Shape Of Response Curve Research Articles

Investigation on vehicle occupant dummy applicability for under-foot impact loading conditions

PurposeUnder-foot impact loadings can cause serious lower limb injuries in many activities, such as automobile collisions and underbody explosions to military vehicles. The present study aims to compare the biomechanical responses of the mainstream vehicle occupant dummies with the human body lower limb model and analyze their robustness and applicability for assessing lower limb injury risk in under-foot impact loading environments. MethodsThe Hybrid III model, the test device for human occupant restraint (THOR) model, and a hybrid human body model with the human active lower limb model were adopted for under-foot impact analysis regarding different impact velocities and initial lower limb postures. ResultsThe results show that the 2 dummy models have larger peak tibial axial force and higher sensitivity to the impact velocities and initial postures than the human lower limb model. In particular, the Hybrid III dummy model presented extremely larger peak tibial axial forces than the human lower limb model. In the case of minimal difference in tibial axial force, Hybrid III's tibial axial force (7.5 KN) is still 312.5% that of human active lower limb's (2.4 KN). Even with closer peak tibial axial force values, the biomechanical response curve shapes of the THOR model show significant differences from the human lower limb model. ConclusionBased on the present results, the Hybrid III dummy cannot be used to evaluate the lower limb injury risk in under-foot loading environments. In contrast, potential improvement in ankle biofidelity and related soft tissues of the THOR dummy can be implemented in the future for better applicability.

Influence of the Shape of Differential Response Curve on Design Calculations of Chemical Reactor

Influence of the Shape of Differential Response Curve on Design Calculations of Chemical Reactor

Tissue response curve-shape analysis of dynamic glucose-enhanced and dynamic contrast-enhanced magnetic resonance imaging in patients with brain tumor.

Dynamic glucose-enhanced (DGE) MRI is used to study the signal intensity time course (tissue response curve) after D-glucose injection. D-glucose has potential as a biodegradable alternative or complement to gadolinium-based contrast agents, with DGE being comparable with dynamic contrast-enhanced (DCE) MRI. However, the tissue uptake kinetics as well as the detection methods of DGE differ from DCE MRI, and it is relevant to compare these techniques in terms of spatiotemporal enhancement patterns. This study aims to develop a DGE analysis method based on tissue response curve shapes, and to investigate whether DGE MRI provides similar or complementary information to DCE MRI. Eleven patients with suspected gliomas were studied. Tissue response curves were measured for DGE and DCE MRI at 7 T and the area under the curve (AUC) was assessed. Seven types of response curve shapes were postulated and subsequently identified by deep learning to create color-coded "curve maps" showing the spatial distribution of different curve types. DGE AUC values were significantly higher in lesions than in normal tissue (p < 0.007). Furthermore, the distribution of curve types differed between lesions and normal tissue for both DGE and DCE. The DGE and DCE response curves in a 6-min postinjection time interval were classified as the same curve type in 20% of the lesion voxels, which increased to 29% when a 12-min DGE time interval was considered. While both DGE and DCE tissue response curve-shape analysis enabled differentiation of lesions from normal brain tissue in humans, their enhancements were neither temporally identical nor confined entirely to the same regions. Curve maps can provide accessible and intuitive information about the shape of DGE response curves, which is expected to be useful in the continued work towards the interpretation of DGE uptake curves in terms of D-glucose delivery, transport, and metabolism.

