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The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD's Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.

From the Publisher: The boom in smart card technology reflects the medium's broad solutions potential. Embedded with a sophisticated microprocessor, smart cards offer unparalleled memory capacity and data encryption capability. From providing secure access to the Internet and mobile radio networks to performing security-sensitive financial transactions in credit card schemes, the Electronic Purse and Pay TV systems, smart card technology is now a multi-billion dollar industry. The Smart Card Handbook presents a state-of-the-art overview of the technology from fundamental information processing through design, manufacture and operation of smart card schemes. Written in a highly accessible style the Smart Card Handbook meets the needs of both novice and expert. This is an essential reference for computer and electronics engineers and students in microchip design and security system development. For professionals developing smart card products, this unique reference will provide an invaluable insight to all the facets of this sophisticated technology.

This paper examines how monitoring power consumption signals might breach smart-card security. Both simple power analysis and differential power analysis attacks are investigated. The theory behind these attacks is reviewed. Then, we concentrate on showing how power analysis theory can be applied to attack an actual smart card. We examine the noise characteristics of the power signals and develop an approach to model the signal-to-noise ratio (SNR). We show how this SNR can be significantly improved using a multiple-bit attack. Experimental results against a smart-card implementation of the Data Encryption Standard demonstrate the effectiveness of our multiple-bit attack. Potential countermeasures to these attacks are also discussed.

The PYRIN and CARD domains are members of the six-helix bundle death domain-fold superfamily that mediates assembly of large signaling complexes in the apoptotic and inflammatory signaling pathways. Here we show that the PYRIN-CARD protein ASC functions as a caspase-1-activating adaptor. ASC interacted specifically with procaspase-1 via CARD-CARD interactions and induced its oligomerization. Consistent with these results ectopic expression of full-length ASC, but not its isolated CARD or PYRIN domain, with procaspase-1 induced activation of procaspase-1 and processing of pro-interleukin-1beta in transfected cells. Substitution of the PYRIN domain of ASC with an inducible FKBP12 oligomerization domain produced a molecule that can induce caspase-1 activation in response to stimulation with the oligomerization drug AP20187, suggesting that the PYRIN domain functions as an oligomerization domain, whereas the CARD domain functions as the effector domain in the caspase-1 activation pathway. Furthermore stable expression of an isolated CARD of ASC in THP-1 cells diminished interleukin-1beta generation in response to pro-inflammatory cytokines. These results indicate that ASC is involved in the caspase-1 signaling pathway by mediating the assembly of a caspase-1-inflammasome signaling complex in response to pro-inflammatory cytokine stimulation.

Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons - first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection technique(s) used. This paper investigates the performance of naïve bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. The three techniques are applied on the raw and preprocessed data. The work is implemented in Python. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision, Matthews correlation coefficient and balanced classification rate. The results shows of optimal accuracy for naïve bayes, k-nearest neighbor and logistic regression classifiers are 97.92%, 97.69% and 54.86% respectively. The comparative results show that k-nearest neighbour performs better than naïve bayes and logistic regression techniques.

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.

We analyze platforms in two-sided markets with network externalities, using the specific context of a payment card association. We study the cooperative determination of the interchange fee by member banks. The interchange fee is the ``access charge'' paid by the merchants' banks (the acquirers) to cardholders' banks (the issuers). We develop a framework in which banks and merchants may have market power and consumers and merchants decide rationally on whether to buy or accept a payment card. After drawing the welfare implications of a cooperative determination of the interchange fee, we describe in detail the factors affecting merchant resistance, compare cooperative and for-profit business models, and make a first cut in the analysis of system competition.

The consumer culture has evolved into one of the most powerful forces shaping individuals and societies (Roberts and Sepulveda 1999 a, b). The desire to become a member of the consumer culture appears to be universal (Droge and Mackoy 1995). Changing attitudes toward money are an important catalyst behind the spread of the consumer culture. Money is important—especially to American college students who have been raised in a credit card society where debt is used freely (Ritzer 1995). Schor (1998) believes that access to easy credit is one of the causes of overspending. Using a causal modeling approach, the present study investigated the role money attitudes and credit card use play in compulsive buying within a sample of American college students (see Figure 1). Findings suggest that the money attitudes powerprestige, distrust, and anxiety (Yamauchi and Templer 1982) are closely related to compulsive buying and that credit card use often moderates these relationships. Study results have important public policy, marketing, and research implications.

Credit card fraud events take place frequently and then result in huge financial losses. Criminals can use some technologies such as Trojan or Phishing to steal the information of other people's credit cards. Therefore, an effictive fraud detection method is important since it can identify a fraud in time when a criminal uses a stolen card to consume. One method is to make full use of the historical transaction data including normal transactions and fraud ones to obtain normal/fraud behavior features based on machine learning techniques, and then utilize these features to check if a transaction is fraud or not. In this paper, two kinds of random forests are used to train the behavior features of normal and abnormal transactions. We make a comparison of the two random forests which are different in their base classifiers, and analyze their performance on credit fraud detection. The data used in our experiments come from an e-commerce company in China.

The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: since only one financial transaction in a thousand is invalid no prediction success less than 99.9% is acceptable. Because of these credit card transaction requirements, completely new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and a neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.

