Digital Twins for Predictive, Preventive Personalized, and Participatory Treatment of Immune-Mediated Diseases.

Digital twins are computational models of complex systems, which aim to understand and optimize those systems more effectively than would be possible in real life. Ideally, digital twins can be translated to individual patients, to characterize and computationally treat their diseases with thousands of drugs, to select the drug or drugs that cure the patients. The background problem is that many patients do not respond adequately to drug treatment. This problem reflects a wide gap between the complexity of diseases and clinical practice. Each disease may involve altered interactions between thousands of genes that vary between different cell types in different organs. To our knowledge, these altered interactions have not been characterized on a genome-, cellulome-, and organ-wide scale in any disease. Thus, clinical translation of the digital twin ideal for predictive, preventive, personalized and participatory treatment involves formidable challenges, which are close to the limits of, or beyond today's technologies. Here, I discuss recent developments and challenges in relation to that ideal focusing on immune-mediated inflammatory diseases, as well as examples from other diseases.

Interrogating the effectiveness of service engagement for underserved populations in complex health and social care systems: towards an equitable engagement strategy.

There are increased sector-wide efforts within health and social care systems to engage those with lived experience in service design, delivery, and monitoring - aiming to secure more equitable health outcomes. However, critical knowledge gaps persist around how national whole-system engagement strategies can account for the challenges experienced by populations that encounter exclusion within complex multi-layered systems. This includes a failure to delineate shared challenges across groups, and to develop transferable cross-group frameworks to assist sector-wide change. There is, therefore, a danger that those groups already least heard will be collectively left behind. With a view to informing a more inclusive engagement strategy in Ireland, this national study aims to investigate multi-level (policy and strategic, operational, on-the-ground services, individual) shared challenges impacting engagement for five populations who have been identified as underserved groups in a complex health and social care system, including: (1) those who misuse drugs and alcohol, (2) those who are experiencing homelessness, (3) those experiencing mental health, (4) migrants and those of minority ethnicies, and (5) Irish Travellers. Adopting a mixed-methods approach which draws on an evidence-informed multistakeholder perspective, this study employs data from: focus groups and life-course interviews with lived-experience populations (n=136), five focus groups (n=39) and a national on-line survey (n=320) with population-specific services providers; and national-level stakeholder interviews (n=9). Two cross-group participatory consultative forums with lived-experience and provider participants (n=28) were used to co-produce priority action areas based on study findings. This article presents findings on shared challenges in engaging these groups around leadership and commitment, implementation and action, population capacities, trust, and representation, stigma, and discrimination. Derived from these challenges, six development areas are presented to advance an inclusive equitable engagement approach in Ireland. These comprise: 1) balancing top-down prioritisation, and bottom-up direction; 2) sustaining multi-level, multi-form implementation; 3) measuring effectiveness and action; 4) embedding inclusive equitable engagement; 5) trust as a prerequisite, and outcome; and 6) an equalising, agency empowering agenda.

Strategies to improve care for older adults who present to the emergency department: a systematic review.

BACKGROUND: The aim of this systematic review was to examine the relationship between strategies to improve care delivery for older adults in ED and evaluation measures of patient outcomes, patient experience, staff experience, and system performance. METHODS: A systematic review of English language studies published since inception to December 2022, available from CINAHL, Embase, Medline, and Scopus was conducted. Studies were reviewed by pairs of independent reviewers and included if they met the following criteria: participant mean age of ≥ 65 years; ED setting or directly influenced provision of care in the ED; reported on improvement interventions and strategies; reported patient outcomes, patient experience, staff experience, or system performance. The methodological quality of the studies was assessed by pairs of independent reviewers using The Joanna Briggs Institute critical appraisal tools. Data were synthesised using a hermeneutic approach. RESULTS: Seventy-six studies were included in the review, incorporating strategies for comprehensive assessment and multi-faceted care (n = 32), targeted care such as management of falls risk, functional decline, or pain management (n = 27), medication safety (n = 5), and trauma care (n = 12). We found a misalignment between comprehensive care delivered in ED for older adults and ED performance measures oriented to rapid assessment and referral. Eight (10.4%) studies reported patient experience and five (6.5%) reported staff experience. CONCLUSION: It is crucial that future strategies to improve care delivery in ED align the needs of older adults with the purpose of the ED system to ensure sustainable improvement effort and critical functioning of the ED as an interdependent component of the health system. Staff and patient input at the design stage may advance prioritisation of higher-impact interventions aligned with the pace of change and illuminate experience measures. More consistent reporting of interventions would inform important contextual factors and allow for replication.

