Correction: Quantifying spatial variability in shell midden formation in the Farasan Islands, Saudi Arabia.

[This corrects the article DOI: 10.1371/journal.pone.0217596.].

Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy.

PURPOSE: OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, which represent plan quality. This study utilised a novel dosimetric validation methodology, comprehensively evaluating a new CT-scanner-based AI contouring solution in terms of PD and RD within an automated planning workflow. METHODS: 20 prostate patients were selected to evaluate AI contouring for rectum, bladder, and proximal femurs. Five planning 'pipelines' were considered; three using AI contours with differing levels of manual editing (nominally none (AIStd), minor editing in specific regions (AIMinEd), and fully corrected (AIFullEd)). Remaining pipelines were manual delineations from two observers (MDOb1, MDOb2). Automated radiotherapy plans were generated for each pipeline. Geometric and dosimetric agreement of contour sets AIStd, AIMinEd, AIFullEd and MDOb2 were evaluated against the reference set MDOb1. Non-inferiority of AI pipelines was assessed, hypothesising that compared to MDOb1, absolute deviations in metrics for AI contouring were no greater than that from MDOb2. RESULTS: Compared to MDOb1, organ delineation time was reduced by 24.9 min (96 %), 21.4 min (79 %) and 12.2 min (45 %) for AIStd, AIMinEd and AIFullEd respectively. All pipelines exhibited generally good dosimetric agreement with MDOb1. For RD, median deviations were within ± 1.8 cm3, ± 1.7 % and ± 0.6 Gy for absolute volume, relative volume and mean dose metrics respectively. For PD, respective values were within ± 0.4 cm3, ± 0.5 % and ± 0.2 Gy. Statistically (p < 0.05), AIMinEd and AIFullEd were dosimetrically non-inferior to MDOb2. CONCLUSIONS: This novel dosimetric validation demonstrated that following targeted minor editing (AIMinEd), AI contours were dosimetrically non-inferior to manual delineations, reducing delineation time by 79 %.

An Undergraduate Health Care Experience Course Increases Confidence and Improves Student Understanding of Health Care Careers.

Increasing the health care work force is critical to underserved communities. Unfortunately, students in these areas lack accessibility to the clinical experiences needed to get an introductory understanding of careers in health care. Therefore, a health care experience (HCE) course was created for undergraduate students that included didactic training, active learning exercises, and coordinated shadowing experiences. To evaluate the effect of the HCE on student interest in science, health care, and rural health a study was performed on HCE participants. This study assessed student background, interest in health care, and plans for future careers in underserved settings. Students who enrolled in the HCE demonstrated high interest in science, health care, and rural health. Evaluation of student reflections indicated students attained novel learning, gained insights, and recognized the importance of communication. The HCE course students exhibited amplified confidence in HCEs and had a significant increase in understanding of health care compared with a control group of students who had not completed the HCE. Undergraduate institutions can include courses like the HCE into curricula to increase accessibility to career experiences for students interested in health care careers.

Synthesis, Characterization, and the Effect of Lewis Bases on the Nuclearity of Iron Alkoxide Complexes.

Inspired by the potential of alkoxides as weak-field ligands and their ability to bridge, we report herein a series of high-spin iron complexes supported by a bis-alkoxide framework PhDbf. A diiron complex [Fe2(PhDbf)2] (1a) is obtained upon metalation of the ligand, whereas addition of substituted pyridines affords five-coordinate mononuclear iron complexes [(R-Py)2Fe(PhDbf)] (2a-4a, R = H, p-tBu, p-CF3). The potential for nuclearity control of the metal complexes via auxiliary ligands is highlighted by the formation of asymmetric diiron species [(p-CF3-Py)Fe2(PhDbf)2] (5a) and [(m-CF3-Py)Fe2(PhDbf)2] (6a) with trifluoromethyl substituted pyridines, while electron-rich pyridines only produced monomeric species. Electronic properties analysis via UV-vis, electron paramagnetic resonance, 57Fe Mössbauer spectroscopy, and time-dependent density functional theory, along with redox capabilities of these complexes are reported to illustrate the effect of nuclearity on reactivity and the potential of these complexes to access higher oxidation states relevant in oxidative chemistry. Species 1a-5a, [(THF)2Fe(PhDbf)][PF6] (7), [PyFe(PhDbf)Cl] (2b), and [Py2Fe(PhDbf)][PF6] (2c) were characterized via SCXRD. Indirect evidence for the formation of dimeric Fe(III) species (1b, 5b, and 6b) is discussed.

