Transmission electron microscopic observations show that easily reducible cations  are exsolved from the perovskite depending on the transition metal-perovskite reducibility. In addition, using density functional calculations we reveal that co-segregation of B-site dopant and oxygen vacancies plays a central role in the exsolution.In perovskites, exsolution of transition metals has been proposed as a smart catalyst design for energy applications. Although there exist transition metals with superior catalytic activity, they are limited by their ability to exsolve under a reducing environment. When a doping element is present in the perovskite, it is often observed that the surface segregation of the doping element is changed by oxygen vacancies. However, the mechanism of co-segregation of doping element with oxygen vacancies is still an open question. Here we report trends in the exsolution of transition metal  on the PrBaMn Exsolution of transition metals from perovskites provides an effective route to useful catalysts, but the underlying factors governing exsolution remain poorly understood. Here, the authors find trends in exsolution in layered perovskites, showing that co-segregation of B-site dopant and oxygen vacancies plays a central role. Perovskites, a class of metal oxides with well-defined structures, have recently occupied a predominant position within the portfolio of compounds that have been explored as the electrode materials for fuel cells, electronic devices, heterogeneous catalysis in syngas production and components for solar cells12356in situ growth of metal nanoparticles from the parent perovskite is an attractive approach for designing nanoparticle-supported perovskite materials. The catalytically active transition metals, such as Pd, Ru, Pt, Co and Ni, are incorporated on the B site of perovskite oxide (ABO3) during material synthesis in air, and then the transition metals are exsolved from the perovskite backbone as highly dispersed nanoparticles under a reducing atmosphere89103 stoichiometry) to promote exsolutionExsolution based on Recently, layered perovskite structures have received considerable attention because of their interesting properties, such as high electrical conductivity, fast surface oxygen exchange and easy oxygen ion diffusion14151618in situ growth of finely dispersed metal nanoparticles on a PrBaMn1.7T0.3Oδ5+  layered perovskite with the aim to verify trends in exsolution and improve the electrochemical performance of solid oxide fuel cell anodes. The exsolution trends of the B-site dopants  are verified by a transmission electron microscopy (TEM) analysis and density functional theory (DFT) calculations.Here we report the contribution of various transition metals for 2Oδ5+ (L-PBMO), PrBaMn1.7Co0.3Oδ5+ (L-PBMCO), PrBaMn1.7Ni0.3Oδ5+ (L-PBMNO) and PrBaMn1.7Fe0.3Oδ5+ (L-PBMFO) after reduction in humidified H2 (3% H2O) at 800 °C for 4 h. The reduced samples present a single phase of the layered perovskite structure with metal or metal oxide phases, indicating that the phase transition from the simple perovskite to the layered perovskite and exsolution occurred in the reducing atmosphere. Although all the samples were reduced under the same conditions, MnO, metallic Co and Ni phases are observed in the L-PBMO, L-PBMCO and L-PBMNO, respectively, and no Fe phase is observed in the L-PBMFO. These results clearly show that MnO, Co and Ni are more easily exsolved to form nanoparticles than Fe, and thus B-site transition metals show some trend to exsolve in the layered perovskite oxide.The crystalline structures of the oxide materials before and after reduction were examined using the X-ray diffraction technique. As shown in 2O) H2 at 800 °C. As shown in the SEM images, the surface morphologies of the reduced samples are similar, and some small spherical nanoparticles with 20–50 nm diameter are only observed on the surface of L-PBMCO and L-PBMNO. Although Co and Ni nanoparticles are readily observed in the SEM images, this is not straightforward in the case of MnO because the population of the exsolved MnO nanoparticles is small in dimension and low in number.0.5Ba0.5MnO3 polycrystalline film was fabricated by pulse laser deposition (PLD) followed by reduction in H2 at 800 °C for the exsolution of the nanoparticles from the lattice. From the bright-field TEM image  in the redox cycling reactions. Among them, Mn3O4  revealed that Mn and O elements coexist in the L-PBMO , speculaEM image , it is oEM image , correspEM image . To idenus study 2021. Basequation  and Mn2Oequation  are easiequation  is thermGr for the CoO (equation 2 (3% H2O) at 800 °C, which is also confirmed by the TEM–EDS analysis results  and Mn3+ (642.8 and 653.7 eV) in L-PBMO3/2 and Fe 2p1/2, accompanied by two shake-up satellite peaks (718.6 and 732 eV) in L-PBMFO. The appearance of two peaks at 710 and 723.7 eV is Fe2+, whereas the other two peaks at 712.5 and 725.5 eV are characteristic of Fe3+. However, in the L-PBMCO, it is not easy to identify exact oxidation state of Co because binding energy peaks of Co 2p and Ba 3d main lines overlap each other (X-ray photoelectron spectroscopy (XPS) was performed to examine oxidation states of B-site dopants in the L-PBMO, L-PBMCO, L-PBMNO and L-PBMFO. As shown in ch other . From thA question that remains to be addressed is how the exsolution phenomenon occurs in layered perovskites. The exsolution phenomenon was confirmed in A-site-deficient simple perovskites, where oxygen vacancies were introduced during reduction, which destabilizes the perovskite structure and results in spontaneous exsolution of B-site cation. We speculate that spontaneous exsolution phenomenon occurs in layered perovskites when considerable amounts of oxygen vacancies and B-site metal vacancies could be introduced instantaneously (co-segregation) by reduction, and then the metal oxide can be converted to the corresponding metal or metal oxide . This mewhere Based on the X-ray diffraction and TEM results, it appears that transition metals show different degrees of exsolution in the B site of the layered perovskite. To verify the exsolution trends of B-site transition metals in the layered perovskite, we performed DFT calculations. As mentioned above, it can be thought that the exsolution process occurs through two key sequential steps: (1) metal segregation towards the surface and (2) phase transition from the segregated phase to metallic phase.et al.xPdx1-O3. Neagu et al.αSrα1-TiβNiβ1-Oγ3- surfaces. These studies imply that the creation of oxygen vacancies at the surfaces or bulk of the perovskite oxides is closely related to the exsolution of metal or metal oxide from the parent lattice. Since these considerations contain both the effect of B-site metal segregation and vacancy formation, it enables us to make a comprehensive comparison of the tendency of B-site transition metal exsolution. The co-segregation energies obtained by DFT calculations are −0.47, −0.55, −0.50 and −0.15 eV for L-PBMO, L-PBMCO, L-PBMNO and L-PBMFO, respectively (To quantitatively compare the exsolution between the Mn (non-doped L-PBMO) and other B-site dopants , we calculated the co-segregation energy of B-site transition metal accompanying oxygen vacancies. This approach involves the assumption that the oxygen vacancy co-segregates with the nearby B-site metal towards the surface . In a prectively , indicatOnce the B-site metal and oxygen vacancy co-segregate towards the surface, stabilization of the segregated phase containing the B-site dopant on the surface is required to maintain the B-site dopant as metallic phase. For this, there should be a preference of oxygen vacancy formation near the B-site dopant compared to other sites. Therefore, we examined the oxygen vacancy formation at various lattice O sites in 0.9Sr0.1Ga0.8Mg0.2Oδ3− (LSGM) electrolyte-supported cell in humidified H2 (3% H2O) as the fuel and ambient air as the oxidant with the configuration of L-PBMTO /La0.4Ce0.6Oδ2- (LDC)/LSGM/NdBa0.5Sr0.5Co1.5Fe0.5Oδ5+-Ce0.9Gd0.1Oδ2−. The electrochemical impedance spectra of single cell corresponding to the I–V polarization curve at 800 °C are presented in 2 at 800 °C, respectively. In previous reports, exsolved Co and Ni nanoparticles increase the catalytic activation in fuel oxidation, which reduces the anode polarization resistance9−2 at 800 °C in H2, respectively. I–V polarization curves of the L-PBMCO (0.331 W cm−2) and L-PBMNO (0.322 W cm−2) single cells in C3H8 at 800 °C. 3H8  among developed ceramic anodes without adding any catalysts externally  containing perovskites and design highly catalytic perovskite oxides for fuel reforming and electro-oxidation.In summary, the present work demonstrates the transition metal  exsolution trends for self-grown catalytic nanoparticle on a layered perovskite, which may be useful for the development of tailored functional materials. On the basis of DFT calculations, we proposed a possible mechanism for the exsolution of transition metals in layered perovskites, wherein co-segregation of B-site metal and oxygen vacancies plays a central role in the exsolution. We found that Co and Ni have high co-segregation energy (−0.55 and −0.50 eV) in the layered perovskite, which facilitates the exsolution of Co and Ni metal particles on the surface. The maximum power densities of an electrolyte-supported cell with L-PBMCO and L-PBMNO anodes reached 1.15 and 1.12 W cm0.5Ba0.5Mn0.85T0.15Oδ3-  powders were prepared by the Pechini method. Stoichiometric amounts of Pr(NO3)3·6H2O , Ba(NO3)2 , Mn(NO3)2·4H2O , Co(NO3)2·6H2O , Ni(NO3)2·6H2O  and Fe(NO3)3·9H2O  were dissolved in distilled water with proper amounts of ethylene glycol and citric acid, followed by combustion to obtain fine powders. These powders were calcined at 600 °C for 4 h and then sintered at 950 °C for 4 h in air. The A-site-layered PrBaMn1.7T0.3Oδ5+  was obtained by annealing Pr0.5Ba0.5Mn0.85T0.15Oδ3-  oxide, respectively, at 800 °C for 4 h in humidified H2. The chemical composition of the synthesized powders and their abbreviations are given in 0.5Ba0.5MnO3 film was deposited on the dense Al2O3 substrate by PLD by using a commercial system . The oxygen pressure was adjusted to 0.67 Pa before the deposition process by introducing commercially available oxygen (without further purification), and the substrate was heated to 800 °C by using an infrared heater. An excimer laser was used with a power of 180 mJ pulse−1 and a frequency of 10 Hz to deposit the film.Pr−10 mm Hg.The crystal structures of the samples were identified by X-ray powder diffraction . The morphologies of the anode materials were investigated using a field emission SEM. TEM images were obtained with a JEOL JEM 2100F with a probe forming (STEM) Cs  corrector at 200 kV. Cross-sectional samples for the TEM analysis were prepared by using a focussed ion beam . XPS analyses were conducted on ESCALAB 250XI from Thermo Fisher Scientific with a monochromated Al-Ka  X-ray source. The base pressure inside the spectrometer during analysis was 1 × 10ab initio Simulation Packagek-point sampling of the Brillouin zone for all slab model calculations−1. In order to take into account on-site Coulomb and exchange interactions, we used GGA+U schemes with the effective U values of 4.0, 3.3, 6.4 and 4.0 to Mn, Co, Ni and Fe, respectivelyP4/mmm structure   . An eighc=7.940) .Evf) was calculated from the total energies of the supercells with various defect positions  with or without oxygen vacancies is defined as the total energy difference between the systems with the exsoluting B-site metal located at the surface and in the bulk:where