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Abstract While most financial engineering and econophysics studies have focused in daytime trading, much less investigation has been devoted to the non-trading or night periods. In this work, the correlation between overnight and daytime return (correlation ND) and the correlation between daytime return and following over night return (correlation DF) were investigated, which led to several findings. First, a weak negative correlation between overnight and daytime return (correlation ND) was observed in Japanese Stocks Market. Secondly, the application of Volatility Constrained correlation (VC correlation) method led to a significant amplification of this signal which benefits for increasing predictability of day time return compared to standard correlation. Furthermore, the analysis of the amplified signal derived from VC correlation for each stock revealed a linear scale relationship between the standard correlation and VC correlation. Therefore, this result indicates that by using the VC correlation, stronger correlation effect can be observed. Taking together, these findings suggest that the combination of VC approach with financial trading data over night paves the way to improve market predictability. Highlights • Weak negative correlation between overnight and daytime return was observed in Japan Stock Market. • VC correlation amplified this weak negative correlation between overnight and daytime return. • A linear scale relationship between the standard correlation and VC correlation was observed. [ABSTRACT FROM AUTHOR]

Financial data, such as financial statements, contain valuable and critical information that may assist stakeholders and investors in optimizing their capital to maximize overall economic growth. Since there are many variables in financial statements, it is crucial to determine the causal relationships, that is, the directional influence between them in a structural way, as well as to understand the associated accounting mechanisms. However, the analysis of variable-to-variable relationships in financial information using standard correlation functions is not sufficient to unveil directionality. Here, we use the volatility constrained correlation (VC correlation) method to predict the directional relationship between two arbitrary variables. We apply the VC correlation method to five significant financial information variables (revenue, net income, operating income, own capital, and market capitalization) of 2321 firms listed on the Tokyo Stock Exchange over 28 years from 1990 to 2018. This study identifies which accounting variables are influential and which are susceptible. Our findings show that operating income is the most influential variable while market capitalization and revenue are the most susceptible variables. Surprisingly, the results differ from the existing intuitive understanding suggested by widely used investment strategy indicators, the price–earnings ratio and the price-to-book ratio, which report that net income and own capital are the most influential variables affecting market capitalization. This analysis may assist managers, stakeholders, and investors to improve financial management performance and optimize firms' financial strategies in future operations. • We apply the VC correlation method to five accounting variables for Japanese firms. • The directions of influence among financial accounting variables are determined. • Operating income is the most influential variable. • Market capitalization and revenue are the most susceptible variables. [ABSTRACT FROM AUTHOR]

To uncover potential disease molecular pathways and signaling networks, we do not only need undirected maps but also we need to infer the directionality of functional or physical interactions between cellular components. A wide range of methods for identifying functional interactions between genes relies on correlations between experimental gene expression measurements to some extent. However, the standard Pearson or Spearman correlation-based approaches can only determine undirected correlations between cellular components. Here, we apply a volatility-constrained correlation method for gene expression profiles that offers a new metric to capture directionality of interactions between genes. To evaluate the predictions we used four datasets distributed by the DREAM5 network inference challenge including an in silico-constructed network and three organisms such as S. aureus , E. coli and S. cerevisiae . The predictions performed by our proposed method were compared to a gold standard of experimentally verified directionality of genetic regulatory links. Our findings show that our method successfully predicts the genetic interaction directionality with a success rate higher than 0.5 with high statistical significance. [ABSTRACT FROM AUTHOR]

The fast increase of ‘multi-omics’ data does not only pose a computational challenge for its analysis but also requires novel algorithmic methodologies to identify complex biological patterns and decipher the ultimate roots of human disorders. To that end, the massive integration of omics data with disease phenotypes is offering a new window into the cell functionality. The minimum dominating set (MDS) approach has rapidly emerged as a promising algorithmic method to analyze complex biological networks integrated with human disorders, which can be composed of a variety of omics data, from proteomics and transcriptomics to metabolomics. Here we review the main theoretical foundations of the methodology and the key algorithms, and examine the recent applications in which biological systems are analyzed by using the MDS approach. [ABSTRACT FROM AUTHOR]

Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called controllers. However, the real systems represented by networks contain unreliable components and modern robust control engineering has not addressed the problem of structural changes on complex networks including scale-free topologies. Here, we introduce the concept of structurally robust control of complex networks and provide a concrete example using an algorithmic framework that is widely applied in engineering. The developed analytical tools, computer simulations, and real network analyses lead herein to the discovery that robust control can be achieved in scale-free networks with exactly the same order of controllers required in a standard nonrobust configuration by adjusting only the minimum degree. The presented methodology also addresses the probabilistic failure of links in real systems, such as neural synaptic unreliability in Caenorhabditis elegans, and suggests a new direction to pursue in studies of complex networks in which control theory has a role. [ABSTRACT FROM AUTHOR]

Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are typically risk-averse with respect to gains and risk-seeking with respect to losses, known as the “reflection effect”. People are much more sensitive to losses than to gains of the same magnitude, a phenomenon called “loss aversion”. Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of the previous studies have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the reflection effect and the loss aversion phenomenon, which are essential in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the impact of the reflection effect and the loss aversion phenomenon. Moreover, we introduce three novel behavioral metrics to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading where traders are allowed to watch and follow the trading activities of others, by predicting potential winners based on their historical trading behavior. [ABSTRACT FROM AUTHOR]

Abstract: Recent financial crises have shown the importance of determining the directionality of the influence between financial assets in order to identify the origin of market instabilities. Here, we analyze the correlation between Japan’s Nikkei stock average index (Nikkei 225) and other financial markets by introducing a volatility-constrained correlation metric. The asymmetric feature of the metric reveals which asset is more influential than the other. As a result, this method allows us to unveil the directionality of the correlation effect, which could not be observed from the standard correlation analysis. Furthermore, we present a theoretical model that reproduces the results observed in empirical analysis. [Copyright &y& Elsevier]

The possibility of controlling and directing a complex system's behavior at will is rooted in its interconnectivity and can lead to significant advances in disparate fields, ranging from nationwide energy saving to therapies that involve multiple targets. In this work, we address complex network controllability from the perspective of the minimum dominating set (MDS). Our theoretical calculations, simulations using artificially generated networks as well as real-world network analyses show that the more heterogeneous a network degree distribution is, the easier it is to control the entire system.We demonstrate that relatively few nodes are needed to control the entire network if the powerlaw degree exponent is smaller than 2, whereas many nodes are required if it is larger than 2. [ABSTRACT FROM AUTHOR]

Increasingly accessible financial data allow researchers to infer market-dynamics-based laws and to propose models that are able to reproduce them. In recent years, several stylized facts have been uncovered. Here we perform an extensive analysis of foreign exchange data that leads to the unveiling of a statistical financial law. First, our findings show that, on average, volatility increases more when the price exceeds the highest (or lowest) value, i.e., breaks the resistance line. We call this the breaking-acceleration effect. Second, our results show that the probability P(T) to break the resistance line in the past time T follows power law in both real data and theoretically simulated data. However, the probability calculated using real data is rather lower than the one obtained using a traditional Black-Scholes (BS) model. Taken together, the present analysis characterizes a different stylized fact of financial markets and shows that the market exceeds a past (historical) extreme price fewer times than expected by the BS model (the resistance effect). However, when the market does, we predict that the average volatility at that time point will be much higher. These findings indicate that any Markovian model does not faithfully capture the market dynamics. [ABSTRACT FROM AUTHOR]

Recent studies have highlighted the importance of interconnectivity in a large range of molecular and human diseaserelated systems. Network medicine has emerged as a new paradigm to deal with complex diseases. Connections between protein complexes and key diseases have been suggested for decades. However, it was not until recently that protein complexes were identified and classified in sufficient amounts to carry out a large-scale analysis of the human protein complex system. We here present the first systematic and comprehensive set of relationships between protein complexes and associated drugs and analyzed their topological features. The network structure is characterized by a high modularity, both in the bipartite graph and in its projections, indicating that its topology is highly distinct from a random network and that it contains a rich and heterogeneous internal modular structure. To unravel the relationships between modules of protein complexes, drugs and diseases, we investigated in depth the origins of this modular structure in examples of particular diseases. This analysis unveils new associations between diseases and protein complexes and highlights the potential role of polypharmacological drugs, which target multiple cellular functions to combat complex diseases driven by gain-of-function mutations. [ABSTRACT FROM AUTHOR]

Abstract: The widespread use of microarrays provided a first glimpse at some simple laws and organizing principles that govern the transcriptome. Previous analyses have shown that the transcriptional organization is very heterogeneous and characterized by a power–law decay for gene expression levels. Moreover, a simple law was unveiled suggesting that gene expression dynamic changes under stress are proportional to their initial expression values. However, to elucidate and assess the underlying governing principles of transcriptional organization, we do not only need to identify them, but also provide theoretical models that are able to faithfully capture and reproduce them. Here we present a method to investigate the gene expression dynamics inspired by the theory of nonlinear transformation of random signals and noise. The model is able to explain not only the well-known power–law decay for abundance of expression levels, but also to reproduce the linear dependence of the standard deviation of gene expression change with respect to the initial expression value (also known as rich-travels-more dynamics). To our knowledge, this is the first model applied to gene expression dynamics that is able to simultaneously predict both statistical features. The theoretical framework derives an indicator to measure the coupling between gene expression and specific perturbations. Using genome-wide transcriptional data, this indicator identifies genes strongly coupled to specific inflammatory responses to different pathogens. The novel application of signal and noise theory to study intracellular responses and gene expression changes offers not only a new theoretical avenue to study transcriptional responses to environmental stresses and chemical signals but also provides predictive capability at the genome scale. [Copyright &y& Elsevier]

