Federal Information Processing Standards Research Articles

Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices.

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Impact of neighborhood deprivation and social vulnerability on long-term outcomes and desire for revision in patients with craniosynostosis.

The authors utilized the area deprivation index (ADI), a validated composite measure of socioeconomic disadvantage, and the social vulnerability index (SVI) to examine whether differences in neighborhood deprivation impact interventions and outcomes among patients with craniosynostosis. Patients who underwent craniosynostosis repair between 2012 and 2017 were included. The authors collected data about demographic characteristics, comorbidities, follow-up visits, interventions, complications, desire for revision, and speech, developmental, and behavioral outcomes. National percentiles for ADI and SVI were determined using zip and Federal Information Processing Standard (FIPS) codes. ADI and SVI were analyzed by tertile. Firth logistic regressions and Spearman correlations were used to assess associations between ADI/SVI tertile and outcomes/interventions that differed on univariate analysis. Subgroup analysis was performed to examine these associations in patients with nonsyndromic craniosynostosis. Differences in length of follow-up among the nonsyndromic patients in the different deprivation groups were assessed with multivariate Cox regressions. In total, 195 patients were included, with 37% of patients in the most disadvantaged ADI tertile and 20% of patients in the most vulnerable SVI tertile. Patients in more disadvantaged ADI tertiles were less likely to have physician-reported desire (OR 0.17, 95% CI 0.04-0.61, p < 0.01) or parent-reported desire (OR 0.16, 95% CI 0.04-0.52, p < 0.01) for revision, independent of sex and insurance status. In the nonsyndromic subgroup, inclusion in a more disadvantaged ADI tertile was associated with increased odds of speech/language concerns (OR 4.42, 95% CI 1.41-22.62, p < 0.01). There were no differences in interventions received or outcomes among SVI tertiles (p ≥ 0.24). Neither ADI nor SVI tertile was associated with risk of loss to follow-up among nonsyndromic patients (p ≥ 0.38). Patients from the most disadvantaged neighborhoods may be at risk for poor speech outcomes and different standards of assessment for revision. Neighborhood measures of disadvantage represent a valuable tool to improve patient-centered care by allowing for modification of treatment protocols to meet the unique needs of patients and their families.

Digital Signature Standard Implementation Strategy by Optimizing Hash Functions Through Performance Optimization

Security method of data transmission process has been growing rapidly with the science of cryptography. Cryptography can provide security services that includes security aspects like confidentiality, data integrity, authentication and non-repudiation. Modern cryptography uses a key that must be kept secret to overcome the problem of cryptographic security. Problem in the use of the same key by two entities that communicate with each other in exchanging messages is a way to distribute the key. This problem can be overcome by using public-key cryptography, which allows users to communicate securely without a shared secret key. Digital signature is the application of public-key cryptography. When accessing important digital documents, it is necessary to verify the signature given. Implementation of digital signature always requires a hash function. Hash function used in this research namely SHA-256, SHA-384 and Tiger. Federal Information Processing Standards (FIPS) set the cryptographic standard for digital signatures is the Digital Signature Standard (DSS). Algorithms included in the DSS are the Digital Standard Algorithm (DSA), Ron Rives, Adi Shamir, and Leonard Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA). So it is necessary to test the best digital signature implementation strategy that can be used by optimizing the performance of the hash function. Performance testing of the three algorithms is done by making an application using a computer programming language C++. Implementation program using C++ class library for cryptographic scheme that is Crypto++ Library 5.6.0. Class libraries used in the classes functions for digital signatures. On the application of digital signatures generated, conducted tests is done by combining each hash function algorithm with each of the DSS in order to compare their performance in terms of time and memory usage. Against the test results are then analyzed using statistical tests. The result shows that pair of Tiger hash function and DSA algorithm is the best combination.

Factors Associated with Remote Patient Monitoring Services Provision by Hospitals and Health Care Systems in the United States.

