Blood-based sample collection is a challenge, and dried blood spots (DBS) represent an attractive alternative. However, for DBSs to be an alternative to venous blood it is important that these samples are able to deliver comparable associations with clinical outcomes. To explore this we looked to see if lipid profile data could be used to predict the concentration of triglyceride, HDL, LDL and total cholesterol in DBSs using markers identified in plasma.To determine if DBSs can be used as an alternative to venous blood in both research and clinical settings, and to determine if machine learning could predict ‘clinical lipid’ concentration from lipid profile data.Lipid profiles were generated from plasma (n = 777) and DBS (n = 835) samples. Random forest was applied to identify and validate panels of lipid markers in plasma, which were translated into the DBS cohort to provide robust measures of the four ‘clinical lipids’.In plasma samples panels of lipid markers were identified that could predict the concentration of the ‘clinical lipids’ with correlations between estimated and measured triglyceride, HDL, LDL and total cholesterol of 0.920, 0.743, 0.580 and 0.424 respectively. When translated into DBS samples, correlations of 0.836, 0.591, 0.561 and 0.569 were achieved for triglyceride, HDL, LDL and total cholesterol.DBSs represent an alternative to venous blood, however further work is required to improve the combined lipidomics and machine learning approach to develop it for use in health monitoring.The online version of this article (10.1007/s11306-020-01703-0) contains supplementary material, which is available to authorized users. Traditionally, in high-income countries there is significant infrastructure to facilitate preclinical population health monitoring and research that is not present in low income countries leaving a significant unmet need for research and health monitoring -cholesterol, High Density Lipoprotein (HDL)-cholesterol and triglyceride. HDL-cholesterol and triglyceride were measured using an enzymatic colorimetric reagent test using a P-800 Modular (Roche/Hitachi), with LDL-cholesterol calculated using the Friedewald formula and total cholesterol determined by combining the concentration of HDL and LDL cholesterol , which was subsequently added to each well.Samples were extracted from plasma (which was stored at − 80 °C until assay) as described previously  was subsequently added to each well.Dried blood spot samples were prepared as described previously  serum:water and 25:75 (v:v) serum:water were prepared from a pooled serum sample with 15 µl extracted following the same procedure applied to DBS and plasma samples. In addition to being used to assess and correct plate to plate variance the quality control levels are also used to aid signal identification with real signals showing a linear increase in abundance with an increase in serum concentration.Three levels of quality controls samples 100% serum, 50:50  using a Triversa Nanomate . Data collection began 20 s after the infusion began, initially analysing samples in the positive ionisation mode with an ionisation voltage of 1.2 kV applied. Data was acquired between 150 and 2000 md et al. .Raw data files were converted to.mzXML files using msConvert (ProteoWizard)  independently. The analysis was performed in two stages, in the first stage of the analysis, we applied a random forest model to all of the data in the ‘discovery’ cohort (the DFBC) to identify a panel of lipid biomarkers capable of robustly predicting the concentration of the clinical biomarker. This was done by splitting the samples into training and test sets (samples split 70:30 respectively) and calculating a random forest model in the training set and assessing its performance in the test set. To determine the number of lipids to include in the predictive panel, iterative random forest models were calculated using the highest ranked variable from the ‘all data’ model first and then adding additional variables to each model (one at the time) until we achieved a model that performed as well as the model calculated using all lipids.In the second stage, we determined if the panel of lipid biomarkers identified in the plasma samples from the DFBC could be used to predict the concentration of the clinical markers in the ‘validation’ cohort. This was done by dividing the DBS cohort (ABCD cohort) into a training and test set (70:30) and calculating a random forest model in the training set and assessing its predictive performance in the testing set based on the correlation between the measured and predicted concentration of the clinical biomarker.p-values passed a Bonferroni adjusted significance threshold of p = 0.0004 calculated based on all 125 lipids measured in this study.The relationships between individual lipid species and given clinical biomarker concentrations were determined using generalised linear models (GLM) applied to the whole of the dataset. All models were calculated in ‘R’ (version 3.4.2). Controlling for type 1 errors was performed by determining if In plasma samples 163 lipids from 11 classes passed quality control procedures, whilst 118 lipids from 11 classes were identified in the dried blood spot samples with 71% of the lipids measured in DBS also measured in plasma.–92 between estimated and measured triglyceride in the test set  produced a model with a mean square of residual (MSR) of 0.199 in the training set (Table S2) and a correlation of r = 0.914 and p = 7.0 × 10–42 between estimated and measure HDL in the test set  was identified that generated a random forest model with an MSR of 0.082 in the training set (Table S2), with a correlation of r = 0.748 and p = 4.5 × 10–21 in the test set  with an MSR of 0.811 in the training set (Table S2), with an estimated and measured LDL concentration correlating with r = 0.556 and p = 5.1 × 10set Fig. a. In theset Fig. b.Fig. 3SThe quartile distribution of LDL showed that the percentage overlap was higher in all quartiles than the 25% that we would expect to see by chance  were used to generate a random forest model with an MSR of 1.05 in the training set Table S2), and a correlation of r = 0.424 and p = 1.8 × 10, and a cOf the 44 lipid associations from the 4 predictive panels 8 (18%) were not measured in the DBS samples whilst 36 (82%) were successfully validated with a strong correlation (r = 0.917) observed between the correlation coefficients measured calculated between the individual lipid abundance and lipoprotein concentration in both plasma and DBS samples.Of the 12 lipids used to predict triglyceride concentration all significantly associated with TriG concentration passing bonferroni correction at (p < 0.0004) in both DFBC and ABCD cohorts, except for DG(34:2) and DG(36:2) which were not measured in DBS samples (Table S2).With the exception of PC(38:5) all significantly associated with HDL concentration passing bonferroni correction at (p < 0.0004) in both DFBC and ABCD cohorts (Table S3). Whilst not passing the bonferroni corrected p-threshold PC(38:5) was significantly (p < 0.05) associated with HDL concentration in both plasma and DBS samples (Table S3).With the exception of CE(14:0) and TG(47:4) which were not measured in dried blood spot samples all lipids were significantly associated with LDL concentration in both sample types passing a bonferroni correct p-threshold (p < 0.0004) (Table S4).Of the 11 lipids total cholesterol concentration all were significantly (p < 0.05) associated with total cholesterol concentration however only 7 passed a bonferroni correct p-threshold (p < 0.0004) with CE(18:2), PC-O(34:1), PC-O(40:5) and TG(56:5) falling short. In dried blood spots CE(14:0), CE(16:1), LPC(20:4) and PC-O(40:5) which were not measured, however the remaining 7 measured lipids were all significantly associated with total concentration and passed multiple comparison correction based on bonferroni correct p-threshold (p < 0.0004) with all of these lipids also exhibiting a stronger association than is observed between these lipids and total cholesterol concentration than is seen in the discovery cohort (Table S5).Plasma samples were stratified into the clinical risk categories based on their estimated lipoprotein concentration Table . Total cStratification of DBS samples in the ABCD cohort was based on quartile rather than clinical cut-offs as the samples came from adolescents only 3% of the measurements fell outside of the healthy range. Classification accuracy was not equal across the quartiles with all of the lipoproteins showing better classification accuracy in the 1st and 4th quartiles Table .To our knowledge this is the first study to compare results obtained from DBSs and plasma samples in complex lipidomics studies and to determine that if we tried to answer the same biological question in both sample types would we achieve the same answer. Whilst we measured more lipids from the plasma samples (163 compared to 118) a high proportion (71%) of the lipids measured in the DBS samples were also measured in plasma, with this lower number of lipids likely the result of less biological material being obtained from a single DBS punch than was extracted for the plasma samples.Applying random forest machine learning to the lipid profile data generated from the plasma samples we were able to obtain good estimates (r > 0.7) of the concentration of triglyceride (12 lipid panel) and HDL (11 lipid panel) and modest estimates (r > 0.4) of LDL (10 lipid panel) and total cholesterol (11 lipid panel) When these lipids were measured in an independent cohort of DBS samples 2 of the TG markers were not measured whilst the other 10 validated the association seen in the plasma samples. For HDL all 11 lipids validated, for LDL 2 were not measured and 8 validated and for total cholesterol 4 lipids were not measured in DBS while the remaining 7 lipids validated between cohorts. Using these panels of lipid biomarkers identified in plasma samples we were also able to use them to obtain a good estimate of triglyceride concentration and modest estimates of HDL, LDL and total cholesterol concentrations. This begins to suggest that dried blood spots can be used as an alternative to plasma samples and that the process of adding and removing the sample from the card is not altering the composition of the lipidome and its association with relationship with outcome measures, in this case the concentration of the four ‘clinical lipids’. This finding is keeping with previous reports that have shown DBS as a viable alternative Below is the link to the electronic supplementary material.


