Relevant bibliographies by topics / Causal graphs

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Causal graphs'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Causal graphs"

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Jonsson, Anders, Peter Jonsson, and Tomas Lööw. "When Acyclicity Is Not Enough: Limitations of the Causal Graph." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 117–25. http://dx.doi.org/10.1609/icaps.v23i1.13550.

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Causal graphs are widely used in planning to capture the internal structure of planning instances. In the past, causal graphs have been exploited to generate hierarchical plans, to compute heuristics, and to identify classes of planning instances that are easy to solve. It is generally believed that planning is easier when the causal graph is acyclic. In this paper we show that this is not true in the worst case, proving that the problem of plan existence is PSPACE-complete even when the causal graph is acyclic. Since the variables of the planning instances in our reduction are propositional, this result applies to STRIPS planning with negative pre-conditions. Having established that planning is hard for acyclic causal graphs, we study a subclass of planning instances with acyclic causal graphs whose variables have strongly connected domain transition graphs. For this class, we show that plan existence is easy, but that bounded plan existence is hard, implying that optimal planning is significantly harder than satisficing planning for this class.
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Nordon, Galia, Gideon Koren, Varda Shalev, Benny Kimelfeld, Uri Shalit, and Kira Radinsky. "Building Causal Graphs from Medical Literature and Electronic Medical Records." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1102–9. http://dx.doi.org/10.1609/aaai.v33i01.33011102.

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Large repositories of medical data, such as Electronic Medical Record (EMR) data, are recognized as promising sources for knowledge discovery. Effective analysis of such repositories often necessitate a thorough understanding of dependencies in the data. For example, if the patient age is ignored, then one might wrongly conclude a causal relationship between cataract and hypertension. Such confounding variables are often identified by causal graphs, where variables are connected by causal relationships. Current approaches to automatically building such graphs are based on text analysis over medical literature; yet, the result is typically a large graph of low precision. There are statistical methods for constructing causal graphs from observational data, but they are less suitable for dealing with a large number of covariates, which is the case in EMR data. Consequently, confounding variables are often identified by medical domain experts via a manual, expensive, and time-consuming process. We present a novel approach for automatically constructing causal graphs between medical conditions. The first part is a novel graph-based method to better capture causal relationships implied by medical literature, especially in the presence of multiple causal factors. Yet even after using these advanced text-analysis methods, the text data still contains many weak or uncertain causal connections. Therefore, we construct a second graph for these terms based on an EMR repository of over 1.5M patients. We combine the two graphs, leaving only edges that have both medical-text-based and observational evidence. We examine several strategies to carry out our approach, and compare the precision of the resulting graphs using medical experts. Our results show a significant improvement in the precision of any of our methods compared to the state of the art.
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Atherton, Juli, Derek Ruths, and Adrian Vetta. "Computation in Causal Graphs." Journal of Graph Algorithms and Applications 23, no. 2 (2019): 317–44. http://dx.doi.org/10.7155/jgaa.00493.

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Lipsky, Ari M., and Sander Greenland. "Causal Directed Acyclic Graphs." JAMA 327, no. 11 (March 15, 2022): 1083. http://dx.doi.org/10.1001/jama.2022.1816.

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Kischka, Peter, and Dietrich Eherler. "Causal graphs and unconfoundedness." Allgemeines Statistisches Archiv 85, no. 3 (August 2001): 247–66. http://dx.doi.org/10.1007/s101820100064.

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Peters, Jonas, and Peter Bühlmann. "Structural Intervention Distance for Evaluating Causal Graphs." Neural Computation 27, no. 3 (March 2015): 771–99. http://dx.doi.org/10.1162/neco_a_00708.

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Causal inference relies on the structure of a graph, often a directed acyclic graph (DAG). Different graphs may result in different causal inference statements and different intervention distributions. To quantify such differences, we propose a (pre-)metric between DAGs, the structural intervention distance (SID). The SID is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements. It is therefore well suited for evaluating graphs that are used for computing interventions. Instead of DAGs, it is also possible to compare CPDAGs, completed partially DAGs that represent Markov equivalence classes. The SID differs significantly from the widely used structural Hamming distance and therefore constitutes a valuable additional measure. We discuss properties of this distance and provide a (reasonably) efficient implementation with software code available on the first author’s home page.
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Kinney, David. "Curie’s principle and causal graphs." Studies in History and Philosophy of Science Part A 87 (June 2021): 22–27. http://dx.doi.org/10.1016/j.shpsa.2021.02.007.

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Mian, Osman A., Alexander Marx, and Jilles Vreeken. "Discovering Fully Oriented Causal Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8975–82. http://dx.doi.org/10.1609/aaai.v35i10.17085.

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We study the problem of inferring causal graphs from observational data. We are particularly interested in discovering graphs where all edges are oriented, as opposed to the partially directed graph that the state of the art discover. To this end, we base our approach on the algorithmic Markov condition. Unlike the statistical Markov condition, it uniquely identifies the true causal network as the one that provides the simplest— as measured in Kolmogorov complexity—factorization of the joint distribution. Although Kolmogorov complexity is not computable, we can approximate it from above via the Minimum Description Length principle, which allows us to define a consistent and computable score based on non-parametric multivariate regression. To efficiently discover causal networks in practice, we introduce the GLOBE algorithm, which greedily adds, removes, and orients edges such that it minimizes the overall cost. Through an extensive set of experiments, we show GLOBE performs very well in practice, beating the state of the art by a margin.
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Bäckström, C., and P. Jonsson. "A Refined View of Causal Graphs and Component Sizes: SP-Closed Graph Classes and Beyond." Journal of Artificial Intelligence Research 47 (July 30, 2013): 575–611. http://dx.doi.org/10.1613/jair.3968.

