean acidification trends across the whole water column of the Northeast Atlantic are evaluated by means of high-quality COIn this study, the carbon analysis for all the hydrographic data involved followed the same analytical methodology and were in situ calibrated against Certified Reference Materials (CRMs).51. The protocols of measurements developed and followed during the cruises, including periodical CRM checks, allowed to achieve an internal consistency and reproducibility of ± 0.0014 pH units27. The large amount of measurements from the deeper waters sampled at the Iberian Abyssal Plain  show a very low standard deviation , which is a useful indicator of consistency and reproducibility since these old waters are expected to be in near steady state9. Total alkalinity (TA) was analyzed by single point titration52 and calibrated with CRM with a measurement precision of ± 2 μmol kg−1. Over 3,200 samples of TA were analyzed for this study, with around three hundred per cruise for the stations shown in Fig. 53 consistent with the GLODAP data products55 and neither carbon related variables  nor oxygen have been modified at the secondary quality control procedure55. Then, results are supported by high-quality and low-uncertainty carbon measurements spanning 21 years.pH was determined with a spectrophotometric method32−]is) were calculated with the CO2SYS toolbox56 using the acid dissociation constants of Mehrbach et al.57 refitted by Dickson and Millero58, and the aragonite solubility of Mucci59. The uncertainty in the concentrations is ± 4.6 μmol kg−1 and ± 3.7 μmol kg−1 for DIC and [CO32−]is, respectively60.Concentrations of dissolved inorganic carbon (DIC) and in situ carbonate ions ([COArg) is the product of the ion concentrations of calcium ([Ca2+]) and carbonate ([CO32−]) divided by the aragonite solubility product (KArg) at in situ conditions (subscript “is”) of temperature, salinity, and pressure12. Following the ΩArg definition:32−] at saturation :2+] is determined only by salinity61. Both KArg and [Ca2+] were calculated with the CO2SYS toolbox56. The difference between [CO32−]is and [CO32−]sat(ΩArg = 1) . Since calcite is more resistant to dissolution than aragonite11, calcifying organisms that form their structures with calcite will be less affected by ocean acidification at shorter time-scales62. Therefore, in this work, we refer exclusively to excess carbonate and the saturation state with respect to aragonite.The in situ degree of aragonite saturation (Ωlcium (Ca+) and caant) was estimated with the biogeochemical back-calculation ϕCT° method63, which has an overall uncertainty of ± 5.2 μmol kg−1. The natural fraction in the total DIC (DICnat) is the difference between DIC and Cant.Finally, anthropogenic carbon (Crefpressure) isopycnals  varies among 0, 1, 2 and 4 (× 103 dbar).The whole-water column was separated following a vertical water mass distribution into five layers delimited by potential density  were based on interpolated bottle data at dbar resolution. Interpolation was done on an area-weighted basis, considering the thickness of the layer and the distance between measurements9. The mean properties are represented along with the 95% confidence interval ; where n is the number of bottle measurements in that layer and cruise, Figs. For each layer, the mean property concentrations  [last time accessed: 28/12/2019]. In this study we used the mean annual concentration of CO2  for the year of the cruise. Shared Socioeconomic Pathways (SSP) are climate scenario frameworks that take into account a wide range of socio-economic futures and policy decisions38. Atmospheric CO2 concentrations for eight representative SSP scenarios belonging to the Coupled Model Intercomparison Project phase 6 (CMIP6) were downloaded from https://greenhousegases.science.unimelb.edu.au [last time accessed: 28/12/2019]39. This data is used in the atmospheric CO2 concentration projections.In this study we contrasted the anthropogenic changes in the ocean carbon cycle with respect to its main driver, the atmospheric COSupplementary Information 1.


