Because of the chemically crosslinked 3D molecular structure of rubbers, their recycling is a challenging task, especially when cost efficiency is also considered. One of the most straightforward procedures is the grinding of discarded rubber products with subsequent devulcanization. The devulcanized rubber can be used as a feedstock for fresh rubber compounds or can be blended with uncured virgin rubber and thermoplastic polymers to form thermoplastic dynamic vulcanizates (TDVs). TDVs combine the beneficial (re)processability of thermoplastics and the elastic properties of rubbers. Our current work focuses on the development of polypropylene (PP)-based TDVs with the use of a tire model rubber (MR) composed of natural rubber (NR) and styrene-butadiene rubber (SBR) in a ratio of 70/30. The research target was the partial substitution of the above fresh MR by microwave devulcanized crumb rubber (dCR). TDVs were produced by continuous extrusion, and the effects of composition (PP/MR/dCR = 40/60/0…50/35/15) and processing parameters  were investigated. Results showed that the fresh rubber compound can be replaced up to 10 wt % without compromising the mechanical properties of the resulting TDV. Polymeric materials have gained enormous importance in the last few decades. Thermoplastics and thermosets are extensively used in almost every facet of life, due to their beneficial and diversely customizable properties. However, polymeric materials pose environmental hazards and their waste management is problematic. As a result of their chemically stable structure, the decomposition of polymeric wastes in most cases is excessively slow, so polymeric products may exist for many decades after they are discarded. This problem is more substantial and acute in the field of thermoset polymers, especially with rubbers, because of their chemically crosslinked 3D molecular structure. Rubbers cannot be recycled like thermoplastics via relatively simple and cost-effective reprocessing  methods. Therefore, the development of feasible recycling technologies for rubbers attracts a great deal of academic and industrial interest. Besides other opportunities such as pyrolytic breakdown , anotherThis paper focuses on the effect of different processing conditions and parameters on the mechanical properties of various TDVs composed of PP, styrene-butadiene rubber (SBR)/natural rubber (NR) fresh tire model rubber blend, and microwave devulcanized crumb rubber. TDV samples were produced continuously by extrusion compounding. The effects of different screw configurations, different PP/model rubber (MR)/devulcanized crumb rubber (dCR) contents, processing temperatures, and feeding methods of the PP were studied. dCR was introduced into the TDV formulations up to 15 wt % based on the overall polymer content. Mechanical and morphological properties of the TDVs were assessed. The mechanical tests covered quasistatic tensile and tension set properties as well.Random polypropylene (PP) copolymer as a thermoplastic material, natural rubber (NR) and styrene-butadiene rubber (SBR) as rubber, and waterjet-milled ground tire rubber as crumb rubber (CR) were selected and used. Manufacturers and types and basic properties of CR, rubbers, and the PP resin are listed in The additives of the tire model rubber (MR) formulation and their suppliers were the following: zinc oxide , stearic acid , N550 carbon black , naphthenic oil , N-cyclohexyl-2-benzothiazolesulfenamide , tetramethyl thiuram disulfide , and sulphur .Microwave devulcanization of the CR was carried out in a BP-125/50 type laboratory microwave oven, produced by Microwave Research Inc.  with batch sizes of 100 g. The final temperature was fixed at 200 °C and the heating rate was 6 °C/min. Prior to microwave treatment, the CR was kept at 150 °C for 2 h in a Venticell LSIS-B2V/VC55 hot air oven . The microwaving of the CR resulted in a significant increment in its sol content. Sol content was determined by Soxhlet extraction in toluene with an extraction time of 16 h. Untreated CR and microwave-treated CRs had sol contents of 11.0 ± 1.6 wt % and 28.5 ± 1.3 wt %, respectively.The formulation of the model rubber (MR) was: SBR 70 phr, NR 30 phr, ZnO 5 phr, stearic acid 1 phr, naphthenic oil 30 phr, N550 carbon black 60 phr, CBS 1.5 phr, TMTD 1.5 phr, sulphur 1.5 phr. The rubber ingredients were compounded by Gumiplast Ltd  in a Banbury-type internal mixer. The curing curves of MR at 160, 170, and 180 °C ) are shown in The curing parameters determined from the curves are listed in The applicability of the dCR and the effect of its content in TDV production were studied by partial substitution of the MR by dCR in 10, 20, and 30 wt % ratios. The component formulations of the TDVs are shown in The compounding and dynamic vulcanization of the blends were performed on a modular corotating twin-screw extruder  with a screw speed of 180 rpm and two different temperature profiles. The temperature profiles of the twin-screw extruder are displayed in –105 °C zone temperatures), and a single rubber filament was obtained with a diameter of 3 mm to ensure the precise and reproducible dosing of the rubber during TDV production. The rubber was dosed by feeding the rubber filament continuously into the hopper of the twin-screw extruder. PP was fed in two different ways so that effects of different feeding methods could be studied: (i) feeding the PP with the rubber filament through the hopper (H) of the twin-screw extruder simultaneously, and (ii) dosing PP in melted state into the second zone of the twin-screw extruder with the help of a single-screw extruder  and a side feeder (S) adaptor. The given MR/PP ratio was set by the following method: net uncured rubber intake at the given screw speed of the twin-screw extruder (180 rpm) was measured (4.5 kg/h), and the necessary amount of additional PP was calculated and set by (i) utilizing and adjusting the built-in hopper feeder of the twin-screw extruder to grant 4.5 kg/h feeding of PP granules (in case of hopper PP feeding and 50/50 MR/PP ratio), or (ii) adjusting the screw speed of the side feeder of the single-screw extruder to obtain 4.5 kg/h (50/50 PP/MR ratio) and 3 kg/h (40/60 PP/MR ratio) throughput for the neat PP melt (in case of side feeding). Three different temperature profiles were set on the single-screw extruder, which are summarized in Prior to compounding and dynamic vulcanization, the rubber mix was shaped on a single-screw extruder  after compounding. From the pellets, ISO 3167 A type dumb-bell specimens were injection-molded with an Arburg Allround Advance 370S 700-290 injection-molding machine . The related parameters were: barrel temperature range: 170–190 °C, mold temperature: 30 °C, injection speed: 50 cmHardness was tested according to the ISO 868 Shore D method on a Zwick H04.3150.000 hardness tester  on the injection-molded specimens. Ten tests were performed on each compound, followed by the calculation of average and standard deviation values.The tensile mechanical properties of the compounds were investigated according to the ISO 527 standard on a Zwick Z250 universal testing machine with a 20 kN load cell  at room temperature at a 100 mm/min crosshead speed. This deformation rate, being below that of the typical rubber testing (500 mm/min), was selected to compare the tensile performance of the TDVs with the PP matrix. The average and standard deviation of the tensile strength and elongation at break were determined with 5 tests on each compound.L0 (mm) is the initial test length (L0 = 70 mm), L1 (mm) is the stretched distance between the markers, and L2 (mm) is the distance between the markers after the given relaxation time.The tension set of the investigated compounds was tested on a Zwick Z020 universal testing machine with a 20 kN load cell  at room temperature with a clamping length of 110 mm. A test length of 70 mm was marked on the narrow section of the specimen by two markers, then the specimen was stretched until clamping length reached 180 mm. Specimens were kept stretched for 24 h at room temperature, then unloaded and the distance between the two markers were measured after 0, 5, 1, 3, 6, 12 and 24 h after the removal of the load. Tension set (%) values were calculated with Equation (1).The morphology of the TDVs was characterized with SEM micrographs. SEM images of cut surfaces produced by cryogenic microtome were taken with a Jeol JSM 6380 LA scanning electron microscope . Prior to investigation, the surfaces of the specimens were sputter-coated with gold.Characteristic tensile curves of the PP, the cured MR (cured at 180 °C), and the “A_H_H_0_50 MR” TDV are shown in The results of the mechanical tests were arranged in four categories based on the various variables studied, namely (i) screw configuration and MR content; (ii) the temperature profile of the compounding twin-screw extruder; (iii) PP feeding method; (iv) dCR content. The tensile strength, elongation at break, and hardness values of the produced TDVs are summarized in Similar tendencies can be observed regarding the tensile stress and elongation at break of the investigated compounds. Screw configuration “B” and lower MR content resulted in improved tensile properties a,b. FiguHigher initial compounding temperature (compounding temperature profile “H”) resulted in improved tensile properties, and the same can be observed in terms of hardness . Figure Feeding the PP at the hopper in the form of granules had a positive effect on the tensile mechanical properties, similar to the higher initial compounding temperature. The melt temperature of the PP in the case of PP side feeding did not have a significant effect on the tensile properties . StresseThe processing parameters of TDVs with incorporated dCR were defined by considering the results of the tensile tests of the TDVs containing MR. Therefore, screw configuration “B”, hopper feeding of the PP, and a higher compounding temperature profile were used in the production of TDVs containing dCR. Replacing 10, 20 and 30 wt % of the MR with dCR resulted in slightly deteriorated tensile behavior; nevertheless, at 10 and 20 wt % ratios, the decline was very modest. Hardness remained practically unchanged by dCR content cf. . The posAccording to the SEM images, screw configuration “B” resulted in better dispersion of the rubber phase, displaying rubber “islands” with small diameter. By contrast, TDVs produced with screw configuration “A” showed a coarser rubber dispersion. At low magnification, even hints for local co-continuity can be found. This can be attributed to lower shear stresses, evolved during compounding with the “A” compared to the “B” screw that delayed the dispersion of rubber (transition to droplets).The comparison of the images displayed in This work was aimed at studying effects of TDV formulation and processing parameters on the mechanical and morphological performance of PP-based TDVs with an SBR/NR rubber phase. Special attention was given to check whether the fresh rubber can be partially replaced by microwave devulcanized crumb rubber (dCR). Note that the use of dCR is an ecologically driven opportunity for rubber waste management. Our results showed that in terms of rubber dispersion and tensile mechanical behavior, it is highly beneficial to subject the compound to intensive mixing/kneading in a relatively early stage of the curing of the rubber phase. This can be achieved with suitable kneading elements in the initial sections of the extruder screw. Feeding both the PP and the rubber into the hopper of the extruder has a positive effect on the dispersion of rubber and thus also on mechanical properties. In this feeding, the melting of the PP occurs in the presence of the rubber compound, thereby generating higher shear stresses within the PP melt, which finally leads to a better dispersion of the rubber particles. Partial substitution of the fresh rubber compound with dCR is possible up to 10 wt % without compromising the tensile mechanical properties of the resulting TDV. Replacing 30 wt % of the fresh rubber, however, led to a pronounced drop in mechanical properties. No additional curatives were added to the dCR-containing rubber compounds in this project, because our goal was to check the effect of “pristine” dCR as a potential substituent of fresh rubbers in TDVs. According to the preliminary results, the incorporation of additional curatives into the compounds containing dCR may improve the tensile mechanical properties of the corresponding TDVs, and thus an even higher amount of dCR can be incorporated without the deterioration of properties.


