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The use of immunophenotyping during immunotoxicity investigations was first popularized in the 1980 s and has since become more integrated into diagnostic and non-clinical assessments. The data provided from immunophenotyping can serve as an initial source of information to guide decisions for additional, more advanced, immunotoxicity testing as well as for human health safety and risk assessment of drugs and chemicals. However, comprehensive guidance describing applications of immunophenotyping data in immunotoxicity investigations is lacking, particularly among regulatory bodies. Therefore, a critical examination is needed for the appropriate interpretations and potential misinterpretations of such data during the assessment of drug safety and chemical risk. As such, the current uses and implications of immunophenotyping data in human health safety and risk assessments has been evaluated to provide additional context for the application of current methodologies and guidelines. In addition, case studies are presented to highlight the challenges of interpreting immunophenotyping results along with incorporating the findings into immunotoxicity investigations. Based on the analyses of current approaches and methodologies, a decision flow is presented for use of immunophenotyping data during risk informed decision making.

Abstract In 2007, the Anaktuvuk River fire burned more than 1000 km2 of arctic tundra in northern Alaska, ~ 50% of which occurred in an area with ice-rich syngenetic permafrost (Yedoma). By 2014, widespread degradation of ice wedges was apparent in the Yedoma region. In a 50 km2 area, thaw subsidence was detected across 15% of the land area in repeat airborne LiDAR data acquired in 2009 and 2014. Updating observations with a 2021 airborne LiDAR dataset show that additional thaw subsidence was detected in

Modeling housing market dynamics is an important component of land use and transport interaction (LUTI) models, particularly for microsimulation models and how they handle the market clearance mechanism. However, most of these models include key assumptions not derived or validated through empirical testing, such as when and what action a seller would take if a property could not be sold within an expected time. However, these are key decision elements of the housing market clearance process. To fill this research gap, this study uses real estate sale listing data to investigate the factors influencing a property listing’s time-on-market (TOM) duration, listing outcome, and correlation. A copula-based structure is developed to jointly estimate the TOM and listing outcome through a competing hazard duration model and a nested logit model. The results show statistically significant and positive correlations between the TOM of terminated listings and termination choices (i.e., whether the terminated listing will be withdrawn from the market, converted to a lease, or re-listed as a sale). This implies that the unobserved factors that may increase a seller’s probability of terminating a listing would decrease its TOM duration until the termination. It is also found that an increase in the asking price of a property listing can significantly increase its TOM duration and probability of being terminated. The copula-based joint model can be integrated into a LUTI microsimulation framework to parameterize the maximum TOM duration of each simulated property for sale in the housing market, improving its market-clearing process to represent real-world behavior better.

Abstract Background For patients with liver-confined metastatic colorectal cancer (mCRC), local therapy of isolated metastases has been associated with long-term progression-free and overall survival (OS). However, for patients with more advanced mCRC, including those with extrahepatic disease, the efficacy of local therapy is less clear although increasingly being used in clinical practice. Prospective studies to clarify the role of metastatic-directed therapies in patients with mCRC are needed. Methods The Evaluating Radiation, Ablation, and Surgery (ERASur) A022101/NRG-GI009 trial is a randomized, National Cancer Institute-sponsored phase III study evaluating if the addition of metastatic-directed therapy to standard of care systemic therapy improves OS in patients with newly diagnosed limited mCRC. Eligible patients require a pathologic diagnosis of CRC, have BRAF wild-type and microsatellite stable disease, and have 4 or fewer sites of metastatic disease identified on baseline imaging. Liver-only metastatic disease is not permitted. All metastatic lesions must be amenable to total ablative therapy (TAT), which includes surgical resection, microwave ablation, and/or stereotactic ablative body radiotherapy (SABR) with SABR required for at least one lesion. Patients without overt disease progression after 16–26 weeks of first-line systemic therapy will be randomized 1:1 to continuation of systemic therapy with or without TAT. The trial activated through the Cancer Trials Support Unit on January 10, 2023. The primary endpoint is OS. Secondary endpoints include event-free survival, adverse events profile, and time to local recurrence with exploratory biomarker analyses. This study requires a total of 346 evaluable patients to provide 80% power with a one-sided alpha of 0.05 to detect an improvement in OS from a median of 26 months in the control arm to 37 months in the experimental arm with a hazard ratio of 0.7. The trial uses a group sequential design with two interim analyses for futility. Discussion The ERASur trial employs a pragmatic interventional design to test the efficacy and safety of adding multimodality TAT to standard of care systemic therapy in patients with limited mCRC. Trial registration ClinicalTrials.gov: NCT05673148, registered December 21, 2022.