Epithelial ovarian cancer (EOC) is a leading cause of cancer-related death in women. Late-stage diagnosis with significant tumor burden, accompanied by recurrence and chemotherapy resistance, contributes to this poor prognosis. These morbidities are known to be tied to events associated with epithelial-mesenchymal transition (EMT) in cancer. During EMT, localized tumor cells alter their polarity, cell–cell junctions, cell–matrix interactions, acquire motility and invasiveness and an exaggerated potential for metastatic spread. Key triggers for EMT include the Transforming Growth Factor-β (TGFβ) family of growth factors which are actively produced by a wide array of cell types within a specific tumor and metastatic environment. Although TGFβ can act as either a tumor suppressor or promoter in cancer, TGFβ exhibits its pro-tumorigenic functions at least in part via EMT. TGFβ regulates EMT both at the transcriptional and post-transcriptional levels as outlined here. Despite recent advances in TGFβ based therapeutics, limited progress has been seen for ovarian cancers that are in much need of new therapeutic strategies. Here, we summarize and discuss several recent insights into the underlying signaling mechanisms of the TGFβ isoforms in EMT in the unique metastatic environment of EOCs and the current therapeutic interventions that may be relevant. SNAI1, SNAI2, SNAI3/SMUC, ZEB1 and ZEB2) or indirectly  on E-box consensus sequences of E-cadherin’s promoter to repress E-cadherin expression [TWIST and ZEB1 that have different roles in breast versus pancreatic cancers [Epithelial-mesenchymal transition (EMT) is a mechanism of trans-differentiation of epithelial cells into mesenchymal cells, wherein polarized epithelial cells alter their contacts with their neighboring cells, basement membrane and the surrounding tissues . EMT is pression –11 which cancers –16. It i cancers , 18. EMT cancers , 20. Suc cancers , howeverNature, the presence of a group of proteins isolated from conditioned media of malignant tumors, that can induce morphological transformation in fibroblasts and promote growth of cells in soft agar. These proteins were called Transforming Growth Factor (TGF) now widely known as the TGFβ family [The discovery of the TGFβs’ can be traced as far back as 1976 when De Larco and Todaro first published in β family . Later, β family –29 and wβ family , 31.The β family  with oveβ family , 34  [Both the R-SMADs and the co-SMAD (SMAD4) have two conserved Mad homology domains MH1 and MH2) at the amino and carboxyl terminus respectively  separate 10−7 M)  compared 10−7 M)  and it i 10−7 M) , 88 ther 10−7 M) . Both in 10−7 M) –92. Nota 10−7 M) , 94. Thu 10−7 M)   and MH2  10−7 M) . Much ofTranscriptional control is the most investigated outcome of SMAD activation downstream of TGFβ, however significant SMAD functions in the cytoplasm have emerged particularly in RNA processing. SMADs have been implicated in RNA splicing, micro-RNA processing, as well as directly in miRNA mediated EMT Fig. . For insLong non-coding RNAs (lncRNA) have also emerged as key regulators of TGFβ mediated EMT Fig.  includinTGFβs can also activate a series of non-SMAD signaling pathways with similar and/or delayed kinetics to the SMAD pathways in a context dependent manner . Most coTGFβR2, AVCR2A, BMPR2), and SMADs  in many non-gynecological cancers [Mutation hotspots exist in genes that encode a subset of TGFβ ligands and receptors  pathway [2O2 both at the level of transcription and at the level of latent TGFβ activation in non-ovarian systems [EMT is part of the normal ovarian physiology during oocyte release . Thus, icadherin , 166. Whcadherin , 168. Mo pathway . STING a pathway . Thus, i pathway . For ins pathway . Similar pathway . A secon pathway . Similar pathway . TGFβ ha systems , 177. Wh“soil” of ECM proteins such as collagen I and other adhesion molecules that can support cell proliferation, migration and invasion. The peritoneum thus provides a site for EOC cells, aggregates, and clusters to attach and invade [The peritoneum is a membrane of mesothelial cells, which lines the wall of the abdominal cavity, lying on abdominal and pelvic organs, including the omentum. The peritoneal mesothelial cells (PMCs) create a mechanical barrier for the abdominal organs. The peritoneal cavity acts as a rich d invade –180. In d invade . Such std invade  will be A variation on the classic cancer EMT of tumor cells is mesothelial-mesenchymal transition (MMT) of the PMCs, wherein PMCs transition into mesenchymal cells (MCs), acquiring migratory and fibroblast like phenotypes , much liAs EOC progresses, metastatic fluid (ascites) accumulates in the peritoneum. Increasing volumes of ascites are thought to generate a favorable tumor microenvironment, enabling transcoelomic tumor spread in a feed forward manner. As such ascitic volume and components have been used to grade, predict stage and survival outcomes including chemoresistance outcomes of EOC patients. Some studies also suggest ascites to be an independent prognostic factor in EOC –195. AscThe ascites constitutes its own environmental niche Fig.  as it noIn addition to the heterogeneous cellular population in the ascites that can produce and respond to TGFβ, several recent studies have also characterized the presence of exosomes Fig.  that havThe omentum is a specialized adipose tissue in the peritoneal cavity and is also the most preferred metastatic site for HGS cancers . AcquisiAnalogous to the peritoneum, mesothelial cells in the omentum can also secrete TGFβ that impacts the fibroblasts and tumor cells, and also the immune state of the omentum . The omeComplete debulking surgery is commonly the first- line of treatment for EOCs, followed up with a carboplatin and paclitaxel chemotherapy regimen. Most EOC patient tumors fall under one of two categories, either they are chemo-resistant at the outset, or will eventually become chemo-resistant. Thus, management of recurrent and resistant disease is one of the biggest challenges for EOC. Approximately 50% of EOC patients who have alterations in BRCA1/2, and/or alterations in other homologous recombination repair deficient pathways (HRD genes), are more likely to be carboplatin sensitive. For such patients, PARP inhibitors hold great promise with significant increases in PFS reported . HoweverChemo-resistant tumors express a more mesenchymal gene signature that also coincides with stem cell like features . NotablyBeing able to target EMT related pathways has been a landmark development for cancer therapies. Several approaches and therapeutic targets have been developed or are being evaluated  includinTGFβ specific targeting can occur at multiple steps Fig.  includinTGFβ ligand neutralizing antibodies and antibodies that block TGFβ receptor ligand interactions have received significant pharmaceutical and clinical attention, however with limited progress specifically in ovarian cancers. Fresolimumab  in cancers particularly ovarian cancers, is currently being actively explored as evidence in multiple other cancer studies indicates this may be an effective approach .That the anti- metastatic effects of the TGFβ inhibitors are potentiated by this combination is also strongly based on prior studies that non-responders to checkpoint inhibitors have elevated levels of the central TGFβ pathway components  . SupportProgress through the clinical pipelines for ovarian cancers has been slow with a major gap being lack of biomarkers to stratify patients and identify those who will benefit the most. This is a central issue for the progression of targeted therapies in EOC. With the acceptance that TGFβ inhibitors are likely to be most beneficial in combination therapy as opposed to as a monotherapy, either with immune checkpoint inhibitors or other approaches including anti- angiogenic strategies (not reviewed here), DNA intercalating agents and even with PARP inhibition, the outlook for the expanded use of TGFβ inhibitors in ovarian cancers remains positive.TGFβ signaling mechanisms have been examined for several decades. Yet fundamental insights both into the mechanism of action and the context of action continue to emerge. The recent advances outlined here have revealed the broad impact of TGFβ signaling mechanisms in EMT and cancer. These combined with our growing understanding of unique disease environments (such as discussed here for ovarian cancer) have provided new contextual information and understanding the detailed mechanisms by which plasticity is dictated will be critical for the future. With the advent of several TGFβ approaches in the clinic, such momentum is prescient. Emphasis should also be laid on understanding the interplay between the multiple TGFβ members that have in recent years emerged as playing key roles in cancer progression.


In this way, the spin and orbital magnetic susceptibilities induced by the bulk and edge electron states were simultaneously measured at atomic scale resolution, providing a pertinent experimental approach in the study of SQPs.Detecting the metallic Dirac electronic states on the surface of Topological Insulators (TIs) is critical for the study of important surface quantum properties (SQPs), such as Majorana zero modes, where simultaneous probing of the bulk and edge electron states is required. However, there is a particular shortage of experimental methods, showing at atomic resolution how Dirac electrons extend and interact with the bulk interior of nanoscaled TI systems. Herein, by applying advanced broadband solid-state  125Te nuclear magnetic resonance on Bi2Te3 nanoplatelets uncovering signals distinguishing edge Dirac electrons and bulk electrons.The detection of topological states is restricted to limited experimental tools. Here, the authors apply broadband solid-state  This fundamental property of electrons is the playground of a number of fascinating phenomena, such as the Dirac edge states in topological insulators (TIs)1, the quantum spin Hall effect2, and the formation of Majorana fermions5. In case of three-dimensional TIs, SOC forces the surface electrons to form helical spin structures, wrapping an odd number of massless Dirac cones, with the simplest and most-studied systems being the Bi2Se3 and Bi2Te3 tetradymides. Angle resolved photoemission spectroscopy (ARPES) experiments in combination with theoretical studies have shown that these systems acquire a single Dirac Cone and large band gap7, thus providing an ideal platform for studying SQPs. However, despite the simplicity of their topological edge states, important quasiparticle excitations of Dirac electrons, such as the Majorana zero mode5, the excitonic superfluid condensate8, or the propagation of chiral spin waves on topological surfaces9 remain to a great extent experimentally unexplored. It is furthermore noticed that the detection of Majorana excitations requires the simultaneous probing of the response from both the bulk and edge states, which is difficult to achieve with current experimental techniques10.In the presence of spin–orbit coupling (SOC), the spin δtot = δFC + δdip + δorb, where the first two terms are the Fermi-contact and spin-dipolar terms originating from the electron spin polarization at the Fermi level, and the third is the orbital term generated by the orbital currents of the Dirac electrons12. According to recent theoretical calculations on TI’s the orbital term δorb from the Dirac electrons dominates over δFC and δdip, and induces large negative shifts, and very short spin-lattice relaxation times T112. However, until now experimental 125Te NMR studies on Bi2Te3 nanoparticles13 and microcrystalline (bulk) powders14 have given contradicting results.Since many SQPs depend on the way that the spin of the Dirac electrons couples with their orbital motion and how this interaction propagates through the crystal, an experimental probe sensitive to both the spin and orbital motion of the Dirac electrons with atomic scale resolution is crucial in the efforts to further understand the physics of topological materials. NMR appears to fulfill these requirements as the nuclear magnetic shielding, and consequently the NMR frequency shift, depends on the spin and orbital magnetic susceptibility at the position of each resonating nucleus. Specifically, the total NMR Knight shift can be expressed as s-corrected scanning transmission electron microscopy (STEM) and DFT calculations on pristine Bi2Te3 nanoplatelets, we succeeded for the first time to resolve the NMR signal assigned to the Dirac electron states, and monitored through the NMR parameters