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The causal graph of a planning instance is an important tool for planning both in practice and in theory. The theoretical studies of causal graphs have largely analysed the computational complexity of planning for instances where the causal graph has a certain structure, often in combination with other parameters like the domain size of the variables. Chen and Giménez ignored even the structure and considered only the size of the weakly connected components. They proved that planning is tractable if the components are bounded by a constant and otherwise intractable. Their intractability result was, however, conditioned by an assumption from parameterised complexity theory that has no known useful relationship with the standard complexity classes. We approach the same problem from the perspective of standard complexity classes, and prove that planning is NP-hard for classes with unbounded components under an additional restriction we refer to as SP-closed. We then argue that most NP-hardness theorems for causal graphs are difficult to apply and, thus, prove a more general result; even if the component sizes grow slowly and the class is not densely populated with graphs, planning still cannot be tractable unless the polynomial hierachy collapses. Both these results still hold when restricted to the class of acyclic causal graphs. We finally give a partial characterization of the borderline between NP-hard and NP-intermediate classes, giving further insight into the problem.
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Habel, Christopher, and Cengiz Acarturk'. "Causal inference in graph-text constellations: Designing verbally annotated graphs." Tsinghua Science and Technology 16, no. 1 (February 2011): 7–12. http://dx.doi.org/10.1016/s1007-0214(11)70002-5.

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Dissertations / Theses on the topic "Causal graphs"

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Choudhry, Arjun. "Narrative Generation to Support Causal Exploration of Directed Graphs." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98670.

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Causal graphs are a useful notation to represent the interplay between the actors as well as the polarity and strength of the relationship that they share. They are used extensively in educational, professional, and industrial contexts to simulate different scenarios, validate behavioral aspects, visualize the connections between different processes, and explore the adversarial effects of changing certain nodes. However, as the size of the causal graphs increase, interpreting them also becomes increasingly tougher. In such cases, new analytical tools are required to enhance the user's comprehension of the graph, both in terms of correctness and speed. To this purpose, this thesis introduces 1) a system that allows for causal exploration of directed graphs, while enabling the user to see the effect of interventions on the target nodes, 2) the use of natural language generation techniques to create a coherent passage explaining the propagation effects, and 3) results of an expert user study validating the efficacy of the narratives in enhancing the user's understanding of the causal graphs. In overall, the system aims to enhance user experience and promote further causal exploration.
Master of Science
Narrative generation is the art of creating coherent snippets of text that cumulatively describe a succession of events, played across a period of time. These goals of narrative generation are also shared by causal graphs – models that encapsulate inferences between the nodes through the strength and polarity of the connecting edges. Causal graphs are an useful mechanism to visualize changes propagating amongst nodes in the system. However, as the graph starts addressing real-world actors and their interactions, it becomes increasingly difficult to understand causal inferences between distant nodes, especially if the graph is cyclic. Moreover, if the value of more than a single node is altered and the cumulative effect of the change is to be perceived on a set of target nodes, it becomes extremely difficult to the human eye. This thesis attempts to alleviate this problem by generating dynamic narratives detailing the effect of one or more interventions on one or more target nodes, incorporating time-series analysis, Wikification, and spike detection. Moreover, the narrative enhances the user's understanding of the change propagation occurring in the system. The efficacy of the narrative was further corroborated by the results of user studies, which concluded that the presence of the narrative aids the user's confidence level, correctness, and speed while exploring the causal network.
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Bernigau, Holger. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-164734.

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Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a state) that interacts with its environment. Common models for this interaction are Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). Learning is then considered to be the maximization of the expectation of a predefined reward function. In order to formulate general principles (like a formal definition of curiosity-driven learning or avoidance of unpleasant situation) in a rigorous way, it might be desirable to have a theoretical framework for the optimization of more complex functionals of the underlying process law. This might include the entropy of certain sensor values or their mutual information. An optimization of the latter quantity (also known as predictive information) has been investigated intensively both theoretically and experimentally using computer simulations by N. Ay, R. Der, K Zahedi and G. Martius. In this thesis, we develop a mathematical theory for learning in the sensorimotor loop beyond expected reward maximization. Approaches and results This thesis covers four different topics related to the theory of learning in the sensorimotor loop. First of all, we need to specify the model of an agent interacting with the environment, either with learning or without learning. This interaction naturally results in complex causal dependencies. Since we are interested in asymptotic properties of learning algorithms, it is necessary to consider infinite time horizons. It turns out that the well-understood theory of causal networks known from the machine learning literature is not powerful enough for our purpose. Therefore we extend important theorems on causal networks to infinite graphs and general state spaces using analytical methods from measure theoretic probability theory and the theory of discrete time stochastic processes. Furthermore, we prove a generalization of the strong Markov property from Markov processes to infinite causal networks. Secondly, we develop a new idea for a projected stochastic constraint optimization algorithm. Generally a discrete gradient ascent algorithm can be used to generate an iterative sequence that converges to the stationary points of a given optimization problem. Whenever the optimization takes place over a compact subset of a vector space, it is possible that the iterative sequence leaves the constraint set. One possibility to cope with this problem is to project all points to the constraint set using Euclidean best-approximation. The latter is sometimes difficult to calculate. A concrete example is an optimization over the unit ball in a matrix space equipped with operator norm. Our idea consists of a back-projection using quasi-projectors different from the Euclidean best-approximation. In the matrix example, there is another canonical way to force the iterative sequence to stay in the constraint set: Whenever a point leaves the unit ball, it is divided by its norm. For a given target function, this procedure might introduce spurious stationary points on the boundary. We show that this problem can be circumvented by using a gradient that is tailored to the quasi-projector used for back-projection. We state a general technical compatibility condition between a quasi-projector and a metric used for gradient ascent, prove convergence of stochastic iterative sequences and provide an appropriate metric for the unit-ball example. Thirdly, a class of learning problems in the sensorimotor loop is defined and motivated. This class of problems is more general than the usual expected reward maximization and is illustrated by numerous examples (like expected reward maximization, maximization of the predictive information, maximization of the entropy and minimization of the variance of a given reward function). We also provide stationarity conditions together with appropriate gradient formulas. Last but not least, we prove convergence of a stochastic optimization algorithm (as considered in the second topic) applied to a general learning problem (as considered in the third topic). It is shown that the learning algorithm converges to the set of stationary points. Among others, the proof covers the convergence of an improved version of an algorithm for the maximization of the predictive information as proposed by N. Ay, R. Der and K. Zahedi. We also investigate an application to a linear Gaussian dynamic, where the policies are encoded by the unit-ball in a space of matrices equipped with operator norm.
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Yang, Karren Dai. "Learning causal graphs under interventions and applications to single-cell biological data analysis." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130806.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 49-51).
This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. This thesis first extends these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them, by defining and characterizing the interventional Markov equivalence class that can be identified from general interventions. Subsequently, this thesis proposes the first provably consistent algorithm for learning DAGs in this setting. Finally, this algorithm as well as related work is applied to analyze biological datasets.
by Karren Dai Yang.
S.M.
S.M.
S.M. Massachusetts Institute of Technology, Department of Biological Engineering
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Giasemidis, Georgios. "Spectral dimension in graph models of causal quantum gravity." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:d0aaa6f2-dd0b-4ea9-81c1-7c9e81a7229e.