Abstract The purpose of this study is to predict the location of new housing supply and compare two different modelling frameworks. Housing supply significantly influences land use simulations in urban microsimulation systems, closely linked with demographic, transportation, and environmental modules. The supply of new dwellings in urban simulation models have evolved from static, exogenous inputs to dynamic, agent-based determinations. This study follows this trend to examine two approaches to modelling the spatial distribution of new housing supply: the first approach models the development choice of each location; the second approach models the location choice of each residential project. Multinomial logit and nested logit models are applied to a Toronto empirical dataset. The results show that although the first approach achieves higher goodness-of-fit and prediction accuracy, the second approach performs better in explaining the locational preference of individual projects. Project characteristics such as structure type and construction cost, as well as location characteristics such as housing price, number of sales, and population density affect the spatial distribution of new housing supply. Both approaches are evaluated regarding estimation, prediction, and microsimulation system integration. The findings enhance housing modelling literature and inform urban microsimulation’s housing supply model configuration.

In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human–machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions.

Stellar and black hole feedback heat and disperse surrounding cold gas clouds, launching gas flows off circumnuclear and galactic disks, producing a dynamic interstellar medium. On large scales bordering the cosmic web, feedback drives enriched gas out of galaxies and groups, seeding the intergalactic medium with heavy elements. In this way, feedback shapes galaxy evolution by shutting down star formation and ultimately curtailing the growth of structure after the peak at redshift 2–3. To understand the complex interplay between gravity and feedback, we must resolve both the key physics within galaxies and map the impact of these processes over large scales, out into the cosmic web. The Advanced X-ray Imaging Satellite (AXIS) is a proposed X-ray probe mission for the 2030s with arcsecond spatial resolution, large effective area, and low background. AXIS will untangle the interactions of winds, radiation, jets, and supernovae with the surrounding interstellar medium across the wide range of mass scales and large volumes driving galaxy evolution and trace the establishment of feedback back to the main event at cosmic noon. This white paper is part of a series commissioned for the AXIS Probe mission concept; additional AXIS white papers can be found at the AXIS website.

The X-ray binary system Cygnus X-3 (4U 2030+40, V1521 Cyg) is luminous but enigmatic owing to the high intervening absorption. High-resolution X-ray spectroscopy uniquely probes the dynamics of the photoionized gas in the system. In this Letter, we report on an observation of Cyg X-3 with the XRISM/Resolve spectrometer, which provides unprecedented spectral resolution and sensitivity in the 2–10 keV band. We detect multiple kinematic and ionization components in absorption and emission whose superposition leads to complex line profiles, including strong P Cygni profiles on resonance lines. The prominent Fe xxv He α and Fe xxvi Ly α emission complexes are clearly resolved into their characteristic fine-structure transitions. Self-consistent photoionization modeling allows us to disentangle the absorption and emission components and measure the Doppler velocity of these components as a function of binary orbital phase. We find a significantly higher velocity amplitude for the emission lines than for the absorption lines. The absorption lines generally appear blueshifted by ∼−500–600 km s ^−1 . We show that the wind decomposes naturally into a relatively smooth and large-scale component, perhaps associated with the background wind itself, plus a turbulent, denser structure located close to the compact object in its orbit.

We present an analysis of the first two XRISM/Resolve spectra of the well-known Seyfert-1.5 active galactic nucleus (AGN) in NGC 4151, obtained in 2023 December. Our work focuses on the nature of the narrow Fe K _α emission line at 6.4 keV, the strongest and most common X-ray line observed in AGN. The total line is found to consist of three components. Even the narrowest component of the line is resolved with evident Fe K _α _,1 (6.404 keV) and K _α _,2 (6.391 keV) contributions in a 2:1 flux ratio, fully consistent with neutral gas with negligible bulk velocity. Subject to the limitations of our models, the narrowest and intermediate-width components are consistent with emission from optically thin gas, suggesting that they arise in a disk atmosphere and/or wind. Modeling the three line components in terms of Keplerian broadening, they are readily associated with (1) the inner wall of the “torus,” (2) the innermost optical “broad-line region” (or “X-ray BLR”), and (3) a region with a radius of r ≃ 100 GM / c ^2 that may signal a warp in the accretion disk. Viable alternative explanations of the broadest component include a fast-wind component and/or scattering; however, we find evidence of variability in the narrow Fe K _α line complex on timescales consistent with small radii. The best-fit models are statistically superior to simple Voigt functions, but when fit with Voigt profiles the time-averaged lines are consistent with a projected velocity broadening of FWHM $\,=\,{1600}_{-200}^{+400}\,\mathrm{km}\,{{\rm{s}}}^{-1}$ . Overall, the resolution and sensitivity of XRISM show that the narrow Fe K line in AGN is an effective probe of all key parts of the accretion flow, as it is currently understood. We discuss the implications of these findings for our understanding of AGN accretion, future studies with XRISM, and X-ray-based black hole mass measurements.