Current animal tracking studies are most often based on the application of external geolocators such as GPS and radio transmitters. While these technologies provide detailed movement data, they are costly to acquire and maintain, which often restricts sample sizes. Furthermore, deploying external geolocators requires physically capturing and recapturing of animals, which poses an additional welfare concern. Natural biomarkers provide an alternative, non-invasive approach for addressing a range of geolocation questions and can, because of relatively low cost, be collected from many individuals thereby broadening the scope for population-wide inference.34S) in cattle tail hair collected in the Greater Serengeti Ecosystem, Tanzania.We developed a low-cost, minimally invasive method for distinguishing between local versus non-local movements of cattle using sulfur isotope ratios (δ34S isoscape across the study area. This isoscape was constructed using spatial smoothers and underpinned by the positive relationship between δ34S values and lithology. We then established a strong relationship between δ34S from recent sections of cattle tail hair and the δ34S from grasses sampled in the immediate vicinity of an individual’s location, suggesting δ34S in the hair reflects the δ34S in the environment. By combining uncertainty in estimation of the isoscape, with predictions of tail hair δ34S given an animal’s position in the isoscape we estimated the anisotropic distribution of travel distances across the Serengeti ecosystem sufficient to detect movement using sulfur stable isotopes.We used a Generalized Additive Model to generate a predicted δWhile the focus of our study was on cattle, this approach can be modified to understand movements in other mobile organisms where the sulfur isoscape is sufficiently heterogeneous relative to the spatial scale of animal movements and where tracking with traditional methods is difficult. Movement is a fundamental characteristic of life , yet its2H) and sulfur (δ34S), are popular geolocators  where X = mgcv package [sp, raster, rdgal and rgeos packages [stats package [gam.check function from mgcv package [δ34SL, j) across the entire ecosystem (i.e. the ‘isoscape’) together with the standard deviation that captured the prediction uncertainty (σL). The predicted δ34S isoscape was at the same spatial resolution as the geology layer (5 km2 per pixel).To develop a sulfur isoscape for Serengeti, we fitted a spatial Generalized Additive Model (GAM) using the  package , 38. The package  using thpackages –44. To cpackages , 46. CHIpackages . The undpackages . The und package . Our fin34S in the ith tail hair sample from the jth individual  linearly reflects the local δ34S signature in vegetation , and to account for multiple observations of tail hair from the same individual, and that the true values of explanatory variable  are latent, and only observed with error (σM) we constructed a latent ‘error in variables’ model wherein:To understand whether δt distributions with 3 degrees of freedom and for standard deviations half t with 3 degrees of freedom, except for σM where the prior was modelled as N. We used the fitted model to generate predictions and associated uncertainty in these predictions for each observation from the most recent segment of tail hair.The model was fitted in Stan .Relocation data from 49 GPS collared cattle in Western Serengeti  sugges34S values of grass range between + 2.82 ‰ and + 13.04 ‰  and b) a negative relationship between sulfur isotope ratios and volcanic ash/tephra . Other relationships that were not statistically significant from the model included: a negative relationship between sulfur isotopes ratios and granitoids  and between sulfur isotope ratios and volcanic lava , as well as a positive relationship between sulfur isotope ratios and mafic volcanic meta-basalts . The mean standard deviation of predictions estimated directly from the GAM was σL = 1.00, and inferred from the full model, σM = 1.810.δ34S values of grasses from locations where tail hair was sampled, and the most recent tail hair section were strongly and positively related . The δted Fig.  with slong grass , 12. Furng grass . With se34S values from the isoscape was typically very close to 1 (range 0.987–1.184). Our power analysis results suggested that an animal needed to move typically about 100 km  sulfur isotope ratios in the vegetation across our study area vary across lithology; 2) sulfur isotope ratios in the vegetation are accurately reflected in cattle tail hairs  and South West (SW) gradients Fig. b. Such aThis study has demonstrated the applicability of sulfur stable isotopes ratios in studying the movement of animals. Historically, sulfur stable isotopes have been difficult and expensive to analyse compared to carbon and nitrogen , 60, 61,34S values of our cattle tail hairs  to study wide-ranging wildlife species (e.g. wildebeest and zebra). For example, this method could distinguish between migratory and non-migratory individuals and establish the frequency of different movement strategies in a partially migratory system such as Serengeti.This study provides a template for understanding animal movement in and outside the Serengeti ecosystem. The question of where, how, and when livestock move remains critical for informing policies aimed at maximizing livestock productivity, minimizing disease spread while also maintaining traditional pastoralist livelihoods. Quantifying livestock movements, such as those occurring in remote agro-pastoralist settings, or through illegal transboundary trade or theft, is challenging to study using other methods. Our approach potentially provides a non-invasive way to infer whether an animal has moved long-distances across sulfur gradients in the previous ~5 months. The same methodology could be applied in other areas with sufficient variation in δ34S isoscape of the Serengeti provides baseline information on how δ34S can be applied to understand spatial and movement ecology of livestock across the landscape. However, the use of a single element, such as δ34S does not capture all the isotopic variation across the landscape and could limit our ability to recreate detailed movement patterns of individual animals. The inclusion of additional isotope tracers such as strontium (87Sr/86Sr), which tends to have a well-defined geological distribution [2H which tends to differ by watershed [This study has shown the potential for using sulfur stable isotopes ratios in studying movement ecology of herbivores. For example, the δribution , or δ2H um 87Sr/8Sr, whichAdditional file 1 Appendix 1. Questionnaires. List of questions asked to cattle owners for exploring movement history of cattle. Appendix 2: Summary statistics and model selection table. Appendix 3: Grass sulfur isotopes across the Serengeti ecosystem. Laboratory results for δ34S data used to interpolate the isoscape. Appendix 4: Distance in Km moved by cattle as per questionnaire report. Distance is the straight-line measure between two points.