the way that the spin and orbital magnetic susceptibilities induced by the Dirac electrons vary across the nanoplatelets.In this article, combining advanced broadband solid-state NMR methods with C2Te3 nanoplatelets were synthesized in the liquid phase using a solvothermal method, as described in the “Methods” section. Figure 15. The Bi and Te atoms are organized in well-defined quintuple atomic layers comprising of five covalently bonded atomic sheets of alternating Bi and Te atoms, i.e. Te(1)–Bi–Te(2)–Bi–Te(1), that are bound to each other by van der Waals interactions, as seen in the intensity profiles in Fig. Bi2Te3 slab comprising 9 quintuplets, corresponding to the mean thickness of the Bi2Te3 nanoplatelets. Details on the DFT calculations are given in the Supplementary Information (SI). Figure 6. In particular, the band structure shows an energy band gap of ≈160 meV and a single surface Dirac cone enclosing the k = Γ symmetry point. The Fermi energy was calculated at EF = 6.5942 eV, i.e. at slightly higher energy than the maximum of the valence band. Figure p〉 and Bi |6p〉 states of the terminating quintuplets. Furthermore, the density of the Dirac states is observed to be substantially reduced in the central region of the nanoplatelets. It is therefore expected that the NMR signals from the surface of the nanoplatelets will be shifted with respect to the signals from the bulk (interior), because the Dirac electrons are predicted to induce large negative orbital Knight shifts12. Despite this expectation, until now efforts to detect the Dirac states through 125Te NMR have not been successful, either with static or with magic-angle-spinning (MAS) one-dimensional (1D) NMR methods. The main reason is the large Knight shift anisotropy, which gives rise to broad unresolved NMR signals that are difficult to excite.In order to examine the way in which the Dirac states propagate through the nanoplatelets and how this feature is encoded into the NMR Knight shift, DFT calculations were carried out on a Bi125Te NMR spectra of two samples (c) microcrystalline (bulk) Bi2Te3 where Dirac electron states are almost absent and (d) Bi2Te3 nanoplatelets where Dirac electron states dominate in the electron band structure. Both spectra exhibit different overall profiles, but with similar features; each is characterized by a central peak at a shift of 250 ppm  and 480 ppm (nanoplatelets), respectively, with a tail at higher shift, and a shoulder at negative shift. In case of the microcrystalline material these features have been explained as originating from the presence of two overlapping signals shifted relative to each other14: one strong narrow resonance at 250 ppm from Te(1), and a broader asymmetric resonance at a shift of −400 ppm from Te(2). Implementation of advanced 1D solid-state MAS NMR methods, such as the double adiabatic echo (DAE) experiment did not improve the resolution, as seen in the Supplementary Fig. The shortcomings of 1D NMR for acquiring well-resolved spectra is clearly seen in Fig. 125Te NMR signals, and to identify those from the topological edge states, the 2D adiabatic magic-angle turning (aMAT) NMR experiment was implemented  and Te(2) NMR signals. No isotropic signal component is observed at negative shifts, as is also clearly shown in Supplementary Fig. 18, and in general in the distribution of the conduction electrons across the nanoplatelets. Most importantly, a third distinct signal component is observed in the aMAT NMR signal of the nanoplatelets at a markedly different shift of −452 ppm, which is assigned to the topological edge states. This signal appears reproducibly in different nanoplatelet samples, including those exhibiting some oxidation of the surface, as observed in Supplementary Fig. In order to resolve these individual ted Fig. , which i17, aMAT signals with very short spin–spin relaxation times T2 may be partially wiped out. The way that nuclear spin coherences dephase across the spectrum is shown in Fig. We note that, because of the long adiabatic pulses (33.33 μs)T2 relaxation, 19. Experimental details on the CPMG inversion are presented in the SI. In the case of the microcrystalline sample the two signals corresponding to Te(1) and Te(2) are resolved, with Te(2) showing a highly anisotropic frequency distribution. In case of the nanoplatelets, the third strong signal component at −452 ppm, exhibits significantly shorter In addition to the inherent 20. The large signal intensity at −452 ppm at short evolution times indicates that a large proportion of the atomic layers in the nanoplatelets have a surface-like electronic structure, and its rapid dephasing at longer evolution times relative to the other signals from the nanoplatelet interior confirms the difference in dephasing times shown in Fig. To test whether such short 21, produced by the surface orbital currents of the Dirac electrons, or otherwise to the Ruderman–Kittel–Kasuya–Yosida (RKKY) interaction between the nuclear spins, also mediated by the Dirac electrons22.On comparing the T1 with the Knight shift is somewhat different, as shown in Supplementary Fig. T1s are uniformly an order of magnitude shorter for the nanoplatelets compared to the microcrystalline sample (200 vs. 20 ms), but see no variation between the bulk Te compared to the surface Te signals, within experimental error, as we do for 12. Nevertheless, the observation of a shorter T1 for the nanoplatelets is evidence that the Dirac electrons prevail on the spin-lattice relaxation mechanism, the effect of which extends into the bulk. This uniformity for T1 across the whole nanoplatelet may be explained by the quasi-2D topology of the Bi2Te3 nanoplatelets12, which confines orbital currents in parallel to the external surfaces. In Fermi liquids with similar orbital current topology, Lee and Nagaosa23 have shown that T1 is independent of the position of the resonating nuclei, which resembles our observation.The variation of the spin-lattice relaxation times 125Te aMAT spectra, and in particular to confirm the assignment of the resonance at −452 ppm, DFT calculations of the NMR shifts were carried out, using the full-potential linearized augmented plane-wave method, as implemented in the Wien2k DFT software package. Figure 2Te3 and a 5-quintuplets slab (with thickness d∼5 nm), representing the microcrystalline Bi2Te3 and the nanoplatelets, respectively. The latter was selected because it allows tractable DFT calculations with satisfactory k-grid, while Dirac electron states remain gapless24. The total isotropic Knight shift is, as previously discussed, equal to δtot = δFC + δdip + δorb. Conventionally, the orbital term is referenced according to δorb = σref − σorb25, with the reference shielding set to σref = 2370 ppm, from Supplementary Fig. δFC and δdip, are set as minus the relevant calculated shielding, i.e., −σFC, respectively −σdip. In case of the microcrystalline material δtot was found to be +720 ppm for Te(1) and −660 ppm for Te(2), as shown in Fig. 14, as shown in the inset of Fig. 125Te NMR spectra.) However, we note that this variation with temperature is similar for the shifts of both Te(1) and Te(2) and, in particular, that the ordering of the two peaks does not change. It is stressed that in the presence of SOC, calculations of NMR shifts require substantial computational efforts, and are dependent on intrinsic errors of the GGA-DFT level of theory, number of k-points, and smearing of the electron occupancy around the Fermi level11. Nevertheless, the calculated NMR shifts, and the predicted ordering of the peaks in the spectrum, are sufficient to facilitate the interpretation of the experimental NMR signals.To strengthen our interpretation of the δtot ∼ −2670 ppm. This is explainable by the fact that in very thin nanoplatelets (d ≤ 5 nm), Dirac electrons spread almost uniformly across the slab  across a 9-quintuplets slab. At the center of the nanoplatelet the DOS drops significantly (gray shaded area) and a band gap begins to open, as shown in Fig. 125Te NMR spectrum of a 9-quintuplet-thick slab was depicted  and Te(2) coalesce to a single resonance, acquiring strong negative shift, ted Fig. , and comets Fig. . In the 14, a nice correlation between the theoretical and experimental spectra is witnessed. This is strong evidence that the isotropic 125Te NMR signal component at −452 ppm belongs to the surface NMR signal shielded by the Dirac electrons. In this perspective, the intensity ratio of the surface-to-bulk NMR signals provides the mean volume of the nanoplatelets that is occupied by the Dirac electrons, whereas T1 , and 1.16 M polyvinylpyrrolidone  were dissolved in 40 mL of ethylene glycol. The mixture was magnetically stirred until it turned highly translucent, then it was transferred and sealed into a Teflon-lined stainless-steel autoclave (capacity of 80 mL). The sealed autoclave was put into an oven at 180 °C for 36 h and cooled to room temperature. The resulting products were collected by repeated centrifugations, and subsequently washed with distilled water and ethanol, two times each, and finally vacuum dried overnight at 90 °C for further characterization.Bi125Te MAS experiments of the Bi2Te3 microcrystalline material were performed with a 4 mm HXY triple-resonance probe, at 14 kHz MAS on a Bruker 400 Avance-III spectrometer operating at a 125Te Larmor frequency of 126.23 MHz. Spectral acquisition was done with a double adiabatic spin-echo sequence (DAE) with a 2.5 μs 90° excitation pulse length, corresponding to an RF field of 100 kHz, followed by a pair of rotor-synchronized short high-power adiabatic pulses (SHAPs)17 of 71.43 μs length and a 5 MHz frequency sweep. For the separation of the isotropic shift and chemical shift anisotropy, which is of significant magnitude in heavy spin-1/2 nuclei26 and strong electron correlated systems, the adiabatic magic-angle-turning (aMAT) pulse sequence was employed16, which consists of a π/2 excitation pulse followed by six refocusing SHAP π-pulses. The same SHAPs as in the DAE were used. Separation of isotropic Knight Shifts was achieved in the isotropic dimension, whereas the MAS dimension corresponds to the conventional MAS spectrum. The diagonal ridges in the sidebands appear because of the inhomogeneous broadening of the sidebands, as they extend along the isotropic shift for each respective Te site. The 125Te MAS spectra of the Bi2Te3 nanoplatelets were acquired on the same spectrometer with a 2.5 mm HX probe, at 30 kHz MAS. For the acquisition of the DAE and the aMAT spectra, rotor synchronized SHAPs sweeping through 5 MHz in 33.33 μs were employed with RF field amplitude of 160 kHz. For the aMAT spectra, the same SHAPs were used with an evolution time of 66.66 μs, excluding the length of the SHAPs, which is equivalent to two rotor periods. Chemical shifts were referenced to TeO227.The 125Te NMR spectra were acquired on a home-built NMR spectrometer under static conditions, operating at Larmor frequency of 126.23 MHz. For the spin-lattice relaxation time T1 and the coherence lifetime 300 with a train of 300 π pulses were implemented, respectively. The T1 and The frequency-sweep 2Te3 nanoplatelets were obtained by measuring 100 randomly selected Bi2Te3 platelets in the SEM image. STEM images and their energy-dispersive X-ray (EDX) elemental mappings were measured using a JEOL JEM-2100F. High-resolution TEM and STEM images were acquired using a Jeol ARM200 with probe Cs-corrector, operated at 200 kV. Cross-sectional TEM samples were prepared by FIB in KBSI . Atomic-coordinates analysis from HAADF images was performed via intensity refinement method. The atomic coordinates analysis was performed with the following steps: (i) HAADF image normalization, (ii) Laplacian of Gaussian filtering, (iii) image erosion, (iv) atom position detection using circular pattern matching, (v) pattern matching errors correction, (vi) center position of atoms correction, (vii) computation of the average pixel intensity around the center of atoms, and finally (viii) two kind of markers based on intensity values has been depicted on the HAADF image, so that the coordinates of Bi and Te are clearly identified from each other.Scanning electron microscopy (SEM) images were recorded on a Hitachi S4800 microscope while the size distribution of the Biλ = 1.5418 Å) with the following experimental conditions: applied voltage of 40 kV, intensity of 30 mA, angular range (2θ) 5–80° and 0.03 steps/s. Rietveld refinement of obtained powder XRD pattern was carried out using the FULLPROF program software. Refined parameters include: overall scale factor, background (BGP), lattice parameters, atomic positions and orientation.The XRD spectra were recorded with an analytical PANalytical X’Pert PRO powder diffractometer. The sample was mounted on a zero-background holder and scanned by using Cu-Kα radiation . Spin–orbit effects were treated self-consistently using fully relativistic projector augmented wave (PAW) pseudopotentials29.DFT calculations were carried out with the QUANTUM ESPRESSO package30. The spin–orbit interaction was considered in a second variational method. Calculations were performed with and without SOC on two different atomic configurations; bulk Bi2Te3, and a five quintuplets Bi2Te3 slab (25 atoms) with thickness ∼5 nm. The k-mesh convergence was checked up to 100,000 points for the bulk materials and up to 5000 points for the slab. Other computational parameters like atomic sphere radii, as well as potentials and wave functions inside the atomic spheres are kept as set by Wien2k defaults. The plane wave basis set size was determined by seting RKmax = 8, and for presented results we have used the PBE generalized gradient approximation31. The orbital part of the Knight shift δorb was calculated by using the x_nmr script of the Wien2k software package, by activating switches to include SOC, the Dirac states metallicity, and Fermi–Dirac smearing between 2 and 8 mRy. The Fermi contact and dipolar terms were calculated in the presence of SOC, using a spin-polarized set-up in an external magnetic field of 100 T, as explained in refs. 30.NMR Knight Shift calculations were performed by using the full-potential linearized augmented plane-wave method, as implemented in the Wien2k DFT software packageSupplementary InformationPeer Review File


