Racial Fairness of Individual- and Community-Level Proxies of Socioeconomic Status Among Birthing Parent-Child Dyads.

BACKGROUND: While precision medicine algorithms can be used to improve health outcomes, concerns have been raised about racial equity and unintentional harm from encoded biases. In this study, we evaluated the fairness of using common individual- and community-level proxies of pediatric socioeconomic status (SES) such as insurance status and community deprivation index often utilized in precision medicine algorithms. METHODS: Using 2012-2021 vital records obtained from the Ohio Department of Health, we geocoded and matched each residential birth address to a census tract to obtain community deprivation index. We then conducted sensitivity and specificity analyses to determine the degree of match between deprivation index, insurance status, and birthing parent education level for all, Black, and White children to assess if there were differences based on race. RESULTS: We found that community deprivation index and insurance status fail to accurately represent individual SES, either alone or in combination. We found that deprivation index had a sensitivity of 61.2% and specificity of 74.1%, while insurance status had a higher sensitivity of 91.6% but lower specificity of 60.1%. Furthermore, these inconsistencies were race-based across all proxies evaluated, with greater sensitivities for Black children but greater specificities for White children. CONCLUSION: This may explain some of the racial disparities present in precision medicine algorithms that utilize SES proxies. Future studies should examine how to mitigate the biases introduced by using SES proxies, potentially by incorporating additional data on housing conditions.

A Hardware Security Protection Method for Conditional Branches of Embedded Systems.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) attacks. Moreover, to prevent attackers from stealing the critical information of branches, the method designs the hybrid arbiter physical unclonable function (APUF) circuit to encrypt and decrypt the directions, addresses, and indexes of branches. This circuit combines the advantages of double APUF and Feed-Forward APUF, which can enhance the randomness of output response and resist machine learning attacks. If attackers still successfully tamper with the branches and disrupt the control flow integrity (CFI), this method detects tampering with the instruction codes, jump addresses, and jump directions in a timely manner through dynamic and static label comparison. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Lifetime residential data collection protocol for the Adolescent Brain Cognitive Development (ABCD) Study.

Understanding the impacts of environmental exposures on health outcomes during development is an important area of research for plenty of reasons. Collecting retrospective and prospective residential history can enrich observational studies through eventual linkages to external sources. Augmenting participant health outcome data with environmental data can better inform on the role of the environment, thereby enhancing prevention and intervention efforts. However, collecting the geospatial information needed for this type of research can be difficult, especially when data are collected directly from participants. Participants' residential histories are unique and often complex. Collecting residential history data often involves capturing precise spatial locations along specific timeframes as well as contending with recall bias and unique, complex living arrangements. When trying to assess lifetime environmental exposures, researchers must consider the many changes in location a person goes through and the timeframes in which these changes occur, ultimately creating a multidimensional and dynamic dataset. Creating data collection protocols that are feasible to administer, result in accurate data, and minimize data missingness is a major challenge to undertake. Here, we provide an overview of the protocol developed to collect the lifetime residential address information of participants in the Adolescent Brain Cognitive Development (ABCD) Study.

Anti-viral Effects of Pavetta indica Methanolic Extract and Acyclovir on Behavioral and Biochemical Parameters in Streptozotocin-induced Alzheimer's Disease in Rats.

BACKGROUND: Alzheimer's disease is a neurological dysfunction of the brain caused by neurodegeneration and oxidative stress. Some viruses, such as herpes viruses, HSV-1, and HSV-2, are causative agents of Alzheimer's disease and result in ß-amyloid peptide and tau protein accumulation in the brain. Some antiviral drugs, such as valacyclovir, acyclovir, and foscarnet, reduce amyloid-beta and P-tau. Pavetta indica leaves are also reported for their antiviral properties. The current study aimed to find out the significance of using Pavetta indica methanolic extract and acyclovir against Alzheimer's disease induced by streptozotocin. METHODS: Wistar rats received acyclovir and Pavetta indica methanolic extract orally at different dose ranges (50, 150, 450 mg/kg) and (125, 250, 500 mg/kg), respectively. The standard therapy, Rivastigmine (2 mg/kg), was given orally. RESULTS: Intracerebroventricular-streptozotocin produced significant alternations in behavioral assessments, including locomotor activity test, Morris water maze test, and elevated plus maze test. Moreover, intracerebroventricular-streptozotocin ameliorated the antioxidant defense activity by decreasing levels of catalase, superoxide dismutase, and reduced glutathione while enhancing the oxidative stress markers, including malondialdehyde, and total nitrite levels. Finally, the main findings showed that intracerebroventricular-streptozotocin significantly increased the inflammatory marker, tumor necrosis factor-α, and disturbed neurotransmitter mediators, including levels of acetylcholinesterase, glutamate, and γ-amino butyric acid. CONCLUSION: In a dose-dependent manner, acyclovir and Pavetta indica methanolic extract treatments abrogated the streptozotocin-induced behavioral and neurological abnormalities in rats. The potential therapeutic effects of PIME and acyclovir administration in intracerebroventricular-streptozotocin-treated rats may be attributed to its potential antiviral, antioxidant, and anti-inflammatory effects. The current study suggests that Pavetta indica methanolic extract and acyclovir are promising therapeutic targets against Alzheimer's disease.

