892 resultados para Generalized Logistic Model
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潜在植被的分布预测与制图对植被恢复规划具有重要的指导价值.利用广义相加模型(generalized additive model,GAM),结合GIS空间分析技术和环境梯度分层采样技术,为延河流域24个地带性物种建立了分布模型,并在考虑群落内部物种种间关系及其分布概率的基础上,对物种分布进行运算,模拟预测了延河流域37种植物群落的分布状况和延河流域的潜在植被分布.结果表明:研究区植被分布预测值与实际调查值间的差异不显著,预测的植被空间分布较好地反映了延河流域潜在的植被分布状况,表明该模型具有较好的预测能力,对于区域植被恢复的目标设定和恢复规划具有重要意义.
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The power-time curves of growth of three strains of petroleum bacteria at different NaCl concentrations at 40.0 and 50.0 degreesC have been determined by using a 2277 Thermometric Thermal Activity Analyser. An equation of a power-time curve, ln[alphaP(K)/P(t) - 1] = ln[(alphaK - N-0)/N-0] - alphakt, was established based on the generalized logistic equation, where P(t) is the thermal power at time t, K the carrying capacity, P-K = P0K, P-0 the thermal power of one cell, N-0 the bacterial population at time zero, alpha = (k - D)/k. The method of four observed points with the same time interval was used to calculate the value of P-K. The growth rate constant k and the death rate constant D were calculated. The NaCl concentration of optimum growth rate of petroleum bacteria at 40.0 and 50.0 degreesC, respectively, have been obtained according to the curves k - D versus NaCl concentration, which are 0.26, 0.54 and 0.57 mol l(-1) for B-1, B-2 and B-3, respectively, at 50.0 degreesC, 0.26, 0.55 and 0.56 mol l(-1) for B-1, B-2 and B-3, respectively, at 40.0 degreesC. The results indicated that the effect of temperature on NaCl concentration of optimum growth rate was small. (C) 2002 Elsevier Science B.V. All rights reserved.
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Neuronal receptive fields (RFs) provide the foundation for understanding systems-level sensory processing. In early visual areas, investigators have mapped RFs in detail using stochastic stimuli and sophisticated analytical approaches. Much less is known about RFs in prefrontal cortex. Visual stimuli used for mapping RFs in prefrontal cortex tend to cover a small range of spatial and temporal parameters, making it difficult to understand their role in visual processing. To address these shortcomings, we implemented a generalized linear model to measure the RFs of neurons in the macaque frontal eye field (FEF) in response to sparse, full-field stimuli. Our high-resolution, probabilistic approach tracked the evolution of RFs during passive fixation, and we validated our results against conventional measures. We found that FEF neurons exhibited a surprising level of sensitivity to stimuli presented as briefly as 10 ms or to multiple dots presented simultaneously, suggesting that FEF visual responses are more precise than previously appreciated. FEF RF spatial structures were largely maintained over time and between stimulus conditions. Our results demonstrate that the application of probabilistic RF mapping to FEF and similar association areas is an important tool for clarifying the neuronal mechanisms of cognition.
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Understanding the mechanisms that structure communities and influence biodiversity are fundamental goals of ecology. To test the hypothesis that the abundance and diversity of upper-trophic level predators (seabirds) is related to the underlying abundance and diversity of their prey (zooplankton) and ecosystem-wide energy availability (primary production), we initiated a monitoring program in 2002 that jointly and repeatedly surveys seabird and zooplankton populations across a 7,500 km British Columbia-Bering Sea-Japan transect. Seabird distributions were recorded by a single observer (MH) using a strip-width technique, mesozooplankton samples were collected with a Continuous Plankton Recorder, and primary production levels were derived using the appropriate satellite parameters and the Vertically Generalized Production Model (Behrenfeld and Falkowski 1997). Each trophic level showed clear spatio-temporal patterns over the course of the study. The strongest relationship between seabird abundance and diversity and the lower trophic levels was observed in March/April ('spring') and significant relationships were also found through June/July ('summer'). No discernable relationships were observed during the September/October ('fall') months. Overall, mesozooplankton abundance and biomass explained the dominant portion of seabird abundance and diversity indices (richness, Simpson's Index, and evenness), while primary production was only related to seabird richness. These findings underscore the notion that perturbations of ocean productivity and lower trophic level ecosystem constituents influenced by climate change, such as shifts in timing (phenology) and synchronicity (match-mismatch), could impart far-reaching consequences throughout the marine food web.
