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151.
152.
153.
Rosenzweig M; Marks DF; Zhu H; Hempel D; Mansfield KG; Sehgal PK; Kalams S; Scadden DT; Johnson RP 《Blood》1996,87(10):4040-4048
154.
Effect of cisapride on functional dyspepsia in patients with and without histological gastritis: A double-blind placebo-controlled trial 总被引:4,自引:0,他引:4
KG YEOH JY KANG HH TAY KA GWEE CC TAN A WEE M TEH HF CHOO W CHINTANA-WILDE 《Journal of gastroenterology and hepatology》1997,12(1):13-18
In the present double-blind placebo-controlled study the effect of cisapride on functional dyspepsia was evaluated in patients with and without histological gastritis. Patients with functional dyspepsia and whose symptoms persisted after a 2 week run-in period with antacid treatment were randomized to receive cisapride (10 mg) or matching placebo three times daily for 4 weeks. Symptoms of epigastric pain, bloating, nausea, belching, early satiety and heartburn were graded on a four-point scale based on patients’ feedback and diary card recording. A global response was also formulated by the investigators. One hundred and four patients entered the study and 76 completed the trial, comprising 36 patients with histological gastritis and 40 patients without gastritis. Symptom scores in both gastritis and non-gastritis groups were significantly improved by both cisapride and placebo; however, the improvement was not statistically different between the two treatment groups. Cisapride produced a good or better global response in 58% of subjects with histological gastritis and in 53% of subjects without gastritis compared with 47% and 52%, respectively, of patients on placebo; this difference was not statistically significant. Gastric histology did not influence the effect of cisapride on the symptoms of functional dyspepsia. 相似文献
155.
Jamal M. Stein Helmut KG Machulla James Deschner Stefan Fickl Yvonne Jockel-Schneider Miriam Tamm Susanne Schulz Stefan Reichert 《Clinical oral investigations》2016,20(4):703-710
Objective
Human leukocyte antigens (HLA) have been associated with periodontitis. Previous studies revealed HLA-A9 and HLA-B15 as potential susceptibility factors, while HLA-A2 and HLA-B5 might have protective effects. The aim of the study was to verify these associations in a group of HLA-typed blood donors with previously unknown periodontal status.Materials and methods
In four German centers, 140 blood donors with known HLA class I status were enrolled and allocated to the following five groups: HLA-A9 (N = 24), HLA-B15 (N = 20), HLA-A2 (N = 30), HLA-B5 (N = 26), and controls (N = 40). Periodontal examination included the measurement of probing depths (PDs), clinical attachment level (CAL), bleeding on probing (BOP), and community periodontal index of treatment needs (CPITN).Results
Carriers with HLA-A9 and HLA-B15 had higher values of mean PD (P < 0.0001), CAL (P < 0.0001), and BOP (P < 0.002) as well as sites with PD and CAL with ≥4 and ≥6 mm (P < 0.0003), respectively, than controls. Multiple regression analyses revealed HLA-A9, HLA-B15, and smoking as risk indicators for moderate to severe (CPITN 3–4; odds ratio (OR): 66.7, 15.3, and 5.1) and severe (CPITN 4; OR: 6.6, 7.4, and 3.8) periodontitis. HLA-A2 and HLA-B5 did not show any relevant associations.Conclusion
The present data support a role of HLA-A9 and HLA-B15 as susceptibility factors for periodontitis, whereas HLA-A2 and HLA-B5 could not be confirmed as resistance factors.Clinical relevance
Both HLA antigens A9 and B15 are potential candidates for periodontal risk assessment.156.
Platelet-derived coagulation factor Va is the primary secreted substrate for a thrombin-stimulation-dependent platelet kinase. Human platelet factor Va, consisting of a molecular weight (M(r)) 105,000 heavy chain and an M(r) 74,000 light chain, incorporates phosphate in at least two sites on the light chain. Phosphorylated factor Va represents 50% of the secreted protein-associated phosphate. This modification occurs exclusively at serine residues and is inhibited by H-7 and staurosporine, which suggests a protein kinase C (PKC)-mediated event. Purified plasma factor V and Va are phosphorylated in the light chain region by rat brain PKC. The activity of platelet factor Va in prothrombinase on platelets is not altered when phosphorylation is inhibited by staurosporine. Plasma-derived factor Va in the presence of thrombin stimulated platelets is phosphorylated on both the heavy chain and the light chain. Plasma factor V and factor Va heavy chain phosphorylation occurs without light chain phosphorylation in the presence of added 32P gamma-ATP and non-stimulated or collagen- stimulated platelets or casein kinase II. This differential phosphorylation of factor Va heavy and light chain shows two independent platelet kinase activities that act on factor Va. The heavy chain factor V/Va kinase activity is similar to casein kinase II, which we have demonstrated previously to act on factor Va and accelerate activated protein C inactivation of the cofactor. Our data show platelet-dependent phosphorylation of platelet and plasma factor V and Va resulting in significant covalent modifications of the cofactor. These modifications may play a role in directing the extracellular distribution of factor V and factor Va. 相似文献
157.