BEC Defender: QR Code-Based Methodology for Prevention of Business Email Compromise (BEC) Attacks.

In an era of ever-evolving and increasingly sophisticated cyber threats, protecting sensitive information from cyberattacks such as business email compromise (BEC) attacks has become a top priority for individuals and enterprises. Existing methods used to counteract the risks linked to BEC attacks frequently prove ineffective because of the continuous development and evolution of these malicious schemes. This research introduces a novel methodology for safeguarding against BEC attacks called the BEC Defender. The methodology implemented in this paper augments the authentication mechanisms within business emails by employing a multi-layered validation process, which includes a MAC address as an identity token, QR code generation, and the integration of timestamps as unique identifiers. The BEC-Defender algorithm was implemented and evaluated in a laboratory environment, exhibiting promising results against BEC attacks by adding an extra layer of authentication.

Pilot Implementation of a Nutrition-Focused Community-Health-Worker Intervention among Formerly Chronically Homeless Adults in Permanent Supportive Housing.

Food insecurity is a known health equity threat for formerly chronically homeless populations even after they transition into permanent housing. This project utilized a human-centered design methodology to plan and implement a nutrition-focused community-health-worker (CHW) intervention in permanent supportive housing (PSH). The project aimed to increase access to healthy foods, improve nutritional literacy, healthy cooking/eating practices, and build community/social connectedness among 140 PSH residents. Validated food-security screening conducted by CHWs identified low or very low food security among 64% of 83 residents who completed the baseline survey, which is similar to rates found in a previous study among formerly homeless populations placed in PSH. Major themes identified through an analysis of resident feedback include (1) lack of needed kitchenware/appliances for food preparation, (2) knowledge gaps on how to purchase and prepare healthier food, (3) positive perceptions of healthy food options, (4) expanded preferences for healthy, easy-to-prepare foods, (5) regaining cooking skills lost during homelessness, (6) positive experiences participating in group activities, (7) community re-entry, and (8) resident ownership. Preliminary findings suggest the use of a human-centered design methodology for planning and implementing this multi-level CHW intervention helped reduce food insecurity, engaged participants in learning and adopting healthy and safe cooking and eating practices, and fostered social connectedness and feelings of community among formerly chronically homeless PSH residents.

The Collaborative Nature of Testimonial Learning.

Children's testimonial learning often occurs in epistemic collaborations with others. In this paper, we will discuss ways in which cultural learning emerges in social and interpersonal contexts, and is intrinsically supported and guided by children's collaborative capacities. Much work in cultural learning has focused on children's examination of speaker and model characteristics, but more recent research has investigated the interactive aspects of testimonial exchanges. We will review evidence that children (1) participate in the interpersonal commitments that are shared in testimonial transactions by way of direct address and epistemic buck passing, (2) participate in social groups that affect their selective learning in nuanced ways, and (3) may detect epistemic harms by listeners who refuse to believe sincere and accurate speakers. Implications for conceptualizing children's testimonial learning as an interactive mechanism of collaboration will be discussed.

FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database.

Introduction: Linking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set. Methods: In this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning-based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual's (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB). Results: We achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement. Discussion: In conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

THE 2023 PRESIDENTAL ADDRESS. Celebrating advocacy: neurosurgeons as advocates.