Purpose: To assess the factors associated with offering remote patient monitoring (RPM) services. Methods: We integrated three datasets: (1) 2019-2020 Area Health Resource Files, (2) 2019 American Community Survey, and (3) 2019 American Hospitals Association annual survey using county Federal Information Processing Standards code to evaluate associations between hospital characteristics and county-level demographic factors with provision of (1) post-discharge, (2) chronic care, (3) other RPM services, and (4) any of these three RPM service categories. These outcomes were analyzed using multi-level, mixed-effects multivariate logistic regression modeling to account for county-level clustering of hospitals. Findings: Among 3,381 hospitals, 1,354 (40.0%) provided any RPM services. Being part of a clinically integrated network (CIN) and private, non-profit (vs. public) ownership were respectively associated with 104.5% (95% confidence interval [CI]: 69.4-146.8%; p < 0.001) and 30.4% (95% CI: 2.5-66.0%; p = 0.031) higher odds of providing any RPM services. Critical access hospital (CAH) designation, for-profit (vs. public) ownership, and location in the South (vs. Northeast) were associated with significantly lowering odds of providing any RPM services by 36.2% (95% CI: 14.2-52.6%; p = 0.003), 70.1% (95% CI: 56.0-79.6%; p < 0.001), and 34.0% (95% CI: 2.8-55.1%; p = 0.035), respectively. Similar trends were found with the various RPM service categories. Conclusions: The factors most associated with provision of any RPM services were hospital-level factors. Specifically, being part of a CIN and private, non-profit ownership had the highest positive associations with offering RPM services whereas location in the South and CAH designation had the strongest negative associations. Further studies are needed to understand the reasons behind these associations.

Abstract PO-172: The impact of environmental quality on colorectal cancer incidence rates in the United States

Abstract Background Colorectal Cancer (CRC) is the third most common cancer and the third leading cause of cancer-related deaths in the US. CRC has been shown to be an environmentally associated disease, and the identification of specific environmental risk factors is critical to identify intervention opportunities in high-risk populations. CRC incidence rates have been increasing among people living in rural areas compared to non-rural. Understanding the relationship between environmental factors and rurality on CRC incidence is imperative to improve CRC prevention and control efforts. We used the Environmental Quality Index (EQI) developed by the US Environmental Protection Agency (EPA) to represent multiple simultaneous environmental domains to measure of the overall environment and examined its association with CRC incidence rates in the US. Methods The 2006-2010 EQI, a county-level index including overall and five specific domains (air, water, land, sociodemographic (SD), and build) for the entire US, was obtained from the US EPA public database. Data for the 2013-2017 county-level age-adjusted CRC incidence rates were extracted from the State Cancer Profiles and linked to the EQI data using the Federal information processing standard (FIPS) state-county code. EQI was categorized into Quintiles (Q1 represents highest environmental quality and Q5 represent poorest environmental quality). Pearson's correlation and linear regression were used to test associations between CRC incidence rate and EQI. Covariates included rural/urban status, percent of Black or African Americans (AAs), and state. Incidence rate differences (IRDs) and 95% confidence intervals (95%CI) were reported. Results Correlations between CRC incidence and EQIs were observed positive in overall (r = -0.06; Р =0.002) and domains including air (r = 0.01; Р = 0.66), build (r = 0.01; Р = 0.67), land (r = 0.08; Р &amp;lt;0.001), SD (r = 0.45; Р &amp;lt; 0.001), and negative in build domain (r = -0.12; Р &amp;lt; 0.001). In the adjusted model, for the SD domain, those counties with lowest quality quintile were associated with an incidence rate increase of 10.26 (8.87, 11.65) cases per 100,000 persons per year compared to counties in the highest quintile. The lowest build EQI (5th quintile) had 1.49 fewer cases per 100,000 persons per year comparing to the 1st quintile. About 82.5% of the observed elevation 7.7 (6.55, 8.84) in CRC incidence rates among rural counties, compared to metropolitan urbanized counties, were explained by the EQI and the percent of Black or AAs residing in the counties. Conclusion The EQI was observed to be a significant factor for increased CRC incidence, although its impact varied when examined by domains. The lowest sociodemographic quality had the strongest association with CRC, followed by highest build environmental quality. Further, sociodemographic disadvantage was found to be a robust predictor and largely explained the rural-urban CRC incidence disparity. Our study provides a broader view of the environmental quality effect on the CRC incidence disparities in rural areas. Citation Format: Tingting Li, Mei-Chin Hsieh, Lee S. McDaniel, Edward S. Peters. The impact of environmental quality on colorectal cancer incidence rates in the United States [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-172.