The land-use/transport interaction (LUTI) modeling framework has become the current state of best practice for analyzing the interdependency between the land-use and transportation systems. This paper presents a comprehensive review of the housing market-clearing mechanisms used in operational LUTI models. Market clearing is a critical component of modeling housing markets, but a systematic review and critique of the current state of the art have not previously been undertaken. In the review paper, the theoretical foundations for modeling household location choice are reviewed, including bid-rent and random utility theories. Five LUTI models are discussed in detail: two equilibrium models, MUSSA and RELU-TRAN, and three dynamic disequilibrium models, UrbanSim, ILUTE, and SimMobility. The discussion focuses on the following key points: the assumptions embedded in the models, the aggregation level of households and locations, computational cost and operationalization of the models. One of the challenges is that there are rarely any empirical studies that compare the performance of equilibrium and dynamic models in the same study context. Future research is recommended to empirically investigate the pros and cons of the two modeling approaches and compare the model performances for their representativeness of real-world behavior, computational efficiencies, and abilities for policy analysis. More sophisticated studies about the impacts of agents’ behavior on the housing market-clearing process are also recommended.

Nucleoside- and nucleotide-based therapeutics are indispensable treatment options for patients suffering from malignant and viral diseases. These agents are most commonly administered to patients as prodrugs to maximize bioavailability and efficacy. While the literature provides a practical prodrug playbook to facilitate the delivery of nucleoside and nucleotide therapeutics, small context-dependent amendments to these popular prodrug strategies can drive dramatic improvements in pharmacokinetic (PK) profiles. Herein we offer a brief overview of current prodrug strategies, as well as a case study involving the fine-tuning of lipid prodrugs of acyclic nucleoside phosphonate tenofovir (TFV), an approved nucleotide HIV reverse transcriptase inhibitor (NtRTI) and the cornerstone of combination antiretroviral therapy (cART). Installation of novel lipid terminal motifs significantly reduced fatty acid hepatic ω-oxidation while maintaining potent antiviral activity. This work contributes important insights to the expanding repertoire of lipid prodrug strategies in general, but particularly for the delivery and distribution of acyclic nucleoside phosphonates.

Key points New mouse for tracking microglia and all mononuclear phagocytes both ex and in vivo within the CNS over time. Dendra2 protein is stable enough to survive tissue processing for immunohistochemistry and flow cytometry quantification. Simultaneous fluorescence imaging of Dendra2 and light scattering measurements can be used to assess the immune response to retinal damage. Chronic in vivo imaging reveals mixed populations of microglia and monocytes during retinal degeneration.

Abstract Background Antimicrobial resistance (AMR) is among the gravest threats to human health and food security worldwide. The use of antimicrobials in livestock production can lead to emergence of AMR, which can have direct effects on humans through spread of zoonotic disease. Pigs pose a particular risk as they are a source of zoonotic diseases and receive more antimicrobials than most other livestock. Here we use a large-scale genomic approach to characterise AMR in Streptococcus suis, a commensal found in most pigs, but which can also cause serious disease in both pigs and humans. Results We obtained replicated measures of Minimum Inhibitory Concentration (MIC) for 16 antibiotics, across a panel of 678 isolates, from the major pig-producing regions of the world. For several drugs, there was no natural separation into ‘resistant’ and ‘susceptible’, highlighting the need to treat MIC as a quantitative trait. We found differences in MICs between countries, consistent with their patterns of antimicrobial usage. AMR levels were high even for drugs not used to treat S. suis, with many multidrug-resistant isolates. Similar levels of resistance were found in pigs and humans from regions associated with zoonotic transmission. We next used whole genome sequences for each isolate to identify 43 candidate resistance determinants, 22 of which were novel in S. suis. The presence of these determinants explained most of the variation in MIC. But there were also interesting complications, including epistatic interactions, where known resistance alleles had no effect in some genetic backgrounds. Beta-lactam resistance involved many core genome variants of small effect, appearing in a characteristic order. Conclusions We present a large dataset allowing the analysis of the multiple contributing factors to AMR in S. suis. The high levels of AMR in S. suis that we observe are reflected by antibiotic usage patterns but our results confirm the potential for genomic data to aid in the fight against AMR.

