Graphical Framework Research Articles

Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model

Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to the existing ones. metaMint also provides interpretable microbe-metabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub.

Estimation of Parkinson's disease severity using speech features and extreme gradient boosting.

In recent years, there is an increasing interest in building e-health systems. The systems built to deliver the health services with the use of internet and communication technologies aim to reduce the costs arising from outpatient visits of patients. Some of the related recent studies propose machine learning-based telediagnosis and telemonitoring systems for Parkinson's disease (PD). Motivated from the studies showing the potential of speech disorders in PD telemonitoring systems, in this study, we aim to estimate the severity of PD from voice recordings of the patients using motor Unified Parkinson's Disease Rating Scale (UPDRS) as the evaluation metric. For this purpose, we apply various speech processing algorithms to the voice signals of the patients and then use these features as input to a two-stage estimation model. The first step is to apply a wrapper-based feature selection algorithm, called Boruta, and select the most informative speech features. The second step is to feed the selected set of features to a decision tree-based boosting algorithm, extreme gradient boosting, which has been recently applied successfully in many machine learning tasks due to its generalization ability and speed. The feature selection analysis showed that the vibration pattern of the vocal fold is an important indicator of PD severity. Besides, we also investigate the effectiveness of using age and years passed since diagnosis as covariates together with speech features. The lowest mean absolute error with 3.87 was obtained by combining these covariates and speech features with prediction level fusion. Graphical Abstract Framework for the proposed UPDRS estimation model.

Flexibility From Distributed Multienergy Systems

Multienergy systems (MES), in which multiple energy vectors are integrated and optimally operated, are key assets in low-carbon energy systems. Multienergy interactions of distributed energy resources via different energy networks generate the so-called distributed MES (DMES). While it is now well recognized that DMES can provide power system flexibility by shifting across different energy vectors, it is essential to have a systematic discussion on the main features of such flexibility. This article presents a comprehensive overview of DMES modeling and characterization of flexibility applications. The concept of “multienergy node” is introduced to extend the power node model, used for electrical flexibility, in the multienergy case. A general definition of DMES flexibility is given, and a general mathematical and graphical modeling framework, based on multidimensional maps, is formulated to describe the operational characteristics of individual MES and aggregate DMES, including the role of multienergy networks in enabling or constraining flexibility. Several tutorial examples are finally presented with illustrative case studies on current and future DMES practical applications.

How do firm and market characteristics affect airports’ Beta risk?

I present a graphical framework based on Subrahmanyam and Thomadakis (1980) that allows to study the impact from firm and market characteristics on systematic risk associated with the return on capital, i.e. Beta risk, for utilities under price control. Within this framework, Beta risk is driven by the magnitude of profit fluctuations following demand shocks. The framework is then applied to airport firm characteristics and airport market environment features. I find that the frequency of price control resets, the level of operating leverage, the extent of capacity constraints, and the degree of market power all have an unambiguous effect on the level of Beta risk. The scope of the regulatory perimeter and the type of traffic mix may also affect Beta risk; however, the magnitude and direction of their impact rely on the specifics of the case. The article may assist policy makers to formulate economically sound recommendations on how the regulatory rate of return for airport operators should be determined. Specifically, my findings suggest criteria that can be used to choose adequate peer companies of comparable systematic risk.

Structural factor equation models for causal network construction via directed acyclic mixed graphs.

Directed acyclic mixed graphs (DAMGs) provide a useful representation of network topology with both directed and undirected edges subject to the restriction of no directed cycles in the graph. This graphical framework may arise in many biomedical studies, for example, when a directed acyclic graph (DAG) of interest is contaminated with undirected edges induced by some unobserved confounding factors (eg, unmeasured environmental factors). Directed edges in a DAG are widely used to evaluate causal relationships among variables in a network, but detecting them is challenging when the underlying causality is obscured by some shared latent factors. The objective of this paper is to develop an effective structural equation model (SEM) method to extract reliable causal relationships from a DAMG. The proposed approach, termed structural factor equation model (SFEM), uses the SEM to capture the network topology of the DAG while accounting for the undirected edges in the graph with a factor analysis model. The latent factors in the SFEM enable the identification and removal of undirected edges, leading to a simpler and more interpretable causal network. The proposed method is evaluated and compared to existing methods through extensive simulation studies, and illustrated through the construction of gene regulatory networks related to breastcancer.

