Model Of Response Research Articles

An in-silico study of conventional and FLASH radiotherapy iso-effectiveness: potential impact of radiolytic oxygen depletion on tumor growth curves and tumor control probability

Objective. This work aims to investigate the iso-effectiveness of conventional and FLASH radiotherapy on tumors through in-silico mathematical models. We focused on the role of radiolytic oxygen depletion (ROD), which has been argued as a possible factor to explain the FLASH effect.Approach. We used a spatiotemporal reaction-diffusion model, including ROD, to simulate tumor oxygenation and response. From those oxygen distributions we obtained surviving fractions (SFs) using the linear-quadratic (LQ) model with the oxygen enhancement ratios (OERs). We then employed the calculated SFs to describe the evolution of preclinical tumor volumes through a mathematical model of tumor response, and we also extrapolated those results to calculate tumor control probabilities (TCPs) using the Poisson-LQ approach.Main results. Our study suggests that the ROD effect may cause differences in SF between FLASH and conventional radiotherapy, especially in lowα/βandpoorly oxygenatedcells. However, a statistical analysis showed that these changes in SF generally do not result in significant differences in the evolution of preclinical tumor growth curves when the sample size is small, because such differences in SF may not be noticeable in the heterogeneity of the population of animals. Nonetheless, when extrapolating this effect to TCP curves, we observed important differences between both techniques (TCP is lower in FLASH radiotherapy). When analyzing the response of tumors with heterogeneous oxygenations, differences in TCP are more important forwell oxygenatedtumors. This apparent contradiction with the results obtained for homogeneously oxygenated cells is explained by the complex interplay between the heterogeneity of tumor oxygenation, the OER effect, and the ROD effect.Significance. This study supports the experimentally observed iso-effectiveness of FLASH and conventional radiotherapy when analyzing the volume evolution of preclinical tumors (that are far from control). However, this study also hints that tumor growth curves may be less sensitive to small variations in SF than tumor control probability: ROD may lead to increased SF in FLASH radiotherapy, which while not large enough to cause significant differences in tumor growth curves, could lead to important differences in clinical TCPs. Nonetheless, it cannot be discarded that other effects not modeled in this work, like radiation-induced immune effects, can contribute to tumor control and maintain the iso-effectiveness of FLASH radiotherapy. The study of tumor growth curves may not be the ideal experiment to test the iso-effectiveness of FLASH, and experiments reporting TCP orD50may be preferred.

Adopting sustainable innovations for remote access to TB and HIV care in South Africa.

For the last decade, South Africa has made substantial progress to control the dual HIV and TB epidemics. However, disruptions in TB and HIV treatment during the COVID-19 pandemic threatened to reverse this. This study aimed to identify adaptations in HIV and TB service delivery models in response to COVID-19 and government restrictions. This information informed the development of an online survey, which was utilized as part of a consultation exercise to further capture adaptations made to HIV/TB service delivery within the South African context. The literature review involved screening 380 titles and abstracts, identifying 30 HIV and TB studies across 19 countries, and categorizing 90 individual interventions into ten thematic areas. Common themes included interventions addressing screening, testing, diagnosis, medication collection and delivery support, and virtual models. Digital health interventions and adaptations to medication collection/delivery were reported in 38% of studies. Analysis of survey responses from 33 stakeholders in South Africa revealed that 47% of interventions targeted HIV, 11% TB, and 23% HIV/TB integrated service delivery. Most interventions (81%) were integrated into the national HIV or TB program, with implementation occurring at various levels: 39% at facility level, 35% at sub-district or district level, and 18% at provincial level. Programmatic data was available for 86% of interventions, with 50% being funded. This study demonstrated that services can be delivered in locations other than in health facilities (e.g., community-based or home-based) and that integrated services can also free up additional resources. Although studies varied, COVID-19 accelerated the adoption of differentiated service delivery (DSD) models for TB care, including multi-month dispensing (MMD) for TB preventative therapy (TPT) and TB treatment, home-based or mobile outreach screening and testing, and community pickup points (PuP) for TB medications. These initiatives had previously lagged behind HIV-focused DSD models, and it is crucial to sustain these services beyond the pandemic. To achieve universal health coverage, it will also be important to capitalize on these experiences and learn from HIV-focused DSD models so programs can deliver integrated person-centered chronic care services for TB, HIV, and non-communicable diseases.

Effect of touch on proprioception: non-invasive trigeminal nerve stimulation suggests general arousal rather than tactile-proprioceptive integration.

Proprioceptive error of estimated fingertip position in two-dimensional space is reduced with the addition of tactile stimulation applied at the fingertip. Tactile input does not disrupt the participants' estimation strategy, as the individual error vector maps maintain their overall structure. This relationship suggests integration of proprioception and tactile information improves proprioceptive estimation, which can also be improved with trained mental focus and attention. Task attention and arousal are physiologically regulated by the reticular activating system (RAS), a brainstem circuit including the locus coeruleus (LC). There is direct and indirect evidence that these structures can be modulated by non-invasive trigeminal nerve stimulation (nTNS), providing an opportunity to examine nTNS effect on the integrative relationship of proprioceptive and tactile information. Fifteen right-handed participants performed a simple right-handed proprioceptive estimation task with tactile feedback (touch) and no tactile (hover) feedback. Participants repeated the task after nTNS administration. Stimulation was delivered for 10 min, and stimulation parameters were 3,000 Hz, 50 μs pulse width, with a mean of 7 mA. Error maps across the workspace are generated using polynomial models of the participants' target responses. Error maps did not demonstrate significant vector direction changes between conditions for any participant, indicating that nTNS does not disrupt spatial proprioception estimation strategies. A linear mixed model regression with nTNS epoch, tactile condition, and the interaction as factors demonstrated that nTNS reduced proprioceptive error under the hover condition only. We argue that nTNS does not disrupt spatial proprioceptive error maps but can improve proprioceptive estimation in the absence of tactile feedback. However, we observe no evidence that nTNS enhances tactile-proprioceptive integration as the touch condition does not exhibit significantly reduced error after nTNS.

