Developmental Dataset Research Articles

Ontogeny of collective behavior reveals a simple attraction rule

The striking patterns of collective animal behavior, including ant trails, bird flocks, and fish schools, can result from local interactions among animals without centralized control. Several of these rules of interaction have been proposed, but it has proven difficult to discriminate which ones are implemented in nature. As a method to better discriminate among interaction rules, we propose to follow the slow birth of a rule of interaction during animal development. Specifically, we followed the development of zebrafish, Danio rerio, and found that larvae turn toward each other from 7 days postfertilization and increase the intensity of interactions until 3 weeks. This developmental dataset allows testing the parameter-free predictions of a simple rule in which animals attract each other part of the time, with attraction defined as turning toward another animal chosen at random. This rule makes each individual likely move to a high density of conspecifics, and moving groups naturally emerge. Development of attraction strength corresponds to an increase in the time spent in attraction behavior. Adults were found to follow the same attraction rule, suggesting a potential significance for adults of other species.

The modular organization of human anatomical brain networks: Accounting for the cost of wiring.

Brain networks are expected to be modular. However, existing techniques for estimating a network’s modules make it difficult to assess the influence of organizational principles such as wiring cost reduction on the detected modules. Here we present a modification of an existing module detection algorithm that allowed us to focus on connections that are unexpected under a cost-reduction wiring rule and to identify modules from among these connections. We applied this technique to anatomical brain networks and showed that the modules we detected differ from those detected using the standard technique. We demonstrated that these novel modules are spatially distributed, exhibit unique functional fingerprints, and overlap considerably with rich clubs, giving rise to an alternative and complementary interpretation of the functional roles of specific brain regions. Finally, we demonstrated that, using the modified module detection approach, we can detect modules in a developmental dataset that track normative patterns of maturation. Collectively, these findings support the hypothesis that brain networks are composed of modules and provide additional insight into the function of those modules.

A New Preprocedure Risk Score for Predicting Contrast-Induced Acute Kidney Injury

BackgroundMost of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for only postcontrast exposure prediction; however, prediction before the procedure is more valuable in practice. This study aimed to develop a risk scoring system based on preprocedural characteristics for early prediction of CI-AKI in patients after coronary angiography or percutaneous coronary intervention (PCI). MethodsWe prospectively recruited 1777 consecutive patients who were randomized in an approximate 3:2 ratio to create a development data set (n = 1076) and a validation data set (n = 701). A risk score model based on preprocedural risk factors was developed using stepwise logistic regression. Validation was performed by bootstrap and split-sample methods. ResultsThe occurrence of CI-AKI was 5.97% (106 of 1777), 5.95% (64 of 1076), and 5.99% (42 of 701) in the overall, developmental, and validation data sets, respectively. The risk score was developed with 5 prognostic factors (age, serum creatinine levels, N-terminal pro b-type natriuretic peptide levels, high-sensitivity C-reactive protein, and primary PCI), ranged from 0-36, and was well calibrated (Hosmer-Lemeshow χ2 = 4.162; P = 0.842). Good discrimination was obtained both in the developmental and validation data sets (C-statistic, 0.809 and 0.798, respectively). The risk score was highly and positively associated with CI-AKI (P for trend < 0.001) in-hospital and long-term outcomes. ConclusionsThe novel risk score model we developed is a simple and accurate tool for early/preprocedural prediction of CI-AKI in patients undergoing coronary angiography or PCI. This tool allows assessment of the risk of CI-AKI before contrast exposure, allowing for timely initiation of appropriate preventive measures.

Selection of reliable reference genes for RT-qPCR analysis during developmental stages and abiotic stress in Setaria viridis.

Real-time PCR (RT-qPCR) expression analysis is a powerful analytical technique, but reliable results depend on the use of stable reference genes for proper normalization. This study proposed to test the expression stability of 13 candidate reference genes in Setaria viridis, a monocot species recently proposed as a new C4 model plant. Gene expression stability of these genes was assayed across different tissues and developmental stages of Setaria and under drought or aluminum stress. In general, our results showed Protein Kinase, RNA Binding Protein and SDH as the most stable genes. Moreover, pairwise analysis showed that two reference genes were sufficient to normalize the gene expression data under each condition. By contrast, GAPDH and ACT were the least stably expressed genes tested. Validation of suitable reference genes was carried out to profile the expression of P5CS and GolS during abiotic stress. In addition, normalization of gene expression of SuSy, involved in sugar metabolism, was assayed in the developmental dataset. This study provides a list of reliable reference genes for transcript normalization in S. viridis in different tissues and stages of development and under abiotic stresses, which will facilitate genetic studies in this monocot model plant.