where 2O3 (Sigma 99.99%), SrCO3 , Ga2O3  and MgO  powders were ball milled in ethanol for 24 h. After drying, the powder was calcined at 1,000 °C for 6 h. The thickness of LSGM electrolyte was polished about 250 μm. LDC was also prepared by ball milling stoichiometric amounts of La2O3 and CeO2  in ethanol and then calcined at 1,000 °C for 6 h. For preparation of the anode ink, Pr0.5Ba0.5Mn0.85T0.15O3  was mixed with an organic binder (Heraeus V006) (1:2 weight ratio). NdBa0.5Sr0.5Co1.5Fe0.5Oδ5+-Ce0.9Gd0.1Oδ2− cathode ink was prepared by precalcined cathode and GDC powders (at a weight ratio of 60:40) were mixed using ball milling, together with an organic binder. The electrode inks were applied on the LSGM pellet by screen printing method and then calcined at 950 °C in air for 4 h. The porous electrode had an active area of 0.36 cm2 and thickness about 20 μm. LDC layer was used as the buffer layer between the anode and the electrolyte to prevent interdiffusion of ionic species between anode and LSGM electrolyte. For fuel cell performance tests, the cells were mounted on alumina tubes with ceramic adhesives . Silver paste and silver wire were used for electrical connections to both the anode and the cathode. The entire cell was placed inside a furnace and heated to the desired temperature. I–V polarization curves were measured using a BioLogic Potentiostat.LSGM powder was prepared by conventional solid state reaction and electrolyte substrate was prepared by pressing and followed by sintering at 1,475 °C. Stoichiometric amounts of LaThe data that support the findings of this study are available from the corresponding authors upon reasonable request.How to cite this article: Kwon, O. et al. Exsolution trends and co-segregation aspects of self-grown catalyst nanoparticles in perovskites. Nat. Commun.8, 15967 doi: 10.1038/ncomms15967 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


An improved understanding of the ways that amyloid-beta peptides are formed could help efforts to find a treatment for Alzheimer’s disease. Related research article Bolduc DM, Montagna DR, Seghers MC, Wolfe MS, Selkoe DJ. 2016. The amyloid-beta forming tripeptide cleavage mechanism of γ-secretase. eLife5:e17578. doi: 10.7554/eLife.17578γ-Secretase is a protease enzyme that can cleave a wide range of different transmembrane proteins. One of these is a protein called C99 that is involved in the production of the Aβ polypeptides that are thought to lead to Alzheimer’s disease . C99 conYears of patient toil by a number of labs have revealed that the γ-secretase enzyme acts by first cleaving C99 near the cytosolic end of its transmembrane domain . This “e40), and a polypeptide that starts with 48 residues is shortened to one with 42 (Aβ42). The production of too much Aβ42, relative to Aβ40, is associated with the rare inherited forms of Alzheimer’s disease; high levels of Aβ42 have also been linked to the more common sporadic form of the disease . Second, these mutations can also disrupt the normal Aβ48–> Aβ45–>Aβ42 and Aβ49–>Aβ46–>Aβ43–>Aβ40 reaction pathways . This is an important observation that provides insight into how certain mutations in C99 that cause the inherited form of Alzheimer’s disease can increase the Aβ42-to-Aβ40 ratio.To begin, for wild-type C99 under both purified and cellular conditions, it was confirmed that epsilon cleavage after residue 49 results in the production of Aβ47 was then shortened by γ-secretase to produce Aβ40, confirming that processive cleavage sometimes involves the "clipping off" of short peptides that contain four rather than three residues  with phenylalanine (which is aromatic) blocked the normal epsilon cleavage reactions and, surprisingly, activated epsilon-like cleavage after site 47 instead. The resulting Aβresidues .Moreover, Bolduc et al. found that replacing entire tracts of residues in the lower transmembrane domain of C99 with aromatic amino acids resulted in cleavage after site 38, which is near the middle of the domain – a shift of some 10–11 residues from the normal epsilon cleavage sites! This provides significant insight because site 37 and site 38 both contain the amino acid glycine (Gly), and it is known that this double-glycine motif destabilizes the helix in the transmembrane domain . This suFor many enzymes, the initial binding event leads directly to the substrate occupying the catalytic site of the enzyme, poised for the chemical reaction. Bolduc et al. found that the γ-secretase enzyme was different: a second step is needed. This result is supported by previous studies of γ-secretase with active site-directed inhibitors (see, for example, The work of Bolduc et al. represents a major advance in our understanding of catalysis by the γ-secretase enzyme. And while many questions remain unanswered, the availability of near-atomic resolution structures for both C99  and γ-se


Previous studies have reported that rod composition and diameter, as well as the correction technique are key factors associated with thoracic kyphosis (TK) restoration. However, few study has analyzed the correlation between screw density and TK restoration in hypokyphotic adolescent idiopathic scoliosis (AIS).Fifty-seven thoracic AIS patients with preoperative TK < 10° treated with all pedicle screw fixation with a minimum 2-year follow-up were recruited. Preoperative and postoperative radiographic measurements, and information of posterior instrumentation were reviewed. Pearson and Spearman correlation coefficient analysis were used to assess relationships between change in TK and number of variables. Then, the included patients were classified into two groups  to evaluate the influence factors of TK restoration.P < 0.001). Significant correlations were found between change in TK and flexibility of major thoracic curve , preoperative TK , and screw density of concave side , respectively. In the subgroup comparison, 17 patients (29.8%) maintain the postoperative TK ≥ 20°, increased flexibility of major thoracic curve (P < 0.001), screw number of concave side (P = 0. 029), and cobalt chromium rods (P = 0.041) were found in the group of postoperative TK ≥ 20°.The average preoperative TK was 4.75°, which was significantly restored to 17.30° (TK restoration remains a challenge for AIS patients with hypokyphosis, especially for the poor flexibility ones. Except for thicker and cobalt chromium rods, screw density of concave side might be another positive predictor of restoring normal kyphosis, which provides a stronger corrective force on the sagittal plane with more pedicle screws. Adolescent idiopathic scoliosis (AIS) is a tridimensional deformity afflicting millions of children who are at risk between the ages of 10–16 years. AIS has a prevalence of 2–4% in this population. The defined threshold for surgical treatment is when the major curve’s Cobb angle is greater than 40°. Untreated AIS patients may have pulmonary limitations, back pain, and changes in appearance and overall motor function [Compared to the coronal correction, sagittal alignment restoration has received more attention –4. HypokA Significant correlation between implant density and coronal correction has been previously observed in the surgical treatment of AIS –15. HowePrevious studies have reported that rod composition and diameter, as well as the correction technique are key factors associated with TK restoration –23. To oAfter receiving approval from the institutional review board, the operative and radiological notes of consecutive AIS patients were retrospectively reviewed. The patients that were reviewed underwent spinal surgery from January 2009 to December 2014 at a single institution.Selection criteria included the following: (1) Lenke type 1–4 AIS; (2) preoperative TK (T5–T12) < 10°; (3) one-stage posterior approach; (4) all pedicle screw construct, and (5) minimum 2-year follow-up. Exclusion criteria included the following: (1) Lenke type 5–6 AIS, early-onset scoliosis, or neuromuscular scoliosis; (2) hybrid instrumentation constituted with hooks or wires; (3) anterior–posterior approach; (4) pedicle subtraction osteotomy or vertebral column resection performed; and (5) reoperations related to the pedicle screw system.Two senior surgeons performed all the surgeries. After the standard posterior midline incision and the anatomical exposure of the spine, multilevel inferior facet resection and superior facet decortication were undergoing. After pedicle screws were implanted with a free hand technique, a metal rod was bended to the anticipated TK prior to insertion, and rod reduction, in order to turn the locking cap to reduce the rod into the screw head, were performed after the insertion of the concave rod. Simple rod rotation technique was applied for deformity correction. After insertion of the second rod and tightening of locking caps, distraction and compression were performed, and locking caps were finally tightened. Transverse connector was selectively used. The transverse processes and laminae were decorticated thoroughly, and allograft bone material was placed for fusion.Clinical measurements included the patients’ age at surgery, gender, rod diameter, rod material, fused vertebral levels, and number of pedicle screws that were used. Screw density was defined as the number of pedicle screws per vertebrae that were implanted. Concave side screw density was calculated using the screw number of the concave side divided by the fused levels; convex side screw density was calculated in a same manner. Total screw density was calculated using the total screw number divided by the fused levels.Standing posterior-anterior and lateral radiographs were measured for parameters before the surgery and at the last follow-up. Radiographic measurements included the patients’ Lenke classification, Risser grade, apical vertebral rotation (Nash-Moe), Cobb angle of main thoracic (MT) curve, convex bending, TK (T5–T12), and lumbar lordosis (T12–S1). The flexibility index, MT correction rate, change in MT curve, and change in TK (postoperative TK – preoperative TK) were all calculated from above radiographic parameters.P < 0.05 was considered statistically significant.In order to explore the correlation between change in TK and potential variables, including age, flexibility of MT curve, preoperative MT Cobb angle, preoperative TK, preoperative LL, fused levels, screw density of concave side, screw density of convex side, and total screw density, Pearson and Spearman correlation coefficient analysis were performed. According to the Lenke classification, the N of the sagittal modifier is from 10° to 40°. However, many scholars hold the view that postoperation TK (T5-T12) less than 20° is still hypokyphosis with, and it is generally agreed that operative treatment of thoracic idiopathic scoliosis should aim to improve TK to a degree more than 20° , 24. In Fifty-seven consecutive thoracic AIS patients with hypokyphosis were carefully reviewed. There were 45 females and 12 males, and the average age at surgery was 14.39 ± 1.82  years old. According to the Lenke classification,  there wer = 0.357, P = 0.006; Spearman: rs = 0.384, P = 0.003. Figure r = −0.408, P = 0.002; Spearman: rs = −0.445, P = 0.001. Figure r = 0.306, P = 0.021; Spearman: rs = 0.290, P = 0.029. Figure In the correlation analysis between change in TK and variables, a linear trend was found between change in TK and flexibility of MT curve (Pearson: P < 0.001) and change in TK (P < 0.001), poor flexibility of MT curve (P < 0.001), lower incidence rate of CoCr rod (P = 0.041), and smaller screw number of concave side (P = 0.029) were found in