A new model formulation for host depletion in parasitoids

A new model of host depletion in the functional response of parasitoids is presented. Most existing models describe the dynamics of prey consumption as a sequential-memoryless/Markovian process of searching for, handling, and consuming prey, but the feeding interaction between a parasitoid and its host is different. One of the most important characteristics is that the host is not consumed, and remains in the arena. On the other hand, functional response models of solitary parasitoids assume that each host can accommodate an infinite number of oviposition events without being killed or becoming unsuitable as a host without the parasitoid noticing it. As a consequence, host depletion is never considered in parasitoid functional response models. To solve that issue, we extended the traditional random predator and random parasitoid functional response models of Rogers into a compartmental model formulated with ordinary differential equations, to group hosts with a different number of attacks to simulate how the parasitoid spreads the attacks between already parasitized and non-parasitized hosts. This model was further modified to simulate non-sequential memoryless search, handling and oviposition. We studied the behaviour of these models to understand how a variation of their parameters led to different shapes of functional response curves and statistical distributions of the number of parasitoid eggs laid per host. The results showed that the expected curves differ from those predicted by the simpler Holling's disk equation (type II functional response), random parasitoid models (no depletion), and the random predator model (proportional depletion). Finally, the non-sequential-memoryless models predict straight curves resembling a type I functional response at low host densities.

Assessing the reliability of species distribution models in the face of climate and ecosystem regime shifts: Small pelagic fishes in the California Current System

Species distribution models (SDMs) are a commonly used tool, which when combined with earth system models (ESMs), can project changes in organismal occurrence, abundance, and phenology under climate change. An often untested assumption of SDMs is that relationships between organisms and the environment are stationary. To evaluate this assumption, we examined whether patterns of distribution among larvae of four small pelagic fishes (Pacific sardine Sardinops sagax, northern anchovy Engraulis mordax, jack mackerel Trachurus symmetricus, chub mackerel Scomber japonicus) in the California Current remained steady across time periods defined by climate regimes, changes in secondary productivity, and breakpoints in time series of spawning stock biomass (SSB). Generalized additive models (GAMs) were constructed separately for each period using temperature, salinity, dissolved oxygen (DO), and mesozooplankton volume as predictors of larval occurrence. We assessed non-stationarity based on changes in six metrics: 1) variables included in SDMs; 2) whether a variable exhibited a linear or non-linear form; 3) rank order of deviance explained by variables; 4) response curve shape; 5) degree of responsiveness of fishes to a variable; 6) range of environmental variables associated with maximum larval occurrence. Across all species and time periods, non-stationarity was ubiquitous, affecting at least one of the six indicators. Rank order of environmental variables, response curve shape, and oceanic conditions associated with peak larval occurrence were the indicators most subject to change. Non-stationarity was most common among regimes defined by changes in fish SSB. The relationships between larvae and DO were somewhat more likely to change across periods, whereas the relationships between fishes and temperature were more stable. Respectively, S. sagax, T. symmetricus, S. japonicus, and E. mordax exhibited non-stationarity across 89%, 67%, 50%, and 50% of indicators. For all species except E. mordax, inter-model variability had a larger impact on projected habitat suitability for larval fishes than differences between two climate change scenarios (SSP1-2.6 and SSP5-8.5), implying that subtle differences in model formulation could have amplified future effects. These results suggest that the widespread non-stationarity in how fishes utilize their environment could hamper our ability to reliably project how species will respond to climatic change.

A general theory for temperature dependence in biology

At present, there is no simple, first principles-based, and general model for quantitatively describing the full range of observed biological temperature responses. Here we derive a general theory for temperature dependence in biology based on Eyring-Evans-Polanyi's theory for chemical reaction rates. Assuming only that the conformational entropy of molecules changes with temperature, we derive a theory for the temperature dependence of enzyme reaction rates which takes the form of an exponential function modified by a power law and that describes the characteristic asymmetric curved temperature response. Based on a few additional principles, our model can be used to predict the temperature response above the enzyme level, thus spanning quantum to classical scales. Our theory provides an analytical description for the shape of temperature response curves and demonstrates its generality by showing the convergence of all temperature dependence responses onto universal relationships-a universal data collapse-under appropriate normalization and by identifying a general optimal temperature, around 25 ∘C, characterizing all temperature response curves. The model provides a good fit to empirical data for a wide variety of biological rates, times, and steady-state quantities, from molecular to ecological scales and across multiple taxonomic groups (from viruses to mammals). This theory provides a simple framework to understand and predict the impact of temperature on biological quantities based on the first principles of thermodynamics, bridging quantum to classical scales.