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The phenomenon of scale dependent spectral dimension has attracted special interest in the quantum gravity community over the last eight years. It was first observed in computer simulations of the causal dynamical triangulation (CDT) approach to quantum gravity and refers to the reduction of the spectral dimension from 4 at classical scales to 2 at short distances. Thereafter several authors confirmed a similar result from different approaches to quantum gravity. Despite the contribution from different approaches, no analytical model was proposed to explain the numerical results as the continuum limit of CDT. In this thesis we introduce graph ensembles as toy models of CDT and show that both the continuum limit and a scale dependent spectral dimension can be defined rigorously. First we focus on a simple graph ensemble, the random comb. It does not have any dynamics from the gravity point of view, but serves as an instructive toy model to introduce the characteristic scale of the graph, study the continuum limit and define the scale dependent spectral dimension. Having defined the continuum limit, we study the reduction of the spectral dimension on more realistic toy models, the multigraph ensembles, which serve as a radial approximation of CDT. We focus on the (recurrent) multigraph approximation of the two-dimensional CDT whose ensemble measure is analytically controlled. The latter comes from the critical Galton-Watson process conditioned on non-extinction. Next we turn our attention to transient multigraph ensembles, corresponding to higher-dimensional CDT. Firstly we study their fractal properties and secondly calculate the scale dependent spectral dimension and compare it to computer simulations. We comment further on the relation between Horava-Lifshitz gravity, asymptotic safety, multifractional spacetimes and CDT-like models.
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CALIGARIS, SILVIA. "A Causal Graphs - based approach for assessing gender disparities: an application to child health & nutrition in China." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/83241.

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Most of gender statistical measures proposed in the last decades are in fact composite indicators, i.e. weighted linear combinations of basic statistics such as ratios, percentages etc. Composite indicators then involves several arbitrary choices -for instance the weighting/aggregating system, variables selection, standardization affecting both indexes transparency and interpretation. Furthermore gender inequality is a complex latent phenomenon, a collection of disparate and inter-linked issues that can be hardly caught in a single indicator. The development of statistical tools and ad hoc models is then required. The aim of this work is to explore the potential of graphical models as a language able to clearly represent the complex relationships among variables involved in the statistical measuring the gender disparities. In particular we will focus on causal graphs allowing to deep and interpret the causal mechanism that may originate gender gaps as well as to explore the effects of gender tailored policies. Causal models indeed provide transparent mathematical tools to formulate the assumptions underlying all causal inference, to translate them in term of joint distribution and to read off the conditional independences using the d-separation criterion (Pearl 2000). It is thus possible deriving causal effects in non-experimental studies, representing policies’ effects and interventions through the do operator, controlling confounders and interpreting counterfactuals. We show the potential of such models through an application to real data from China Health and Nutrition Survey 2011 ; in particular we explore the eventual existence of gender discrimination in children’ nutrition and health as possible indicator of preference for sons. The analysis takes in exam socio-demographic, economical as well as biological variables. Resorting to the PC algorithm and the IDA algorithm, we aim to learn the underlying causal structure and to estimate causal effect of siblings on children’ nutrition from observational data.
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Bernigau, Holger [Verfasser], Nihat [Akademischer Betreuer] Ay, Nihat [Gutachter] Ay, and Shun-ichi [Gutachter] Amari. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop : Causal Models over Infinite Graphs and their Application to theSensorimotor Loop / Holger Bernigau ; Gutachter: Nihat Ay, Shun-ichi Amari ; Betreuer: Nihat Ay." Leipzig : Universitätsbibliothek Leipzig, 2015. http://d-nb.info/1239565127/34.

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Chong, Hogun. "A causal model of linkages among strategy, structure, and performance using directed acyclic graphs: A manufacturing subset of Fortune 500 industrials 1990-1998." Texas A&M University, 2003. http://hdl.handle.net/1969.1/58.

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This research explored the causal relationships among strategies, corporate structure, and performance of the largest U.S. non-financial firms using Directed Acyclic Graphs (DAGs). Corporate strategies and structure have been analyzed as major variables to influence corporate performance in management and organizational studies. However, their causal relationships in terms of which variables are leaders and followers, as well as the choices of variables to configure them, are controversial. Finding of causal relationships among strategic variables, structural variables, and corporate performance is beneficial to researchers as well as corporate mangers. It provides guidance to researchers how to build a model in order to measure influences from one variable to the other, lowering the risk of drawing spurious conclusions. It also provides managers a prospect of how certain important variables would change by making a certain strategic decision. Literatures from agency theory, transactional cost economics, and traditional strategic management perspective are used to suggest variables essential to analyze corporate performance. This study includes size and multi-organizational ownership hierarchy as variables to configure corporate structure. The variables to configure corporate strategies are unrelated and related diversification, ownership by institutional investors, debt, investment in R&D, and investment in advertisement. The study finds that most of the variables classified as corporate strategy and corporate structure variables are either direct or indirect causes of corporate accounting performance. Generally, results supports the relational model: corporate structure® corporate strategy® corporate performance. Ownership hierarchy structure, unrelated diversification, advertising expenses, and R&D intensity have direct causal influences on corporate accounting performance. Size and related diversification affected corporate accounting performance indirectly, both through ownership hierarchy structure. Theoretical causal relationships from agency theory are less supported than those from transaction cost economics and traditional strategic management perspective. Further my study suggests that, in general, good corporate performance in 1990s was mainly achieved by internal expansion through investment in R&D and advertisement, rather than external expansion of firms through unrelated diversification, related diversification, and expansion of ownership hierarchy.
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Aka, Niels Mariano [Verfasser]. "Three Essays on Model Selection in Time Series Econometrics : Model Averaging, Causal Graphs, and Structural Identification / Niels Mariano Aka." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1229436685/34.