Abstract Background In patients with stage III melanoma, the use of adjuvant radiation therapy (RT) after lymph node dissection (LND) may be currently considered in selected high-risk patients to improve tumor control. Melanomas harbor BRAF mutations (BRAF+) in 40–50% of cases, the majority of which are on the V600E residue. This study sought to compare the clinical outcomes after RT between patients with BRAF+ and BRAF− melanoma. Methods This was a retrospective review of 105 Stage III melanoma patients treated at our institution with LND followed by adjuvant RT from 2006 to 2019. BRAF mutational status was determined on the primary skin or nodal tissue samples from all patients. We compared characteristics of the BRAF+ and BRAF− groups using Fisher’s exact test and Wilcoxon rank sum test and performed univariate and multivariate analysis using Kaplan–Meier estimates, log-rank tests, and Cox proportional hazards modeling with the clinical outcomes of local–regional lymph node control, distant metastasis-free survival (DMFS), recurrence-free survival (RFS), and overall survival (OS). Results Fifty-three (50%) patients harbored a BRAF mutation (92%, pV600E). BRAF+ patients were younger and had primary tumors more commonly found in the trunk vs head and neck compared to BRAF- patients (p 4 times increased risk of local recurrence or regional lymph node recurrence. These results could be useful for adjuvant RT consideration in lymph node positive melanoma patients and supports other data that BRAF mutation confers radiation resistance.

Clean energy research and development (R and D) leading to commercial technologies is vital to economic development, technology competitiveness, and reduced environmental impact. Over the past 30 years, such efforts have advanced technology performance and reduced cost by leveraging network effects and economies of scale. After demonstrating promise in applied R and D, successful clean energy and energy efficiency technologies are incorporated into an initial product sold by the private sector. Despite its importance, processes by which first commercialization occurs are difficult to generalize while capturing specific insights from practitioners in markets and technologies. This paper presents a policy-focused qualitative assessment of the first commercialization of four diverse energy technologies: thin film photovoltaics, wind turbine blades, dual-stage refrigeration evaporators, and fuel cells for material handling equipment. Each technology presents distinct value propositions, markets, and regulatory drivers. The case studies indicate three common characteristics of successful first commercialization for new energy technologies: 1) good fit between the technology, R&D infrastructure, and public-private partnership models; 2) high degree of alignment of government regulations and R&D priorities with market forces; and 3) compatibility between time scales required for R&D, product development, and opportunities. These findings may inform energy investment decision-making, maximize benefits from R&D, and advance the transition to a low-emission future.

Tolyporphins were discovered some 30 years ago as part of a global search for antineoplastic compounds from cyanobacteria. To date, the culture HT-58-2, comprised of a cyanobacterium–microbial consortium, is the sole known producer of tolyporphins. Eighteen tolyporphins are now known—each is a free base tetrapyrrole macrocycle with a dioxobacteriochlorin (14), oxochlorin (3), or porphyrin (1) chromophore. Each compound displays two, three, or four open β-pyrrole positions and two, one, or zero appended C-glycoside (or –OH or –OAc) groups, respectively; the appended groups form part of a geminal disubstitution motif flanking the oxo moiety in the pyrroline ring. The distinct structures and repertoire of tolyporphins stand alone in the large pigments-of-life family. Efforts to understand the cyanobacterial origin, biosynthetic pathways, structural diversity, physiological roles, and potential pharmacological properties of tolyporphins have attracted a broad spectrum of researchers from diverse scientific areas. The identification of putative biosynthetic gene clusters in the HT-58-2 cyanobacterial genome and accompanying studies suggest a new biosynthetic paradigm in the tetrapyrrole arena. The present review provides a comprehensive treatment of the rich science concerning tolyporphins.