The central focus of the 2023 annual gathering of the American Association of Neurological Surgeons in Los Angeles revolved around the concept of neurosurgeons as advocates. Beyond their roles in clinical practice and patient care, neurosurgeons frequently unite in their commitment to advocacy. This shared dedication empowers them to thrive in areas such as innovation, teaching, advanced research, and comprehensive training to shape the future of the neurosurgical field. The substantial outcome of this approach is the establishment of an environment dedicated to delivering the utmost quality of care to neurosurgery patients.

Brief Interventions for Cannabis Using Adolescents.

This article discusses the application of brief interventions to address adolescents with a cannabis use problem. Topics include a general model of brief interventions, the outcome literature, existing brief interventions that focus on youth cannabis use, adjustments to a brief intervention when addressing cannabis, referral to treatment issues, personalizing a brief intervention, the need to address coexisting problems, and future directions.

A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics.

Neuromorphic cognitive computing offers a bio-inspired means to approach the natural intelligence of biological neural systems in silicon integrated circuits. Typically, such circuits either reproduce biophysical neuronal dynamics in great detail as tools for computational neuroscience, or abstract away the biology by simplifying the functional forms of neural computation in large-scale systems for machine intelligence with high integration density and energy efficiency. Here we report a hybrid which offers biophysical realism in the emulation of multi-compartmental neuronal network dynamics at very large scale with high implementation efficiency, and yet with high flexibility in configuring the functional form and the network topology. The integrate-and-fire array transceiver (IFAT) chip emulates the continuous-time analog membrane dynamics of 65 k two-compartment neurons with conductance-based synapses. Fired action potentials are registered as address-event encoded output spikes, while the four types of synapses coupling to each neuron are activated by address-event decoded input spikes for fully reconfigurable synaptic connectivity, facilitating virtual wiring as implemented by routing address-event spikes externally through synaptic routing table. Peak conductance strength of synapse activation specified by the address-event input spans three decades of dynamic range, digitally controlled by pulse width and amplitude modulation (PWAM) of the drive voltage activating the log-domain linear synapse circuit. Two nested levels of micro-pipelining in the IFAT architecture improve both throughput and efficiency of synaptic input. This two-tier micro-pipelining results in a measured sustained peak throughput of 73 Mspikes/s and overall chip-level energy efficiency of 22 pJ/spike. Non-uniformity in digitally encoded synapse strength due to analog mismatch is mitigated through single-point digital offset calibration. Combined with the flexibly layered and recurrent synaptic connectivity provided by hierarchical address-event routing of registered spike events through external memory, the IFAT lends itself to efficient large-scale emulation of general biophysical spiking neural networks, as well as rate-based mapping of rectified linear unit (ReLU) neural activations.

Capturing emergency dispatch address points as geocoding candidates to quantify delimited confidence in residential geolocation.

BACKGROUND: In response to citizens' concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients' residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. RESULTS: We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62-1.00) and ED_sRGDP (0.36-1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work. CONCLUSIONS: Our methodology produces simple metrics - sRGDP - to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust.

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses.

BACKGROUND: Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration. METHOD: In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data. RESULTS: We found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants. CONCLUSIONS: The results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.

Processing DNA Storage through Programmable Assembly in a Droplet-Based Fluidics System.

DNA can be used to store digital data, and synthetic short-sequence DNA pools are developed to store high quantities of digital data. However, synthetic DNA data cannot be actively processed in DNA pools. An active DNA data editing process is developed using splint ligation in a droplet-controlled fluidics (DCF) system. DNA fragments of discrete sizes (100-500 bps) are synthesized for droplet assembly, and programmed sequence information exchange occurred. The encoded DNA sequences are processed in series and parallel to synthesize the determined DNA pools, enabling random access using polymerase chain reaction amplification. The sequencing results of the assembled DNA data pools can be orderly aligned for decoding and have high fidelity through address primer scanning. Furthermore, eight 90 bps DNA pools with pixel information (png: 0.27-0.28 kB), encoded by codons, are synthesized to create eight 270 bps DNA pools with an animation movie chip file (mp4: 12 kB) in the DCF system.

Evaluation Mechanism of Political Discourse: A Holistic Approach.

