Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy.

Background: The timely identification of nulliparas at high risk of adverse fetal and neonatal outcomes during pregnancy is crucial for initiating clinical interventions to prevent perinatal complications. Although machine learning methods have been applied to predict preterm birth and other pregnancy complications, many models do not provide explanations of their predictions, limiting the clinical use of the model.

Objective: This study aimed to develop interpretable prediction models for a composite adverse perinatal outcome (stillbirth, neonatal death, estimated Combined Apgar score of <10, or preterm birth) at different points in time during the pregnancy and to evaluate the marginal predictive value of individual predictors in the context of a machine learning model.

Comprehensive transcript-level analysis reveals transcriptional reprogramming during the progression of Alzheimer's disease.

Background: Alzheimer's disease (AD) is a common neurodegenerative disorder that has a multi-step disease progression. Differences between moderate and advanced stages of AD have not yet been fully characterized.

Materials And Methods: Herein, we performed a transcript-resolution analysis in 454 AD-related samples, including 145 non-demented control, 140 asymptomatic AD (AsymAD), and 169 AD samples.

County augmented transformer for COVID-19 state hospitalizations prediction.

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT).

CSSQ: a ChIP-seq signal quantifier pipeline.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has revolutionized the studies of epigenomes and the massive increase in ChIP-seq datasets calls for robust and user-friendly computational tools for quantitative ChIP-seq. Quantitative ChIP-seq comparisons have been challenging due to noisiness and variations inherent to ChIP-seq and epigenomes. By employing innovative statistical approaches specially catered to ChIP-seq data distribution and sophisticated simulations along with extensive benchmarking studies, we developed and validated CSSQ as a nimble statistical analysis pipeline capable of differential binding analysis across ChIP-seq datasets with high confidence and sensitivity and low false discovery rate with any defined regions.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as (WiMob).

Critical success factors for high routine immunization performance: A case study of Senegal.

Background: The essential components of a vaccine delivery system are well-documented, but robust evidence is lacking on policies and implementation strategies are operationalized to drive catalytic improvements in coverage. To address this gap, we identified success factors that supported improvements in routine immunization coverage in Senegal, especially from 2000 to 2019.

Methods: We identified Senegal as an exemplar in the delivery of childhood vaccines through analysis of DTP1 and DTP3 coverage data.

The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection.

Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods.

Predicting a kidney transplant patient's pre-transplant functional status based on information from waitlist registration.

With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration.

A disposable impedance-based sensor for in-line cell growth monitoring in CAR-T cell manufacturing.

This paper presents the development of low-cost, disposable impedance-based sensors for real-time, in-line monitoring of suspension cell culture. The sensors consist of electrical discharge machining (EDM) cut aluminum electrodes and polydimethylsiloxane (PDMS) spacers, both of which are low-cost materials that can be safely disposed of. Our research demonstrates the capability of these low-cost sensors for in-line, non-invasive monitoring of suspension cell growth in cell manufacturing.

Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information.

Background: As the prolonged COVID-19 pandemic continues, severe seasonal Influenza (flu) may happen alongside COVID-19. This could cause a "twindemic", in which there are additional burdens on health care resources and public safety compared to those occurring in the presence of a single infection. Amidst the raising trend of co-infections of the two diseases, forecasting both Influenza-like Illness (ILI) outbreaks and COVID-19 waves in a reliable and timely manner becomes more urgent than ever.

Estimating and Assessing Differential Equation Models with Time-Course Data.

Ordinary differential equation (ODE) models are widely used to describe chemical or biological processes. This Article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations, time-course data are often noisy, and some components of the system may not be observed.

Assessing the effectiveness of training programme on the competency of medical staffs in public health emergency.

Background: The competencies of medical staff in the public health emergency system and evaluated the effects of system-based professional training were investigated.

Material And Methods: A competency model for individuals in a public health emergency management system was developed, which contained 33 items with 5 domains. A competency-based intervention was performed.

Temporal Network Analysis of Neurocognitive Functioning and Psychological Symptoms in Collegiate Athletes After Concussion.