Tm shifts: JNK Inhibitor II for hNek1(Δ262-1258)-(T162A), Isogranulatimide for hNek6(S206A), andGSK-3 Inhibitor XIII for hNek7wt. Each one of these compounds was also validated by reducing the kinases activity by at least 25%. The binding sites for these compounds were identified by in silico docking at the ATP-binding site of the respective hNeks. Potential inhibitors were first screened by thermal shift assays, had their efficiency tested by a kinase assay, and were finally analyzed by molecular docking. Our findings corroborate the idea of ATP-competitive inhibition for hNek1 and hNek6 and suggest a novel non-competitive inhibition for hNek7 in regard to GSK-3 Inhibitor XIII. Our results demonstrate that our approach is useful for finding promising general and specific hNekscandidate inhibitors, which may also function as scaffolds to design more potent and selective inhibitors.Human Neks are a conserved protein kinase family related to cell cycle progression and cell division and are considered potential drug targets for the treatment of cancer and other pathologies. We screened the activation loop mutant kinases hNek1 and hNek2, wild-type hNek7, and five hNek6 variants in different activation/phosphorylation statesand compared them against 85 compounds using thermal shift denaturation. We identified three compounds with significant  Protein kinases (PKs) play an important role in the activation of biochemical pathways in eukaryotic cells. With over 500 described kinases encoded in the human genome, virtually every signal transduction process is wired through a phosphotransfer cascade. Therefore, the tight regulation of phosphorylation is crucial for cell growth and development, and this regulation relies on the proper regulation of kinase proteins. Protein kinase domains are found in ~2% of eukaryotic genes , which aAspergillus nidulans, which performs mitotic roles similar to the mammalian Neks . Kinase and ATP concentrations that matched these characteristics had been obtained in a previous assay, using a kinase concentration range between 2.5 nM and 640 nM, and an ATP concentration range between 0 and 25 µM. Inhibitor final concentration was 2×, 1× and 0.5× the ATP Km[apparent] concentration found for each kinase: 0.625 µM for hNek6(S206A) and hNek7wt, and 25 µM for hNek1(Δ262-1258)-(T162A).Kinase assays were performed using LANCEhttp://www.yasara.org). The alignment with the target sequence is improved using sequence-based profiles obtained from related Uniprot sequences  along with secondary structure prediction, to ensure high model quality.Homology models for hNek1(Δ262-1258)-(T162A) and hNek6(S206A) were obtained based on the protein structure of hNek1 (PDB: 4APC) (unpublished) and hNek7 (PDB: 2WQN) , respectTm (ΔTm ≥ 4.0 °C) and increased inhibitory activity (percent phosphorylation ≤ 75%) were selected as best candidates and used as ligands for molecular docking against Neks. JNK Inhibitor II  and Isogranulatimide  were used considering hNek1(Δ262-1258)-(T162A) and hNek6(S206A) molecular models as targets, respectively. GSK-3 Inhibitor XIII  was used considering the hNek7 crystal structure as the molecular target. Compound structures were obtained from the commercial SDF catalog  and submitted to energetic parametrization and minimization on YASARA (http://www.yasara.org). Global molecular docking simulations were conducted using Autodock VINA [Compounds showing the most significant Δock VINA  with 25 Tm shift for hNek1(Δ262-1258)-(T162A), JNK Inhibitor II, and ten hit compounds for hNek7wt. Of these compounds, JNK Inhibitor II was validated by reducing hNek1(Δ262-1258)-(T162A) activity to 71.5% ± 0.1% at a 50 µM concentration, while GSK-3 Inhibitor XIII was validated by reducing hNek7wt activity to 46.3% ± 2.9%, at a 1.25 µM concentration. JNK Inhibitor II was also predicted to bind to the ATP-binding site of hNek1(Δ262-1258)-(T162A) as an ATP-competitive inhibitor, in contrast to GSK-3 Inhibitor XIII, which showed a novel possible non-ATP-competitive inhibition of hNek7, but a most probable peptide/substrate-competitive inhibition.We screened the activation loop mutant kinases hNek1 and hNek2, wild-type hNek7 and five hNek6 variants, with different activation/phosphorylation states, and compared them against 85 compounds using thermal shift denaturation and identified one compound with significant Tmshift for hNek6(S206A), but not for the other variants, which was later confirmed to reduce hNek6(S206A) activity to 74.9% ± 10.4% at 0.625 µM and to bind to its ATP-binding site.We also found that mutant hNek6, without the activation loop conserved phosphorylation, is a better target for inhibitor stabilization than an activated more phosphorylated hNek6 kinase. From this experiment, we retrieved one compound, Isogranulatimide, which produced a significant Further functional experiments in living cells are required to validate this findings, and structural studies with atomic resolution will be important to characterize the mechanistic association of hNek1(Δ262-1258)-(T162A), hNek6(S206A), and wild-type hNek7 with these promising compounds. Furhtermore, they may also function as scaffolds to design more potent and selective inhibitors for hNeks.