158.
C. Brock Woodson Steven Y. Litvin 《Proceedings of the National Academy of Sciences of the United States of America》2015,112(6):1710-1715
Long-term changes in nutrient supply and primary production reportedly foreshadow substantial declines in global marine fishery production. These declines combined with current overfishing, habitat degradation, and pollution paint a grim picture for the future of marine fisheries and ecosystems. However, current models forecasting such declines do not account for the effects of ocean fronts as biogeochemical hotspots. Here we apply a fundamental technique from fluid dynamics to an ecosystem model to show how fronts increase total ecosystem biomass, explain fishery production, cause regime shifts, and contribute significantly to global biogeochemical budgets by channeling nutrients through alternate trophic pathways. We then illustrate how ocean fronts affect fishery abundance and yield, using long-term records of anchovy–sardine regimes and salmon abundances in the California Current. These results elucidate the fundamental importance of biophysical coupling as a driver of bottom–up vs. top–down regulation and high productivity in marine ecosystems.Globally, marine primary production is considered to set the limits of fishery production (1), drive ecosystem functioning (2), and contribute substantially to biogeochemical cycles (3). Recent evidence of increased ocean temperatures (4, 5) and declines in global nutrient supply and primary production (6), combined with overfishing and other increasing human demands on the ocean (7–9), therefore raises significant concerns about fishery sustainability, ecosystem health, and maintaining global biogeochemical cycles (10). However, the degree of patchiness, instead of total biomass, may be the primary regulator of marine production and food web structure (11–16). Fronts in the ocean are boundaries between distinct water masses with sharp gradients in temperature or salinity (density) that can increase patchiness through flow convergence and, for density fronts, increase vertical mixing and nutrient supply (11, 17). Due to flow convergence at fronts, the spatiotemporal overlap of prey and predators can be immense, leading to a cascade of impacts across multiple scales from local prey size structure to global biogeochemical fluxes (11–13). However, the effects of fronts as fishery productivity and biogeochemical cycling hotspots have not been included in models that assess fisheries production and ecosystem health (18) or addressed at scales (tens to hundreds of kilometers) relevant to climate change (19).Here we use an ecosystem model to explore why fronts appear to have a strong influence on marine fishery production and biogeochemical cycling. Existing ecosystem models currently account only for the mean concentration of predator and prey with relatively large grid cells (20). In a simple case of a single autotrophic prey (A) and a single heterotrophic predator (B) the governing equations are[1]These equations describe the change in biomass of predator and prey relative to nutrient supply (N), intrinsic growth (μ), grazing (g), and mortality (m) rates with a reactive term, gμzAB, coupling the two equations, and the mortality terms, maA and mbB, represent the contribution of the interaction to biogeochemical cycling.In the ocean, concentrations of prey and predator are typically very low, and consequently, the production term in an ecosystem model will be even lower because it depends on the concentration of both (Fig. 1A). However, near fronts and other regions of sharp ocean gradients, the covariance of prey and predators is driven by (i) fluid dynamic processes that concentrate or disperse organisms (convergence/divergence, confluence/diffluence, mixing) and (ii) species-specific behaviors (11). Predator–prey covariance at fronts can be orders of magnitude higher than mean oceanic values (Fig. 1B and Fig. 1B). The decomposition results in an additional term, ?A′B′?, that represents the influence of the spatial covariance of prey and predator on the trophic dynamics of the ecosystem and imposes a similar closure problem to the dynamic equations.Open in a separate windowFig. 1.Effects of spatial covariance between predators and prey. (A) Evenly dispersed condition currently assumed in models. (B) Highly aggregated condition representative of most ocean environments. (C) Magnitude of the effect of spatial covariance on species production.