Background: After IR stress, DNA double-strand breaks (DSBs) occur and repair proteins (RPs) bind to them, generating DSBRP complexes (DSBCs), which results in repaired DSBs (RDSBs). In recent experimental studies, it is suggested that the ATM proteins detect these DNA lesions depending on the autophosphorylation of ATM which exists as a dimer before phosphorylation. Interestingly, the ATM proteins can work as a sensor for a small number of DSBs (approximately 18 DSBs in a cell after exposure to IR). Thus the ATM proteins amplify the small input signals based on the phosphorylation of the ATM dimer proteins. The true DSB-detection mechanism depending on ATM autophosphorylation has yet to be clarified. Methodology/Principal Findings: We propose a mathematical model for the detection mechanism of DSBs by ATM. Our model includes both a DSB-repair mechanism and an ATM-phosphorylation mechanism. We model the former mechanism as a stochastic process, and obtain theoretical mean values of DSBs and DSBCs. In the latter mechanism, it is known that ATM autophosphorylates itself, and we find that the autophosphorylation induces bifurcation of the phosphorylated ATM (ATM*). The bifurcation diagram depends on the total concentration of ATM, which makes three types of steady state diagrams of ATM*: monostable, reversible bistable, and irreversible bistable. Bistability exists depending on the Hill coefficient in the equation of ATM autophosphorylation, and it emerges as the total concentration of ATM increases. Combining these two mechanisms, we find that ATM* exhibits switch-like behaviour in the presence of bistability, and the detection time after DNA damage decreases when the total concentration of ATM increases. Conclusions/Significance: This work provides a mathematical model that explains the DSB-detection mechanism depending on ATM autophosphorylation. These results indicate that positive auto-regulation works both as a sensor and amplifier of small input signals. [ABSTRACT FROM AUTHOR]

Background: It is becoming increasingly accepted that a shift is needed from the traditional target-based approach of drug development towards an integrated perspective of drug action in biochemical systems. To make this change possible, the interaction networks connecting drug targets to all components of biological systems must be identified and characterized. Results: We here present an integrative analysis of the interactions between drugs and metabolism by introducing the concept of metabolic drug scope. The metabolic drug scope represents the full set of metabolic compounds and reactions that are potentially affected by a drug. We constructed and analyzed the scopes of all US approved drugs having metabolic targets. Our analysis shows that the distribution of metabolic drug scopes is highly uneven, and that drugs can be classified into several categories based on their scopes. Some of them have small scopes corresponding to localized action, while others have large scopes corresponding to potential largescale systemic action. These groups are well conserved throughout different topologies of the underlying metabolic network. They can furthermore be associated to specific drug therapeutic properties. Conclusion: These findings demonstrate the relevance of metabolic drug scopes to the characterization of drug-metabolism interactions and to understanding the mechanisms of drug action in a system-wide context. [ABSTRACT FROM AUTHOR]

We present an extended version of max-plus algebra which includes the inverse operator of “max.” This algebra enables us to ultradiscretize the system including subtractions and obtain new ultradiscrete equations. The known ultradiscrete equations can also be recovered by this construction. [ABSTRACT FROM AUTHOR]

Background: Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. In general, the second network may be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. While the chemical compound network has been classified as hierarchical network, a detailed study of the chemical reaction network had not been carried out. Results: We have applied the line graph transformation to a hierarchical network and the degree-dependent clustering coefficient C(k) is calculated for the transformed network. C(k) indicates the probability that two nearest neighbours of a vertex of degree k are connected to each other. While C(k) follows the scaling law C(k) ~ k-1.1 for the initial hierarchical network, C(k) scales weakly as k0.08 for the transformed network. This theoretical prediction was compared with the experimental data of chemical reactions from the KEGG database finding a good agreement. Conclusions: The weak scaling found for the transformed network indicates that the reaction network can be identified as a degree-independent clustering network. By using this result, the hierarchical classification of the reaction network is discussed. [ABSTRACT FROM AUTHOR]