This data article describes how the Dig It Check It (DICI) model operates. The purpose of the DICI model is to assess the effectiveness of a specified subsurface sampling program in the detection of archaeological sites   Specifications tableValue of the data•Dig It, Check It is the first model to be presented which employs simulation to assess the effectiveness of irregular, real-world subsurface sampling programs in the detection of concentrations of artefacts.•One of the principal points of difference between this new model and previous work is that this tool allows real-world and irregular test-pit layouts to be analysed through the importation of a GIS shapefile.•This means that a sampling strategy which includes test-pits of varying sizes and intervals can be assessed, as can survey areas of non-rectangular shape.•This allows the archaeologist to easily conduct a statistically rigorous bias assessment of any subsurface testing program.1http://www.digittools.net/The data presented here is a NetLogo 2There are three entities in the model: survey area, sites and test-pits. A simulation is run which randomly places sites in a survey area which contains the specified test-pits to determine the probability of overlap between the test-pits and sites. When overlap does occur, the site is recorded as intersected. The model then calculates the site density at that point and compares it with the test-pit size to determine whether the density is great enough for detection to also occur.2.1The survey area is loaded through the importation of two shapefiles via the interface. The survey area boundary file sets the world envelope and scales and resizes the display of the world/NetLogo screen. The second shapefile draws the survey area boundary and codes the area within the boundary as ‘surveyed’. The following pseudo-code describes this process:2.2x coordinate, y coordinate, length, width, area, artifact-density, number of sites intersected and number of sites detected. The ID, x coordinate, y coordinate, length and width are imported with the shapefile, and the area is then calculated from the length and width variables.Pits have the variables: ID, Each pit consists of a pit-set, which includes a single seed-pit, which is imported as a point during the importation of the shapefile, and additional pit-squares. The initial seed-pits create a pit-set of multiple pit-squares, which together define the extent of each test-pit. This allows test-pits of different sizes to be imported and constructed. These pit-squares have the same ID as the seed-pit, and the seed-pit and pit-squares keep a tally of how many sites the test-pit intersects and detects. This allows sites which partially overlap a pit-set to be recorded as intersected/detected by the test-pit as a single unit. The following pseudo-code describes this process:3The sites consist of concenrations of artefacts. The site-properties are established during the initialisation on the interface with the variables ‘average-density’, ‘site-diameter’ and ‘density-distribution’. These properties can be adjusted between runs.The size of the site is determined by the variable ‘site-diameter’. The density of each point within the site is calculated according to the average-density and the density-distribution. The density distributions which can be selected are: uniform, linear, cosinusoidal, and the Lake George Regression (LGR) curve. The density distributions are described below.r = the distance of the point of intersection from the centre of the site.In the uniform density distribution all points within the site will have the same density as the average density. If the density distribution is set to a linear, then a regular density decay occurs. The centre of the site will have the highest density, which is set at 3.0 times the average density. Decay then proceeds linearly to zero at the margin of the site. This density distribution is calculated with the following equation:In the cosinusoidal distribution, the centre of the site has a maximum density of 3.36 times the average density. Density decay occurs slowly in the central area of the site and then increases towards the margin, until it falls to zero at the perimeter. This creates a site with a large central area of high density, a medium density through the centre of the site, and a low density margin. This distribution is determined by the equation:y-score is the standard deviation of the site at that point as determined by the radius.In the LGR density distribution the centre of the site has a maximum density of 6.66 times the average density. This curve produces a small central area of high density, a steep decay through the centre of the site, and a large marginal area of low density, which falls to zero at the perimeter of the site. This distribution is determined by the equation:The sites are added to the model by randomly placing a seed-site within the surveyed area. The seed-site then builds the rest of the site according to its diameter and density-distribution.k = clustering parameter and d = expected artifact density in test-pitIn some situations, artifacts are not distributed in these ways, but are clustered. Similar to Kintigh׳s 4The survey area and the test-pits are now loaded and drawn, and the characteristics of the sties have been established on the user-interface. The simulation is now ready to run. During the simulation the sites are added to random locations within the survey area. The number of sites added is determined by the user through the variable ‘trial-runs’, which is set by default to 1000. If a site overlaps a single test-pit, the intersection tally for that test-pit increases by 1. During this calculation, all the pit-squares in a pit-set ask their fellow pit-squares if they intersected any sites in that run. This allows sites which only partially overlap a test-pit to be tallied as intersected by the test-pit as a single unit.The detection calculation occurs in a similar manner, however a detection threshold of 1 is set here. If this site overlaps a test-pit, the artifact density of the site at the point of overlap is transferred to the pit-square. All the pit-squares then calculate the total pit density by adding together the artifact-densities of each pit-square in the pit-set and then dividing that figure by the total pit area. If the total pit density is greater than 1, the site is recorded as detected, and the site detection tally is increased by 1. The pseudo-code for this process is as follows:The simulation runs until the number of trial runs is reached. At this point, the monitors can be inspected see . The fir5p is the probability of finding one or more artifacts, a is the area of the test-pit and d is the artifact density The concepts of intersection probability and detection probability, which were first introduced by Krakker et al. Randomness/stochasticity is also necessary for modelling the presence of artifacts within an intersected test-pit. The Poisson distribution (see equation above) is used to calculate the probability of finding at least one artifact in a test-pit based on the expected number of artifacts at the point of intersection Randomness/stochasticity is also required when a site is generated. During the simulation a number of trials are run. During each trial a site is randomly placed within the survey area. In this way stochasticity is used to simulate the unknown nature of the location of buried sites in the landscape. If detection and/or intersection occur, this is tallied. Each trial is independent of the others.The purpose of the model is to determine the probability that a site will be detected or intersected by a particular sampling strategy.62 to prevent the model from running too slowly. As such, the test-pits are restricted by this format and can only consist of combinations of 0.25 m2 squares. They cannot be circular.The minimum test-pit size is hard-coded into the model to be 0.25 mThe base unit for the construction of the sites is the NetLogo ‘turtle’, which can be combined to produce a unit of any shape. However, the sites constructed in this model are circular. In this paper ‘site’ means an artifact concentration, and after Krakker et al.


Haemaphysalis longicornis salivary cystatin (HlSC-1) and subolesin by RNA interference. Adult unfed female ticks were injected with double-stranded RNA of (A) subolesin, (B) cystatin, (C) subolesin plus cystatin, and (D) injection buffer, then fed alongside normal unfed males up to spontaneous drop-down. The percentage of knockdowns was determined by real-time polymerase chain reaction. Sixty-three percent and 53% knockdown rates were observed in subolesin and cystatin double-stranded RNA-injected ticks respectively, while 32 and 26% knockdown rates of subolesin and cystatin transcript were observed in subolesin plus cystatin double-stranded RNA-injected ticks. Subolesin and/or cystatin knockdown causes a significant (p < 0.05) reduction in tick engorgement, egg mass weight, and egg conversion ratio. Most importantly, combined silencing did not act synergistically, but caused a similarly significant (p < 0.05) reduction in tick engorgement, egg mass weight, and egg conversion ratio. Therefore, the elucidation of multiple antigens may be helpful in the future of vaccines.Currently, multi-antigenic vaccine use is the method of choice for the strategic control of ticks. Therefore, determining the efficacy of combined antigens is a promising avenue of research in the development of anti-tick vaccines. The antigen responsible for blood intake and reproduction has proven suitable as a vaccine antigen. It has been shown to silence  Ceriodaphnia cornuta [The hard tick (Ixodidae) is an obligate, blood-sucking ectoparasite that has gained attention due its role in medicine and veterinary science in the transmission of significant pathogens . As an e cornuta . Althoug cornuta ,7. ThereHaemaphysalis longicornis was reported to transmit a wide variety of pathogens, including Babesia spp., Anaplasma bovis, Theileria spp., Coxiella burnetii, Ehrlichia chaffeensis, spotted fever group rickettsiae, and severe fever with thrombocytopenia syndrome (SFTS) virus throughout China, Korea, and Japan [Among Ixodidae, nd Japan ,12,13,14H. longicornis completes each of its life stages , with the exception of the egg stage, by successive feeding before dropping to molt for the next stage. Tick salivary glands play a vital role in blood engorgement and the transmission of tick-borne pathogens to vertebrates [Blood is the only nutrient that ticks ingest from vertebrates in order to provide themselves with energy to progress through their life stages. As a three-host tick, tebrates . During tebrates . TherefoH. longicornis, three cystatins  have been characterized [H. longicornis, and was found to play a crucial role in the blood-feeding process [Amblyomma americanum [Cystatin, a large, superfamily natural inhibitor of papain-like cysteine proteases, consists of three groups—type 1 (stefin), type 2 (cystatin), and type 3 (kininogen)—which are widely present in vertebrates, invertebrates, and plants, as well as protozoa ,17,18. Tcterized ,22. Amon process . One eleericanum . The othIxodes scapularis [I. scapularis affects fertility in males, and promotes a degeneration of the salivary glands, gut, and reproductive and embryonic tissues in females [A tick protective antigen known as subolesin was recently identified in the apularis . Subolesapularis ,27,28. R females ,29,30,31Rhipicephalus haemaphysaloides has been shown to cause a significant decrease in engorgement rate (5.4%), body weight of engorged ticks (206.15 mg), and egg weight (87.84 mg), in comparison with a control group  [I. scapularis infestations . Additionally, Nijhof et al. [Rhipicephalus microplus resulted in a silencing in both eggs and larvae. The silencing of subolesin expression with RNAi resulted in a reduction in fertility by 93% and 71% in Dermacentor variabilis and I. scapularis, respectively [Injection of subolesin double-stranded RNA (ds RNA) into ctively) . The resf et al.  showed tectively .Boophilus microplus. Two commercial vaccines have been developed for the control of tick infestations in cattle: TickGARD Plus® in Australia, and Gavac® in Cuba. Both vaccines are based on the recombinant antigen Bm86, located on the mid-gut digestive cell. Bm 91 is a low-abundance glycoprotein located in the salivary glands and the midgut of B. microplus . Bm 91 is a homolog of carboxydipeptidase, and shares many biochemical and enzymatic properties with mammalian angiotensin-converting enzymes. An anti-tick vaccine containing multiple antigens is considered more likely to be effective than one containing a single antigen . TherefoIn the present study, using RNAi we silenced salivary subolesin and/or cystatin in order to determine their role in feeding and reproduction. This information may help in selecting multiple antigens as candidate vaccines. H. longicornis (Jeju strain), has been maintained on rabbits at the Laboratory of Veterinary Parasitology, College of Veterinary Medicine and Bio-Safety Research Institute at Chonbuk National University in Iksan, Republic of Korea since 2003 (as described previously) [The hard tick, viously) . The aniH. longicornis were placed on the ears of specific pathogen-free (SPF) New Zealand White rabbits  using a cloth sock attached with tape. After five days of feeding, partially engorged females were removed for salivary gland collection. Ticks were kept for one hour at room temperature for the removal of host tissue. To prevent surface contamination, the ticks were cleaned with distilled water and 70% ethanol. Salivary glands were collected as described elsewhere [Twenty unfed, adult female and male ticks of lsewhere , but witTotal RNA was extracted from the collected salivary glands using a total RNA extraction kit (RiboEx™) in accordance with the manufacturer’s instructions. The concentration of RNA was determined using a NanoDrop™ 2000 spectrophotometer . The sample was then stored at −70 °C. Complementary DNA (cDNA) was synthesized using a transcriptor first-strand cDNA synthesis kit  in accordance with the manufacturer’s instructions, using 1 µg of total RNA and an anchored oligo (dT)18 primer.RT-PCR was performed using BioFACT™ 2X Multi-Star PCR Master Mix with a master cycler gradient . For each gene, cDNA was PCR-amplified using a gene-specific primer . Actin cPCR product was visualized by 1% agarose gel electrophoresis. Then, PCR products were purified using an EZ-Pure™ PCR Purification Kit ver. 2  in accordance with the manufacturer’s instructions. Purified PCR products were sent for sequencing. H. longicornis (HlSC-1) cystatin and subolesin respectively, using an NCBI nucleotide blast search (www.ncbi.nih.gov/blast) (data not shown) (GenBank accession No. EU289292.1).Sequence similarities of 97–100% and 97–98% were observed with The PCR products of subolesin (218 bp) and cystatin (194 bp) were joined to a T7 promoter sequence using T7 promoter-linked (at both the 5′ and 3′ ends) oligonucleotide primers . A T7 pr® 33-gauge needle. After injection, the ticks were kept overnight in a 25 °C incubator with high humidity to observe their survival. The injected ticks of each group were mixed with an equal number of male ticks and then placed on the ears of four SPF rabbits. To observe gene silencing, 10 female ticks of each group were collected after five days of feeding. Their salivary glands were subsequently collected, and real-time PCR was performed for gene expression analysis. The rest of the ticks were fed up to spontaneous drop-down. Feeding time, blood engorgement, egg mass weight, and hatching rate were recorded.Eighty adult, unfed female ticks were divided into four groups, with each group containing an equal number (20) of ticks. The injection dose for the target gene was selected using methods described in previous research ,30. Two ® PrimeScript™ RT-PCR kit II  with a Thermal Cycler Dice™ system . The primer used for gene expression is cited in Ct value, and the percentage of knockdowns was calculated in the same manner as with previous RNAi experiments [Total RNA was collected from the salivary glands of the injected female ticks who had fed for five days, as described above. Real-time PCR was performed to determine the gene expression after knockdown, using a One-step SYBReriments ,39.t-test , executed with Graph Pad Prism 5 . Values were represented in the format of mean ± SE. Values for p that were 0.05 or less were considered to be statistically significant in comparison with the control group.Statistical analysis was performed with a Student’s H. longicornis (HlSC-1) was more abundant in the salivary glands than in the midgut; it plays a crucial role in blood engorgement [The salivary cystatin of orgement . Salivarorgement . CystatiH. longicornis subolesin gene encoding 396 bp  decreased transcript levels (63%) after five days of feeding, in comparison with the control group (p < 0.05) decreased transcript levels (53%), compared with the control group. It was interesting to observe that the cystatin plus subolesin ds RNA-injected group showed significantly (p < 0.05) decreased transcript levels—by 32% and 26% respectively—for subolesin and cystatin  increase in feeding duration (p < 0.05) increased feeding duration  reduced in comparison to the control group (average: 174.13 mg)  B, but nop < 0.05) reduced egg mass (average: 37.25 mg) in comparison with that of the control group (average: 79.87 mg) (p < 0.05), compared with the control group  C. The egol group D. No difp < 0.001) retardation of egg mass and egg conversion ratio (average: 50 mg and 40% respectively), compared with those of the control group, (79.87 mg and 50% respectively)  C,D. No dH. longicornis depends on host blood for survival and reproduction. Tick feeding capability largely depends on its major secretory organ, the salivary glands [Ornithodoros moubata, salivary cystatin OmC2 is an inhibitor of cysteine proteases that resembles human cystin C [A. americanum [A. americanum. Both subolesin and cystatin have significant roles in blood intake. Therefore, we silenced both of these salivary antigens in order to determine their synergistic role in blood intake, as well as their effects on egg production. In our research, it was clear that cystatin knockdown hampers blood engorgement and subsequent egg production. However, the combined effect of subolesin and cystatin silencing has not yet been studied. Our present study aimed to determine the effects of subolesin and/or cystatin knockdown on feeding and reproduction in the H. longicornis.The hard tick y glands . Therefoy glands . The syny glands ,34. Anoty glands . A previcystin C . Therefocystin C . The silericanum . The autR. microplus, I. scapularis, R. haemaphysaloides, Dermacentor marginatus, and D. variabilis [R. haemaphysaloides, indicating their possible future use as part of a cocktail of vaccine antigens [The silencing of the tick protective antigen subolesin by RNAi resulted in a significant reduction of body weight and egg mass, and increased mortality in riabilis ,30,31,32p < 0.05) reduction of engorgement weight in comparison with that of the control group [I. scapularis, A. americanum, and D. marginatus 4D8, ds RNA abrogated egg production [In our present study, the silencing of subolesin by RNAi yielded a significant  reductions of engorgement weight, egg mass, and egg conversion ratio in comparison with the control group were observed. Similarly, 50% knockdown of salivary cystatin caused a significant reduction of tick engorgement in A. americanum [We detected and purified salivary cystatin for the synthesis of ds RNA. After cystatin ds RNA-injected ticks were fed for five days, they showed a reduction of 53% in transcript level. Resultant significant (ericanum . A. americanum, a cystatin and synaptobrevin knockdown reduced successful tick feeding [B. microplus females reportedly led to a significant reduction in tick weight and egg/tick mass, and also to an increased tick mortality [Rhipicephalus sanguineus, silencing of Rs 86 and subA caused a reduction in attachment, engorgement, and oviposition [Amblyomma hebraeum ticks resulted in the silencing of only the subolesin in females which fed together, this was not the case for voraxin [Caenorhabditis elegans, the injection of zfp-1 ds RNA suppressed the lethality produced by mom-2 and hmp-2 ds RNAs [Currently, multi-antigenic vaccines are preferred over single antigenic vaccines ,46. In R feeding . The silortality . In Rhipposition . While t voraxin . However ds RNAs . In our H. longicornis. As multi-antigenic vaccines are more efficient than single-antigenic ones, our findings are expected to lead to subolesin and/or cystatin being considered as vaccine antigens.In the future, combined antigens will be used for controlling ticks and tick-borne diseases. RNAi can be successfully utilized to determine the efficacy of combined gene silencing before vaccination trials. However, it remains a challenge to determine the success of vaccine antigens working together. To our knowledge, the present study is the first to describe the effects of subolesin and/or cystatin knockdown in


Here we provide evidence of this based on the δ30Si composition of meltwater runoff from a Greenland Ice Sheet catchment. Glacier runoff has the lightest δ30Si measured in running waters (−0.25 ± 0.12‰), significantly lower than nonglacial rivers (1.25 ± 0.68‰), such that the overall decline in glacial runoff since the Last Glacial Maximum (LGM) may explain 0.06–0.17‰ of the observed ocean δ30Si rise (0.5–1.0‰). A marine sediment core proximal to Iceland provides further evidence for transient, low-δ30Si meltwater pulses during glacial termination. Diatom Si uptake during the LGM was likely similar to present day due to an expanded Si inventory, which raises the possibility of a feedback between ice sheet expansion, enhanced Si export to the ocean and reduced CO2 concentration in the atmosphere, because of the importance of diatoms in the biological carbon pump.Globally averaged riverine silicon (Si) concentrations and isotope composition (δ The role ice sheets play in the silica cycle over glacial−interglacial timescales remains unclear. Here, based on the measurement of silica isotopes in Greenland meltwater and a nearby marine sediment core, the authors suggest expanding ice sheets considerably increased isotopically light silica in the oceans. Marine diatoms account for 35–70% of marine primary production2, and are therefore key in maintaining ecosystem health, the ocean biological pump, and atmospheric carbon fixation3. The input of Si from terrestrial weathering via rivers is crucial as it sustains diatom productivity over the ocean’s Si residence time1. Thus, understanding the sensitivity of the Si cycle in the past, and its likely response to future climate warming, is important for marine ecosystem change, biogeochemical carbon cycling, and by association the efficiency of the ocean’s biological carbon pump.Silicon (Si) plays a crucial role in global biogeochemical cycles because it is an essential nutrient for a number of marine