Predicting drought using Bayesian structural time series model

The western U.S. has been experiencing a mega-scale drought since 2000. By killing trees and drying out forests, the drought triggers widespread wildfire activities. In the 2020 California fire season alone, more than 10.3 million acres of land were burned and over 10000 structures were damaged. The estimated cost is over $12 billion. Drought also devastates agriculture and drains the social and emotional well-being of impacted communities. &#x0D; This work aims at predicting the occurrence and severity of drought, and thus helping mitigate drought related adversaries. A machine learning based framework was developed, including time series data collection, model training, forecast and visualization. The data source is from the National Drought Monitor center with FIPS (Federal Information Processing Standards) geographic identification codes. For model training and forecasting, a Bayesian structural time series (BSTS) based statistical model was employed for a time-series forecasting of drought spatially and temporally. In the model, a time-series component captures the general trend and seasonal patterns in the data; a regression component captures the impact of the drought in measurements such as severity of drought, temperature, etc. The statistical measure, Mean Absolute Percentage Error, was used as the model accuracy metric. The last 10 years of drought data up to 2020-09-01 was used for model training and validation. Back-testing was implemented to validate the model . Afterwards, the drought forecast was generated for the upcoming 3 weeks of the United States based on the unit of county level. 2-D heat maps were also integrated for visual reference. &#x0D;

Development of the Advanced Encryption Standard.

Strong cryptographic algorithms are essential for the protection of stored and transmitted data throughout the world. This publication discusses the development of Federal Information Processing Standards Publication (FIPS) 197, which specifies a cryptographic algorithm known as the Advanced Encryption Standard (AES). The AES was the result of a cooperative multiyear effort involving the U.S. government, industry, and the academic community. Several difficult problems that had to be resolved during the standard's development are discussed, and the eventual solutions are presented. The author writes from his viewpoint as former leader of the Security Technology Group and later as acting director of the Computer Security Division at the National Institute of Standards and Technology, where he was responsible for the AES development.

Trends in Medical School Application and Matriculation Rates Across the United States From 2001 to 2015: Implications for Health Disparities.

Socioeconomic and geographic determinants of medical school application and matriculation may help explain the unequal distribution of physicians in the United States. This study describes trends in MD-granting medical school application and matriculation rates and explores the relationship between county median family income, proximity to a medical school, and medical school application and matriculation rates. Data were obtained from the Association of American Medical Colleges, including the age, gender, and Federal Information Processing Standards code for county of legal residence for each applicant and matriculant to U.S. MD-granting medical schools from 2001 through 2015. The application and matriculation rates in each county were calculated using the number of applicants and matriculants per 100,000 residents. Counties were classified into 4 groups according to the county median family income (high-income, middle-income, middle-low-income, low-income). The authors performed chi-square tests to assess trends across the study period and the association of county median family income with application and matriculation rates. There were 581,833 applicants and 262,730 (45.2%) matriculants to MD-granting medical schools between 2001 and 2015. The application rates per 100,000 residents during 2001-2005, 2006-2010, and 2011-2015 were 57.2, 62.7, and 69.0, respectively, and the corresponding matriculation rates were 27.5, 28.1, and 29.8. The ratios of the application rate in high-income counties to that in low-income counties during the 3 time periods were 1.9, 2.4, and 2.8, respectively. The application and matriculation rates to MD-granting medical schools increased steadily from 2001 to 2015. Yet, applicants and matriculants disproportionately came from high-income counties. The differences in the application and matriculation rates between low-income and high-income counties grew during this period. Exploring these differences can lead to better understanding of the factors that drive geographic differences in physician access and the associated health disparities across the United States.

Crowdsourced Tick Image-Informed Updates to U.S. County Records of Three Medically Important Tick Species.

Burgeoning cases of tick-borne disease present a significant public health problem in the United States. Passive tick surveillance gained traction as an effective way to collect epidemiologic data, and in particular, photograph-based tick surveillance can complement in-hand tick specimen identification to amass distribution data and related encounter demographics. We compared the Federal Information Processing Standards (FIPS) code of tick photos submitted to a free public identification service (TickSpotters) from 2014 to 2019 to published nationwide county reports for three tick species of medical concern: Ixodes scapularis Say (Ixodida: Ixodidae), Ixodes pacificus Cooley and Kohls (Ixodida: Ixodidae), and Amblyomma americanum Linneaus (Ixodida: Ixodidae). We tallied the number of TickSpotters submissions for each tick species according to "Reported" or "Established" criteria per county, and found that TickSpotters submissions represented more than half of the reported counties of documented occurrence, and potentially identified hundreds of new counties with the occurrence of these species. We detected the largest number of new county reports of I. scapularis presence in Michigan, North Carolina, and Texas. Tick image submissions revealed potentially nine new counties of occurrence for I. pacificus, and we documented the largest increase in new county reports of A. americanum in Kentucky, Illinois, Indiana, and Ohio. These findings demonstrate the utility of crowdsourced photograph-based tick surveillance as a complement to other tick surveillance strategies in documenting tick distributions on a nationwide scale, its potential for identifying new foci, and its ability to highlight at-risk localities that might benefit from tick-bite prevention education.

