Modelling customers credit card behaviour using bidirectional LSTM neural networks

Availability of data and materials: The dataset used for supporting the conclusions of this paper is available from the public data repository at https://archive.ics.uci.edu/ml/index.php. The dataset is available at: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients.Copyright © The Author(s) 2021. With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.Office of Research, Zayed University under Grant Number R20053

Improving binary classification using filtering based on k-NN proximity graphs

One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data can confuse classifier and lead it to catch wrong patterns in training data. The common approach in data filtering is using proximity graphs. However, the problem of the optimal filtering parameters selection is still insufficiently researched. In this paper filtering procedure based on k-nearest neighbours proximity graph was used. Filtering parameters selection was adopted as the solution of outlier minimization problem: k-NN proximity graph, power of distance and threshold parameters are selected in order to minimize outlier percentage in training data. Then performance of six commonly used classifiers (Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine and Decision Tree) and one heterogeneous classifiers combiner (DES-LA) are compared with and without filtering. Dynamic ensemble selection (DES) systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of base classifiers, such as, its accuracy in local regions of the feature space around the query instance. In our case the combiner is based on the local accuracy of single classifiers and its output is a linear combination of single classifiers ranking. As results of filtering, accuracy of DES-LA combiner shows big increase for low-accuracy datasets. But filtering doesn’t have sufficient impact on DES-LA performance while working with high-accuracy datasets. The results are discussed, and classifiers, which performance was highly affected by pre-processing filtering step, are defined. The main contribution of the paper is introducing modifications to the DES-LA combiner, as well as comparative analysis of filtering impact on the classifiers of various type. Testing the filtering algorithm on real case dataset (Taiwan default credit card dataset) confirmed the efficiency of automatic filtering approach

Organ-specific toxicity evaluation of stearamidopropyl dimethylamine (SAPDMA) surfactant using zebrafish embryos

Surfactants are widely used in the industry of detergents, household products, and cosmetics. SAPDMA is a cationic surfactant that is used mostly in cosmetics, conditioning agents and has recently gained attention as a corrosion inhibitor in the sea pipelines industry. In this regard, literature concerning the ecotoxicological classification of SAPDMA on aquatic animals is lacking. This study aims to evaluate the potential ecotoxicity of SAPDMA using the aquatic zebrafish embryo model. The potential toxic effects of SAPDMA were assessed by different assays. This includes (i) mortality/survival assay to assess the median lethal concentration (LC50); (ii) teratogenicity assay to assess the no observed effect concentration (NOEC); (iii) organ-specific toxicity assays including cardiotoxicity, neurotoxicity (using locomotion assay), hematopoietic toxicity (hemoglobin synthesis using o-dianisidine staining), hepatotoxicity (liver steatosis and yolk retention using Oil Red O (ORO) stain); (iv) cellular cytotoxicity (mitochondrial membrane potential) by measuring the accumulation of JC-1 dye into mitochondria. Exposure of embryos to SAPDMA caused mortality in a dose-dependent manner with a calculated LC50 of 2.3 mg/L. Thus, based on the LC50 value and according to the Fish and Wildlife Service (FWS) Acute Toxicity Rating Scale, SAPDMA is classified as “moderately toxic”. The No Observed Effect Concentration (NOEC) concerning a set of parameters including scoliosis, changes in body length, yolk, and eye sizes was 0.1 mg/L. At the same NOEC concentration (0.1 mg/L), no organ-specific toxicity was detected in fish treated with SAPDMA, except hepatomegaly with no associated liver dysfunctions. However, higher SAPDMA concentrations (0.8 mg/L) have dramatic effects on zebrafish organ development (eye, heart, and liver development). Our data recommend a re-evaluation of the SAPDMA employment in the industry setting and its strictly monitoring by environmental and public health agencies

Developments in nanoparticles for use in biosensors to assess food safety and quality

The following will provide an overview on how advances in nanoparticle technology have contributed towards developing biosensors to screen for safety and quality markers associated with foods. The novel properties of nanoparticles will be described and how such characteristics have been exploited in sensor design will be provided. All the biosensor formats were initially developed for the health care sector to meet the demand for point-of-care diagnostics. As a consequence, research has been directed towards miniaturization thereby reducing the sample volume to nanolitres. However, the needs of the food sector are very different which may ultimately limit commercial application of nanoparticle based nanosensors. © 2014 Elsevier Ltd

