AI DIGS

An analysis of side effects reports to the Food and Drug Administration reported that of those sent in by trans men and women taking hormone therapies, the majority were seriously dangerous.

DignifAI With the power of AIwe will clothe the instahotswe will purify them of their tattooswe will liberate them of their piercingswe will lengthen their skirtsIt will be nearly impossible to build counter narratives to this. We will piggy back off the outrage around AI porn and commit unrelenting psychological warfare to our own ends.We […]

#DignifAI is a hashtag campaign created by users of 4chan's /pol/ board in which they use artificial intelligence tools to edit real pictures of women in provocative clothing to give them a more modest outfit and, if applicable, remove their tattoos. The trend began spreading on Twitter / X in February 2024.

DignifAI is gaining attention in the social media lately. Unlike the increasing number of apps that controversially remove clothes from

Get ready to dive into the fascinating world of DignifAI!

Wearable electronics and photonics have advanced rapidly and impacted significantly on the way of healthcare monitoring/treatment, ambient monitoring/intervention, prosthetics, soft robotics, flexibl...

The State of Upper Austria sees artificial intelligence (AI) as one of the most important technology trends of the next decade.

University of Minnesota Ph.D. dissertation. February 2019. Major: Bioproducts/Biosystems Science Engineering and Management. Advisors: Ce Yang, Peter Marchetto. 1 computer file (PDF); xx, 173 pages.

For information visit: https://www.plant-phenotyping.org/index.php?index=580&event=http_ieeeagra_com_events_webinar_september_24_2019

When the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019, researchers rapidly recalibrated epidemiological computer models originally developed for other pandemics to serve as decision support tools for policy-makers and health care professionals planning public health responses. However, no current tools can predict the course of disease and help a doctor decide on the most appropriate treatment for an individual COVID-19 patient. “Digital twins” are software replicas of the dynamic function and failure of engineered products and processes. The medical analog, patient-specific digital twins, could integrate known human physiology and immunology with real-time patient-specific clinical data to produce predictive computer simulations of viral infection and immune response. Such medical digital twins could be a powerful addition to our arsenal of tools to fight future pandemics, combining mechanistic knowledge, observational data, medical histories, and the power of artificial intelligence (AI). An industrial digital twin of a device, such as a specific jet engine, combines a predictive template computational model calibrated using historical data aggregated from many devices with regularly collected operational data for the particular device. Digital twins allow continual forecasting, and small-scale interventions to prevent problems before they become serious. This greatly reduces the frequency of critical failures. The most sophisticated engineering digital twins are also self-improving—they continuously monitor divergence between predictions and observations and use these divergences to improve their own accuracy. Although medical digital twins are much more difficult to develop than those for engineered devices, they have begun to find applications in improving human health. Examples include the “artificial pancreas” for type 1 diabetes patients ([ 1 ][1], [ 2 ][2]). In the artificial pancreas model, a template mathematical model of human glucose metabolism and a closed-loop control algorithm modeling insulin delivery and data from an implanted glucose sensor are customized into a patient-specific digital twin that continuously calculates insulin needs and drives an implanted pump that adjusts blood insulin concentrations. Additionally, pediatric cardiac digital twins combine template models of the heart with patient-derived clinical measurements to optimize some heart surgeries ([ 3 ][3]) and assess the risk of thrombosis ([ 4 ][4]). These examples illustrate how current digital twins can operate in real time to maintain health continuously, or they can be used off-line to design personalized medical interventions. The ARCHIMEDES diabetes model expands these technologies by including models not only of the progression of diabetes within individual patients but also of medical diagnosis, treatments, and the functioning of the health care system that is providing the treatment ([ 5 ][5]). These examples provide a vision of the potential of medical digital twins. Medical digital twins that combine mechanistic understanding of physiology and viral replication with AI-based models derived from population and individual clinical data are promising as tools for optimizing the treatment of patients infected with a virus. As clinical outcomes of SARS treatment revealed, therapies such as steroidal anti-inflammatories can be lifesaving but also ineffective or even lethal if they are not adjusted carefully to suit individual patient responses ([ 6 ][6]). Thus, more complex therapies combining antivirals and multiple immune-stimulating and anti-inflammatory drugs would need to be personalized for time of application and dose of each component to be both effective and safe. Validated digital twins could greatly reduce the cost and complexity of such combinatorial clinical applications. Even primitive digital twins could improve diagnosis, prognosis, and treatment by providing a framework to combine patient-specific population data in a consistent framework. Their deployment would enable rapid refinement and improvement, especially if they were designed in a modular fashion to permit the parallel development and optimization of their component submodels. However, a digital twin that can continuously replicate the complexity of an infection and immune responses sufficiently well to guide individual treatment is currently out of reach. To realize the potential of digital twins in the treatment of viral diseases, there are a number of issues that need to be addressed. The spread of infection within the body and the immune response to viral pathogens are still poorly understood, as are the factors determining if and when specific components of the immune response are beneficial (viral clearance) or harmful (hyperinflammation). Viral infections can be complex, with pathologies developing in organs beyond the sites of primary infection, requiring an understanding of the responses of multiple organs. Therapies are also complex, with combinations integrating phased doses of antivirals, anti-inflammatory drugs, antibodies, and immune-stimulating drugs such as interferon (IFN) or interleukin-7 (IL-7). Where to start in building viral-infection digital twins? When considering how to build mechanistic model components for a viral-infection digital twin, many of the necessary submodels of relevant pathways and processes already exist ([ 7 ][7]) or could be developed using existing experimental methodologies (see the figure). For example, at the subcellular scale, transcriptomics data analysis of human macrophages can be used to construct dynamic network models of the interactions of highly expressed genes for each macrophage subtype ([ 8 ][8]), forming the basis for dynamic models of gene regulatory networks. At the multicellular scale, imaging technology reveals spatial aspects of the immune response ([ 9 ][9]). At the tissue scale, a simulation of an alveolar sac can capture the spatiotemporal variability of the immune response. At the organ scale, computational fluid dynamics models can simulate airflow in the lungs ([ 10 ][10]). And at the whole-body scale, computational models calibrated with simultaneous data from different organs, such as respiratory contractions and cerebral blood flow, can be used to integrate different organ systems ([ 11 ][11]). Physiologically based kinetic models ([ 12 ][12]) are widely used in the development and regulation of pharmaceuticals. Virologists have developed template models that capture aspects of viral spread, immune response, viral replication in individual cells, physiology, and dysfunction of specific organ systems, transport in the blood and lymph, airway transport of viruses, and aerosol therapies ([ 13 ][13]). Digital twins describing infection and treatment require the development, validation, and integration of numerous component submodels in the context of a rapidly developing scientific understanding of biological behaviors and continual generation of new experimental and clinical data. Although individual laboratories may construct submodels, the development of comprehensive digital twins will require laboratories and research groups around the world to integrate and validate submodels independently, with only limited central coordination. Enabling such parallel development requires a flexible simulation architecture that uses a multiscale map of all the relevant components of a patient's response to viral infection, as well as responses to available treatments. Community efforts such as the COVID-19 Disease Map Project and Computational Modeling in Biology Network (COMBINE) are working to build such infrastructure, although much work needs to be done to adapt those for use in digital-twin technology. ![Figure][14] Building a personalized digital twin Data from multiple scales are needed to build computational representations of biological processes and body systems that are affected by viral infection. These submodels are integrated and personalized with clinical data from individual patients. The digital twin can then be used to derive predictions about diagnosis, prognosis, and efficacy and optimization of therapeutic interventions. GRAPHIC: N. CARY/ SCIENCE Another substantial challenge is the generation of the heterogeneous data required to both calibrate template models to known human biology and physiology and to personalize template models into digital twins. Data from clinical trials are an important resource for model validation and discovery, as are high-resolution time-series data characterizing the immune response in a variety of individuals and settings. Model construction and validation require collection of synchronous measurements at different physiological scales: 'omics data from tissues and single cells, from diverse experimental systems, including two-dimensional (2D) and 3D cell cultures, in vivo and ex vivo animal models, and patients; at the tissue level, data characterizing immune cell trafficking and patterns of damage and recovery; and biophysical and structural data from tissues and organs, combined with data characterizing transport throughout the body. To personalize a digital twin, the model template must integrate with clinical records and time courses, such as vital signs, immune cell counts, computed tomography scans of infected organs, measured viral loads, and treatment responses. Given the likely complexity of the underlying models, even the abundance of such data will leave considerable challenges in model validation and uncertainty quantification. Data-driven AI digital twins for prognosis and treatment optimization are often viewed as alternatives to mechanistic modeling. However, AI and mechanistic approaches are most valuable when used together. Mechanistic modeling can provide important constraints for training AI algorithms ([ 14 ][15]). Conversely, AI can assist mechanistic modelin...

Many disease-causing bacteria use a molecular syringe to inject dozens of their proteins, called effectors, into intestinal cells, blocking key immune responses. Ruano-Gallego et al. used the mouse pathogen Citrobacter rodentium to model effector function in vivo. They found that effectors work together as a network, allowing the microbe great flexibility in maintaining pathogenicity. An artificial intelligence platform correctly predicted colonization outcomes of alternative networks from the in vivo data. However, the host was able to bypass the obstacles erected by different effector networks and activate complementary immune responses that cleared the pathogen and induced protective immunity. Science , this issue p. [eabc9531][1] ### INTRODUCTION Infections with many Gram-negative pathogens, including Escherichia coli , Salmonella , Shigella , and Yersinia , rely on the injection of effectors via type III secretion systems (T3SSs). The effectors hijack cellular processes through multiple mechanisms, including molecular mimicry and diverse enzymatic activities. Although in vitro analyses have shown that individual effectors can exhibit complementary, interdependent, or antagonistic relationships, most in vivo studies have focused on the contribution of single effectors to pathogenesis. Citrobacter rodentium is a natural mouse pathogen that shares infection strategies and virulence factors with the human pathogens enteropathogenic and enterohemorrhagic E. coli (EPEC and EHEC). The ability of these pathogens to colonize the gastrointestinal tract is mediated by the injection of effectors via a T3SS. Although C. rodentium infects 31 effectors, the prototype EPEC strain E2348/69 translocates 21 effectors. ### RATIONALE The aim of this study was to test the hypotheses that, rather than operating individually, the T3SS effectors form robust intracellular networks that can sustain large contractions and that expanded effector repertoires play a role in distinct disease phenotypes and host adaption. ### RESULTS We tested the effector-network paradigm by infecting mice with >100 C. rodentium effector mutant combinations. First, using machine learning prediction algorithms, we discovered additional effectors, NleN and NleO. We then sequentially deleted effector genes from two distinct starting points to reach sustainable endpoints, which resulted in strains missing 19 unrelated effectors (CR14) or 10 effectors involved in the modulation of innate immune responses in intestinal epithelial cells (IECs) (CRi9). Moreover, we deleted Map and EspF, which target the mitochondria and disrupt tight junctions. Unexpectedly, all strains colonized the colon and activated conserved metabolic and antimicrobial processes in the IECs while eliciting distinct cytokine and immune cell infiltration responses. In particular, although infection with C. rodentium Δ map /Δ espF failed to induce secretion of interleukin-22 (IL-22), CR14 and CRi9 triggered heightened secretion of IL-6 and granulocyte-macrophage colony-stimulating factor (GM-CSF) and of IL-22, interferon-γ (IFN-γ), and IL-17 from colonic explants, respectively. Nonetheless, infection with CR14 or CRi9 induced protective immunity against secondary infections. Although Tir, EspZ, and NleA are essential, other effectors exhibit context-dependent essentiality in vivo. Moreover, C. rodentium expressing the effector repertoire of EPEC E2348/69 failed to efficiently colonize mice. We used curated functional information and our in vivo data to train a machine learning model that predicted values for colonization efficiency of previously uncharacterized mutant combinations. Notably, a mutant with a low predicted value, lacking only nleF , nleG8 , nleG1 , nleB , and espL , failed to colonize. ### CONCLUSION Our analysis revealed that T3SS effectors form robust networks, which can sustain substantial contractions while maintaining virulence, and that the composition of the effector network contributes to host adaptation. Alternative effector networks within a single pathogen triggered markedly different immune responses yet induced protective immunity. CR14 did not tolerate any further contraction, which suggests that this network reached its robustness limit with only 12 effectors. As the robustness limits of other effector networks depend on the contraction starting point and the order of the deletions, machine learning models could transform our ability to predict alternative network functions. Together, this study demonstrates the robustness of T3SS effector networks and the ability of IECs to withstand drastic perturbations while maintaining antibacterial functions. ![Figure][2] T3SS effectors form robust intracellular networks. T3SS effector networks can sustain substantial contractions while maintaining virulence. Using C. rodentium as a model showed that although triggering the conserved infection signatures in IECs, distinct networks induce divergent immune responses and affect host adaption. Because the robustness limit depends on the contraction sequence, machine learning models could transform our ability to predict the virulence potential of alternative networks. Infections with many Gram-negative pathogens, including Escherichia coli , Salmonella , Shigella , and Yersinia , rely on type III secretion system (T3SS) effectors. We hypothesized that while hijacking processes within mammalian cells, the effectors operate as a robust network that can tolerate substantial contractions. This was tested in vivo using the mouse pathogen Citrobacter rodentium (encoding 31 effectors). Sequential gene deletions showed that effector essentiality for infection was context dependent and that the network could tolerate 60% contraction while maintaining pathogenicity. Despite inducing very different colonic cytokine profiles (e.g., interleukin-22, interleukin-17, interferon-γ, or granulocyte-macrophage colony-stimulating factor), different networks induced protective immunity. Using data from >100 distinct mutant combinations, we built and trained a machine learning model able to predict colonization outcomes, which were confirmed experimentally. Furthermore, reproducing the human-restricted enteropathogenic E. coli effector repertoire in C. rodentium was not sufficient for efficient colonization, which implicates effector networks in host adaptation. These results unveil the extreme robustness of both T3SS effector networks and host responses. [1]: /lookup/doi/10.1126/science.abc9531 [2]: pending:yes

Artificial Intelligence at the Edge

Artificial Intelligence at the Edge

Defense Advanced Research Projects Agency Program Detail

Defense Advanced Research Projects Agency News Detail

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