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Martiel, Simon. "Approches informatique et mathématique des dynamiques causales de graphes." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4043/document.

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Le modèle des automates cellulaires constitue un des modèles le mieux établi de physique discrète sur espace euclidien. Ils implantent trois symétries fondamentales de la physique: la causalité, l'homogénéité et la densité finie de l'information. Bien que l'origine des automates cellulaires provienne de la physique, leur utilisation est très répandue comme modèles de calcul distribué dans l'espace (machines auto-réplicantes, problèmes de synchronisation,...), ou bien comme modèles de systèmes multi-agents (congestion du trafic routier, études démographiques,...). Bien qu'ils soient parmi les modèles de calcul distribué les plus étudiés, la rigidité de leur structure interdit toute extension triviale vers un modèle de topologie variant dans le temps, qui se trouve être un prérequis fondamental à la modélisation de certains phénomènes biologiques, sociaux ou physiques, comme par exemple la discrétisation de la relativité générale. Les dynamiques causales de graphes généralisent les automates cellulaires aux graphes arbitraires de degré borné et pouvant varier dans le temps. Dans cette thèse, nous nous attacherons à généraliser certains des résultats fondamentaux de la théorie des automates cellulaires. En munissant nos graphes d'une métrique compacte, nous présenterons deux approches différentes du modèle. Une première approche axiomatique basée sur les notions de continuité et d'invariance par translation, et une deuxième approche constructive, où une règle locale est appliquée en parallèle et de manière synchrone sur l'ensemble des sommets du graphe
Cellular Automata constitute one of the most established model of discrete physical transformations that accounts for euclidean space. They implement three fundamental symmetries of physics: causality, homogeneity and finite density of information. Even though their origins lies in physics, they are widely used to model spatially distributed computation (self-replicating machines, synchronization problems,...), as well as a great variety of multi-agents phenomena (traffic jams, demographics,...). While being one of the most studied model of distributed computation, their rigidity forbids any trivial extension toward time-varying topology, which is a fundamental requirement when it comes to modelling phenomena in biology, sociology or physics: for instance when looking for a discrete formulation of general relativity. Causal graph dynamics generalize cellular automata to arbitrary, bounded degree, time-varying graphs. In this work, we generalize the fundamental structure results of cellular automata for this type of transformations. We endow our graphs with a compact metric space structure, and follow two approaches. An axiomatic approach based on the notions of continuity and shift-invariance, and a constructive approach, where a local rule is applied synchronously on every vertex of the graph. Compactness allows us to show the equivalence of these two definitions, extending the famous result of Curtis-Hedlund-Lyndon’s theorem. Another physics-inspired symmetry is then added to the model, namely reversibility
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Encardes, Nicole A. "Causal factors of Macrophoma rot observed on Petit Manseng grapes." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99083.

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Macrophoma rot is a general term for fruit rots of Vitis spp. caused by the fungus Neofusicoccum ribis (syn. Botryosphaeria ribis) or closely related or renamed taxa, including Botryosphaeria dothidea. While mainly observed as a fruit pathogen of muscadine grape, the disease has recently been observed on bunch grapes in Virginia. Isolates (N = 835) were collected from Petit Manseng fruit clusters from seven Virginia vineyards in 2018 and 2019. A subset of these isolates was sequenced using three primer sets (ITS, RPB2, and EF). The preliminary result showed a single taxonomic strain of N. ribis. A controlled inoculation study of Petit Manseng clusters verified that infection could occur anytime between bloom and 2 weeks post-veraison; however, both the mean cluster incidence and the severity of Macrophoma rot did not differ from each other at any growth stage during the season. A season-long cluster exposure experiment showed that any amount of sun exposure significantly increased Macrophoma rot severity compared to shaded clusters, and that full sun exposure was associated with greatest rot severity. This finding contravenes current management recommendations for Macrophoma rot, and it raises yet unanswered questions as to why exposed clusters are more susceptible to Macrophoma rot than are shaded clusters. An in vitro fungicide assay study using nine fungicides identified captan, thiophanate-methyl, and tetraconazole as potential candidates for management of Macrophoma rot which need to be investigated further.
Master of Science in Life Sciences
Macrophoma rot is a general term for fruit rots of grapes caused by the pathogenic fungi in the family Botryosphaeriaceae. The rot is mainly observed on Muscadine grapes, but recently more cases were found on a wine grape cultivar Petit Manseng in Virginia. Macrophoma rot symptoms begin as dark brown, circular lesions on the surface of the berry and look similar to sunburn and other fruit rots. As the disease progresses, the lesion envelopes the entire berry and black fruiting bodies develop. Severe cases may lead to crop loss. The same group of pathogens is also associated with rots on other crops including apple, pear, olive, and kiwis. Very little is known about the disease cycle and the control of Macrophoma rot, therefore, an investigation into this fungal pathogen was needed. Multiple studies with the wine grape variety Petit Manseng were conducted during the 2018-2019 growing seasons, including a survey, leaf removal trial, and an inoculation study. Results showed that a species called Neofusicoccum ribis was found in vineyards across northern and central Virginia based on the genetic identification of fungal isolates collected at seven vineyards in those areas. Macrophoma symptoms were observed to be more prevalent and severe in more exposed clusters based on a leaf removal experiment. An artificial inoculation experiment revealed that grape clusters are susceptible to Neofusicoccum ribis at any time during the season. Based on the screening of nine fungicides, three chemicals (captan, thiophanate-methyl, and tetraconazole) showed promising results as possible management tools for Macrophoma rot. The knowledge collected will lead to an increase in understanding of this fungal pathogen and to further studies to manage Macrophoma rot.
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Books on the topic "Causal graphs"

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Yao, Qing. Directed acyclic graphs, linear recursive regression, and inference about causal ordering. Toronto: University of Toronto, Dept. of Statistics, 1993.

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Isakov, Vladimir. Speak the language of schemes. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860649.