N = 8,964) and cows (N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15–45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic  effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers ( Hanwoo is an indigenous beef cattle breed in Korea, originated from a Bos taurus breed in north-east Asia . The breThe national breeding system for Hanwoo includes two genetic evaluation programs, individual performance and progeny test, in which candidate bulls are selected based on their own growth performance, and they are further evaluated with a progeny test . From thTo estimate genotype-by-environment (G×E) interaction, two kinds of models have mainly been used in previous studies, i.e., a bivariate linear mixed model for modeling effects of distinct environments and a reaction norm model (RNM) for effects of continuous environmental gradients. Bivariate linear mixed models can detect G×E interaction by estimating a genetic correlation between different environmental groups for the trait of interest and test if the estimated genetic correlation is significantly different from 1, using the Wald test statistics. For instance, While most interaction studies focus on G×E interaction (genetic heterogeneity), it has also been recognized that the heterogeneity of residual variance exists. Residual variance heterogeneity may be due to outliers, scale effects or non-normality of data, which can be remedied by standard quality control including normalization  . HoweverIn livestock, genetic and residual correlations play a critical role in genetic evaluation. However, most G×E interaction studies have overlooked the fact that correlations between the main trait and an environmental modulator, i.e., genotype-environment (G-E) correlation, can cause spurious G×E interaction signals . AlthougA total of 10,215 Hanwoo Korean cattle  born between 2006 and 2016 were included in this study. The data were provided by the BioGreen 21 Program (Molecular Breeding Program) of National Institute of Animal Science (NIAS), Rural Development Administration (RDA), South Korea, and the experimental procedures were approved by The Animal Care and Use Committee of the NIAS, RDA, South Korea. The contemporary group (CG) was based on the classification of individuals belonging to the same year of birth, season, resulting in 29 contemporary groups. Farm information was collected from 15 provinces located in the northern , middle  and southern regions  of the Republic of Korea. The average temperature in the southern area is about 3–6°C higher than that of north area.Phenotypic data in this study included carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) . BFT, EMWe used the meteorological data from 15 regional weather stations nearest to the farms. Weather variables used in our study included daily maximum temperature and daily average humidity . THI recT and H are the average maximum daily temperature  and average relative humidity (%), which were measured between 15 and 45 days before the slaughter date  was used for DNA concentration and purity, and SNP marker data  was obtained using the Illumina Bovine SNP50 BeadChip (50K) platform. Quality control procedures were applied to SNP filtering using PLINK 1.9. software . SNPs weIn preliminary analyses, to avoid any confounding effects, the phenotypes of each trait were adjusted for fixed effects such as sex, CG, farm location and age. We additionally tested if there were significant linear or quadratic fixed effects of THI on the phenotypes. We also accounted for genetic population structure by fitting the first 10 principal components (PCs) estimated from the genomic relationship matrix. For this, we used eight different linear models, and applied model comparisons using Akaike information criteria (AIC) . AccordiAn association between THI and genetic effects can be revealed by checking whether the genomic relationships between the samples (which were inferred from their genotypes in this study) explains any of the variation in THI. Such an association would then lead to a spurious heritability estimate of THI, obviously not because of THI truly having a heritable component, but because of confounding between breeding values and THI, e.g., somehow more of the low merit sires for a trait are measured in warm/humid areas or vice versa. This association can also generate spurious G×E interaction signals when regressing breeding value on THI unless it is correctly controlled, i.e., modeling the G-E correlations as well as the G×E interaction in MRNM . In ordeThe genetic parameters of the four carcass traits consisting of CW, BFT, EMA and MS were estimated from a four-traits linear mixed model using genome-based restricted maximum likelihood (GREML). The four-traits linear mixed model can be written asyi is the Nrecords vector of observed phenotypes, bi is the vector of fixed effects, gi is the Nindividuals vector of additive genetic, and ei is Nrecords vector of the residual effects for the ith trait . The fixed effects include 29 CG classified according to birth year and season information, 15 farm locations, 2 sex classes, ages, the first 10 PCs and a linear or/and quadratic function of THI. With other fixed effects, the linear and quadratic function of THI values and the first 10 PCs were used as fixed effects for CW and EMA, respectively  is computed from A = WW′/NSNPs, where W is a column-standardized Nindividuals × NSNPs matrix including the genotype information of Nindividuals. The terms, i, and σg_ij and σe_ij represent the genetic and residual covariances between the traits i and j , respectively. The random genetic and residual effects are assumed to be normally distributed with mean zero and variance where ormation , and I isoftware , the genTHI for each carcass trait of which the genetic effects are modulated by THI. The model assumes homogeneous residual variance and does not consider the correlation between carcass traits and THI values. Following A univariate reaction norm model (URNM) can be used to estimate G×E0 and α1 are the Nindividuals vector of the zero and first order of random regression coefficients for the random genetic effects, c is Nindividuals