Teaching jazz double bass in the 21st century

My initial plan was to do research and catalogue the available existing method books for Jazz bass education. But during the process I realized that matters were more complex than I had foreseen. Jazz (bass) methods were often based on classical music methods but Jazz education demands a different skillset and approach. I reexamined the fundamental building blocks necessary to successfully teach Jazz double bass. For this I designed a graphic theoretical framework that helped me analyze the existing methods. With this information I built a data base and webpage that offers analytical information about the available methods. Methods not limited to the traditional books but also on line learning programs and other methodical teaching material. Furthermore I examined the influence of the technological developments that the computer, smartphones, tablets and the internet have to offer. Teaching Jazz in the 21st century has proven to be much more of a modulair approach than the traditional linear approach.

SimilCatch: Enhanced social spammers detection on Twitter using Markov Random Fields

The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate the possibility of using the output of previously proposed supervised classification systems as a tool for spammers discovery. The hypothesis is that these systems are still highly capable of detecting spammers reliably even when their recall is far from perfect. We then propose to use the output of these classifiers as prior beliefs in a probabilistic graphical model framework. This framework allows beliefs to be propagated to similar social accounts. Basing similarity on a who-connects-to-whom network has been empirically critiqued in recent literature and we propose here an alternative definition based on a bipartite users-content interaction graph. For evaluation, we build a Markov Random Field on a graph of similar users and compute prior beliefs using a selection of state-of-the-art classifiers. We apply Loopy Belief Propagation to obtain posterior predictions on users. The proposed system is evaluated on a recent Twitter dataset that we collected and manually labeled. Classification results show a significant increase in recall and a maintained precision. This validates that formulating the detection problem with an undirected graphical model framework permits to restore the deteriorated performances of previously proposed statistical classifiers and to effectively mitigate the effect of spam evolution.

ATP-Based Fuzzy Logic Controller Model for Electromagnetic Transients Studies

Alternative Transient Program (ATP) has been steadily developed and improved in the last decades. Today, ATP Draw, a graphical framework, is available, which has improved the use of the program significantly. However, fuzzy controller is one of complex models for which a simplified model can be a very useful tool. This paper introduces a model for fuzzy logic controller in ATP for electromagnetic transients' studies of power system. The proposed model introduces a first model of fuzzy logic in ATP. This facilitates power system and fuzzy logic controller simulation in ATP without needing any other programs. The proposed model is implemented in ATPDraw environment. The proposed fuzzy logic controller model consists of three parts; Membership functions (triangular and trapezoidal shapes), Rules and Defuzzification. The proposed model is verified using numerical examples. The membership degree, the rules, and the defuzzification are calculated using both numerical calculation and the proposed model. The proposed model verification shows acceptable results.

The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis

Data analysis is a risky endeavor, particularly among people who are unaware of its dangers. According to some researchers, “statistical conclusions validity” threatens all research subjected to the dark arts of statistical magic. Although traditional statistics classes may advise against certain practices (e.g., multiple comparisons, small sample sizes, violating normality), they may fail to cover others (e.g., outlier detection and violating linearity). More common, perhaps, is that researchers may fail to remember them. In this article, rather than rehashing old warnings and diatribes against this practice or that, I instead advocate a general statistical-analysis strategy. This graphic-based eight-step strategy promises to resolve the majority of statistical traps researchers may fall into—without having to remember large lists of problematic statistical practices. These steps will assist in preventing both false positives and false negatives and yield critical insights about the data that would have otherwise been missed. I conclude with an applied example that shows how the eight steps reveal interesting insights that would not be detected with standard statistical practices.

Learning soft sensors using time difference-based multi-kernel relevance vector machine with applications for quality-relevant monitoring in wastewater treatment.