In chronic kidney disease altered cardiac metabolism precedes cardiac hypertrophy.

Conduit arterial disease in chronic kidney disease (CKD) is an important cause of cardiac complications. Cardiac function in CKD has not been studied in the absence of arterial disease. In an Alport syndrome model bred not to have conduit arterial disease, mice at 225 days of life (dol) had CKD equivalent to humans with CKD stage 4-5. Parathyroid hormone (PTH) and FGF23 levels were one log order elevated, circulating sclerostin was elevated, and renal activin A was strongly induced. Aortic Ca levels were not increased, and vascular smooth muscle cell (VSMC) transdifferentiation was absent. The CKD mice were not hypertensive, and cardiac hypertrophy was absent. Freshly excised cardiac tissue respirometry (Oroboros) showed that ADP-stimulated O2 flux was diminished from 52 to 22 pmol/mg (P = 0.022). RNA-Seq of cardiac tissue from CKD mice revealed significantly decreased levels of cardiac mitochondrial oxidative phosphorylation genes. To examine the effect of activin A signaling, some Alport mice were treated with a monoclonal Ab to activin A or an isotype-matched IgG beginning at 75 days of life until euthanasia. Treatment with the activin A antibody (Ab) did not affect cardiac oxidative phosphorylation. However, the activin A antibody was active in the skeleton, disrupting the effect of CKD to stimulate osteoclast number, eroded surfaces, and the stimulation of osteoclast-driven remodeling. The data reported here show that cardiac mitochondrial respiration is impaired in CKD in the absence of conduit arterial disease. This is the first report of the direct effect of CKD on cardiac respiration.NEW & NOTEWORTHY Heart disease is an important morbidity of chronic kidney disease (CKD). Hypertension, vascular stiffness, and vascular calcification all contribute to cardiac pathophysiology. However, cardiac function in CKD devoid of vascular disease has not been studied. Here, in an animal model of human CKD without conduit arterial disease, we analyze cardiac respiration and discover that CKD directly impairs cardiac mitochondrial function by decreasing oxidative phosphorylation. Protection of cardiac oxidative phosphorylation may be a therapeutic target in CKD.

OCA-B/Pou2af1 is sufficient to promote CD4+ T cell memory and prospectively identifies memory precursors.

The molecular mechanisms leading to the establishment of immunological memory are inadequately understood, limiting the development of effective vaccines and durable antitumor immune therapies. Here, we show that ectopic OCA-B expression is sufficient to improve antiviral memory recall responses, while having minimal effects on primary effector responses. At peak viral response, short-lived effector T cell populations are expanded but show increased Gadd45b and Socs2 expression, while memory precursor effector cells show increased expression of Bcl2, Il7r, and Tcf7 on a per-cell basis. Using an OCA-B mCherry reporter mouse line, we observe high OCA-B expression in CD4+ central memory T cells. We show that early in viral infection, endogenously elevated OCA-B expression prospectively identifies memory precursor cells with increased survival capability and memory recall potential. Cumulatively, the results demonstrate that OCA-B is both necessary and sufficient to promote CD4 T cell memory in vivo and can be used to prospectively identify memory precursor cells.

Living in disadvantaged neighborhoods linked to less intervention for severe aortic stenosis.

To investigate the association between area deprivation index (ADI) and aortic valve replacement (AVR) in patients with severe aortic stenosis (AS). Patients aged 40-95 years with severe AS confirmed by echocardiography were included. The 9-digit zip code of patient residence address was used to identify the ADI ranking, based on which patients were divided into 5 groups (with Group E being most deprived). The rates of AV intervention were compared among 5 groups using competing risks analysis, with death as a competing event. We included 1751 patients with severe AS from 2013 to 2018 followed for a median 2.8 (interquartile range, 1.5-4.8) years. The more distressed ADI groups tended to be younger (P = 0.002), female (P < 0.001), and of African American race (P < 0.001), have higher presentation of sepsis (P = 0.031), arrhythmia (P = 0.022), less likely to have previous diagnosis of AS (P < 0.001); and were less likely to undergo AVR (52.5% vs 46.9% vs 46.1% vs 48.9% vs 39.7%, P = 0.023). Using competing risk analysis, the highest ADI group (E) were the least and the lowest ADI group (A) the most likely to undergo AVR (Gray's test, P = 0.025). The association between ADI ranking and AVR rates was influenced by sex and race. Within group analysis, there was significant association between race and AVR (Gray's test, P < 0.001), and between sex and AVR (Gray's test, P < 0.001). Patients with severe AS living in more deprived neighborhoods were less likely to undergo aortic valve interventions, which was influenced by female gender, and African American race.