vector of THI values that modulates the main phenotypes, and e is the residual effects. The genetic variance and covariance matrix of random regression coefficients (gK) iswhere μ is the mean of the adjuted phenotyeps, αα0,1 is the covariance between α0 and α1, assuming that each individual has a unique covariate  value such that the number of individuals is the same as the number of unique THI values. The genetic variance and covariance matrix of all individuals is a function of the matrix gK and polynomials, and can be expressed aswhere Φ is Nindividuals × 2 matrix of the zero and first order polynomials of the THI values of Nindividuals, i.e., Φ = .where THI interaction model assumes that the residual variance of main trait is homogeneous across THI values, however, this could be violated. The R×ETHI interaction model can capture such residual heterogeneity and can be written asThe G×E0 and τ1 are the zero and first order of random regression coefficients for residual effects. The residual effects can be modeled with random regression coefficients and the variance and covariance matrix of random regression coefficients (eK) can be represented aswhere ττ0,1 is the covariance between τ0 and τ1. The residual variance and covariance matrix is a function of the matrix eK and polynomials, and can be expressed aswhere Φ is Nindividuals x 2 matrix of the zero and first order of the THI values of Nindividuals, i.e., Φ = .where THI or R-ETHI correlation, i.e., no spurious genetic or residual correlation between the carcass traits and THI. However, it has been reported that unmodeled correlation can generate spurious G×ETHI or R×ETHI interaction signals and biased estimates (THI or R-ETHI correlations). Following URNM assumes that there is no G-Estimates . To avoic* indicates the THI values adjusted for the fixed effects  can also cause spurious G × E, we explicitly model those variance components.where  effects  as the p0α and 1α) and the component of variance in THI explained by genetic effects (β) can be written asThe covariance between the genetic random regression coefficients of carcass traits (0τand1τ) and residual effects of THI (ε) can be written asIn a similar manner, the covariance between the residual random regression coefficients of carcass traits . Ai* or Ai* indicates the ith column or row vector of the A matrix. All models described above can be fitted using MTG2.14 . In a similar manner, the magnitude of significance for the orthogonal effects of G×ETHI conditional on R×ETHI, and vice versa, can be obtained by log-likelihood comparison between the combined (G×ETHI and R×ETHI interaction) model and a reduced model (with either R×ETHI or G×ETHI interaction only), referred to as THI|R×ETHI) or THI| G×ETHI). From these quantities, the amount of collinearity between G×ETHI and R×ETHI interactions can be approximately quantified asThe magnitude of significance for G×EIn a preliminary analysis without considering interaction model, we estimated genetic variance within and covariance between four carcass traits using the four-traits linear mixed model. As shown in THI and R×ETHI interactions for each of four carcass traits . We conducted various model comparisons .In the main analysis, we used URNM and MRNM fitting THI  to estimate G×ETHI signal from URNM and MRNM, after adjusting for R×ETHI interaction (p = 6.78E–01 and 1.17E–01 in M2 and M5). The orthogonal R×ETHI interaction was not significant for URNM, after adjusting for G×ETHI interaction (p = 2.22E–01 in M3). Using MRNM, which could model the G-E correlation and G×E interaction jointly, we found a significant orthogonal R×ETHI interaction  (−0.034 ± 0.012) as shown in THI and R×ETHI interactions are important in a genetic evaluation (p = 2.63E–02 in M4) whereas URNM had no significance (p = 2.56E–01 in M1). THI and R×ETHI interaction effects were mostly independent . The estimated G×ETHI or R×ETHI interactions were dependent to each other and there was a substantial collinearity between two estimated interactions . We applied MRNM and also found a significance for G×ETHI interaction (p = 2.72E–02 in M5). However, R×ETHI interaction for EMA was not significant regardless of using URNM or MRNM (M3 and M6). The significant signal of the combined G×ETHI and R×ETHI interactions was found in MRNM (p = 3.32E–03 in M4). This was expected due to the large magnitude of orthogonal G×ETHI interaction and there was dependency between G×ETHI and R×ETHI interactions , which was consistent with the result of using MRNM (M5). The parameters of G×ETHI interaction components estimated from MRNM were not different from zero (THI interaction from URNM and MRNM (p = 3.68E–04 and 1.60E–03 in M3 and M6). As shown in THI interaction was significantly different from zero (0.046 ± 0.013). We also note that there was negligible residual correlation between MS and THI  and cov in MRNM (THI and R×ETHI interactions from MRNM (p = 3.30E–03 in M4), mostly due to the large magnitude of R×ETHI interaction  . The residual variances from bivariate GREML and MRNM for BFT were not significantly different from one another (p = 3.73E–01 in We estimated consistent genetic variances from bivariate GREML (the null model) and MRNM (combined G×EI model) , confirmI model) . Supplemtability . For EMATHI and R-ETHI correlations correctly. From the MRNM analyses, we show that there are significant effects of R×ETHI interactions for CW that are orthogonal to each other. For BFT, there is not any interactions and they are not orthogonal to one another. We also show for EMA and MS that there are significant combined G×ETHI and R×ETHI interactions, where EMA has a significant signal for orthogonal G×ETHI whereas MS has a significance for orthogonal R×ETHI. We found that interactions are ubiquitous among carcass traits, indicating that it is necessary to include interaction components in the genetic evaluation of Hanwoo cattle.In this study, we used MRNM to estimate genetic and non-genetic variance components that were changed with respect to THI values, accounting for G-ETHI and R×ETHI interaction and the orthogonal R×ETHI interaction were detected from MRNM only. It is also noted