Considering the time-varying, uncertain and non-linear properties of the wastewater treatment process (WWTPs), a novel multi-kernel relevance vector machine (MRVM) soft sensor based on time difference (TD) is proposed to predict the quality-relevant but difficult-to-measure variable. Firstly, a novel dimension reduction technique is introduced to reduce data dimension and model complexity. Secondly, the parameters of the kernel model are optimized by the intelligent optimization algorithm (PSO). Besides, the TD strategy is introduced to enhance the robustness of MRVM when exposing to dynamic environments. Finally, the proposed model was assessed through two simulation studies and a real WWTP with the results demonstrating the effectiveness of the proposed model. Graphical abstract Framework of Lasso-TD-MRVM soft sensor model.

Interpreting Pattern in Plant-Soil Feedback Experiments with Co-occurring Invasive Species: A Graphical Framework and Case Study

Despite the ubiquity of multiple plant invasions, the underlying mechanisms of invasive-invasive interactions remain relatively unknown. Given the importance of plant–soil feedback (PSF) in contributing to single species invasions, it may be an important factor influencing invasive–invasive species interactions as well. PSF between multiple invaders has rarely been examined, but could inform the nature of invasive–invasive interactions and advance understanding of how multiple invaders impact plant communities. Alternative mechanisms of plant invasions include novel weapons and enemy escape. We develop graphical PSF predictions based on these mechanisms and other possible invasive–invasive dynamics. Comparing these predictions to observed results is a first step in interpreting PSF among co-occurring invasive species. We illustrate this with a case study of net pairwise PSF among three common invaders of tallgrass prairie: Lotus corniculatus (birdsfoot trefoil), Phalaris arundinacea (reed canarygrass), and Cirsium arvense (Canada thistle). We found that feedback among all pairwise combinations of these invasive species was neutral. Neutral feedback can arise from a mutual lack of soil borne pathogens, consistent with the enemy escape hypothesis, although we cannot rule out shared benefit from generalist mutualists. While both facilitative and competitive interactions among these three species have previously been shown, our data suggest that such interactions are unlikely to operate through a legacy effect of PSF. Our results inform follow-up PSF experiments that would help to confirm the existence and nature of PSF interactions among these species.

Continuous toolpath planning in a graphical framework for sparse infill additive manufacturing

We develop a framework that creates a new polygonal mesh representation of the sparse infill domain of a layer-by-layer 3D printing job. We guarantee the existence of a single, continuous tool path covering each connected piece of the domain in every layer in this graphical model. We also present a tool path algorithm that traverses each such continuous tool path with no crossovers.The key construction at the heart of our framework is a novel Euler transformation which converts a 2-dimensional cell complex K into a new 2-complex Kˆ such that every vertex in the 1-skeleton Gˆ of Kˆ has even degree. Hence Gˆ is Eulerian, and an Eulerian tour can be followed to print all edges in a continuous fashion without stops.We start with a mesh K of the union of polygons obtained by projecting all layers to the plane. First we compute its Euler transformation Kˆ. In the slicing step, we clip Kˆ at each layer using its polygon to obtain a complex that may not necessarily be Euler. We then patch this complex by adding edges such that any odd-degree nodes created by slicing are transformed to have even degrees again. We print extra support edges in place of any segments left out to ensure there are no edges without support in the next layer above. These support edges maintain the Euler nature of the complex. Finally, we describe a tree-based search algorithm that builds the continuous tool path by traversing “concentric” cycles in the Euler complex. Our algorithm produces a tool path that avoids material collisions and crossovers, and can be printed in a continuous fashion irrespective of complex geometry or topology of the domain (e.g., holes).We implement our test our framework on several 3D objects. Apart from standard geometric shapes including a nonconvex star, we demonstrate the framework on the Stanford bunny. Several intermediate layers in the bunny have multiple components as well as complicated geometries.

A Process Model of Causal Reasoning.

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model-the mutation sampler-that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis-Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process-level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it.

The Networks Infection Contagious Diseases Positioning System (NICDP-System): The Case of Wuhan-COVID-19

This paper formulates an analytical framework to understand the spatiotemporal patterns of epidemic disease occurrence, its relevance and implications to economic activity, their impact on the vulnerability of different populations, and to develop public health policy decisions on disease prevention issues. The paper suggests a paradigm shift: a new multidimensional geometric approach to capture all symmetrical and asymmetrical strategic graphical movements in real space and time. The Networks Infection Contagious Diseases Positioning System (NICDP-System) extends the traditional two-dimensional graphical representation into a multidimensional graphical framework that synchronizes the optimal virus behavior in real-time. The model investigates the epidemic 2019 novel coronavirus (COVID-19) identified in Wuhan City, Hubei Province, China.