Open in a separate window*Humans are not included explicitly in the present model, but the estimate of ζAB illustrates the magnitude of the fishing effect on fish populations.To resolve the ecosystem closure problem, we developed a parameterization for the new covariance term in an ecosystem model (Fig. 1C) that reasonably represents existing field observational data across trophic levels, using a range of front properties (convergence rate, density difference) and species-specific swimming speeds. We define a front as a sharp gradient in ocean temperature or salinity. Whereas density fronts can drive upwelling and be a direct source of nutrients, fronts with no density signature can also lead to aggregation and may be supplied nutrients indirectly through advection of remote sources. Here, we do not directly address the source of nutrients for frontal productivity, but only the aggregative effect of fronts, but a similar technique could be used to address increased nutrient flux at fronts. We used a frontal scaling factor for prey (ζA = A′/?A?) and predator (ζB = B′/?B?) and their covariance (ζAB = 1 + ?A′B′?/?A??B?) calculated as the ratio of the covariance term (?A′B′?) to the product of the mean biomass of prey and predator (?A??B?). The frontal scaling factor between two species describes the relative influence of fronts on production (grams per square meter per year) due to enhanced trophic interactions (Fig. 1C). As the strength and stability of a front increase, the production of a species increases due to increased prey availability. Similarly, as the ability of consumers to exploit the front increases, the production of that species increases. For secondary consumers (zooplankton), fronts produce up to a 5-fold increase in production. For larger, commercially important species, such as anchovies, sardines, and salmon, fronts can account for a 20- to 40-fold increase in production and even higher for top predators such as sharks and marine mammals (Fig. 1C), similar to observed values (21), (ii) many large predators migrate to highly active frontal regions such as the California Current (22), and (iii) aggregation plays such an important role in trophic regulation of pelagic ecosystems (2, 13), we incorporated this covariance parameterization into a hybridized size-spectral/higher trophic-level ecosystem model (20, 23). The model contains 40 spectrally distributed size classes each of phytoplankton and microzooplankton and 16 classes of higher trophic levels. We used published values for prey preferences, ingestion rates, and swimming speeds that have been shown to be realistic and stable in the Ecopath model (20). We ran the model for 100 y and discarded the first 25 y as model spin-up for analysis, using a 10-km × 10-km × 20-m deep surface layer ecosystem. Examples of low front (weak convergence, low persistence) and high front (strong convergence, high persistence) runs are given in Figs. S1 and S2. We then computed the mean and 95% confidence intervals of the annual production for several species (Fig. 2 A–C). At low levels of frontal activity, the model performs comparably to other ecosystem models for the California Current [mean production of salmon 0.31 tons (t)⋅km−2 compared with 0.286 t⋅km−2; black dashed line in Fig. 2C] (20). When the covariance term is near zero or negative, the system is highly sensitive to nutrient supply and primary production as has been shown in previous studies (1, 2). However, at moderate and high levels of frontal activity, higher trophic-level production is up to 25-fold higher than in the low front case and relatively insensitive to nutrient supply levels. Ultimately fronts, by changing the susceptibility of prey to consumers, lead to changes in plankton community structure that translate into increased trophic complexity, higher diversity, and higher overall biomass (Fig. 2 and Open in a separate windowFig. 2.Front parameterized ecosystem model results. (A–C) Productivity vs. frontal strength (ΔU/Δx) and nutrient supply (μM N⋅d−1) for (A) phytoplankton community structure, (B) ardine/anchovy regimes, and (C) salmon production. Dashed black line in C represents production in an ecosystem model of California Current (20). (D) Relationship of phytoplankton size structure, zooplankton abundance, and anchovy–sardine regimes to front parameter.