Anonymized location data reveals trends in legal Cannabis use in communities with increased mental health risks at the start of the COVID-19 pandemic

Background The coronavirus disease 2019 (COVID-19) pandemic has led to increases in felt negative affect for many. This is concerning as individuals at increased risk for mental health issues are often more likely to use substances to cope with stressors. Objectives The aim of the current study is to examine whether communities reporting an increased risk for developing mental health issues showed differential patterns of legal cannabis use as the pandemic began. A secondary goal is to examine the feasibility of using anonymized location data to uncover community consumption patterns of potential concern. Methods Anonymized location data from approximately 10% of devices in the United States provided a count of the number of visitors to 3,335 cannabis retail locations (medical and recreational) each day from December 1st 2019 through April 2020. Visitor counts were merged with the average number of mentally unhealthy days (aMUDs) reported in the Federal Information Processing Standard (FIPS) county the retailer was located along with FIPS county population and poverty rate estimates. A Poisson spline regression predicting visitors by day, aMUDs, as well as their interaction was performed, entering population and poverty rate as covariates. Results As the pandemic began communities reporting a greater aMUDs showed greater visitation to cannabis retailers. Conclusions These results suggest that the COVID-19 pandemic may have led to increased legal cannabis use in at risk communities. They also highlight the value anonymized location data can provide policymakers and practitioners in uncovering community level trends as they confront an increasingly uncertain landscape.

A secure data parallel processing based embedded system for internet of things computer vision using field programmable gate array devices

SummaryTechnologies and new standards have converted the traditional networks and systems into new smart cities integrated networks. Different factors are behind to build these new networks in terms of hardware, software, standards, technologies, and infrastructure. Internet of things (IoT) is one of the popular network types based on small and portable devices. Some of the IoT applications are more critical and sensitive like smart healthcare systems wherein time data delivery is needed without any delay. Keeping in view the criticality of such applications, this study proposes a secure storage, processing and transmission (SSPT) technique. This technique is based on two modules named Advanced Encryption Standard (AES) in Electronic Code Book (ECB) mode and AES in Ciphered Message Authentication Code (CMAC) mode. In the first module, the AES is implemented in ECB mode to provide encryption to IoT computer vision data. In the second module, the AES is integrated with the CMAC algorithm to provide encryption, integrity, and authentication. The said modules are implemented in a field programmable gate array (FPGA) device which acts as a secure edge computing device. The SSPT is verified by the test vectors given by international standards such as the National Institute of Standards and Technologies (NIST) and Federal Information Processing Standards (FIPS) which are adaptable, proficient, and vigorous as compared to previous techniques.

Microcontroller-based Random Number Generator Implementation by Using Discrete Chaotic Maps

In recent decades, chaos theory has been used in different engineering applications of different disciplines. Discrete chaotic maps can be used in encryption applications for digital applications. In this study, firstly, Lozi, Tinkerbell and Barnsley Fern discrete chaotic maps are implemented based on microcontroller. Then, microcontroller based random number generator is implemented by using the three different two-dimensional discrete chaotic maps. The designed random number generator outputs are applied to NIST (National Institute of Standards and Technology) 800-22 and FIPS (Federal Information Processing Standard) tests for randomness validity. The random numbers are successful in all tests.

Proposing cybersecurity regulations for smart contracts

ABSTRACT Smart contracts have the potential to improve many existing transactions and to enable entirely new business models. However, the technology supporting this new method of transacting is complex and the legal framework applying to it is somewhat unclear. Though theorised several decades ago, it was not until the advent of the distributed ledger technology known as blockchain that smart contracts were able to be practically implemented. This paper summarises the concept of smart contracts while providing the background and context of its development. It then distinguishes those smart contracts which are considered legally binding within the scope of US laws from those that may not have legal effect. Next, it provides an in-depth example of an exploitation of smart contracts and explores how the legal reaction to it is inadequate. To reduce the likelihood of future smart contract exploitations and to improve confidence for contracting parties, this article suggests adding explicit smart contract cybersecurity provisions to existing US legal frameworks. Specifically, I propose adapting several of the National Institute of Standards and Technology’s Federal Information Processing Standards to create minimum cybersecurity requirements for all legally binding smart contracts. I also examine the shortcomings of the Computer Fraud and Abuse Act while identifying it as a piece of legislation ripe for reform which, if done adequately, may provide a legal deterrent to would-be cyber hackers of smart contracts.