Comparison of Power of Trend Tests for Toxicological Application Based on Dose-Response Profiles Enumerated Using a Nonparametric Algorithm.

Tests for dose-response trend may be used to support conclusions on the toxicity of a substance in a range of doses evaluated in a comparative experiment. Criteria are proposed for evaluating and comparing alternative tests, and are applied in a comparison of certain tests applicable with continuous responses, namely, a multiplicity-adjusted pairwise comparison procedure, a trend test based on a single contrast, and four multiple-contrast trend tests. Methods considered treat dose as ordinal information. A particular focus was comparison of the simple test of a single contrast, with coefficients proportional to dose rank, with the multiple-contrast methods available with modern computing. To explore the role of the dose-response curve shape, regular dose-response shapes (or profiles), defined by the mean response for each dose level, using a simple grid-based algorithm were generated. Power for individual profiles may be used to illustrate the effect of curve shape on the power of alternative tests. To facilitate selection of tests, we report average power over profiles, averaging at different levels of granularity, first separately for each of several curve shape categories, and then over all profiles, weighting shape categories equally. Power results for the six tests evaluated at least provide clear distinctions between some procedures that are more-preferred, versus less-preferred. Our analyses expand the comparisons of tests available and contribute a methodology for choice of statistical methods for toxicological data analysis. Environ Toxicol Chem 2022;41:1754-1764. Published 2022. This article is a U.S. Government work and is in the public domain in the USA.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.

Investigating the applicability of a standardised growth curve approach on Middle Pleistocene sediments from northern Switzerland

The introduction of the Single Aliquot Regenerative Dose (SAR) protocol established luminescence dating as an indispensable tool in Quaternary research. A major impediment of this technique is the time required for measurements, since the protocol is repeated for various aliquots of each sample to establish a sound statistical basis. To reduce the demand on machine time, Standardised Growth Curve (SGC) approaches have been developed and successfully applied for samples from some regions. However, differences in luminescence properties require careful testing of this techniques when applied to samples with other geological background. In this study, the application of the SGC approach of Li et al. (2016) is successfully verified for multi-grain aliquots of coarse-grained quartz and feldspar samples from three sites in northern Switzerland. In-depth quality control measures ensure the reproducibility of equivalent dose (D e ) values obtained by the common SAR protocol and sample-specific SGCs. For both minerals little sensitivity was found to the re-normalisation dose and the sample-specific SGCs performed well. In contrast to other studies, no different types of dose response curve shape were observed for quartz. A minimum number of full SAR measurements of eight and six aliquots per sample has been found appropriate for quartz and feldspar, respectively. For the fading corrected feldspar signals, site-specific SGC worked well and D e values of up to 800 Gy were consistently replicated. In summary, sample-specific SGCs for samples from northern Switzerland perform well and their application reduces measurement times by up to 70%. The construction of a regional SGC may well be beneficial, however, caution regarding the choice of given doses and curve fitting is recommended and a thorough verification of SGC results is needed before the technique is widely applied. • Application of the standardised growth curve approach (SGC). • Sample- and site-specific SGCs successful for feldspar and quartz. • Application of a regional SGC for fading corrected feldspar signals with caution.

Shape of the OGTT glucose response curve: relationship with β-cell function and differences by sex, race, and BMI in adults with early type 2 diabetes treated with metformin

IntroductionThe shape of the glucose curve during an oral glucose tolerance test (OGTT) reflects β-cell function in populations without diabetes but has not been as well studied in those with...

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution.

