scholarly journals Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Chao Wang ◽  
Samantha M. Scott ◽  
Kanagaraj Subramanian ◽  
Salvatore Loguercio ◽  
Pei Zhao ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Membrane Trafficking ◽  
Histone Deacetylases ◽  
Gaussian Process Regression ◽  
Rapid Onset ◽  
Cholesterol Homeostasis ◽  
Spatial Covariance ◽  
Npc1 Gene ◽  
The Individual ◽  
The Impact

Abstract To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual.

scholarly journals Managing the Spatial Covariance of Genetic Diversity in Niemann-Pick C1 Through Modulation of the Hsp70 Chaperone System

2018 ◽  
Author(s):  
Chao Wang ◽  
Samantha M Scott ◽  
Darren M Hutt ◽  
Pei Zhao ◽  
Hao Shao ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Transmembrane Protein ◽  
Genetic Disorder ◽  
Rapid Onset ◽  
Cholesterol Homeostasis ◽  
Nucleotide Exchange ◽  
Spatial Covariance ◽  
Hsp70 Chaperone ◽  
Niemann Pick ◽  
The Impact

ABSTRACTGenetic diversity provides a rich repository for understanding the role of proteostasis in the management of the protein fold to allow biology to evolve through variation in the population and in response to the environment. Failure in proteostasis can trigger multiple disease states affecting both human health and lifespan. Niemann-Pick C (NPC) disease is a genetic disorder mainly caused by mutations in NPC1, a multi-spanning transmembrane protein that is trafficked through the exocytic pathway to late endosomes and lysosomes (LE/Ly) to manage cholesterol homeostasis. Proteostatic defects triggered by >600 NPC1 variants found in the human population inhibit export of NPC1 protein from ER or function in downstream LE/Ly, leading to accumulation of cholesterol and rapid onset neurodegeneration in childhood for most patients. We now show that chemical allosteric inhibitors, such as JG98, targeting the cytosolic Hsp70 chaperone/co-chaperone complex improves the trafficking and stability of NPC1 variants with diverse NPC1 genotypes. By exploiting the knowledge-base of NPC1 variants found in the world-wide patient population using Variation Spatial Profiling (VSP), a Gaussian-process based machine learning (ML) approach, we show how the Hsp70 chaperone system alters the spatial covariance (SCV) tolerance of the ER and the SCV set-points for each residue of the NPC1 polypeptide chain differentially to improve trafficking efficiency and post-ER stability for variants distributed across the entire NPC1 polypeptide. The impact of JG98 is supported by the observation that silencing of Hsp70 specific nucleotide exchange factors (NEF) (BCL-anthogene (BAG) family) co-chaperones significantly improve the folding status of NPC1 variants. Together, these studies suggest that targeting the cytosolic Hsp70 system to adjust the SCV tolerance of the proteostasis network can improve recognition of the plasticity of the NPC1 fold found in the disease population for trafficking to the LE/Ly compartments.

scholarly journals Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

2021 ◽  
Vol 19 ◽  
pp. 41-48
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Monte Carlo Analysis ◽  
Computational Effort ◽  
Benchmark Problem ◽  
Yield Optimization ◽  
Multi Objective ◽  
Electromagnetic Devices ◽  
The Impact

Abstract. Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

scholarly journals Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Majedeh Gheytanzadeh ◽  
Alireza Baghban ◽  
Sajjad Habibzadeh ◽  
Amin Esmaeili ◽  
Otman Abida ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Fossil Fuels ◽  
Global Climate ◽  
Gaussian Process Regression ◽  
Accurate Estimate ◽  
Kernel Functions ◽  
Predictive Tool ◽  
Input Variables ◽  
Capture Techniques ◽  
The Impact

AbstractIn recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

scholarly journals Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method

2021 ◽  
Vol 13 (7) ◽  
pp. 3665
Author(s):  
Ying Wang ◽  
Bo Feng ◽  
Qing-Song Hua ◽  
Li Sun
Keyword(s):  
Gaussian Process ◽  
Hybrid Model ◽  
Solar Power ◽  
Short Term Memory ◽  
Gaussian Process Regression ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Individual ◽  
Solar Power Forecasting

Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

scholarly journals Spatial Covariant Management of Thermodynamics Contributes to Protein Fold Function through Quality Assurance

2021 ◽  
Author(s):  
Frederic Angles ◽  
Chao Wang ◽  
William Balch
Keyword(s):  
Quality Assurance ◽  
Thermodynamic Properties ◽  
Gaussian Process Regression ◽  
Protein Fold ◽  
Spatial Covariance ◽  
General Quality ◽  
Er Export ◽  
The Impact

Abstract Although the impact of genome variation on the thermodynamic properties of the protein fold has been studied in vitro, it remains a challenge to assign these relationships across the entire polypeptide sequence in vivo. Using the Gaussian process regression-based principle of Spatial CoVariance (SCV), we globally assign on a residue-by-residue the biological thermodynamic properties contributing to the functional fold in the cell using CFTR as an example. We demonstrate the existence of a thermodynamically sensitive region of the CFTR fold involving the interface between NBD1 and ICL4 that contributes to the endoplasmic reticulum (ER) export. At the cell surface a new set of residues contribute uniquely to the management of channel function. These results support a general 'quality assurance' (QA) view of global protein fold management as an SCV principle describing the differential pre- and post-ER residue interactions contributing to compartmentalization of the energetics of the protein fold for function.

