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This article is from Schizophrenia Research , volume 158 . Abstract This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses – to predictable and unpredictable visual targets – to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia.

This article is from Schizophrenia Research , volume 158 . Abstract This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses – to predictable and unpredictable visual targets – to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia.

This is a preregistration for our first experiment.

This is a preregistration for our first experiment.

Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science---the most precise photometric measurements of stars ever made---appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits in the target star. In addition, we use the target star's future and past (auto-regression). By appropriately separating, for each data point, the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the model. The method has four hyper-parameters (the number of predictor stars, the auto-regressive window size, and two L2-regularization amplitudes for model components), which we set by cross-validation. We determine a generic set of hyper-parameters that works well for most of the stars and apply the method to a corresponding set of target stars. We find that we can consistently outperform (for the purposes of exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for exoplanet discovery.

The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force s (1999) on Statistical Methods in Psychology Journals: Guidelines and Explanations. In discussing SEM, the report starts with the usual warning: It is sometimes thought that correlation does not prove causation but causal modeling does. [Wrong! There are] dangers in this practice. But then ends with a startling conclusion: The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects. The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation.

This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to time-dependent patient characteristics. The treatment effect cannot be estimated by simply conditioning on these time-dependent patient characteristics, as they may themselves be indications of the treatment effect. This time-dependent confounding is common in observational studies. Robins [(1992) Biometrika 79 321--334, (1998b) Encyclopedia of Biostatistics 6 4372--4389] has proposed the so-called structural nested models to estimate treatment effects in the presence of time-dependent confounding. In this article we provide a conceptual framework and formalization for structural nested models in continuous time. We show that the resulting estimators are consistent and asymptotically normal. Moreover, as conjectured in Robins [(1998b) Encyclopedia of Biostatistics 6 4372--4389], a test for whether treatment affects the outcome of interest can be performed without specifying a model for treatment effect. We illustrate the ideas in this article with an example.

This study evaluated the implementation of and the outcomes from a local child parent center compensatory education program. It intended to contribute to a better understanding of one phase of compensatory education; and, in the process, it proposed to develop a relatively simple and practical evaluation strategy which would verify the implementation of a program and relate the implementation measures to the outcome measures. The evaluation strategy included practical methods for modeling the program, gathering data, and analyzing data. Causal modeling techniques were used to relate implementation measures to outcome measures. The strategy was than analyzed regarding its usefulness as an evaluation design which would measure program implementation and provide explanatory power. (Author/PN)

Statistical Analysis Plan (SAP) As part of the larger TIP project, 61 SAD patients and 41 healthy controls underwent an emotion regulation task with negative and neutral faces during fMRI scanning. We will use dynamic causal modeling (DCM) to shed light on potential disturbances in the effective connectivity of emotion regulation networks in social anxiety disorder (SAD).

Hierarchial causal models are described as pictorial representations of multiple regression equations. These models are particularly helpful for three reasons: (1) the formulation of problems in a path analytic framework forces a degree of explicitness that is often not present in research reports that rely solely on regression; (2) they provide a powerful aid to the substantive interpretation of results; and (3) they aid in the interpretation of relationships between unmeasured variables. Though causal modeling techniques are very powerful, important prerequisites are a thorough knowledge of one's subject matter and a stylish appreciation of alternative explanations. (BW)

Resourced Bayesian Networks * Missile Defense Example * How it has been done - Extension to Bayes Technology * Resource Constrained Persistence * Relationship to other common approaches.

This is the preregistration for our second experiment.

Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.

While theoretical models aimed at explaining or predicting employee turnover outcomes have been developed, minimal consideration has been given to the same task regarding safety, often measured as the probability of a crash in a given time frame. The present literature review identifies four constructs from turnover literature, which are believed to be relevant to safety research. A theoretical model of safety built upon these constructs, as they apply to the trucking industry, is presented. (Contains 3 figures.)