The widely used Maximum Matching (MM) method identifies the minimum driver nodes set to control biological and technological systems. Nevertheless, it is assumed in the MM approach that one driver node can send control signal to multiple target nodes, which might not be appropriate in certain complex networks. A recent work introduced a constraint that one driver node can control one target node, and proposed a method to identify the minimum target nodes set under such a constraint. We refer such target nodes to driven nodes. However, the driven nodes may not be uniquely determined. Here, we develop a novel algorithm to classify driven nodes in control categories. Our computational analysis on a large number of biological networks indicates that the number of driven nodes is considerably larger than the number of driver nodes, not only in all examined complete plant metabolic networks but also in several key human pathways, which firstly demonstrate the importance of use of driven nodes in analysis of real-world networks. [ABSTRACT FROM AUTHOR]

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high 'control centrality' of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks. [ABSTRACT FROM AUTHOR]

It is difficult to control multilayer networks in situations with real-world complexity. Here, we first define the multilayer control problem in terms of the minimum dominating set (MDS) controllability framework and mathematically demonstrate that simple formulas can be used to estimate the size of the minimum dominating set in multilayer (MDSM) complex networks. Second, we develop a new algorithm that efficiently identifies the MDSM in up to 6 layers, with several thousand nodes in each layer network. Interestingly, the findings reveal that the MDSM size for similar networks does not significantly differ from that required to control a single network. This result opens future directions for controlling, for example, multiple species by identifying a common set of enzymes or proteins for drug targeting. We apply our methods to 70 genome-wide metabolic networks across major plant lineages, unveiling some relationships between controllability in multilayer networks and metabolic functions at the genome scale. [ABSTRACT FROM AUTHOR]

Recent controllability analyses have demonstrated that driver nodes tend to be associated to genes related to important biological functions as well as human diseases. While researchers have focused on identifying critical nodes, intermittent nodes have received much less attention. Here, we propose a new efficient algorithm based on the Hamming distance for computing the importance of intermittent nodes using a Minimum Dominating Set (MDS)-based control model. We refer to this metric as criticality. The application of the proposed algorithm to compute criticality under the MDS control framework allows us to unveil the biological importance and roles of the intermittent nodes in different network systems, from cellular level such as signaling pathways and cell-cell interactions such as cytokine networks, to the complete nervous system of the nematode worm C. elegans. Taken together, the developed computational tools may open new avenues for investigating the role of intermittent nodes in many biological systems of interest in the context of network control. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is a progressive disorder with high incidence in the ageing human population that still has no treatment currently. This disorder induces the breakdown of articular cartilage, leading to the exposure and damage of bone surfaces. For a global understanding of OA development, the systematic integration of known OA-related proteins with protein-protein interaction (PPI) networks is required. In this work, the OA-related interactome was reconstructed using multiple data sources to have the most up-to-date information on OA-related proteins and their interactions. We then combined emergent concepts in network medicine to detect new unclassified OA-related proteins. The mapping of known OA-related proteins with PPI networks showed that these proteins are locally connected to each other and agglomerated in a large component. To expand this module, we applied a diffusion-based algorithm that probabilistically induces more searches in the vicinity of the seed OA-related proteins. As a result, the 10 topmost ranked proteins were connected to the OA disease module, supporting the local hypothesis. We computed structural modules and selected those that had the highest enrichment of OA-related proteins. The identified molecules show a link between structural topology and disease dysfunctionality. Interestingly, the protein Q6EEV6 was highlighted for OA association by both methods, reinforcing the potential involvement of this protein. These results suggest that similar disease-connected modules may exist in different human disorders, which could lead to systematic identification of genes or proteins that have a joint role in specific disease phenotypes. [ABSTRACT FROM AUTHOR]

The availability of huge volume of financial data has offered the possibility for understanding the markets as a complex system characterized by several stylized facts. Here we first show that the time evolution of the Japan’s Nikkei stock average index (Nikkei 225) futures follows the resistance and breaking-acceleration effects when the complete time series data is analyzed. However, in stock markets there are periods where no regular trades occur between the close of the market on one day and the next day’s open. To examine these time gaps we decompose the time series data into opening time and intermediate time. Our analysis indicates that for the intermediate time, both the resistance and the breaking-acceleration effects are still observed. However, for the opening time there are almost no resistance and breaking-acceleration effects, and volatility is always constantly high. These findings highlight unique dynamic differences between stock markets and forex market and suggest that current risk management strategies may need to be revised to address the absence of these dynamic effects at the opening time. [ABSTRACT FROM AUTHOR]

