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.

Role of rheology in formulation and process design of hot melt extruded amorphous solid dispersions.

Hot melt extrusion (HME) has been widely used as a continuous and highly flexible pharmaceutical manufacturing process for the production of a variety of dosage forms. In particular, HME enables preparation of amorphous solid dispersions (ASDs) which can improve bioavailability of poorly water-soluble drugs. The rheological properties of drug-polymer mixtures can significantly influence the processability of drug formulations via HME and eventually the end-use product properties such as physical stability and drug release. The objective of this review is to provide an overview of various rheological techniques and properties that can be used to evaluate the flow behavior and processability of the drug-polymer mixtures as well as formulation characteristics such as drug-polymer interactions, miscibility/solubility, and plasticization to improve the HME processability. An overview of the thermodynamics and kinetics of ASD processing by HME is also provided, as well as aspects of scale-up and process modeling, highlighting rheological properties on formulation design and process development. Overall, this review provides valuable insights into critical rheological properties which can be used as a predictive tool to optimize the HME processing conditions.

Stretch of the papillary insertion triggers reentrant arrhythmia: an in silico patient study.

Background: The electrophysiological mechanism connecting mitral valve prolapse (MVP), premature ventricular complexes and life-threatening ventricular arrhythmia is unknown. A common hypothesis is that stretch activated channels (SACs) play a significant role. SACs can trigger depolarizations or shorten repolarization times in response to myocardial stretch. Through these mechanisms, pathological traction of the papillary muscle (PM), as has been observed in patients with MVP, may induce irregular electrical activity and result in reentrant arrhythmia. Methods: Based on a patient with MVP and mitral annulus disjunction, we modeled the effect of excessive PM traction in a detailed medical image-derived ventricular model by activating SACs in the PM insertion region. By systematically varying the onset of SAC activation following sinus pacing, we identified vulnerability windows for reentry with 1 ms resolution. We explored how reentry was affected by the SAC reversal potential ( E SAC ) and the size of the region with simulated stretch (SAC region). Finally, the effect of global or focal fibrosis, modeled as reduction in tissue conductivity or mesh splitting (fibrotic microstructure), was investigated. Results: In models with healthy tissue or fibrosis modeled solely as CV slowing, we observed two vulnerable periods of reentry: For E SAC of -10 and -30 mV, SAC activated during the T-wave could cause depolarization of the SAC region which lead to reentry. For E SAC of -40 and -70 mV, SAC activated during the QRS complex could result in early repolarization of the SAC region and subsequent reentry. In models with fibrotic microstructure in the SAC region, we observed micro-reentries and a larger variability in which times of SAC activation triggered reentry. In these models, 86% of reentries were triggered during the QRS complex or T-wave. We only observed reentry for sufficiently large SAC regions ( > = 8 mm radius in models with healthy tissue). Conclusion: Stretch of the PM insertion region following sinus activation may initiate ventricular reentry in patients with MVP, with or without fibrosis. Depending on the SAC reversal potential and timing of stretch, reentry may be triggered by ectopy due to SAC-induced depolarizations or by early repolarization within the SAC region.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies.

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

Higher-order correction of persistent batch effects in correlation networks.

MOTIVATION: Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data is corrected using standard batch correction. RESULTS: In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. AVAILABILITY AND IMPLEMENTATION: COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io). SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

Neural Dynamic Principles for an Intentional Embodied Agent.

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.

Exploring Early Number Abilities With Multimodal Transformers.

Early number skills represent critical milestones in children's cognitive development and are shaped over years of interacting with quantities and numerals in various contexts. Several connectionist computational models have attempted to emulate how certain number concepts may be learned, represented, and processed in the brain. However, these models mainly used highly simplified inputs and focused on limited tasks. We expand on previous work in two directions: First, we train a model end-to-end on video demonstrations in a synthetic environment with multimodal visual and language inputs. Second, we use a more holistic dataset of 35 tasks, covering enumeration, set comparisons, symbolic digits, and seriation. The order in which the model acquires tasks reflects input length and variability, and the resulting trajectories mostly fit with findings from educational psychology. The trained model also displays symbolic and non-symbolic size and distance effects. Using techniques from interpretability research, we investigate how our attention-based model integrates cross-modal representations and binds them into context-specific associative networks to solve different tasks. We compare models trained with and without symbolic inputs and find that the purely non-symbolic model employs more processing-intensive strategies to determine set size.