Educational and social programs often develop from a weak or imprecise conceptualization relating the program's system of input variables to its claimed outcomes. Evaluation personnel can contribute both to the final development of a program and to the fair evaluation of such programs by learning to formally characterize programs and to construct causal models of them. The evaluation effort represents an attempt to determine the correctness of the program's existing conceptualization, and if properly carried out, permits the developer/sponsor to strengthen, add, or delete components which are found to be nonfunctional. In this paper, the authors discuss the concept of causal model building and illustrate their ideas with an example of how causal model construciton procedures were used to assist in the evaluation of a complex early childhood program. (Author)

Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Further, in many cases only some students may be nested within a unit while other students may not. Such instances of partial nesting requires a more flexible framework for estimating treatment effects so that the model coefficients are correctly estimate. Although several recommendations have been offered to the field on handling partially nested data, few are comprehensive in their treatment of manifest and latent variables in the context of partial nesting, full nesting, and cross-classification. The present study introduces "n"-level SEM (Mehta, 2013a) as a flexible measurement and analytic framework for the estimation of treatment effects for complex data structures that frequently present in randomized controlled trials. In this tutorial, we explore how the notation of "n"-level SEM allows for parsimonious model specification whether data are observed or latent and in the presence of partial nested or cross-classified designs. By using the xxm package in R, the advantage of using "n"-level SEM framework is demonstrated through five examples for single outcome manifest variables, as in the traditional multilevel model, as well as latent applications as in multilevel SEM. [This paper was published in "Educational and Psychological Measurement" v79 p1075-1102 2019 (EJ1227460).]

The finite element simulation of blast wave formation, wave interactions with the head and subsequent response in the brain to blast exposure various conditions were carried out. Based on Bowen's curve, the maximum peak pressure transmitted to the scalp, skull and brain were about 3, 12 and 4 times respectively higher than the blast pressure received by the head. Increasing levels of overpressure produced higher intracranial pressure and strain. In contrast, increasing levels of impulse had adverse effects on the brain pressure. A person in a prone head-on position subjected to the ground explosion would sustain a greater damage in the brain as compared to one standing in a free blast condition. The effects of being adjacent to a reflecting wall were noticeable only on the region of the brain closer to the wall. The blast threats based on Bowen iso-damage curve of short duration regimen do not always produce the same level of compressive stress responses in the brain. These variations in tissue response predict potential multi-level damage outcomes rather than the same level estimated using the blast input-based tolerance curve of Bowen.

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes' theory of probabilistic causation, depicted in graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are generative probabilistic graphical models that recapitulate the potential ordering of accumulation of such DNA changes during the progression of the disease. Such models can be inferred from data by exploiting likelihood-based model-selection strategies with regularization. In this paper we discuss the theoretical foundations of our approach and we investigate in depth the influence on the model-selection task of: (i) the poset based on Suppes' theory and (ii) different regularization strategies. Furthermore, we provide an example of application of our framework to HIV genetic data highlighting the valuable insights provided by the inferred.

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes' theory of probabilistic causation, depicted in graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are generative probabilistic graphical models that recapitulate the potential ordering of accumulation of such DNA changes during the progression of the disease. Such models can be inferred from data by exploiting likelihood-based model-selection strategies with regularization. In this paper we discuss the theoretical foundations of our approach and we investigate in depth the influence on the model-selection task of: (i) the poset based on Suppes' theory and (ii) different regularization strategies. Furthermore, we provide an example of application of our framework to HIV genetic data highlighting the valuable insights provided by the inferred.

A study explored the development of self-efficacy and outcome expectancy beliefs for reading and writing--examining specifically (1) the structure of the relationships both within reading and within writing, and the influences of writing beliefs on reading and reading beliefs on writing; and (2) the development of writing beliefs. Subjects were 606 children in grades 4, 7, and 10 from a midwestern city school system. Self-efficacy for reading and writing, outcome expectancy (contingency beliefs and causal attributions), reading achievement, and writing achievement were measured with various instruments. Multiple regression analysis of the resulting data supported previous research that has found significant relationships between self-efficacy and outcome expectancy beliefs and reading and writing. Results also suggest that beliefs about reading and writing ability become increasingly important factors in predicting reading and writing skill as children age and master skills. To be fully effective readers and writers, children must develop the positive self-efficacy and outcome expectancies necessary to effectively organize and apply the cognitive reading and writing skills they possess. (Three tables of data are included; 14 references are attached.) (SR)