A New Z Score Curve of the Coronary Arterial Internal Diameter Using the Lambda-Mu-Sigma Method in a Pediatric Population

Several coronary artery Z score models have been developed. However, a Z score model derived by the lambda-mu-sigma (LMS) method has not been established. Echocardiographic measurements of the proximal right coronary artery, left main coronary artery, proximal left anterior descending coronary artery, and proximal left circumflex artery were prospectively collected in 3,851 healthy children ≤18years of age and divided into developmental and validation data sets. In the developmental data set, smooth curves were fitted for each coronary artery using linear, logarithmic, square-root, and LMS methods for both sexes. The relative goodness of fit of these models was compared using the Bayesian information criterion. The best-fitting model was tested for reproducibility using the validation data set. The goodness of fit of the selected model was visually compared with that of the previously reported regression models using a Q-Q plot. Because the internal diameter of each coronary artery was not similar between sexes, sex-specific Z score models were developed. The LMS model with body surface area as the independent variable showed the best goodness of fit; therefore, the internal diameter of each coronary artery was transformed into a sex-specific Z score on the basis of body surface area using the LMS method. In the validation data set, a Q-Q plot of each model indicated that the distribution of Z scores in the LMS models was closer to the normal distribution compared with previously reported regression models. Finally, the final models for each coronary artery in both sexes were developed using the developmental and validation data sets. A Microsoft Excel-based Z score calculator was also created, which is freely available online (http://raise.umin.jp/zsp/calculator/). Novel LMS models with which to estimate the sex-specific Zscore of each internal coronary artery diameter were generated and validated using a large pediatric population.

Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI.

While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in-scanner motion on morphological analysis of structural MRI is relatively under-studied. Even among "good quality" structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in-scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend-level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non-overlapping sets of structural MRI scans, convergent evidence showed that in-scanner motion-even at levels which do not manifest in visible motion artifact-can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non-sedated humans. Hum Brain Mapp 37:2385-2397, 2016. © 2016 Wiley Periodicals, Inc.

Comparison of Thermal Performance Equations in Describing Temperature-Dependent Developmental Rates of Insects: (I) Empirical Models

Temperature greatly affects the developmental duration of insects at their different stages, and many mathematical models exist for describing their temperature-dependent developmental rates. It is important to choose a suitable model to predict outbreaks of pest insects under climate change. However, previous comparisons among these models were usually based on a single species. In the present study, we compared the six nonlinear models (the Briere-1, Briere-2, Lactin, Performance-2, beta, and Ratkowsky models) based on the goodness of fit and the trade-off between the model’s goodness of fit and structural complexity, using 10 temperature-dependent developmental rate datasets on insects to make the conclusions general. We found that the square root model (i.e., the Ratkowsky model) fitted all datasets well, and the curve shape produced by this model also approximates the curve shape of thermodynamically based mathematical models. The square root model was originally derived to be applicable to the growth rates of bacteria, and until now it has been generally ignored in entomology. We were mainly concerned with the predicted results obtained by using this model on observations of temperature-dependent developmental rates. We found that the square root model described well the pooled developmental rates in the low-, mid-, and high-temperature ranges, and we believe that it merits wider use in entomology.

Web-Based Malignancy Risk Estimation for Thyroid Nodules Using Ultrasonography Characteristics: Development and Validation of a Predictive Model.

To establish a practical and simplified method for analyzing thyroid nodules in a clinical setting, the development of a new practical prediction model was required. This study aimed to construct and validate a simple and reliable web-based predictive model using the ultrasonography characteristics of thyroid nodules to stratify the risk of malignancy. To analyze ultrasonography images, radiologists were asked to assess thyroid nodules according to the following criteria: internal content, echogenicity of the solid portion, shape, margin, and calcifications. Multivariate logistic regression was performed to predict whether nodules were diagnosed as malignant or benign. The developmental data set included 849 nodules (January-June 2003). The validation set included different data (n = 453, June 2008-February 2009). Ultrasonography features, including solid content, taller-than-wide shape, spiculated margin, ill-defined margin, hypoechogenicity, marked hypoechogenicity, microcalicifications, and rim calcifications, were selected as predictors for malignant nodules in the development set. A 14-point risk scoring system was developed. Malignancy risk ranged from 3.8% to 97.4%, and the risk of malignancy was positively associated with increases in risk scores. The areas under the receiver operating characteristic curve of the development and validation sets were 0.903 and 0.897, respectively. A simple and reliable web-based predictive model was designed using ultrasonography characteristics to stratify thyroid nodules according to the probability of malignancy.

The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-of-hospital cardiac arrest.

Survival after out-of-hospital cardiac arrest (OHCA) remains disappointingly low. Among patients admitted alive, early prognostication remains challenging. This study aims to establish a stratification score for patients admitted in intensive care unit (ICU) after OHCA, according to their neurological outcome. The CAHP (Cardiac Arrest Hospital Prognosis) score was developed from the Sudden Death Expertise Center registry (Paris, France). The primary outcome was poor neurological outcome defined as Cerebral Performance Category 3, 4, or 5 at hospital discharge. Independent prognostic factors were identified using logistic regression analysis and thresholds defined to stratify low-, moderate-, and high-risk groups. The CAHP score was validated in both a prospective and an external data set (Parisian Cardiac Arrest Registry). The developmental data set included 819 patients admitted from May 2011 to December 2012. After multivariate analysis, seven variables were independently associated with poor neurological outcome and subsequently included in the CAHP score (age, non-shockable rhythm, time from collapse to basic life support, time from basic life support to return of spontaneous circulation, location of cardiac arrest, epinephrine dose, and arterial pH). Three risks groups were identified: low risk (score ≤150, 39% of unfavourable outcome), medium risk (score 150-200, 81% of unfavourable outcome) and high-risk group (score ≥200, 100% of unfavourable outcome). The AUC of the CAHP score were 0.93, and the discrimination value in the validation data sets was consistent (respectively, AUC 0.91 and 0.85). The CAHP score represents a simple tool for early stratification of patients admitted in ICU after OHCA. A high-risk category of patients with very poor prognosis can be easily identified.

4D atlas of the mouse embryo for precise morphological staging.

After more than a century of research, the mouse remains the gold-standard model system, for it recapitulates human development and disease and is quickly and highly tractable to genetic manipulations. Fundamental to the power and success of using a mouse model is the ability to stage embryonic mouse development accurately. Past staging systems were limited by the technologies of the day, such that only surface features, visible with a light microscope, could be recognized and used to define stages. With the advent of high-throughput 3D imaging tools that capture embryo morphology in microscopic detail, we now present the first 4D atlas staging system for mouse embryonic development using optical projection tomography and image registration methods. By tracking 3D trajectories of every anatomical point in the mouse embryo from E11.5 to E14.0, we established the first 4D atlas compiled from ex vivo 3D mouse embryo reference images. The resulting 4D atlas comprises 51 interpolated 3D images in this gestational range, resulting in a temporal resolution of 72 min. From this 4D atlas, any mouse embryo image can be subsequently compared and staged at the global, voxel and/or structural level. Assigning an embryonic stage to each point in anatomy allows for unprecedented quantitative analysis of developmental asynchrony among different anatomical structures in the same mouse embryo. This comprehensive developmental data set offers developmental biologists a new, powerful staging system that can identify and compare differences in developmental timing in wild-type embryos and shows promise for localizing deviations in mutant development.

Validating Internal Control Genes for the Accurate Normalization of qPCR Expression Analysis of the Novel Model Plant Setaria viridis.

Employing reference genes to normalize the data generated with quantitative PCR (qPCR) can increase the accuracy and reliability of this method. Previous results have shown that no single housekeeping gene can be universally applied to all experiments. Thus, the identification of a suitable reference gene represents a critical step of any qPCR analysis. Setaria viridis has recently been proposed as a model system for the study of Panicoid grasses, a crop family of major agronomic importance. Therefore, this paper aims to identify suitable S. viridis reference genes that can enhance the analysis of gene expression in this novel model plant. The first aim of this study was the identification of a suitable RNA extraction method that could retrieve a high quality and yield of RNA. After this, two distinct algorithms were used to assess the gene expression of fifteen different candidate genes in eighteen different samples, which were divided into two major datasets, the developmental and the leaf gradient. The best-ranked pair of reference genes from the developmental dataset included genes that encoded a phosphoglucomutase and a folylpolyglutamate synthase; genes that encoded a cullin and the same phosphoglucomutase as above were the most stable genes in the leaf gradient dataset. Additionally, the expression pattern of two target genes, a SvAP3/PI MADS-box transcription factor and the carbon-fixation enzyme PEPC, were assessed to illustrate the reliability of the chosen reference genes. This study has shown that novel reference genes may perform better than traditional housekeeping genes, a phenomenon which has been previously reported. These results illustrate the importance of carefully validating reference gene candidates for each experimental set before employing them as universal standards. Additionally, the robustness of the expression of the target genes may increase the utility of S. viridis as a model for Panicoid grasses.