Recently, network controllability studies have proposed several frameworks for the control of large complex biological networks using a small number of life molecules. However, age-related changes in the brain have not been investigated from a controllability perspective. In this study, we compiled the gene expression profiles of four normal brain regions from individuals aged 20–99 years and generated dynamic probabilistic protein networks across their lifespan. We developed a new algorithm that efficiently identified critical proteins in probabilistic complex networks, in the context of a minimum dominating set controllability model. The results showed that the identified critical proteins were significantly enriched with well-known ageing genes collected from the GenAge database. In particular, the enrichment observed in replicative and premature senescence biological processes with critical proteins for male samples in the hippocampal region led to the identification of possible new ageing gene candidates. [ABSTRACT FROM AUTHOR]

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application of convolutional neural networks (CCNs) to "omics" data poses some difficulties, such as the processing of complex networks structures as well as its integration with transcriptome data. Here, we propose a CNN approach that combines spectral clustering information processing to classify lung cancer. The developed spectral-convolutional neural network based method achieves success in integrating protein interaction network data and gene expression profiles to classify lung cancer. The performed computational experiments suggest that in terms of accuracy the predictive performance of our proposed method was better than those of other machine learning methods such as SVM or Random Forest. Moreover, the computational results also indicate that the underlying protein network structure assists to enhance the predictions. Data and CNN code can be downloaded from the link: https://sites.google.com/site/nacherlab/analysis [ABSTRACT FROM AUTHOR]

Controlling complex networks through a small number of controller vertices is of great importance in wide-ranging research fields. Recently, a new approach based on the minimum feedback vertex set (MFVS) has been proposed to find such vertices in directed networks in which the target states are restricted to steady states. However, multiple MFVS configurations may exist and thus the selection of vertices may depend on algorithms and input data representations. Our attempts to address this ambiguity led us to adopt an existing approach that classifies vertices into three categories. This approach has been successfully applied to maximum matching-based and minimum dominating set-based controllability analysis frameworks. In this article, we present an algorithm as well as its implementation to compute and evaluate the critical, intermittent, and redundant vertices under the MFVS-based framework, where these three categories include vertices belonging to all MFVSs, some (but not all) MFVSs, and none of the MFVSs, respectively. The results of computational experiments using artificially generated networks and real-world biological networks suggest that the proposed algorithm is useful for identifying these three kinds of vertices for relatively large-scale networks, and that the fraction of critical and intermittent vertices is considerably small. Moreover, an analysis of the signal pathways indicates that critical and intermittent MFVSs tend to be enriched by essential genes. [ABSTRACT FROM AUTHOR]

One of the most aggressive forms of breast cancer is inflammatory breast cancer (IBC), whose lack of tumour mass also makes a prompt diagnosis difficult. Moreover, genomic differences between common breast cancers and IBC have not been completely assessed, thus substantially limiting the identification of biomarkers unique to IBC. Here, we developed a novel statistical analysis of gene expression profiles corresponding to microdissected IBC, non-IBC (nIBC) and normal samples that enabled us to identify a set of genes significantly associated with a specific disease state. Second, by using advanced methods based on controllability network theory, we identified a set of critical control proteins that uniquely and structurally control the entire proteome. By mapping high change variance genes in protein interaction networks, we found that a large statistically significant fraction of genes whose variance changed significantly between normal and IBC and nIBC disease states were among the set of critical control proteins. Moreover, this analysis identified the overlapping genes with the highest statistical significance; these genes may assist in developing future biomarkers and determining drug targets to disrupt the molecular pathways driving carcinogenesis in IBC. [ABSTRACT FROM AUTHOR]

Background: Current technology has demonstrated that mutation and deregulation of non-coding RNAs (ncRNAs) are associated with diverse human diseases and important biological processes. Therefore, developing a novel computational method for predicting potential ncRNA-disease associations could benefit pathologists in understanding the correlation between ncRNAs and disease diagnosis, treatment, and prevention. However, only a few studies have investigated these associations in pathogenesis. Results: This study utilizes a disease-target-ncRNA tripartite network, and computes prediction scores between each disease-ncRNA pair by integrating biological information derived from pairwise similarity based upon sequence expressions with weights obtained from a multi-layer resource allocation technique. Our proposed algorithm was evaluated based on a 5-fold-cross-validation with optimal kernel parameter tuning. In addition, we achieved an average AUC that varies from 0.75 without link cut to 0.57 with link cut methods, which outperforms a previous method using the same evaluation methodology. Furthermore, the algorithm predicted 23 ncRNA-disease associations supported by other independent biological experimental studies. Conclusions: Taken together, these results demonstrate the capability and accuracy of predicting further biological significant associations between ncRNAs and diseases and highlight the importance of adding biological sequence information to enhance predictions. [ABSTRACT FROM AUTHOR]