In addition, we present complementary analyses to those included in the main article, such as the detailed analysis of the farming activity influence on anticoagulant rodenticide exposure of raptors, by island and by raptor species.The dataset presented in this article supports “Intensive livestock farming as a major determinant of the exposure to anticoagulant rodenticides in raptors of the Canary Islands (Spain)” . A Geographic Information System (GIS) analysis on the influence of the influence of livestock activity on exposure to anticoagulant rodenticides in raptors in the Canary Islands was performed. This dataset provides geographic information on the localization of each raptor (either positive or negative for anticoagulant rodenticides,  They will also contribute in a very relevant way to the elaboration of the map of the exposure to rodenticides of raptors in Europe , as well as to implement the Information Platform for Chemical Monitoring (IPCheM) database, which is the European Commission's reference access point for searching, accessing and retrieving chemical occurrence data collected and managed in Europe.•By providing not only pollution data, but also the GPS coordinates of 308 raptor specimens, some of them belonging to species very rarely observed in Europe , allows feeding databases, such as the Census of raptor specimens for pan-European contaminant biomonitoring.•Buteo buteo (n = 53); Falco tinnunculus (n = 83); Tyto alba (n = 8); Asio otus (n = 68); as well as data from many other species of interest.In addition, the censuses that are currently being carried out in Europe focus in a special way on those species that have a pan-European distribution, since the contamination data in raptors is crossed with those of the human population and in environmental samples of each country. In this work, we provide data from numerous specimens of the species that have been identified as being of greatest interest for this purpose: 1The data presented here are part of a larger series of 831 animals that have been investigated for anticoagulant rodenticides in the Canary Islands over a decade (2011–2020), and which are presented in the article by Rial-Berriel et al. Falco tinnunculus,Buteo buteo, Asio otus, Neophron percnopterus,Additionally, detailed analysis is presented for those raptor species in which more than 50 individuals were analysed. In these analyses we present the comparative statistics of the distribution of anticoagulant rodenticide values between the groups of birds that were found inside or outside the 1.5 km-buffer zone of farms: common kestrel (22.1Accipiter nisus granti (n = 9); Actitis hypoleucos (n = 1); Asio otus canariensis (n = 68); Buteo buteo insularum (n = 53); Circus aeruginosus (n = 1); Falco eleonorae (n = 4); Falco peregrinus pelegrinoides (n = 13); Falco subbuteo (n = 1); Falco tinnunculus canariensis (n = 69); Falco tinnunculus dacotiae (n = 14); Neophron percnopterus majorensis (n = 67); Tyto alba alba (n = 5); and Tyto alba gracilirostris (n = 3). The animals were sent by environmental officers or patrols if found dead, or by wildlife recovery centers if they had been admitted alive but euthanized or death within a week of admission. All carcasses were kept frozen at −20 °C, until they were necropsied. No animals were sacrificed for the purpose of this study. The livers, as the main organ for accumulation and storage of rodenticides, were used for this study This study was carried out in the Canary Islands, and the samples were taken in the context of the Poisoning Control and Prevention Strategy in the Canaries 2.2During these 10 years, we employed two extraction-detection methods for the quantitative analysis of all the anticoagulant rodenticides permitted in the EU  2.3The data about the place where the carcass was found were collected by Canary Islands environmental patrols and obtained by GPS tracking. We employed the QGIS Desktop software (version 3.12) for the analyses of geospatial data. The images were projected to the UTM 28N zone based on the WGS84 Geographic Coordinates System. Several vectorial map layers were created for animals positive for anticoagulant rodenticides; animals negative for anticoagulant rodenticides, farms of pig/cattle/sheep/goat production, and 1.5 km-buffer zones for farms, and all these were superimposed on the base map (OpenStreetMap).2.4The statistical analyses were done using the software package using GraphPad Prism v8.0 . First, the adjustment to the series of data to normality was examined using the Kolmogorov–Smirnov test. The distributions of the anticoagulant rodenticides did not adjust to normality and therefore, non-parametric tests were employed. Thus, we used the Mann–Whitney tests for the analysis. Probability levels of less than 0.05 (two tailed) were considered statistically significant.All samples were collected after obtaining the corresponding permits and following the animal welfare protocols during the sampling.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


Fonticula slime moulds. Taken together, our data identifies additional roles for eukaryotic microbes in the marine carbon cycle; where putative osmotrophic–saprotrophic protists represent a significant active microbial-constituent of the upper sediment layer.Marine sediments are one of the largest carbon reservoir on Earth, yet the microbial communities, especially the eukaryotes, that drive these ecosystems are poorly characterised. Here, we report implementation of a sampling system that enables injection of reagents into sediments at depth, allowing for preservation of RNA in situ. Using the RNA templates recovered, we investigate the ‘ribosomally active’ eukaryotic diversity present in sediments close to the water/sediment interface. We demonstrate that in situ preservation leads to recovery of a significantly altered community profile. Using SSU rRNA amplicon sequencing, we investigated the community structure in these environments, demonstrating a wide diversity and high relative abundance of stramenopiles and alveolates, specifically: Bacillariophyta (diatoms), labyrinthulomycetes and ciliates. The identification of abundant diatom rRNA molecules is consistent with microscopy-based studies, but demonstrates that these algae can also be exported to the sediment as active cells as opposed to dead forms. We also observe many groups that include, or branch close to, osmotrophic–saprotrophic protists (e.g. labyrinthulomycetes and Pseudofungi), microbes likely to be important for detrital decomposition. The sequence data also included a diversity of abundant amplicon-types that branch close to the  Marine sediments are microbially driven ecosystems and encompass one of the largest reservoirs of organic carbon on Earth –3. TheseAn issue for molecular diversity studies is that cell-free DNA and DNA of dead cells can persist over long periods of time, misleading DNA-based analysis. The cold temperatures found at depth (e.g. 4 °C) can also play a role in DNA preservation. For example, the DNA profiles of terrestrial soils can be ‘contaminated’ with sequences from dormant-, dead- or lysed cells . SequencTo date, remotely operated vehicle (ROVs push core and scoop approaches) have precluded preservation at depth, despite there being several chemical possibilities for preserving RNA. LifeGuard™ is an RNA preservation buffer which, according to US patent , contains no compounds that induce the precipitation of proteins and organic compounds, which can lead to the drastic loss of nucleic acid molecules or biases in community structure . LifeGuaTo tackle this problem, we have developed a tool for the injection of preservation buffer into sedimentary samples at the point of sampling . This ‘pilot cruise’ site was 12 km from the 200 m site and 22 km to the 881/957 m sites (see below). All these sites had similar sedimentary characteristics. The injector was deployed by an ROV preloaded with PBS-containing fluorescein  and 0.75 μm YG fluorescent-polystyrene beads (Polysciences Inc.). The buffer was pumped into the sediment cores using the ROV manipulator claw (taking 1–2 min) to test buffer penetrance. Follow-up microscopy demonstrated the injected buffer saturated the top 1–2 cm layer of the sediment core. All further sampling was limited to the top 1 cm of the core.Pilot experiments were conducted using a standard push core  modified as a reagent injector Fig.  with 5 cPrior to, and after each use, the injector was set up/decommissioned in a laminar flow hood, with the system repeatedly washed through with 70% ethanol, with all liquid expelled before being loaded with sampling buffer. The system was then fully discharged, the flow-through sample buffer discarded and then reloaded with new sampling buffer. The reserve bladder and injector pump were filled using sterile syringes with a mix of 5 mg of fluorescein dissolved in 100 ml of LifeGuard™ (MoBio). The system was then primed prior to deployment so that ~5 ml of buffer was released from the needles recovered and stored at −80 °C for SSU rRNA gene analysis as a control for contamination.Doc Ricketts from the North Pacific Ocean (Monterey Bay) at 200 m , 650 m , 881 m  and 957 m  depth . The paired cores with the different treatments were each deployed within 1 m of each other.Samples for diversity analyses were collected in September 2011 using the ROV pth Fig. . The samtem Fig.  followedDuring preparation and ROV recovery, samples were taken to control for potential contamination introduced during set-up and/or ROV operation. Specifically, 5 ml of buffer was recovered from the wash through as the system was primed (described above) and another 5 ml of un-injected buffer sample was recovered from the buffer reserve bladder after deployment. These control samples were immediately frozen at –80 °C. Across the three needle-injected samples 10 (650 m sample), 30 (881 m) and 20 ml (957 m) were left in the bladder/system after deployment indicating that between 65 and 85 ml were injected into 1–2 cm top layer of the sediments. On ship, ~12 g of surface sediment taken from around the needle insertion points from each sampling core was transferred immediately to a 50 ml centrifuge tube, using a sterilised spatula in a laminar flow hood. Additional LifeGuard™ solution was added , shaken vigorously and stored at –80 °C and then shipped on dry ice to the UK. We note this process may fail to sample some eukaryotes lysed by the process of sampling and for which the RNA molecules of lysed cells were released into the water column space of the core or the resuspension buffer during preservation.g for 5 min at 4 °C to remove supernatant. Each push core sample  and analysed using a Bioanalyzer, which demonstrated the absence of clear RNA or DNA signal. These control samples were then subject to PCR amplification using 18S-V9 and 16S-V6 primers and PCR protocols as outlined above. These were both negative (i.e. no discernible 18S or 16S band present compared with positive controls). The RNA samples were then reverse transcribed and PCR amplified using 18S rDNA primers (as above) and again the PCR reactions were negative.