Functional pathway mapping analysis for hypoxia-inducible factors

Background: Hypoxia-inducible factors (HIFs) are transcription factors that play a crucial role in response to hypoxic stress in living organisms. The HIF pathway is activated by changes in cellular oxygen levels and has significant impacts on the regulation of gene expression patterns in cancer cells. Identifying functional conservation across species and discovering conserved regulatory motifs can facilitate the selection of reference species for empirical tests. This paper describes a cross-species functional pathway mapping strategy based on evidence of homologous relationships that employs matrix-based searching techniques for identifying transcription factorbinding sites on all retrieved HIF target genes. Results: HIF-related orthologous and paralogous genes were mapped onto the conserved pathways to indicate functional conservation across species. Quantitatively measured HIF pathways are depicted in order to illustrate the extent of functional conservation. The results show that in spite of the evolutionary process of speciation, distantly related species may exhibit functional conservation owing to conservative pathways. The novel terms OrthRate and ParaRate are proposed to quantitatively indicate the flexibility of a homologous pathway and reveal the alternative regulation of functional genes. Conclusion: The developed functional pathway mapping strategy provides a bioinformatics approach for constructing biological pathways by highlighting the homologous relationships between various model species. The mapped HIF pathways were quantitatively illustrated and evaluated by statistically analyzing their conserved transcription factor-binding elements

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively

Towards Assumption-free Bias Mitigation

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attributes are often either unavailable or missing in real-world scenarios. Therefore, several existing works alleviate the bias without sensitive attributes. Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias. The latter requires strong assumptions about the correlation between sensitive and non-sensitive attributes. As data distribution and task goals vary, the strong assumption on non-sensitive attributes may not be valid and require domain expertise. In this work, we propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. The proposed framework aims to mitigate the unfair impact of identified biased feature interactions. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors by considering biased feature interactions

A Shallow Ritz Method for Elliptic Problems with Singular Sources

In this paper, a shallow Ritz-type neural network for solving elliptic equations with delta function singular sources on an interface is developed. There are three novel features in the present work; namely, (i) the delta function singularity is naturally removed, (ii) level set function is introduced as a feature input, (iii) it is completely shallow, comprising only one hidden layer. We first introduce the energy functional of the problem and then transform the contribution of singular sources to a regular surface integral along the interface. In such a way, the delta function singularity can be naturally removed without introducing a discrete one that is commonly used in traditional regularization methods, such as the well-known immersed boundary method. The original problem is then reformulated as a minimization problem. We propose a shallow Ritz-type neural network with one hidden layer to approximate the global minimizer of the energy functional. As a result, the network is trained by minimizing the loss function that is a discrete version of the energy. In addition, we include the level set function of the interface as a feature input of the network and find that it significantly improves the training efficiency and accuracy. We perform a series of numerical tests to show the accuracy of the present method and its capability for problems in irregular domains and higher dimensions

Recent Developments in Quantitative Finance: An Overview

Quantitative finance combines mathematical finance, financial statistics, financial econometrics and empirical finance to provide a solid quantitative foundation for the analysis of financial issues. The purpose of this special issue on “Recent developments in quantitative finance” is to highlight some areas of research in which novel methods in quantitative finance have contributed significantly to the analysis of financial issues, specifically fast methods for large-scale non-elliptical portfolio optimization, the impact of acquisitions on new technology stocks: the Google-Motorola case, the effects of firm characteristics and recognition policy on employee stock options prices after controlling for self-selection, searching for landmines in equity markets, whether CEO incentive pay improves bank performance, using a quantile regression analysis of U.S. commercial banks, testing price pressure, information, feedback trading, and smoothing effects for energy exchange traded funds, actuarial implications of structural changes in El Niño-Southern Oscillation Index dynamics, credit spreads and bankruptcy information from options data, QMLE of a standard exponential ACD model: asymptotic distribution and residual correlation, and using two-part quantile regression to analyze how earnings shocks affect stock repurchases

Twin-Free GaAs Nanosheets by Selective Area Growth: Implications for Defect-Free Nanostructures

Highly perfect, twin-free GaAs nanosheets grown on (111)B surfaces by selective area growth (SAG) are demonstrated. In contrast to GaAs nanowires grown by (SAG) in which rotational twins and stacking faults are almost universally observed, twin formation is either suppressed or eliminated within properly oriented nanosheets are grown under a range of growth conditions. A morphology transition in the nanosheets due to twinning results in surface energy reduction, which may also explain the high twin-defect density that occurs within some III–V semiconductor nanostructures, such as GaAs nanowires. Calculations suggest that the surface energy is significantly reduced by the formation of {111}-plane bounded tetrahedra after the morphology transition of nanowire structures. By contrast, owing to the formation of two vertical {11̅0} planes which comprise the majority of the total surface energy of nanosheet structures, the energy reduction effect due to the morphology transition is not as dramatic as that for nanowire structures. Furthermore, the surface energy reduction effect is mitigated in longer nanosheets which, in turn, suppresses twinning

Electrochemical Oxidation of Fragrances 4-Allyl and 4-Propenylbenzenes on Platinum and Carbon Paste Electrodes

The electrochemical oxidation behaviors of 4-allylbenzenes (estragole, safrole and eugenol) and 4-propenylbenzenes (anethole, asarone and isoeugenol) on platinum and carbon paste electrodes were investigated in a Britton-Robinson buffer (pH = 2.93 and 10.93), acetate buffer, phosphate buffer solutions (pH = 2.19 and 6.67), and acetonitrile containing various supporting electrolytes examined lithium perchlorate. Their oxidation potential with Hammett (free-energy relationships) and possible reaction mechanisms were discussed

Recent Developments in Quantitative Finance: An Overview

Quantitative finance combines mathematical finance, financial statistics, financial econometrics and empirical finance to provide a solid quantitative foundation for the analysis of financial issues. The purpose of this special issue on “Recent developments in quantitative finance” is to highlight some areas of research in which novel methods in quantitative finance have contributed significantly to the analysis of financial issues, specifically fast methods for large-scale non-elliptical portfolio optimization, the impact of acquisitions on new technology stocks: the Google-Motorola case, the effects of firm characteristics and recognition policy on employee stock options prices after controlling for self-selection, searching for landmines in equity markets, whether CEO incentive pay improves bank performance, using a quantile regression analysis of U.S. commercial banks, testing price pressure, information, feedback trading, and smoothing effects for energy exchange traded funds, actuarial implications of structural changes in El Niño-Southern Oscillation Index dynamics, credit spreads and bankruptcy information from options data, QMLE of a standard exponential ACD model: asymptotic distribution and residual correlation, and using two-part quantile regression to analyze how earnings shocks affect stock repurchases

Layered Capacity-Based Relay-and-Antenna Joint Selection for MIMO-AF-Multiple-Relay Systems in Correlated Channels

A computationally efficient two-stage greedy capacity maximization (GCM) relay-and-antenna joint selection is proposed for a dual-hop nonregenerative amplify-and-forward (AF) multiple-input multiple-output (MIMO) multiple-relay system with multiple antennas equipped at each node in correlated fading channels. This modified GCM (MGCM) antenna selection strategy selects a subset of antenna pairs from available relays based on the concept of channel capacity maximization subject to an optimal power allocation constraint across the activated antenna pairs. In order to reduce system hardware complexity, antenna selection schemes are performed at the destination node as well. Finally, simulations are conducted to compare the channel capacity of the proposed two-layered antenna selection technique with other existing antenna selection algorithms for half-duplex AF-MIMO multiple-relay systems