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Mid-ocean ridges are common features of the world’s oceans but there is a lack of understanding as to how their presence affects overlying pelagic biota. The Mid-Atlantic Ridge (MAR) is a dominant feature of the Atlantic Ocean. Here, we examined data on euphausiid distribution and abundance arising from several international research programmes and from the continuous plankton recorder. We used a generalized additive model (GAM) framework to explore spatial patterns of variability in euphausiid distribution on, and at either side of, the MAR from 60°N to 55°S in conjunction with variability in a suite of biological, physical and environmental parameters. Euphausiid species abundance peaked in mid-latitudes and was significantly higher on the ridge than in adjacent waters, but the ridge did not influence numerical abundance significantly. Sea surface temperature (SST) was the most important single factor influencing both euphausiid numerical abundance and species abundance. Increases in sea surface height variance, a proxy for mixing, increased the numerical abundance of euphausiids. GAM predictions of variability in species abundance as a function of SST and depth of the mixed layer were consistent with present theories, which suggest that pelagic niche availability is related to the thermal structure of the near surface water: more deeply-mixed water contained higher euphausiid biodiversity. In addition to exposing present distributional patterns, the GAM framework enables responses to potential future and past environmental variability including temperature change to be explored.
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We study the spatial and seasonal variability of phytoplankton biomass (as phytoplankton color) in relation to the environmental conditions in the North Sea using data from the Continuous Plankton Recorder survey. By using only environmental fields and location as predictor variables we developed a nonparametric model (generalized additive model) to empirically explore how key environmental factors modulate the spatio-temporal patterns of the seasonal cycle of algal biomass as well as how these relate to the ,1988 North Sea regime shift. Solar radiation, as manifest through changes of sea surface temperature (SST), was a key factor not only in the seasonal cycle but also as a driver of the shift. The pronounced increase in SST and in wind speed after the 1980s resulted in an extension of the season favorable for phytoplankton growth. Nutrients appeared to be unimportant as explanatory variables for the observed spatio-temporal pattern, implying that they were not generally limiting factors. Under the new climatic regime the carrying capacity of the whole system has been increased and the southern North Sea, where the environmental changes have been more pronounced, reached a new maximum.
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Results are reported on the a-b plane dielectric function (epsilon) of thin-film c-axis NdBa2Cu3O7-delta with close to optimal oxygen doping (T-c similar to 90 K) in the mid-infrared (wavelength 3.392 mum) over the temperature range 85 K to 300 K. An attenuated total reflectance technique based on the excitation of surface plasmon polaritons is used. The results show that \epsilon (r)\ decreases quasi-linearly with increasing temperature, while Ei is invariant with temperature to within experimental uncertainties. Representative values are epsilon = [epsilon (r) + i epsilon (i)] = (-12.9 +/- 0.6) + i(23.0 +/- 1.5) at T similar to 295 K and epsilon = (-15.7 +/- 0.7) + i(23.5 +/- 1.1) at T similar to 90 K. The raw data an interpreted in terms of the generalized Drude model which gives effective scattering rates (1/tau*) that increase with temperature from about 3800 cm(-1) at 90 K to about 4300 cm(-1) at 295 K. There are indications of a superlinear T-dependence in the scattering, 1/tau*: a fit to a function of the form 1/tau* = A + BTalpha gives alpha = 2.8 +/- 0.7. The effective plasma frequency, omega (p)*, with an average value of approximately 21 000 cm(-1) was independent of temperature.