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Schematization and visualization are the necessary means to ensure the activity of a modern specialist. Schematization allows you to highlight the main thing in an object, to discover its constituent elements, to show their relationship, gives impetus to the construction of conceptual approaches. Visualization "dresses" schematic concepts in a bright, expressive artistic and graphic form. The handbook provides descriptions of the most popular means of analytical graphics - maps, graphs, tables, graphs, diagrams, flowcharts (algorithms), chronolents, maps, methodological schemes, etc. The ways of using schemes for analyzing goals, causes, problems, versions are considered. A thematic dictionary of terms and definitions, a "hot twenty" useful schematization are given. For students, undergraduates, postgraduates, teachers of law schools and faculties, as well as for representatives of other specialties - everyone who draws diagrams and works with them.
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Ruiz, Dana Catharine De. La Causa: The Migrant Farmworkers' Story. Austin: Raintree Steck-Vaughn, 1993.

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Murray, Myles N. William Murray, Esq.: Land agent in the Illinois Territory before the Revolutionary War. Brooklyn, N.Y: T. Gaus, 1987.

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Ghere, David L. Causes of the American Revolution: Focus on Boston : a unit of study for grades 7-12 / David L. Ghere, Jan F. Spreeman. Los Angeles, Calif: Organization of American Historians : National Center for History in the Schools, 1998.

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Moneysmith, Marie. Grasas que engordan, grasas que curan: Conozca la diferencia entre las grasas que le hacen bien y las que le pueden causar enfermedades. México, D.F: Panorama Editorial, 2008.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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The abolition of feudalism: Peasants, lords, and legislators in the French Revolution. University Park, Pa: Pennsylvania State University Press, 1996.

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Boller, David. Die letzten Tage der Menschheit: Eine Graphic Novel nach Karl Kraus. Edited by Pietsch Reinhard adapter editor and Kraus Karl 1874-1936. München: Herbert Utz Verlag GmbH, 2014.

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Burgan, Michael. The Boston Massacre. Mankato, Minn: Capstone Press, 2006.

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Book chapters on the topic "Causal graphs"

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Brumback, Babette A. "Causal Directed Acyclic Graphs." In Fundamentals of Causal Inference with R, 81–98. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003146674-5.

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Águeda, Cristina Puente. "Causal Relations, Text Mining and Causal Graphs." In Accuracy and Fuzziness. A Life in Science and Politics, 61–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18606-1_2.

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Jensen, Finn V. "Causal and Bayesian Networks." In Bayesian Networks and Decision Graphs, 3–34. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3502-4_1.

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Gebharter, Alexander, and Marie I. Kaiser. "Causal Graphs and Biological Mechanisms." In Explanation in the Special Sciences, 55–85. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7563-3_3.

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Pearl, J. "Statistics, Causality, and Graphs." In Causal Models and Intelligent Data Management, 3–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58648-4_1.

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Mohan, Karthika. "Causal Graphs for Missing Data: A Gentle Introduction." In Probabilistic and Causal Inference, 655–66. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3501714.3501750.

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Eherler, Dietrich, and Peter Kischka. "Decision Making Based on Causal Graphs." In Models, Methods and Decision Support for Management, 323–48. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57603-4_18.

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Neufeld, Eric, and Sonje Kristtorn. "Picturing Causality – The Serendipitous Semiotics of Causal Graphs." In Smart Graphics, 252–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11536482_24.

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Pearl, Judea, and Nanny Wermuth. "When can association graphs admit a causal interpretation?" In Selecting Models from Data, 205–14. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2660-4_21.

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Kourani, Humam, Chiara Di Francescomarino, Chiara Ghidini, Wil van der Aalst, and Sebastiaan van Zelst. "Mining for Long-Term Dependencies in Causal Graphs." In Business Process Management Workshops, 117–31. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25383-6_10.

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Conference papers on the topic "Causal graphs"

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Huang, Hao. "Causal Relationship over Knowledge Graphs." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3511808.3557818.

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Li, Jiangnan, Fandong Meng, Zheng Lin, Rui Liu, Peng Fu, Yanan Cao, Weiping Wang, and Jie Zhou. "Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/584.

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Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs to overcome implicit contextual modelling of the original entailment style. Following the previous work, we further introduce the emotion information into graphs. Emotion information can markedly promote the detection of causal utterances whose emotion is the same as the targeted utterance. However, it is still hard to detect causal utterances with different emotions, especially neutral ones. The reason is that models are limited in reasoning causal clues and passing them between utterances. To alleviate this problem, we introduce social commonsense knowledge (CSK) and propose a Knowledge Enhanced Conversation graph (KEC). KEC propagates the CSK between two utterances. As not all CSK is emotionally suitable for utterances, we therefore propose a sentiment-realized knowledge selecting strategy to filter CSK. To process KEC, we further construct the Knowledge Enhanced Directed Acyclic Graph networks. Experimental results show that our method outperforms baselines and infers more causes with different emotions from the targeted utterance.
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Balashankar, Ananth, and Lakshminarayanan Subramanian. "Learning Faithful Representations of Causal Graphs." In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.acl-long.69.

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Simonne, Lucas, Nathalie Pernelle, Fatiha Saïs, and Rallou Thomopoulos. "Differential Causal Rules Mining in Knowledge Graphs." In K-CAP '21: Knowledge Capture Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460210.3493584.

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Gonzalez-Soto, Mauricio, Ivan Feliciano-Avelino, Luis Sucar, and Hugo Escalante. "Learning a causal structure: a Bayesian Random Graph approach." In LatinX in AI at Neural Information Processing Systems Conference 2020. Journal of LatinX in AI Research, 2020. http://dx.doi.org/10.52591/lxai202012121.

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A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures.
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Bäckström, Christer, Peter Jonsson, and Sebastian Ordyniak. "A Refined Understanding of Cost-optimal Planning with Polytree Causal Graphs." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/848.

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Complexity analysis based on the causal graphs of planning instances is a highly important research area. In particular, tractability results have led to new methods for constructing domain-independent heuristics. Important early examples of such results were presented by, for instance, Brafman & Domshlak and Katz & Keyder. More general results based on polytrees and bounding certain parameters were subsequently derived by Aghighi et al. and Ståhlberg. We continue this line of research by analyzing cost-optimal planning for instances with a polytree causal graph, bounded domain size and bounded depth. We show that no further restrictions are necessary for tractability, thus generalizing the previous results. Our approach is based on a novel method of closely analysing optimal plans: we recursively decompose the causal graph in a way that allows for bounding the number of variable changes as a function of the depth, using a reording argument and a comparison with prefix trees of known size. We then transform the planning instances into tree-structured constraint satisfaction instances.
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Hyttinen, Antti, Paul Saikko, and Matti Järvisalo. "A Core-Guided Approach to Learning Optimal Causal Graphs." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/90.