that the significance of URNM tends to be different from MRNM, given the results of p = 2.22E–01 (URNM) and 2.99E–02 (MRNM) for orthogonal R×ETHI interaction on CW, and p = 1.49E–02 (URNM) and 2.72E–02 (MRNM) for G×ETHI interaction on EMA. This difference was probably due to the fact that MRNM is better to disentangle interaction effects from (G-E and R-E) correlations between the traits (CW and EMA) and THI , probably not due to the genuine genetic effects of THI. Although this genetic correlation is not biological correlation, this also can cause spurious G×E signals as like G-E correlation would cause. Unless this correlation is correctly accounted for, it is possible to get biased interaction signals. This problem is likely to be reduced when applying MRNM that can disentangle interactions from any confounding correlation.In the presence of interactions, the estimated heritability in bivariate GREML was biased due to unmodeled G×E and R×E interactions. This result agrees with previous studies, e.g., There are a number of limitations in this study. First, we used a single environmental variable only, THI, although there can be multiple (unknown) environmental variables that can increase the proportion of phenotypic variance explained by interaction effects. Second, the covariance structure used to estimate R×E interaction in this study was based on an identity matrix because there were no repeated measures for the animals. It is desirable to collect repeated measures or construct the covariance structure for R×E interaction based on the product of multiple environmental variables, e.g., an environmental relationship matrix, in a further study, which can increase the power to estimate R×E interaction. Third, only the first order of G×E or R×E interaction was considered in this study, and a further study is required to validate our findings with higher order interactions. Fourth, because the period around the slaughter date is most important for the quality of carcass traits , we did THI interaction for EMA and significant R×ETHI interactions for CW and MS, which has an important implication in the genetic evaluation of the traits , Rural Development Administration (RDA), South Korea.YC, SHoL, and SHwL conceived the idea. SHoL and SHwL directed and supervised the study. H-KL and DL organized the database. YC and SHoL wrote the first draft of the manuscript. YC, SHoL, SHwL, and JW contributed to manuscript revision. All authors contributed to read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits (“macrocolumns”), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay ; (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations. Understanding the exact computations performed by the mammalian neocortex has been a “Holy Grail” of Neuroscience for over 100 years. This is in part inspired by the fact that the only known unique attribute of the human brain in comparison to other mammals is the relative size of our neocortex . The most notable differences exist between the “frontal cortex” and “sensory cortex” , bayesian inference, and Adaptive Resonance Theory (ART). These have all been broadly categorized as “predictive processing framework.” All these predictive processing frameworks share two essential features. First, they all assume that the purpose of the sensory cortex is to predict its sensory input. Second, they all assume that the neocortex performs computations, at least in part, hierarchically—whereby the outputs of lower-order regions are provided as inputs to higher-order regions. Although there is a broad consensus on these two features, there are notable differences between each framework, which I describe in more detail within the “Relationship to Previous Models” section. This work integrates and extends ideas from all four of these frameworks, but is built almost directly on top of HTM. HTM is uniquely attractive in that it models sequence and object learning using only Hebbian plasticity, whereas other models tend to require less biologically plausible learning mechanisms (see “Relationship to Previous Models” section).However, three key elements are missing from prior HTM models that I seek to extend in this article. First, the neocortex can learn sequences even when input elements are separated by long time intervals , even though short term synaptic plasticity can only occur on the timescale of <100 ms  perform working memory and connect sequences separated by long time intervals; (2) coordinate its activity and processing with other macrocolumns and structures on extremely precise time intervals; and (3) can be modulated by attention. I will go on to show how this model directly explains a wide range of seemingly disparate experimental observations about the neocortex.My approach will be to start with a basic overview of the overall organizational principles of neocortical neurons, macrocolumns, and thalamocortical networks. I will go on to assign specific computational roles to individual types of neurons within a macrocolumn. I will then incorporate input from the frontal cortex into these macrocolumns; and lastly, I will go on to show how networks of these macrocolumns can recognize and learn objects and sequences.To model the computations within canonical neocortical microcircuit, we must first model the computations of a single neuron. Most of the excitatory neurons within the neocortex are pyramidal neurons, with many apical and basal dendritic segments. Most synapses on a pyramidal neuron are not proximal to the soma but rather found far away from soma on basal dendrites or apical dendrites . Presynaptic firing at distal synapses has very little effect on somatic membrane potential  to “first order neocortex,” while higher-order relay nuclei pass information between different levels of neocortex within the hierarchy see . Early odriving input (synapses close to soma) to core relay neurons that project to L4 in other “higher-order” macrocolumns  back to the relay neurons that projected to L4-ST neurons in a given macrocolumn  neurons are the receiver of bottom-up input from lower-order cortical areas, primarily passing information up from the thalamus , which