Genetic structure and climate niche differentiation among populations of Leopardus geoffroyi.

Geoffroy's cat (Leopardus geoffroyi) is a small-sized felid native to South America. Given the species' distribution covering a wide variety of habitats, and the presence of high levels of anthropization in part of its range, it is possible that genetically differentiated groups exist and that they occupy different climatic niches. We assessed patterns of contemporary genetic diversity and structure in the species across most of its range, characterizing each inferred genetic group based on ecological niche models. We genotyped 11 microsatellites for 142 samples covering most of Geoffroy's cat distribution, and investigated patterns of genetic structure and diversity, applying spatial and nonspatial Bayesian clustering methods and a spatial principal component analysis. We created ecological niche models for each genetic cluster, evaluating whether these clusters occupy different climatic spaces and display differences in the suitability of different values of the climatic variables analyzed. We identified two genetic clusters, one in the north-northeast and the other in the south-southwest of the species' distribution. These clusters showed moderate FST values between them and differences in dispersal/genetic diversity. We found isolation-by-distance patterns globally and within each cluster. We observed lower expected heterozygosity compared with other studies and a north-south gradient in allelic richness. The southern cluster showed lower genetic variability and a more restricted climatic niche suggesting that this group is more vulnerable to the effects of the current context of climate change. Individuals from the southern genetic cluster are under different pressures, likely a product of the particularly dry habitat they occupy. Climatic variables associated with habitat suitability suggest the southern cluster has affinity for the arid and semiarid conditions present in its distribution. Conservation measures should consider the genetic structure observed and differences in climatic spaces to maintain the evolutionary potential of the species.

Effect of multi-level social risk factors on developmental trajectories of sexual risk behaviors among Bahamian middle-to-late adolescents.

Background: Few studies have examined how multi-level social factors interact and affect developmental patterns of sexual risk among middle-to-late adolescents who are at risk of experiencing sexual risk behaviors. We examined developmental trajectories of sexual risk behaviors of boys and girls in middle-to-late adolescence and the effects of exposure to three social risk factors (poor parental monitoring, peer risk, and neighborhood risk). Methods: We followed 2,332 Bahamian adolescents every six months from Grades 10-12. We used group-based trajectory modeling to identify distinct trajectories of sexual risk behaviors for boys and girls. Results: We identified three trajectories each for boys and girls. Peer risk and neighborhood risk predicted a high sexual-risk trajectory for boys, and peer risk (alone or combined with other risk factors) had the greatest impact on the membership of moderate-to-high-risk trajectory for girls. Parental monitoring had a relatively small effect on adolescents' sexual risk behavior. Conclusion: Our results underscore the importance of early identification of adolescents with sexual risk behavior and development of targeted prevention interventions to improve adolescent health outcomes.

3D physiologically-informed deep learning for drug discovery of a novel vascular endothelial growth factor receptor-2 (VEGFR2).

Angiogenesis is an essential process in tumorigenesis, tumor invasion, and metastasis, and is an intriguing pathway for drug discovery. Targeting vascular endothelial growth factor receptor 2 (VEGFR2) to inhibit tumor angiogenic pathways has been widely explored and adopted in clinical practice. However, most drugs, such as the Food and Drug Administration -approved drug axitinib (ATC code: L01EK01), have considerable side effects and limited tolerability. Therefore, there is an urgent need for the development of novel VEGFR2 inhibitors. In this study, we propose a novel strategy to design potential candidates targeting VEGFR2 using three-dimensional (3D) deep learning and structural modeling methods. A geometric-enhanced molecular representation learning method (GEM) model employing a graph neural network (GNN) as its underlying predictive algorithm was used to predict the activity of the candidates. In the structural modeling method, flexible docking was performed to screen data with high affinity and explore the mechanism of the inhibitors. Small -molecule compounds with consistently improved properties were identified based on the intersection of the scores obtained from both methods. Candidates identified using the GEM-GNN model were selected for in silico modeling using molecular dynamics simulations to further validate their efficacy. The GEM-GNN model enabled the identification of candidate compounds with potentially more favorable properties than the existing drug, axitinib, while achieving higher efficacy.

Research on detailed design methods of precast components in prefabricated buildings based on BIM technology.

There are still problems such as low standardization and low interoperability in the prefabricated building precast component design with BIM technology, which lead to the absence and separation of precast components in the deepening design stage and other stages. Therefore, how to solve the problem of mutual loss and separation among the various levels of the precast component deepening design process has become one of the key problems to be urgently resolved. To link up each stage of the deepening design and improve its efficiency. Based on semi-structured interview, the primary and secondary functional modules required for the detailed design of precast components were clarified. Based on C# programming language and Revit API, the Revit API-based the design platform for deepening precast components (DPDPC) of prefabricated buildings was independently developed by using secondary development technology. Based on the research method of case analysis, this paper uses the DPDPC to study and demonstrate the actual project, and finally verifies the feasibility of the precast component deepening design process. The research results show that, the prototype of the DPDPC constructed includes three first-level function modules and eleven second-level function modules. The detailed design process of precast components with Revit as the core is component split → component design → optimized design → component drawing → material list. This paper provides clarity on the main process of precast deepening design. The prototype of DPDPC constructed not only helps to better connect each level of prefabricated building precast component deepening design, but also helps to display the functional requirements of each level of deepening design process through a systematic platform from the technical level. To provide a reference for further design of precast components.

Kinome state is predictive of cell viability in pancreatic cancer tumor and cancer-associated fibroblast cell lines.

Numerous aspects of cellular signaling are regulated by the kinome-the network of over 500 protein kinases that guides and modulates information transfer throughout the cell. The key role played by both individual kinases and assemblies of kinases organized into functional subnetworks leads to kinome dysregulation driving many diseases, particularly cancer. In the case of pancreatic ductal adenocarcinoma (PDAC), a variety of kinases and associated signaling pathways have been identified for their key role in the establishment of disease as well as its progression. However, the identification of additional relevant therapeutic targets has been slow and is further confounded by interactions between the tumor and the surrounding tumor microenvironment. In this work, we attempt to link the state of the human kinome, or kinotype, with cell viability in treated, patient-derived PDAC tumor and cancer-associated fibroblast cell lines. We applied classification models to independent kinome perturbation and kinase inhibitor cell screen data, and found that the inferred kinotype of a cell has a significant and predictive relationship with cell viability. We further find that models are able to identify a set of kinases whose behavior in response to perturbation drive the majority of viability responses in these cell lines, including the understudied kinases CSNK2A1/3, CAMKK2, and PIP4K2C. We next utilized these models to predict the response of new, clinical kinase inhibitors that were not present in the initial dataset for model devlopment and conducted a validation screen that confirmed the accuracy of the models. These results suggest that characterizing the perturbed state of the human protein kinome provides significant opportunity for better understanding of signaling behavior and downstream cell phenotypes, as well as providing insight into the broader design of potential therapeutic strategies for PDAC.

Extending the theory of planned behavior to predict the behavior of farmers in choosing low-water-intensive medicinal plants.

Water scarcity has become a serious challenge in many parts of the world due to increasing demands and the impacts of climate change. The agriculture sector globally accounts for a major portion of water consumption, yet it also holds substantial potential for water conservation. Among the most effective ways to conserve water is to cultivate low-water-demanding crops, such as medicinal plants (MPs), instead of water-demanding crops (WDC). However, the voluntary participation of farmers, largely influenced by socio-psychological drivers, is crucial for successfully implementing most water conservation programs and needs to be addressed. Therefore, the main objectives of this paper were: (1) to identify the determinants that explain farmers' intention and behavior in cultivating MPs instead of WDC; and (2) to examine the effectiveness and performance of an extended version of the theory of planned behavior (TPB) in predicting farmers' intention and behavior toward cultivating MPs by innovatively incorporating four new variables into the original TPB model: perceived barriers, moral norms, compatibility, and relative advantage. The applicability of the theoretical framework was evaluated in the Sojasroud Plain, Zanjan province, Iran. The results of the structural equation modeling revealed that: (1) farmers' intention to cultivate MPs instead of WDC is significantly influenced by perceived barriers, moral norms, subjective norms, and perceived behavior control (the strongest predictor); and (2) farmers' behavior in cultivating MPs instead of WDC is predicted by relative advantage, compatibility, and intention (the most prominent determinant). The R2 values for predicting intention and behavior were 55% and 53%, respectively. Based on the results, some practical policies were proposed to increase the cultivation of MPs in the study area.

Characterization of lignin degrading enzyme PmdC, which catalyzes a key step in the synthesis of polymer precursor 2-pyrone-4,6-dicarboxylic acid (PDC).

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is CHMS dehydrogenase, which acts on the substrate 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS). We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme's mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

Parents' clinical depression and children's problem behaviors: A multi-level meta-analytic examination.

BACKGROUND: Previous meta-analyses considering associations between parental depression (PD) and child symptoms have considered PD based primarily on self-report of depression symptoms. The present meta-analysis, in contrast, evaluated the effect of parents' clinically-diagnosed depressive disorders (PDD) on child internalizing and externalizing symptoms and considered both family- and study-level variables that influenced the strength of these effects. METHODS: We examined 111 effect sizes nested in 40 studies including a clinical assessment of parents' major or persistent depressive disorder and measures of children's internalizing or externalizing behaviors published between 2000 and 2020. We used a multi-level meta-analytic framework to account for nesting of multiple effect sizes within studies. RESULTS: PDD was associated with children's internalizing (weighted mean r = 0.211) and externalizing (weighted mean r = 0.204) behaviors. Family- and study-level variables moderated these relations, including the inclusion of fathers in the sample, the specific measure of internalizing behavior, reporting of diagnostic reliability, and informant for problem behaviors. LIMITATIONS: Limitations include exclusive consideration of internalizing and externalizing symptoms (versus other symptom types or problems) and the limited number of father-only studies from which to base conclusions about the relative effect of maternal vs. paternal depression. CONCLUSIONS: The similarity between the current findings and previous meta-analyses suggests that researchers studying the effects of PD may be able to bypass more exhaustive clinical interviews for less burdensome depression symptom inventories. Furthermore, our findings suggest that researchers and clinicians should consider how PD impacts not just child depressive symptoms, but myriad problem behaviors.

Phylogeographic patterns match the floristic subdivisions: The diversification history of a widespread herb in subtropical China.

BACKGROUND AND AIMS: Subtropical China is dominated by evergreen broad-leaved forests (EBLFs) and is acknowledged as a critical region for its high floristic richness and endemism. Understanding of evolutionary mechanisms of such global biodiversity hotspots comes almost exclusively from long-lived tree species. Herbaceous plants represent critical biodiversity components in forests, however, the diversification history of understory herbs in subtropical EBLFs remain poorly understood. Here, we investigated the phylogeographic patterns and demographic history of Oreocharis auricula, a widespread perennial herb endemic to the EBLFs of subtropical China. METHODS: Both cpDNA sequences and single-copy nuclear genes were used to investigate the genetic variation among 657 individuals from 68 populations. Evidences from molecular dating, demographic history construction, and species distribution modeling were also combined to infer the phylogeography and evolutionary history of O. auricula. KEY RESULTS: Strong phylogeographic signals have been congruently observed using nuclear and plastid DNA markers, with the diversification patterns generally consistent with the recognized floristic subdivisions of subtropical China. Notably, we revealed an important phylogeographic barrier along the Nanling mountain range, which is also around a climatic transition at 24-26°N latitude in subtropical China, separating the south monsoon subtropical EBLFs from the mid-subtropical EBLFs. Demographic expansion and significant niche divergence were detected among the extant lineages, which may have diverged during the early Pleistocene. CONCLUSIONS: The inherent characteristics of understory herbs with limited dispersal and short generation time intensify the genetic divergence response of O. auricula to abiotic forces, contributing to the profound phylogeographic imprints of mountains and climate in such herbaceous flora. To further substantiate the generality of the identified patterns, it is paramount to extend phylogeographic investigations to other understory herbaceous taxa in subtropical China. These results have expanded our understanding of the diversification processes of subtropical forests in China.

Metapopulation model of phage therapy of an acute Pseudomonas aeruginosa lung infection.

Infections caused by multidrug resistant (MDR) pathogenic bacteria are a global health threat. Bacteriophages ("phage") are increasingly used as alternative or last-resort therapeutics to treat patients infected by MDR bacteria. However, the therapeutic outcomes of phage therapy may be limited by the emergence of phage resistance during treatment and/or by physical constraints that impede phage-bacteria interactions in vivo. In this work, we evaluate the role of lung spatial structure on the efficacy of phage therapy for Pseudomonas aeruginosa infections. To do so, we developed a spatially structured metapopulation network model based on the geometry of the bronchial tree, including host innate immune responses and the emergence of phage-resistant bacterial mutants. We model the ecological interactions between bacteria, phage, and the host innate immune system at the airway (node) level. The model predicts the synergistic elimination of a P. aeruginosa infection due to the combined effects of phage and neutrophils, given the sufficient innate immune activity and efficient phage-induced lysis. The metapopulation model simulations also predict that MDR bacteria are cleared faster at distal nodes of the bronchial tree. Notably, image analysis of lung tissue time series from wild-type and lymphocyte-depleted mice revealed a concordant, statistically significant pattern: infection intensity cleared in the bottom before the top of the lungs. Overall, the combined use of simulations and image analysis of in vivo experiments further supports the use of phage therapy for treating acute lung infections caused by P. aeruginosa, while highlighting potential limits to therapy in a spatially structured environment given impaired innate immune responses and/or inefficient phage-induced lysis. IMPORTANCE: Phage therapy is increasingly employed as a compassionate treatment for severe infections caused by multidrug-resistant (MDR) bacteria. However, the mixed outcomes observed in larger clinical studies highlight a gap in understanding when phage therapy succeeds or fails. Previous research from our team, using in vivo experiments and single-compartment mathematical models, demonstrated the synergistic clearance of acute P. aeruginosa pneumonia by phage and neutrophils despite the emergence of phage-resistant bacteria. In fact, the lung environment is highly structured, prompting the question of whether immunophage synergy explains the curative treatment of P. aeruginosa when incorporating realistic physical connectivity. To address this, we developed a metapopulation network model mimicking the lung branching structure to assess phage therapy efficacy for MDR P. aeruginosa pneumonia. The model predicts the synergistic elimination of P. aeruginosa by phage and neutrophils but emphasizes potential challenges in spatially structured environments, suggesting that higher innate immune levels may be required for successful bacterial clearance. Model simulations reveal a spatial pattern in pathogen clearance where P. aeruginosa are cleared faster at distal nodes of the bronchial tree than in primary nodes. Interestingly, image analysis of infected mice reveals a concordant and statistically significant pattern: infection intensity clears in the bottom before the top of the lungs. The combined use of modeling and image analysis supports the application of phage therapy for acute P. aeruginosa pneumonia while emphasizing potential challenges to curative success in spatially structured in vivo environments, including impaired innate immune responses and reduced phage efficacy.

Computational Synthetic Biology Enabled through JAX: A Showcase.

Mathematical modeling is indispensable in synthetic biology but remains underutilized. Tackling problems, from optimizing gene networks to simulating intracellular dynamics, can be facilitated by the ever-growing body of modeling approaches, be they mechanistic, stochastic, data-driven, or AI-enabled. Thanks to progress in the AI community, robust frameworks have emerged to enable researchers to access complex computational hardware and compilation. Previously, these frameworks focused solely on deep learning, but they have been developed to the point where running different forms of computation is relatively simple, as made possible, notably, by the JAX library. Running simulations at scale on GPUs speeds up research, which compounds enable larger-scale experiments and greater usability of code. As JAX remains underexplored in computational biology, we demonstrate its utility in three example projects ranging from synthetic biology to directed evolution, each with an accompanying demonstrative Jupyter notebook. We hope that these tutorials serve to democratize the flexible scaling, faster run-times, easy GPU portability, and mathematical enhancements (such as automatic differentiation) that JAX brings, all with only minor restructuring of code.