What might make nurses stay? A protocol for discrete choice experiments to understand NHS nurses' preferences at early-career and late-career stages.

INTRODUCTION: Like many countries, England has a national shortage of registered nurses. Employers strive to retain existing staff, to ease supply pressures. Disproportionate numbers of nurses leave the National Health Services (NHS) both early in their careers, and later, as they near retirement age. Research is needed to understand the job preferences of early-career and late-career nurses working in the NHS, so tailored policies can be developed to better retain these two groups. METHODS AND ANALYSIS: We will collect job preference data for early-career and late-career NHS nurses, respectively using two separate discrete choice experiments (DCEs). Findings from the literature, focus groups, academic experts and stakeholder discussions will be used to identify and select the DCE attributes (ie, job features) and levels. We will generate an orthogonal, fractional factorial design using the experimental software Ngene. The DCEs will be administered through online surveys distributed by the regulator Nursing and Midwifery Council. For each group, we expect to achieve a final sample of 2500 registered NHS nurses working in England. For early-career nurses, eligible participants will be registered nurses who graduated in the preceding 5 years (ie, 2019-2023). Eligible participants for the late-career survey will be registered nurses aged 55 years and above. We will use conditional and mixed logit models to analyse the data. Specifically, study 1 will estimate the job preferences of early-career nurses and the possible trade-offs. Study 2 will estimate the retirement preferences of late-career NHS nurses and the potential trade-offs. ETHICS AND DISSEMINATION: The research protocol was reviewed and approved by the host research organisation Ethics Committees Research Governance (University of Southampton, number 80610) (https://www.southampton.ac.uk/about/governance/regulations-policies/policies/ethics). The results will be disseminated via conference presentations, publications in peer-reviewed journals and annual reports to key stakeholders, the Department of Health and Social Care, and NHS England/Improvement retention leaders. REGISTRATION DETAILS: Registration on OSF http://doi.org/10.17605/OSF.IO/RDN9G.

Examining household effects on individual Twitter adoption: A multilevel analysis based on U.K. household survey data.

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members' Twitter adoption, (b) whether the influences are mediated by the member's activity variety and self-reported efficacy with mobile technology, and (c) whether the members' traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members' Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals' Twitter adoption.

Glioblastoma and Radiotherapy: a multi-center AI study for Survival Predictions from MRI (GRASP study).

BACKGROUND: The aim was to predict survival of glioblastoma at eight months after radiotherapy (a period allowing for completing a typical course of adjuvant temozolomide), by applying deep learning to the first brain MRI after radiotherapy completion. METHODS: Retrospective and prospective data were collected from 206 consecutive glioblastoma, IDH-wildtype patients diagnosed between March 2014-February 2022 across 11 UK centers. Models were trained on 158 retrospective patients from three centers. Holdout test sets were retrospective (n=19; internal validation), and prospective (n=29; external validation from eight distinct centers).Neural network branches for T2-weighted and contrast-enhanced T1-weighted inputs were concatenated to predict survival. A non-imaging branch (demographics/MGMT/treatment data) was also combined with the imaging model. We investigated the influence of individual MR sequences; non-imaging features; and weighted dense blocks pretrained for abnormality detection. RESULTS: The imaging model outperformed the non-imaging model in all test sets (area under the receiver-operating characteristic curve, AUC p=0.038) and performed similarly to a combined imaging/non-imaging model (p>0.05). Imaging, non-imaging, and combined models applied to amalgamated test sets gave AUCs of 0.93, 0.79, and 0.91. Initializing the imaging model with pretrained weights from 10,000s of brain MRIs improved performance considerably (amalgamated test sets without pretraining 0.64; p=0.003). CONCLUSIONS: A deep learning model using MRI images after radiotherapy, reliably and accurately determined survival of glioblastoma. The model serves as a prognostic biomarker identifying patients who will not survive beyond a typical course of adjuvant temozolomide, thereby stratifying patients into those who might require early second-line or clinical trial treatment.