A 56-year-old woman with a history of cerebral amyloid angiopathy (CAA) complicated by prior intracranial hemorrhage (ICH) was evaluated for an asymptomatic ischemic stroke discovered on screening brain MRI. On echocardiogram, she was found to have a mass on her mitral valve and strongly positive antiphospholipid antibodies. She was diagnosed with nonbacterial thrombotic (Libman-Sacks) endocarditis associated with the primary antiphospholipid syndrome (APS). The treatment decision was complicated by the history of CAA with ICH within the last year with very high risk for bleeding complications if on anticoagulation. A multidisciplinary decision was made to initiate a trial of warfarin for 3 months. She fared well and warfarin was continued. She has not had any further bleeding or ischemic events over the subsequent 1.5 years and remains on warfarin for her APS.

Abstract Human machine interfaces that can track head motion will result in advances in physical rehabilitation, improved augmented reality/virtual reality systems, and aid in the study of human behavior. This paper presents a head position monitoring and classification system using thin flexible strain sensing threads placed on the neck of an individual. A wireless circuit module consisting of impedance readout circuitry and a Bluetooth module records and transmits strain information to a computer. A data processing algorithm for motion recognition provides near real-time quantification of head position. Incoming data is filtered, normalized and divided into data segments. A set of features is extracted from each data segment and employed as input to nine classifiers including Support Vector Machine, Naive Bayes and KNN for position prediction. A testing accuracy of around 92% was achieved for a set of nine head orientations. Results indicate that this human machine interface platform is accurate, flexible, easy to use, and cost effective.

This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.

Abstract We conducted an institutional study to compare the clinical and pathological efficacy between the neoadjuvant therapy (NAT)‐modified FOLFIRINOX (mFOLF) vs nanoparticle albumin–bound paclitaxel plus gemcitabine (nab‐P/G) for borderline resectable pancreatic cancer (BRPC) and locally advanced pancreatic cancer (LAPC) patients who completed resection. The study retrospectively enrolled patients with pathologically confirmed BRPC or LAPC from 2010 to 2018 at our institution. The survival rates were determined by the Kaplan‐Meier method and log‐rank test was used to test differences. Cox's proportional hazard model was used to assess survival with respect to covariates. Seventy‐two patients who completed at least two cycles of neoadjuvant chemotherapy and surgical resection were included, with 52 (72.2%) patients receiving mFOLF and 20 (27.8%) receiving nab‐P/G. Patients treated with mFOLF had statistically higher rates of RECIST 1.1 partial or complete response (16/52 vs 1/20, P = .028). Additionally, mFOLF patients had greater pathological tumor size reduction, fewer positive lymph nodes, and higher treatment response grade compared to the nab‐P/G patients (all P

Abstract Background Metastatic prostate cancer (PC) is highly lethal. The ability to identify primary tumors capable of dissemination is an unmet need in the quest to understand lethal biology and improve patient outcomes. Previous studies have linked chromosomal instability (CIN), which generates aneuploidy following chromosomal missegregation during mitosis, to PC progression. Evidence of CIN includes broad copy number alterations (CNAs) spanning > 300 base pairs of DNA, which may also be measured via RNA expression signatures associated with CNA frequency. Signatures of CIN in metastatic PC, however, have not been interrogated or well defined. We examined a published 70-gene CIN signature (CIN70) in untreated and castration-resistant prostate cancer (CRPC) cohorts from The Cancer Genome Atlas (TCGA) and previously published reports. We also performed transcriptome and CNA analysis in a unique cohort of untreated primary tumors collected from diagnostic prostate needle biopsies (PNBX) of localized (M0) and metastatic (M1) cases to determine if CIN was linked to clinical stage and outcome. Methods PNBX were collected from 99 patients treated in the VA Greater Los Angeles (GLA-VA) Healthcare System between 2000 and 2016. Total RNA was extracted from high-grade cancer areas in PNBX cores, followed by RNA sequencing and/or copy number analysis using OncoScan. Multivariate logistic regression analyses permitted calculation of odds ratios for CIN status (high versus low) in an expanded GLA-VA PNBX cohort (n = 121). Results The CIN70 signature was significantly enriched in primary tumors and CRPC metastases from M1 PC cases. An intersection of gene signatures comprised of differentially expressed genes (DEGs) generated through comparison of M1 versus M0 PNBX and primary CRPC tumors versus metastases revealed a 157-gene “metastasis” signature that was further distilled to 7-genes (PC-CIN) regulating centrosomes, chromosomal segregation, and mitotic spindle assembly. High PC-CIN scores correlated with CRPC, PC-death and all-cause mortality in the expanded GLA-VA PNBX cohort. Interestingly, approximately 1/3 of M1 PNBX cases exhibited low CIN, illuminating differential pathways of lethal PC progression. Conclusions Measuring CIN in PNBX by transcriptome profiling is feasible, and the PC-CIN signature may identify patients with a high risk of lethal progression at the time of diagnosis.

