Marin Community Foundation

Precision medicine demands a shift from static, single-analyte diagnostics toward dynamic, systems-level understanding of health and disease. This review explores how the convergence of systems biology, multiomics, and artificial intelligence (AI) redefines biomarker discovery to drive early disease detection and personalized intervention. We highlight pioneering efforts that use longitudinal, multimodal data to map individual health trajectories and uncover early disease signals. Advances in AI, including machine learning and contextualization using knowledge graphs and digital twins, are accelerating clinical translation by enabling predictive, context-aware analyses. Real-world applications, including omics-informed diagnostics and digital health monitoring, demonstrate the potential of this approach to transform health care from reactive treatment to proactive wellness. These technologies also inform the development of targeted therapeutics that intervene earlier, personalize treatment, and potentially halt or reverse disease progression. We outline challenges, emerging solutions, and future directions that position AI-driven systems biology at the center of next-generation precision health.

Abstract Multi‐species occupancy models for estimating the effects of environmental covariates on species occurrences while accounting for the effects of false‐negative errors in detection were developed more than 20 years ago. Only recently have these models been extended to include correlations in occurrence between species. Tobler et al. (2019) proposed two of these models wherein species occurrences are specified using a probit‐regression model of correlated multivariate binary outcomes. In one model, correlations in occurrence between species are formal parameters. In the other model, a factor‐analytic approximation of these correlations is used. After conducting extensive simulation studies to compare the performance of these models, Tobler et al. (2019) recommended the latter model in favour of the former owing to difficulties in fitting the former to simulated or real data. The same software (JAGS) was used to fit both models. Since the former model specifies the actual process used to generate species occurrences, we hypothesized that shortcomings in the software were responsible for the difficulties reported by Tobler et al. (2019). We therefore devised an efficient Markov chain Monte Carlo algorithm for fitting the data‐generating, multi‐species occupancy model using parameter‐expanded data augmentation. Using this algorithm, we conducted analyses of simulated data sets to re‐evaluate and compare the performance of the two multi‐species occupancy models. We also compared the results of fitting these models to a real dataset collected in camera‐trap surveys of mammalian species on the Tibetan Plateau. Our analyses of simulated data revealed that estimators of species occurrence, correlation and detection parameters had similar performance in terms of average bias, average root‐mean‐squared error and average coverage of 95% credible intervals regardless of whether the data‐generating model or its factor‐analytic approximation was used to analyse the data. However, our analyses also revealed the presence of systematic bias in the correlation estimates of both models, wherein strongly negative correlations were overestimated and strongly positive correlations were underestimated. Systematic bias was not evident in correlations estimated by fitting a multivariate probit regression model to latent species occurrences; therefore, the systematic bias in correlation estimates of the two occupancy models must have been produced by the failure of these models to estimate latent species occurrences accurately.

La transformación digital ha impulsado cambios profundos en la forma en que las organizaciones gestionan la calidad del servicio, especialmente en entornos virtuales. Este estudio examina las estrategias administrativas implementadas para captar y fidelizar usuarios mediante herramientas tecnológicas, procesos innovadores y enfoque centrado en el cliente. Se aplicó una metodología mixta con encuestas a usuarios y análisis comparativo de plataformas digitales, destacando el impacto de la personalización, la respuesta ágil y la atención multicanal. Los resultados muestran que las organizaciones con mayor integración tecnológica presentan mejores niveles de satisfacción y retención de usuarios. Se concluye que la transformación digital, cuando está alineada con políticas administrativas efectivas, se convierte en un factor estratégico para la competitividad en el ecosistema digital contemporáneo.

Complications from diabetes, such as diabetic neuropathy, may greatly limit a person's range of mobility and walking. To address this problem, we provide an innovative system that tracks and evaluates diabetes patients' walking patterns using wearable Internet of Things (IoT) sensors connected with artificial intelligence (AI). Wearable sensors integrated into our system collect stride length, gait velocity, and pressure distribution in real time. Subtle variations and patterns may indicate the start of diabetes issues, and this data is evaluated using advanced neural network methods. Patterns connected to neuropathy and other walking defects, particularly those of diabetes patients, are provided to the neural network models. The AI technology enables early intervention and personalized recommendations by providing quick feedback and useful data. Patients may continually monitor their walking patterns by incorporating this technology into their everyday lives, which may help to avoid serious consequences and improve their quality of life. This research provides a realistic solution for continuously monitoring and controlling diabetic walking health by integrating wearable IoT technology with neural networks, which can improve diabetic management.