® High-Fidelity DNA Polymerase (BioLabs). PCR reactions consisted of a denaturation step at 98 °C for 30 s, followed by 10 cycles of 10 s at 98 °C, 30 s at 53 °C and 30 s at 72 °C, and then 15 cycles of 10 s at 98 °C, 30 s at 48 °C and 30 s at 72 °C, and an elongation step at 72 °C for 10 min. Amplicons were checked on 1% agarose gels for amplification and purified using a GeneJET PCR Purification Kit (ThermoFisher), eluted in 25 µl of elution buffer and quantified with a Qubit dsDNA HS kit (Invitrogen).For each cDNA sample three PCR amplifications were conducted using the eukaryotic V4 SSU primers: TAReuk454FWD1 (5′-CCAGCASCYGCGGTAATTCC-3′) and TAReukREV3 (5′-ACTTTCGTTCTTGATYRA-3′) . PCR reaLG-ship)  were sequenced for a second time using one lane of Illumina MiSeq (run 2) . Each library was subject to a 300 bp paired-end sequencing run on an Illumina MiSeq 2500 platform (run 1) at the University of Exeter Sequencing Service. All samples were pooled on the same ‘lane’, so that any trend identified between sample sets was not the product of between-lane sequencing efficiency/dynamics. All the cores with LifeGuard™ added at the surface ( 2) Fig. . Sequenchttps://github.com/frederic-mahe/stampa) against PR2 V4 SSU rRNA database (version gb 203) [Raw data from the Illumina libraries were assigned to individual samples by their barcodes. Merged (overlapped) paired-end reads were created using PEAR  and any  gb 203) . Chimeri gb 203)  implemenLG-in situ and LG-ship). Any OTUs assigned as Metazoa (19% of the reads) were excluded. The Shannon, Chao1 and PD calculation methodology include a default rarefaction approach and so were calculated using the QIIME v1 scripts  resulting in 18,564 reads per sample. An analysis of variance (ANOVA) was conducted to test for group differences using R [Alpha- (within sample) and beta- (among samples) diversity analyses were conducted on both sample types from run 1 ( using R .Species (OTUs) abundance distribution and species (OTUs) accumulation curves, based on the number of reads, were compared between treatment groups using R (ggplot2 library ). For thFor the beta-diversity, Bray Curtis distance matrices from a relative abundance and a presence/absence (Sorenson’s index) OTU table were performed using R (Vegan library). Unweighted and weighted UniFrac  distanceLG-ship samples were subjected to additional sequencing (run 2) to further explore the diversity profiles. Runs ‘1’ and ‘2’, were pooled by sediment core and normalised . Stacked bar charts were generated with the reshape2 and ggvis libraries in R to identify taxonomic composition of these samples.As mentioned above, the SSU phylogenetic trees were calculated for the OTUs identified from runs ‘1’ and ‘2’ combined. We limited our phylogenetic analysis to OTUs that were composed of ≥20 reads and so these results are limited to OTU groups with increased relative representation. The resulting trees encompass a large amount of sequence diversity, yet are derived from a short alignment of the ~400 bp V4 region of SSU rRNA gene, resulting in a highly skewed data matrix with relatively few informative alignment positions but very many sequences. This in part explains why many of the nodes recovered in the phylogenies have low bootstrap values. To construct the different phylogenies, published reference trees of known OLS eukaryotic groups , were divided into three equivalent portions. These were considered separate biological replicates. The SSU PCR was conducted in triplicate for each cDNA sample . In the best cases, we generated nine replicates per core. In a subset of cases some steps failed, limiting sampling to six or more replicates Fig. . After bLG-in situ and one LG-ship. For the fourth set of samples at 200 m, the injection system failed and only LG-ship samples were recovered. For the 200 m, 650 m and 881 m depth samples, it took 17, 40 and 37 min to recover the samples, respectively. Due to the depth, tide and rough sea state, the 957 m depth sample took 127 min to recover. Abiotic parameter tracking demonstrated considerable changes in pressure during ROV recovery, while temperature changes of around 6–10 °C occurred largely in the last ¼ (5–20 min) of the ROV recovery journey  two cores were recovered, one ney Fig. .LG-in situ treatment resulted in a different abundance/diversity profile compared with the paired LG-ship samples. After normalising the sampling effort, alpha diversity analyses   a lower total number of OTUs with equivalent sequencing efforts and (ii) the abundant OTUs contained higher relative representation  is higher than the median with LG-in situ, indicating a significantly increased diversity profile identified in non-injected samples in eight of nine ANOVA tests  Fig. , speciesty) Fig.  and poolty) Fig. ) and spety) Fig.  were comion Fig. . This reion Fig. . FurtherLG-in situ and LG-ship) group together, forming clusters of 50.3–51.6% of similarity from the presence/absence analysis  and weighted (quantitative) Unifrac distances were also analysed , a highly abundant group, showed a consistent and strong pattern of change between LG-in situ and LG-ship sampling strategies . An overview of the taxonomic composition of the most abundant groups detected in these sediments is shown in Fig. To further investigate the diversity, we performed additional sequencing of one core from each depth of the Within the stramenopiles, the ochrophyta Bacillariophyta (diatoms) are the dominant RNA-derived signature. Indeed, of the phytoplankton groups, only the diatoms show high relative OTU abundance (21.52% S.E.M. ± 3.36) confirming that ribosomally active and likely intact diatom cells are exported to the sediments , 14, 45 The Rhizaria also demonstrated a high relative abundance of transcribed SSU rRNA sequences, with relative abundance of 10–20% across the samples Fig. . Within Corallochytrium [Next, we aimed to investigate the diversity of OTUs putatively branching with OLS taxa. These classifications were based on STAMPA analysis against a PR2 rRNA database , followechytrium  and the chytrium , thraustchytrium , 50. In chytrium , 51 but 97 pyrotags’ [OTUs with ≥20 reads classified as MAST or as pseudofungal were incorporated into a stramenopile phylogeny  Fig. 4a4a. This yrotags’ , yet theDevelopayella [For the pseudofungi groups, we identified an additional 33 OTUs within the oomycete clade Figs.  and S2; opayella , 52.Labyrinthulomycete (Labyrinthulea) OTUs with ≥20 reads, were integrated into a labyrinthulomycete phylogeny , 53 Fig and S3, Fonticula alba [OTUs with ≥20 reads assigned as opisthokonts  were incorporated into a published tree  Figs. 4 and S4. ula alba  and haveula alba . This grula alba . In geneula alba ), suggesThe phylogenetic analysis demonstrated only 30 OTUs were placed within the Fungi clade and none of these OTUs showed a high relative abundance Figs.  and S4. LG-in situ samples contained an increased representation (read-count) of the abundant OTUs. Linked to this skew in sampling, we also identified a reduction in the breadth of the diversity profile recovered in LG-in situ samples. This effect is significant  fixation vs RNA loss dynamics and (ii) community change during sampling are playing a role in driving the observed community changes and indeed these phenomena are not mutually exclusive. Depending on the aim of a given study, in situ preservation could be interpreted as a weakness as it limits the diversity profile recovered. Yet, in situ preserved samples are likely to recover a more realistic RNA community structure as a product of blocking RNA degradation or community change during sampling. As such, experimental design, with-or-without in situ RNA preservation, should be tailored to the specific aim of a study. We also note that overall the Our analysis identified a large relative abundance of rRNA sequences classified as diatoms. This pattern was consistent over multiple cores/sites. The extent of this rRNA signature was unexpected given that diatoms predominantly function as phototrophic organisms. Export of organic carbon to the deep sea is known to occur episodically and rapidly, in the order of days, in eastern North Pacific waters South of our study region , and sinFonticula-like protist lineages, suggesting this group, which branches with organisms that form slime-mould cellular structures, constitutes an important component of marine sedimentary communities [Fungi are primarily osmotrophic and often encode potent lysotrophic functions , making munities .The analysis of MAST groups demonstrated that a number of these protist phylogroups, initially identified in the marine water column , are alsFonticula-like groups identified). The patterns identified here suggest these groups may be important organic matter degraders. If this is the case, it is likely that these unicellular eukaryotes are therefore adding to key biogeochemical functions.Collectively, the pattern of OTU diversity identified demonstrates a number of candidate phylogenetic groups, specifically: pseudofungi, labyrinthulomycetes and fonticulids, which are ribosomally active, diverse and in some cases show high SSU rRNA relative abundances in these environments. It is not possible to confidently predict the function of these eukaryotic microbes based on SSU rRNA phylogenetic associations, yet the diversity pattern reported provides a number of candidate groups that potentially encode OLS function associated with detrital processing in marine sediments Fig. . HoweverSupplementary Materials Figure LegendsSupplementary Film S1Supplementary Figure S1Supplementary Figure S2Supplementary Figure S3Supplementary Figure S4Supplementary Table S1Supplementary Table S2


We found that in this model, TGFβ1 rescues neurite outgrowth under growth inhibitory conditions via the canonical TGFβR2/ALK5 signaling axis. Thereby, protein kinase A (PKA)-mediated phosphorylation of the E3 ubiquitin ligase SMURF1 induces a switch of its substrate preference from PAR6 to the Ras homolog A (RhoA), in this way enhancing outgrowth on the level of the cytoskeleton. This proposed mechanism of TGFβ1 signaling could underly the observed increase in structural plasticity after stroke in vivo as suggested by the temporal and spatial expression of TGFβ1. In accordance with previous publications, this study corroborates the potential of TGFβ1 and associated signaling cascades as a target for future therapeutic interventions to enhance structural plasticity and functional recovery for stroke patients.Transforming growth factor (TGF)β1 has repeatedly been associated with axonal regeneration and recovery after injury to the CNS. We found TGFβ1 upregulated in the stroke-denervated mouse spinal cord after ischemic injury to the motor cortex as early as 4 d postinjury (dpi) and persisting up to 28 dpi. Given the potential role of TGFβ1 in structural plasticity and functional recovery after stroke highlighted in several published studies, we investigated its downstream signaling in an  This study addresses a mechanism for Transforming growth factor (TGF)β1 to increase compensatory axonal sprouting and growth after cortical stroke, e.g., in the stroke-denervated cervical spinal cord, where it was previously implicated as a potential growth-inducer. The signaling pathway includes the canonical receptor components ALK5 and SMAD3 and a downstream modulation of the cytoskeleton via protein kinase A (PKA)/SMURF1 induced downregulation of Ras homolog A (RhoA) and upregulation of PAR6. Defining the downstream signaling pathway through which TGFβ1 can induce neurite outgrowth may provide new clinical targets for future therapeutic interventions to increase compensatory sprouting, thus contributing to functional recovery.Interruption of the brain’s blood supply, e.g., in case of ischemic stroke, can result in life-long disability because of the loss of neurons . Most avin vitro in primary neurons, including cortical neurons  through the canonical TGFβ1/ALK5 signaling axis. Further hypothesis driven pharmacological blockade studies suggest an underlying signaling mechanism involving a PKA-mediated phosphorylation of the E3 ubiquitin ligase SMURF1, switching its substrate preference from PAR6 to Ras homolog A (RhoA). In conclusion, we show that besides canonical transcriptional changes associated with TGFβ1, it also enhances neurite outgrowth by downregulating RhoA, the downstream signaling mediator of many CNS-associated growth inhibitory molecules. Thereby TGFβ1 has the potential to prime the neuronal cytoskeleton into a growth permissive state despite of the inhibitory CNS environment.In the present study, we show that TGFβ1 is transcriptionally upregulated in the region of the axotomized cervical CST and in the premotor layers of the CST-deprived spinal gray matter as early as 4 d postinjury (dpi). Using a simple but highly reproducible n = 21 adult C57BL/6J mice  were used in this study. Only one sex was used to minimize lesion size variability and animal numbers. Animals were housed in groups of four to five under a constant 12/12 h light/dark cycle with food and water ad libitum.All animal experiments were performed with the approval of and in strict accordance with the guidelines of the Zurich Cantonal Veterinary Office. A total of For all surgeries, mice were initially anesthetized using 3–4% isoflurane, transferred to a stereotactic frame (Kopf Instruments) and kept at 1–2% isoflurane throughout the surgery. Body temperature was maintained at 37°C on a heating pad. All animals received a unilateral photothrombotic stroke to lesion the right side sensorimotor cortex as previously described . Brieflyn = 12). Paw dragging of the affected limb was scored as the percentage of paw drags divided by total number of paw touches (n = 12). The number of foot errors was measured as the number of total misplacements (slips) divided by the total number of steps taken. All recorded steps were analyzed and no videos were excluded for the analyses to avoid bias toward stroked groups .Behavioral tests were performed before surgery (baseline) as well as 4, 7, 14, and 28 d after photothrombotic stroke (dpi) of the right motor cortex. Poststroke impairment and recovery of forelimb function was assessed using the cylinder test. Forelimb paw touches to the cylinder wall during spontaneous rearing behavior were recorded for 20 min or 30 rears in total ( touches . The hor touches . Mice we5 IU/l heparin (Roche) and 0.25% NaNO2]. Brains and spinal cords were quickly dissected and snap-frozen on dry ice to preserve RNA quality. The brains were immersed in 4% PFA overnight before being transferred to 30% sucrose in phosphate buffer (PB) for cryoprotection. Samples were blinded from the point of tissue harvesting.All mice were terminally anesthetized with 3–5% isoflurane followed by injection of pentobarbital . Animals were transcardially perfused with ice-cold Ringer’s solution  was applied in 1% BSA in PBS and incubated over night at 4°C. The samples were washed three times in PBS, followed by incubation with secondary antibodies  in 1% BSA in PBS for 1 h at RT. After washing three times in PBS, cells were counterstained with 1:100 Phalloidin Alexa Fluor 488  to visualize the actin cytoskeleton and DAPI . To detect differences between groups and within groups over time, two-way ANOVA followed by Dunnett’s multiple-comparisons (MC) was used. Other symbols might be used to indicate two comparisons in one graph. In bar graphs, all data are plotted as mean standard error of the mean (±SEM), while data were normalized to solvent controls in the neurite outgrowth assays. Dots in the behavioral graphs represent individual animals. For all neurite outgrowth assays, In situ hybridization for TGFβ1 in the stroke-denervated spinal cord showed an increase of TGFβ1 mRNA at 4 dpi in both analyzed regions, the CST domain of the dorsal funiculus as well as the iGM  in the cervical spinal cord after a large stroke to the motor cortex, a time point when compensatory axonal sprouting of the CST may be triggered. This increased expression persisted in the CST area in the dorsal funiculus up to 28 dpi. in vitro studies correlated TGFβ1 treatment with axonal growth promotion in several subtypes of neurons (in vivo is thus evident, however, interpretation of the results may be complex owing to the pleiotropic roles of TGFβ in the control of cell proliferation, differentiation, wound healing and immune system (Following stroke, TGFβ1 has been reported to exert neuroprotective effects. Administration of TGFβ1 led to reduced infarct sizes , while b neurons , while o neurons . These v neurons ; our pree system .Previous studies have reported indirect effects of TGFβ1 on modulation of axonal growth , e.g., bA previous study reported an upregulation of TGFβ1 mRNA in the stroke-denervated spinal cord at 28 dpi , suggestin vitro model the neuroblastoma-derived cell line N1E-115 treated with crude SCE (in vitro model of N1E-115 cells was dependent on ALK5 and SMAD3, as was shown to be the case for cortical neurons in vivo (We addressed the potential downstream pathways of TGFβ1 leading to increased neurite outgrowth under growth inhibitory conditions. We conducted our studies using aa rude SCE . While t in vivo .We then went on to pharmacologically block selected downstream targets of TGFβ1/ALK5 signaling to identify potential effects on neurite outgrowth in this model. By applying the candidate factors in a well-defined time frame after initial neurite outgrowth and in combination with inhibitory SCE, we were able to address post-translational signaling mechanisms, including phosphorylation and ubiquitination. While these studies allow us to address the direct effects of pharmacological interventions, we did not address effects on a transcriptional level in this study.Application of SCE was shown to increase RhoA levels both in the cell body as well as in the growth cone. RhoA activity is associated with neurite retraction in response to growth cone collapsing agents . PharmacWe further saw an accumulation of PAR6 protein in the TGFβ1-treated N1E-115 cells, suggesting increased protein stability or synthesis resulting from the TGFβ1 treatment. Previous studies have described TGFβ1-mediated regulation of proteasomal processes in neuronal cells . Along tin vitro and neuronal polarization in vivo (in vivo and in particular in CSNs after a cortical stroke.Notably, in our study, the TGFβ1 induced neurite outgrowth was also dependent on PKA activation. Previous studies have shown that PKA is a downstream target of TGFβ1 and is associated with SMAD proteins . PKA-med in vivo . It is p in vivo . Based oin vitro and in vivo, to develop a successful therapeutic agent.Endogenous TGFβ1 levels were shown to be increased in early phases after stroke, within the critical period of plasticity . While tin vitro and possibly compensatory axonal sprouting in vivo. This signaling pathway includes the canonical receptor components ALK5 and SMAD3 and a downstream activation of the cytoskeleton via PKA/SMURF1 induced downregulation of RhoA and upregulation of PAR6.The present study investigates a potential signaling mechanism through which TGFβ1 induces neurite outgrowth in a growth inhibitory environment


Protein–protein interactions (PPIs) represent an extremely attractive class of potential new targets for therapeutic intervention; however, the shallow extended character of many PPIs can render developing inhibitors against them as exceptionally difficult. Yet this problem can be made tractable by taking advantage of the fact that large interacting surfaces are often characterized by confined “hot spot” regions, where interactions contribute disproportionately to overall binding energies. Peptides afford valuable starting points for developing PPI inhibitors because of their high degrees of functional diversity and conformational adaptability. Unfortunately, contacts afforded by the 20 natural amino acids may be suboptimal and inefficient for accessing both canonical binding interactions and transient “cryptic” binding pockets. Oxime ligation represents a class of biocompatible “click” chemistry that allows the structural diversity of libraries of aldehydes to be rapidly evaluated within the context of a parent oxime-containing peptide platform. Importantly, oxime ligation represents a form of post solid-phase diversification, which provides a facile and empirical means of identifying unanticipated protein–peptide interactions that may substantially increase binding affinities and selectivity. The current review will focus on the authors’ use of peptide ligation to optimize PPI antagonists directed against several targets, including tumor susceptibility gene 101 (Tsg101), protein tyrosine phosphatases (PTPases) and the polo-like kinase 1 (Plk1). This should provide insights that can be broadly directed against an almost unlimited range of physiologically important PPIs. There are estimated to be more than 400,000 protein–protein interactions (PPIs) comprising the human “interactome” and collectively, these represent an extremely attractive class of potential new targets for therapeutic intervention against a broad range of diseases, including several cancers ,2,3. TheApplication of rapid and efficient strategies that can extend chemical diversity beyond that afforded by the coded amino acids has the potential to significantly enhance the ability of peptides to explore unexpected binding interactions, thereby making the identification of high affinity PPI inhibitor leads more probable. Post solid-phase diversification is particularly appealing, since it eliminates the necessity of individually preparing each peptide by total synthesis. Utilizing azide-alkyne “click” chemistry can be a rapid and easy way to introduce structural diversity . However1, 1). For post-solid-phase diversification, a solution of 1 in DMSO can be aliquoted into the wells of microtiter plates and into each well is then added a single, unique alkyl or aryl aldehyde in the presence of a slight excess of acetic acid at room temperature (RT) overnight. This results in a single oxime product (2) for each well, which is typically of sufficient purity to be used directly for biological testing without a need for further intermediate purification . F1E2P3T4A5P6P7E8E9” retains modest binding affinity. In order to probe the nature of the binding interactions of the parent peptide with Tsg101, we conducted a systematic examination of each residue using an oxime library approach ..N-termin11 was not obvious based on the structure of PBD-bound parent peptide 9 , we found that while the “HSpT” residues of 11 were nearly superimposable with those of the parent in the 3HIK structure, differences in the psi angles of the Leu residues placed the adjacent N-terminal Pro residues in nearly opposing directions (R) phenylbutyloxy group of 11 so that it terminates with its phenyl ring in a “cryptic binding pocket” that is occluded in the crystal structure of 9. Surprisingly, the interactions of the “-(CH2)4-Ph” moiety of 11 are highly similar to what are observed with the peptide PLH*SpT, where H* indicates the presence of a “-(CH2)8Ph” group on the His N3 (π) nitrogen -(CH2)8Ph])   . Peptideeactions . The app12, little effort had been directed at optimizing these interactions. This was due in large part to the fact that introducing the –(CH2)8Ph moiety into parent 12 requires a total peptide synthesis using the reagent N-Fmoc-His-[N(π)-(CH2)8Ph]-OH -OH . Such a ligation . We syntntations . Subsequptide 12 . This isWe hypothesized that the branched bi-aryl ring systems allowed access to a previously unidentified “auxiliary” binding region, proximal to the hydrophobic cryptic pocket .17–19, which showed up to three orders-of-magnitude selectivity for the PBD of Plk1 relative to Plk3 (We also thought that by taking advantage of subtle differences within the proximal auxiliary binding pockets, it might be possible to enhance selectivity for the Plk1 PBD relative to the PBDs of Plk2 and Plk3, since several residues in this region differ among the three PBDs. Accordingly, in further work we were able to arrive at peptides, such as  to Plk3  36]..17–19, wOxime ligation is an emerging and powerful tool that allows one to interrogate and optimize the interactions of peptides with protein binding partners. Viewed as a “tethered fragment” approach, oxime ligation has distinct advantages over traditional fragment-based screening, where the low binding affinities of fragment libraries present inherent limitations. It combines the power of fragment screening together with affinity enhancement afforded by multi-valency. In this review, we have presented a number of studies, in which we have utilized this approach to optimize peptide–protein interactions. Used effectively, oxime ligation has the potential to profoundly impact the development of PPI inhibitors.


The paper presents experimental data on heat transfer and kinematic structure of steady and pulsating flows behind a rib. Several forcing frequencies and one non-dimensional amplitude of pulsation are considered. Distributions of heat transfer coefficient were obtained in the separation region. Optical measurements yielded the profiles of velocity and turbulent characteristics of flow at representative coordinates of the separation region. Distributions of heat transfer coefficient are generalized well when the dimensionless frequency (Stanton number) is used.•Profiles of velocity and turbulent characteristics were obtained at the representative coordinates of the separation region in steady and pulsating flows.•The research also focuses on the vortex street behind the rib, which is an important component of the kinematic structure of flow. Parameters of this vortex street depend on the frequency and amplitude of pulsations.•Simultaneous analysis of heat transfer and hydrodynamics of pulsating flows was performed; correlation between local heat transfer and transverse velocity was revealed.1The obtained heat transfer results contain distributions of the heat transfer coefficient over the channel wall behind the rib in steady and pulsating flows (air with ambient parameters). Experimental data were acquired for several flow rates, several frequencies and single amplitude of forced pulsations.The data on kinematic structure of flow include the profiles of velocity and turbulent characteristics at representative coordinates of the separation region. These data were obtained in much detail for one flow rate. Steady-state flow and pulsating flow at a single one forcing frequency and one level of forced amplitude were considered. Similarity of geometry and flow regimes allowed direct comparison between heat transfer and hydrodynamic parameters.22.1F0=0.115 × 0.15 m2. An aluminum square rib with the height e = 30 mm (rib dimensions: 30 × 30 × 150 mm3) was installed on one of the 0.15 m wide walls at the distance of 0.1 m from the inlet. A 455 mm long section for heat transfer measurements was mounted immediately downstream of the rib. Stable flow rate was provided by critical flow nozzles. Close to harmonic pulsation pattern was generated by a rotating flap at the channel outlet.Heat transfer behind a rib in pulsating air flows was investigated using a special experimental setup. The test section was a 1.2 m long rectangular channel with a cross section Transparent walls (glass and polycarbonate) facilitated experimental investigation of kinematic structure of flow. Aerosol was supplied from the preparation chamber to the channel entrance in order to visualize the flow structure. Vector fields of velocity were estimated from the analysis of displacements of turbulent structures visualized by aerosol. The flow patterns were filmed with a high-speed camera in a light sheet illuminating the axial plane of the channel.2.2Heat transfer between the wall and the flow was provided by heating the measurement section of the wall by direct current supplied from a battery. A printed circuit board was employed as a heated wall. The tracks of this board simultaneously served as resistance thermometers registering the local wall temperatures. The distributions of heat transfer coefficient behind the rib were derived from convective heat fluxes and difference between the wall and flow temperatures. Convective heat fluxes were estimated based on the heat generated by electrical current taking into account the heat losses estimated from the heat balance equation and heat flux equations . Measurement uncertainties were estimated. The method was described and tested in Velocity fields and Reynolds stresses were measured using the optical method SIV (Smoke Image Velocimetry) based on digital processing of flow pattern videos. SIV estimated vector fields of velocity analyzing the displacements of turbulent structures visualized by aerosol 33.1Flow parameters considered in the heat transfer study are given in Q, was provided by the critical flow nozzles. The flow rates brought to standard conditions (20 °C and 1.013•105 Pa) were estimated taking into consideration the temperature and pressure before the nozzles. The bulk velocity, U0, was calculated for the coordinate located far away from the rib. The experimental data on heat transfer coefficient along the separation region of steady and pulsating flows are given in x normalized by the rib height e. Y-axis is the heat transfer coefficient h. Distributions were obtained in the streamwise axial plane of the channel. Heat transfer in the corners was not considered. For illustrative purposes, the rib is shown as a black square with a proper scale in the figures.The volumetric air flow rate, 3.2x/e = 1.05; 2; 3; 4; 5; 6; 8; 10 (x is the streamwise coordinate with the origin at the leading edge of the rib).Detailed investigation of hydrodynamics was carried out for steady and pulsating flow cases . The floU) and transverse (V) velocities, their turbulent components (Uʹ and Vʹ), Reynolds stresses (UʹVʹ) and vorticity (ω) normalized by bulk velocity and rib height (e).The profiles of velocity and turbulent characteristics in steady flows are presented in U) and transverse (V) velocities are plotted. Here, the fluctuations are considered as a combination of periodic and turbulent components of velocity p=Ũ+UʹU and p=Ṽ+VʹV, where Ũ=AUSin(2πfτ) and Ṽ=AVSin(2πfτ).Profiles of velocity and fluctuations in pulsating flows are demonstrated in fτ) of forced pulsations were built for two representative coordinates (x/e = 2 and x/e = 3) with the intention to analyze the evolution of hydrodynamic processes in separated pulsating flows (Profiles of flow parameters in different phases φ (φ=2πng flows . Each cu4The experimental data on heat transfer and hydrodynamics obtained in the channels of identical geometry at identical regime parameters can be employed for combined analysis of these data and for the search for correlation between these processes. Such an analysis was performed in related research paper This material is the authors' own original work, which has not been previously published elsewhereIrek Davletshin: Formal analysis, Writing - review & editing. Andrey Mikheev: Methodology, Writing - review & editing. Nikolay Mikheev: Investigation. Radif Shakirov: Investigation.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The physics of weak itinerant ferromagnets is challenging due to their small magnetic moments and the ambiguous role of local interactions governing their electronic properties, many of which violate Fermi-liquid theory. While magnetic fluctuations play an important role in the materials’ unusual electronic states, the nature of these fluctuations and the paradigms through which they arise remain debated. Here we use inelastic neutron scattering to study magnetic fluctuations in the canonical weak itinerant ferromagnet MnSi. Data reveal that short-wavelength magnons continue to propagate until a mode crossing predicted for strongly interacting quasiparticles is reached, and the local susceptibility peaks at a coherence energy predicted for a correlated Hund metal by first-principles many-body theory. Scattering between electrons and orbital and spin fluctuations in MnSi can be understood at the local level to generate its non-Fermi liquid character. These results provide crucial insight into the role of interorbital Hund’s exchange within the broader class of enigmatic multiband itinerant, weak ferromagnets. The rich magnetic phase behaviour of MnSi reflects the complexity of the physics underlying itinerant ferromagnetism. Here the authors present evidence that MnSi is strongly influenced by Hund’s coupling effects, suggesting a broader class of materials may fall into the class of Hund metals. The seminal theory of itinerant spin fluctuations by Moriya3 provides important insight, yet neglects the effects of local and short-range effects in their complex electronic properties. Clear indications of these strong correlation effects such as the considerable enhancement of the low-temperature electronic specific-heat and non-Fermi-liquid (NFL) behavior have posed a persistent challenge to synthesizing a complete understanding of both the many-body phenomenology and the  electronic structure of these transition-metal compounds. Common, highly local, approaches built from Mott-physics usually associated with various transition-metal oxides also fail in this endeavor.Understanding the underlying interactions in weak itinerant ferromagnets 11 coupling to charge carriers13 as well at the short-wavelength limit, where weak ferromagnetism provides a rare experimental realization of magnon decay within a particle–hole (Stoner) continuum14. Underlying this is the notion of prototypical itinerancy in MnSi’s charge and spin degrees of freedom—a view supported by the observation of strongly screened moments16, and the success of conventional band-based models in capturing the qualitative features of low-energy magnon propagation17.The phase behaviors found in MnSi are particularly emblematic of the complexity of the interplay between local interactions and itinerancy at this frontier, and the coupling between itinerant electrons and magnetic order in MnSi has remained of sustained interest for decades20. An extended NFL phase has been identified under pressure4, and indications for NFL behavior are also observed in the optical conductivity21 at ambient conditions. The root of these departures from the expectations of conventional Fermi-liquid theory and the role of electron correlations remain open questions.MnSi nevertheless exhibits signs of significant electronic correlations. Large electron mass renormalization and strong quasiparticle damping have been detected in both de Haas-van-Alphen and angle-resolved photoemission (ARPES) measurementsχ″23. Inelastic neutron scattering measurements mapping broad regions of momentum (q) and energy (E) space directly sample χ″, and, when combined with density functional theory (DFT) paired with dynamical mean-field theory (DMFT) to describe realistic correlation effects, considerable insight can be gained. For instance, DFT+DMFT can probe the energy scales of local orbital and local spin coherence directly accessible in neutron measurements25. This aspect is critical for identifying and understanding properties of Hund metals28.The interplay of the charge and spin degrees of freedom in itinerant magnets is effectively studied via measurements of the dynamic magnetic susceptibility U, but instead mainly arise from the effects of the interorbital Hund’s exchange JH acting within a broad bandwidth orbital manifold—optimally one with an occupation of one additional electron or hole away from half filling29. The separation of spin and orbital screening energy scales is a common feature of these unusual metals, with Fermi-liquid behavior only appearing well below the lowest screening scale. In the separation regime, incoherent phenomenology appears such as bad-metal or non-Fermi-liquid behavior29, and prominent