Designing and Building Secure Electronic Medical Record Application by Applying AES-256 and RSA Digital Signature

Medical records consist of health data that must be kept confidential and is regulated in Indonesian law. Until now the implementation of electronic medical records in Indonesia has not been clearly regulated in a law, while Regulation of the Minister of Health of the Republic of Indonesia state that medical records must be made in writing, complete, and clearly or electronically. In view of this, another approach than the regulation is needed to be able to meet the confidentiality of the data in the regulation, in electronic form. In this study, an electronic medical record application was designed and built that can meet the requirements of the legislation. The application encrypts patient’s health data stored on medical records using the AES-256 algorithm for data confidentiality. The use of AES-256 has been standardized based on the Federal Information Processing Standards Publication 197 and RSA Digital Signature digital signature because it has been standardized based on Federal Information Processing Standards Publication 186-4 and NIST Special Publication 800-89. The proposed application have been made have passed security testing using 6 testcase based on the reference paper.

Abstract B098: Individual and contextual factors associated with prostate cancer stage at diagnosis: A multistate study

Abstract Background: The success of prostate cancer (PCa) prevention and control programs should be measurable as a decrease in the likelihood of PCa diagnosis at a late stage in vulnerable and underserved populations. Objective: To examine the relationship between individual socioeconomic status (SES), county-level measures of social determinants, and PCa stage at diagnosis. Methods: Men aged 40 and older diagnosed with stage 0 to III PCa from 2000 to 2012 were identified from the Surveillance, Epidemiology, and End Results (SEER) tumor registries in 12 states. These states were retained because they had complete information on Federal Information Processing Standard (FIPS) county codes and across the study period. These data were combined with data from the Area Health Resource Files (AHRF). Stage at diagnosis was dichotomized as early stage (in situ, localized) versus late stage (regional, distant). Individual-level factors included age at diagnosis, race (white, black, others), ethnicity (Hispanic/Latino, non-Hispanic/Latino), marital status (married, unmarried), insurance status (insured, uninsured, unknown), tumor stage, tumor grade (undifferentiated, poorly differentiated, moderately differentiated, well differentiated), and year of diagnosis. Contextual-level factors included county level SES status, residence in rural/urban county, and county-level availability of health care resource. Student's t test for continuous variables and chi-square test for categorical variables were used for statistical comparisons. Hierarchical logistic regression models were used to identify factors associated with late-stage diagnosis. Results: The sample consisted of N = 707,108 PCa cases of which 16.48 % (N = 116,511) are late stage and 16.48 % (N= 590,597) are early stage. The mean diagnosis age was 66 years old. The majority of these individuals were married (76%), White (80.76%), had unknown health insurance coverage (56.30%), were diagnosed in the year 2007 (8.56%), and lived in the state of California (38.48%). In the multivariate analysis, the odds of late-stage PCa diagnosis were higher in older men (odds ratio [OR] =1.013, 95% CI=1.012, 1.014), Blacks (OR=1.07, 95% CI=1.05, 1.09), and those with unknown insurance status (OR=1.12, 95% CI= 1.10, 1.15). County-level socioeconomic indicators including unemployment rates, educational attainment, poverty rates, rural/urban classification were not predictors of late-stage diagnosis. Furthermore, county-level contextual variables related to the availability and accessibility of health care providers and health services were not associated with late-stage diagnosis. Conclusions: Community-based interventions designed to increase utilization of PCa testing should continue to target vulnerable and underserved populations especially Black, and uninsured/underinsured men. It is hope that these efforts will improve early detection and contribute to improve survival. Citation Format: Georges Adunlin, Maisha K. Freeman, Hadiza I. Galadima, Matthew Asare. Individual and contextual factors associated with prostate cancer stage at diagnosis: A multistate study [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B098.