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Discovery of causal relations is an important part of data analysis. Recent exact Boolean optimization approaches enable tackling very general search spaces of causal graphs with feedback cycles and latent confounders, simultaneously obtaining high accuracy by optimally combining conflicting independence information in sample data. We propose several domain-specific techniques and integrate them into a core-guided maximum satisfiability solver, thereby speeding up current state of the art in exact search for causal graphs with cycles and latent confounders on simulated and real-world data.
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Hans, Atharva, Ashish M. Chaudhari, Ilias Bilionis, and Jitesh H. Panchal. "Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22613.

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Abstract Extracting an individual’s knowledge structure is a challenging task as it requires formalization of many concepts and their interrelationships. While there has been significant research on how to represent knowledge to support computational design tasks, there is limited understanding of the knowledge structures of human designers. This understanding is necessary for comprehension of cognitive tasks such as decision making and reasoning, and for improving educational programs. In this paper, we focus on quantifying theory-based causal knowledge, which is a specific type of knowledge held by human designers. We develop a probabilistic graph-based model for representing individuals’ concept-specific causal knowledge for a given theory. We propose a methodology based on probabilistic directed acyclic graphs (DAGs) that uses logistic likelihood function for calculating the probability of a correct response. The approach involves a set of questions for gathering responses from 205 engineering students, and a hierarchical Bayesian approach for inferring individuals’ DAGs from the observed responses. We compare the proposed model to a baseline three-parameter logistic (3PL) model from the item response theory. The results suggest that the graph-based logistic model can estimate individual students’ knowledge graphs. Comparisons with the 3PL model indicate that knowledge assessment is more accurate when quantifying knowledge at the level of causal relations than quantifying it using a scalar ability parameter. The proposed model allows identification of parts of the curriculum that a student struggles with and parts they have already mastered which is essential for remediation.
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Amblard, Pierre-Olivier, and Olivier J. J. Michel. "Causal conditioning and instantaneous coupling in causality graphs." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319633.

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Hintz, Kenneth J., and Andrew S. Hintz. "From Social Network Graphs to Causal Bayes Nets." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011199.

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Reports on the topic "Causal graphs"

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Naugle, Asmeret, Laura Swiler, Kiran Lakkaraju, Stephen Verzi, Christina Warrender, and Vicente Romero. Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/1884926.

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Castleman, Benjamin, and Bridget Terry Long. Looking Beyond Enrollment: The Causal Effect of Need-Based Grants on College Access, Persistence, and Graduation. Cambridge, MA: National Bureau of Economic Research, August 2013. http://dx.doi.org/10.3386/w19306.

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Lichter, Amnon, Joseph L. Smilanick, Dennis A. Margosan, and Susan Lurie. Ethanol for postharvest decay control of table grapes: application and mode of action. United States Department of Agriculture, July 2005. http://dx.doi.org/10.32747/2005.7587217.bard.

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Original objectives: Dipping of table grapes in ethanol was determined to be an effective measure to control postharvest gray mold infection caused by Botrytis cinerea. Our objectives were to study the effects of ethanol on B.cinerea and table grapes and to conduct research that will facilitate the implementation of this treatment. Background: Botrytis cinerea is known as the major pathogen of table grapes in cold storage. To date, the only commercial technology to control it relied on sulfur dioxide (SO₂) implemented by either fumigation of storage facilities or from slow release generator pads which are positioned directly over the fruits. This treatment is very effective but it has several drawbacks such as aftertaste, bleaching and hypersensitivity to humans which took it out of the GRAS list of compounds and warranted further seek for alternatives. Prior to this research ethanol was shown to control several pathogens in different commodities including table grapes and B. cinerea. Hence it seemed to be a simple and promising technology which could offer a true alternative for storage of table grapes. Further research was however required to answer some practical and theoretical questions which remained unanswered. Major conclusions, solutions, achievements: In this research project we have shown convincingly that 30% ethanol is sufficient to prevent germination of B. cinerea and kill the spores. In a comparative study it was shown that Alternaria alternata is also rather sensitive but Rhizopus stolonifer and Aspergillus niger are less sensitive to ethanol. Consequently, ethanol protected the grapes from decay but did not have a significant effect on occurrence of mycotoxigenic Aspergillus species which are present on the surface of the berry. B. cinerea responded to ethanol or heat treatments by inducing sporulation and transient expression of the heat shock protein HSP104. Similar responses were not detected in grape berries. It was also shown that application of ethanol to berries did not induce subsequent resistance and actually the berries were slightly more susceptible to infection. The heat dose required to kill the spores was determined and it was proven that a combination of heat and ethanol allowed reduction of both the ethanol and heat dose. Ethanol and heat did not reduce the amount or appearance of the wax layers which are an essential component of the external protection of the berry. The ethanol and acetaldehyde content increased after treatment and during storage but the content was much lower than the natural ethanol content in other fruits. The efficacy of ethanol applied before harvest was similar to that of the biological control agent, Metschnikowia fructicola, Finally, the performance of ethanol could be improved synergistically by packaging the bunches in modified atmosphere films which prevent the accumulation of free water. Implications, both scientific and agricultural: It was shown that the major mode of action of ethanol is mediated by its lethal effect on fungal inoculum. Because ethanol acts mainly on the cell membranes, it was possible to enhance its effect by lowering the concentration and elevating the temperature of the treatment. Another important development was the continuous protection of the treated bunches by modified atmosphere that can solve the problem of secondary or internal infection. From the practical standpoint, a variety of means were offered to enhance the effect of the treatment and to offer a viable alternative to SO2 which could be instantly adopted by the industry with a special benefit to growers of organic grapes.
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Reisch, Bruce, Avichai Perl, Julie Kikkert, Ruth Ben-Arie, and Rachel Gollop. Use of Anti-Fungal Gene Synergisms for Improved Foliar and Fruit Disease Tolerance in Transgenic Grapes. United States Department of Agriculture, August 2002. http://dx.doi.org/10.32747/2002.7575292.bard.