ABSTRACT GP101 is a single chain diabody composed of Fv regions of antibodies directed to CD3 and CD19. It is designed to mimic commercial blinatumomab (Blincyto®) that simultaneously co-engages patient T lymphocytes and CD19 positive B cell leukemias and lymphomas, facilitating their targeted destruction. This study details the purification of GP101 protein and development of a highly sensitive ELISA for its detection, achieving a sensitivity of 15pg/mL in 25% mouse serum. This ELISA has been validated according to GLP standards.

Ground-level ozone (O3) has emerged as a significant air pollutant in China, attracting increasing attention from both the scientific community and policymakers. Chemical transport models (CTM) serve as crucial tools in addressing O3 pollution, with frequent applications in predicting O3 concentrations, identifying source contributions, and formulating effective control strategies. The accuracy and reliability of the simulated O3 concentrations are typically assessed through model performance evaluation (MPE). However, the wide array of CTMs available, variations in input data, model setups, and other factors result in a broad range of simulated O3 concentration differences from observed values, highlighting the necessity for standardized benchmarks in O3 evaluation. Built upon our previous work, this study conducted a thorough literature review of CTM applications simulating O3 in China from 2006 to 2021. 216 relevant articles out of a total of 667 reviewed were identified to extract quantitative MPE results and key model configurations. From our analysis, two sets of benchmark values for six commonly used MPE metrics are proposed for CTM applications in China, categorized into "goal" benchmarks representing optimal model performance and "criteria" benchmarks representing achievable model performance across a majority of studies. It is recommended that the normalized mean bias (NMB) for hourly O3 and daily 8hr maximum O3 concentrations should ideally fall within ±15% and ±10%, respectively, to meet the "goal" benchmark. If the "criteria" benchmarks are to be met, the NMB should be within ±30% and ±20%, respectively. Moreover, uncertainties in O3 predictions due to uncertainties in various model inputs were quantified using the decoupled direct method (DDM) in a commonly used CTM. For the simulation period of June 2021, the total uncertainty of simulated O3 ranged 4-25 μg/m 3 , with anthropogenic volatile organic compound (AVOC) emissions contributing most to the uncertainty of O3 in coastal regions and O3 boundary conditions playing a dominant role in the northwest region. The proposed benchmarks for assessing simulated O3 concentrations, in conjunction with our previous studies on PM2.5 and other criteria air pollutants, represent a comprehensive and systematic effort to establish a model performance framework for CTM applications in China. These benchmarks aim to support the growing modeling community in China by offering a robust set of evaluation metrics and establishing a consistent evaluation methodology relative to the body of prior research, thereby helping to establish the credibility and reliability of their CTM applications. These statistical benchmarks need to be periodically updated as models advance and better inputs become available in the future.

The Comprehensive Air Quality Model with extensions (CAMx) is an open-source, state-of-the-science photochemical grid model that addresses tropospheric air pollution (ozone, particulates, air toxics) over spatial scales ranging from neighborhoods to continents. CAMx has been in continuous development for over 25 years and has been used by numerous entities ranging from government to industry to academia to support regulatory actions and scientific research addressing a variety of air quality issues. Here, we describe the technical formulation of CAMx v7.20, publicly released in May 2022. To illustrate an example of regional and seasonal model performance for predicted ozone and fine particulate matter (PM2.5), we summarize a model evaluation from a recent 2016 national-scale CAMx application over nine climate zones contained within the conterminous US. We show that statistical performance for warm season maximum 8 h ozone is consistently within benchmark statistical criteria for bias, gross error, and correlation over all climate zones, and often near statistical goals. Statistical performance for 24 h PM2.5 and constituents fluctuate around statistical criteria with more seasonal and regional variability that can be attributed to different sources of uncertainty among PM2.5 species (e.g., weather influences, chemical treatments and interactions, emissions uncertainty, and ammonia treatments). We close with a mention of new features and capabilities that are planned for the next public releases of the model in 2024 and beyond.