respond to on-center off-surround circular stimuli in specific locations in their receptive field  have basal dendrites that extend laterally throughout the entire macrocolumn. They have apical dendrites that extend throughout L1 in the macrocolumn. Axonal projections from L2/3-PY neurons project back onto themselves as well as laterally throughout layers 2, 3, and 5 of the entire macrocolumn . This effectively implements a competitive network, where cells responsive to “A” will excite other cells responsive to “A” while inhibiting those responsive to other stimuli , the competitive network in L2/3 will force only one representation to be active  neurons play the computational role of learning and replaying To demonstrate the proposed computation of L5a-RS neurons, let us consider a simplified  setup where sequences occur in small time windows supportive of Hebbian and spike-timing-dependent plasticity . Suppose you input a rapid sequence of already known patterns  into a macrocolumn; and suppose each element follows each other immediately with no delay.unique to the sequence “A→B.” This is the case because the pattern of neurons inhibited and excited in the minicolumns of “B,” was generated by the L5a-RS neurons specific to “A.” If the preceding element in the sequence were “Z,” then a different sparse pattern of “B” would have been activated. The unique code of “A→B” then similarly activates a pattern separated L5a-RS code  neurons represent the key output code of the macrocolumn: L5-IB neurons project directly to motor areas, striatum, and provide driving input to higher-order thalamic relay neurons that project to higher-order cortical areas  will have very different output codes depending on the prior elements in the sequence. In other words, the output code for “A” coming right after “Z” is completely different from the output code for “A” coming right after “X.”In final element of the sequence triggers an L5-IB output representation that is unique for that exact sequence. So in bottom right example in B→C” (from dynamics described in L5a-RS neurons), and hence the L5b-IB output is a unique code representing the exact sequence “A→B→C.” This provides a mechanism for how a macrocolumn can output a unique sequence code based on its inputs.Furthermore, if you chain elements together, the L2/3 representation in the I propose that after learning, the L5b-IB sequence code will stabilize across the whole sequence to represent the pattern of the last element. Later in this article, I will describe in detail how this occurs.Although far from conclusive, evidence of L5b-IB response properties is at least consistent with the proposal that they generate invariant sequence codes. First, L5b-IB neurons have been shown to have much wider receptive fields than L5a-RS or L2/3-PY neurons  neurons encode Thomson, . DendritThomson, . L6a-CT Thomson, , which IThomson, . The majThomson, . These pThomson, . I hypotactive state, as well as putting a specific pattern of L6a-CT neurons into a predictive state . Third, connectivity could be highly biased from peripheral visual field macrocolumns to fovea field macrocolumns and have very little transference from the fovea to peripheral fields. All of these would dramatically reduce the required connectivity of L6-CC neurons while still enabling the overarching motor prediction mechanism to function.I propose that another function of the frontal projection to L6a-CT and L2/3-PY neurons in the sensory cortex is to enable top-down attention. I use the term “top-down attention” to refer to two abilities Knudsen, —the abildecreasing the responsiveness of neurons that are tuned to other feature or objects  due to their rhythmic bursting at the alpha rhythm . I propose that the purpose of this oscillatory slowing is threefold. First, the default oscillatory dynamics of the higher-order frontal cortex and hippocampus are in the theta frequency Buzsáki, , hence tThere are several mechanisms by which oscillations in the sensory cortex might be slowed from alpha to theta during attentive processing. It has been shown that L5b-IB neurons can modulate their bursting rate within ranges encapsulating both theta and alpha frequencies based on apical input  “integration”—progressively more stable representations of input get formed higher in the hierarchy, as proposed by HTM theory  sequence disambiguation—an ambiguous sequence can be disambiguated  and (b) object disambiguation—an ambiguous input can be disambiguated .To demonstrate how the sequence disambiguation occurs, let us consider how a simple network of macrocolumns could learn to differentiate between two songs . ConsideE→D, and C→B→A. This means that on beat #1, macrocolumn #1 does not know what song is being played; it is within an ambiguous sequence.Suppose this network has only ever heard one of two songs  will win out. Hence the representation for only song 1 gets activated in L2/3 (step 3).To see how this network of macrocolumns implements this sequence disambiguation, let us play out the processing steps in our two-level network of macrocolumns see . Step 1 Note, the ability to co-activate multiple representations in L5b-IB, without a winner-take-all mechanism preventing such co-activation, is in stark contrast to the L2/3 network and is consistent with the experimental data on L5b-IB neurons. The inhibition observed amongst L5b-IB neurons seems to not implement lateral inhibition, but rather drive coordinated burst firing amongst L5b-IB neurons. The L5b-IB to L5b-IB inhibition exhibits a remarkable delay in firing, inhibiting other L5b-IB neurons only after 100–200 ms , there will be only excitement of thalamic relay neurons active during song 1, which thereby provides biased top-down input to macrocolumn #2. This top-down bias leads to the activation of the L2/3 representation of only the C within the sequence CED, and not the C within sequence CBA. In the next output state (step 6), macrocolumn #2 will now output an unambiguous sequence code only for the sequence CED. Further, this will lead to an unambiguous prediction of “high E” as the next element in the sequence through L6a-CT neurons in macrocolumn #2. At this point, this network has now achieved a stable state, and if sensory input is unchanged, this network will oscillate back and forth between step 5 and step 6.In the next output state of the network (step 4), an unambiguous sequence code gets output from macrocolumn #3. Furthermore, the L6a-CT neuron back-propagation provides modulatory input back to the thalamic relay neurons for song 1, whileThis sequence disambiguation could happen within the timescale of hundreds of milliseconds, and it can enable this network to unambiguously know that we are playing song 1 and not song 2, even when just hearing beat #1.Object disambiguation occurs very similarly to sequence disambiguation, except instead of disambiguating between sequence codes, networks disambiguate between conflicting inputs. Suppose a two-level network is learning the difference between an oval and a line, and that each level 1 macrocolumn receives input from only a specific location in a visual field. The learned pattern representations can be seen in conflicting input? The same dynamics in sequence disambiguation will play out where the competitive network in level 2 will lead to top-down bias in the macrocolumn with conflicting input, eventually leading the entire network to actually see the full oval, even though not all input is consistent with the oval just most of the input  by the activation of different sets of two minicolumns see .Computationally five specific states will occur during the example procedure of learning this sequence:(1)Receiving the input of “A” for 1 s:(2)Pause (no input) for 5 s:(3)Receiving the input of “B” for 1 s:(4)Pause (no input) for 5 s:(5)Receiving the input of “C” for 1 s.In order to accomplish this task, the brain must store each of these elements in working memory. As such, I propose that the relevant networks lock into an “attentive processing” mode during this procedure. They do this to enable coordinated processing with the frontal cortex and hippocampus, hence oscillating at the theta rhythm. In During the output state, I propose the frontal cortex and/or CA1 activates a unique pattern of L6a-CT neurons, which gets mapped to the network state “A” through STDP. I further propose that CA1 generates unique “episodic memory codes,” consistent with the concept of place cells O’Keefe,  and thatAfter the first replay of “A,” a new L5a-RS code is activated, sequence biasing L2/3-PY neurons. If the subsequent input in the input state is the same , then the L5a-RS projection fails to change the L2/3 representation. In contrast, if the subsequent input in the input state is different , then the combination of L5a-RS sequence biasing and L4-ST input will generate a sparse representation in L2/3 unique for the sequence “A→B.”When the sensory input of “A” is removed during the pause, as long as the frontal/CA1 episode code continues to replay itself during this delay period, then the L6a-CT episode code will continue to independently replay “A” during each output state. Crucially, this means that at the beginning of each input state. L2/3 is sequence biased from the L5a-RS “A” representation, waiting to be mapped to the next incoming L2/3 representation. I propose that this continuous replay of an episode code is one of the key underlying computational processes performed by the brain during working memory tasks.After 5 s of a pause, the sensory input of “B” is provided to the macrocolumn. Due to the sequence biasing from L5a-RS neurons, a sparse representation of “B” is activated that is unique to “A→B,” and the L5a-RS code is mapped to this sparse representation of B using STDP. Due to this unique representation of “B,” now L5b-IB neurons output an “A→B” sequence code instead of just the “A” sequence code see .Taken together, this means that although “A” and “B” were separated by 5 s, in the macrocolumn they were only separated by ~10 ms due to the repeated working memory replay of “A.” This enables rapid STDP plasticity between the L5a-RS neurons activated by “A” and the L2/3 representation of “B,” despite a 5-s separation between the actual sensory stimuli.When the repeating frontal/CA1 episode code comes around and reactivates “A” during the output state, the entire sequence “A→B” will be replayed automatically, instead of just “A.”Due to the same dynamics described in step #2, as long as frontal cortex/CA1 continues to replay the same episode code, our model macrocolumn will continue to replay the sequence “A→B” on each output state even when stimuli “B” is removed.The key difference between step #4 and step #2 is that now: (a) there are two elements replayed and hence two gamma cycles (A and then B); and (b) the output state now ends with a sequence bias from the L5a-RS code for “A→B,” instead of the L5a-RS code for just “A.”B→C” . If this replay occurs a sufficient quantity of times, these synaptic connections will go through long-term potentiation (LTP). This LTP then makes it such that when this macrocolumn receives the input “A,” during the output state it will output the code “A→B→C” automatically instead of just the output for sequence “A.” Note that multiple L5b-IB representations can be active simultaneously, meaning that if “A” leads to multiple different sequences, multiple ambiguous sequence codes can be output for higher cortical areas to disambiguate.Each time the sequence “A” then “B” then “C” replays during an output state while L5b-IB neurons are firing the “A→This aricle provides a novel theory for how the neocortex learns to recognize complex multi-sensory objects and sequences across realistic timescales, and in doing so provides a model for how working memory may function. It extends prior HTM models in three ways: (1) it shows how macrocolumns perform working memory and connect sequences separated by long time intervals; (2) it shows how networks of macrocolumns can coordinate processing with other macrocolumns on precise time intervals; and (3) it explicitly incorporates top-down attention.time and attention. Inputs that tend to co-occur, or to quickly follow each other in sequence, will over time get unique output codes from a network of macrocolumns. These unique