Improvements in the Probabilistic Prediction of Tropical Cyclone Rapid Intensification with Passive Microwave Observations

Abstract The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.

Mortality of People with Intellectual and Developmental Disabilities from Select US State Disability Service Systems and Medical Claims Data.

Monitoring population trends including mortality within subgroups such as people with intellectual and developmental disabilities and between countries provides crucial information about the population's health and insights into underlying health concerns and the need for and effectiveness of public health efforts. Data from both US state intellectual and developmental disabilities service system administrative data sets and de-identified state Medicaid claims were used to calculate average age at death and crude mortality rates. Average age at death for people in state intellectual and developmental disabilities systems was 50.4-58.7 years and 61.2-63.0 years in Medicaid data, with a crude adult mortality rate of 15.2 per thousand. Age at death remains lower and mortality rates higher for people with intellectual and developmental disabilities. Improved case finding (e.g. medical claims) could provide more complete mortality patterns for the population with intellectual and developmental disabilities to inform the range of access and receipt of supportive and health-related interventions and preventive care.

LOGISMOS-B for Primates: Primate Cortical Surface Reconstruction and Thickness Measurement.

Cortical thickness and surface area are important morphological measures with implications for many psychiatric and neurological conditions. Automated segmentation and reconstruction of the cortical surface from 3D MRI scans is challenging due to the variable anatomy of the cortex and its highly complex geometry. While many methods exist for this task in the context of the human brain, these methods are typically not readily applicable to the primate brain. We propose an innovative approach based on our recently proposed human cortical reconstruction algorithm, LOGISMOS-B, and the Laplace-based thickness measurement method. Quantitative evaluation of our approach was performed based on a dataset of T1- and T2-weighted MRI scans from 12-month-old macaques where labeling by our anatomical experts was used as independent standard. In this dataset, LOGISMOS-B has an average signed surface error of 0.01 ± 0.03mm and an unsigned surface error of 0.42 ± 0.03mm over the whole brain. Excluding the rather problematic temporal pole region further improves unsigned surface distance to 0.34 ± 0.03mm. This high level of accuracy reached by our algorithm even in this challenging developmental dataset illustrates its robustness and its potential for primate brain studies.

Improved Tropical-Cyclone Flight-Level Wind Estimates Using Routine Infrared Satellite Reconnaissance

AbstractA new and improved method for estimating tropical-cyclone (TC) flight-level winds using globally and routinely available TC information and infrared (IR) satellite imagery is presented. The developmental dataset is composed of aircraft reconnaissance (1995–2012) that has been analyzed to a 1 km × 10° polar grid that extends outward 165 km from the TC center. The additional use of an azimuthally average tangential wind at 500 km, based on global model analyses, allows the estimation of winds at larger radii. Analyses are rotated to a direction-relative framework, normalized by dividing the wind field by the observed maximum, and then decomposed into azimuthal wavenumbers in terms of amplitudes and phases. Using a single-field principal component method, the amplitudes and phases of the wind field are then statistically related to principal components of motion-relative IR images and factors related to the climatological radius of maximum winds. The IR principal components allow the wind field to be related to the radial and azimuthal variability of the wind field. Results show that this method, when provided with the storm location, the estimated TC intensity, the TC motion vector, and a single IR image, is able to estimate the azimuthal wavenumber 0 and 1 components of the wind field. The resulting wind field reconstruction significantly improves on the method currently used for satellite-based operational TC wind field estimates. This application has several potential uses that are discussed within.

Weather-Based Forecasting of Rhizoctonia Web Blight Development on Container-Grown Azalea.

The most reliable approach for timing fungicides to control Rhizoctonia web blight on container-grown azalea has been a fixed calendar date. The purpose of this study was to model periods when a preventive control could be applied in advance of significant increases in leaf blight intensity (LBI) in response to a daily accumulated risk value indicating conducive conditions of temperature (18 or more hours between 20 and 30°C when maximum temperature is less than 35°C), leaf wetness (16 or more hours), and/or rainfall (greater than 6.7 mm above the maximum daily irrigation). Analysis used weather measurements taken every 30 minutes from 11 site-year datasets from 2006 to 2011, and weekly or biweekly LBI assessments. Six developmental site-year datasets were analyzed using receiver operating characteristic (ROC) curve procedures to evaluate models. Only a single model predicted true positives and negatives significantly greater than a 50:50 probability. Subsequently, five site-year validation datasets were evaluated. Similar results were obtained from both the developmental and validation datasets, which produced ROC areas of 0.7715 (P = 0.0120) and 0.8393 (P = 0.0034), respectively. The practical application of weather-based forecasting for management of web blight in nurseries is discussed.