the postoperative TK < 20°group. Interestingly, the MT curve correction (%) in the postoperative TK ≥20° group was six percentage points lower than the other group, even though no significant difference was found . The clinical parameters are presented in Table In the subgroup analysis, the postoperative TK ≥ 20°group consisted of 17 patients , whereas the postoperative TK < 20°group had 40 patients . No significant differences were found with respect to the following parameters: gender, age at surgery, Riser sign, apical vertebra rotation, convex-Bending Cobb angle, preoperative MT Cobb angle, postoperative MT Cobb angle, change in MT Cobb angle, MT curve correction, preoperative TK, preoperative LL, postoperative LL, change in LL, rod diameter (5.5 mm/6.0 mm), fused levels, total screw number, screw number of convex side, total screw density, screw density of concave side, screw density of convex side. Except for significant differences in postoperative TK , and the mechanism was unclear [TK restoration is a continuing surgical challenge for AIS patients with hypokyphosis . In our  unclear . To our P = 0.029) were found in the postoperative TK < 20°group in our study. A biomechanical study reported that the correctional force produced by the Ti 30-degree pre-bend rod was approximately 67% that of a CoCr rod [P < 0.001) in hypokyphotic curves treated with Ponte osteotomy [r = 0.492, P = 0.002) [The most commonly studied factors influencing TK restoration include the implant rod stiffness, , 33 implCoCr rod . SimilarCoCr rod , 39. HowCoCr rod . Shah etsteotomy . Sudo et= 0.002) . TherefoP < 0.05) [r = 0.351) but not with convex side screw density (r = 0.144) [n = 375) and type 2 (n = 245) curves [Few studies have evaluated the correlation between implant density and sagittal correction, and conclusions were controversial. Liu et al. compared the effects of high versus low implant density on sagittal plane correction, and greater TK restoration was found in AIS patients with high screw density on the concave side ( < 0.05) . Similar= 0.144) . Contrar) curves . While, Through this study, we found that concave side screw density was positively correlated with TK restoration in thoracic AIS patients with hypokyphosis, In addition, smaller screw number of concave side were found in the postoperative TK < 20°group. Which suggested that higher concave side screw density provided a beneficial effect on TK restoration in hypokyphotic AIS. A possible explanation is that the concave side rod provides a stronger corrective force on the sagittal plane with more pedicle screws. Cidambi et al. reported that the resulting deformations were likely to be associated with substantial in vivo deforming forces, particularly for concave rods . In addiAn important limitation of the study was that the analysis of rod curvature prior to implantation was limited due to the retrospective study. In addition, the relatively small sample size and short follow-up time were underpowered to find more significant difference, and larger-scale and longer follow-up research should be performed.TK restoration remains a challenge for AIS patients with hypokyphosis, especially for poor flexibility ones. Except for thicker and cobalt chromium rods, screw density of concave side might be another positive predictor of restoring normal kyphosis, which provides a stronger corrective force on the sagittal plane with more pedicle screws.


Recently obtained geophysical data show sets of parallel erosional features on the Lomonosov Ridge in the central Arctic Basin, indicative of ice grounding in water depths up to 1280 m. These features have been interpreted as being formed by an ice shelf—either restricted to the Amerasian Basin (the “minimum model”) or extending across the entire Arctic Basin. Here, we use a numerical ice sheet-shelf model to explore how such an ice shelf could form. We rule out the “minimum model” and suggest that grounding on the Lomonosov Ridge requires complete Arctic ice shelf cover; this places a minimum estimate on its volume, which would have exceeded that of the modern Greenland Ice Sheet. Buttressing provided by an Arctic ice shelf would have increased volumes of the peripheral terrestrial ice sheets. An Arctic ice shelf could have formed even in the absence of a hypothesised East Siberian Ice Sheet. Ice grounding features discovered in the Arctic Basin, in water depths exceeding 1 km and dated to the penultimate glacial, suggest a past Arctic ice shelf. Here, the authors undertake numerical simulations that shed light on how such an ice shelf could have formed, its dynamics and most likely configuration. Despite the implications of an ice shelf for Arctic glaciology, ocean circulation, and ice volume estimates, the hypothesis received little subsequent attention due to a lack of supporting data8. The hypothesis has attracted renewed interest arising from a number of research cruises to the Arctic that have provided evidence for ice grounding in water depths exceeding 1 km, including in the central Arctic Basin on the Lomonosov Ridge10. It continues to be debated whether erosional features are indicative of grounding by isolated iceberg keels or an extensive ice shelf14. Recently acquired high-resolution sonar imagery have revealed sets of linear erosional features that are parallel and spatially coherent across many tens of kilometres. These features do not resemble the chaotic cross-cutting of iceberg scouring and are the strongest evidence yet for ice shelf grounding10. Dating of the thin sediment drape covering the erosional features indicate that grounding occurred during the penultimate glacial maximum . Although subject to age model uncertainties, the return of polynya-type conditions as evident in sea-ice reconstructions suggests that such an ice shelf would have begun to break up during the latest MIS615.A thick ice shelf covering the Arctic Basin during glacial stages was proposed in the 1970s, in broad analogy to the marine-based West Antarctic Ice Sheet16. Reconstructions of Arctic Ocean temperatures indicate that, following the mid-Brunhes event (~400 ka), intermediate-depth temperatures were warmer than modern during glacial stages18. One hypothesis is that a thickening halocline, driven by decreased freshwater inputs12 and/or the formation of an Arctic ice shelf, caused the polar surface layer and inflowing warm Atlantic waters to deepen. Although there are considerable uncertainties, this could suggest that a thick ice shelf (but one that may not have grounded) formed during each of the last four glacial maxima18. The ice grounding locations are collated in Fig. It is possible that large Arctic ice shelves also formed during other glacial stages, although interestingly no erosional features deeper than ~600 m water depth have yet been dated to the Last Glacial Maximum (LGM)19 or analytical16 approaches to explore the plausibility of a thick Arctic ice shelf and numerical simulations have addressed the potential for an East Siberian ice shelf20. Here we simulate an Arctic sheet-shelf system using a hybrid shallow ice-shallow shelf model22, with climate forcing provided from existing coupled climate model simulations configured for MIS623 but without dynamical ice-ocean coupling. A number of experiments are performed with different imposed ice sheet extents  to determine what impact the different ice source regions have on ice shelf thickness and flow direction. Although our focus is on explaining the bathymetric features on the central Lomonosov Ridge we also consider whether an ice shelf can explain other ice grounding features. A simulation is performed with the ice shelf constrained to the Amerasian Basin, in order to test the “minimum model” that the ice shelf was prevented from expanding into the Eurasian Basin due to the inflow of warm North Atlantic waters10. We also explore what impact the Lomonosov Ridge  has on the ice-shelf dynamics, by performing simulations with lowered seafloor elevation.Previous studies have followed statistical22, unless otherwise stated. We first constrain the ice shelf calving parameterisation for simulations of the Barents-Kara Ice Sheet to its known LGM extent . However, this analytical solution does not consider lateral drag, which could thicken an ice shelf embayed in the Amerasian Basin enough to ground on the Lomonosov Ridge. We test this “minimum model” by performing an experiment with a high oceanic sub-ice melt rate in the Eurasian Basin, preventing the expansion of the ice shelf across the entire Arctic Basin. With a Eurasian Ice Sheet extending into Eastern Siberia this is the most favourable configuration of peripheral ice sheets for the growth of the ice shelf. Even with grounded ice flowing into the Arctic Basin from the Laurentide Ice Sheet and an Eastern Siberian Ice Sheet, the mean ice shelf thickness is only ~250 m . For the LGM, the extent of the major ice sheets is well known, constrained by a large volume of dated geomorphological and geological evidence30. For prior glaciations such as MIS6, the evidence-base in North America is sparse and its geographic spread is insufficient to develop a continent-wide reconstruction of ice extent at this time. But it is known for example that during MIS6, that the southern Laurentide Ice Sheet extended some 200 km further south in the US state of Illinois than during the LGM31. In Ohio the MIS6 extent is a short distance (10–20 km) further south32. In Minnesota, pre-LGM glacial deposits also extend beyond the known LGM ice limits33. However, this picture of greater MIS6 extent is not repeated around the rest of the perimeter of the ice sheet, likely because the ice sheet was smaller than during the LGM and the evidence has been erased or obscured. The lack of icebergs being discharged through the Hudson Strait during MIS6 would indicate that the North Atlantic sector of the Laurentide Ice Sheet had a reduced extent (or different dynamics) relative to the LGM. Finally, the LGM sea level lowstand was likely lower than during MIS6, with one estimate proposing a difference between the two glacial stages of 21 ± 14 m34. Because the MIS6 Eurasian Ice Sheet had a greater extent than during the LGM35, it is likely that the North American Ice Sheets had a smaller volume during MIS636.Two different extents of the North American Ice Sheets are considered in the climate model forcing (the ice sheet model margin is not constrained), one is equivalent to the LGM extent Fig. , and one−1 total input into the Arctic Basin, compared with 950 Gt year−1 for the larger North American Ice Sheets) because the western most ice streams, the MacKenzie Trough and Amundsen Gulf Ice Streams, are less active. However, this reduced flow into the basin is partially offset by higher surface accumulation on the Arctic ice shelf (370 Gt year−1 compared to 240 Gt year−1). The lower elevation and extent of the North American Ice Sheets generates a shift in atmospheric planetary waves, increasing temperatures and precipitation over the Eurasian Ice Sheet and leading to cooler temperatures over Eastern Siberia. The smaller North American Ice Sheets also generate increased precipitation in the Arctic, in particular in the Amerasian Basin23. Although these two effects partially cancel out, the reduction in ice flow from North American ice streams results in a mean shelf thickness of 830 m, compared with 1070 m for the thicker LGM-equivalent North American Ice Sheets. Despite the reduced shelf thickness, this ice shelf still grounds at most of the locations where there is evidence for grounding , relative to the MIS6 