Major Role of Multiscale Entropy Evolution in Complex Systems and Data Science.

Complex systems are prevalent in various disciplines encompassing the natural and social sciences, such as physics, biology, economics, and sociology. Leveraging data science techniques, particularly those rooted in artificial intelligence and machine learning, offers a promising avenue for comprehending the intricacies of complex systems without necessitating detailed knowledge of underlying dynamics. In this paper, we demonstrate that multiscale entropy (MSE) is pivotal in describing the steady state of complex systems. Introducing the multiscale entropy dynamics (MED) methodology, we provide a framework for dissecting system dynamics and uncovering the driving forces behind their evolution. Our investigation reveals that the MED methodology facilitates the expression of complex system dynamics through a Generalized Nonlinear Schrödinger Equation (GNSE) that thus demonstrates its potential applicability across diverse complex systems. By elucidating the entropic underpinnings of complexity, our study paves the way for a deeper understanding of dynamic phenomena. It offers insights into the behavior of complex systems across various domains.

Advances in Adjoint Functions of Connection Number in Water Resources Complex Systems: A Systematic Review.

The adjoint function of connection number has unique advantages in solving uncertainty problems of water resource complex systems, and has become an important frontier and research hotspot in the uncertainty research of water resource complex problems. However, in the rapid evolution of the adjoint function, some problems greatly limit the application of the adjoint function in the research of water resources. Therefore, based on bibliometric analysis, development, practical application issues, and prospects of the hot directions are analyzed. It is found that the development of the connection number of water resource set pair analysis can be divided into three stages: (1) relatively sluggish development before 2005, (2) a period of rapid advancement in adjoint function research spanning from 2005 to 2017, and (3) a subsequent surge post-2018. The introduction of the adjoint function of connection number promotes the continuous development of set pair analysis of water resources. Set pair potential and partial connection number are the crucial research directions of the adjoint function. Subtractive set pair potential has rapidly developed into a relatively independent and important trajectory. The research on connection entropy is comparatively less, which needs to be further strengthened, while that on adjacent connection number is even less. The adjoint function of set pair potential can be divided into three major categories: division set pair potential, exponential set pair potential, and subtraction set pair potential. The subtraction set pair potential, which retains the original dimension and quantity variation range of the connection number, is widely used in water resources and other fields. Coupled with the partial connection number, a series of new connection number adjoint functions have been developed. The partial connection number can be mainly divided into two categories: total partial connection number, and semi-partial connection number. Among these, the calculation expression and connotation of total partial connection numbers have not yet reached a consensus, accompanied by the slow development of high-order partial connection numbers. Semi-partial connection number can describe the mutual migration movement between different components of the connection number, which develops rapidly. With the limitations and current situation described above, promoting the exploration and application of the adjoint function of connection number in the field of water resources and other fields of complex systems has become the focus of future research.

3D printing technology for prepared dishes: printing characteristics, applications, challenges and prospects.