Many species distribution models (SDMs) are built with precise but geographically restricted presence-absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species' niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

Empirical analyses of the factors influencing fire severity in southeastern Australia

AbstractFire severity is a key component of fire regimes, and understanding the factors affecting it is critical given the increasing incidence of wildfires globally. We quantified the factors affecting the severity of the 2019–2020 fires in Victoria, southeastern Australia. We constructed statistical models of relationships between fire severity (as reflected by two measures: Crown Burn and the composite measure of Crown Burn/Crown Scorch) and the main and interacting effects of five key covariates: fire progression zone (reflecting fire weather), time since previous major disturbance in the forest, forest type, slope, and aspect. The best supported models for the probability of a Crown Burn and the probability of a Crown Burn/Crown Scorch contained evidence of a three‐way interaction between fire weather, forest type, and time since previous major disturbance as well as two‐way interactions between (1) fire weather and slope, and (2) fire weather and aspect. There was an increase in the probability of Crown Burn and Crown Burn/Crown Scorch under more extreme fire weather in all forest types, with the effect especially elevated in dry forest. Our analyses also revealed a range of response curve shapes for the relationships between time since previous major disturbance and fire severity relationships and these varied by fire weather classes and forest type. Under severe fire weather conditions, we found that relationships between time since previous major disturbance and fire severity relationships often exhibited non‐linear, negative polynomial shape with a peak around 10–40 yr, especially for Crown Burn, although there also were instances of this distinctive curve shape in our Crown Burn/Crown Scorch fire severity analysis. Our analyses also contained strong evidence that fire severity was higher on steeper slopes and on more exposed northerly aspects under extreme fire weather. Our analyses suggest that forests managed for timber production near settlements may be at increased risk of high‐severity fire. This is because logging resets stand age to zero, after which there is a subsequent period of increased probability of high‐severity fire, particularly under extreme fire weather conditions. Therefore, policies to maintain cover of older forest near settlements should be considered.

Fault detection in rotor system by discrete wavelet neural network algorithm

This study identifies a method for detection of irregularities such as open cracks or grooves on a rotating stepped shaft with multiple discs, based on the wavelet transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotors with and without grooves. Discrete and continuous 1D wavelet transforms are applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete wavelet transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by wavelets transforms is analysed for single as well as multiple grooves with increase in groove depth. Increase in groove depth can be quantified by increase in wavelet coefficient, and it can be an indicator. White Gaussian noise with low signal-to-noise ratio is added to response curves and analysed for crack location identification. Intelligent techniques such as artificial neural networks are used to quantify the location and depth of crack. Discrete wavelet transforms coefficients are provided as input to the neural network. Feed forward artificial neural networks are trained with Levenberg–Marquardt back propagation algorithm. Trained networks are able to quantify the crack location and depth accurately.

Statistical Shape Mapping Pre-Processing of Metal Oxide Gas Sensor Response for Machine Learning Detection Improvement