simulations (240–370 Gt year−1). Overall, this results in a lower equilibrium thickness (940 m) of the ice shelf, relative to a simulation with an MIS6 equivalent Eurasian Ice Sheet (1070 m). However, our simulated LGM ice shelf would have left ice grounding traces. These simulations have identical ocean forcing and the same duration as the MIS6 simulations. Given the strong sensitivity of the ice shelf thickness to changes in the ocean melt rate , which will increase with the presence of such a large floating meteoric ice mass2. There is a 1.66‰ offset in δ18O of benthic foraminifera between the Holocene and MIS626, which includes contributions from decreasing deep sea temperature and changes in δ18Osw. Assuming a simplified constant ice shelf δ18O composition of −40‰, the Arctic ice shelf would increase δ18Osw by 0.11–0.14‰, reducing the magnitude of MIS6 sea level lowstand estimates by up to 14 m.The volume of the simulated Arctic ice shelf varies between 3.5 and 4.8 × 1036. As mentioned previously, there is a 21 ± 14 m offset in sea level between MIS6 and the greater sea level fall of the LGM. However, this sea level offset is smaller in deep-sea δ18Osw records38, compared with sea level reconstructions based on water residence times in the semi-enclosed Mediterranean Sea and Red Sea39. The formation of an Arctic ice shelf would influence the Mediterranean and Red Sea reconstructions, which are also affected by changes in δ18Osw. However, due to the larger amplitude of the glacial to interglacial δ18O shift for the semi-enclosed basin records, this bias would be larger in the deep-sea δ18Osw reconstructions. The sea level offset, and the discrepancies between the sea level records, can be explained by the formation of a large ice shelf during MIS6, but would require negligible floating ice mass during the LGM34.The presence of an Arctic ice shelf may help to explain existing discrepancies between sea level reconstructions for MIS624. The routing of meltwater from terrestrial ice sheets into the Arctic and the subsequent release of large amounts of freshwater through the Fram Strait has the potential to disrupt the thermohaline circulation in the North Atlantic40; an Arctic ice shelf is an additional source of freshwater. More work is needed to determine whether the magnitude and rate of ice shelf break-up would be sufficient to influence ocean circulation. It is noteworthy that because of the thickness and spreading rate of the ice shelf, once the calving front has retreated back into the Arctic Basin, calving laws suggest that break-up of the shelf would have been rapid41.The significance of an Arctic ice shelf to ocean circulation and northern hemisphere glacial climates has been noted previously, both through the direct impact of a permanent thick ice cover on the atmosphere and ocean below, and additional impacts on ocean circulation during ice shelf break up21, we briefly describe the model, which is setup as documented in ref. 22 unless otherwise mentioned below. Vertical shearing and basal stress balances the gravitational driving stress for slow flowing, grounded ice regimes, whereas horizontal stretching dominates in fast flowing grounded ice regimes and floating ice shelves. The combined shallow ice-shallow shelf (SIA-SSA) equations account for both flow regimes, with computational demand reduced in slow-flowing areas with limited basal sliding that can be satisfactorily modeled with solely SIA dynamics. The grounding line can freely migrate using a sub-grid parameterisation that relates ice velocity to ice thickness at the grounding line43. This parameterisation eliminates the resolution-dependence of ice flow at the grounding line  and overrides the ice velocities computed by the SIA-SSA at the grounding line. The parameterisation also accounts for buttressing by downstream islands, pinning points or side shear that can generate back stress, reducing grounding line velocities. Basal sliding coefficients (C) vary based on the distribution of deformable sediments, with a sediment map44 used to specify regions of hard bedrock (C = 10–10 m a−1 Pa−2) and deformable sediment (C = 10−6 m a−1 Pa−2).The ice sheet model used is a hybrid model that combines the scaled shallow ice and shallow shelf equations45. An added calving constraint is imposed which restricts the minimum thickness of ice shelves . In Antarctic model runs this parameter is needed to prevent the seaward extension of the Filchner-Ronne and Ross ice shelves beyond their modern-day calving fronts, however the parameter prohibits the regrowth of the West-Antarctic Ice Sheet from a deglaciated state. Experiments conducted here show that the default calving thickness threshold (150 m) also prevents the growth of the Barents-Kara Ice Sheet (BKIS) and formation of an ice sheet in Hudson Bay during the LGM . Two separate MIS6 simulations are used with different configurations for the North American Ice Sheets, either with the same extent as LGM, or with a reduced extent and elevation required to maintain sea level budgets given the larger extent of the Eurasian Ice Sheet during MIS635. The reduced North American Ice Sheets are based on the ICE5G model for 13 ka50, although the actual extent of the North American Ice Sheets during MIS6 is uncertain. The differences in the extent and elevation of the North American Ice Sheets has a significant impact on the climate of the Arctic and Eurasia23. Surface temperatures are lapse rate corrected to account for differences in GCM elevation and the ice-model surface elevation. The surface temperatures from CCSM4 are also anomaly (bias) corrected based on errors between a modern-day control simulation and an observational temperature dataset. Precipitation is not anomaly corrected. Bias correcting leads to a greater agreement between the simulated LGM ice thicknesses and the ICE6G model. Surface ablation is calculated using the positive-degree day method. Because we are using a constant climate forcing, there may be more rapid transitions that are not currently accounted for.Surface temperatures and precipitation from the Community Climate System Model, version 4 (CCSM4) are used to calculate the surface mass balance. These simulations do not include an Arctic ice shelf and there is therefore no feedback from the formation of an ice shelf on the ocean and climate system. A thick ice shelf cover would reduce heat exchange between the ocean and atmosphere, relative to a thin sea ice cover, and also lower surface air temperatures due to the elevation lapse rate effect−1 in that sector . A similar adjustment is required to allow glacial inception on Svalbard for ice sheet model runs starting from ice-free conditions, with an initial ablation limit of <0.2 m year−1 required in that sector , this does not affect floating ice cells.The MIS6 climate forcing produces a strong negative surface mass balance along the North Alaskan margin. This can result in the export of shelf ice via re-grounding, followed by subsequent surface melting and instances with spurious ice flow. As there is no evidence for export of ice/meltwater via the North Alaskan margin, we limit the surface ablation for grid cells with grounded ice to no greater than 0.5 m year17. The presence of an ice shelf in the Arctic could affect the ocean structure and circulation10, however no simulations have yet examined these effects on basal melt rates. We therefore adopt a conservative approach to sub-ice melting, calculating basal melt rates based on a last glacial simulation51 that has been calibrated for Antarctica52 and exploring model sensitivity through large adjustments (50–800%) of a melt enhancement factor (Ofac). Changes in the basal melt rate can lead to variation in the ice shelf equilibrium thickness of 100 s of metres Peer Review File(PDF 173 kb)Description of Additional Supplementary Files(PDF 68 kb)Supplementary Movie 1


Spring sea ice phenology regulates the timing of the two consecutive pulses of marine autotrophs that form the base of the Arctic marine food webs. This timing has been suggested to be the single most essential driver of secondary production and the efficiency with which biomass and energy are transferred to higher trophic levels. We investigated the chronological sequence of productivity pulses and its potential cascading impacts on the reproductive performance of the High Arctic seabird community from Svalbard, Norway. We provide evidence that interannual changes in the seasonal patterns of marine productivity may impact the breeding performance of little auks and Brünnich’s guillemots. These results may be of particular interest given that current global warming trends in the Barents Sea region predict one of the highest rates of sea ice loss within the circumpolar Arctic. However, local- to regional-scale heterogeneity in sea ice melting phenology may add uncertainty to predictions of climate-driven environmental impacts on seabirds. Indeed, our fine-scale analysis reveals that the inshore Brünnich’s guillemots are facing a slower advancement in the timing of ice melt compared to the offshore-foraging little auks. We provide a suitable framework for analyzing the effects of climate-driven sea ice disappearance on seabird fitness. Among the main climate-driven environmental changes occurring in the Arctic, it has been suggested that the recent and dramatic loss of sea ice5 is most strongly impacting the structure and functioning of polar communities9. However, the actual consequences of sea ice loss on key biological processes remain largely unknown9. This is, in part, because polar systems, especially marine ones, are less accessible and notoriously difficult to study10.Arctic ecosystems are rapidly changing as climate warming affects these regions faster than the global average5. It is expected that this trend will continue well into the future, with the strongest losses predicted in the southern Arctic seas, including the Barents Sea area13. These changes in the physical environment are evident in the marine food webs through phenological changes and mismatching processes16, or alterations in predator-prey dynamics with cascading impacts on their populations18. However, one of the most alarming, albeit largely undocumented impact, probably concerns changes in bottom-up processes affecting the efficiency with which biomass and energy are transferred through marine food webs10.Since the late ‘70 s, the Arctic sea ice has decreased at a rate of 5% per decade9. Sea-ice algae begin to grow within and underneath the sea ice in early spring, concurrently with increasing day length and with the start of the ice melting process20. The pelagic algal bloom, in contrast, normally occurs in open waters several weeks after ice breakup, leading to a discontinuity between sea-ice and open-water primary production20. As the seasonal growth period becomes narrower at higher latitudes, it is suggested that the timing for these two blooms is probably the most essential driver of secondary production10. Indeed, the breeding phenology of Arctic zooplankton  has been shown to closely track the early sea-ice algae bloom, whereas the growth of this secondary producer’s offspring is dependent on the later bloom of pelagic phytoplankton9. Changes in the timing of these productivity pulses may therefore result in a mismatch between zooplankton requirements and resource availability, thereby affecting zooplankton recruitment, hence the efficiency of transfer of biomass and energy to higher trophic levels20. Ultimately, this may affect some valued ecosystems services, such as fish stocks, as well as