Sport-related concussion (SRC) is associated with several post-injury consequences, including neurocognitive decrements and psychological distress. Yet, how these clinical markers interact with each other, the magnitude of their interrelationships, and how they may vary over time following SRC are not well understood. Network analysis has been proposed as a statistical and psychometric method to conceptualize and map the complex interplay of interactions between observed variables (e.

Test-Retest Reliability and Efficacy of Individual Symptoms in Concussion Management.

Objective: (1) To determine test-retest reliability of individual Sport Concussion Assessment Tool-Third Edition (SCAT-3) symptom scores and symptom severity scores, (2) to examine the specificity/sensitivity of individual SCAT-3 symptom severity scores acutely (24-48 hours) postconcussion, and (3) to develop a model of symptoms best able to differentiate concussed from nonconcussed student athletes and cadets.

Design: Prospective, longitudinal, and cross-sectional.

Setting: Twenty-six civilian schools and 3 US service academies.

Explainable AI: A review of applications to neuroimaging data.

Deep neural networks (DNNs) have transformed the field of computer vision and currently constitute some of the best models for representations learned hierarchical processing in the human brain. In medical imaging, these models have shown human-level performance and even higher in the early diagnosis of a wide range of diseases. However, the goal is often not only to accurately predict group membership or diagnose but also to provide explanations that support the model decision in a context that a human can readily interpret.

Relation of blood lead levels and lead in gasoline: an updated systematic review.

Background: Millions of tons of lead were added to gasoline worldwide beginning in 1922, and leaded gasoline has been a major source of population lead exposure. In 1960s, lead began to be removed from automotive gasoline. Removal was completed in 2021.

Are more charging piles imperative to future electrified transportation system?

Scholars and practitioners believe that the large-scale deployment of charging piles is imperative to our future electric transportation systems. Major economies ambitiously install charging pile networks, with massive construction spending, maintenance costs, and urban space occupation. However, recent developments in technology may significantly reduce the necessary charging capacity required by the system.

Flexible analytic model to inform multi-stakeholder pediatric vaccine scheduling decisions.

Background: Pediatric immunization is important for preventing potentially life-threatening diseases in children. Over time, the number of recommended pediatric vaccines has increased and is likely to increase further as new vaccines are developed. Given the different number of doses for available vaccines and various constraints (e.

Incorporating New Technologies in EEIO Models.

We propose a methodology to add new technologies into Environmentally Extended Input-Output (EEIO) models based on a Supply and Use framework. The methodology provides for adding new industries (new technologies) and a new commodity under the assumption that the new commodity will partially substitute for a functionally-similar existing commodity of the baseline economy. The level of substitution is controlled by a percentage (%) as a variable of the model.

Critical success factors for high routine immunization performance: A case study of Nepal.

Introduction: The essential components of a vaccine delivery system are well-documented, but robust evidence on how and why the related processes and implementation strategies drive catalytic improvements in vaccination coverage are not well established. To address this gap, we identified critical success factors that may have led to substantial improvements in routine childhood immunization coverage in Nepal from 2000 through 2019.

Methods: We identified Nepal as an exemplar in the delivery of early childhood immunization through analysis of DTP1 and DTP3 coverage data.

Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids with PVs: An Online High-Dimensional Data-driven Approach.

Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI).

Projecting COVID-19 Mortality as States Relax Nonpharmacologic Interventions.

Importance: A key question for policy makers and the public is what to expect from the COVID-19 pandemic going forward as states lift nonpharmacologic interventions (NPIs), such as indoor mask mandates, to prevent COVID-19 transmission.

Objective: To project COVID-19 deaths between March 1, 2022, and December 31, 2022, in each of the 50 US states, District of Columbia, and Puerto Rico assuming different dates of lifting of mask mandates and NPIs.

Design Setting And Participants: This simulation modeling study used the COVID-19 Policy Simulator compartmental model to project COVID-19 deaths from March 1, 2022, to December 31, 2022, using simulated populations in the 50 US states, District of Columbia, and Puerto Rico.