IntroductionModulation of metal oxide (MOX) gas sensors working temperature offers possibilities of highly selective detection of various gases, when coupled with signal processing and machine learning [1]. However, stability of response is still a major issue [2], hampering widespread practical application of MOX sensors in the fields of industrial safety, public security, ecological control, non-invasive medical diagnostics etc. Most of the modern approaches of signal pre-processing, aimed at elimination of baseline drift effects, signal amplitude fluctuations due to sensor aging or background variations of analyzed air, act through selection of the most significant features of the response, leading to the loss of some useful part of the signal [3]. This may lead to the unstable system performance in variable ambience conditions, which are characteristic to urban air or air at industrial facilities.The present work introduces shape-space mapping pre-processing of MOX gas sensor response for further application of machine learning in order to improve the identification and quantification of common flammable and pollutant gases.ExperimentalThree different gas detection tasks were studied. First - identification of propane vs hydrogen in the flow of real urban air; second – identification of methane vs propane in same conditions, third – determination of CO concentration in clean humid air. Laboratory made SnO2-based MOX gas sensors [4] were used for the solution of the first and second tasks. Sensors were linearly thermally modulated between 100 and 500 oC in the constant flow of outdoor air, to which gases were admixed. Statistical analysis of response curve shape (statistical shape mapping - SSM - in 2D space of sensing element electrical resistance and temperature) was performed as a pre-processing procedure prior to machine learning algorithm use. The performance of the obtained ML model was evaluated with the use of completely independent set of data, obtained in separate days of experiment [5]. The ability of SSM to improve the gas identification accuracy of the ML model was compared to raw sensor response, normalized response as well as response, pre-processed via principal component analysis (PCA), discrete wavelet transform (DWT) and polynomial curve fitting (PCF) approach. Same comparison has been made during the solution of the third task with the use of publicly available database of response of thermally modulated commercially available MOX gas sensors [6,7].Results and discussionThe results of the ML model testing in the case of methane vs propane discrimination task (Table 1) shows that improvement of sensitivity (AuPd/SnO2 sensor [4]) improves the accuracy of detection. Normalization of sensor response doers not necessary lead to the reduced error of model answer and other pre-processing techniques lead to the decreased accuracy of detection compared to the raw sensor response, used for the machine learning. The only exception is SSM pre-processing approach, improving the accuracy of identification in the case of all three used sensors. The day-to day error variation does not correlate with the atmospheric pressure, air temperature, humidity or regional air quality index (AQI), indicating local uncontrolled pollution as a source of erroneous gas identification [8]. Similar results are obtained in the case of hydrogen vs propane identification task [9]. For CO quantification task by commercial sensors with rectangular impulse type working temperature modulation much better performance of ML model is observed in the case of SSM pre-processed response of TGS 3870-A04 gas sensor type (Figaro), than in case of SB-500-12 (FIS) sensors compared to other pre-processing technique [8].ConclusionsThe application of statistical shape space pre-processing to the signal of temperature modulated metal oxide gas sensors improves the selectivity of gases identification with the use of ANN based machine learning algorithm in comparison to other methods – PCA, DWT, PCF, data normalization. It helps to eliminate effects of sensor response drift, amplitude fluctuations, while the major source of erroneous gas identification may rise from the lack of shape features in the original sensor response and the uncontrolled local background air pollution. Improvement in general sensitivity of sensor, is favorable for shape space signal pre-processing in order to enhance the selectivity of chemically similar gases detection at low concentrations in realistic air conditions. The proposed approach can be considered as versatile as its performance is successfully tested on different type of sensors and gases in different range of concentrations in air with different types of working temperature modulation.AcknowledgementsThe reported study was funded by RFBR according to the research project № 18-33-20220.

In defense of intrinsic efficacy: The didactic value of a modern implementation of Furchgott's views on agonism

Mastery of fundamental pharmacology concepts is a defining characteristic of basic science graduate pharmacology programs. The acquisition of these skills, however, is challenging for most trainees and a solid understanding of the meaning and use of appropriate pharmacology terms such as efficacy, potency, receptor reserve, allostery and biased agonism is often not fully realized. In an effort to overcome these challenges, I have developed a consistent stepwise approach to the introduction of concepts that involves a modern and expanded implementation of Furchgott's classical views of agonism. Although largely superseded by the Black and Leff operational model of agonism, Furchgott's approach has distinct didactic advantages by clearly separating properties of the drug/receptor complex (intrinsic efficacy) from the specific properties of the system that interprets the stimulus generated by the receptor species (sensitivity). This approach lends itself readily to useful didactic analogies that can be implemented and illustrated in the teaching environment and are easier to conceptualize by the learners. To illustrate the behavior of this model, an easy to use simulation tool has been developed in the widely used Excel platform that allows learners to manipulate individual parameters such as affinity, intrinsic efficacy, number of receptors and sensitivity of the system to explore their influence on the shape and position of concentration response and ligand occupancy curves. Furthermore, this formalism is readily expanded in a stepwise manner to introduce progressively more advanced concepts such as interactions between two different ligands, as well as concepts of constitutive activity and allostery. These functionalities are revealed progressively in the simulation tool such that nearly all forms of major scenarios can be explored, including concepts such as receptor reserve, competitive antagonism, inverse agonism, and allosteric effects on both affinity and/or efficacy. The power of this approach is also evidenced by the fact that it is easy to illustrate to the learners not only how different mechanisms can yield similar functional outcomes but also to allow them to explore and identify experiments that can most clearly distinguish between them.