marine mammals, seabirds and other marine-dependent Arctic predators21. Indeed, trophic disruptions affecting marine productivity patterns23, species distributions10 or phenological processes9 may lead to rapid and sensitive responses in the reproductive performance of top-predators22. Monitoring these reproductive parameters could therefore serve as a sensitive tool for tracking changes in the functioning of Arctic marine ecosystems on a near real-time basis, provided we are able to link the large degree of climatic variability to changes in the breeding performance of top-predators24.Sea ice plays a dual role in primary production in polar seas by driving the timing of the two consecutive pulses of marine autotrophs that form the base of the Arctic food webs: sea-ice algae and pelagic phytoplankton7) and biological  processes in oceans worldwide. These environmental data products are now provided at a high enough spatiotemporal resolution for capturing the fine-scale spatial heterogeneity inherent to marine systems7, and the short-term variability typical of these seasonal environments23. The Svalbard archipelago (Norway) provides, in turn, an ideal case study for assessing the impact of climate-driven trophic disruptions on the fitness of Arctic top-predators. This is because environmental cycles and productivity patterns in Svalbard are largely driven by sea ice phenology10, and because this region has recently experienced one of the highest rates of sea ice loss within the circumpolar Arctic27. We used remote-sensing data on sea ice extent and chlorophyll-a concentration (CHL-a) to investigate interannual changes in seasonal patterns for each of the two blooms of autotrophs forming the base of the Arctic marine food web. We then assessed whether the timing of these two pulses of primary production affects the reproductive performance of the High-Arctic seabird community from Svalbard via bottom-up processes. Finally, we framed these results within the current trends of global warming and changes in sea ice melting patterns in the Arctic11.Here, we used remotely sensed records to investigate the chronological sequence of the primary productivity pulses  affecting the transfer of biomass and energy through Arctic food webs. Satellite remote sensing has emerged as an essential tool for studying the most recent and striking trends and patterns in both physical , the black-legged kittiwake (Rissa tridactyla) and the little auk  (see Supporting Information). The foraging areas during the summer period differed among the three seabird species . Thus, these were likely independent from sea-ice driven productivity patterns and were excluded from the subsequent analyses.We focused on three of the most abundant, though ecologically diverse, seabird species breeding in the Svalbard archipelago: the Brünnich’s guillemot  and 2011–2016 (Brünnich’s guillemot). In the case of CHL-a, this difference accounted for 6 to 12 weeks. Accordingly, the temporal lag between the annual peaks of sea ice extent and CHL-a ranged from 2 to 10 weeks in the case of little auks, and from 4 to 21 weeks for Brünnich’s guillemots.Seasonal patterns in sea ice extent and CHL-6,11 = 4.49, p = 0.03, R2 = 0.69), which decreased at an overall rate of 2% per week of lag between the two pulses of marine autotrophs, regardless of the species and colony considered .Seabird hatching dates were relatively constant throughout our study period Table . On aver−1  in the onset of the melting process over the last few decades . A less pronounced , albeit non-significant, trend was observed for the inshore Brünnich’s guillemot .Spatially-explicit information on the long-term (1979–2012) trends in the melting phenology suggested that seabirds might be facing different environmental changes according to their foraging ranges Fig. . In part28. In turn, climate driven environmental changes  may affect marine productivity patterns, either directly through metabolic processes29 or indirectly through trophic disruptions caused by shifts in species distributions or abundances30 or changes in the timing of seasonal processes30. Here, we provide evidence that interannual changes in the seasonal patterns of marine productivity in the Arctic may have a cascading effect on seabirds. In particular, we showed that increasing temporal lag between sea-ice melting, i.e. the physical process driving the annual bloom of sea ice algae20, and the bloom of pelagic phytoplankton resulted in rapidly decreasing breeding performance for little auks and Brünnich’s guillemots. The timing of these two productivity pulses is considered as an essential driver of recruitment, and hence abundance, of secondary producers9. Therefore, climate-driven trophic disruptions seem to be an important mechanism underlying the breeding performance of Arctic seabirds and, potentially, of other marine-dependent Arctic top-predators.Food availability during key periods such as reproduction is widely considered as one of the most important drivers of marine predator breeding performance21. Previously reported impacts on Arctic predators mostly concern changes in resource availability and interspecific relationships caused by species distribution shifts17 or changes in seasonal patterns of marine productivity16. Nevertheless, resource availability for an organism is a complex feature that encompasses food abundance and accessibility30. In the Arctic, it has been suggested that food abundance and the efficient transfer of nutrients and energy through ice-dependent food webs largely rest on the timing of the blooms of sea ice algae and pelagic phytoplankton10. Previous studies on the impacts of environmentally-driven changes in food abundances have largely focused on the lower levels of Arctic food webs (primary and secondary production9). Observed trends in the breeding performance of seabirds feeding on the seasonal ice zone  suggest, however, that these impacts may also extend through entire food webs via bottom-up processes, from primary producers up to predators. Remarkably, these trends contrasted between the two considered breeding parameters, i.e. hatching success and chick survival. This might be partially explained by the fact that maximum food requirements occur during chick rearing31. Additionally, egg synthesis can be partially sourced by resources acquired prior reproduction34, thereby mitigating food availability during the egg production period35.Current trends towards sea ice disappearance are known to have a profound impact on the structure and functioning of sea-ice dependent Arctic ecosystems10. Even at the very low sunlight intensity conditions of the early spring, the onset of snow and ice melting in March  substantially enhances light availability within the water column and promoting ice algae growth until the sea ice substratum melts in May. Concurrently with the sea ice breakup, light availability within the water column peaked resulting in the annual bloom of pelagic phytoplankton. The timing of these physical and biological processes is largely affected by the high degree of environmental variability that characterizes Arctic ecosystems8. Such environmental variability is expected to become even higher within the current context of global warming21, thus warranting more studies of the mechanisms through which it can affect entire food webs.Seasonal patterns in marine productivity at high latitudes are largely constrained by sea ice cover and darkness during winter months13. This will certainly affect the timing of key processes such as primary productivity pulses, with potential deleterious consequences for secondary producers10 that could spread up to higher trophic levels and impact predators such as seabirds  is adjusted to food abundance and climate variability43, and this timing to some extent affects their reproductive success28. Similarly, common murres , Cassin’s auklet  and rhinoceros auklet (Cerorhinca monocerata) tend to match their breeding phenology to the annual peak of abundance in some of their prey, again with temporal mismatch influencing their reproductive performance46. As expected for species that operate at the upper limit of their energetic capabilities48, Arctic seabirds should be particularly sensitive to environmental changes affecting the seasonal patterns in resource availability and food abundance28.The advancement in ice breakup may result in an earlier onset of the pelagic phytoplankton bloom49 and that some Arctic seabirds like the Brünnich’s guillemot, are already declining in a large part of their range, including Svalbard50. Changes thus far may seem relatively limited but seabirds may be unable to adjust their life cycle to these changes, which may have detrimental consequences on their reproduction and thus population dynamics.The warming of the Arctic is leading to changes in sea ice melting phenology, a key driver of primary production and potentially of the reproductive performance of Arctic predators. This is especially concerning considering that warming may accelerateδ13C). All captures, handling, tissue sampling and logger deployments were approved and conducted according to the permits provided by the Governor of Svalbard (project RIS 361) and by the Norwegian Animal Research Authority . Birds were handled about 10 m away from their nest, and released after handling, typically after 5 to 15 minutes. The attachment methods for the GPS devices were developed to minimize the impact of the logger on the birds51 and have been successfully applied to seabirds previously52.Between 2011 and 2016, an average of 103 (range 70–151) and 51 (range 35–66) nests of Brünnich’s guillemots were monitored during the breeding season at Diabasodden (Isfjorden) and Ossian sarsfjellet (Kongsfjorden), respectively  for little auks, we identified the main foraging areas for each species and colony. The isotopic approach consists of evaluating the temporal covariance between seabird δ13C values and SST values on a pixel basis53 (see Supporting Information).Based on tracking data from Brünnich’s guillemots  and black-legged kittiwakes , and on the comparison of sea-surface temperature (SST) during the breeding period and blood isotopic ratios  was used as an index of light availability within the water column, the ultimate physical factor driving marine productivity at these high latitudes. We used remote sensing data on sea ice extent within defined seabird foraging grounds  to unravel intra-annual trends in sea ice phenology, a proxy for seasonal patterns in sea-ice algae productivity. Finally, CHL-a  was used to examine the seasonal patterns in phytoplankton production after sea ice break-up (see details in Supporting Information).The diffuse attenuation coefficient for downwelling irradiance at 490 nm (hereafter KD490 in mst) at a 25-km spatial resolution and has been previously considered as a suitable climate proxy in the Arctic sea ice zone54.To frame our results within the current context of climate-driven environmental changes at these latitudes, we used long-term (1979–2012), satellite data on the onset of snowmelt  to evaluate spatiotemporal changes in the melting phenology. This dataset includes yearly snowmelt onset dates  were used to remove excessive noise in the original signal.Whereas information on ice extent is provided on a daily basis, we used weekly composites of KD490 and CHL-a (in weeks from January 1st) were respectively considered as suitable surrogates of the dual peak of primary productivity driving the Arctic bloom: the annual peak of ice algae growing within and beneath the sea ice, and the annual peak of phytoplankton growing in open waters after ice break-up20. The timing of these blooms is crucial for the quantity and quality of primary and secondary production20. The temporal delay -in weeks- between the annual peaks of sea ice extent and CHL-a was therefore considered as an index of the efficiency with which biomass and energy are transferred from primary producers to higher trophic levels. Seabird breeding performance  was therefore modeled (linear models) by using estimated temporal lags as the main explanatory variable. Additionally, our models included the “year” as a factor to account for other, unknown drivers of seabirds breeding performance that might vary over time. A three level factor combining the ‘species’ and ‘colony  and its interaction with the estimated temporal lags were also considered to account for potential differences in populations’ responses (see details in Supporting Information).The annual peaks of sea ice extent and CHL-Pixel-based, least squares linear regressions of snow melt onset dates were used for deriving the magnitudes of temporal trends in melting phenology over the past three decades within our study area, thereby providing information about spatial heterogeneity in current climate impacts. Further, the long-term records of average melt onsets within seabirds’ foraging grounds were used to depict the ongoing changes in melting processes  that these seabirds have faced in recent decades.Supporting Information