Did George Bush's use of the Willie Horton story during the1988 presidential campaign communicate most effectively when no one noticed its racial meaning? Do politicians routinely evoke racial stereotypes, fears, and resentments without voters' awareness? This controversial, rigorously researched book argues that they do. Tali Mendelberg examines how and when politicians play the race card and then manage to plausibly deny doing so. In the age of equality, politicians cannot prime race with impunity due to a norm of racial equality that prohibits racist speech. Yet incentives to appeal to white voters remain strong. As a result, politicians often resort to more subtle uses of race to win elections. Mendelberg documents the development of this implicit communication across time and measures its impact on society. Drawing on a wide variety of research--including simulated television news experiments, national surveys, a comprehensive content analysis of campaign coverage, and historical inquiry--she analyzes the causes, dynamics, and consequences of racially loaded political communication. She also identifies similarities and differences among communication about race, gender, and sexual orientation in the United States and between communication about race in the United States and ethnicity in Europe, thereby contributing to a more general theory of politics. Mendelberg's conclusion is that politicians--including many current state governors--continue to play the race card, using terms like "welfare" and "crime" to manipulate white voters' sentiments without overtly violating egalitarian norms. But she offers some good news: implicitly racial messages lose their appeal, even among their target audience, when their content is exposed.

The Wisconsin Card Sorting Task (WCST) has been used to assess dysfunction of the prefrontal cortex and basal ganglia. Previous brain imaging studies have focused on identifying activity related to the set-shifting requirement of the WCST. The present study used event-related functional magnetic resonance imaging (fMRI) to study the pattern of activation during four distinct stages in the performance of this task. Eleven subjects were scanned while performing the WCST and a control task involving matching two identical cards. The results demonstrated specific involvement of different prefrontal areas during different stages of task performance. The mid-dorsolateral prefrontal cortex (area 9/46) increased activity while subjects received either positive or negative feedback, that is at the point when the current information must be related to earlier events stored in working memory. This is consistent with the proposed role of the mid-dorsolateral prefrontal cortex in the monitoring of events in working memory. By contrast, a cortical basal ganglia loop involving the mid-ventrolateral prefrontal cortex (area 47/12), caudate nucleus, and mediodorsal thalamus increased activity specifically during the reception of negative feedback, which signals the need for a mental shift to a new response set. The posterior prefrontal cortex response was less specific; increases in activity occurred during both the reception of feedback and the response period, indicating a role in the association of specific actions to stimuli. The putamen exhibited increased activity while matching after negative feedback but not while matching after positive feedback, implying greater involvement during novel than routine actions.

BACKGROUND: Accumulating sufficient moderate to vigorous physical activity is recognized as a key determinant of physical, physiological, developmental, mental, cognitive, and social health among children and youth (aged 5-17 y). The Global Matrix 3.0 of Report Card grades on physical activity was developed to achieve a better understanding of the global variation in child and youth physical activity and associated supports. METHODS: Work groups from 49 countries followed harmonized procedures to develop their Report Cards by grading 10 common indicators using the best available data. The participating countries were divided into 3 categories using the United Nations' human development index (HDI) classification (low or medium, high, and very high HDI). RESULTS: A total of 490 grades, including 369 letter grades and 121 incomplete grades, were assigned by the 49 work groups. Overall, an average grade of "C-," "D+," and "C-" was obtained for the low and medium HDI countries, high HDI countries, and very high HDI countries, respectively. CONCLUSIONS: The present study provides rich new evidence showing that the situation regarding the physical activity of children and youth is a concern worldwide. Strategic public investments to implement effective interventions to increase physical activity opportunities are needed.

Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers' habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.

Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Metadata, however, contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.

The Internet has taken its place beside the telephone and the television as an important part of people's lives. Consumers rely on the Internet to shop, bank and invest online. Most online shoppers use credit cards to pay for their purchases. As credit card becomes the most popular mode of payment, cases of fraud associated with it are also increasing. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is trained with normal behavior of cardholder. If an incoming credit card transaction is not accepted by the HMM with sufficiently high probability, it is considered to be fraudulent. We present detailed experimental results to show the effectiveness of our approach.

Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time. The neural network was trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, mail-order fraud and NRI (non-received issue) fraud. The network detected significantly more fraud accounts (an order of magnitude more) with significantly fewer false positives (reduced by a factor of 20) over rule-based fraud detection procedures. We discuss the performance of the network on this data set in terms of detection accuracy and earliness of fraud detection. The system has been installed on an IBM 3090 at Mellon Bank and is currently in use for fraud detection on that bank's credit card portfolio.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In a previous study, a bilingual advantage for preschool children in solving the dimensional change card sort task was attributed to superiority in inhibition of attention (Bialystok, 1999). However, the task includes difficult representational demands to encode and interpret the task stimuli, and bilinguals may also have profited from superior representational abilities. This possibility is examined in three studies. In Study 1, bilinguals outperformed monolinguals on versions of the problem containing moderate representational demands but not on a more demanding condition. Studies 2 and 3 demonstrated that bilingual children were more skilled than monolinguals when the target dimensions were perceptual features of the stimulus and that the two groups were equivalent when the target dimensions were semantic features. The conclusions are that bilinguals have better inhibitory control for ignoring perceptual information than monolinguals do but are not more skilled in representation, confirming the results of the original study. The results also identify the ability to ignore an obsolete display feature as the critical difficulty in solving this task.

BACKGROUND: Impaired Wisconsin Card Sorting Test (WCST) performance has been one critical piece of evidence suggesting frontal lobe dysfunction in schizophrenia. However, the specific cognitive processes underlying impaired performance have not been identified. Impaired WCST performance in schizophrenia might in part reflect a fundamental working memory deficit. METHODS: We examined the performance of 30 normal subjects and 36 patients with schizophrenia on a neuropsychological battery including a novel measure of working memory-letter-number (LN) span. RESULTS: Patients with schizophrenia were impaired on LN span performance, which was also highly correlated with WCST performance (r = 0.74). Between-group WCST differences were eliminated when we covaried LN span. Regression analyses suggested that LN span performance predicted the WCST category achieved score, whereas measures of set shifting, verbal fluency, and attention were predictive of perseveration. CONCLUSION: Working memory may be a critical determinant of one aspect of WCST performance in schizophrenia.