Effector-Phase IL-2 Signals Drive Th1 Effector and Memory Responses Dependently and Independently of TCF-1.

Following viral infection, CD4+ T cell differentiation is tightly regulated by cytokines and TCR signals. Although most activated CD4+ T cells express IL-2Rα after lymphocytic choriomeningtis virus infection, by day 3 postinfection, only half of activated T cells maintain expression. IL-2Rα at this time point distinguishes precursors for terminally differentiated Th1 cells (IL-2Rαhi) from precursors for Tfh cells and memory T cells (IL-2Rαlo) and is linked to strong TCR signals. In this study, we test whether TCR-dependent IL-2 links the TCR to CD4+ T cell differentiation. We employ a mixture of anti-IL-2 Abs to neutralize IL-2 throughout the primary CD4+ T cell response to lymphocytic choriomeningitis virus infection in mice or only after the establishment of lineage-committed effector cells (day 3 postinfection). We report that IL-2 signals drive the formation of Th1 precursor cells in the early stages of the immune response and sustain Th1 responses during its later stages (after day 3). Effector-stage IL-2 also shapes the composition and function of resulting CD4+ memory T cells. Although IL-2 has been shown previously to drive Th1 differentiation by reducing the activity of the transcriptional repressor TCF-1, we found that sustained IL-2 signals were still required to drive optimal Th1 differentiation even in the absence of TCF-1. Therefore, we concluded that IL-2 plays a central role throughout the effector phase in regulating the balance between Th1 and Tfh effector and memory cells via mechanisms that are both dependent and independent of its role in modulating TCF-1 activity.

Myomatrix arrays for high-definition muscle recording.

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous system's actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ('Myomatrix arrays') that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a 'motor unit,' during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous system's motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and identifying pathologies of the motor system.

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes.

Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.

Determining the Educational Value of an Emergency Medicine Rotation for Non-Emergency Medicine Residents.

Background Residents from diverse specialties perform clinical rotations in the emergency department (ED). There is little research about the value of the ED rotation for them. Objectives We sought to determine the learning objectives of non-EM residents (NEMRs) in the ED, the effectiveness of the rotation, and the highest-yield components of their experience. Methods From 2017-2019, we surveyed NEMR on their pre-rotation learning objectives and their comfort level with 15 common ED presentations/procedures before and after the rotation. We assessed how well their objectives were met, the highest-yield components of their rotation, and opportunities for improvement. Results We collected responses from 56 (47%) pre-rotation and 61 (51%) post-rotation residents over a two-year period. The five most commonly cited learning goals were: management of acutely ill patients, triage skills, procedural competence, and ultrasound. Seventy-eight percent (78%) of residents reported their learning goals were moderately to very well met during their rotation. NEMRs' level of comfort improved in all the commonly encountered clinical experiences in the ED in a statistically significant manner. They cited on-shift teaching by attending physicians and senior EM residents as the most valuable learning resource. Conclusion NEMR from diverse medical and surgical specialties could identify specific learning objectives for their EM rotation with common themes, and the majority felt their educational goals were met. They gained comfort with the management and triage of all the assessed common ED conditions. By collecting and defining their specific needs and goals, we are better equipped to improve the quality and value of the rotation.

Testing Times: Challenges in Disentangling Admixture Histories in Recent and Complex Demographies.

Paleogenomics has expanded our knowledge of human evolutionary history. Since the 2020s, the study of ancient DNA has increased its focus on reconstructing the recent past. However, the accuracy of paleogenomic methods in answering questions of historical and archaeological importance amidst the increased demographic complexity and decreased genetic differentiation within the historical period remains an open question. We used two simulation approaches to evaluate the limitations and behavior of commonly used methods, qpAdm and the f3-statistic, on admixture inference. The first is based on branch-length data simulated from four simple demographic models of varying complexities and configurations. The second, an analysis of Eurasian history composed of 59 populations using whole-genome data modified with ancient DNA conditions such as SNP ascertainment, data missingness, and pseudo-haploidization. We show that under conditions resembling historical populations, qpAdm can identify a small candidate set of true sources and populations closely related to them. However, in typical ancient DNA conditions, qpAdm is unable to further distinguish between them, limiting its utility for resolving fine-scaled hypotheses. Notably, we find that complex gene-flow histories generally lead to improvements in the performance of qpAdm and observe no bias in the estimation of admixture weights. We offer a heuristic for admixture inference that incorporates admixture weight estimate and P-values of qpAdm models, and f3-statistics to enhance the power to distinguish between multiple plausible candidates. Finally, we highlight the future potential of qpAdm through whole-genome branch-length f2-statistics, demonstrating the improved demographic inference that could be achieved with advancements in f-statistic estimations.