Proposing Cybersecurity Regulations for Smart Contracts

Smart contracts have the potential to improve many existing transactions and to enable entirely new business models. However, the technology supporting this new method of transacting is complex and the legal framework applying to it is somewhat unclear. Though theorized several decades ago, it was not until the advent of the distributed ledger technology known as blockchain that smart contracts were able to be practically implemented. This paper summarizes the concept of smart contracts while providing the background and context of its development. It then distinguishes those smart contracts which are considered legally binding within the scope of US laws from those that may not have legal effect. Next, it provides an in-depth example of an exploitation of smart contracts and explores how the legal reaction to it is inadequate. To reduce the likelihood of future smart contract exploitations and to improve confidence for contracting parties, this article suggests adding explicit smart contract cybersecurity provisions to existing US legal frameworks. Specifically, I propose adapting several of the National Institute of Standards and Technology’s Federal Information Processing Standards to create minimum cybersecurity requirements for all legally binding smart contracts. I also examine the shortcomings of the Computer Fraud and Abuse Act while identifying it as a piece of legislation ripe for reform which, if done adequately, may provide a legal deterrent to would-be cyber hackers of smart contracts.

A Novel Approach of Data Content Zeroization Under Memory Attacks

Protecting user’s secret data on the devices like smartphones, tablets, wearable devices etc, from memory attacks is always a challenge for system designers. The most stringent security requirements and protocols in today’s state-of-the-art systems are governed by Federal Information Processing Standards (FIPS). Specifically, it ensures the protection of sensitive data by erasing them from random access memories (RAMs) and associated flip-flip based registers, as soon as security violation(s) is(are) detected. Traditionally, the sensitive data like authentication credentials, cryptographic keys and other on-chip secrets are erased (or zeroized) by sequential write transactions initiated either by dedicated hardware or using software programs. This paper, for the first time, proposes a novel approach of erasing secured data content from on-chip RAMs using conventional memory built-in-self-test (MBIST) hardware in mission mode. The proposed zeroization approach is proved to be substantially faster than the traditional techniques in erasing data content. As it helps in re-using Memory BIST hardware for on-chip data content zeroization, this guarantees to save silicon area and power by removing dedicated conventional hardware from the device. This paper also discusses the micro-architectural implementation and security challenges of using Memory BIST hardware in mission mode and proposes practical solutions to fill the gaps.

Neighborhood Racial Diversity, Socioeconomic Status, and Perceptions of HIV-Related Discrimination and Internalized HIV Stigma Among Women Living with HIV in the United States.

Relationships that traverse sociodemographic categories may improve community attitudes toward marginalized groups and potentially protect members of those groups from stigma and discrimination. The present study evaluated whether internalized HIV stigma and perceived HIV-related discrimination in health care settings differ based on individual- and neighborhood-level characteristics of women living with HIV (WLHIV). We also sought to extend previous conceptual and empirical work to explore whether perceived HIV-related discrimination mediated the association between neighborhood racial diversity and internalized HIV stigma. A total of 1256 WLHIV in the Women's Interagency HIV Study (WIHS) attending 10 sites in metropolitan areas across the United States completed measures of internalized HIV stigma and perceived HIV-related discrimination in health care settings. Participants also provided residential information that was geocoded into Federal Information Processing Standard (FIPS) codes and linked with census-tract level indicators. In cross-sectional analyses, greater neighborhood racial diversity was associated with less internalized HIV stigma and less perceived HIV-related discrimination regardless of individual race. Neighborhood median income was positively associated with internalized HIV stigma and perceived discrimination, while individual income was negatively associated with perceptions of stigma and discrimination. In an exploratory mediation analysis, neighborhood racial diversity had a significant indirect effect on internalized HIV stigma through perceived HIV-related discrimination. An indirect effect between neighborhood income and internalized stigma was not supported. These findings suggest that greater neighborhood racial diversity may lessen HIV stigma processes at the individual level and that HIV stigma-reduction interventions may be most needed in communities that lack racial diversity.

