scholarly journals Higher-order clinical risk factor interaction analysis for overall mortality in maintenance hemodialysis patients

2020 ◽  
Vol 11 ◽  
pp. 204062232094906
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Li-Yeh Chuang ◽  
Jin-Bor Chen
Keyword(s):  
Interaction Analysis ◽  
Interaction Model ◽  
Higher Order ◽  
Survival Outcome ◽  
Maintenance Hemodialysis ◽  
Order Interaction ◽  
Statistical Approaches ◽  
The Impact ◽  
Factor Interaction ◽  
Overall Mortality

Background and Aims: In Taiwan, approximately 90% of patients with end-stage renal disease receive maintenance hemodialysis. Although studies have reported the survival predictability of multiclinical factors, the higher-order interactions among these factors have rarely been discussed. Conventional statistical approaches such as regression analysis are inadequate for detecting higher-order interactions. Therefore, this study integrated receiver operating characteristic, logistic regression, and balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction (MDR-ER) analyses to examine the impact of interaction effects between multiclinical factors on overall mortality in patients on maintenance hemodialysis. Meterials and Methods: In total, 781 patients who received outpatient hemodialysis dialysis three times per week before 1 January 2009 were included; their baseline clinical factor and mortality outcome data were retrospectively collected using an approved data protocol (201800595B0). Results: Consistent with conventional statistical approaches, the higher-order interaction model could indicate the impact of potential risk combination unique to patients on maintenance hemodialysis on the survival outcome, as described previously. Moreover, the MDR-based higher-order interaction model facilitated higher-order interaction effect detection among multiclinical factors and could determine more detailed mortality risk characteristics combinations. Conclusion: Therefore, higher-order clinical risk interaction analysis is a reasonable strategy for detecting non-traditional risk factor interaction effects on survival outcome unique to patients on maintenance hemodialysis and thus clinically achieving whole-scale patient care.

The impact of high-order interactions on the rate of synchronous discharge and information transmission in somatosensory cortex

2009 ◽  
Vol 367 (1901) ◽  
pp. 3297-3310 ◽  
Author(s):  
Fernando Montani ◽  
Robin A. A. Ince ◽  
Riccardo Senatore ◽  
Ehsan Arabzadeh ◽  
Mathew E. Diamond ◽  
...  
Keyword(s):  
Neural Networks ◽  
Somatosensory Cortex ◽  
Local Population ◽  
Interaction Model ◽  
Higher Order ◽  
High Order ◽  
Neural Population ◽  
Somatosensory Information ◽  
Representational Capacity ◽  
The Impact

Understanding the operations of neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a local population of neurons, a macroscopic index of the activation of the neural network that can be measured experimentally. We analyse a model based on physics’ maximum entropy principle that evaluates whether the probability of synchronous discharge can be described by interactions up to any given order. When compared with real neural population activity obtained from the rat somatosensory cortex, the model shows that interactions of at least order three or four are necessary to explain the data. We use Shannon information to compute the impact of high-order correlations on the amount of somatosensory information transmitted by the rate of synchronous discharge, and we find that correlations of higher order progressively decrease the information available through the neural population. These results are compatible with the hypothesis that high-order interactions play a role in shaping the dynamics of neural networks, and that they should be taken into account when computing the representational capacity of neural populations.

Keynote

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Jeanne LIEDTKA
Keyword(s):  
Organizational Performance ◽  
Design Thinking ◽  
Social Systems ◽  
Field Research ◽  
Higher Order ◽  
Lens Design ◽  
Social Sector ◽  
Extensive Field ◽  
Innovative Solutions ◽  
The Impact