This article is from Cerebral Cortex (New York, NY) , volume 21 . Abstract Perception entails interactions between activated brain visual areas and the records of previous sensations, allowing for processes like figure–ground segregation and object recognition. The aim of this study was to characterize top-down effects that originate in the visual cortex and that are involved in the generation and perception of form. We performed a functional magnetic resonance imaging experiment, where subjects viewed 3 groups of stimuli comprising oriented lines with different levels of recognizable high-order structure (none, collinearity, and meaning). Our results showed that recognizable stimuli cause larger activations in anterior visual and frontal areas. In contrast, when stimuli are random or unrecognizable, activations are greater in posterior visual areas, following a hierarchical organization where areas V1/V2 were less active with “collinearity” and the middle occipital cortex was less active with “meaning.” An effective connectivity analysis using dynamic causal modeling showed that high-order visual form engages higher visual areas that generate top-down signals, from multiple levels of the visual hierarchy. These results are consistent with a model in which if a stimulus has recognizable attributes, such as collinearity and meaning, the areas specialized for processing these attributes send top-down messages to the lower levels to facilitate more efficient encoding of visual form.

The technique of causal modeling as applied to theoretical constructs in teacher education is demonstrated. The abstract principles of causality are explained, and are applied to various educational research needs. An example is made using data collected from a sample of 44 secondary level students who participated in a one semester student teaching program at Texas A and M University. Diagrams and statistical information are included. (LH)

"The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors."

Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. In this regard, root-cause analysis becomes highly intractable due to complex fault propagation mechanisms in combination with diverse operating modes. This paper presents a new data-driven framework for root-cause analysis for addressing such issues. The framework is based on a spatiotemporal feature extraction scheme for multivariate time series built on the concept of symbolic dynamics for discovering and representing causal interactions among subsystems of a complex system. We propose sequential state switching ($S^3$) and artificial anomaly association ($A^3$) methods to implement root-cause analysis in an unsupervised and semi-supervised manner respectively. Synthetic data from cases with failed pattern(s) and anomalous node are simulated to validate the proposed approaches, then compared with the performance of vector autoregressive (VAR) model-based root-cause analysis. The results show that: (1) $S^3$ and $A^3$ approaches can obtain high accuracy in root-cause analysis and successfully handle multiple nominal operation modes, and (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy.

In this report we give a high-level description of the computational approach for the Causal Modeler (CModeler) tool. The tool provides a capability for capturing the cause/effect constraints in a Special Operations plan and for reasoning tasks in support of plan execution. The input to the tool is a plan created by a human planner in a mixed initiative environment using custom graphical interface (a program called SOFTools TPE) and the output is a minimal directed acyclic graph (DAG) representing a parsimonious potential causality graph. The nodes of the DAG are the actions and the directed arcs represent potential causal links. The term potential emphasizes the uncertainty in the abduced links since no requirement is placed on the availability of domain theory. Our approach relies on the structural information only; namely the temporal ordering of the actions and the task hierarchy of the plan. We describe one main application of the tool for the Special Operations domain to support the task of run-time replanning. The replanning task takes the unexpected events in the execution of the plan (e.g., late or aborted actions) and uses the causal model to compute the impact on future actions and reconfigure the plan. We summarize at the end of the report our views of the lessons learned and give concluding remarks about future directions for developing this technology.

This study investigates the interrelationships of academic causal attributions, academic self-concept, learning approaches, and their effects on academic achievement among Hong Kong Chinese tertiary students. It was hypothesized that academic causal attributions and academic self-concept affect the learning approaches students adopt and subsequently influence achievement outcomes. Structural equation modeling was used to clarify the interrelationships of these variables and their relative contributions to academic achievement. The participants were 162 first-year full-time Hong Kong Chinese university students. Results show that, as predicted, both academic causal attributions and academic self-concept have direct effects on students' learning approaches that in turn influenced their academic achievement. (Contains 2 figures, 4 tables, and 90 references.) (Author/SLD)

The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the same processes produce higher-quality causal maps. This study analyzed the first five nodes that students (n = 37) placed in their causal maps to reveal that: 1) use of backward, forward, breadth-first, and depth-first processing produced maps of similar quality; and 2) backward processing had three times more impact on maps scores than depth-first processing to suggest that linking events into chains using backward chaining is one approach to constructing higher quality causal maps. These findings are compared with prior research findings and discussed in terms of noted differences in the task demands of constructing argument versus causal maps to gain insights into why, how, and when specific processes/strategies can be applied to create higher-quality causal maps and argument maps. These insights provide guidance on ways to develop diagramming and analytic tools that automate, analyze, and provide real-time support to improve the quality of students' maps, learning, understanding, and problem-solving skills. [For the full proceedings, see ED636095.]