Selecting nonlinear piezoelectricity for fully autonomous self-sensing synchronized switch damping on inductor technique

Recently, the importance of nonlinear piezoelectricity in smart piezoelectric devices has been receiving attention. Considering two sophisticated models of nonlinear piezoelectricity, we proposed a systematic methodology to select a model that is suitable for a piezoelectric smart attenuation device employing a fully autonomous self-sensing synchronized switch damping on inductor (SSDI) circuit. We derived the electromechanical equation based on the two models by considering a practical structure where the piezoelectric materials are attached using an adhesive and cover the structure partially. Then, the nonlinear parameters were determined using harmonic balance method (HBM) and nonlinear data fitting. We selected a suitable model by comparing the numerical analysis and experimental results of three datasets for the open, short, and SSDI circuits. It is difficult to distinguish the two models clearly from the frequency response peak values of the displacement or piezoelectric voltage. However, the difference between the two models was observed in the backbone curve properties of the peak frequency and the shape of frequency response curves, particularly for the short circuit. The methodology established in this work allows us to systematically select a suitable model of nonlinear piezoelectricity for a smart attenuation device.

Dynamic Analysis of Multi Disc Rotating Stepped Shaft Through Signal Processing

This study identifies a method for detection of irregularities like open cracks or grooves on a rotating stepped shaft with multiple discs, based on the Wavelet Transforms. Cracks are represented as reduction in diameter of shaft (groove) with small width. Single as well as multiple grooves are considered on stepped shaft at locations of stress concentration. Translational or rotational response curves/mode shapes are extracted from finite element analysis of rotor with and without grooves. Discrete and continuous 1D wavelet transforms applied on resultant response curve or mode shapes. The results show that rotational response curves or mode shapes are more sensitive to shaft cracks and key contributors to identify the location of cracks than translation response curves or mode shapes. Discrete Wavelet Transforms are accurate enough to locate the groove of smaller size. Effectiveness of detection by Wavelets Transforms is analyzed for Single as well as Multiple grooves with increase in grove depth. Increase in groove depth can be quantified by increase in wavelet coefficient and it can be an indicator.

Modeling the Ecological Responses of Tree Species to the Flood Pulse of the Amazon Negro River Floodplains

The large flood pulse of the Amazon basin is a principal driver of environmental heterogeneity with important implications for ecosystem function and the assembly of natural communities. Understanding species ecological response to the flood pulse is thus a key question with implications for theories of species coexistence, resource management, and conservation. Yet these remain largely undescribed for most species, and in particular for trees. The large flood pulse and high tree diversity of the Negro River floodplain makes it an ideal system to begin filling this knowledge gap. We merged historical hydrologic data with 41 forest inventories under variable flooding conditions distributed across the Negro River basin, comprising a total area of 34 ha, to (i) assess the importance of flood duration as a driver of compositional variation, (ii) model the response curve shapes of 111 of the most frequent tree species in function of flood duration, and (iii) derive their niche properties (optima and tolerance). We found that flood duration is a strong driver of compositional turnover, although the majority site-to-site variation in forest composition still remains unexplained. About 73% of species responded to the flood duration gradient, exhibiting a diversity of shapes, but most frequently skewed. About 29% of species were clearly favored by flood durations &amp;gt;120 days year–1, and 44% of species favored by shorter floods. The median niche breadth was 85 flood days year–1, corresponding to approximately 30% of the flood duration gradient. A significant subset of species (27%) did not respond to flooding, but rather exhibited wide tolerance to the flood gradient. The response models provided here offer valuable information regarding tree species differential capacity to grow, survive, and regenerate along an ecologically important gradient and are spatially valid for the Amazon Negro basin. These attributes make them an appealing tool with wide applicability for field and experimental studies in the region, as well as for vegetation monitoring and simulation models of floodplain forest change in the face of hydrologic alteration.