Open in a separate windowPREV, previous year anchovy–sardine ratio; FSI, frontal strength index; FPI, frontal probability index; PDO, Pacific Decadal Oscillation; OUT, Sacramento River outflow.The change in zooplankton size spectra due to front dynamics offers an alternate mechanism for regime shifts from anchovy- to sardine-dominated systems beyond those related to physical, biochemical, and climate drivers (24–27). Weak fronts lead to higher levels of smaller phytoplankton and zooplankton (e.g., dinoflagellates and microzooplankton, Fig. 2 A and D), because top–down forcing of zooplankton on phytoplankton is weaker relative to bottom–up regulation and therefore to a sardine-dominated system (Fig. 2 B and D). Strong fronts lead to a size structure shift from smaller phytoplankton and microzooplankton to larger zooplankton and phytoplankton (e.g., diatoms), because zooplankton can exploit phytoplantkon patches, and eventually to an anchovy-dominated system (Fig. 2 B and D). Although other mechanisms of anchovy–sardine dynamics have been proposed related to coastal vs. wind-stress curl upwelling (24) and oxygen content of waters (27), results here suggest that prey preferences and variation in the feedback loop between bottom–up and top–down forcing due to fronts lead to similar patterns. It is likely that a combination of these mechanisms works in concert to drive observed fluctuations between anchovies and sardines.To further illustrate the importance of fronts on ocean production, we show that anchovy–sardine regimes and salmon production are closely correlated with front strength and density, using 30+ y of satellite-derived front probability and strength estimates for the central California Current (Fig. 3). Front probability is defined as the probability of a front at a pixel and computed using a probabilistic method from satellite-derived sea surface temperature (28). The front probability index (FPI) is then the first empirical orthogonal function (EOF) of the front probability and represents the frequency of frontal occurrence on the continental shelf (Fig. 3A). The frontal strength index (FSI) is estimated as the distance from the mean to the mode in the sea surface temperature (SST) gradient and represents a skewed lognormal distribution toward high values. We used these two indexes, along with major climate indexes [Pacific Decadal Oscillation (PDO), El Nino Southern Oscillation (ENSO), North Pacific Gyre Oscillation (NPGO), freshwater outflow, and previous year abundance], to develop an integrated index (29) for each that was incorporated into a general linear model for both anchovy–sardine ratios and salmon abundance in the form of the Sacramento index (SI) (30). The best model was selected using the Akaike information criterion corrected for low sample size (AICc). The anchovy–sardine ratio best-fit model (lowest AICc) incorporated an interaction between FSI and previous year abundance (Fig. 3B and Fig. 3D and Open in a separate windowFig. 3.Effects of fronts on ecosystem dynamics. (A) Spatial distribution of the first EOF of front probability, the front probability index (FPI). (B) Anchovy–sardine ratio [AS, normalized (sardine biomass − anchovy biomass)/(combined biomass); − indicates anchovy dominated, + indicates sardine dominated] with best-fit model and predictors. (C) Climate and environmental indexes used in Generalized Linear Models for anchovy–sardine regimes and salmon abundance. (D) Salmon abundance (Sacramento index, SI) with best-fit model and predictors. Dashed red lines in B and C show 95% confidence intervals. Symbols in equations refer to the Pacific Decadal Oscillation (PDO), El Nino Southern Oscillation (ENSO), North Pacific Gyre Oscillation (NPGO), Sacramento River outflow (OUT), front probability index (FPI), front strength index (FSI), and previous year anchovy–sardine ratio (PREV).Overall, fronts cover ∼4–10% of the area of the California Current during the spring–summer productive upwelling season as estimated from the remote sensing analysis for 1983–2011, yet have disproportionately large contributions to fisheries production (1, 6, 31) (Fig. 3C). These results also suggest that conservation of large marine predators and fishery management that incorporates persistent frontal features may allow for a comparatively rapid return to historical abundances of fishes and other top predators (18).
Open in a separate windowPercentage columns refer to the amount (1%, 5%, 10%) of the ocean area classified as a front. Values in parentheses are 95% confidence intervals or the percentage of total production that occurs at fronts.Recent evidence of enhanced vertical mixing along fronts has led to the suggestion that these oceanographic features play an important role in global biogeochemical cycles and consequently must be considered in global climate change models (19, 32, 33). Results from our front-parameterized ecosystem model suggest that in addition to vertical mixing, biogeochemical cycling and carbon export at fronts are also mediated and enhanced by biological processes (Fig. 4). Specifically, the enhanced transfer of nutrients, carbon, and energy to higher trophic levels at fronts increases biogeochemical fluxes to the deep ocean. The resulting carbon and nutrient fluxes at frontal zones are an order of magnitude higher than in surrounding regions (Fig. 4) through processes not currently accounted for in global-scale climate and ecosystem models (19). In the California Current, fronts that cover 5% of the ocean contribute more than 40% of the total biogeochemical fluxes (Fig. 4) (32).Open in a separate windowFig. 4.Rates of carbon export vs. frontal strength (ΔU/Δx) and nutrient supply (μM N⋅d−1).The effects of climate change on fisheries production and biogeochemical cycling will likely be determined by the counteractive effects of stratification (front development and persistence) and winds (front destruction) (33). Increasing sea surface temperatures and stratification related to climate change would likely increase frontal strength and persistence (33) and consequently increase fishery production, as suggested by the model. Conversely, increasing winds may limit front development and persistence and therefore decrease fishery production despite the effects of increased upwelling and nutrient flux (34). Evaluating how these climate change-related scenarios will interact to affect fisheries and ecosystem functioning is critical to our ability to predict and manage future change.Because fronts are critical, dynamic features of the marine environment, influencing a range of processes from recruitment and fishery production to biogeochemical cycling (1, 18, 19), incorporation of frontal parameterizations into climate and ecosystem models is critical. Current Intergovernmental Panel on Climate Change class models do capture large-scale fronts and important features of primary production, but not the effects of smaller-scale fronts on higher trophic-level interactions and biogeochemical cycling. Reynolds decomposition provides an elegant, computationally inexpensive, easily parameterized mechanism to include fronts in both climate and ecosystem models that will likely improve both climate change and fishery forecasts (Fig. 1D). This formulation is also easily exported to other models that may better represent phytoplankton dynamics using self-selection criteria (35). Explicit data on the spatial covariance of prey and predators due to fronts will be needed to improve the parameterizations used in this study, and novel technological developments in video and acoustic imaging and dynamical systems analysis provide an excellent opportunity to acquire these data across a wide range of oceanographic conditions and trophic levels (14, 36, 37). 相似文献
Table 1.