Application of a Graphical Depiction of Longitudinal Study Designs to Managed Care Pharmacy Research.

Managed care pharmacists apply real-world evidence (RWE) to support activities such as pipeline forecasting, clinical policy development, and contracting for pharmaceutical products. Managed care pharmacy researchers strive to produce studies that can be applied in practice. While asking the right research question is necessary, it is not sufficient. As with all studies, consumers of RWE look for internal and external validity, as well as sources of bias, to determine how the study findings can be applied in their work. To date, however, some of the safeguards that exist for clinical trials-such as public registration of study protocols-are lacking for RWE. Several leading professional organizations have initiatives dedicated to improving the credibility and reliability of such research. One component common to these initiatives is enhanced transparency and completeness of methodologic reporting. Graphical representations of study designs can improve the reporting and design of research conducted in health care databases, specifically by enhancing the transparency and clarity of often complex studies. As such, Schneeweiss et al. (2019) proposed a graphical framework for longitudinal study designs in health care databases. Herein, we apply this framework to 2 studies published in the Journal of Managed Care & Specialty Pharmacy that represent common research designs and report how application of the framework revealed deficiencies in reporting. We advocate for adoption of this framework in the effort to increase the usability of RWE studies using health care databases by managed care pharmacy. DISCLOSURES: No funding was provided for this work. Gatwood has received research funding from Merck & Co., AstraZeneca, and GlaxoSmithKline, unrelated to this work. Schneeweiss is a consultant to Aetion, of which he also owns equity. He is the principal investigator of investigator-initiated grants to the Brigham and Women's Hospital from Bayer, Genentech, Boehringer Ingelheim, and Vertex. Wang reports support from investigator-initiated grants from Novartis, Boehringer Ingelheim, and Johnson & Johnson to Brigham and Women's Hospital, unrelated to this work. Happe and Brown have nothing to disclose.

Simulation study of transcatheter heart valve implantation in patients with stenotic bicuspid aortic valve.

Bicuspid aortic valve (BAV) anatomy has routinely been considered an exclusion in the setting of transcatheter aortic valve implantation (TAVI) because of the large dimension of the aortic annulus having a more calcified, bulky, and irregular shape. The study aims to develop a patient-specific computational framework to virtually simulate TAVI in stenotic BAV patients using the Edwards SAPIEN 3 valve (S3) and its improved version SAPIEN 3 Ultra and quantify stent frame deformity as well as the severity of paravalvular leakage (PVL). Specifically, the aortic root anatomy of n.9 BAV patients who underwent TAVI was reconstructed from pre-operative CT imaging. Crimping and deployment of S3 frame were performed and then followed by fluid-solid interaction analysis to simulate valve leaflet dynamics throughout the entire cardiac cycle. Modeling revealed that the S3 stent frame expanded well on BAV anatomy with an elliptical shape at the aortic annulus. Comparison of predicted S3 deformity as assessed by eccentricity and expansion indices demonstrated a good agreement with the measurement obtained from CT imaging. Blood particle flow analysis demonstrated a backward blood jet during diastole, whereas the predicted PVL flows corresponded well with those determined by transesophageal echocardiography. This study represents a further step towards the use of personalized simulations to virtually plan TAVI, aiming at improving not only the efficacy of the implantation but also the exploration of "off-label" applications as the TAVI in the setting of BAV patients. Graphical abstract Computational frameworks of TAVI in patients with stenotic bicuspid aortic valve.

