scholarly journals Author Correction: Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Nature Energy ◽  
2020 ◽  
Vol 5 (12) ◽  
pp. 1051-1052
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  
Nature Energy ◽  
2020 ◽  
Vol 5 (9) ◽  
pp. 666-673 ◽  
Author(s):  
Shiqi Ou ◽  
Xin He ◽  
Weiqi Ji ◽  
Wei Chen ◽  
Lang Sui ◽  
...  

2021 ◽  
Author(s):  
Jasmine A. Fels ◽  
Gabriella Casalena ◽  
Csaba Konrad ◽  
Holly Holmes ◽  
Ryan W. Dellinger ◽  
...  

Abstract Background: Majority of ALS cases are sporadic (sALS), as they lack defined genetic causes. Metabolic alterations shared between the nervous system and skin fibroblasts have emerged in ALS. Recently, we found that a subgroup of sALS fibroblasts (sALS1) is characterized by metabolic profiles (metabotype) distinct from other sALS cases (sALS2) and controls, suggesting that metabolic therapies could be effective in sALS. The metabolic modulators nicotinamide riboside and pterostilbene (EH301) are under clinical development for the treatment of ALS. Here, we studied the metabolome and transcriptome of sALS cells to understand the molecular bases of sALS metabotypes and the impact of EH301.Methods: Six fibroblast cell lines (3 male and 3 female subjects of similar ages) were used for each group (sALS1, sALS2, and controls). Metabolomics and transcriptomics were investigated at baseline and after EH301 treatment. Differential gene expression (DEGs) and metabolite abundance were assessed by a Wald Test and ANOVA, respectively, with FDR correction, and pathway analyses were performed. EH301 protection against metabolic stress was tested by thiol depletion. Weighted gene co-expression network analysis (WGCNA) was used to investigate the association of metabolic and clinical features and was also performed on the Answer ALS dataset from induced motor neurons (iMN). A machine learning model based on DEGs was tested as a sALS disease progression predictor. Results: We found that the sALS1 transcriptome is distinct from sALS2 and that EH301 modifies gene expression differently in sALS1, sALS2, and controls. Furthermore, EH301 had strong protective effects against metabolic stress, which is linked to anti-inflammatory and antioxidant pathways. WGCNA revealed that ALS functional rating scale and metabotypes are associated with gene modules enriched for cell cycle, immunity, autophagy, and metabolism terms, which are modified by EH301. Meta-analysis of publicly available transcriptomics data from iMNs confirmed functional associations of genes correlated with disease traits. A small subset of genes differentially expressed in sALS fibroblasts could be used in a machine learning model to predict disease progression.Conclusions: Multi-omics analyses of patient-derived fibroblasts highlighted differential metabolic and transcriptomic profiles in sALS metabotypes, which translate into differential responses to the investigational drug EH301.


2022 ◽  
Vol 14 (2) ◽  
pp. 691
Author(s):  
David Dominguez ◽  
Luis de Juan del Villar ◽  
Odette Pantoja ◽  
Mario González-Rodríguez

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.


2021 ◽  
Vol 192 ◽  
pp. 2624-2632
Author(s):  
Laura Verde ◽  
Fiammetta Marulli ◽  
Stefano Marrone

Author(s):  
Tong Wang ◽  
Cheng He ◽  
Fujie Jin ◽  
Yu Jeffrey Hu

We develop a novel interpretable machine learning model, GANNM, and use newly available data to evaluate how different types of marketing campaigns and budget allocations influence malls’ customer traffic. We observe that the response curves that measure the impact of campaign budget on customer traffic differ for different categories of campaigns, with sales incentives or experience incentives, during peak periods, off-peak periods, or online promotion periods. Based on such accurate response curves from GANNM, the optimized budget allocation is estimated to yield a 11.2% increase in customer traffic compared with the original allocation. Our findings provide novel insights on managing mall campaigns. Mall managers should increase marketing spending to areas that were likely overlooked before and avoid over-crowding budget to campaigns during times with high levels of competition and are likely already over-marketed. We provide empirical evidence showing that the recent trend of employing novel approaches for enhancing customer experience in physical stores can effectively encourage customers to visit malls. Furthermore, we show that online promotions could also create opportunities for offline businesses—investing in campaigns in the major online promotion periods could significantly increase customer traffic for malls, given sufficient investment in the campaigns to raise customer awareness.


2021 ◽  
Vol 7 ◽  
Author(s):  
Yabei Xu ◽  
Qingzhao Chu ◽  
Dongping Chen ◽  
Andrés Fuentes

A large number of PAH molecules is collected from recent literature. The HOMO-LUMO gap value of PAHs was computed at the level of B3LYP/6-311+G (d,p). The gap values lie in the range of 0.64–6.59 eV. It is found that the gap values of all PAH molecules exhibit a size dependency to some extent. However, the gap values may show a big variation even at the same size due to the complexity in the molecular structure. All collected PAHs are further classified into seven groups according to features in the structures, including the types of functional groups and the molecular planarity. The impact of functional groups, including –OH, –CHO, –COOH, =O, –O– and –CnHm on the bandgap is discussed in detail. The substitution of ketone group has the greatest reduction on the HOMO-LUMO gap of PAH molecules. Besides functional groups, we found that both local structure and the position of five-member rings make critical impacts on the bandgap via a detailed analysis of featured PAHs with unexpected low and high gap values. Among all these factors, the five-member rings forming nonplanar PAHs impact the gap most. Furthermore, we developed a machine learning model to predict the HOMO-LUMO gaps of PAHs, and the average absolute error is only 0.19 eV compared with the DFT calculations. The excellent performance of the machine learning model provides us an accurate and efficient way to explore the band information of PAHs in soot formation.


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