Accent Policy and Accent Modification Enterprises as Potential Indicators of Intercultural Power Relations: A Call for an Updated Research Agenda

2013 ◽  
Vol 3 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Brynne D. Ovalle ◽  
Rahul Chakraborty
Keyword(s):  
International Relations ◽  
Identity Construction ◽  
Social Theory ◽  
Power Relations ◽  
Linguistic Diversity ◽  
Cultural Organization ◽  
Using Data ◽  
The Individual ◽  
The Impact ◽  
The Relationship

This article has two purposes: (a) to examine the relationship between intercultural power relations and the widespread practice of accent discrimination and (b) to underscore the ramifications of accent discrimination both for the individual and for global society as a whole. First, authors review social theory regarding language and group identity construction, and then go on to integrate more current studies linking accent bias to sociocultural variables. Authors discuss three examples of intercultural accent discrimination in order to illustrate how this link manifests itself in the broader context of international relations (i.e., how accent discrimination is generated in situations of unequal power) and, using a review of current research, assess the consequences of accent discrimination for the individual. Finally, the article highlights the impact that linguistic discrimination is having on linguistic diversity globally, partially using data from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and partially by offering a potential context for interpreting the emergence of practices that seek to reduce or modify speaker accents.

Investigating the Relationship Between Self-Esteem and Stigma Among Young Adults With History of Suicide Attempts

Crisis ◽  
2016 ◽  
Vol 37 (4) ◽  
pp. 265-270 ◽  
Author(s):  
Meshan Lehmann ◽  
Matthew R. Hilimire ◽  
Lawrence H. Yang ◽  
Bruce G. Link ◽  
Jordan E. DeVylder
Keyword(s):  
Young Adults ◽  
Suicide Attempts ◽  
Self Esteem ◽  
High Risk Population ◽  
High Functioning ◽  
People With Mental Illness ◽  
History Of ◽  
The Individual ◽  
The Impact ◽  
The Relationship

Abstract. Background: Self-esteem is a major contributor to risk for repeated suicide attempts. Prior research has shown that awareness of stigma is associated with reduced self-esteem among people with mental illness. No prior studies have examined the association between self-esteem and stereotype awareness among individuals with past suicide attempts. Aims: To understand the relationship between stereotype awareness and self-esteem among young adults who have and have not attempted suicide. Method: Computerized surveys were administered to college students (N = 637). Linear regression analyses were used to test associations between self-esteem and stereotype awareness, attempt history, and their interaction. Results: There was a significant stereotype awareness by attempt interaction (β = –.74, p = .006) in the regression analysis. The interaction was explained by a stronger negative association between stereotype awareness and self-esteem among individuals with past suicide attempts (β = –.50, p = .013) compared with those without attempts (β = –.09, p = .037). Conclusion: Stigma is associated with lower self-esteem within this high-functioning sample of young adults with histories of suicide attempts. Alleviating the impact of stigma at the individual (clinical) or community (public health) levels may improve self-esteem among this high-risk population, which could potentially influence subsequent suicide risk.

The Role of Threat and Economic Uncertainty in Support for Scottish Independence

2014 ◽  
Vol 23 (1) ◽  
pp. 103-124 ◽  
Author(s):  
Daniel Kopasker
Keyword(s):  
Economic Consequences ◽  
Perceived Threat ◽  
Constitutional Change ◽  
Lack Of Information ◽  
Economic Expectations ◽  
Scottish Independence ◽  
The Individual ◽  
The Impact ◽  
Levels Of Support

Existing research has consistently shown that perceptions of the potential economic consequences of Scottish independence are vital to levels of support for constitutional change. This paper attempts to investigate the mechanism by which expectations of the economic consequences of independence are formed. A hypothesised causal micro-level mechanism is tested that relates constitutional preferences to the existing skill investments of the individual. Evidence is presented that larger skill investments are associated with a greater likelihood of perceiving economic threats from independence. Additionally, greater perceived threat results in lower support for independence. The impact of uncertainty on both positive and negative economic expectations is also examined. While uncertainty has little effect on negative expectations, it significantly reduces the likelihood of those with positive expectations supporting independence. Overall, it appears that a general economy-wide threat is most significant, and it is conjectured that this stems a lack of information on macroeconomic governance credentials.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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