The value delivered by design thinking is almost always seen to be improvements in the creativity and usefulness of the solutions produced. This paper takes a broader view of the potential power of design thinking, highlighting its role as a social technology for enhancing the productivity of conversations for change across difference. Examined through this lens, design thinking can be observed to aid diverse sets of stakeholders’ abilities to work together to both produce higher order, more innovative solutions and to implement them more successfully. In this way, it acts as a facilitator of the processes of collectives, by enhancing their ability to learn, align and change together. This paper draws on both the author’s extensive field research on the use of design thinking in social sector organizations, as well as on the literature of complex social systems, to discuss implications for both practitioners and scholars interested in assessing the impact of design thinking on organizational performance.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

Statistical Approaches to Structural Change in Regional Interindustry Models

1986 ◽  
Vol 18 (9) ◽  
pp. 1189-1207
Author(s):  
B Ó Huallacháin
Keyword(s):  
Structural Change ◽  
Structural Changes ◽  
Washington State ◽  
Multivariate Statistical Analyses ◽  
Input Output ◽  
Multivariate Statistical ◽  
State Data ◽  
System A ◽  
Statistical Approaches ◽  
The Impact

The conventional approach to assessing structural change in regional input – output tables is to measure the impact of coefficient change on the estimation of outputs and multipliers. The methods developed and tested in this paper focus exclusively on the coefficients. Univariate and multivariate statistical analyses can be used to identify and measure various types of changes ranging from coefficient instability to changes in interindustry relationships as a system. A distinction is made between structural changes in input relationships and those in output relationships. The methods are tested by using Washington State data for the years 1963 and 1967. The results are compared with previous analyses of change in these data.

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

VERY HIGH-ORDER SPATIALLY IMPLICIT SCHEMES FOR COMPUTATIONAL ACOUSTICS ON CURVILINEAR MESHES

2001 ◽  
Vol 09 (04) ◽  
pp. 1259-1286 ◽  
Author(s):  
MIGUEL R. VISBAL ◽  
DATTA V. GAITONDE
Keyword(s):  
Three Dimensional ◽  
Radiation Condition ◽  
Higher Order ◽  
High Order ◽  
High Frequencies ◽  
Decomposition Strategies ◽  
The Impact ◽  
Spurious Modes ◽  
Very High ◽  
Computational Strategy

A high-order compact-differencing and filtering algorithm, coupled with the classical fourth-order Runge–Kutta scheme, is developed and implemented to simulate aeroacoustic phenomena on curvilinear geometries. Several issues pertinent to the use of such schemes are addressed. The impact of mesh stretching in the generation of high-frequency spurious modes is examined and the need for a discriminating higher-order filter procedure is established and resolved. The incorporation of these filtering techniques also permits a robust treatment of outflow radiation condition by taking advantage of energy transfer to high-frequencies caused by rapid mesh stretching. For conditions on the scatterer, higher-order one-sided filter treatments are shown to be superior in terms of accuracy and stability compared to standard explicit variations. Computations demonstrate that these algorithmic components are also crucial to the success of interface treatments created in multi-domain and domain-decomposition strategies. For three-dimensional computations, special metric relations are employed to assure the fidelity of the scheme in highly curvilinear meshes. A variety of problems, including several benchmark computations, demonstrate the success of the overall computational strategy.

Overcoming resistance to product rebranding

2015 ◽  
Vol 24 (6) ◽  
pp. 621-632 ◽  
Author(s):  
Véronique Collange ◽  
Adrien Bonache
Keyword(s):  
Indirect Effect ◽  
Consumer Attitudes ◽  
Higher Order ◽  
Brand Name ◽  
Negative Effects ◽  
Marketing Practice ◽  
Content Type ◽  
Name Change ◽  
Marketing Managers ◽  
The Impact