Abstract 55: The CAHP (Cardiac Arrest Hospital Prognosis) Score: Predicting Neurological Outcome After Out-of-Hospital Cardiac Arrest

Background: Out-of-hospital cardiac arrest (OHCA) carries a very poor prognosis. Early prognostication of patients admitted in ICU after resuscitated OHCA is a key issue but remains challenging. The aim of that study was to establish a new scoring system to predict poor neurological outcome in these patients. Materials and Methods: The CAHP (Cardiac Arrest Hospital Prognosis) score was developed from the Sudden Death Expertise Center registry (SDEC, Paris, France). Objective risk factors were weighted on the basis of a logistic regression analysis. The primary outcome was poor neurological outcome defined as Cerebral Performance Category 3, 4 or 5. Thresholds were defined to distinguish low, moderate and high-risk groups. The CAHP score was then validated in an external dataset (Parisian OHCA Registry). Score calibration and discrimination characteristics were assessed in the validation dataset. Results: The developmental dataset included 819 patients admitted in ICU from May 2011 to December 2012. After logistic regression, 7 variables were independently associated with poor neurological outcome: age, initial shockable rhythm, time form collapse to basic life support (BLS), time from BLS to return of spontaneous circulation (ROSC), location of cardiac arrest, epinephrine dose during resuscitation and arterial pH at admission. These variables were included in the CAHP score. 3 risks groups were identified: a low risk group (score ≤ 150, 39 % of unfavorable outcome), medium risk group (score 150-200, 81% of unfavorable outcome) and high-risk group (CAHP score ≥ 200, 100 % of unfavorable outcome). AUC of the CAHP score was 0.93. In the external validation dataset, discrimination value of the CAHP score was consistent with an AUC of 0.85. Conclusion: The CAHP score is a simple and objective tool for early assessment of prognosis in patients admitted to ICU after OHCA. Moreover it allows to stratify the probability of poor neurological outcome by identifying a very high-risk category of patients (score ≥ 200).

Prediction models for the risk of postoperative nausea and vomiting

This is the protocol for a review and there is no abstract. The objectives are as follows: Identify all prognostic models developed for estimating the risk of PONV. Describe the characteristics (e.g. included predictive factors, apparent predictive accuracy in the developmental data set) of prognostic models. Appraise the predictive accuracy (external validation, including discrimination and calibration) of these models. Report and, if possible, meta-analyse the performance of models, particularly those that have been validated in external populations. Identify impact assessments of PONV prediction models that have been performed. Appraise and make recommendations for PONV prediction models that may be useful for patient care.

RNA-seq analysis of placental transcriptional landscape in normal and complicated pregnancies

RNA-seq analysis of placental transcriptional landscape in normal and complicated pregnancies

Developmental variation among Cochliomyia macellaria Fabricius (Diptera: Calliphoridae) populations from three ecoregions of Texas, USA.

Forensic entomologists rely on published developmental datasets to estimate the age of insects developing on human remains. Currently, these datasets only represent populations of targeted insects from specific locations. However, recent data indicate that populations can exhibit genetic variation in their development, including signatures of local adaptation demonstrated by regionally distinct plastic responses to their environments. In this study, three geographically distinct populations of the secondary screwworm, Cochliomyia macellaria Fabricius (Diptera: Calliphoridae; College Station, Longview, and San Marcos, TX, USA), a common blow fly collected from human remains in the southern USA, were reared in two distinct environments (cool 21 °C, 65 % relative humidity (RH); and warm 31 °C, 70 % RH) over 2 years (2011 and 2012) in order to determine differences in development time and mass. Significant differences in immature and pupal development time, as well as pupal mass, were shown to exist among strains derived from different populations and years. For immature development times, there was evidence of only an environmental effect on phenotype, while genotype by environment interactions was observed in pupal development times and pupal mass. College Station and San Marcos populations exhibited faster pupal development and smaller pupal sizes in the cooler environment relative to the Longview population, but showed an opposite trend in the warm environment. Rank order for College Station and Longview populations was reversed across years. Failure to take genetic variation into consideration when making such estimates can lead to unanticipated error and bias. These results indicate that genetics will have little impact on error when working with Texas genotypes of C. macellaria at ~30 °C and 70 % RH, but will have a more meaningful impact on error in postmortem interval estimates with this species in cooler, drier environments.