Data-driven simulations for training AI-based segmentation of neutron images.

Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs → Validate PDFs → Design Image Masks → Generate Intensities → Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks.

Real-Time Cell Cycle Imaging in a 3D Cell Culture Model of Melanoma, Quantitative Analysis, Optical Clearing, and Mathematical Modeling.

Aberrant cell cycle progression is a hallmark of solid tumors. Therefore, cell cycle analysis is an invaluable technique to study cancer cell biology. However, cell cycle progression has been most commonly assessed by methods that are limited to temporal snapshots or that lack spatial information. In this chapter, we describe a technique that allows spatiotemporal real-time tracking of cell cycle progression of individual cells in a multicellular context. The power of this system lies in the use of 3D melanoma spheroids generated from melanoma cells engineered with the fluorescent ubiquitination-based cell cycle indicator (FUCCI). This technique, combined with mathematical modeling, allows us to gain further and more detailed insight into several relevant aspects of solid cancer cell biology, such as tumor growth, proliferation, invasion, and drug sensitivity.

Orphan GPR52 as an emerging neurotherapeutic target.

GPR52 is a highly conserved, brain-enriched, Gs/olf-coupled orphan G protein-coupled receptor (GPCR) that controls various cyclic AMP (cAMP)-dependent physiological and pathological processes. Stimulation of GPR52 activity might be beneficial for the treatment of schizophrenia, psychiatric disorders and other human neurological diseases, whereas inhibition of its activity might provide a potential therapeutic approach for Huntington's disease. Excitingly, HTL0048149 (HTL'149), an orally available GPR52 agonist, has been advanced into phase I human clinical trials for the treatment of schizophrenia. In this concise review, we summarize the current understanding of GPR52 receptor distribution as well as its structure and functions, highlighting the recent advances in drug discovery efforts towards small-molecule GPR52 ligands. The opportunities and challenges presented by targeting GPR52 for novel therapeutics are also briefly discussed.

Thermoreversible gels of hollow silica nanorod dispersions.

Colloidal suspensions of anisotropic particles are ubiquitous in particle-based industries. Consequently, there is a need to quantify the effects of particle shape on equilibrium phases and kinetic state transitions, particularly at lower aspect ratios (L/D ≈ 1-10). We present a new, colloidal system comprised of hollow, octadecyl-coated silica rods with 40 nm diameter with controlled aspect ratio and thermoreversible short-range attractions. Rheology and dynamic light scattering measurements on suspensions of these hollow adhesive hard rods with nominal aspect ratio ≈3 suspended in tetradecane exhibit thermoreversible gelation without complicating effects of gravitational settling. Small angle neutron scattering measurements of the microstructure are analyzed to determine the effective strength of attraction in the form of Baxter sticky parameter. Quantitative agreement is found with simulation predictions of the thermoreversible gel transition as a function of volume fraction, further validating a universal state diagram and providing guidance for the effects of aspect ratio on gelation.

Modelling count data with partial differential equation models in biology.

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.

Throughout the life sciences, we routinely seek to interpret measurements and observations using parametrized mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis and prediction, called profile-wise analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.

Prophylactic Prednisolone Promotes AAV5 Hepatocyte Transduction Through the Novel Mechanism of AAV5 Coreceptor Platelet-Derived Growth Factor Receptor Alpha Upregulation and Innate Immune Suppression.

Adeno-associated virus (AAV) vectors are used to deliver therapeutic transgenes, but host immune responses may interfere with transduction and transgene expression. We evaluated prophylactic corticosteroid treatment on AAV5-mediated expression in liver tissue. Wild-type C57BL/6 mice received 6 × 1013 vg/kg AAV5-HLP-hA1AT, an AAV5 vector carrying a human α1-antitrypsin (hA1AT) gene with a hepatocyte-specific promoter. Mice received 4 weeks of daily 2 mg/kg prednisolone or water starting day -1 or 0 before vector dosing. Mice that received prophylactic corticosteroids had significantly higher serum hA1AT protein than mice that did not, starting at 6 weeks and persisting to the study end at 12 weeks, potentially through a decrease in the number of low responders. RNAseq and proteomic analyses investigating mechanisms mediating the improvement of transgene expression found that prophylactic corticosteroid treatment upregulated the AAV5 coreceptor platelet-derived growth factor receptor alpha (PDGFRα) on hepatocytes and downregulated its competitive ligand PDGFα, thus increasing the uptake of AAV5 vectors. Evidently, prophylactic corticosteroid treatment also suppressed acute immune responses to AAV. Together, these mechanisms resulted in increased uptake and preservation of the transgene, allowing more vector genomes to be available to assemble into stable, full-length structures mediating long-term transgene expression. Prophylactic corticosteroids represent a potential actionable strategy to improve AAV5-mediated transgene expression and decrease intersubject variability.

Formation and Growth of Co-Culture Tumour Spheroids: New Compartment-Based Mathematical Models and Experiments.

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights.

Intravascular Leukocyte Labeling Refines the Distribution of Myeloid Cells in the Lung in Models of Allergen-induced Airway Inflammation.

Innate immune cell populations are critical in asthma with different functional characteristics based on tissue location, which has amplified the importance of characterizing the precise number and location of innate immune populations in murine models of asthma. In this study, we performed premortem intravascular (IV) labeling of leukocytes in mice in two models of asthma to differentiate innate immune cell populations within the IV compartment versus those residing in the lung tissue or airway lumen. We performed spectral flow cytometry analysis of the blood, suspensions of digested lung tissue, and bronchoalveolar lavage fluid. We discovered that IV labeled leukocytes do not contaminate analysis of bronchoalveolar lavage fluid but represent a significant proportion of cells in digested lung tissue. Exclusion of IV leukocytes significantly improved the accuracy of the assessments of myeloid cells in the lung tissue and provided important insights into ongoing trafficking in both eosinophilic and neutrophilic asthma models.

Extracting microscopic insight from transient dielectric measurements during large amplitude oscillatory shear.

Probing the transient microstructure of soft matter far from equilibrium is an ongoing challenge to understanding material processing. In this work, we investigate inverse worm-like micelles undergoing large amplitude oscillatory shear using time-resolved dielectric spectroscopy. By controlling the Weissenburg number, we compare the non-linear microstructure response of branched and unbranched worm-like micelles and isolate distinct elastic effects that manifest near flow reversal. We validate our dielectric measurements with small angle neutron scattering and employ sequence of physical processes to disentangle the elastic and viscous contributions of the stress.

Age and political leaning predict COVID-19 vaccination status at a large, multi-campus, public university in Pennsylvania: A cross-sectional survey.

INTRODUCTION: Vaccine hesitancy during the COVID-19 pandemic impacted many higher education institutions. Understanding the factors associated with vaccine hesitancy and uptake is instrumental in directing policies and disseminating reliable information during public health emergencies. OBJECTIVE: This study evaluates associations between age, gender, and political leaning in relationship to COVID-19 vaccination status among a large, multi-campus, public university in Pennsylvania. METHODS: From October 5-November 30, 2021, a 10-minute REDCap survey was available to students, faculty, and staff 18 years of age and older at the Pennsylvania State University (PSU). Recruitment included targeted email, social media, digital advertisements, and university newspapers. 4,231 responses were received. Associations between the selected factors and vaccine hesitancy were made with Chi-square tests and generalized linear regression models using R version 4.3.1 (2023-06-16). RESULTS: Logistic regression approach suggested that age and political leaning have a statistically significant association with vaccine hesitancy at the 5% level. Adjusted for political leaning, odds of being vaccinated is 4 times higher for those aged 56 years or older compared to the ones aged 18 to 20 (OR = 4.35, 95% CI = (2.82, 6.85), p-value < 0.05). The results also showed that adjusted for age, the odds of being vaccinated is about 3 times higher for liberal individuals compared to far-left individuals (OR = 2.85, 95% CI = (1.45, 5.41), p-value = 0.001). CONCLUSIONS: Age and political leaning are key predictors of vaccine uptake among members of the PSU community, knowledge of which may inform campus leadership's public health efforts such as vaccine campaigns and policy decisions.

Near Room-Temperature Intrinsic Exchange Bias in an Fe Intercalated ZrSe2 Spin Glass.

Some magnetic systems display a shift in the center of their magnetic hysteresis loop away from zero field, a phenomenon termed exchange bias. Despite the extensive use of the exchange bias effect, particularly in magnetic multilayers, for the design of spin-based memory/electronics devices, a comprehensive mechanistic understanding of this effect remains a longstanding problem. Recent work has shown that disorder-induced spin frustration might play a key role in exchange bias, suggesting new materials design approaches for spin-based electronic devices that harness this effect. Here, we design a spin glass with strong spin frustration induced by magnetic disorder by exploiting the distinctive structure of Fe intercalated ZrSe2, where Fe(II) centers are shown to occupy both octahedral and tetrahedral interstitial sites and to distribute between ZrSe2 layers without long-range structural order. Notably, we observe behavior consistent with a magnetically frustrated and multidegenerate ground state in these Fe0.17ZrSe2 single crystals, which persists above room temperature. Moreover, this magnetic frustration leads to a robust and tunable exchange bias up to 250 K. These results not only offer important insights into the effects of magnetic disorder and frustration in magnetic materials generally, but also highlight as design strategy the idea that a large exchange bias can arise from an inhomogeneous microscopic environment without discernible long-range magnetic order. In addition, these results show that intercalated TMDs like Fe0.17ZrSe2 hold potential for spintronic technologies that can achieve room temperature applications.

Comparing 7-Day Versus 6-8-Day Penicillin Treatment Intervals Among Pregnant People With Syphilis of Late or Unknown Duration: No Difference Found in Incidence of Congenital Syphilis.

Background: Guidelines recommend that pregnant patients with syphilis of late/unknown duration be treated with benzathine penicillin G, dosed as 3 weekly intramuscular injections (BPGx3) given ideally at strict 7-day intervals. Given limited pharmacokinetic data, it is unknown whether more flexible BPG treatment intervals might be effective in preventing congenital syphilis (CS). Methods: We used California surveillance data to identify birthing parent/infant dyads wherein the pregnant parent had syphilis of late/unknown duration between January 1, 2016 - June 30, 2019. We divided the dyads into 3 groups based on prenatal treatment: (1) BPGx3 at strict 7-day intervals, (2) BPGx3 at 6-8 day intervals, and (3) no/inadequate treatment. We then compared CS incidence among infants in each group. Results: We analyzed 1,092 parent/infant dyads: 607 (55.6%) in the 7-day treatment group, 70 (6.4%) in the 6-8 day treatment group, and 415 (38.0%) in the no/inadequate treatment group. The incidence proportion of infants meeting CS criteria in each group was, respectively, 5.6%, 5.7%, and 36.9%. Compared with BPGx3 at 7-day intervals, the odds of CS were 1.0 [95% CI 0.4-3.0] in the 6-8 day group and 9.8 [95% CI 6.6-14.7] in the no/inadequate treatment group. Conclusions: Prenatal BPGx3 at 6-8 days was no more likely to lead to CS in infants than 7-days. These findings hint that 6-8-day intervals might be adequate to prevent CS among pregnant people with syphilis of late/unknown duration. Consequently, it is possible that CS evaluation beyond an RPR at delivery may be unnecessary in asymptomatic infants whose parents received BPGx3 at 6-8 days.

Initial impacts of the COVID-19 pandemic on Australian fisheries production, research organisations and assessment: shocks, responses and implications for decision support and resilience.

Australia's fisheries have experience in responding individually to specific shocks to stock levels (for example, marine heatwaves, floods) and markets (for example, global financial crisis, food safety access barriers). The COVID-19 pandemic was, however, novel in triggering a series of systemic shocks and disruptions to the activities and operating conditions for all Australia's commercial fisheries sectors including those of the research agencies that provide the information needed for their sustainable management. While these disruptions have a single root cause-the public health impacts and containment responses to the COVID-19 pandemic-their transmission and effects have been varied. We examine both the impacts on Australian fisheries triggered by measures introduced by governments both internationally and domestically in response to the COVID-19 pandemic outbreak, and the countermeasures introduced to support continuity in fisheries and aquaculture production and supply chains. Impacts on fisheries production are identified by comparing annual and monthly catch data for Australia's commercial fisheries in 2020 with averages for the last 4-5 years. We combine this with a survey of the short-term disruption to and impacts on research organisations engaged in fisheries monitoring and assessment and the adaptive measures they deployed. The dominant impact identified was triggered by containment measures both within Australia and in export receiving countries which led to loss of export markets and domestic dine-in markets for live or fresh seafood. The most heavily impact fisheries included lobster and abalone (exported live) and specific finfishes (exported fresh or sold live domestically), which experienced short-term reductions in both production and price. At the same time, improved prices and demand for seafood sold into domestic retail channels were observed. The impacts observed were both a function of the disruptions due to the COVID-19 pandemic and the countermeasures and support programs introduced by various national and state-level governments across Australia to at least partly mitigate negative impacts on harvesting activities and supply chains. These included protecting fisheries activities from specific restrictive COVID-19 containment measures, pro-actively re-establishing freight links, supporting quota roll-overs, and introducing wage and businesses support packages. Fisheries research organisations were impacted to various degrees, largely determined by the extent to which their field monitoring activities were protected from specific restrictive COVID-19 containment measures by their state-level governments. Responses of these organisations included reducing fisheries dependent and independent data collection as required while developing strategies to continue to provide assessment services, including opportunistic innovations to harvest data from new data sources. Observed short run impacts of the COVID-19 pandemic outbreak has emphasised both the vulnerability of fisheries dependent on export markets, live or fresh markets, and long supply chains and the resilience of fisheries research programs. We suggest that further and more comprehensive analysis over a longer time period of the long-run impacts of subsequent waves of variants, extended pandemic containment measures, autonomous and planned adaptive responses would be beneficial for the development of more effective counter measures for when the next major external shock affects Australian fisheries.

Distinct Epithelial-Innate Immune Cell Transcriptional Circuits Underlie Airway Hyperresponsiveness in Asthma.

Rationale: Indirect airway hyperresponsiveness (AHR) is a highly specific feature of asthma, but the underlying mechanisms responsible for driving indirect AHR remain incompletely understood. Objectives: To identify differences in gene expression in epithelial brushings obtained from individuals with asthma who were characterized for indirect AHR in the form of exercise-induced bronchoconstriction (EIB). Methods: RNA-sequencing analysis was performed on epithelial brushings obtained from individuals with asthma with EIB (n = 11) and without EIB (n = 9). Differentially expressed genes (DEGs) between the groups were correlated with measures of airway physiology, sputum inflammatory markers, and airway wall immunopathology. On the basis of these relationships, we examined the effects of primary airway epithelial cells (AECs) and specific epithelial cell-derived cytokines on both mast cells (MCs) and eosinophils (EOS). Measurements and Main Results: We identified 120 DEGs in individuals with and without EIB. Network analyses suggested critical roles for IL-33-, IL-18-, and IFN-γ-related signaling among these DEGs. IL1RL1 expression was positively correlated with the density of MCs in the epithelial compartment, and IL1RL1, IL18R1, and IFNG were positively correlated with the density of intraepithelial EOS. Subsequent ex vivo modeling demonstrated that AECs promote sustained type 2 (T2) inflammation in MCs and enhance IL-33-induced T2 gene expression. Furthermore, EOS increase the expression of IFNG and IL13 in response to both IL-18 and IL-33 as well as exposure to AECs. Conclusions: Circuits involving epithelial interactions with MCs and EOS are closely associated with indirect AHR. Ex vivo modeling indicates that epithelial-dependent regulation of these innate cells may be critical in indirect AHR and modulating T2 and non-T2 inflammation in asthma.

Patient-Reported Experiences in Voxelotor-Treated Children and Adults with Sickle Cell Disease: A Semistructured Interview Study.

Objective: Voxelotor is a first-in-class sickle hemoglobin-polymerization inhibitor that was approved in 2019 by the US Food and Drug Administration for treatment of patients with sickle cell disease (SCD) aged ≥12 years; in 2021, the approval was extended to children with SCD aged 4 to 11 years. Additionally, both the Ministry of Health and Prevention for the United Arab Emirates and the European Commission granted marketing authorization for voxelotor in September 2021 and February 2022, respectively, for treatment of SCD in adults and pediatric patients aged ≥12 years. Thus, additional information on the patient experience with voxelotor would be useful for patients, caregivers, and healthcare professionals alike. The purpose of this study was to conduct semistructured interviews in an effort to understand the experiences and perspectives of voxelotor-treated patients with SCD. Methods: One-time semistructured interviews with adults, adolescents, and children with SCD and their primary caregivers were conducted in the United States. Twenty-three adults and adolescents were recruited across 4 clinical sites, and 10 children-caregiver dyads were recruited from a single site. The interview was designed to elicit patient perspectives on symptomatic changes with voxelotor and the impact of treatment on patients' perceived health-related quality of life. Individual interview transcripts were analyzed using a thematic analytic approach, and concept saturation was assessed in both cohorts. Results: Most patients reported improvements in their SCD symptoms with voxelotor treatment, specifically regarding pain crises, jaundice, and fatigue. Almost all patients experienced improvements in self-reported health-related quality of life with voxelotor treatment. Conclusions: This study provides patient and caregiver perspectives on the symptomatic benefits of voxelotor treatment. These findings not only highlight the benefits of voxelotor treatment in improving symptoms and increasing health-related quality of life across the entire SCD population but also can inform further research on SCD-specific patient-reported outcomes.