Higher Socioeconomic Status Is Associated with Improved Overall Survival in Adults with Philadelphia Chromosome Negative Acute Lymphoblastic Leukemia: A Population Study

▪Background:Socioeconomic status (SES) and other non-disease (social and demographic) characteristics are known to predict overall survival (OS) in children with acute lymphoblastic leukemia (ALL) (Petridou et al. Ann Oncol 2015). Less is known about the impact of these factors on survival of adults with ALL. We studied which non-biological risks impact OS in adults with Philadelphia chromosome negative (Ph negative) ALL, with emphasis on the impact of SES on survival.Methods:We assembled data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER-18) registry from 2000 to 2014. This registry covers 27.8% of the US population. We identified patients 18-69 years old diagnosed with Ph negative ALL between 2000-2012 using ICD-O-3 codes. We merged data with the Federal Information Processing Standards county codes and designated patients as metropolitan or non-metropolitan based on state and country 2013 rural-urban continuum codes. We assigned county-level median household income using the Census American Community Survey (www.census.gov).Descriptive statistics were calculated for age at diagnosis, sex, race/ethnicity, marital status, insurance status,median household income, and status with respect to the 200% of the federal poverty line (FPL). We stratified the population by age: 18-39 years (adolescent and young adult, or AYA, cohort) and 40-69 years (adult cohort). We also identified 3 eras of interest: 2000 to 2003, 2004 to 2007, and 2008 to 2012. Multivariable Cox proportional hazards (PH) regression assessed predictors of OS, with separate models for AYAs and adults. Analyses were performed using SAS.Results:In total, 5,858 patients met criteria for analysis. Median age was 41.4 years (25-75th%: 26.9-54.8), with 45.8%patients in the AYA cohort. 57.8% of patients were male, and half were non-Hispanic white (50.8%). Over 90% of all patients lived in metropolitan counties. One-third (35%) of patients lived below the 200% FPL. Median household income was $55,901 (25-75th%: $51,389-67,677). 49% of patients had missing data about insurance, so we omitted this variable from analysis. ALL lineage was B cell in 57%, T-cell in 10%, and unspecified in 33% of cases. 52% were married overall with more AYAs (61.3%) not married compared to older adults (34%).In Cox regression model for AYAs, higher median income was associated with better OS (HR=0.95 for every $10,000 above national household income, p=0.03). We also found that later era of diagnosis (2008-2012 vs. 2000-2003) was associated with better OS (HR=0.70, p<0.001), while older age (HR=1.04, p<0.0001), unspecified lineage (vs. B-cell, HR=1.20, p=0.04), and all races compared to non-Hispanic white were associated with poorer OS with the exception of Asian/Pacific Islander: American Indian (HR=2.82, p<0.001), Hispanic (HR=1.58, p<0.001), and non-Hispanic black (HR=1.50, p<0.001). Sex, marital status, and rural residence were not associated with OS in the AYA cohort.In the adult cohort, in a Cox regression model, patients with higher income had better OS (HR=0.95 for every $10,000 above median national household income, p=0.001). We also found that later era of diagnosis (2008-2012 and 2004-2007 vs. 2000-2003), and non-metropolitan geography (HR=0.81, p<0.001) predicted better OS, while older age (HR=1.03, p<0.0001), Hispanic race (HR=1.16, p=0.03), male sex (HR=1.09, p=0.03), and non-married status conferred poorer OS (HR=1.31, p<0.0001) (Table).Conclusion:We identify non-biological predictors of OS in adults with ALL in a large population-based registry. Notably, higher SES portends better OS in both AYAs and older adults with ALL, consistent with findings in the pediatric literature. Marital status affects OS only in older patients, while race/ethnicity in AYAs greatly impacts OS. All ages had improved OS in more recent years, suggesting that all age cohorts are benefitting from new ALL treatment approaches. Our findings of the impact of SES on OS call for more investigation and action to improve social support and possibly adherence in the adult population. DisclosuresKumar:Seattle Genetics: Research Funding. Rodday:Seattle Genetics: Research Funding. Stock:Jazz Pharmaceuticals: Consultancy. Parsons:Seattle Genetics: Research Funding.

Corrected runs distribution test for pseudorandom number generators

Runs distribution test is an important statistical test based on Golomb's randomness postulates. It is an important randomness test included in some famous evaluation standards and statistical test suites such as Application Notes and Interpretation of the Scheme (AIS20), the Federal Information Processing Standards (FIPS140), CryptX, and so on. However, when it is applied on some well-known good Deterministic Random Bit Generators (DRBGs), the test results show apparent bias from randomness. This shows that there exist some inaccuracies in the runs distribution test. The problem is solved and the runs distribution test through two ways is corrected. First, the degree of freedom of statistical value V is adjusted. Secondly, the expected number of different lengths runs is modified. The experiment results show that the corrected runs distribution test is more accurate under two-level evaluation approach proposed by National Institute of Standards and Technology (NIST) Special Publication (SP) 800–22.