This article is from Frontiers in Neuroscience , volume 8 . Abstract Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.

This article is from Frontiers in Neuroscience , volume 8 . Abstract Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.

This article is from Frontiers in Neuroscience , volume 8 . Abstract Dynamic Causal Modeling (DCM) can be used to quantify cognitive function in individuals as effective connectivity. However, ambiguity among subjects in the number and location of discernible active regions prevents all candidate models from being compared in all subjects, precluding the use of DCM as an individual cognitive phenotyping tool. This paper proposes a solution to this problem by treating missing regions in the first-level analysis as missing data, and performing estimation of the time course associated with any missing region using one of four candidate methods: zero-filling, average-filling, noise-filling using a fixed stochastic process, or one estimated using expectation-maximization. The effect of this estimation scheme was analyzed by treating it as a preprocessing step to DCM and observing the resulting effects on model evidence. Simulation studies show that estimation using expectation-maximization yields the highest classification accuracy using a simple loss function and highest model evidence, relative to other methods. This result held for various dataset sizes and varying numbers of model choice. In real data, application to Go/No-Go and Simon tasks allowed computation of signals from the missing nodes and the consequent computation of model evidence in all subjects compared to 62 and 48 percent respectively if no preprocessing was performed. These results demonstrate the face validity of the preprocessing scheme and open the possibility of using single-subject DCM as an individual cognitive phenotyping tool.

Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often referred to as unstructured. A first task in its analytics is to derive a low dimensional representation, a graph or discrete manifold, that describes well the interrelations among the time series and their intrarelations across time. This paper presents a computationally tractable algorithm for estimating this graph that structures the data. The resulting graph is directed and weighted, possibly capturing causal relations, not just reciprocal correlations as in many existing approaches in the literature. A convergence analysis is carried out. The algorithm is demonstrated on random graph datasets and real network time series datasets, and its performance is compared to that of related methods. The adjacency matrices estimated with the new method are close to the true graph in the simulated data and consistent with prior physical knowledge in the real dataset tested.

How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oil, gas and coal prices, with German wind power and Nordic water reservoir levels as exogenous variables, we estimate a causal model for the price dynamics, both for contemporaneous and lagged relationships. In contemporaneous time, Nordic and German electricity prices are interlinked through gas prices. In the long run, electricity prices and British gas prices adjust themselves to establish the equlibrium price level, since oil, coal, continental gas and EUR/USD are found to be weakly exogenous.

Five causal models relating several aspects of end-of-term student evaluation of a graduate course in nursing research methods were proposed and tested empirically. The course evaluation form consisted of four Likert-type subscales, on which students rated the following aspects of the course: (1) the extent to which the course met its objectives; (2) the utility of various instructional resources and activities in meeting course objectives (e.g., reading materials, quizzes, examinations); (3) the effectiveness of a number of specific course policies (e.g., open book examinations and quizzes) in promoting learning; and (4) instructor effectiveness. For each model, a prediction was made in terms of vanishing partial or zero-order correlations. The most plausible models suggested that ratings of the extent to which course objectives were met, and of instructor effectiveness, were both linked with ratings of course policy effectiveness via ratings of the utility of instructional resources in meeting course objectives. Course policies may best be implemented by means of appropriate instructional resources and techniques; students will then rate the instructor as effective and regard course objectives as having been met. (Author/RL)

This article is from BMC Genomics , volume 14 . Abstract Background: It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions. Results: To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs - from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Drosophila melanogaster. The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies. Conclusions: Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications.

turing tests probabilistic humanity

turing tests probabilistic humanity

turing tests probabilistic humanity