Hyperaspis polita Weise functional response: effects of predator stages, prey species, and previous feeding experiences

At the moment, Hyperaspis polita Weise is a key predator of mealybugs in southern Iran. In this study, the efficiency of this predator on Phenacoccus solenopsis Tinsley, a serious pest, and Planococcus citri Risso as a factitious prey in predator mass rearing, was examined. The functional response experiment was performed for 4th instar larvae, adult male and female of H. polita at five prey densities (2, 4, 8, 12, 16, and 32 adult female stage) on these two preys. The shape of functional response curves well fitted the type II for 4th instar larvae, adult male and female of H. polita on both preys, whenever reared on one prey, while being exposed to the other prey (previous feeding experiences). Comparisons of attack rates and handling time for 4th instar larvae and adult female did not show any difference for various preys as well as previous feeding experiences. For the adult male, when reared on P. solenopsis, the handling time was significantly longer, while it was shorter on P. citri and again the same prey was given to it. The predation was highest for the 4th instar larva followed by adult females and adult male on all treatments. In addition, the maximum predation rate was shown when these predators were reared on P. solenopsis and were exposed to P. citri. The results of this study indicated that H. polita can be an effective biological control agent for mealybugs. It also emphasizes the importance of previous feeding experiences in release program protocols for H. polita.

2297-PUB: The Shape of Glucose Response Curve during Oral Glucose Tolerance Tests (OGTT) and Long-Term Near-Normoglycemia Remission in African Americans Presenting with Hyperglycemic Crises

Approximately 70% of obese African American (AA) patients with new-onset diabetic ketoacidosis (DKA) and severe hyperglycemia (SH) achieve near-normoglycemia remission (defined as HbA1c &amp;lt;7%, fasting blood glucose [BG]&amp;lt; 130 mg/dl, and off insulin for 1 month) with intensive insulin treatment. The shape of glucose response curve during OGTT has been shown predictive of future glycemic failure and beta(β)-cell function in adolescents with diabetes and adults. Biphasic (Bφ) curves showing better glycemic control and β-cell function compared to monophasic (Mφ) or incessant rise (IR) curves. We hypothesize that in AA patients with new-onset DKA and SH, Bφ shape at remission is associated with lower hyperglycemia relapse compared to Mφ or IR curves. Seventy AA patients underwent a 75g two-hour OGTT after near-normoglycemia remission and at end of study (median follow-up, 392 days). Insulin sensitivity (Si, oral minimal model) and β-cell function (AUCi: incremental area under the curve of insulin), and disposition index (Di= Si x AUCi) were compared among the different OGTT response curves. Hyperglycemia relapse-free survival (fasting BG &amp;gt; 130 mg/dl, 2 random BG &amp;gt; 180 mg/dl or HbA1c &amp;gt; 7%) was calculated with Kaplan-Meier curve. At remission, initial OGTT showed 56% of patients with Mφ, 16% with Bφ, and 29% with IR curve shape. Baseline characteristics did not differ. Even though, more patients in the Bφ group (88%) sustained remission than the Mφ (57%) and IR (53%), hyperglycemia relapse-free survival did not differ by curve shape (log rank, p=0.26). On initial OGTT, Si (p=0.09), AUCi (p=0.12) and Di (p=0.06) did not differ between curve shapes. At last OGTT, Si (p=0.43), AUCi (0.47) and Di (p=0.22) did not differ. In obese AA patients with new-onset DKA and SH, the shape of the glucose response curve during OGTT does not predict long-term glycemic control or β-cell function. Disclosure L. Cotten: None. D. Stefanovski: None. O. Oladejo: None. G.E. Umpierrez: None. P. Vellanki: Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Funding National Institutes of Health (K08DK0830361, K23DK113241-01A1, UL1TR002378, 1P30DK111024-01)