ARD) prevalence in low‐income countries may be partly attributed to helminth infections. In the Schistosoma mansoni (Sm)‐endemic Lake Victoria islands (Uganda), we recently observed positive helminth‐allergy associations, despite low ARD prevalence. To understand how Sm‐induced cytokine and antibody profiles might influence allergic response profiles in this population, we assessed Schistosoma worm (SWA)‐ and egg antigen (SEA)‐specific Th1 (IFN‐γ), Th2  and regulatory (IL‐10) cytokine profiles (n = 407), and total (n = 471), SWA‐, SEA‐ and allergen (house dust mite [HDM] and cockroach)‐specific (as)IgE and IgG4 profiles (n = 2117) by ELISA. Wheeze was inversely associated with SWA‐specific IFN‐γ (P < .001) and IL‐10 (P = .058), and SEA‐specific IL‐5 (P = .004). Conversely, having a detectable asIgE response was positively associated with SWA‐specific IL‐5 (P = .006) and IL‐10 (P < .001). Total, SWA‐, SEA‐ and allergen‐specific IgE and IgG4 responses were higher among Sm Kato‐Katz positive (SmKK+) and skin prick test (SPT)+ individuals compared to SmKK‐ and SPT‐ individuals. However, total and asIgG4/IgE ratios were lower among SPT+ and wheezing individuals. We conclude that, in this population, helminth‐induced antibody and cytokine responses may underlie individual positive helminth‐atopy associations, while the overall IgG4‐IgE balance may contribute to the low overall prevalence of clinical allergies in such settings.Low allergy‐related disease ( Hypothesized pathways that underpin these associations are shown in Figure Schistosoma mansoni (Sm)‐endemic Lake Victoria islands, albeit against a backdrop of low ARD prevalence.Sm‐induced cytokine and antibody profiles underpinned helminth‐allergy associations in the above survey, we here describe an assessment of Sm‐specific cytokine profiles, as well as total, allergen‐ and Sm‐specific IgE and IgG4 profiles, and their relationship with Sm infection status, wheeze and atopy.Emerging epidemiological data on helminth‐allergy associations in Uganda reflect the complex interaction between helminths and allergens: while observational analyses in a birth cohort suggested a protective effect of childhood and maternal helminths against childhood eczema22.1Dermatophagoides mix, Blomia tropicalis or German cockroach (Blattella germanica) , and (ii) detectable IgE response (>312 ng/mL by ELISADermatophagoides pteronyssinus [hereinafter “house dust mite (HDM)”] and/or German cockroach whole allergen extracts .Samples were collected during the baseline household survey preceding a cluster‐randomized trial of standard vs intensive anthelminthic intervention  described elsewhere.Ethics committees of Uganda Virus Research Institute, London School of Hygiene and Tropical Medicine and Uganda National Council for Science and Technology approved the study.2.2Sm eggs using the Kato‐Katz method.Two slides from one stool sample per individual were independently examined by different technicians for Schistosoma worm (SWA) and egg antigens (SEA), as previously described.2) with 10 μg/mL SWA or SEA  or mitogen , or left unstimulated. Supernatants were harvested on day six and stored at −80°C until analysis. Cytokine levels in supernatants were measured by ELISA . The net response to each stimulus was calculated by subtracting the concentration in the unstimulated control well. Response values that were below the dynamic range of the assay and those that were negative after subtraction of the response in the unstimulated well were assigned a value of zero.We assessed IFN‐γ (Th1‐type), IL‐5, IL‐13 (Th2‐type) and IL‐10 (regulatory) levels by ELISA using supernatants from six‐day whole blood cultures stimulated with ® 96‐well plates  were coated overnight at 4°C with 5 μg/mL HDM or cockroach allergens and twofold dilutions of human IgE  or IgG4 (Sigma‐Aldrich) standards. Plates were blocked at room temperature (RT) with 1% skimmed milk and incubated overnight at 4°C with plasma samples diluted 1/20 (IgE assay) or 1/40 (IgG4 assay) with 10% foetal bovine serum in PBS‐Tween 20. Specific IgE or IgG4 was detected using biotinylated monoclonal mouse anti‐human IgE or IgG4 (BD Pharmingen™) and a streptavidin‐horseradish peroxidase conjugate . O‐phenylenediamine (Sigma‐Aldrich) was used as a substrate, and the reaction stopped with 2M sulphuric acid. Optical density values were measured at 490nm (reference wavelength 630nm) on a 96‐well plate ELISA reader. IgE or IgG4 concentrations (ng/mL) were interpolated from standard curves using a five‐parameter curve fit using Gen5 data collection and analysis software . Total, SWA‐ and SEA‐specific IgE and IgG4 ELISAs were performed using similar in‐house procedures (detailed in this article's supporting information).HDM and cockroach extract‐specific IgE and IgG4 were measured in plasma using an in‐house ELISA described previously.2.3S. mansoni‐induced cytokines and antibodies on allergy‐related outcomes is illustrated in Figure Sm Kato‐Katz positivity and allergy‐related outcomes were associated with antibody and cytokine levels, using the “svy” command to allow for the non–self‐weighting cluster survey design. Raw cytokine and antibody responses were skewed, so log10 (concentration+1)‐transformed antibody and cytokine data were used in our regression models; we back‐transformed the results to obtain geometric mean ratios and 95% confidence intervals. Crude and age‐ and sex‐adjusted analyses were performed. Associations between antibody responses were estimated using Spearman's correlation coefficient (rs). We used a 5% significance level for all analyses. P values quoted in the main text are from adjusted analyses.Our hypothesized mode of action of 3Questionnaire data were obtained from 2316 participants.3.1Sm by Kato‐Katz (SmKK+) had higher geometric mean concentrations of SWA‐specific type 2 and regulatory cytokines compared to SmKK‐ individuals (Table P = .034). However, there was no dose‐response relationship with infection intensity  and IL‐10 .Wheeze was inversely associated with SWA‐specific IFN‐γ  and IL‐10 responses , with HDM‐specific IgE (P = .006) and HDM‐ and cockroach‐specific IgG4  , total IgG4/total IgE ratios (P = .005) and total IgE/asIgE ratios (P < .05) were positively associated with Kato‐Katz positivity. In addition, there was a general dose‐response relationship between S. mansoni infection intensity and antibody responses  Table , which wes Table B.). In contrast, cockroach SPT reactivity was inversely associated with total IgG4/total IgE ratios (P = .022), and HDM SPT reactivity with total IgE/HDM‐specific IgE ratios (P < .001).Cockroach‐specific IgE and total IgE were positively associated with cockroach SPT reactivity. HDM‐specific IgE and IgG4, SWA‐ and SEA‐specific IgE and total IgE, were all positively associated with HDM SPT reactivity Table . In conP = .032), cockroach‐specific IgE (P = .003) and cockroach‐specific IgG4 (P = .001) were all inversely associated with wheeze.Associations between wheeze and antibody responses Table , when si4Sm‐endemic setting, associations between wheeze and Sm‐specific cytokines and antibodies, when significant, were inverse. However, SPT reactivity and detectable asIgE were positively associated with the same Sm‐specific responses.In this highly Sm exposure is almost universal, and infection much higher than indicated by Kato‐Katz: urine assessment for Sm circulating cathodic antigen (CCA) indicated a prevalence of over 70%, compared to 51.4% prevalence by Kato‐Katz.In this population, Sm‐specific cytokine responses were positive. Associations with whole blood cytokine responses are best interpreted taking into account total cell counts, but these data were unavailable. However, atopy‐antibody associations were also positive. Besides, these results mirror our previous epidemiological observations in this population, where Sm infection was positively associated with Dermatophagoides‐specific IgE, and atopy‐wheeze associations were stronger in the presence of Sm infection.All statistically significant associations between atopy and Our results were unexpected in view of earlier findings from GabonIn keeping with the original hypothesis, associations between wheeze and cytokine and antibody responses, when significant, were inverse. Furthermore, total and allergen‐specific IgG4/IgE ratios were mostly inversely associated with atopy, implying that the regulatory role of IgG4 against allergy might best be assessed relative to IgE. Also, lower total/asIgE ratios among HDM SPT+ individuals are consistent with the perception that high total/asIgE ratios may be protective against allergic responses, because nonspecific polyclonal IgE may compete with asIgE to saturate IgE receptors.One limitation of assessing helminth‐allergy associations and underlying mechanisms in this population is the almost universal exposure to helminths, and lack of data on duration of infection. We also report a large number of statistical tests, so some apparently “significant” findings could have occurred by chance. As we anticipated that some of our measures might be correlated, we did not formally adjust for multiplicity, instead we focussed on patterns of association and consistency of results, and on biological plausibility with reference to other findings. Another potential limitation is that wheeze was relatively rare in the study population, and hence, some of our comparison groups (such as the age group 1‐17 years) had a low prevalence. Besides, reported wheeze could easily be misclassified in this population due to lack of a direct translation of “wheeze” in the native languages.Nonetheless, our results generally agree with our epidemiological observations in the same population,None.AME conceived the main study. AME, RES and MN led the field and clinic teams. AME, GN and JK participated in the design of laboratory studies. GN, JK, BW and JN performed the experiments. GN and ELW analysed the results. GN wrote the manuscript, with all authors contributing to the interpretation of the results, and revision and approval of the final manuscript. GN is the guarantor of the article. Click here for additional data file.


Sea ice has been suggested to be an important factor for dispersal of vascular plants in the Arctic. To assess its role for postglacial colonization in the North Atlantic region, we compiled data on the first Late Glacial to Holocene occurrence of vascular plant species in East Greenland, Iceland, the Faroe Islands and Svalbard. For each record, we reconstructed likely past dispersal events using data on species distributions and genetics. We compared these data to sea-ice reconstructions to evaluate the potential role of sea ice in these past colonization events and finally evaluated these results using a compilation of driftwood records as an independent source of evidence that sea ice can disperse biological material. Our results show that sea ice was, in general, more prevalent along the most likely dispersal routes at times of assumed first colonization than along other possible routes. Also, driftwood is frequently dispersed in regions that have sea ice today. Thus, sea ice may act as an important dispersal agent. Melting sea ice may hamper future dispersal of Arctic plants and thereby cause more genetic differentiation. It may also limit the northwards expansion of competing boreal species, and hence favour the persistence of Arctic species. OceanSome of the most remote Arctic islands are found in the North Atlantic sector, which has also been identified as one of the major barriers to dispersal of Arctic-alpine plants ,14. Desp15Here, we compile data on the first Late Glacial to Holocene occurrence of vascular plant species, likely dispersal routes, as well as reconstructions of past sea-ice extent to assess the importance of sea ice in facilitating plant dispersal during postglacial re-colonization. Driftwood has limited floating capacity in open water but may be transported over long distances by sea ice . Thus, i2.(a)We compiled data on first occurrences of plant taxa on Svalbard, Iceland, East Greenland and the Faroe Islands based on macrofossils, pollen and/or ancient DNA that were identified to species level or could be ascribed to such because only one species within the genera is currently present in the region .The past sea-ice pathways were estimated based on a compilation of published sea-ice proxy records (e.g. ) from thFor driftwood, we compiled published records from the Amphi-Atlantic region dated and traced by means of dendrochronology .(b)genetic dataset), colonization patterns in the North Atlantic had previously been inferred on the basis of phylogeographic analysis of amplified fragment length polymorphism [We used the first occurrences of plant species to create two datasets. For a subset of species  or based on the dominant colonization direction for the target area inferred by Alsos et al. [All retrieved first occurrences were included in the s et al.  .genetic dataset (14 first occurrences from eight species), the number of individuals assigned to the respective source region increased 2.22 times for an increase of one in sea-ice index  = 1.03–5.68; p = 0.049), whereas distance had a significant negative effect . Moreover, analysis of the floristic dataset revealed that sea ice was significantly denser along the ‘most likely’ dispersal routes than along routes classified as ‘not likely’ or ‘potential’  shows that the presence of driftwood mainly is confined to regions with sea ice. Further, the inferred driftwood routes mainly follow the sea-ice and surface ocean current patterns .Figure 4.Overall, the denser sea ice along the most likely colonization routes, and the close link between driftwood and sea-ice distribution, strongly support that sea ice has been and probably still is an important vector for dispersal of plants to Arctic islands.While the presence of sea ice at the first time of occurrence strongly indicates the importance of this vector, it does not preclude dispersal by other means such as wind, birds and ocean currents –22. The Both dispersal ,14 and c


The phenomenon that both activation and inhibition relationships exist between two interacting proteins is computationally modelled by multi-label learning framework. The problem of GO (gene ontology) sparsity is tackled by introducing the homolog knowledge as independent homolog instances. ℓ2-regularized logistic regression is accordingly adopted here to penalize the homolog noise and to reduce the computational complexity of the double-sized training data. Computational results show that the proposed method achieves satisfactory multi-label learning performance and outperforms the existing phenotype correlation method on the experimental data of Drosophila melanogaster. Several predictions have been validated against recent literature. The predicted activation/inhibition relationships in human PPI networks are provided in the Protein-protein interaction (PPI) networks are naturally viewed as infrastructure to infer signalling pathways. The descriptors of signal events between two interacting proteins such as upstream/downstream signal flow, activation/inhibition relationship and protein modification are indispensable for inferring signalling pathways from PPI networks. However, such descriptors are not available in most cases as most PPI networks are seldom semantically annotated. In this work, we extend  Protein-protein interactions (PPIs) play important roles in mediating gene expression & regulation, cell signalling and organismal development. Aberrant protein-protein interactions could lead to diseases23Drosophila melanogaster PPI networks. Activation/inhibition relationships play important roles in relaying signals between physically interacting proteins and in mediating cross-talks between signalling pathways. Activation of oncogenes and/or inhibition of tumor suppressor genes to some extent could lead to serious diseases. To the best of our knowledge, there is to date no computational method developed for predicting the activation/inhibition relationships in human PPI networks. The only existing computational method that predicts activation/inhibition relationships focuses on relatively small-scale Drosophila melanogaster PPI networksDrosophila melanogaster PPI networks, wherein positive Pearson correlation coefficient between two genotypes’ phenotypes indicates activation relationship, while negative Pearson correlation coefficient indicates inhibition relationship. The idea behind the method is simple and easy to implement. Nevertheless, there are several concerns to be addressed. Firstly, the method needs phenotype data to derive genotype-phenotype matrix. The requirement may be practical for small-scale Drosophila melanogaster PPI networks. For large-scale human PPI networks, phenotype data may not be available and the requirement imposes demanding data constraint on computational modelling. Secondly, the method used indirect phenotype data to predict activation/inhibition relationships. Actually the experimental activation/inhibition data that contain more reliable and direct information are not exploited at all. Finally, dissimilar phenotypic patterns between two interacting genes  do not necessarily indicate an inhibition relationship between the two genes. Maybe a third gene c inhibits the signalling interaction that gene a activates gene b.In recent years, much effort has been made to semantically annotate protein-protein interaction networks. For instance, statistical or machine learning methods are proposed to predict the upstream/downstream directionality between two interacting proteins34578ℓ2-regularized logistic regression method to multi-label learning scenario for predicting the activation/inhibition relationships in human PPI networks. In this method, the available experimental activation/inhibition data are directly exploited as training data. The phenomenon that both activation and inhibition exist between two interacting proteins is computationally modelled by multi-label learning framework. In addition, a third class named others is introduced to classify those interacting protein pairs that possess neither activation relationship nor inhibition relationship. Here GO (gene ontology) terms are used as features to represent protein-protein interactions. To address the problems of GO sparsity and null-feature vectors, homolog knowledge transfer is conducted by treating the homolog knowledge as independent homolog instances. Drosophila melanogaster activation/inhibition data. Lastly, we apply the trained model to annotate human PPI networks with activation/inhibition relationships for further biomedical research.In this work, we extend To our knowledge, several major databases including STRINGothers, we randomly sample in the physical PPI space that is generated by combining the PPIs in HPRD and HitPredict and then excluding those activation/inhibition relationships that already occur in the training set. The size of class others is the same as that of the class activation to reduce the risk of predictive bias toward the large class activation. The physical PPI space minus the training set yields the prediction set that contains 151,201 PPIs.As shown in As shown in Gene ontology (GO) is a hierarchically organized and controlled vocabulary to characterize gene products19202122202122As regards explicit binary feature representation, there are also two methods to exploit GO terms. One method is to directly use the GO terms extracted from the GOA database alone2122i and U is defined as follows.In this work, we use GO term as feature and represent protein pair in the form of flat binary feature vector, such that the shared GO terms and the distinct GO terms between two interacting proteins are easily represented. The GO terms are simply retrieved from the GOA databaseBased on these notations, the target instance and the homolog instance for each protein pair g of the target instance g of the homolog instance i1 and protein i2 share the same GO term g, then the corresponding component in the feature vector g, then the component is set 0; otherwise, the component is set 1. If either where Activation/inhibition relationships between two interacting proteins actually need to embrace upstream/downstream directionality. Since prediction of signal directionality is often treated as an independent computational problem5Multi-label learning is easily converted to traditional supervised learning by two approaches, namely label combination method and binary methodl test instances, yi denotes the true label vector of the ith instance and In the scenario of multi-label learning, three metrics, i.e. exact match ratio, macro-average F-measure and micro-average F-measure, are commonly used to measure model performance. Exact match ratio is used to measure the model performance of correctly recognizing all the associated class labels. The demerit of exact match ratio is that it does not count partial label matches that also provide useful information. To take partial label matches into account, macro-average F-measure and micro-average F-measure are especially proposed for performance estimation in the scenario of multi-label learningI denotes an indicator function as defined below.where L={1, 2, 3, … d}, for the ith instance, the true label set is denoted as Li and the predicted label set is denoted as ith instance are formally defined by d-dimensional binary vectors as follows.Given the set of class labels j, the performance metrics precision (P) and recall (R) are defined as