examples of Hund metals are established in iron-based superconductors and in ruthenates31.In a Hund metal, strong electronic correlations do not stem from the intraorbital Coulomb repulsion χ″ unveiling signatures of strong correlation effects consistent with those of a Hund metal. Overdamped low-frequency magnon modes disperse within the particle–hole continuum until an anomalous, intermediate energy scale is reached and the modes’ coherence are abruptly lost into a non-dispersive continuum of excitations. This energy scale is captured within DFT+DMFT calculations incorporating both JH and a modest Hubbard U, where the momentum-integrated local orbital susceptibility 21 and demonstrate that correlations originating from JH drive substantial renormalization of the electronic spectrum and low-energy properties. This establishes MnSi as an example of an unconventional Hund metal, where a lower-lying orbital coherence scale cuts off damped-magnon propagation, and suggests an application of the Hund-metal framework to a broader class of anomalous, weak itinerant ferromagnets33.In this work, we present a study of the MnSi high-frequency dynamic susceptibility 11; however, the spin excitations at higher energies relevant for understanding interactions within the metallic state are relatively unexplored. This is largely due to the fact that, as the spin waves disperse upward in energy, they enter the Stoner continuum, access a decay channel into the particle–hole excitations above E ≈ 3 meV (q > 0.2 Å−1), and rapidly become damped34. Once in the Stoner continuum, these damped magnons continue dispersing toward the zone boundary with a similar spin stiffness as that observed in the low-q regime—an overall phenomenology consistent with a number of itinerant magnets38. Figure Well-defined spin waves at low energies are known to appear in the ferromagnetic phase of MnSiE and q values, the low-energy/long-wavelength helical modulation of the ferromagnetic state can be neglected, and the system may be viewed as damped ferromagnetic spin waves. Figure q =  magnetic zone center both upon entering the Stoner continuum above 3 meV and their transformation into a continuum of non-dispersive fluctuations at higher energies. Notably, this high-energy continuum develops before the magnetic zone boundary is reached at ξ = 0.5 r.l.u., which is further illustrated in Fig. Our inelastic neutron scattering measurements begin by exploring spin excitations within the damped regime where the spin waves have already entered the Stoner continuum. At these ure q = −, 0, −1 mq, and the modes’ lifetimes decrease. This is parameterized by fitting the excitations to the form E0 is the mode energy and γ plotted in Fig. γ increases linearly with increasing energy, consistent with models of coupled localized spins and conduction electrons39. At the lowest energy measured (E = 3 meV), the spectrometer’s resolution may convolve broadening into the γ-term due to helimagnon modes from multiple allowed domains17. However, this effect is quickly superseded as the dynamics cross the threshold into the Stoner continuum (E > 3 meV) and become intrinsically damped14. Near 30 meV, the dispersion toward the zone boundary ceases and the values of γ diverge. This marks the onset of a true continuum of magnetic fluctuations between q = 0.6 − 0.75 Å−1 depending on the position within the zone. At all points however, the continuum develops before the wave-vector of the magnetic zone boundary (marked as dashed lines) in Fig. Figure 41 (which we label as the Δ-mode). Although the Δ-mode is difficult to measure directly, it can propagate  through the continuum, and when its dispersion crosses with that of the transverse spin fluctuations  character as identified experimentally16. Figure U = 2 eV and JH = 0.65 eV on the Mn sites, the multi-sheet Fermi surface is strongly modified. The spectral function A of paramagnetic MnSi  appear. A comparison of the Fermi surfaces computed with DFT versus DFT+DMFT is further shown in Supplementary Fig. The DFT+DMFT scheme was employed to understand the correlated electronic structure for  MnSiʼs predominant Mn in Matsubara-frequency space and fitting its imaginary part to F(ωn) = Kωnα, the exponent α ≈ 0.5–0.6 indeed markedly deviates from the Fermi-liquid value αFL = 1  moment value of 0.3 μB, which differs from the larger reported local moment of 2.2 μB obtained via Curie–Weiss analysis of the high temperature susceptibility45. This supports the arguments of Moriya and Kawabata46, who demonstrated that the true local-moment response can be obscured via long-range spin fluctuations near the zone center, producing a tail in the magnetic susceptibility well above Tc. As q-dependent spin fluctuations are not included in single-site DMFT, the temperature dependence of the local magnetic moment is very weak, and instead our theoretical result describes the screened long-time moment. Instead, computing the instantaneous moment however renders a larger μB. This strong deviation between long- and short-time moment is another fingerprint of Hund metals25.From experiments, the low-temperature saturated moment of MnSi is measured to be 0.4 Εs = 55 meV and Εo = 38 meV for the spin and the orbital degrees of freedom, respectively. The splitting of these two coherence scales in energy is a key feature of Hund metals33. How these processes evolve with changing the JH value is described in Supplementary Fig. The imaginary part of the local (momentum-integrated) magnetic susceptibility U and the loss of coherence Eo < Es in MnSi is unusual. Theoretical calculations require vertex corrections to capture these features , in line with its ideal Hund-metal local d6 occupation of the transition-metal site. Other weak ferromagnets (e.g. ZrZn2 or Ni3Al) formed away from this ideal occupation likely still realize similar local correlation effects where electron counts larger or smaller than the ideal local occupation can carry over relevant Hund physics for a sizable JH/U ratio47.Our findings suggest a much broader classification of 48, Spallation Neutron Source at Oak Ridge National Laboratory. Data were collected with different incident energies Ei = 18, 30, 70, 150, 300, 700 meV at T = 5 K. Some higher temperature data (up to T = 300 K) were also collected at select incident energies; however, no appreciable difference is found for high-energy spin wave excitations even at T = 300 K. The background is corrected using the intensity at larger Q zone centers where magnetic contributions are negligible. The magnetic form factor term for Mn3+ in the scattering cross section is accounted for and removed for after nonmagnetic background contributions were removed. Sample growth and characterization details can be found in ref. 49. Data were analyzed using the software package Horace50 as described in Supplementary Note A single crystal with a mass of 52 g was used for time-of-flight inelastic neutron scattering experiments at SEQUOIA41. The transport and dynamical equations for the FFLT follow from the steady state and time evolution equations of the magnetization distribution function. This leads directly to the prediction of the existence of the amplitude mode when the l = 1 Fermi-liquid parameter is included we used other experimental measurements to get the magnitude of the amplitude mode for MnSi41. This is predicted to be about 50 meV at q = 0 and it drops to about 25 meV when the Goldstone mode crosses with the amplitude mode inside the particle–hole continuum. The spin hydrodynamic equations were developed here to make the plot of Fig. We use the version of the ferromagnetic Fermi-liquid theory (FFLT), where the Fermi-liquid phenomenology is augmented with the Ginzburg–Landau theory for a small moment ferromagnet13) of MnSi has an internal trigonal distortion along the <111> cube diagonal, which breaks inversion symmetry. As a result, there are seven Si neighbors for each Mn, grouped into three classes of different bond lengths (Supplementary Note d) states split into three symmetry classes: eg (degeneracy 2), eg′ (degeneracy 2) and a1g (degeneracy 1). A charge self-consistent combination of density functional theory (DFT) and dynamical mean-field theory (DMFT) is employed for the one-particle properties51. A mixed-basis pseudopotential method53 is used for the DFT part with the generalized-gradient approximation. Within the mixed basis, localized functions for Mn(3d), Si(3s), (3p) and Si(3d) are utilized to reduce the plane-wave energy cutoff. The correlated subspace for the DMFT part consists of the effective Mn(3d) Wannier-like functions as obtained from a projected-local-orbital formalism54. A five-orbital Slater–Kanamori Hubbard Hamiltonian parametrized by a Hubbard U and a Hund’s exchange JH is applied on the Mn sites. Four symmetry-equivalent Mn sites are in the primitive MnSi cell with lattice parameter a = 4.558 Å56. The single-site DMFT impurity problem is solved by the continuous-time quantum Monte Carlo scheme in hybridization expansion as implemented in the TRIQS package58. A double-counting correction of fully localized-limit type is used59. The system temperature is set to T = 300 K. Paramagnetism is assumed in all the computations and spin–orbit coupling is neglected. One-particle spectral information is obtained from analytical continuation via the maximum-entropy method as well as the Pade method.The cubic B20 crystal structure  for U = 2 eV and U = 4 eV. Because of the large effective Mn bandwidth, there are no essential differences in Atot(ω) between these different interaction strengths; however, the established renormalization of the strong original Mn(3d) peak at −1.9 eV within DFT toward the −1.5 eV observed in valence-band photoemission is better described for the lower U = 2 eV value. The site and orbital projection Aproj(ω) unravels the strong intermixing between especially Si(3p) and Mn(3d) states and shows that electronic correlations are effective in shifting the Si states into lower-energy regions. The remaining contributions close to εF within the Mn manifold demonstrate that the Mn-eg′ orbital supports the itinerant-electron character strongest.Experimental estimates of the strength of local Coulomb interactions in MnSi favor a rather low value of 60 (i.e. Mishchenko method) as implemented in the TRIQS/SOM package61. The standard SOM procedure is combined with projection on a very fine energy grid and subsequent rebinning onto a sparser grid. This approach strongly suppresses the stochastic noise and allows to reliably resolve the low-energy spectral features in the computed susceptibilities. The paramagnetic local magnetic moment M on the Mn sites is obtained from computing χloc(ω = 0) by integrating the local spin susceptibility over imaginary times, and employing the Curie–Weiss formula χloc =M2/(3T).Local susceptibilities are computed by DFT+DMFT without charge self-consistency using the Hirsch–Fye quantum Monte Carlo scheme. Analytical continuation of the local susceptibilities is performed with the stochastic optimization method (SOM)Supplementary InformationPeer Review File