Prepared dishes are popular convenience foods that meet the needs of consumers who pursue delicious tastes while saving time and effort. As a new technology, food 3D printing (also known as food additive manufacturing technology) has great advantage in the production of personalized food. Applying food 3D printing technology in the production of prepared dishes provides the solution to microbial contamination, poor nutritional quality and product standardization. This review summarizes the problems faced by the prepared dishes industry in traditional food processing, and introduces the characteristics of prepared dishes and 3D printing technology. Food additives are suitable for 3D prepared dishes and novel 3D printing technologies are also included in this review. In addition, the challenges and possible solutions of the application of food 3D printing technology in the field of prepared dishes are summarized and explored. Food additives with advantages in heat stability, low temperature protection and bacteriostasis help to accelerate the application of 3D printing in prepared dishes industry. The combination of 3D printing technology with heat-assisted sources (microwave, laser) and non-heat-assisted sources (electrolysis, ultrasound) provides the possibility for the development of customized prepared dishes in the future, and also promotes more 3D food printing technologies for commercial use. It is noteworthy that these technologies are still at research stage, and there are challenges for the formulation design, the stability of printed ink storage, as well as implementation of customized nutrition for the elderly and children.

Understanding the dynamics of the obesity transition associated with physical activity, sedentary lifestyle, and consumption of ultra-processed foods in Colombia.

OBJECTIVE: We investigate the obesity transition at the country- and regional-levels, by age, gender, and socioeconomic status (SES) and its relationship to three health behavior attributes, including physical activity (PA), sedentary activities (ST), and consumption of ultra-processed foods (CUPF) within the urban population of Colombia, from 20,010 to 2050. METHODS: The study is informed by cross-sectional data from ENSIN survey. We used these data to develop a system dynamics model that simulates the dynamics of obesity by body mass index (BMI) categories, gender, and SES. This model also uses a conservative co-flow structure for three health-related behaviors (PA, ST, and CUPF). RESULTS: At the national level, our results indicate that the burden of obesity is shifting towards populations with lower SES as the gross domestic product (GDP) increases, particularly women aged 20-59 years with lower SES. Among this group of women, the highest burden of obesity is among those who do not meet the PA, ST and CUPF recommendations. At the regional level, our findings suggest that the regions are at different stages in the obesity transition. CONCLUSIONS: The burden of obesity is shifting towards women with lower SES as GDP increases at the national level and across several regions. This obesity transition is paralleled by a high prevalence of women from low SES groups who do not meet the minimum recommendations for PA, CUPF, and ST. Our findings can be used by decision-makers to inform age- and SES- specific policies seeking to tackle the obesity.

Analysis of the spatiotemporal patterns and decoupling effects of China's water resource spatial equilibrium.

The significance of water resource spatial equilibrium (WRSE) research is to maximally remove the spatial restrictions of water on regional development, including social development, economic development and eco-environmental maintenance. Although great achievements have been made, national-scale WRSE research is rare; besides, the spatiotemporal patterns and decoupling effects of WRSE have been poorly studied in current research. Thus, the aim of this research is to measure the WRSE in China for the period 2008-2019 by using an improved coupling coordination model and to empirically analyse its distribution dynamics and decoupling effects. The results show that the WRSE status of China's 31 provincial administrative regions from 2008 to 2019 is at a moderate level. Based on the spatiotemporal patterns and decoupling effects analysis, areas in urgent need of improving WRSE status are identified, and tailored countermeasures are provided for each area. To our knowledge, this paper is the first nationwide study of the spatiotemporal patterns and decoupling effects of WRSE.

Simulating the multicausality of Alzheimer's disease with system dynamics.

INTRODUCTION: In Alzheimer's disease (AD), cognitive decline is driven by various interlinking causal factors. Systems thinking could help elucidate this multicausality and identify opportune intervention targets. METHODS: We developed a system dynamics model (SDM) of sporadic AD with 33 factors and 148 causal links calibrated with empirical data from two studies. We tested the SDM's validity by ranking intervention outcomes on 15 modifiable risk factors to two sets of 44 and 9 validation statements based on meta-analyses of observational data and randomized controlled trials, respectively. RESULTS: The SDM answered 77% and 78% of the validation statements correctly. Sleep quality and depressive symptoms yielded the largest effects on cognitive decline with which they were connected through strong reinforcing feedback loops, including via phosphorylated tau burden. DISCUSSION: SDMs can be constructed and validated to simulate interventions and gain insight into the relative contribution of mechanistic pathways.