ζAB estimated from observations in available literature for multiple trophic interactionPrey, A | Predator, B | ?A??B? | ?A′B′? | ζAB | Ref. |
Phytoplankton | Copepods | 1,680 | 21,000 | 13.5 | (14) |
Copepods | Micronekton | 96 | 1,340 | 14.9 | (14) |
Micronekton | Dolphins | 20 | 480 | 26.0 | (14) |
Micronekton | Tunas | 0.094 | 3.24 | 35.5 | (39) |
Copepods | Whales | 0.066 | 3.35 | 56.8 | (40) |
Salmon | Humans | 0.088 | 7.62 | 87.5 | (41)* |
Table 2.
Stepwise linear model results for anchovy–sardine regimes and salmon abundancePredictor index | Anchovy–sardine estimate | Predictor index | Salmon estimate |
PREV | 0.915 | FPI | 0.530 |
FSI | −0.436 | PDO×FPI | −0.187 |
PREV×FSI | −0.542 | PDO×OUT | −0.187 |
Table 3.
Fishery production and biogeochemical cycling at frontsSpecies/dimension | Mean | At front | 1% | 5% | 10% |
Salmon, 103 kg⋅km−⋅y−1 | 0.23 | 6.16 (0.15) | 0.29 (21) | 0.53 (59) | 0.77 (71) |
Pelagic sharks, 103 kg⋅km−2⋅y−1 | 0.05 | 7.78 (0.38) | 0.08 (95) | 0.39 (99) | 0.78 (99) |
Baleen whales, 103 kg⋅km−2⋅y−1 | 0.08 | 9.34 (0.25) | 0.09 (97) | 0.47 (99) | 0.94 (99) |
Shannon diversity index, H′ | 0.85 | 2.79 | 0.86 | 1.08 | 1.22 |
Simpson reciprocal index of diversity, 1/D | 3.36 | 9.98 | 3.67 | 4.92 | 6.24 |
Mean trophic level | 1.67 | 3.33 | 1.68 | 2.77 | 3.18 |
N, 103 kg⋅km−2⋅y−1 | 0.11 | 1.65 (0.08) | 0.13 (14) | 0.19 (44) | 0.26 (63) |
C, 103 kg⋅km−2⋅y−1 | 11.66 | 148.10 (8.77) | 13.02 (11) | 18.47 (40) | 25.29 (59) |
P, 103 kg⋅km−2⋅y−1 | 1.88 | 28.29 (1.33) | 2.14 (13) | 3.20 (41) | 4.52 (62) |
159.
160.
Studies on the structure of bovine factor V by scanning transmission electron microscopy 总被引:2,自引:0,他引:2
We studied purified bovine factor V (mol wt 330,000) by scanning transmission electron microscopy (STEM) of freeze-dried unstained or negatively contrasted preparations. Freeze-dried molecules revealed discrete shapes ranging from roughly spheroidal (100 to 120 nm) to oblong (140 to 200 nm in length X 50 to 100 nm in width). Oblong shapes could often be resolved into two or three distinct domains, ranging from 60 to 100 nm in diameter. A "satellite" nodular structure (30 to 50 nm in diameter) connected to the main molecule by a thin stalk (approximately 10 nm wide) up to 80 nm in length was occasionally seen. Glutaraldehyde-treated preparations yielded the same shapes as were seen in unfixed preparations but revealed better definition of submolecular features and "satellite" nodules. STEM mass analysis confirmed that each of the different shapes represented a monomolecular form of factor V. Negatively stained images revealed objects having the same general shapes as freeze-dried molecules, although greater detail was evident. Some images suggested that molecules consist of five or more discrete parts. Taken together, these observations indicate that factor V molecules are multidomainal, flexible structures that tend to have an irregular oblong shape with an axial ratio between 3:2 and 2:1. 相似文献