organisms, including some species of sponges, radiolarians, silicoflagellates, and diatoms30Si of natural waters, sediments, and biogenic silica reflect fractionation during continental and oceanic biogeochemical processing4. Lighter isotopes are incorporated into solids, for example during the precipitation of secondary weathering products and diatom frustule formation, thus inducing fractionation from parent material values and usually leading to accumulation of heavier isotopes in the dissolved Si phase5. The δ30Si of biogenic silica in marine sediment cores has been used as a proxy to explore past oceanic dissolved silica concentrations (DSi)6, infer diatom utilisation of Si8, and investigate changes in Si source inputs4. There has been a focus on the δ30Si change from the Last Glacial Maximum  to the present day, with Southern Ocean marine biogenic opal records showing a shift in δ30Si of ~+0.5–1.0‰, according to Southern Ocean core records8. This has been explained by changes in dissolved silica utilisation in surface waters7, variation in terrestrial silica inputs9 and by oscillations in intermediate and deep-water DSi as a result of changes in oceanic circulation8. Until recently, modelling studies have assumed that riverine δ30Si input was uniform over glacial−interglacial timescales in their first-order interpretation of downcore diatom records11. However, past research indicates that this is unlikely, and that at least part of the δ30Si shift from the LGM to the present day can be explained by a change in the δ30Si and/or magnitude of input fluxes, due to temporal changes in terrestrial weathering regimes13. There is additional uncertainty in interpretation of palaeo-records as downcore biogenic opal isotopic composition are likely to be a complex mixture of changes in silicic acid utilisation and concentrations, the δ30Si of the whole ocean silicon isotope inventory, and more localised changes in inputs. This is problematic given most core records come from the Southern Ocean at present. The role of the changing extent of ice sheets  since the LGM on the Si cycle has yet to be fully considered14, despite their known impact on the global hydrological cycle15 and weathering of continental rocks16.Variations in the δ17. Melting of this ice during deglaciation raised sea levels by ~130 m and exported large quantities of eroded sediment into the oceans19. The last deglaciation contained two major, rapid ice melt events: Meltwater Pulse 1a  where sea levels rose by 12–22 m in <350 years20 and Meltwater Pulse 1b  where sea levels may have risen by up to 10 m in ~500 years21. Glaciers and ice sheets are dynamic components of regional nutrient cycles23, exporting significant quantities of dissolved24 and labile amorphous silica (ASi) attached to fine-grained glacial suspended particulate matter (SPM)14, which are likely to impact primary productivity in surrounding oceans25. Large silica fluxes from glaciated regions likely lead to preferential growth of diatoms in downstream marine ecosystems compared to other nonsilicifying phytoplankton species24. However, silica fluxes and their associated δ30Si signature from the PIS have been overlooked in studies of ancient Si cycling28, despite evidence suggesting the role of terrigenous sediment delivery to the ocean is underappreciated in global elemental cycles29. Thus far it has been found that glacial rivers in Iceland have a distinctive low δ30Si13, but these data are based on point measurements rather than seasonal time series, and no data exist for large glacial systems more representative of PIS.Glaciers and ice sheets covered nearly 30% of the Earth’s land surface at their greatest extent during the LGM, including much of North America and northwestern Eurasia30Si composition for subglacial meltwaters exiting Leverett Glacier, a large (~600 km2)30 catchment of the Greenland Ice Sheet (GrIS). These data are used as a modern-day analogue for PIS runoff to investigate its potential influence on the Si cycle over glacial−interglacial and millennial timescales. Our findings suggest changing glacial silica fluxes could explain roughly 10–30% of the silicon isotope variation recorded in siliceous organisms since the LGM, instead of previously invoked changes in marine biological productivity or ocean circulation. Ice sheets are likely to have delivered large quantities of isotopically light silica to the oceans during periods of greater glacial activity, thereby augmenting the ocean’s Si inventory and sustaining diatom productivity.Here, we present a unique time series of DSi and ASi concentrations and associated δ3 s−1  μM for DSi and 229 (69.8–336.6) μM for ASi. This equates to estimated DSi and ASi catchment fluxes of 30 (13–83) Mmol year−1 and 331 (101–488) Mmol year−1, within the same range as those reported at Leverett Glacier for the 2009–2012 period (396–1575 Mmol year−1)22. Meltwater outburst events , extracted using a weak alkaline leach (see Methods and SI). Our δ30ASi values are lighter than those of local bedrock collected near the sampling site  and bulk suspended sediment δ30Si  is extremely light, at −0.25 ± 0.12‰ (n = 16), which is similar to δ30ASi, but significantly lower than the bedrock and SPM.We collected the first measurements of SPM δ33 with an ensemble of 50 simulations . The results show relatively large changes in the ocean Si inventory over the deglaciation , where MWP1a leads to an excursion in δ30Si of up to −0.08‰.We used a modified version of a previously published three-box ocean model30Si, extracted from a sediment core off southeast Iceland  was used to investigate possible changes in isotopic inputs in the high-latitudes during a glacial termination, and corroborate box model findings . It is lower than the only other measurements of δ30DSi in glacial meltwaters from glaciers in Iceland (0.02 ± 0.18‰)13, and much lower than global rivers (mean = 1.25 ± 0.68‰)4. It is also lower than the estimated mean groundwater δ30DSi (0.19 ± 0.86‰)4, and most similar to measurements of hydrothermal fluids (−0.30 ± 0.15‰)4. The anomalously low δ30DSi signal from glacial meltwaters requires either a light δ30DSi source or a heavy δ30DSi sink. The latter has only been documented with acidic hydrofluoric leaching of basalts41; therefore, secondary phase dissolution or rapid leaching of the mineral surface is more likely the source. The glacial meltwaters are significantly undersaturated with respect to ASi (mean SIASi = −2.1)14 and have high pH 42, which may have an even lighter δ30Si signature than ASi43, explaining the lowest δ30DSi values observed. Dissolution of secondary weathering products is thought to occur in long residence time groundwaters43, and could reflect drainage of more isolated subglacial water sources further into the glacial catchment as the melt season progresses44. It is possible that either glacial chemical weathering preferentially removes 28Si during initial dissolution of silicate surface layers5, heavier 30Si isotopes have either been stripped out during previous chemical weathering , or heavier isotopes are retained in the mineral weathering crust, to balance the long-term isotopic mass balance of the system.The mean δ pH Fig. , so ASi 30DSi also shows a significant temporal shift of >1.3‰ toward lower values from early season low discharge to peak discharge waters, while δ30ASi shows little temporal variation , during a high discharge meltwater outburst event (~230 m3 s−1) characterised by a large spike in SPM concentration, electrical conductivity and pH 4, changes in glacial land coverage would affect the ocean’s Si inventory and isotopic composition, for example over glacial cycles4. On shorter  temporal and spatial scales, the extremely low δ30Si of glacial meltwater could influence regional marine silicon cycling and isotopic budgets. The extremely light isotopic signature of GrIS runoff is likely to be broadly representative of PIS during deglaciation. Leverett Glacier is likely to be a crude analogue of ice sheet meltwaters because it is a large glacial catchment (~600 km2), has bedrock geology broadly representative of the shield bedrock that underlies much of the land on which the Eurasian and North American ice sheets sat  explains a portion of the glacial to present-day δ30Si increase found in diatomaceous remains, which is usually quoted as 0.5–1.0‰10. Much of this increase is reliant on ASi dissolution and bioavailability to marine organisms. ASi dissolution is catalyzed by the presence of the alkali metals50, which are found in high dissolved concentrations in seawater. Furthermore, a recent study demonstrates rapid dissolution of ASi from glacial SPM in natural seawater (up to 25% in less than 30 days), at high sediment loading concentrations (1 gL−1 of SPM)14, corroborating previous evidence of synthetic ASi dissolution in artificial seawater51. The isotopic signature from ASi will likely imprint upon ocean waters upon dissolution.The three-box model used as a thought experiment to simulate potential oceanic response to changes in glacier silica fluxes , when the model is run to equilibrium  on the isotopic composition recorded in the marine diatom record. The modelled change in external input  can explain only around ~0.1‰ of LGM–present change in marine diatom δ30Si records in this box (i.e. up to 20% of the observed change in Southern Ocean diatom core records), which is analogous to the Southern Ocean. There are several possible candidates to explain the discrepancy between modelled and observed δ30Si. First, an obvious candidate for the observed change is a difference in Si utilisation between the LGM and today7. Second, changes in external inputs not modelled in our simulations such as the input of Si through the dissolution of aeolian dust, or change in the nonglacial weathering regime4, may have a further impact.The δ2 drawdown and ecosystem function, via increased diatom productivity, possibly at the expense of other phytoplankton groups52, as has been observed in Greenlandic fjords24. This has important implications for marine biogeochemical cycles, as higher Si input favours the growth of diatoms relative to other phytoplankton groups53. This is likely to have an impact on the organic carbon export , surface alkalinity , and the “silica pump”, which controls the ratio of nutrients reaching the deep ocean55. The model results further indicate that MWP1a and MWP1b34 impart a signature on marine DSi and δ30Si  and elevated DSi concentrations (up to 1.5 μM) in all simulations , Eurasia (~2.5 m58) and Greenland (~0.5 m). Runoff from melting terrestrial ice therefore likely made up the dominant portion of MWP1a freshwater flux and there is little evidence to suggest iceberg calving contributed anywhere near as much to sea level rise during this period. The importance of iceberg Si inputs from large ice calving events (e.g. Heinrich events such as H1) are not included in our model but should be addressed in future research due to the potentially large associated Si fluxes14.The model predicted that whole-ocean DSi concentrations were higher during the deglaciation and likely higher during the LGM than present day Fig. . A large30Si measurement, we would expect proximal downstream PIS effects to be more pronounced than whole ocean model simulations indicate , variation in seawater δ30DSi at the time of spicule formation, or a combination of both increased DSi concentrations and lower seawater δ30DSi. Such significant and rapid changes would require a local source that is highly enriched in DSi and/or contribute low δ30DSi to ocean