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PURPOSE: To investigate whether the 2 subtypes of advanced age-related macular degeneration (AMD), choroidal neovascularization (CNV), and geographic atrophy (GA) segregate separately in families and to identify which genetic variants are associated with these 2 subtypes. DESIGN: Sibling correlation study and genome-wide association study (GWAS). PARTICIPANTS: For the sibling correlation study, 209 sibling pairs with advanced AMD were included. For the GWAS, 2594 participants with advanced AMD subtypes and 4134 controls were included. Replication cohorts included 5383 advanced AMD participants and 15 240 controls. METHODS: Participants had the AMD grade assigned based on fundus photography, examination, or both. To determine heritability of advanced AMD subtypes, a sibling correlation study was performed. For the GWAS, genome-wide genotyping was conducted and 6 036 699 single nucleotide polymorphisms (SNPs) were imputed. Then, the SNPs were analyzed with a generalized linear model controlling for genotyping platform and genetic ancestry. The most significant associations were evaluated in independent cohorts. MAIN OUTCOME MEASURES: Concordance of advanced AMD subtypes in sibling pairs and associations between SNPs with GA and CNV advanced AMD subtypes. RESULTS: The difference between the observed and expected proportion of siblings concordant for the same subtype of advanced AMD was different to a statistically significant degree (P = 4.2×10(-5)), meaning that in siblings of probands with CNV or GA, the same advanced subtype is more likely to develop. In the analysis comparing participants with CNV to those with GA, a statistically significant association was observed at the ARMS2/HTRA1 locus (rs10490924; odds ratio [OR], 1.47; P = 4.3×10(-9)), which was confirmed in the replication samples (OR, 1.38; P = 7.4×10(-14) for combined discovery and replication analysis). CONCLUSIONS: Whether CNV versus GA develops in a patient with AMD is determined in part by genetic variation. In this large GWAS meta-analysis and replication analysis, the ARMS2/HTRA1 locus confers increased risk for both advanced AMD subtypes, but imparts greater risk for CNV than for GA. This locus explains a small proportion of the excess sibling correlation for advanced AMD subtype. Other loci were detected with suggestive associations that differ for advanced AMD subtypes and deserve follow-up in additional studies. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.
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OBJECTIVE To assess the association between circulating angiogenic and antiangiogenic factors in the second trimester and risk of preeclampsia in women with type 1 diabetes.
RESEARCH DESIGN AND METHODS Maternal plasma concentrations of placental growth factor (PlGF), soluble fms-like tyrosine kinase 1 (sFlt-1), and soluble endoglin (sEng) were available at 26 weeks of gestation in 540 women with type 1 diabetes enrolled in the Diabetes and Preeclampsia Intervention Trial.
RESULTS Preeclampsia developed in 17% of pregnancies (n = 94). At 26 weeks of gestation, women in whom preeclampsia developed later had significantly lower PlGF (median [interquartile range]: 231 pg/mL [120–423] vs. 365 pg/mL [237–582]; P < 0.001), higher sFlt-1 (1,522 pg/mL [1,108–3,393] vs. 1,193 pg/mL [844–1,630] P < 0.001), and higher sEng (6.2 ng/mL [4.9–7.9] vs. 5.1 ng/mL[(4.3–6.2]; P < 0.001) compared with women who did not have preeclampsia. In addition, the ratio of PlGF to sEng was significantly lower (40 [17–71] vs. 71 [44–114]; P < 0.001) and the ratio of sFlt-1 to PlGF was significantly higher (6.3 [3.4–15.7] vs. 3.1 [1.8–5.8]; P < 0.001) in women who later developed preeclampsia. The addition of the ratio of PlGF to sEng or the ratio of sFlt-1 to PlGF to a logistic model containing established risk factors (area under the curve [AUC], 0.813) significantly improved the predictive value (AUC, 0.850 and 0.846, respectively; P < 0.01) and significantly improved reclassification according to the integrated discrimination improvement index (IDI) (IDI scores 0.086 and 0.065, respectively; P < 0.001).
CONCLUSIONS These data suggest that angiogenic and antiangiogenic factors measured during the second trimester are predictive of preeclampsia in women with type 1 diabetes. The addition of the ratio of PlGF to sEng or the ratio of sFlt-1 to PlGF to established clinical risk factors significantly improves the prediction of preeclampsia in women with type 1 diabetes.
Preeclampsia is characterized by the development of hypertension and new-onset proteinuria during the second half of pregnancy (1,2), leading to increased maternal morbidity and mortality (3). Women with type 1 diabetes are at increased risk for development of preeclampsia during pregnancy, with rates being two-times to four-times higher than that of the background maternity population (4,5). Small advances have come from preventive measures, such as low-dose aspirin in women at high risk (6); however, delivery remains the only effective intervention, and preeclampsia is responsible for up to 15% of preterm births and a consequent increase in infant mortality and morbidity (7).