Connection number based model for coordination development evaluation of regional water resources, social economy and ecological environment complex system.

The development of social economy often requires a large consumption of water resources, and will also discharge a large amount of pollutants to the environment. Currently, the rapid development of regional water resources, social economy and ecological environment (WSE) complex system encounters significant challenges, and the coordination development of WSE complex system becomes important and necessary condition of regional sustainable development. Therefore, to scientifically evaluate the coordination development state of WSE system, based on the establishment of evaluation index system, the connection number and distance coordination model coupling approach for the coordination development evaluation of WSE complex system was proposed in this manuscript. The application results of the proposed method in Anhui Province, China indicate that, during 2011-2020, the coordination level of Anhui province is relatively high, and the coordination grade of most cities are in grade I or II. The coordination development degree of Anhui province presented a distinct improving trend with time, from most cities in grade IV or V in 2011 to most cities in grade II in 2020, from the worst 0.0580 in 2011 to the best 0.9200 in 2020. In terms of space, the coordinated development level of southern Anhui is higher than that of northern Anhui. Meanwhile, the coordination development status of the 16 cities in Anhui province can be divided into three patterns according to its historical variation characteristics, i.e., coordination development mode, ecological environment backward mode, and social and economic backward mode. Compared with the commonly used coordination evaluation method, the method of this paper can solve the problem of homogenization, and its calculation results are more reasonable and practical.

Neural Causal Information Extractor for Unobserved Causes.

Causal inference aims to faithfully depict the causal relationships between given variables. However, in many practical systems, variables are often partially observed, and some unobserved variables could carry significant information and induce causal effects on a target. Identifying these unobserved causes remains a challenge, and existing works have not considered extracting the unobserved causes while retaining the causes that have already been observed and included. In this work, we aim to construct the implicit variables with a generator-discriminator framework named the Neural Causal Information Extractor (NCIE), which can complement the information of unobserved causes and thus provide a complete set of causes with both observed causes and the representations of unobserved causes. By maximizing the mutual information between the targets and the union of observed causes and implicit variables, the implicit variables we generate could complement the information that the unobserved causes should have provided. The synthetic experiments show that the implicit variables preserve the information and dynamics of the unobserved causes. In addition, extensive real-world time series prediction tasks show improved precision after introducing implicit variables, thus indicating their causality to the targets.

An Entropy Generation Rate Model for Tropospheric Behavior That Includes Cloud Evolution.

A postulate that relates global warming to higher entropy generation rate demand in the tropospheric is offered and tested. This article introduces a low-complexity model to calculate the entropy generation rate required in the troposphere. The entropy generation rate per unit volume is noted to be proportional to the square of the Earth's average surface temperature for a given positive rate of surface warming. The main postulate is that the troposphere responds with mechanisms to provide for the entropy generation rate that involves specific cloud morphologies and wind behavior. A diffuse-interface model is used to calculate the entropy generation rates of clouds. Clouds with limited vertical development, like the high-altitude cirrus or mid-altitude stratus clouds, are close-to-equilibrium clouds that do not generate much entropy but contribute to warming. Clouds like the cumulonimbus permit rapid vertical cloud development and can rapidly generate new entropy. Several extreme weather events that the Earth is experiencing are related to entropy-generating clouds that discharge a high rate of rain, hail, or transfer energy in the form of lightning. The water discharge from a cloud can cool the surface below the cloud but also add to the demand for a higher entropy generation rate in the cloud and troposphere. The model proposed predicts the atmospheric conditions required for bifurcations to severe-weather clouds. The calculated vertical velocity of thunderclouds associated with high entropy generation rates matches the recorded observations. The scale of instabilities for an evolving diffuse interface is related to the entropy generation rate per unit volume. Significant similarities exist between the morphologies and the entropy generation rate correlations in vertical cloud evolution and directionally solidified grainy microstructures. Such similarities are also explored to explore a generalized framework of pattern evolution and establish the relationships with the corresponding entropy generation rate. A complex system like the troposphere can invoke multiple phenomena that dominate at different spatial scales to meet the demand for an entropy generation rate. A few such possibilities are presented in the context of rapid and slow changes in weather patterns.

A COMPREHENSIVE APPROACH TO MEDICAL-PSYCHOLOGICAL SUPPORT FOR SERVICE WOMEN IN MODERN UKRAINE.

OBJECTIVE: The aim: To analyze the current state of the problem and develop a modern methodology for the correction and prevention of stress disorders in female veterans. PATIENTS AND METHODS: Materials and methods: The following methods were used during the research: theoretical and interdisciplinary analysis, complex, сlinical and psychopatho¬logical examination and methods of mathematical and statistical data processing. RESULTS: Results: In the course of our work, it was developed an algorithm for medical and psychological support for women who suffered from results of the fighting includes the following components: monitoring of the psychological and mental state of veteran women; increased psychological care; psychological support for veteran women; psychotherapy; psychoeducation; creation of a re-adaptation atmosphere; formation of a health-centred living style and strengthening of psychosocial resources. CONCLUSION: Conclusions: The complex system of treatment and prevention of stress-social disorders in women veterans should be aimed at decreasing the level of anxiety-depressive symptoms and the excessive nervous and psychological tension; infective re-evaluation of the traumatic experience; building a positive attitude towards the future and creating a new cognitive model of life.

Novel approach to analysis of the immune system using an ungated model of immune surface marker abundance to predict health outcomes.

Traditionally, the immune system is understood to be divided into discrete cell types that are identified via surface markers. While some cell type distinctions are no doubt discrete, others may in fact vary on a continum, and even within discrete types, differences in surface marker abundance could have functional implications. Here we propose a new way of looking at immune data, which is by looking directly at the values of the surface markers without dividing the cells into different subtypes. To assess the merit of this approach, we compared it with manual gating using cytometry data from the Singapore Longitudinal Aging Study (SLAS) database. We used two different neural networks (one for each method) to predict the presence of several health conditions. We found that the model built using raw surface marker abundance outperformed the manual gating one and we were able to identify some markers that contributed more to the predictions. This study is intended as a brief proof-of-concept and was not designed to predict health outcomes in an applied setting; nonetheless, it demonstrates that alternative methods to understand the structure of immune variation hold substantial progress.

Measurement and analysis of the resilience characteristics for a regional agricultural soil-water resource composite system.

Resilience is a significant attribute used to measure the sustainable development of the environment, and research on optimal measurement models is very important. This study took 15 farms in the Jiansanjiang Administration of Heilongjiang Province in China as the research object and constructed a resilience evaluation indicator system containing 31 indicators for the regional agricultural soil-water resource composite system (ASWRS). The combined weight (BFCM-CRITIC) and entropy weight (EW) combined with the variable fuzzy assessment (VFA) model and the improved technique for order preference by similarity to an ideal solution (TOPSIS) model were used to calculate the resilience exponent and to analyse the characteristics of space-time variation. The stability and reliability of the two models under different weights were verified by the Spearman correlation coefficient and discrimination theory to determine the optimal resilience exponent diagnosis method. The results show that according to the BFCM-CRITIC-VFA model, the levels of resilience were the highest at the Nongjiang, Hongwei and Erdaohe farms, and the resilience levels were strong and scattered. The resilience of the Jiansanjiang Administration has been increasing over time, and the spatial distribution has generally decreased from north to south and from the Heilong River and Wusuli River basins inland. A comparison of the reliability and stability of the two models for different weights indicates that the VFA model optimized based on combined weights was superior to the other methods in terms of stability and reliability, which verifies that the BFCM-CRITIC-VFA model is the most suitable for measuring the resilience exponent.

Anticipation of common buzzard population patterns in the changing UK landscape.

Harmonious coexistence between humans, other animals and ecosystem services they support is a complex issue, typically impacted by landscape change, which affects animal distribution and abundance. In the last 30 years, afforestation on grasslands across Great Britain has been increasing, motivated by socio-economic reasons and climate change mitigation. Beyond expected benefits, an obvious question is what are the consequences for wider biodiversity of this scale of landscape change. Here, we explore the impact of such change on the expanding population of common buzzards Buteo buteo, a raptor with a history of human-induced setbacks. Using Resource-Area-Dependence Analysis (RADA), with which we estimated individuals' resource needs using 10-day radio-tracking sessions and the 1990s Land Cover Map of GB, and agent-based modelling, we predict that buzzards in our study area in lowland UK had fully recovered (to 2.2 ind km-2) by 1995. We also anticipate that the conversion of 30%, 60% and 90% of economically viable meadow into woodland would reduce buzzard abundance nonlinearly by 15%, 38% and 74%, respectively. The same approach used here could allow for cost-effective anticipation of other animals' population patterns in changing landscapes, thus helping to harmonize economy, landscape change and biodiversity.

Emerging properties of malaria transmission and persistence in urban Accra, Ghana: evidence from a participatory system approach.

BACKGROUND: Several studies that aim to enhance the understanding of malaria transmission and persistence in urban settings failed to address its underlining complexity. This study aims at doing that by applying qualitative and participatory-based system analysis and mapping to elicit the system's emergent properties. METHODS: In two experts' workshops, the system was sketched and refined. This system was represented through a causal loop diagram, where the identification of leverage points was done using network analysis. RESULTS: 45 determinants interplaying through 56 linkages, and three subsystems: urbanization-related transmission, infection-prone behaviour and healthcare efficiency, and Plasmodium resistance were identified. Apart from the number of breeding sites and malaria-positive cases, other determinants such as drug prescription and the awareness of householders were identified by the network analysis as leverage points and emergent properties of the system of transmission and persistence of malaria. CONCLUSION: Based on the findings, the ongoing efforts to control malaria, such as the use of insecticide-treated bed nets and larvicide applications should continue, and new ones focusing on the public awareness and malaria literacy of city dwellers should be included. The participatory approach strengthened the legitimacy of the recommendations and the co-learning of participants.

Assessment of long-range cross-correlations in cardiorespiratory and cardiovascular interactions.

We propose higher-order detrending moving-average cross-correlation analysis (DMCA) to assess the long-range cross-correlations in cardiorespiratory and cardiovascular interactions. Although the original (zeroth-order) DMCA employs a simple moving-average detrending filter to remove non-stationary trends embedded in the observed time series, our approach incorporates a Savitzky-Golay filter as a higher-order detrending method. Because the non-stationary trends can adversely affect the long-range correlation assessment, the higher-order detrending serves to improve accuracy. To achieve a more reliable characterization of the long-range cross-correlations, we demonstrate the importance of the following steps: correcting the time scale, confirming the consistency of different order DMCAs, and estimating the time lag between time series. We applied this methodological framework to cardiorespiratory and cardiovascular time series analysis. In the cardiorespiratory interaction, respiratory and heart rate variability (HRV) showed long-range auto-correlations; however, no factor was shared between them. In the cardiovascular interaction, beat-to-beat systolic blood pressure and HRV showed long-range auto-correlations and shared a common long-range, cross-correlated factor. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities.

Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.

The Causal Interaction between Complex Subsystems.

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems and is expected to have applications in many real world problems in different disciplines such as climate science, fluid dynamics, neuroscience, financial economics, etc.