PPARγ1 and LXRα face a new regulator of macrophage cholesterol homeostasis and inflammatory responsiveness, AEBP1

Peroxisome proliferator-activated receptor γ1 (PPARγ1) and liver X receptor α (LXRα) are nuclear receptors that play pivotal roles in macrophage cholesterol homeostasis and inflammation; key biological processes in atherogenesis. The activation of PPARγ1 and LXRα by natural or synthetic ligands results in the transactivation of ABCA1, ABCG1, and ApoE; integral players in cholesterol efflux and reverse cholesterol transport. In this review, we describe the structure, isoforms, expression pattern, and functional specificity of PPARs and LXRs. Control of PPARs and LXRs transcriptional activity by coactivators and corepressors is also highlighted. The specific roles that PPARγ1 and LXRα play in inducing macrophage cholesterol efflux mediators and antagonizing macrophage inflammatory responsiveness are summarized. Finally, this review focuses on the recently reported regulatory functions that adipocyte enhancer-binding protein 1 (AEBP1) exerts on PPARγ1 and LXRα transcriptional activity in the context of macrophage cholesterol homeostasis and inflammation

AEO-7 surfactant is “super toxic” and induces severe cardiac, liver and locomotion damage in zebrafish embryos

© 2020, The Author(s). Background: Fatty alcohol polyoxyethylene ether-7 (AEO-7), a non-ionic surfactant, has recently been receiving extensive attention from the ocean pipeline industry for its ability to inhibit corrosion. However, the present lack of information concerning the potential environmental toxicity of AEO-7, especially towards aquatic organisms, is a major impediment to its wider application. Here, we assess potential adverse effects of AEO-7 on zebrafish embryos employing a variety of assays, including (i) a mortality/survival assay which allowed the median lethal concentration (LC50) to be calculated; (ii) a teratogenicity assay on the basis of which the no observed effect concentration (NOEC) was determined; and (iii) specific assays of cardiotoxicity, neurotoxicity (based on locomotion), hematopoietic toxicity (the level of hemoglobin as revealed by o-dianisidine staining) and hepatotoxicity (liver steatosis and yolk retention examined by staining with Oil Red O). Results: AEO-7 caused mortality with a calculated LC50 of 15.35 μg/L, which, according to the U.S. Fish and Wildlife Service (USFWS) Acute Toxicity Rating scale, should be considered “super toxic”. Although at its NOEC (0.8 μg/L), there were no signs of significant teratogenicity, cardiotoxicity, or hemopoiesis toxicity, 3.2 µg/L AEO-7 exerted dramatic detrimental effects on organ development. Conclusion: On the basis of these findings, we recommend that the industrial usage and environmental impact of AEO-7 be re-evaluated and strictly monitored by environmental and public health organizations

Carboxypeptidase-M is regulated by lipids and CSFs in macrophages and dendritic cells and expressed selectively in tissue granulomas and foam cells

Granulomatous inflammations, characterized by the presence of activated macrophages (MAs) forming epithelioid cell (EPC) clusters, are usually easy to recognize. However, in ambiguous cases the use of a MA marker that expresses selectively in EPCs may be needed. Here, we report that carboxypeptidase-M (CPM), a MA-differentiation marker, is preferentially induced in EPCs of all granuloma types studied, but not in resting MAs. As CPM is not expressed constitutively in MAs, this allows utilization of CPM-immunohistochemistry in diagnostics of minute granuloma detection when dense non-granulomatous MAs are also present. Despite this rule, hardly any detectable CPM was found in advanced/active tubercle caseous disease, albeit in early tuberculosis granuloma, MAs still expressed CPM. Indeed, in vitro both the CPM-protein and -mRNA became downregulated when MAs were infected with live mycobacteria. In vitro, MA-CPM transcript is neither induced remarkably by interferon-γ, known to cause classical MA activation, nor by IL-4, an alternative MA activator. Instead, CPM is selectively expressed in lipid-laden MAs, including the foam cells of atherosclerotic plaques, xanthomatous lesions and lipid pneumonias. By using serum, rich in lipids, and low-density lipoprotein (LDL) or VLDL, CPM upregulation could be reproduced in vitro in monocyte-derived MAs both at transcriptional and protein levels, and the increase is repressed under lipid-depleted conditions. The microarray analyses support the notion that CPM induction correlates with a robust progressive increase in CPM gene expression during monocyte to MA maturation and dendritic cell (DC) differentiation mediated by granulocyte–MA-colony-stimulating factor+IL-4. M-CSF alone also induced CPM. These results collectively indicate that CPM upregulation in MAs is preferentially associated with increased lipid uptake, and exposure to CSF, features of EPCs, also. Therefore, CPM-immunohistochemistry is useful for granuloma and foam MA detections in tissue sections. Furthermore, the present data offer CPM for the first time to be a novel marker and cellular player in lipid uptake and/or metabolism of MAs by promoting foam cell formation

