One of the most important research issues in finance is building effective corporate bankruptcy prediction models because they are essential for the risk management of financial institutions. ...Researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques, and many of them have been proved to be useful. Case-based reasoning (CBR) is one of the most popular data-driven approaches because it is easy to apply, has no possibility of overfitting, and provides good explanation for the output. However, it has a critical limitation—its prediction performance is generally low. In this study, we propose a novel approach to enhance the prediction performance of CBR for the prediction of corporate bankruptcies. Our suggestion is the simultaneous optimization of feature weighting and the instance selection for CBR by using genetic algorithms (GAs). Our model can improve the prediction performance by referencing more relevant cases and eliminating noises. We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional CBR may be improved significantly by using our model. Our study suggests ways for financial institutions to build a bankruptcy prediction model which produces accurate results as well as good explanations for these results.
The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often ...challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.
As a result of the extensive variety of products available in e-commerce settings during the last decade, recommender systems have been highlighted as a means of mitigating the problem of information ...overload. Collaborative filtering (CF) is the most widely used algorithm to build such systems, and improving the predictive accuracy of CF-based recommender systems has been a major research challenge. This research aims to improve the prediction accuracy of CF by incorporating social network analysis (SNA) and clustering techniques. Our proposed model identifies the most influential people in an online social network by SNA and then conducts clustering analysis using these people as initial centroids (cluster centres). Finally, the model makes recommendations using cluster-indexing CF based on the clustering outcomes. In this step, our model adjusts the effect of neighbours in the same cluster as the target user to improve prediction accuracy by reflecting hidden information about his or her social community. The experimental results indicate that the proposed model outperforms other comparison models, including conventional CF, with statistical significance.
Methylated cytosines are associated with gene silencing. The ten-eleven translocation (TET) hydroxylases, which oxidize methylated cytosines to 5-hydroxymethylcytosine (5hmC), are essential for ...cytosine demethylation. Gene silencing and activation are critical for intestinal stem cell (ISC) maintenance and differentiation, but the potential role of TET hydroxylases in these processes has not yet been examined. Here, we generated genome-wide maps of the 5hmC mark in ISCs and their differentiated progeny. Genes with high levels of hydroxymethylation in ISCs are strongly associated with Wnt signaling and developmental processes. We found Tet1 to be the most abundantly expressed Tet gene in ISCs; therefore, we analyzed intestinal development in Tet1-deficient mice and determined that these mice are growth-retarded, exhibit partial postnatal lethality, and have significantly reduced numbers of proliferative cells in the intestinal epithelium. In addition, the Tet1-deficient intestine displays reduced organoid-forming capacity. In the Tet1-deficient crypt, decreased expression of Wnt target genes such as Axin2 and Lgr5 correlates with lower 5hmC levels at their promoters. These data demonstrate that Tet1-mediated DNA hydroxymethylation plays a critical role in the epigenetic regulation of the Wnt pathway in intestinal stem and progenitor cells and consequently in the self-renewal of the intestinal epithelium.
The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers’ needs and expectations is considered a prerequisite for activating the consumer-oriented ...electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied
K-means clustering whose initial seeds are optimized by GA, which is called GA
K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA
K-means to those of a simple
K-means algorithm and self-organizing maps (SOM). The results showed that GA
K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.
In this study, we investigated the influence of the electrode structure on ion beam extraction in a cold-cathode ion source. Numerical simulations were performed for three typical structures of ...cold-cathode ion sources with different cathode and anode geometries using in- house 2-D ion ray-tracing code. We found that the potential structure inside the plasma source, which is greatly influenced by the electrode structure, plays a significant role in determining the amount and divergence of the extracted ion beam. The electrode structure influences the potential structure inside the plasma source and forms a radial electric field between the sheath edge and the extraction aperture. The radial electric field guides the ion beam to focus or diverge in this region, thus changing the characteristics of the ion beam extracted from the aperture. The simulation results reveal that the ion source with the cylindrical anode geometry extracts a higher current than those with other geometries due to its potential structure, which focuses the ion beam more efficiently. The simulation results are consistent with those of our experiments with a small-sized cold-cathode ion source.
The heart undergoes dramatic developmental changes during the prenatal to postnatal transition, including maturation of cardiac myocyte energy metabolic and contractile machinery. Delineation of the ...mechanisms involved in cardiac postnatal development could provide new insight into the fetal shifts that occur in the diseased heart and unveil strategies for driving maturation of stem cell-derived cardiac myocytes.
To delineate transcriptional drivers of cardiac maturation.
We hypothesized that ERR (estrogen-related receptor) α and γ, known transcriptional regulators of postnatal mitochondrial biogenesis and function, serve a role in the broader cardiac maturation program. We devised a strategy to knockdown the expression of ERRα and γ in heart after birth (pn-csERRα/γ postnatal cardiac-specific ERRα/γ) in mice. With high levels of knockdown, pn-csERRα/γ knockdown mice exhibited cardiomyopathy with an arrest in mitochondrial maturation. RNA sequence analysis of pn-csERRα/γ knockdown hearts at 5 weeks of age combined with chromatin immunoprecipitation with deep sequencing and functional characterization conducted in human induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CM) demonstrated that ERRγ activates transcription of genes involved in virtually all aspects of postnatal developmental maturation, including mitochondrial energy transduction, contractile function, and ion transport. In addition, ERRγ was found to suppress genes involved in fibroblast activation in hearts of pn-csERRα/γ knockdown mice. Disruption of
and
in mice during fetal development resulted in perinatal lethality associated with structural and genomic evidence of an arrest in cardiac maturation, including persistent expression of early developmental and noncardiac lineage gene markers including cardiac fibroblast signatures. Lastly, targeted deletion of
and
in hiPSC-CM derepressed expression of early (transcription factor 21 or TCF21) and mature (periostin, collagen type III) fibroblast gene signatures.
ERRα and γ are critical regulators of cardiac myocyte maturation, serving as transcriptional activators of adult cardiac metabolic and structural genes, an.d suppressors of noncardiac lineages including fibroblast determination.
A deep understanding of the kinetic properties of the electrons in a magnetic nozzle (MN), which is attracting attention as an acceleration stage for thrusters, is of great significance as it ...directly contributes to the development of the MN performance. In the sense that a conversion of the electron momentum to the ion kinetic energy determines the characteristics of the MN, fundamental research on the kinetic feature of a magnetically expanding plasma has focused on the spatial distribution of the electron properties and proposed directions to the desired application. Unlike the common perception of this importance, various research groups have proposed contradictory arguments based on their theoretical approaches regarding the ion beam acceleration from the viewpoint of heat flow of electrons. We point out that the main reason for the absence of a theoretical consensus for the nozzle efficiency improvements arises from the lack of the clear interpretation of the plasma properties by focusing only on the final state of the electrons. In this Letter, time-resolved measurement of the electron energy distributions has been performed to grasp a detailed series of expansion processes. It has been revealed that the effective potential well gradually formed by the self-generated electric field acts as a limiting factor in the motion of electrons; this effect attributes to the changes of the electron energy distribution represented as the accumulation of the trapped electrons. The accumulation over the entire region diminishes the degree of the cooling rate of a system and decreases the electric field in the downstream region initially generated by the adiabatic expansion. The present study emphasizes that the kinetic features of an MN are strongly affected by the non-stationary motion of the trapped electrons; thus, the temporal behavior of the trapped electrons must be considered for prediction and analysis of nozzle performances.
Autism spectrum disorders (ASDs) are common neurodevelopmental disorders characterized by deficits in social interactions and communication, restricted interests, and repetitive behaviors. Despite ...extensive study, the molecular targets that control ASD development remain largely unclear. Here, we report that the dormancy of quiescent neural stem cells (qNSCs) is a therapeutic target for controlling the development of ASD phenotypes driven by Shank3 deficiency. Using single-cell RNA sequencing (scRNA-seq) and transposase accessible chromatin profiling (ATAC-seq), we find that abnormal epigenetic features including H3K4me3 accumulation due to up-regulation of Kmt2a levels lead to increased dormancy of qNSCs in the absence of Shank3. This result in decreased active neurogenesis in the Shank3 deficient mouse brain. Remarkably, pharmacological and molecular inhibition of qNSC dormancy restored adult neurogenesis and ameliorated the social deficits observed in Shank3-deficient mice. Moreover, we confirmed restored human qNSC activity rescues abnormal neurogenesis and autism-like phenotypes in SHANK3-targeted human NSCs. Taken together, our results offer a novel strategy to control qNSC activity as a potential therapeutic target for the development of autism.
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