Purpose – The purpose of this article is to understand how and why consumers resist or accept product rebranding. It seeks to identify and to quantify the drivers of attitudes toward this marketing practice to guide marketing managers in the execution of an effective changeover. Design/methodology/approach – The research is conducted in three stages. First, a qualitative study is run among 45 consumers to identify variables that might influence attitudes toward product rebranding. Second, a review of literature on the emotion of surprise is carried out to specify the relationships between the variables previously identified and to formulate hypotheses. Third, a quantitative study is conducted among 480 consumers to test the hypotheses and to quantify the impact of each variable. Findings – Surprise impacts attitudes toward product rebranding through a three-way process (automatic, higher-order cognitive, higher-order affective): a direct negative effect, an indirect effect mediated by incomprehension about the reasons for the change and an indirect effect mediated by the negative emotions generated by the change. Moreover, trust in firms diminishes the negative effects of anger, fear and sadness on attitudes toward product rebranding. Research limitations/implications – The research offers a better understanding of processes involved in the building of consumer attitudes toward brand name change. However, it only constitutes a first step in the attempt to understand the phenomena. Practical implications – This practice of brand name change is increasingly popular, but marketing managers are skeptical about the best way to implement it. The paper provides a better understanding of consumer reactions to product rebranding, so that marketing managers can make better decisions. It reveals guidance for successful brand name changes. Originality/value – This paper is the first to propose and to test a comprehensive model of the mental processes involved in the building of consumer attitudes toward product rebranding.

scholarly journals Influence of Even-Order Dispersion on Super-Sech Soliton Transmission Quality under Coherent Crosstalk

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Aleksandra Panajotovic ◽  
Daniela Milovic ◽  
Anjan Biswas ◽  
Essaid Zerrad
Keyword(s):  
Fourth Order ◽  
Optical Network ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Higher Order ◽  
Trailing Edges ◽  
Even Order ◽  
Transmission Quality ◽  
The Impact ◽  
Order Dispersion

The transmission speed of optical network strongly depends on the impact of higher order dispersion. In presence of coherent crosstalk, which cannot be otherwise controlled by optical filtering, the impact of higher order dispersions becomes more pronounced. In this paper, the general expressions, that describe pulse deformation due to second- and fourth-order dispersions in a single-mode fiber, are given. The responses for such even-order dispersions, in presence of coherent crosstalk, are characterized by waveforms with long trailing edges. The transmission quality of optical pulses, due to both individual and combined influence of second- and fourth-order dispersions, is studied in this paper. Finally, the pulse shape and eye diagrams are obtained.

The impact of higher-order state and control effects on local relational reasoning

2012 ◽  
Vol 22 (4-5) ◽  
pp. 477-528 ◽  
Author(s):  
DEREK DREYER ◽  
GEORG NEIS ◽  
LARS BIRKEDAL
Keyword(s):  
Recent Work ◽  
Possible Worlds ◽  
Higher Order ◽  
Local State ◽  
Relational Reasoning ◽  
Logical Relation ◽  
Game Semantics ◽  
And Control ◽  
The Impact ◽  
Order State

AbstractReasoning about program equivalence is one of the oldest problems in semantics. In recent years, useful techniques have been developed, based on bisimulations and logical relations, for reasoning about equivalence in the setting of increasingly realistic languages—languages nearly as complex as ML or Haskell. Much of the recent work in this direction has considered the interesting representation independence principles enabled by the use of local state, but it is also important to understand the principles that powerful features like higher-order state and control effects disable. This latter topic has been broached extensively within the framework of game semantics, resulting in what Abramsky dubbed the “semantic cube”: fully abstract game-semantic characterizations of various axes in the design space of ML-like languages. But when it comes to reasoning about many actual examples, game semantics does not yet supply a useful technique for proving equivalences.In this paper, we marry the aspirations of the semantic cube to the powerful proof method of step-indexed Kripke logical relations. Building on recent work of Ahmed et al. (2009), we define the first fully abstract logical relation for an ML-like language with recursive types, abstract types, general references and call/cc. We then show how, under orthogonal restrictions to the expressive power of our language—namely, the restriction to first-order state and/or the removal of call/cc—we can enhance the proving power of our possible-worlds model in correspondingly orthogonal ways, and we demonstrate this proving power on a range of interesting examples. Central to our story is the use of state transition systems to model the way in which properties of local state evolve over time.

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