surface waters, which our data indicate could be of glacial origin59. Data from Icelandic glaciers, including a spot measurement from Skeiðarárjökull , corroborates the observation that an enriched and light δ30DSi source from local glacial meltwaters is likely13. This interpretation is supported by the observation that the excursions in spicule δ30Si from our record coincide, within the age model uncertainty, with estimates of flux pulses derived from sea-level changes and collapse of the local Icelandic ice sheet 15,000 to 14,700 years before present59 62. Core evidence suggests that localised polar influence from meltwaters, sea-ice formation and brine rejection would likely have transferred a surface δ30Si signal to depth via overflow waters62. These overflow waters originated locally and from the Nordic Seas62, which would have been heavily influenced by ice melt from northern hemisphere ice sheets, with a corresponding light δ30Si composition and elevated Si concentrations.Data from the sponge spicule record of a marine sediment core proximal to Iceland reveal high-frequency variability in spicule δ63. Here we show that these systems act as a significant source of isotopically light Si, either directly via dissolved silica or indirectly as dissolvable amorphous silica attached to SPM. This has the potential to impact the marine Si inventory over a range of different spatial and temporal scales, given the meltwater input from the wastage of large palaeo-ice sheets since the LGM. Our model indicates that the magnitude of meltwater-derived Si inputs is sufficient to drive significant changes in the ocean’s Si inventory on glacial/interglacial and deglacial timescales, thereby modulating the productivity of diatoms relative to other primary producers. Results provide evidence for significant low δ30Si release during rapid ice sheet wastage in the δ30Si composition of a high latitude North Atlantic sediment core record, which corroborates the hypothesis that terrestrial ice cover impacted the oceanic Si cycle, derived from modern ice sheet data and modelling results. These findings highlight the important role played by glacial meltwater in the marine Si cycle, aiding in our interpretation of palaeoceanographic proxies and our understanding of past and present carbon cycling. Our data demonstrate the potential for a feedback between PIS growth and decay, increased Si delivery to the ocean and CO2 drawdown via stimulating the productivity of diatoms.The part played by ice sheets and glaciers in marine nutrient and nutrient isotope cycling is only just starting to be appreciated65.). Stage (for conversion to discharge), electrical conductivity and turbidity (a proxy for suspended sediment concentration) were logged every 10 min at a stable bedrock section of the river ~2.2 km downstream of the glacier portal. Permanent (fixed in place) and mobile (re-located to keep pace with river stage) temporary pressure transducers monitored stage, which was converted into discharge using a stage-discharge rating curve of 26 Rhodamine-WT dye dilution injections . Discharge was calculated by dividing the amount of dye injected by the area under the return curve. The errors associated with discharge measurements are ±12.1 % following the methods of Bartholomew et al.32. Calibrating turbidity (in mV) against 23 manually collected samples (using a USDH48) allowed formation of a continuous suspended sediment record (in g L−1). Around 300–500 mL of meltwater was filtered through a pre-weighed 47 mm 0.45 µm cellulose nitrate filter (Whatman®), with the amount of meltwater filtered accurately measured using a measuring cylinder. On return to labs in the UK, filters were oven dried overnight at 40 °C and reweighed to four decimal places. Suspended sediment concentration was plotted against the turbidity at sampling time points to derive a linear relationship (R2 = 0.73). The linear regression between suspended sediment and turbidity was used to derive suspended sediment concentrations at each 10-min interval over the measurement period. Errors associated with suspended sediment measurements are estimated to be ±6%30.Leverett Glacier runoff was hydrologically gauged from the onset (28 May) to the end of the ablation season (15 September) 2015. Gauging of Leverett Glacier meltwater river has been extensively discussed by others  and filtered immediately through 0.45 μm cellulose nitrate filters (Whatman®). Samples were stored in clean HDPE 30 ml Nalgene bottles and kept refrigerated until analysis. Cellulose nitrate filters were retained and also stored refrigerated until ASi and bulk SPM analysis.All water samples were collected from the same location ~1 km from Leverett Glacier terminus . The methodological limit of detection was 0.3 μmol, precision was ±1.3% and accuracy was +2.1%, as determined from five replicates of a 250 μg L−1 (8.9 μmol) standard prepared by gravimetric dilution from a 1000 mg L−1 Si stock (CertiPur®).Dissolved silica (as silicic acid) was determined using LaChat QuickChem66, used to determine biogenic opal and, increasingly, inorganic amorphous silica in terrestrial soils and sediments68. The DeMaster66 method uses 0.1 M sodium carbonate (Na2CO3) solution, a weak base, which maximises the dissolution of amorphous Si with minimal impact on more refractory crystalline material. Approximately 30 mg of sample (accurately weighed) was placed in a clean 60 mL HDPE bottle  with 50 mL of 85 °C 0.1 M Na2CO3 solution. Bottles were placed in a hot water bath at 85 °C for the duration of the extraction. Aliquots of 1 mL were taken at 2, 3 and 5 h and stored refrigerated in a new, clean 2 mL microcentrifuge tube (polypropelene). Samples were measured using the same method as DSi (above) within 24 h immediately after dilution of 0.5 mL sample in 4.5 mL of 0.021 M HCl. At least two blanks were processed alongside samples. Precision was ±2.9% and accuracy was +0.4%, as determined from five replicates of a 250 μg L−1 (8.9 μmol) standard prepared by gravimetric dilution from a 1000 mg L−1 Si stock (CertiPur®). Amorphous silica was determined by using the intercept of the regression line. This method was also compared to the method of extracting total reactive silica for δ30ASi using 0.2 M sodium hydroxide (NaOH), to ensure consistency between %ASi results and silicon isotope composition . To this, 1 mL 0.2 M NaOH was added per mg of sediment. Samples were heated at 100 °C for 30 min to extract reactive silica , diluted 1 in 5, and then centrifuged for 5 min at 4000 rpm before being filtered through a 0.22 μm PES syringe filter . A threshold minimum value for %ASi was used for samples (0.01% ASi), as samples with very low %ASi may have resulted in more refractory material also being extracted, skewing the δn = 3) and bulk SPM (n = 4) δ30Si were determined after alkaline fusion, adapted from the method of Georg et al.69. Bedrock samples  were initially crushed using a sledgehammer on a metal plate (within multiple thick polyethylene bags), then ground for 1 min in a Fritsch Planetary Mono Mill Pulverisette 6 at 500 rpm, following the methods of Telling et al.70. Approximately 15 mg of bulk rock sample powder or suspended particulate material was subsequently accurately weighed into a silver crucible with ~200 mg of NaOH pellets . Crucibles were then placed in a muffle furnace, heated to 730 °C for 10 min to fuse, and allowed to cool for 10 min. Samples were added to 30 mL of deionised water (18.2 MΩ cm−1 Milli-Q Millipore), left overnight and then sonicated for 10 min to aid final dissolution. Samples were acidified and diluted with Milli-Q water using a ratio of 2.1 mL 8 N HNO3 per 500 mL water, before analysis as below.Bedrock (+ form) columns and eluted with MQ water3, before being spiked with a Mg solution (Inorganic Ventures). Freshwater samples and their bracketing standards were additionally spiked with 50 μL 0.01 M sulphuric acid (Romil-UpA) to reduce mass bias resulting from high anion loading71. Silicon isotope composition  was determined by mass spectrometry using a Thermo ScientificTM Neptune PlusTM High Resolution MC-ICP-MS in the Bristol Isotope Group laboratories at the University of Bristol  for δ30Si and ±0.05‰ (2σ) for δ29Si. The long-term reproducibility was determined by analysis of two international reference standards, characterised by a number of research groups. The mean for diatomite was +1.26‰ ± 0.11‰ (2σ) for 27 measurements73 and the average for LMG08 (sponge) was −3.33‰ ± 0.15‰ (2σ) for 53 measurements74. External reproducibility of freshwater δ30Si was assessed using a lake water standard (RMR4) from the NERC Isotope Geosciences Laboratory UK (NIGL), which had mean δ29Si and δ30Si values of +0.46‰ ± 0.02‰ (2σ) and +0.91‰ ± 0.03‰ (2σ) respectively (n = 3) in good agreement with previous measurements from NIGL75. A three-isotope plot of all the samples measured during the study can be plotted along a straight line with a gradient of 0.523 ± 0.025  were purified using precleaned BioRad exchange resin AG50W-X12  are not precisely resolved in time, their meltwater contribution to the global oceans is captured in the longer term. In addition to this deglaciation meltwater, we include a flux from modelled precipitation minus evaporation  over the palaeo ice sheets77, which was calculated from the best 30 members of an ensemble of simulations validated against both the preindustrial and LGM climate. At the LGM, this yields an ice sheet runoff estimate of ~7700 ± 770 km3 year−1, peaking at 21,600 ± 2160 km3 year−1 during the period encompassing Meltwater Pulse 1a (MWP1a), and falling to ~1400 ± 140 km3 year−1 at present78. These values give a reasonable first-order approximation of changes in glacial runoff over the last 21,000 years.Glacial meltwater flux from the palaeo ice sheets into the oceans is calculated using the ICE-6G_C (VM5a) reconstructed ice mass loss leading to sea level rise from the LGM to present day 79 and the percentage difference in precipitation−evaporation simulated over non-ice covered land area for the modern day compared to LGM using the same 30 climate model ensemble as above 77. Changes in runoff at each time point during the deglaciation are approximated by scaling to the percentage land ice cover34 from these two end members.Riverine runoff input fluxes are calculated using two end members: modern-day runoff , Conley82 (ASi) and Frings et al.4 (δ30DSi and δ30ASi composition) to calculate fluxes and δ30Si of riverine inputs at each model time step. Glacial meltwater DSi and ASi fluxes and δ30Si composition are taken from samples measured at Leverett Glacier in this paper (Supplementary Table 30Si of the input flux of +0.33 (+0.23 to +0.47)‰ from LGM conditions to present day, using these mass balance calculations  input fluxes  are taken from Tréguer et al.33 to simulate the deglacial oceanic cycle of Si and its isotopes in an open-system ocean, i.e. with Si inputs into and outputs from the ocean. The inputs to the ocean are computed as described above (note Heinrich event H1 is not included in our model due to the uncertainties in associated freshwater fluxes), while outputs are parameterised as a temporally constant fraction of export that is lost from the ocean by burial in sediment (see Model ensemble below). The model was run at a 1-year time step with a time-transient Si input (magnitude and weighted δ30Si of the input flux) dependent on the balance between ice sheet meltwater flux and riverine flux over the last 21,000 years  and two surface-ocean boxes, one representing the low latitudes  and one representing the high latitudes . These boxes are connected by a simplified representation of the ocean circulation as represented in Supplementary Fig. The three-box model of Sarmiento and Toggweilerkl and kh (see Supplementary Table 1). A small fraction fb of this export flux does not dissolve within the deep ocean but is lost to burial in sediment, representing the output term that, in equilibrium, balances the input of Si from external sources.The model’s Si cycle is driven by Si uptake in the two surface boxes, which is parameterised as a first-order function of Si concentration, with the rate constants 4, and include input of Si from aeolian deposition, groundwater discharge, hydrothermalism and seafloor weathering, each of which contributes Si to different boxes of the model , and are assumed to be constant over the deglaciation in order to isolate the effect of changes in glacial/riverine Si input on the oceanic Si system  relative to that for the Si tracer. No isotope fractionation during dissolution is modelled. The isotope composition of all input fluxes to the ocean  is simulated as a temporally constant value . The resulting dependency of the whole-ocean mean Si concentration on fb  on request.Supplementary InformationPeer Review File