Although the etiology of preeclampsia remains unclear, abnormal placental vascular remodeling and placental ischemia, together with maternal endothelial dysfunction, hemodynamic changes, and renal pathology, contribute to its pathogenesis (8). In addition, over the past decade accumulating evidence has suggested that an imbalance between angiogenic factors, such as placental growth factor (PlGF), and antiangiogenic factors, such as soluble fms-like tyrosine kinase 1 (sFlt-1) and soluble endoglin (sEng), plays a key role in the pathogenesis of preeclampsia (8,9). In women at low risk (10–13) and women at high risk (14,15), concentrations of angiogenic and antiangiogenic factors are significantly different between women who later develop preeclampsia (lower PlGF, higher sFlt-1, and higher sEng levels) compared with women who do not.
Few studies have specifically focused on circulating angiogenic factors and risk of preeclampsia in women with diabetes, and the results have been conflicting. In a small study, higher sFlt-1 and lower PlGF were reported at the time of delivery in women with diabetes who developed preeclampsia (16). In a longitudinal prospective cohort of pregnant women with diabetes, Yu et al. (17) reported increased sFlt-1 and reduced PlGF in the early third trimester as potential predictors of preeclampsia in women with type 1 diabetes, but they did not show any difference in sEng levels in women with preeclampsia compared with women without preeclampsia. By contrast, Powers et al. (18) reported only increased sEng in the second trimester in women with pregestational diabetes who developed preeclampsia.
The aim of this study, which was significantly larger than the previous studies highlighted, was to assess the association between circulating angiogenic (PlGF) and antiangiogenic (sFlt-1 and sEng) factors and the risk of preeclampsia in women with type 1 diabetes. A further aim was to evaluate the added predictive ability and clinical usefulness of angiogenic factors and established risk factors for preeclampsia risk prediction in women with type 1 diabetes.
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Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.
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Structures and catalytic activities of Au thin films supported at anatase TiO(2)(101)) and a Au substrate are studied by using density functional theory calculations. The results show that O(2) can hardly adsorb at flat and stepped Au thin films, even supported by fully reduced TiO(2)(101) that can highly disperse Au atoms and offer strong electronic promotion. Interestingly, in both oxide-supported and pure Au. systems, wire-structured Au can adsorb both CO and O(2) rather strongly, and kinetic analysis suggests its high catalytic activity for low-temperature CO oxidation. The d-band center of Au at the catalytic site is determined to account for the unusual activity of the wire-structured film. A generalized structural model based on the wire-structured film is proposed for active Au, and possible support effects are discussed: Selected oxide surfaces can disperse Au atoms and stabilize the formation of a filmlike structure; they may also serve as a template for the preferential arrangement of Au atoms in a wire structure under low Au coverage.
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Many cancer patients die in institutional settings despite their preference to die at home. A longitudinal, prospective cohort study was conducted to comprehensively assess the determinants of home death for patients receiving home-based palliative care. Data collected from biweekly telephone interviews with caregivers (n=302) and program databases were entered into a multivariate logistic model. Patients with high nursing costs (odds ratio [OR]: 4.3; confidence interval [CI]: 1.8-10.2) and patients with high personal support worker costs (OR: 2.3; CI: 1.1-4.5) were more likely to die at home than those with low costs. Patients who lived alone were less likely to die at home than those who cohabitated (OR: 0.4; CI: 0.2-0.8), and those with a high propensity for a home-death preference were more likely to die at home than those with a low propensity (OR: 5.8; CI: 1.1-31.3). An understanding of the predictors of place of death may contribute to the development of effective interventions that support home death.
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The cognitive reflection test (CRT) is a short measure of a person's ability to resist intuitive response tendencies and to produce a normatively correct response, which is based on effortful reasoning. Although the CRT is a very popular measure, its psychometric properties have not been extensively investigated. A major limitation of the CRT is the difficulty of the items, which can lead to floor effects in populations other than highly educated adults. The present study aimed at investigating the psychometric properties of the CRT applying item response theory analyses (a two-parameter logistic model) and at developing a new version of the scale (the CRT-long), which is appropriate for participants with both lower and higher levels of cognitive reflection. The results demonstrated the good psychometric properties of the original, as well as the new scale. The validity of the new scale was also assessed by measuring correlations with various indicators of intelligence, numeracy, reasoning and decision-making skills, and thinking dispositions. Moreover, we present evidence for the suitability of the new scale to be used with developmental samples. Finally, by comparing the performance of adolescents and young adults on the CRT and CRT-long, we report the first investigation into the development of cognitive reflection.