Microcanonical Analysis of Helical Homopolymers: Exploring the Density of States and Structural Characteristics.

This study investigates the density of states and structural characteristics of helical homopolymers. Comprising repeating identical units, the model enables the exploration of complex behaviors arising from a simple, yet generalized, set of potentials. Utilizing microcanonical analysis, transitions between helical structures are identified and categorized. Through a systematic comparison of results under varying conditions, we develop a nuanced understanding of the system's general behavior. A two-dimensional plot illustrates the relative distribution of different structural types, effectively showcasing their prevalence. The findings of this study substantially advance our understanding of the density of states and structural transformations of helical homopolymers across a range of conditions. Additionally, the prevalence plot offers valuable insights into the occurrence of suppressed intermediate states, particularly in models featuring stiff helix segments. This research significantly enhances our understanding of the complex interactions governing helix bundling phenomena within the context of helical homopolymers.

False discovery rates of qpAdm-based screens for genetic admixture.

Although a broad range of methods exists for reconstructing population history from genome-wide single nucleotide polymorphism data, just a few methods gained popularity in archaeogenetics: principal component analysis (PCA); ADMIXTURE, an algorithm that models individuals as mixtures of multiple ancestral sources represented by actual or inferred populations; formal tests for admixture such as f3-statistics and D/f4-statistics; and qpAdm, a tool for fitting two-component and more complex admixture models to groups or individuals. Despite their popularity in archaeogenetics, which is explained by modest computational requirements and ability to analyze data of various types and qualities, protocols relying on qpAdm that screen numerous alternative models of varying complexity and find "fitting" models (often considering both estimated admixture proportions and p-values as a composite criterion of model fit) remain untested on complex simulated population histories in the form of admixture graphs of random topology. We analyzed genotype data extracted from such simulations and tested various types of high-throughput qpAdm protocols ("rotating" and "non-rotating", with or without temporal stratification of target groups and proxy ancestry sources, and with or without a "model competition" step). We caution that high-throughput qpAdm protocols may be inappropriate for exploratory analyses in poorly studied regions/periods since their false discovery rates varied between 12% and 68% depending on the details of the protocol and on the amount and quality of simulated data (i.e., >12% of fitting two-way admixture models imply gene flows that were not simulated). We demonstrate that for reducing false discovery rates of qpAdm protocols to nearly 0% it is advisable to use large SNP sets with low missing data rates, the rotating qpAdm protocol with a strictly enforced rule that target groups do not pre-date their proxy sources, and an unsupervised ADMIXTURE analysis as a way to verify feasible qpAdm models. Our study has a number of limitations: for instance, these recommendations depend on the assumption that the underlying genetic history is a complex admixture graph and not a stepping-stone model.

ARF6-dependent endocytic trafficking of the Interferon-γ receptor drives adaptive immune resistance in cancer.

Adaptive immune resistance (AIR) is a protective process used by cancer to escape elimination by CD8+ T cells. Inhibition of immune checkpoints PD-1 and CTLA-4 specifically target Interferon-gamma (IFNγ)-driven AIR. AIR begins at the plasma membrane where tumor cell-intrinsic cytokine signaling is initiated. Thus, plasma membrane remodeling by endomembrane trafficking could regulate AIR. Herein we report that the trafficking protein ADP-Ribosylation Factor 6 (ARF6) is critical for IFNγ-driven AIR. ARF6 prevents transport of the receptor to the lysosome, augmenting IFNγR expression, tumor intrinsic IFNγ signaling and downstream expression of immunosuppressive genes. In murine melanoma, loss of ARF6 causes resistance to immune checkpoint blockade (ICB). Likewise, low expression of ARF6 in patient tumors correlates with inferior outcomes with ICB. Our data provide new mechanistic insights into tumor immune escape, defined by ARF6-dependent AIR, and support that ARF6-dependent endomembrane trafficking of the IFNγ receptor influences outcomes of ICB.