Taking the economic issue of Trump's First State of the Union Address (SUA) as original data, the present study examined the evaluation features of political speeches by adopting a holistic approach, which includes both macro and micro dimensions. At the macro level, a series of semantic patterns were identified, with Goal-Achievement and General-Example Patterns being the most prevalent. They predetermine the evaluative tone, giving the surrounding statements evaluative meanings, exhibiting the radiating nature of evaluative meaning; at the micro level, a variety of resources have been identified, both explicit and implicit, lexical and syntactical, attitudinal and gradational, which collaborate to reinforce the subjective evaluation, revealing the holistic characteristic in the realization of evaluative meaning. Throughout the analysis, three evaluative mechanisms have been proposed, which are the coupling of meaning, semantic prosody, and tense switching. They collaborate and promote the subjective evaluation to be established and reinforced in a cumulative, gradient or hybrid pattern. In a narrow sense, the present study has partially revealed Trump's political discourse feature. Broadly speaking, it contributes to the theoretical development of the appraisal framework by refining existing evaluation systems through a holistic research paradigm, which in turn facilitates accurate interpretation of various types of discourse.

Evidential value of country location evidence obtained from IP address geolocation.

Knowledge of the previous location of an Internet device is valuable information in forensics. The previous device location can be obtained via the IP address that the device used to access Internet services, such as email, banking, and online shopping. However, the problem with the device location using its IP address is the unknown evidential value, which is used to admit the evidence in the case. This work introduces a method to process free and constantly updated data to assess the evidential value of the IP country location. The evidential value is assessed for several countries by analyzing historical data over 8 years. Tampering with the location evidence is discussed, as well as its detection. The source code to replicate the results and to apply the updated data to future evidence is available.

Descobri que tenho câncer de mama: significados no discurso do sujeito coletivo / Descubrí que tengo cáncer de mama: significados en el discurso del sujeto colectivo / I discovered that I have breast cancer: meanings in the collective subject discourse

Objetivo: Conhecer significados contemporâneos do diagnóstico de câncer de mama. Método: Pesquisa narrativa realizada com 11 mulheres com diagnóstico de câncer de mama, submetidas à quimioterapia, em remissão da doença. As narrativas foram coletadas, entre agosto e dezembro de 2018, por entrevistas gravadas, transcritas e analisadas pela técnica do Discurso do Sujeito Coletivo. Resultados: Da análise emergiram quatro ideias centrais e neste estudo serão apresentadas três ideias centrais: Descobrindo o câncer de mama; Sentimentos atuais e; Conselhos às mulheres com câncer de mama. Conclusão: O discurso revela o medo e os sofrimentos das mulheres, em especial, na fase do diagnóstico e de remissão do câncer, apesar dos avanços no controle da doença dos tempos atuais. Além disso, revela as mudanças no viver após o diagnóstico. Conhecer o significado da doença permite melhor planejamento de enfermagem

Objective: To know the contemporary meaning of breast cancer diagnosis. Method: Narrative research, carried out with 11 women diagnosed with breast cancer, submitted to chemotherapy, in remission of the disease. The narratives were collected, between August and December 2018, by recorded interviews, transcribed, and analyzed using the Collective Subject Discourse technique. Results: From the analysis techniques four central ideas emerged, in this study, three central ideas are presented: Discovering breast cancer; Current feelings; and Advice for women with breast cancer. Conclusion: The discourse reveals the fear and suffering of women, especially in the stage of cancer diagnosis and remission, despite the advances in the control of the disease in current times. Furthermore, it reveals the changes in living after diagnosis. Knowing the meaning of the disease allows for better nursing planning.

Objetivo: Conocer el significado contemporáneo del diagnóstico de cáncer de mama. Método: Investigación narrativa, realizada con 11 mujeres diagnosticadas de cáncer de mama, sometidas a quimioterapia, en remisión de la enfermedad. Se recolectaron las narrativas entre agosto y diciembre de 2018, mediante la grabación de entrevistas para ser transcritas y analizadas por la técnica del Discurso del Sujeto Colectivo. Resultados: De las cuatro ideas que surgieron del análisis, en este estudio se presentan tres ideas centrales: Descubriendo el cáncer de mama; Sentimientos actuales; y Consejos para mujeres con cáncer de mama. Conclusión: El discurso revela el miedo y el sufrimiento de las mujeres, especialmente en la etapa de diagnóstico y remisión del cáncer, a pesar de los avances en el control de la enfermedad en los tiempos actuales. Además, muestra los cambios en la vida después del diagnóstico. Conocer el significado de la enfermedad permite una mejor planificación de la enfermería.

Neuromorphic-P2M: processing-in-pixel-in-memory paradigm for neuromorphic image sensors.

Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2× lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.