Atmospheric warming is increasing surface melting across the Antarctic Peninsula, with unknown impacts upon glacier dynamics at the ice-bed interface. Using high-resolution satellite-derived ice velocity data, optical satellite imagery and regional climate modelling, we show that drainage of surface meltwater to the bed of outlet glaciers on the Antarctic Peninsula occurs and triggers rapid ice flow accelerations . This provides a mechanism for this sector of the Antarctic Ice Sheet to respond rapidly to atmospheric warming. We infer that delivery of water to the bed transiently increases basal water pressure, enhancing basal motion, but efficient evacuation subsequently reduces water pressure causing ice deceleration. Currently, melt events are sporadic, so efficient subglacial drainage cannot be maintained, resulting in multiple short-lived (<6 day) ice flow perturbations. Future increases in meltwater could induce a shift to a glacier dynamic regime characterised by seasonal-scale hydrologically-driven ice flow variations. Surface meltwater is known to influence the dynamics of some glaciers and the Greenland ice sheet. Here, the authors have identified the first examples of the drainage of surface meltwater to the bed of outlet glaciers on the Antarctic Peninsula that trigger large and rapid accelerations of ice flow. Surface meltwater is known to influence the dynamics of many glaciers7 and portions of the Greenland Ice Sheet9. At these locations, drainage of surface water to the ice-bed interface impacts basal water pressure, leading to variations in ice motion on sub-daily to decadal timescales13. This mechanism couples ice flow to atmospheric processes over a range of timescales down to the sub-diurnal level, making affected ice masses respond rapidly to changes in atmospheric circulation patterns induced by climate change9.The melting of snow and ice creates networks of meltwater streams and ponds on glacier and ice-sheet surfaces3. Antarctic-wide melting is projected to double by 2050, with the greatest increases in meltwater production expected across the Antarctic Peninsula, where atmospheric warming is already increasing surface melting15. Surface meltwater had catastrophic consequences for some ice shelves, where meltwater expedites ice-shelf disintegration by hydraulically driven fracture16. However, whether surface meltwater impacts the behaviour of grounded ice in Antarctica, implying a rapid and direct coupling of ice dynamics with atmospheric conditions, is currently unknown17.Surface meltwater has recently been reported to be widespread across the Antarctic Ice SheetIn this paper, we use high-resolution satellite-derived ice velocity data, optical satellite imagery and regional climate modelling to study five Antarctic Peninsula outlet glaciers and ascertain whether surface meltwater drains to the bed of these glaciers, influencing dynamics at the ice-bed interface. The data reveal that multiple short-lived (<6 day) rapid and large accelerations  termed speed-up events occur. That the relative magnitude of speed-up events increases away from the marine margin, and that tidal fluctuations, iceberg calving events and sea-ice break-up are asynchronous with speed-ups, leads us to rule-out marine processes as a cause. Instead, the spatial pattern of speed-ups, their concurrence with periods of modelled surface melting, and observations of potential routes for surface meltwater to reach the bed from optical satellite imagery are consistent with a surface meltwater trigger. For the first time, we show that surface meltwater affects the dynamics of grounded ice in Antarctica, proving a mechanism by which atmospheric and ice-dynamic processes are coupled over short-timescales.−1 magnitude. Finally, short-lived rapid accelerations in ice velocity to values >20% greater than the annual mean  in November 2017  speed-up occurring 9 km from the grounding line of Hektoria Glacier during the November 2017 event  but short-lived (<1 week) spikes in melt are modelled, separated by periods of little-to-no melting. The large spikes in melt are coincident with austral summer Föhn events. There is a striking qualitative correspondence between periods of modelled surface melting and speed-up events . Surface melt features were observed upglacier of the grounding line of each glacier, including small lakes fed by streams and water-filled crevasses Fig. –12. Alth22. Efficient evacuation of both surface-derived meltwater and water stored at the ice base would then lead to a subsequent reduction in water pressure and ice motion22. This mechanism is known to occur in alpine glaciers5, some polythermal glaciers23, and across some marginal regions of the Greenland Ice Sheet9, but this is the first time that surface meltwater-induced speed-up has been observed in Antarctica.Given the spatial pattern of speed-up events, their temporal coincidence with modelled melt, lack of coincident variations in glacier frontal position, and observations of surface meltwater drainage, we infer that the observed speed-up events were caused by surface meltwater drainage to the base of the glaciers. The lack of a detectable lag  between spikes in snowmelt and speed-up events suggests that the surface is well-connected to the bed, with meltwater drainage and the resulting dynamic response occurring within 6 days. We suggest that once at the ice base, this water causes a rapid acceleration of ice by promoting enhanced basal sliding through a spike in basal water pressure21, which are short-lived. As a result, the supraglacial hydrological network is not extensive, with few moulins and restricted growth of surface lakes; a configuration that favours refreezing rather than drainage of surface lakes  and eventually toward a Greenlandic or Alaskan style meltwater-driven system of glacier flow32  adapted for quantifying ice flow. Computationally efficient sub-pixel displacement estimates for each image patch were made by obtaining an initial estimate of the cross-correlation peak using a fast Fourier transform, and then up-sampling the discrete Fourier transform using matrix-multiplication of a small neighbourhood (1.5 × 1.5 pixels) around the original estimated location37. We oversampled the amplitude images in the azimuth direction by a factor of two38 and used image patch sizes of 96 × 256 single-look oversampled azimuth and range pixels, with an overlap of 64 and 24 pixels respectively between patches. Correlation signal-to-noise ratios were used to filter the velocity results, with a threshold set at 5.835. Spurious correlations which evaded this first sift were removed by a threshold strain filter39, and a kernel density filter based on the paired displacements in the range and azimuth directions for each image patch40. A Visible Structured Noise Filter (VSNR)41 was used to remove any anomalous stripes in the azimuth displacement data which sometimes result from fluctuating electron density along the sensor path through the ionosphere38. Noise in the azimuth displacement data was quantified using a Blind/Referenceless Image Spatial Quality Evaluator, and the VSNR filter was only employed if it helped to improve image quality . The filtered velocity fields were transformed from radar to map coordinates using the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model. The median velocity error was estimated to be 30.5 m a−1 by measuring the mean velocity over bedrock areas.Each image was split into many image patches, and cross-correlation of each image patch between repeat-pass image pairs was used to determine the offset of features  over timeRegions of interest (ROIs) 1 km by 1 km were drawn along the central flow lines of each glacier, allowing area-averaged velocity values to be extracted. The number and positioning of ROIs for each glacier was primarily dictated by velocity data coverage, with a minimum threshold of 80% required. ROIs were numbered sequentially inland from the grounding line, with each ROI indicating an approximate number of kilometres inland. We calculated velocities both for each ROI, and also a mean value for each glacier by averaging all the respective ROIs. A single chain of between six and ten ROIs were drawn for each glacier.42. This model is available at http://volkov.oce.orst.edu/tides/global.html. The Tide Model Driver package was run in MATLAB to produce modelled tidal amplitudes and frequencies for the study period from these constituents. These data were used as an approximation of the tidal signal for the whole study region. We calculated a continuous wavelet transform (using a standard Morse wavelet) of daily-averaged tidal amplitudes and the 6-day velocity data to investigate temporal correlations between the tides and the area-averaged ice velocity from each ROI for each glacier. The results of this analysis are plotted as a scalogram  and signal frequency (y-axis). The colours represent the strength of a signal of a particular frequency at a given point in time. The scalogram can be conceptualised as similar to a Fast Fourier Transform of a time-series, but one that retains information about the strength of signals of different frequencies at different times (or indeed distances) rather than just displaying a time-independent visualisation of the frequency of the strongest signals within a time-series.Tidal constituents for a location just off the east coast of the Antarctic Peninsula  were generated using the Oregon State University TOPEX/Poseidon Global Inverse Solution tide model TPXO43 in Google Earth Engine ©. This analysis used cloud free Landsat 8 and Sentinal 2 optical imagery, as well as Sentinel 1 radar return images. In total 750 ice front margins were manually identified and mapped . Where the ice surface was visible (in 600 instances), images were classified as to whether obvious signs of surface meltwater, in the form of surface lakes, meltwater-filled crevasses and streams, were present. Meltwater features were found to recur in the same positions on glacier surfaces, meaning the Supplementary Figs. To look for possible routes of water getting to the ice-bed interface, visible waveband Landsat 4,5,7, and 8, and Sentinel 2 imagery was analysed to identify patterns of surface melt across the studied glaciers. GEEDiT was used to cycle through available imagery record 184 onward21. The model run was initialised on 1st January 1979, with outputs from October 2016 to mid-April 2018 analysed here. The surface mass balance model has been validated against 132 observational records21. Mean values of snowmelt were extracted from the modelled snowmelt data in 1 km by 5 km areas along the centreline and extending inland from the grounding line of each glacier. These regions of analysis overlap with the ROIs used to generate velocity data. Data were extracted using MATLAB and ArcMap.We used a high-resolution (5.5 km) version of the Regional Atmospheric Climate Model (RACMO) version 2.3p2 of the Antarctic Peninsula regionSupplementary InformationPeer Review File