The Threonine Protease Activity of Testes-Specific Protease 50 (TSP50) Is Essential for Its Function in Cell Proliferation

Background: Testes-specific protease 50 (TSP50), a newly discovered threonine enzyme, has similar amino acid sequences and enzymatic structures to those of many serine proteases. It may be an oncogene. TSP50 is up-regulated in breast cancer epithelial cells, and ectopic expression of TSP50 in TSP50-deficient Chinese hamster ovary (CHO) cells has been found to promote cell proliferation. However, the mechanisms by which TSP50 exerts its growth-promoting effects are not yet fully understood. Methodology/Principal Findings: To delineate whether the threonine protease activity of TSP50 is essential to its function in cell proliferation, we constructed and characterized a mutant TSP50, called TSP50 T310A, which was identified as a protease-dead mutant of TSP50. By a series of proliferation analyses, colony formation assays and apoptosis analyses, we showed that T310A mutation significantly depresses TSP50-induced cell proliferation in vitro. Next, the CHO stable cell line expressing either wild-type or T310A mutant TSP50 was injected subcutaneously into nude mice. We found that the T310A mutation could abolish the tumorigenicity of TSP50 in vivo. A mechanism investigation revealed that the T310A mutation prevented interaction between TSP50 and the NF-kBIkBa complex, which is necessary for TSP50 to perform its function in cell proliferation. Conclusion: Our data highlight the importance of threonine 310, the most critical protease catalytic site in TSP50, to TSP50induce

Validation of a Novel Fluorescent Lateral Flow Assay for Rapid Qualitative and Quantitative Assessment of Total Anti-SARS-CoV-2 S-RBD Binding Antibody Units (BAU) from Plasma or Fingerstick Whole-Blood of COVID-19 Vaccinees

Background: Limited commercial LFA assays are available to provide a reliable quantitative measurement of the total binding antibody units (BAU/mL) against the receptor-binding domain of the SARS-CoV-2 spike protein (S-RBD). Aim: This study aimed to evaluate the performance of the fluorescence LFA FinecareTM 2019-nCoV S-RBD test along with its reader (Model No.: FS-113) against the following reference methods: (i) the FDA-approved GenScript surrogate virus-neutralizing assay (sVNT); and (ii) three highly performing automated immunoassays: BioMérieux VIDAS®3, Ortho VITROS®, and Mindray CL-900i®. Methods: Plasma from 488 vaccinees was tested by all aforementioned assays. Fingerstick whole-blood samples from 156 vaccinees were also tested by FinecareTM. Results and conclusions: FinecareTM showed 100% specificity, as none of the pre-pandemic samples tested positive. Equivalent FinecareTM results were observed among the samples taken from fingerstick or plasma (Pearson correlation r = 0.9, p < 0.0001), suggesting that fingerstick samples are sufficient to quantitate the S-RBD BAU/mL. A moderate correlation was observed between FinecareTM and sVNT (r = 0.5, p < 0.0001), indicating that FinecareTM can be used for rapid prediction of the neutralizing antibody (nAb) post-vaccination. FinecareTM BAU results showed strong correlation with VIDAS®3 (r = 0.6, p < 0.0001) and moderate correlation with VITROS® (r = 0.5, p < 0.0001) and CL-900i® (r = 0.4, p < 0.0001), suggesting that FinecareTM can be used as a surrogate for the advanced automated assays to measure S-RBD BAU/mL.This work was made possible by grant number UREP28-173-3-057 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors