Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river.

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River-which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang-Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

Impact of Structural Differences on the Modeled Cost-Effectiveness of Noninvasive Prenatal Testing.

BACKGROUND: Noninvasive prenatal testing (NIPT) was developed to improve the accuracy of prenatal screening to detect chromosomal abnormalities. Published economic analyses have yielded different incremental cost-effective ratios (ICERs), leading to conclusions of NIPT being dominant, cost-effective, and cost-ineffective. These analyses have used different model structures, and the extent to which these structural variations have contributed to differences in ICERs is unclear. AIM: To assess the impact of different model structures on the cost-effectiveness of NIPT for the detection of trisomy 21 (T21; Down syndrome). METHODS: A systematic review identified economic models comparing NIPT to conventional screening. The key variations in identified model structures were the number of health states and modeling approach. New models with different structures were developed in TreeAge and populated with consistent parameters to enable a comparison of the impact of selected structural variations on results. RESULTS: The review identified 34 economic models. Based on these findings, demonstration models were developed: 1) a decision tree with 3 health states, 2) a decision tree with 5 health states, 3) a microsimulation with 3 health states, and 4) a microsimulation with 5 health states. The base-case ICER from each model was 1) USD$34,474 (2023)/quality-adjusted life-year (QALY), 2) USD$14,990 (2023)/QALY, (3) USD$54,983 (2023)/QALY, and (4) NIPT was dominated. CONCLUSION: Model-structuring choices can have a large impact on the ICER and conclusions regarding cost-effectiveness, which may inadvertently affect policy decisions to support or not support funding for NIPT. The use of reference models could improve international consistency in health policy decision making for prenatal screening. HIGHLIGHTS: NIPT is a clinical area in which a variety of modeling approaches have been published, with wide variation in reported cost-effectiveness.This study shows that when broader contextual factors are held constant, varying the model structure yields results that range from NIPT being less effective and more expensive than conventional screening (i.e., NIPT was dominated) through to NIPT being more effective and more expensive than conventional screening with an ICER of USD$54,983 (2023)/QALY.Model-structuring choices may inadvertently affect policy decisions to support or not support funding of NIPT. Reference models could improve international consistency in health policy decision making for prenatal screening.

Interfacial Dynamics Study of NCM523-Based Semi-Solid-State Lithium-Ion Batteries by Electrochemical Impedance Spectroscopy.

The use of solid electrolytes (SE) in solid-state batteries holds the promise of achieving higher energy densities and enhancing safety. However, current solid-state batteries face significant interface impedance issues, mainly dealing with the effect of the evolution of the solid-solid interface on ion transport. Semi-solid-state batteries (SSB), containing a small amount of liquid electrolyte, serve as appropriate transitional products in the development process of solid-state batteries. More importantly, the clarity of the relevant interface dynamics can provide theoretical guidance for the subsequent all-solid-state batteries. Therefore, this paper investigates SSB through Electrochemical Impedance Spectroscopy (EIS), primarily employing a combination of theoretical modeling, simulation predictions, and experimental analyses to elucidate the complex electrochemical processes within these batteries. Based on detailed exploration of the complex electrochemical processes within SSB, we have discovered additional electrochemical processes beyond Li+ penetration through the solid-electrolyte interphase (SEI) film and charge transfer. We attribute the additional electrochemical reaction processes to the resistance present at the SE/SEI interface of SSB on account of numerical analysis and interface characterization. Furthermore, this interface resistance exhibits a trend of initial decrease followed by continuous increase, elucidating the attribution and numerical variations of various impedance components within the EIS. The application of EIS techniques to analyze ion transport processes in SSB serves as a suitable transition toward achieving all-solid-state batteries as well as provides guidance for subsequent interface optimization of solid-state batteries and propels their transition from laboratory experimentation to commercialization.

Performance analysis of mathematical methods used to forecast the 2022 New York City Mpox outbreak.

In mid-2022, New York City (NYC) became the epicenter of the US mpox outbreak. We provided real-time mpox case forecasts to the NYC Department of Health and Mental Hygiene to aid in outbreak response. Forecasting methodologies evolved as the epidemic progressed. Initially, lacking knowledge of at-risk population size, we used exponential growth models to forecast cases. Once exponential growth slowed, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) model. Retrospectively, we explored if forecasts could have been improved using an SEIR model in place of our early exponential growth model, with or without knowing the case detection rate. Early forecasts from exponential growth models performed poorly, as 2-week mean absolute error (MAE) grew from 53 cases/week (July 1-14) to 457 cases/week (July 15-28). However, when exponential growth slowed, providing insight into susceptible population size, an SEIR model was able to accurately predict the remainder of the outbreak (7-week MAE: 13.4 cases/week). Retrospectively, we found there was not enough known about the epidemiological characteristics of the outbreak to parameterize an SEIR model early on. However, if the at-risk population and case detection rate were known, an SEIR model could have improved accuracy over exponential growth models early in the outbreak.

Anthropogenic Fingerprint Detectable in Upper Tropospheric Ozone Trends Retrieved from Satellite.

Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability. We show that the anthropogenic signal ("fingerprint") in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño-Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe. Our analysis reveals a significant human effect on Earth's atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

Process Optimization for Biosurfactant Production by Bacillus aryabhattai SPS1001 using Taguchi Method.

This study employs Taguchi design of experiments (DOE) to optimize biosurfactant yield by analyzing the impact of various input parameters. Signal-to-noise ratio analysis was utilized for optimization, corroborated by ANOVA findings. Regression equations depicted response behaviour and are validated through a confirmation test. Taguchi methodology identified optimal conditions for maximum biosurfactant yield: agitation (180 rpm), inoculum size (2%), beef extract (5 g/L), diesel (20 ml/L), peptone (5 g/L), NaCl (7 g/L), incubation time (4 days), pH (7.9), and yeast extract (6 g/L). This yielded an 8.33% increase to 1.53 g/L, with initial optimum parameters projecting 1.41 g/L. ANOVA ranked and quantified control factor contributions, revealing agitation's significant (31.41%) impact on yield. The study underscores the viability of Taguchi's optimal conditions for substantial yield improvement within specific ranges. The strong alignment between expected and experimental yields affirmed the reliability of developed models for optimal yield selection. This study underscores the power of statistical techniques like Taguchi DOE and ANOVA in systematically enhancing biosurfactant production by Bacillus aryabhattai SPS1001 and paves the way for future advancements in bioprocess optimization.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction.

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Formation and kinetic analysis of AGEs in Pacific white shrimp during frying.

Advanced glycation end products (AGEs) are complex and heterogeneous compounds closely associated with various chronic diseases. The changes in Nε-carboxymethyllysine (CML), Nε-carboxyethyllysine (CEL), Nε-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MG-H1), and fluorescent AGEs (F-AGEs) in fried shrimp during frying (170 °C, 0-210 s) were described by kinetic models. Besides,the correlations between AGEs contents and physicochemical indicators were analyzed to reveal their intrinsic relationship. Results showed that the changes of four AGEs contents followed the zero-order kinetic, and their rate constants were ranked as kCML < kCEL ≈ kMG-H1 < kF-AGEs. Oil content and lipid oxidation were critical factors that affected the AGEs levels of the surface layer. Protein content and Maillard reaction were major factors in enhancing the CML and CEL levels of the interior layer. Furthermore, the impact of temperature on the generation of CML and CEL was greater than that of MG-H1 and F-AGEs.

DN-ODE: Data-driven neural-ODE modeling for breast cancer tumor dynamics and progression-free survivals.

Pharmacokinetic/Pharmacodynamic (PK/PD) modeling is crucial in the development of new drugs. However, traditional population-based PK/PD models encounter challenges when modeling for individual patients. We aim to explore the potential of constructing a pharmacodynamic model for individual breast cancer pharmacodynamics leveraging only limited data from early clinical trial phases. While previous studies on Neural Ordinary Differential Equations (ODEs) suggest promising results in clinical trial practices, they primarily focused on theoretical applications or independent PK/PD modeling. PD modeling from complex and irregular clinical trial data, especially when interacting with PK parameters, is still unclear. To achieve that, we introduce a Data-driven Neural Ordinary Differential Equation (DN-ODE) modeling for breast cancer tumor dynamics and progression-free survival data. To validate this approach, experiments are conducted with early-phase clinical trial data from the Amcenestrant (an oral treatment for breast cancer) dataset (AMEERA 1-2), aiming to predict pharmacodynamics in the later phase (AMEERA 3). DN-ODE model achieves RMSE scores of 8.78 and 0.21 in tumor size and progression-free survival, respectively, with R2 scores over 0.9 for each task. Compared to PK/PD methodologies, DN-ODE is able to predict robust individual tumor dynamics with only limited cycle data. We also introduce Principal Component Analysis visualizations for encoder results, demonstrating the DN-ODE's capability to discern individual distributions and diverse tumor growth patterns. Therefore, DN-ODE facilitates comprehensive drug efficacy assessments, pinpoints potential responders, and aids in trial design.

MotionTrack: Learning motion predictor for multiple object tracking.

Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains a challenge. This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT. In this context, we introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor that relies solely on object trajectory information. This predictor comprehensively integrates two levels of granularity in motion features to enhance the modeling of temporal dynamics and facilitate precise future motion prediction for individual objects. Specifically, the proposed approach adopts a self-attention mechanism to capture token-level information and a Dynamic MLP layer to model channel-level features. MotionTrack is a simple, online tracking approach. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT, characterized by highly complex object motion.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

Modeling of Microplastics Migration in Soil and Groundwater: Insights into Dispersion and Particle Property Effects.

Migration of microplastics (MPs) in soil-groundwater systems plays a pivotal role in determining its concentration in aquifers and future threats to the terrestrial environment, including human health. However, existing models employing an advection-dispersion equation are insufficient to incorporate the holistic mechanism of MP migration. Therefore, to bridge the gap associated with MP migration in soil-groundwater systems, a dispersion-drag force coupled model incorporating a drag force on MPs along with dispersion is developed and validated through existing laboratory and field-scale experiments. The inclusion of the MP dispersion notably increased the global maximum particle velocity (vmaxp) of MPs, resulting in a higher concentration of MPs in the aquifer, which is also established by sensitivity analysis of MP dispersion. Additionally, increasing irrigation flux and irrigation areas significantly accelerates MP migration downward from soil to deep saturated aquifers. Intriguingly, vmaxp of MPs exhibited a nonlinear relationship with MPs' sizes smaller than 20 µm reaching the highest value (=1.64 × 10-5 m/s) at a particle size of 8 µm, while a decreasing trend was identified for particle sizes ranging from 20 to 100 µm because of the hindered effect by porous media and the weaker effect of the drag force. Moreover, distinct behaviors were observed among different plastic types, with poly(vinyl chloride), characterized by the highest density, displaying the lowest vmaxp and minimal flux entering groundwater. Furthermore, the presence of a heterogeneous structure with lower hydraulic conductivity facilitated MP dispersion and promoted their migration in saturated aquifers. The findings shed light on effective strategies to mitigate the impact of MPs in aquifers, contributing valuable insights to the broader scientific fraternity.

Analysis of Longitudinal Patterns and Predictors of Medicine Use in Residential Aged Care Using Group-Based Trajectory Modeling: The "MEDTRAC-Cardiovascular" Longitudinal Cohort Study.

AIM: Cardiovascular diseases are the leading cause of death globally. Ensuring ongoing use of medicines-medication persistence-is crucial, yet no prior studies have examined this in residential aged care facilities (RACFs). We aimed to identify long-term trajectories of persistence with cardiovascular medicines and determine predictors of persistence trajectories. METHOD: A longitudinal cohort study of 2837 newly admitted permanent residents from 30 RACFs in New South Wales, Australia. We monitored weekly exposure to six cardiovascular medicine classes-lipid modifiers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEI/ARBs), beta-blockers, diuretics, calcium channel blockers (CCB), and cardiac therapy-over 3 years. Group-based trajectory modeling was employed to determine persistence trajectories for each class. RESULTS: At baseline, 76.6% (n = 2172) received at least one cardiovascular medicine with 41.2% receiving lipid modifiers, 31.4% ACEI/ARBs, 30.2% beta-blockers, 24.4% diuretics, 18.7% CCBs, and 14.8% cardiac therapy. The model identified two persistence trajectories for CCBs and three trajectories for all other classes. Sustained high persistence rates ranged from 68.4% (ACEI/ARBs) to 79.8% (beta-blockers) while early decline in persistence and subsequent discontinuation rates ranged from 7.6% (cardiac therapy) to 25.3% (CCBs). Logistic regressions identified 11 predictors of a declining persistence across the six medicine classes. CONCLUSION: Our study revealed varied patterns of cardiovascular medicine use in RACFs, with 2-3 distinctive medicine use trajectories across different classes, each exhibiting a unique clinical profile, and up to a quarter of residents discontinuing a medicine class. Future studies should explore the underlying reasons and appropriateness of nonpersistence to aid in identifying areas for improvement.

Using latent variables to improve the management of depression among hemodialysis patients.

BACKGROUND: Screening for depression can be challenging among hemodialysis patients due to the overlap of depressive symptoms with dialysis or kidney disease related symptoms. The aim of this study was to understand these overlapping symptoms and develop a depression screening tool for better clinical assessment of depressive symptoms in dialysis patients. METHODS: We surveyed 1,085 dialysis patients between March 1, 2018 and February 28, 2023 at 15 dialysis facilities in Northeast Ohio with the 9-item patient health questionnaire (PHQ-9) and kidney disease quality of life (KDQOL) instrument. To evaluate overlap across questionnaire items, we used structural equation modeling (SEM). We predicted and transformed factor scores to create a hemodialysis-adjusted PHQ-9 (hdPHQ-9). In exploratory analysis (N = 173), we evaluated the performance of the hdPHQ-9 relative to the PHQ-9 that also received a Mini-International Neuropsychiatric Interview. RESULTS: Our study sample included a high percentage of Black patients (74.6%) and 157 (14.5%) survey participants screened positive for depression (PHQ-9 ≥ 10). The magnitude of overlap was small for (respectively, PHQ-9 item with KDQOLTM item) fatigue with washed out, guilt with burden on family, appetite with nausea and movement with lightheaded. The hdPHQ-9 showed reasonably high sensitivity (0.81 with 95% confidence interval [CI] 0.58, 0.95) and specificity (0.84 with 95% CI 0.77, 0.89); however, this was not a significant improvement from the PHQ-9. CONCLUSION: There is little overlap between depressive symptoms and dialysis or kidney disease symptoms. The PHQ-9 was found to be an appropriate depression screening instrument for dialysis patients.

Current advances in experimental and computational approaches to enhance CAR T cell manufacturing protocols and improve clinical efficacy.

Since the FDA's approval of chimeric antigen receptor (CAR) T cells in 2017, significant improvements have been made in the design of chimeric antigen receptor constructs and in the manufacturing of CAR T cell therapies resulting in increased in vivo CAR T cell persistence and improved clinical outcome in certain hematological malignancies. Despite the remarkable clinical response seen in some patients, challenges remain in achieving durable long-term tumor-free survival, reducing therapy associated malignancies and toxicities, and expanding on the types of cancers that can be treated with this therapeutic modality. Careful analysis of the biological factors demarcating efficacious from suboptimal CAR T cell responses will be of paramount importance to address these shortcomings. With the ever-expanding toolbox of experimental approaches, single-cell technologies, and computational resources, there is renowned interest in discovering new ways to streamline the development and validation of new CAR T cell products. Better and more accurate prognostic and predictive models can be developed to help guide and inform clinical decision making by incorporating these approaches into translational and clinical workflows. In this review, we provide a brief overview of recent advancements in CAR T cell manufacturing and describe the strategies used to selectively expand specific phenotypic subsets. Additionally, we review experimental approaches to assess CAR T cell functionality and summarize current in silico methods which have the potential to improve CAR T cell manufacturing and predict clinical outcomes.

History of the development of isolated heart perfusion experimental model and its pioneering role in understanding heart physiology.

The isolated heart perfusion model, a fundamental tool in cardiovascular research, has evolved significantly since its inception in the late 19th century. This review traces the development of the isolated heart model, from its early adaptations by pioneers such as Langendorff and Starling to modern advancements by researchers like Morgan and Neely. We discuss the various applications of the model in pharmacological testing, disease modeling, and educational settings, emphasizing its crucial role in understanding cardiac function and disease mechanisms. Recent technological enhancements, including high-resolution imaging, integration with bioengineering, and advanced genomic and proteomic analyses, have significantly broadened the capabilities of these models. Looking forward, we explore potential future developments such as the integration of precision medicine, stem cell research, and artificial intelligence, which promise to revolutionize the use of isolated heart perfusion models. This review highlights the model's crucial role in bridging experimental research and clinical applications.

Highly defended nudibranchs "escape" to visually distinct background habitats.

The "escape and radiate" hypothesis predicts that once species have evolved aposematism, defended species can utilize more visually diverse visual backgrounds as they "escape" the need to be well camouflaged. This enables species to explore new ecological niches, resulting in increased diversification rates. To test this hypothesis "escape" component, we examined whether the background habitats of 12 nudibranch mollusk species differed among species depending on the presence and strength of chemical defenses. We obtained a rich array of color pattern statistics using quantitative color pattern analysis to analyze backgrounds viewed through the eyes of a potential predator (triggerfish, Rhinecanthus aculeatus). Color pattern analysis was done at viewing distances simulating an escalating predation sequence. We identified 4 latent factors comprising 17 noncorrelated color pattern parameters, which captured the among-species variability associated with differences in chemical defenses. We found that chemically defended species, indeed, were found on visually distinct backgrounds with increased color and luminance contrast, independent of viewing distance. However, we found no evidence for increased among-species background diversity coinciding with the presence and strength of chemical defenses. Our results agree with the "escape and radiate" hypothesis, suggesting that potent chemical defenses in Dorid nudibranchs coincide with spatiochromatic differences of visual background habitats perceived by potential predators.

Bite hard: Linking cranial loading mechanics to ecological differences in gnawing behavior in caviomorph rodents.

The mammalian skull is very malleable and has notably radiated into highly diverse morphologies, fulfilling a broad range of functional needs. Although gnawing is relatively common in mammals, this behavior and its associated morphology are diagnostic features for rodents. These animals possess a very versatile and highly mechanically advantageous masticatory apparatus, which, for instance, allowed caviomorph rodents to colonize South America during the Mid-Eocene and successfully radiate in over 200 extant species throughout most continental niches. Previous work has shown that differences in bite force within caviomorphs could be better explained by changes in muscle development than in mechanical advantages (i.e., in cranial overall morphology). Considering the strong bites they apply, it is interesting to assess how the reaction forces upon the incisors (compression) and the powerful adductor musculature pulling (tension) mechanically affect the cranium, especially between species with different ecologies (e.g., chisel-tooth digging). Thus, we ran finite element analyses upon crania of the subterranean Talas' tuco-tuco Ctenomys talarum, the semi-fossorial common degu Octodon degus, and the saxicolous long-tailed chinchilla Chinchilla lanigera to simulate: (A) in vivo biting in all species, and (B) rescaled muscle forces in non-ctenomyid rodents to match those of the tuco-tuco. Results show that the stress patterns correlate with the mechanical demands of distinctive ecologies, on in vivo-based simulations, with the subterranean tuco-tuco being the most stressed species. In contrast, when standardizing all three species (rescaled models), non-ctenomyid models exhibited a several-fold increase in stress, in both magnitude and affected areas. Detailed observations evidenced that this increase in stress was higher in lateral sections of the snout and, mainly, the zygomatic arch; between approximately 2.5-3.5 times in the common degu and 4.0-5.0 times in the long-tailed chinchilla. Yet, neither species, module, nor simulation condition presented load factor levels that would imply structural failure by strong, incidental biting. Our results let us conclude that caviomorphs have a high baseline for mechanical strength of the cranium because of the inheritance of a very robust "rodent" model, while interspecific differences are associated with particular masticatory habits and the concomitant level of development of the adductor musculature. Especially, the masseteric and zygomaticomandibular muscles contribute to >80% of the bite force, and therefore, their contraction is responsible for the highest strains upon their origin sites, that is, the zygomatic arch and the snout. Thus, the robust crania of the subterranean and highly aggressive tuco-tucos allow them to withstand much stronger forces than degus or chinchillas, such as the ones produced by their hypertrophied jaw adductor muscles or imparted by the soil reaction.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.