Purpose: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in cancer survivors, particularly after chest radiation therapy (RT). However, the extent to which CVD events are consistently reported in contemporary prospective trials is unknown. Methods and Materials: From 10 high-impact RT, oncology, and medicine journals, we identified all latter phase trials from 2000 to 2019 enrolling patients with breast, lung, lymphoma, mesothelioma, or esophageal cancer wherein chest-RT was delivered. The primary outcome was the report of major adverse cardiac events (MACEs), defined as incident myocardial infarction, heart failure, coronary revascularization, arrhythmia, stroke, or CVD death across treatment arms. The secondary outcome was the report of any CVD event. Multivariable regression was used to identify factors associated with CVD reporting. Pooled annualized incidence rates of MACEs across RT trials were compared with contemporary population rates using relative risks (RRs). Results: The 108 trials that met criteria enrolled 59,070 patients (mean age, 58.0 ± 10.2 years; 46.0% female), with 273,587 person-years of available follow-up. During a median follow-up of 48 months, 468 MACEs were reported (including 96 heart failures, 75 acute coronary syndrome, 1 revascularization, 94 arrhythmias, 28 strokes, and 20 CVD deaths; 307 occurred in the intervention arms vs 144 in the control arms; RR, 1.96; P < .001). Altogether, 50.0% of trials did not report MACEs, and 37.0% did not report any CVD. The overall weighted-trial incidence was 376 events per 100,000 person-years compared with 1408 events per 100,000 person-years in similar nontrial patients (RR, 0.27; P < .001). There were no RT factors associated with CVD reporting. Conclusion: In contemporary chest RT–based clinical trials, reported CVD rates were lower than expected population rates.

Few fires are known to have burned the tundra of the Arctic Slope north of the Brooks Range in Alaska, USA. A total of 90 fires between 1969 and 2022 are known. Because fire has been rare, old burns can be detected by the traces of thermokarst and distinct vegetation they leave in otherwise uniform tundra, which are visible in aerial photograph archives. Several prehistoric tundra burns have been found in this way. Detection of tundra fires in this sparsely populated and remote area has been historically inconsistent and opportunistic, relying on reports by aircraft pilots. Fire reports have been logged into an administrative database which, out of necessity, has been used to scientifically evaluate changes in the fire regime. To improve the consistency of the record, we completed a systematic search of Landsat Collection 2 for the Brooks Range Foothills ecoregion over the period 1972–2022. We found 57 unrecorded tundra burns, about 41% of the total, which now numbers 138. Only 15% and 33% of all fires appear in MODIS and VIIRS satellite-borne thermal anomaly products, respectively. The fire frequency in the first 37 years of the record is 0.89 y−1 for natural ignitions that spread ≥10 ha. Frequency in the last 13 years is 2.5 y−1, indicating a nearly three-fold increase in fire frequency.

BackgroundChest radiation therapy (RT) has been associated with increased cardiac morbidity and mortality in numerous studies including the landmark Darby study published in 2013 demonstrating a linear increase in cardiac mortality with increasing mean heart radiation dose. However, the extent to which cardiotoxicity has been incorporated as an endpoint in prospective RT studies remains unknown.MethodsWe queried clincaltrials.gov to identify phase II/III trials in lung, esophageal, lymphoma, mesothelioma, thymoma, or breast cancer from 1/1/2006-2/1/2021 enrolling greater than 100 patients wherein chest RT was delivered in at least one treatment arm. The primary endpoint was the rate of inclusion of cardiotoxicity as a specific primary or secondary endpoint in the pre- (enrollment started prior to 1/1/2014) versus post-Darby era using the Chi-square test (p