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BACKGROUND: Offspring of women with diabetes mellitus (DM) during pregnancy have a risk of developing metabolic disease in adulthood greater than that conferred by genetics alone. The mechanisms responsible are unknown, but likely involve fetal exposure to the in utero milieu, including glucose and circulating adipokines. The purpose of this study was to assess the impact of maternal DM on fetal adipokines and anthropometry in infants of Hispanic and Native American women.
METHODS: We conducted a prospective study of offspring of mothers with normoglycemia (Con-O; n = 79) or type 2 or gestational DM (DM-O; n = 45) pregnancies. Infant anthropometrics were measured at birth and 1-month of age. Cord leptin, high-molecular-weight adiponectin (HMWA), pigment epithelium-derived factor (PEDF) and C-peptide were measured by ELISA. Differences between groups were assessed using the Generalized Linear Model framework. Correlations were calculated as standardized regression coefficients and adjusted for significant covariates.
RESULTS: DM-O were heavier at birth than Con-O (3.7 ± 0.6 vs. 3.4 ± 0.4 kg, p = 0.024), but sum of skinfolds (SSF) were not different. At 1-month, there was no difference in weight, SSF or % body fat or postnatal growth between groups. Leptin was higher in DM-O (20.1 ± 14.9 vs. 9.5 ± 9.9 ng/ml in Con-O, p < 0.0001). Leptin was positively associated with birth weight (p = 0.0007) and SSF (p = 0.002) in Con-O and with maternal hemoglobin A1c in both groups (Con-O, p = 0.023; DM-O, p = 0.006). PEDF was positively associated with birth weight in all infants (p = 0.004). Leptin was positively associated with PEDF in both groups, with a stronger correlation in DM-O (p = 0.009). At 1-month, HMWA was positively associated with body weight (p = 0.004), SSF (p = 0.025) and % body fat (p = 0.004) across the cohort.
CONCLUSIONS: Maternal DM results in fetal hyperleptinemia independent of adiposity. HMWA appears to influence postnatal growth. Thus, in utero exposure to DM imparts hormonal differences on infants even without aberrant growth.
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In this work physical and behavioral models for a bulk Reflective Semiconductor Optical Amplifier (RSOA) modulator in Radio over Fiber (RoF) links are proposed. The transmission performance of the RSOA modulator is predicted under broadband signal drive. At first, the simplified physical model for the RSOA modulator in RoF links is proposed, which is based on the rate equation and traveling-wave equations with several assumptions. The model is implemented with the Symbolically Defined Devices (SDD) in Advanced Design System (ADS) and validated with experimental results. Detailed analysis regarding optical gain, harmonic and intermodulation distortions, and transmission performance is performed. The distribution of the carrier and Amplified Spontaneous Emission (ASE) is also demonstrated. Behavioral modeling of the RSOA modulator is to enable us to investigate the nonlinear distortion of the RSOA modulator from another perspective in system level. The Amplitude-to-Amplitude Conversion (AM-AM) and Amplitude-to-Phase Conversion (AM-PM) distortions of the RSOA modulator are demonstrated based on an Artificial Neural Network (ANN) and a generalized polynomial model. Another behavioral model based on Xparameters was obtained from the physical model. Compensation of the nonlinearity of the RSOA modulator is carried out based on a memory polynomial model. The nonlinear distortion of the RSOA modulator is reduced successfully. The improvement of the 3rd order intermodulation distortion is up to 17 dB. The Error Vector Magnitude (EVM) is improved from 6.1% to 2.0%. In the last part of this work, the performance of Fibre Optic Networks for Distributed and Extendible Heterogeneous Radio Architectures and Service Provisioning (FUTON) systems, which is the four-channel virtual Multiple Input Multiple Output (MIMO), is predicted by using the developed physical model. Based on Subcarrier Multiplexing (SCM) techniques, four-channel signals with 100 MHz bandwidth per channel are generated and used to drive the RSOA modulator. The transmission performance of the RSOA modulator under the broadband multi channels is depicted with the figure of merit, EVM under di erent adrature Amplitude Modulation (QAM) level of 64 and 254 for various number of Orthogonal Frequency Division Multiplexing (OFDM) subcarriers of 64, 512, 1024 and 2048.