Graphical Framework for Categorizing Data Capabilities and Properties of Objects in the Internet of Things

Things are the core of the Internet of Things (IoT) and must be properly characterized according to the different functions they accomplish. Identifying their capabilities and combining them as sets provides a view on the single or joint properties of existing things and guide in properly designing and building new things while maximizing their potential benefits within an IoT system or application. Building on five essential but independent capabilities of things (Identification, Localization, Sensing, Actuation, and Processing), four categories or groups of things are defined. These groups comprise a particular view of the diversity of objects found in the IoT, as trackable, data, interactive, or smart objects. In this paper, a description of the aforementioned capabilities is presented, stating how each of the groups of objects includes them. Then, given that data are the most important assets for both organizations and individuals a further description of the data objects group is made, proposing a graphical categorization framework that thoroughly describes and measures the level in which each of these capabilities is contained and how it contributes to the performance and data properties of any data object.

Multivariate analyses to determine fungicide efficacy on Ecuadorian bananas for consumption

Half maximal effective concentration EC 50 is considered the main reference for evaluating the efficacy of the products in any plantation using doses and inhibition percentages from laboratory data. However, EC 50 is not the best representation when other relevant variables and their relationships could be involved. As an agricultural case study, fungicide sensitivity of Pseudocercospora fijiensis , the causal agent of black sigatoka, was evaluated on bananas’ plantations in three provinces of Ecuador. In this study, multivariate statistical process control was adjusted to a fungicide efficacy evaluation case considering multiple data tables from different locations and years at the same time. The threshold conveyed by inhibition percentages, related to the EC , along with locations and years allowed the multivariate analyses carried out in the proposal. The multivariate statistical control techniques applied were Multilinear Principal Component Analysis (MPCA) and Dual STATIS-Parallel Coordinates approach (DS-PC). A comparison was developed and showed that both methods discriminate correctly between the normal and anomalous conditions within plantations along years, validating the ability of the novel method DS-PC for exhibiting better signaling of anomalous plantations and performing variable-wise analysis to find out possible causes of this behavior in an easier time-saving graphical framework.

Quantum Codes From Classical Graphical Models

We introduce a new graphical framework for designing quantum error correction codes based on classical principles. A key feature of this graphical language, over previous approaches, is that it is closely related to that of factor graphs or graphical models in classical information theory and machine learning. It enables us to formulate the description of the recently-introduced `coherent parity check' quantum error correction codes entirely within the language of classical information theory. This makes our construction accessible without requiring background in quantum error correction or even quantum mechanics in general. More importantly, this allows for a collaborative interplay where one can design new quantum error correction codes derived from classical codes.

Classifying aerosol particles through the combination of optical and physical-chemical properties: Results from a wintertime campaign in Rome (Italy)

The “Carbonaceous Aerosol in Rome and Environs” (CARE) experiment took place at a Mediterranean urban background site in Rome (Italy) deploying a variety of instrumentation to assess aerosol physical-chemical and optical properties with high-time resolution (from 1 min to 2 h). In this study, aerosol optical properties, chemical composition, and size distribution data were examined with a focus on the analysis of several intensive optical properties obtained from multi-wavelength measurements of aerosol scattering and absorption coefficients. The spectral behaviour of several quantities related to both aerosol composition and size was explored, analysing their high-time resolved temporal patterns and combining them in order to extract the maximum information from all the available data. A methodology to separate aerosol types using optical data only is here proposed and applied to an urban area characterised by a complex mixture of particles. A key is given to correctly disentangle cases that could not be distinguished observing only one or few parameters, but that can be clearly separated using a suitable ensemble of optical properties. The SSCAAE, i.e. the wavelength dependence of the Single Scattering co-albedo 1-SSA (where SSA is the Single Scattering Albedo) - that efficiently responds to both aerosol size and chemical composition – resulted to be the best optical intensive parameter to look at for the discrimination between episodes characterised by specific aerosol types (e.g. sea salt, Saharan dust) and more mixed conditions dominated by local emissions. However, this study also highlighted that it is necessary to combine temporal patterns of different optical parameters to robustly associate SSCAAE features to specific aerosol types. In addition, the complete chemical speciation and the high-time resolved size distribution were used to confirm the aerosol types identified via a combination of aerosol optical properties. Look-up tables with most suitable ranges of values for optical variables were produced; therefore, these pieces of information can be used at the same site or at locations with similar features to quickly identify the occurrence of aerosol episodes. Graphical frameworks (both from the literature and newly designed) are also proposed; for each scheme features, advantages, and limitations are discussed.