Summary: T cell exhaustion and dysfunction are hallmarks of severe COVID-19. To gain insights into the pathways underlying these alterations, we performed a comprehensive transcriptome analysis of peripheral-blood-mononuclear-cells (PBMCs), spleen, lung, kidney, liver, and heart obtained at autopsy from COVID-19 patients and matched controls, using the nCounter CAR-T-Characterization panel. We found substantial gene alterations in COVID-19-impacted organs, especially the lung where altered TCR repertoires are noted. Reduced TCR repertoires are also observed in PBMCs of severe COVID-19 patients. ENTPD1/CD39, an ectoenzyme defining exhausted T-cells, is upregulated in the lung, liver, spleen, and PBMCs of severe COVID-19 patients where expression positively correlates with markers of vasculopathy. Heightened ENTPD1/CD39 is paralleled by elevations in STAT-3 and HIF-1α transcription factors; and by markedly reduced CD39-antisense-RNA, a long-noncoding-RNA negatively regulating ENTPD1/CD39 at the post-transcriptional level. Limited TCR repertoire and aberrant regulation of ENTPD1/CD39 could have permissive roles in COVID-19 progression and indicate potential therapeutic targets to reverse disease.

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

Non-parametric and distribution-free two-sample tests have been the foundation of many change point detection algorithms. However, randomness in the test statistic as a function of time makes them susceptible to false positives and localization ambiguity. We address these issues by deriving and applying filters matched to the expected temporal signatures of a change for various sliding window, two-sample tests under IID assumptions on the data. These filters are derived asymptotically with respect to the window size for the Wasserstein quantile test, the Wasserstein-1 distance test, Maximum Mean Discrepancy squared (MMD2), and the Kolmogorov-Smirnov (KS) test. The matched filters are shown to have two important properties. First, they are distribution-free, and thus can be applied without prior knowledge of the underlying data distributions. Second, they are peak-preserving, which allows the filtered signal produced by our methods to maintain expected statistical significance. Through experiments on synthetic data as well as activity recognition benchmarks, we demonstrate the utility of this approach for mitigating false positives and improving the test precision. Our method allows for the localization of change points without the use of ad-hoc post-processing to remove redundant detections common to current methods. We further highlight the performance of statistical tests based on the Quantile-Quantile (Q-Q) function and show how the invariance property of the Q-Q function to order-preserving transformations allows these tests to detect change points of different scales with a single threshold within the same dataset.

Background: Intrahepatic cholangiocarcinoma (ICC) is a highly lethal malignancy. For patients with locally advanced, unresectable disease, numerous liver-directed therapy options exist, including chemoradiation (CRT), stereotactic body radiation therapy (SBRT), and transarterial radioembolization (TARE). There is no randomized data to inform clinicians regarding the optimal treatment modality. Method: We used the National Cancer Database (NCDB) to study the overall survival (OS) of patients with ICC treated with CRT, SBRT, and TARE. We used Cox proportional hazards modeling and inverse probability of treatment weighting (IPTW) to account for confounding variables. Results: We identified 170 patients with unresected ICC treated with SBRT (n = 37), CRT (n = 61), or TARE (n = 72). SBRT was associated with higher OS compared to CRT (hazard ratio [HR] = 0.37; 95% confidence interval [CI] 0.20–0.68; p = 0.001) and TARE (HR = 0.40; 95% CI 0.22–0.74; p = 0.003). On multivariable analysis, SBRT remained associated with higher OS compared to CRT (HR = 0.44; 95% CI 0.21–0.91; p = 0.028) and TARE (HR = 0.42; 95% CI 0.21–0.84; p = 0.014). After IPTW (Bonferroni-adjusted significance threshold, α = 0.017), SBRT again had a statistically significant association with higher OS compared to CRT (HR = 0.22; 95% CI 0.11–0.44; p

BackgroundThe COVID-19 pandemic has demonstrated the possibility of severe ventilator shortages in the near future. ObjectiveWe aimed to develop an acute shortage ventilator. MethodsThe ventilator was designed to mechanically compress a self-inflating bag resuscitator, using a modified ventilator patient circuit, which is controlled by a microcontroller and an optional laptop. It was designed to operate in both volume-controlled mode and pressure-controlled assist modes. We tested the ventilator in 4 modes using an artificial lung while measuring the volume, flow, and pressure delivered over time by the ventilator. ResultsThe ventilator was successful in reaching the desired tidal volume and respiratory rates specified in national emergency use resuscitator system guidelines. The ventilator responded to simulated spontaneous breathing. ConclusionsThe key design goals were achieved. We developed a simple device with high performance for short-term use, made primarily from common hospital parts and generally available nonmedical components to avoid any compatibility or safety issues with the patient, and at low cost, with a unit cost per ventilator is less than $400 US excluding the patient circuit parts, that can be easily manufactured.