sequence codes can then be used by other structures, such as the striatum, amygdala, or other cortical areas to respond to recognized objects or sequences.Taken together, this sheds light on the overall evolutionary purpose of the mammalian neocortex. As theorized by Hawkins’ HTM, I propose the neocortex is in the business of generating unique sequence and object codes. This neocortical microcircuit engages in “unsupervised learning.” There is no labeling. The only “supervisor” is how working memory, sequence prediction, and object recognition might work in the neocortex, this theory also explains why certain neuroscientific findings are observed. I will review a selection of these below.In addition to explaining It has long been observed that there are distinct electrophysiological oscillations within the brain. However, the computational purpose has been unclear. Many computational models have attempted to explain these oscillations as emergent dynamics of feedback inhibition and attractor states .2. This theory predicts that “multiareal matrix neurons” seen in Clascá et al.  are the 3. This theory suggests that when multi-areal matrix neurons burst fire, they “restart” sequence representations stored within macrocolumns. A way to test this would be to selectively inhibit multi-areal matrix neurons during tasks where subjects are observing well-learned sequences . This model predicts that in such a condition if surprising stimuli are inserted into these sequences, it will take subjects longer to identify these new objects. This model further predicts that subjects will struggle to identify which stimuli are in fact “surprising” at all.4. This theory suggests that attention facilitates learning of sequences by replaying previously received elements through the activation of L6a-CT neurons. A way to test this would be to selectively inhibit frontal and hippocampal input to L6a-CT neurons during learning tasks. This model predicts that subjects should be severely impaired in their ability to learn sequences of stimuli if the presentation of these stimuli is separated by a delay.5. This theory suggests that intended motor movements arise through frontal projections to L6a-CT neurons. A way to test this would be to selectively inhibit frontal input to L6a-CT neurons within the visual cortex. This model predicts that this will be very disorienting for subjects as they will be unable to use intended volitional movements to predict changes in visual stimuli. This difference could be tested using measures of arousal and surprise.6. This theory suggests that the origin of differences in observed responses within L2/3 from predicted vs. surprising stimuli  how sequences get learned when there is a realistic time delay between inputs (seconds to minutes); and (4) how sequences get “restart.” I extend Hawkins’ article by proposing an explanation for all four. A notable difference in the circuit model presented here vs. Hawkins and Ahmad  attractor network models; (2) bistability models; and (3) synaptic weight models  explain the observed delay activity in the brain during working memory tasks; and (b) describe a circuit mechanism for multi-item storage.This model proposes a “synaptic weight model” that solves both of these challenges. This model explains the existence of delay activity as well as how multiple items can be stored simultaneously (through “replays”), all without the requirement for a stable attractor state.Predictive coding is undeniably the most broadly accepted computational model of the sensory cortex. The canonical model of predictive coding assumes that hierarchical layers sensory neocortex pass predictions to lower level neocortex, and pass prediction errors up to higher levels (Rao and Ballard, Continuous state-space models of attention and working memory (Parr and Friston, The model presented here is also consistent with Friston’s seminal Free-Energy Principle, a foundational principle guiding most predictive coding models Friston, . Put simHowever, despite these many similarities, there are also important differences between the theory presented here and predictive coding. Most directly, predictive coding typically theorizes that L2/3-PY neurons compute prediction errors, and L5/6 neurons compute predictions (Bastos et al., ART is built on the idea that the neocortex tries to assign incoming sensory input to a known classification, and if the mismatch between incoming input and all known classifications surpasses a threshold, then a new classification is generated for that input (Grossberg and Versace, The original contributions presented in the study are included in the article/MB conceived the overall theory and wrote the entire manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.https://www.frontiersin.org/articles/10.3389/fncir.2020.00040/full#supplementary-material.The Supplementary Material for this article can be found online at: FIGURE S1Fliegende Blätter.The duck or rabbit illusion. Example of how top-down bias can change object perception without any changes to bottom-up input. Originally printed in the 1892 issue of Click here for additional data file.FIGURE S1Visual depiction of example paradigm for learning a sequence of elements separated by realistic time delays. The top part of the figure shows the timeline of the learning paradigm. The “zoom in” depicts the repeating oscillatory states of macrocolumns during the learning paradigm. See text for details.Click here for additional data file.TABLE S1References for connectivity. References for the connectivity modeled and cited in this article.Click here for additional data file.


Scientific Reports 10.1038/s41598-020-62466-1, published online 26 March 2020Correction to: This Article contains an incorrect version of Figure 3. The correct Figure 3 has been reproduced below as Figure Additionally, there is an error in the Methods section.“This study was approved by the Ethics Committee of Shanxi Cancer Hospital, and written informed consent was obtained from all patients.”should read:“This study was approved by the Ethics Committee of Shanxi Tumor Hospital, and written informed consent was obtained from all patients.”