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Original objectives . 1. Test anti-fungal gene products for activity against Uncinula necator, Aspergillus niger, Rhizopus stolonifer and Botrytis cinerea. 2. For Agrobacterium transformation, design appropriate vectors with gene combinations. 3. Use biolistic bombardment and Agrobacterium for transformation of important cultivars. 4. Characterize gene expression in transformants, as well as level of powdery mildew and Botrytis resistance in foliage of transformed plants. Background The production of new grape cultivars by conventional breeding is a complex and time-consuming process. Transferring individual traits via single genes into elite cultivars was proposed as a viable strategy, especially for vegetatively propagated crops such as grapevines. The availability of effective genetic transformation procedures, the existence of genes able to reduce pathogen stress, and improved in vitro culture methods for grapes, were combined to serve the objective of this proposal. Effective deployment of resistance genes would reduce production costs and increase crop quality, and several such genes and combinations were used in this project. Progress The efficacy of two-way combinations of Trichoderma endochitinase (CHIT42), synthetic peptide ESF12 and resveratrol upon the control of growth of Botrytis cinerea and Penicillium digitatum were evaluated in vitro. All pairwise interactions were additive but not synergistic. Per objective 2, suitable vectors with important gene combinations for Agrobacterium transformation were designed. In addition, multiple gene co-transformation by particle bombardment was also tested successfully. In New York, transformation work focused on cultivars Chardonnay and Merlot, while the technology in Israel was extended to 41B, R. 110, Prime, Italia, Gamay, Chardonnay and Velika. Transgenic plant production is summarized in the appendix. Among plants developed in Israel, endochitinase expression was assayed via the MuchT assay using material just 1-5 days after co-cultivation. Plants of cv. Sugraone carrying the gene coding for ESF12, a short anti-fungal lytic peptide under the control of the double 358 promoter, were produced. Leaf extracts of two plants showed inhibition zones that developed within 48 h indicating the inhibitory effect of the leaf extracts on the six species of bacteria. X fastidiosa, the causal organism of Pierce's disease, was very sensitive to leaf extracts from ESF12 transformed plants. Further work is needed to verify the agricultural utility of ESF12 transformants. In New York, some transformants were resistant to powdery mildew and Botrytis fruit rot. Major conclusions, solutions, achievements and implications The following scientific achievements resulted from this cooperative BARD project: 1. Development and improvement of embryogenesis and tissue culture manipulation in grape, while extending these procedures to several agriculturally important cultivars both in Israel and USA. 2. Development and improvement of novel transformation procedures while developing transformation techniques for grape and other recalcitrant species. 3. Production of transgenic grapevines, characterization of transformed vines while studying the expression patterns of a marker gene under the control of different promoter as the 35S CaMV in different part of the plants including flowers and fruits. 4. Expression of anti-fungal genes in grape: establishment of transgenic plants and evaluation of gene expression. Development of techniques to insert multiple genes. 5. Isolation of novel grape specific promoter to control the expression of future antimicrobial genes. It is of great importance to report that significant progress was made in not only the development of transgenic grapevines, but also in the evaluation of their potential for increased resistance to disease as compared with the non engineered cultivar. In several cases, increased disease resistance was observed. More research and development is still needed before a product can be commercialized, yet our project lays a framework for further investigations.
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Homan, H. Jeffrey, Ron J. Johnson, James R. Thiele, and George M. Linz. European Starlings. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, September 2017. http://dx.doi.org/10.32747/2017.7207737.ws.

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European starlings (Sturnus vulgaris, Figure 1)are an invasive species in the United States. The first recorded release of the birds was in 1890 in New York City’s Central Park. Because starlings easily adapt to a variety of habitats, nest sites and food sources, the birds spread quickly across the country. Today, there are about 150 million starlings in North America. Conflicts between people and starlings occur mostly in agricultural settings. Starlings damage apples, blueberries, cherries, figs, grapes, peaches, and strawberries. Starlings gather at concentrated animal feeding operations (CAFOs) during late fall and winter. Starlings also cause human health problem, airplane hazards, and nuisance problems. European starlings are not protected by the Migratory Bird Treaty Act (MBTA).
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Desai, Jairaj, Jijo K. Mathew, Howell Li, Rahul Suryakant Sakhare, Deborah Horton, and Darcy M. Bullock. National Mobility Report for All Interstates–December 2022. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317591.

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In January 2023, Wejo Data Services Inc. provided Purdue with a national data set comprised of approximately 503 billion connected vehicle records covering all 50 states for the month of December 2022. The goal of the evaluation data set was to create a series of summary graphics to evaluate the scalability of work zone/winter weather analytics graphics and electric/hybrid vehicle counts at a national level as well as visualize the mobility impacts of winter weather activity on the national interstate network. This report illustrates several performance measures developed using this dataset for all interstate routes, both state wise and cross-country. Statewide graphics are presented for each interstate in the 50 states. There are also a series of multi-state graphics for I-5, I-10, I-15, I-35, I-55, I-65, I-75, I-80, I-90, and I-95 and selected other routes that witnessed winter storm impacts. Performance measures include absolute and normalized trip counts classified by type of trip (electric vehicle or hybrid vehicle or internal combustion engine vehicle), weekly heatmaps based on vehicle speed (for the two-week period from December 12-25, 2022) and finally, speed profiles by interstate mile markers. Traffic tickers depicting miles of congestion as well as mile-hours of congestion for the national interstate network and selected multi-state routes are also included to provide a unified visual of nationwide mobility impact of recurring congestion as well as non-recurring congestion caused by winter weather. The generated performance measures can be found in the supplemental files.
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Liu, Miao, Hongan Wang, Jing Lu, Zhiyue Zhu, Chaoqun Song, Ye Tian, Xinzhi Chen, et al. Vitamin D supplementation in the treatment of Myasthenia Gravis A protocol for a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0129.