follows.For label j is formally defined as follows.Similar to the definition of Macro-average F-measure is defined as the unweighted mean of the F-measures of all class labels.Micro-average F-measure considers the predictions from all instances and calculates the F-measure across all class labels.Homolog knowledge transfer via homolog instances is an effective way to tackle the problems of GO term sparsity and null-feature vectors. However, homolog instances also have two adverse effects. First, the homolog instances double the size of training data and according increase the computational complexity. For large training data, things will become much worse. Second, the homolog instances could introduce a certain level of noise. As such, a noise-tolerant machine learning framework that can handle large-scale training data is needed for the homolog knowledge transfer. To the best of our knowledge, ω denotes the weight vector and C denotes the penalty parameter or regularizer. The second term could penalize noise/outlier fitting. The optimization of the objective function (10) can be solved via its dual form:where αi denotes Lagrangian operator and where i is larger than that of lj, then the combined label is LT; otherwise, the combined label is LH. However, it is more complicated to yield the final class labels in the scenario of multi-label learning, because both the target instance and the homolog instance are predicted to multiple class labels. For example, the target instance is predicted to the labels Ten-fold cross validation is first conducted on the training data collected from the STRING database23Next we further check whether the proposed method yields predictive bias. As shown in Independent test is further conducted here to study how well the proposed method generalizes to unseen data. The independent test sets are provided in NetPathDrosophila melanogaster PPI networksDrosophila melanogaster PPI networks instead of the human PPI networks. The phenotype correlation method exploits 49 phenotype data of Drosophila melanogaster to construct a genotype-phenotype matrix, and then calculates the Pearson correlation coefficient of the phenotypes between two genes to predict activation/inhibition relationships. In the method, activation is treated as positive class and inhibition is treated as negative class. On the independent test data that contain 69 activation relationships and 37 inhibition relationships, the method achieves 97.2% true positive rate  and 41% true negative rate . This independent test performance is used as the baseline for model comparison.To further demonstrate the efficacies of the proposed method, we need to further compare it with the existing methods. To our knowledge, there are to date no other methods developed to predict the activation/inhibition relationships in human PPI networks. The only comparable method is the phenotype correlation method that is developed to predict the activation/inhibition relationships in Drosophila melanogaster (69 activation relationships and 37 inhibition relationships), we need to first collect experimental data to train a predictive model. Since the training data of 49 phenotypes used as training dataDrosophila melanogaster PPI networks, which are disjoint with the independent test set. This data source is used as training data. The 10-fold cross validation performance on this data set is provided in others. Similarly, the performance on the small class inhibition is still not satisfactory.Before performance estimation on the independent test data of Next we use the trained model to evaluate the performance on the independent test data used inothers. The physical PPIs that are predicted to the class activation and/or the class inhibition are provided in the Before interactome-wide predictions, we have attempted to train a more robust model on a larger training set by merging the training set from the STRING database with the independent test sets from the Reactome database and the KEGG database see . HoweverBDNF (Brain-derived neurotrophic factor) signalling pathway. Brain-derived neurotrophic factor (BDNF) is a member of family of neurotrophins that plays a major role in the growth, differentiation, plasticity and survival of neurons. BDNF is also involved in the biological processes such as energy metabolism, mental health, behavior, learning, memory, stress, pain and apoptosis29PTPN11 and {NTRK2, FRS3, FRS2, SIRPA}. According to the Uniprot database (http://www.uniprot.org/uniprot/Q06124), PTPN11 mediates cross-talk in multiple signalling pathways, e.g. fibroblast growth factor receptor signalling pathway, epidermal growth factor receptor signalling pathway, FRS2-mediated cascade, brain development, etc. These results may suggest that PTPN11 plays important roles in coordinating the activation/inhibition of multiple cross-talk pathways.AR (Androgen receptor) signalling pathway. The androgen receptor is a member of nuclear receptor family of ligand activated transcription factors, stimulation of which activates the SMAD signalling moduleAR is predicted to activate or to be activated by most of the other genes. The activation relationships between AR and {NR3C1, PXN, ESR1} have been experimentally verifiedAR and the genes {CTNNB1, PIAS1}. InPIAS1 and AR expression in the malignant tissues of prostate cancer, while the Pearson’s correlation between the expressions of these two genes is low in the benign tissues, indicating that the predicted inhibition relationship between AR and PIAS1 is consistent with the experimental evidence.IL (Interleukin) signalling pathways. Interleukin are a group of cytokines  that were first seen to be expressed by white blood cells (leukocytes), and the function of immune system depends in a large part on interleukinsIL7R and the genes {IL2RG, JAK1, JAK3} have been experimentally validated inIL7 receptor alpha chain (IL7R) and the common gamma chain (IL2RG). InIL7 to IL7R could activate the receptor complex and further activate Janus kinase 1 (JAK1) and JAK3. In addition, the mutation in IL7R, IL2RG, JAK1, or JAK3 could also activate the receptor complex, which would lead to impaired B & T cell development, phosphorylation of STAT proteins, and thus cause the activation of survival and proliferation pathways. This statement suggests that the activation of receptor complex {IL7R, IL2RG} may activate {JAK1, JAK3} to cause immunodeficiency disease. According to this evidence, the receptor complex {IL7R, IL2RG} can be inferred to keep inactivated with {JAK1, JAK3} in normal cells, which validates our predictions.Drosophila melanogaster. There is to date no computational method that focuses on predicting activation/inhibition relationships in human PPI networks.Computationally annotating protein-protein interaction (PPI) networks has drawn much attention in recent years. Assignment of semantic annotations to the interactions of PPI networks facilitates the derivation of signalling pathways. At present, most existing computational methods focus on predicting the descriptors of signal events between two interacting proteins, such as upstream/downstream directionality, activation/inhibition relationship, chemical reaction, protein modification, etc. Among these signal events, activation/inhibition relationships are significant to reveal the spatiotemporal relay of signalling events in biological processes and to understand the cross-talk mechanism between signalling pathways. Activation of oncogenes and/or inhibition of tumor suppressor genes to some extent cause diseases. To our knowledge, there is only one computational method developed to predict the activation/inhibition relationships in the PPI networks of In this work, we extend Drosophila melanogaster PPI networks.From the aspect of state-of-art computational modelling, we should choose the best one that could achieve the highest performance from a variety of machine learning methods, such as support vector machine (SVM), neural networks and random forest, etc. SVMAs regards the feature construction method using GO terms, there is still a problem that is worth further discussing. As mentioned in the section of feature construction, we adopt the simple method of using the GO terms from the GOA database directly or taking the most specific annotated GO terms. This method has its demerit. As the GO knowledge is unevenly distributed between well-studied proteins and less-studied proteins, the less-studied proteins would lack lower-level or more specific GO terms, so that no shared GO terms are found between these two kinds of proteins even though they are functionally correlated. The method of lowest-common-ancestora is annotated with a GO term Termi at the ith level, and a less-studied protein b is only annotated to the jth level with a GO term Termj that is ancestor to Termi. As Termi is distinct from Termj, it would be taken for granted that the set of shared GO terms is empty and protein a is little functionally correlated with protein b. Fortunately, the homologs of protein b could increase the coverage of well-studied proteins. If protein a is functionally related with protein b, there is a large chance that a homolog c of protein b is annotated with Termi. As shown in Drosophila melanogaster PPI networks the homolog instance correctly recognizes 86.21% inhibition relationships, while the homolog instance correctly recognizes only 65.52% inhibition relationships. The results show that the homolog knowledge transfer enriches the feature information and to some extent recovers the functional relationships between two GO terms at different levels of GO DAG. Similarly, homolog knowledge transfer also has its demerit, that’s, a certain level of noise could be introduced via homolog instances. This is the reason why we introduce Homolog knowledge transfer is another effective way to recover the functional relationships between two GO terms at different levels of GO DAG2328others. We have attempted to sample the training data to create approximately even class distributions, for instances, oversampling the small class inhibition, undersampling the larger classes {activation, others}, or developing ensemble of classifiers on the larger classes {activation, others}. Unfortunately, no substantial performance gain is obtained. Maybe accumulation of more training data for the class inhibition is the ultimate way to achieve balanced performance among the three classes.Imbalanced distribution of training data among multiple classes is a hard computational problem in the fields of bioinformatics and machine learning. In this work, the larger classes {activation, others} possess much more training instances than the small class inhibition. As a result, the performance on the class inhibition is much lower than that on the class activation and the class others} and relatively poor performance on the small class inhibition. Nevertheless, the performance on the class inhibition is still quite promising as compared to that of the phenotype correlation methodComputational results show that the proposed method achieves excellent performance on the two large classes {activation, How to cite this article: Mei, S. and Zhang, K. Multi-label ℓ2-regularized logistic regression for predicting activation/inhibition relationships in human protein-protein interaction networks. Sci. Rep. 6, 36453; doi: 10.1038/srep36453 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.