Evidence suggests that several elements  of the Earth’s climate system could tip into a qualitatively different state due to on-going and future anthropogenically induced climate change. Risks associated with tipping could form a component of critical climate risks, and their consideration should be indispensable in decision-making. However, there is lack of scientific knowledge about the risks associated with tipping elements, inhibiting their incorporation into comprehensive risk assessments of climate change . Using two major tipping elements (Arctic summer sea-ice loss and Greenland ice-sheet melting) as examples, this study attempted to address this lack of knowledge by conducting several calculations under various policy choices based on target temperature, including (i) the probability of passing a threshold temperature in this century and (ii) the potential impact of passing a threshold temperature on a millennial timescale beyond this century. The first theme of this study [Item (i) above] suggested that probability of exceeding the threshold within this century is 24.8% for the Greenland ice sheet and 2.7% for Arctic summer sea ice under a 1.5 °C temperature goal. However, it should be noted that the estimated probabilities of exceeding the threshold are largely dependent on the specification of the probability density function and key assumptions. With regard to the second theme of this study [Item (ii) above], estimation of the potential global coastal exposure using the estimated sea level exhibited a significant gap between scenarios not exceeding the threshold (1.5 °C target) and those exceeding the threshold. Tipping elements (TEs), as introduced by Lenton et al.  and descThe Integrated Climate Assessment—Risks, Uncertainties and Society (ICA-RUS) was a 5-year (2012–2016) research project that had the aim of developing and proposing strategies for global climate risk management, from which several papers, including this paper, have been submitted to the special feature of this journal. Although many of the research components of ICA-RUS focused on the risks and impacts that could appear within this century, and mitigation options that could be implemented in this century, the choice of strategies could lead to drastically different implications with regard to TEs , we assess (i) the probability of passing thresholds within this century both for Arctic summer sea-ice loss and Greenland ice sheet melting, and (ii) the potential impact of Greenland ice sheet melting beyond the threshold on a millennium timescale. In addition, this investigation provides estimations of the potential impact and probability of exceeding thresholds performed under consistent scenarios using the IAM simulations from ICA-RUS. The ultimate goal of this research was to support the incorporation of TEs and their threshold temperatures into strategies for the global-scale management of climate change risks, which could be realized through the incorporation of TEs and their thresholds into IAMs in a technical sense. Although we have not reached this ultimate goal, the analyses presented herein represent an indispensable preliminary step toward this goal.The structure of this paper is as follows. The next section focuses on the two investigated TEs. We explain the details of our framework to estimate the probability of exceeding the threshold temperatures of TEs. Then, we apply the proposed framework to two TEs and discuss the results. Following this, we focus on the special case that has the highest probability of occurrence within our framework, and perform a millennial-scale estimation of sea level rise and its consequences considering the threshold behavior of the Greenland ice sheet.Exceeding threshold temperatures could have catastrophic impacts. However, existing studies on the impacts of temperature increase seldom consider the threshold behavior of climate systems in their assessments. Difficulties in considering climate system thresholds in impact assessments arise from uncertainties associated with the thresholds, including the possible range of threshold temperatures. However, a probabilistic approach can be used to cope with these uncertainties. Several studies using IAMs have evaluated climate impacts while considering the probability of crossing threshold temperatures . The parameters of the PDFs are determined based on a literature review, enabling us to obtain the PDF for each TE separately. In this study, temperature increase represents the temperature anomaly with respect to preindustrial levels.Figure First, the threshold of Greenland ice-sheet melting is defined as the temperature increase at which the surface mass balance of the Greenland ice sheet becomes negative. It should be noted that it may take hundreds of years to reach a negative mass balance after the temperature increases to the threshold. In the AR5 IPCC , the thrTo estimate the probability of exceeding the thresholds by the end of this century, we used a set of global mean temperature increase projections conducted as IAM simulations in ICA-RUS. The IAM simulations were performed for several policy choices based on the target temperature at the end of this century. Details of the IAM simulations can be found in Su et al. , who comIn addition to T15, T20, and T25, Su et al.  computedFigure Using the 16 emission pathways  and assumptions of the PDFs of the threshold temperatures, the algorithm in Fig. x = 3.65 in Fig. It should be noted that, although linear response theory of climate sensitivity suggests that the multiplicative  of climate sensitivity should correspond to the same multiplicative  of temperature increase, such multiplicative features do not appear in Fig. Next, we made a joint PDF from the PDFs of the temperature increase at 2100 and the threshold temperature (Step 4). Finally, we integrated this PDF over the domain where the threshold temperature was lower than the temperature increase at 2100 to obtain probability of exceeding the threshold temperature (Step 5).From the above-described algorithm, we obtained the probabilities of exceeding the threshold temperatures by 2100 . As examples of how we integrated the joint PDFs to obtain the probabilities of exceeding the threshold, Fig. Table The results showed that if the policy target temperature were low, the likelihood of exceeding the threshold for the Greenland ice sheet would be higher than that of Arctic summer sea ice. This was due to our assumption that the lower bound of the threshold for the Greenland ice sheet was lower than that of Arctic summer sea ice. The assumption that the PDF of the Greenland ice sheet was right-skewed also explained the relatively high probability of exceeding the threshold even though the temperature target was low.However, under a high temperature increase , the probability of crossing the threshold for Arctic summer sea ice would rapidly increase, as indicated by a sudden increase in the probability from 37.3% (T2.5) to 82.4% (BaU). This rapid increase in probability arose because the upper bound of the threshold temperature of Arctic summer sea ice was lower than that of the Greenland ice sheet, even though the lower bound of the threshold temperature of Arctic summer sea ice was higher than that of the Greenland ice sheet. Therefore, the probability of exceeding the threshold of Arctic summer sea ice increases rapidly under high temperature increases. However, it should be noted that these trends in the probability of exceeding thresholds strongly depend on the assumptions of the form of the PDF and range of the confidence interval.Our probabilistic assessment quantitatively showed the variations in the probability of exceeding thresholds according to policy target temperature. By placing assumptions on climate sensitivity and tipping points, this computation suggested that identifying the lower bound of the threshold and narrowing the uncertainty range of the threshold, as well as identifying their PDFs could greatly improve the confidence of the estimated probability of exceeding the threshold temperature.It should be noted that the probabilities of exceeding thresholds were derived from several assumptions. For instance, we assumed a log-normal distribution for the Greenland ice sheet and a normal distribution for the Arctic summer sea ice, despite the fact that the probability distributions for those thresholds remain unknown. Thereby, a sensitivity analysis of the estimated probabilities was performed using a uniform distribution as the PDF of tipping points. The ranges of uniform distribution for the Greenland ice sheet and Arctic summer sea ice were set as 1.0–4.0) and (1.95–2.95), respectively. The sensitivity analysis  to determine (i) the probability of passing the thresholds within this century and (ii) the potential impact of the Greenland ice-sheet melting beyond the threshold.In the first part of this study, we estimated the probability of crossing the thresholds under several policy choices, which indicated how policy choices could alter the probability of crossing thresholds within this century. The estimated probabilities for BaU were significantly larger than those for T25 and T20, whereas the estimated probabilities for T15 were considerably lower. Such information could be used to support decision-making. However, the estimated probability in this study is not definitive, because the derived probability depends on several assumptions, as described in the methodology. Therefore, applying different methods of making assumptions or different analytical approaches would help in evaluating the confidence and robustness of the estimated probabilities. For example, a recent study by Screen and Williamson  used a dIn the second part of this study, we estimated the global mean sea level rise until the year 3000 using a semi-empirical model assigning three millennial temperature pathways. In addition, we estimated the potential coastal exposure using the estimated sea level.There was a large gap between the scenarios exceeding the threshold (T25 and T20) and those not exceeding the threshold (T15) in the global total. It should be noted that the results are greatly affected by the assumption that the threshold temperature of the Greenland ice sheet is 1.6 °C. Therefore, the parameters used to describe ice sheets and sea level distribution should be explored further to yield more plausible sea level and exposure estimations on a millennial timescale.Overall, although our study has presented a framework for probabilistic assessment and added estimates of probabilities of exceeding thresholds while also outlining the potential consequences of exceeding thresholds, enhancing the scientific basis for various TEs and their thresholds through additional research is necessary, because understanding on these parameters is still limited.To this end, several studies on TEs and their thresholds (e.g., Abe-Ouchi et al.