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Review question / Objective: The patients should meet the internationally recognized diagnostic criteria for myasthenia gravis and be definitely diagnosed as myasthenia gravis, excluding MG patients caused by congenital, drug and other factors, as well as patients with serious primary diseases, autoimmune diseases or mental diseases. Patients are not restricted by race, region, gender, age, background, course of disease and other factors. We will focus on trials using vitamin D as an intervention at any dose and in any regimen (eg daily/weekly/monthly intake). The control group was routinely given western medicine, including cholinesterase inhibitors, glucocorticoids, immunosuppressants, alone or in combination, or placebo. The intervention group was treated with vitamin D on the basis of western medicine treatment in the control group. The specific dosage form and dose were not limited, and the shortest course of treatment should be 4 weeks. Main outcome measures: (1) Quantitative score of myasthenia gravis (QMG); (2) Recurrence rate; (3) Effective. Secondary outcome measures: (1) The level of serum acetylcholine receptor antibody (AchRab); (2) The levels of inflammatory factors such as IL-6 and IL-10; (3) Clinical absolute score; (4) TCM syndrome score scale; (5) Quality of life score (QOL); (6) Incidence rate of adverse events. All randomized controlled trials (RCT) literatures from the establishment to September 2022 were retrieved and classified.
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Firon, Nurit, Prem Chourey, Etan Pressman, Allen Hartwell, and Kenneth J. Boote. Molecular Identification and Characterization of Heat-Stress-Responsive Microgametogenesis Genes in Tomato and Sorghum - A Feasibility Study. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7591741.bard.

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Exposure to higher than optimal temperatures - heat-stress (HS) - is becoming increasingly common to all crop plants worldwide. Heat stress coinciding with microgametogenesis, especially during the post-meiotic phase that is marked by starch biosynthesis, is often associated with starch-deficient pollen and male sterility and ultimately, greatly reduced crop yields. The molecular basis for the high sensitivity of developing pollen grains, on one hand, and factors involved in pollen heat-tolerance, on the other, is poorly understood. The long-term goal of this project is to provide a better understanding of the genes that control pollen quality under heat-stress conditions. The specific objectives of this project were: (1) Determination of the threshold heat stress temperature(s) that affects tomato and sorghum pollen quality whether: a) Chronic mild heat stress conditions (CMHS), or b) Acute heat stress (AHS). (2) Isolation of heat-responsive, microgametogenesis-specific sequences. During our one-year feasibility project, we have accomplished the proposed objectives as follows: Objectrive 1: We have determined the threshold HS conditions in tomato and sorghum. This was essential for achieving the 2nd objective, since our accumulated experience (both Israeli and US labs) indicate that when temperature is raised too high above "threshold HS levels" it may cause massive death of the developing pollen grains. Above-threshold conditions have additional major disadvantages including the "noise" caused by induced expression of genes involved in cell death and masking of the differences between heatsensitive and heat-tolerant pollen grains. Two different types of HS conditions were determined: a) Season-long CMHS conditions: 32/26°C day/night temperatures confirmed in tomato and 36/26°C day maximum/night minimum temperatures in sorghum. b) Short-term AHS: In tomato, 2 hour exposure to 42-45°C (at 7 to 3 days before anthesis) followed by transfer to 28/22±2oC day/night temperatures until flower opening and pollen maturation, caused 50% reduced germinating pollen in the heat-sensitive 3017 cv.. In sorghum, 36/26°C day/night temperatures 10 to 5 days prior to panicle emergence, occurring at 35 days after sowing (DAS) in cv. DeKalb28E, produced starch-deficient and sterile pollen. Objective 2: We have established protocols for the high throughput transcriptomic approach, cDNA-AFLP, for identifying and isolating genes exhibiting differential expression in developing microspores exposed to either ambient or HS conditions and created a databank of HS-responsivemicrogametogenesis-expressed genes. A subset of differentially displayed Transcript-Derived Fragments (TDFs) that were cloned and sequenced (35 & 23 TDFs in tomato and sorghum, respectively) show close sequence similarities with metabolic genes, genes involved in regulation of carbohydrate metabolism, genes implicated in thermotolerance (heat shock proteins), genes involved in long chain fatty acids elongation, genes involved in proteolysis, in oxidation-reduction, vesicle-mediated transport, cell division and transcription factors. T-DNA-tagged Arabidopsis mutants for part of these genes were obtained to be used for their functional analysis. These studies are planned for a continuation project. Following functional analyses of these genes under HS – a valuable resource of genes, engaged in the HS-response of developing pollen grains, that could be modulated for the improvement of pollen quality under HS in both dicots and monocots and/or used to look for natural variability of such genes for selecting heat-tolerant germplasm - is expected.
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Mikhaleva, E., E. Babikova, G. Bezhashvili, M. Ilina, and I. Samkova. VALUE STREAM PROGRAM. Sverdlovsk Regional Medical College, December 2022. http://dx.doi.org/10.12731/er0618.03122022.

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In order to increase the efficiency of the work of a medical organization, it is necessary to train medical workers, employees of medical organizations, students of educational organizations in the techniques and methods of lean production, followed by the application of the acquired skills directly at the workplace in a medical organization. The purpose of the training under the program is to acquire new competencies necessary to perform professional tasks using lean manufacturing tools - mapping the value stream to ensure maximum operational efficiency of production processes. The program provides for independent work: mapping the value stream of the current, ideal and target states of the process, analysis of the value stream of the current state of the process (problem identification: spaghetti method, pyramid of problems, graph-links, summary table of root causes and contribution of the solution), development of a plan measures to achieve the target state of the process.
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McCall, Jamie, Brittany Weston, James Onorevole, John Roberson, and Jamie Andrews. Extraordinary Times Call for Extraordinary Measures: Use of Loans and Grants for Small Business Assistance During the COVID-20 Pandemic. Carolina Small Business Development Fund and ResilNC, October 2022. http://dx.doi.org/10.46712/extraordinary.times.

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Abstract:
The financial losses caused by COVID-19 are extraordinary in scope and have affected every facet of the small business ecosystem. For entrepreneurs, recovery from the pandemic has similarly required extraordinary levels of assistance. We assess the connection between COVID-19 grant and loan emergency interventions with a variety of positive community economic development impacts. Our analysis draws from a survey of aid recipients across ten COVID-19 relief programs offered or administered by Carolina Small Business Development Fund between February 2020 and February 2022. The data highlight three main findings. First, while the pandemic’s economic damage was high across all underprivileged communities, in many cases the harms disproportionately accrued to Black-owned firms. Second, the results show that grant initiatives are better for short-term financial stability and are likely to position recipients for future financing opportunities. Finally, disaster loans are favored for outcomes related to higher employment retention and a creating a more favorable business sentiment outlook.
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