Numerical Modelling of Fish Cage Structural Responses in Regular and Irregular Waves Using Modified XPBD

Abstract The structural response of gravity-type fish cages in waves and current is numerically investigated. A Morison model is adopted to compute the hydrodynamic forces acting on the net ropes of the aquaculture nets and a screen model is used to compute the hydrodynamic forces on the net twines of the fish cages. The irregular waves are created based on the JONSWAP wave spectrum. The structure of the aquaculture nets is modeled using a modified extended position-based dynamics (XPBD) algorithm. The proposed method is effectively validated against the published experiment data to ensure its accuracy and reliability. The static response of the fish cage at various current speeds and the dynamic responses of the fish cage in regular and irregular waves are analyzed. Results show that the volume of the investigated fish cage is reduced by 30% in a current of 0.5m/s. The tension at the bottom net can reach about five times of maximum tension of the case without current. In the regular or irregular waves, the drag force of the fish cage is higher than that in a uniform flow with the same current speed and the fish cage volume decreases. The increase in drag force rises from the coupling between the waves and current. Therefore, the hydrodynamic model including the regular and irregular modeling, Morison model and screen model is successfully implemented into the framework of the present modified XPBD algorithm to provide the structural response of fish cages in waves and current.

Investigating In Vivo Tumor Biomolecular Changes Following Radiation Therapy Using Raman Spectroscopy.

Treatment resistance is a major bottleneck in the success of cancer therapy. Early identification of the treatment response or lack thereof in patients can enable an earlier switch to alternative treatment strategies that can enhance response rates. Here, Raman spectroscopy was applied to monitor early tumor biomolecular changes in sensitive (UM-SCC-22B) and resistant (UM-SCC-47) head and neck tumor xenografts for the first time in in vivo murine tumor models in response to radiation therapy. We used a validated multivariate curve resolution-alternating least-squares (MCR-ALS) model to resolve complex multicomponent Raman spectra into individual pure spectra and their respective contributions. We observed a significant radiation-induced increase in the contributions of lipid-like species (p = 0.0291) in the radiation-sensitive UM-SCC-22B tumors at 48 h following radiation compared to the nonradiated baseline (prior to commencing treatment). We also observed an increase in the contribution of collagen-like species in the radiation-resistant UM-SCC-47 tumors at 24 h following radiation compared to the nonradiated baseline (p = 0.0125). In addition to the in vivo analysis, we performed ex vivo confocal Raman microscopic imaging of frozen sections derived from the same tumors. A comparison of all control and treated tumors revealed similar trends in the contributions of lipid-like and collagen-like species in both in vivo and ex vivo measurements; however, when evaluated as a function of time, longitudinal trends in the scores of collagen-like and lipid-like components were not consistent between the two data sets, likely due to sample numbers and differences in sampling depth at which information is obtained. Nevertheless, this study demonstrates the potential of fiber-based Raman spectroscopy for identifying early tumor microenvironmental changes in response to clinical doses of radiation therapy.

Improving multiple stressor-response models through the inclusion of nonlinearity and interactions among stressor gradients.

Stressor-response models are used to detect and predict changes within ecosystems in response to anthropogenic and naturally occurring stressors. While nonlinear stressor-response relationships and interactions between stressors are common in nature, predictive models often do not account for them due to perceived difficulties in the interpretation of results. We used Irish river monitoring data from 177 river sites to investigate if multiple stressor-response models can be improved by accounting for nonlinearity, interactions in stressor-response relationships and environmental context dependencies. Out of the six models of distinct biological responses, five models benefited from the inclusion of nonlinearity while all six benefited from the inclusion of interactions. The addition of nonlinearity means that we can better see the exponential increase in Trophic Diatom Index (TDI3) as phosphorus increases, inferring ecological conditions deteriorating at a faster rate with increasing phosphorus. Furthermore, our results show that the relationship between stressor and response has the potential to be dependent on other variables, as seen in the interaction of elevation with both siltation and nutrients in relation to Ephemeroptera, Plecoptera and Trichoptera (EPT) richness. Both relationships weakened at higher elevations, perhaps demonstrating that there is a decreased capacity for resilience to stressors at lower elevations due to greater cumulative effects. Understanding interactions such as this is vital to managing ecosystems. Our findings provide empirical support for the need to further develop and employ more complex modelling techniques in environmental assessment and management.

Towards a framework for predicting immunotherapy outcome: a hybrid multiscale mathematical model of immune response to vascular tumor growth.

Studying tumor immune microenvironment (TIME) is pivotal to understand the mechanism and predict the outcome of cancer immunotherapy. Systems biology mathematical models can consider and control various factors of TIME and therefore explore the anti-tumor immune response meticulously. However, the role of tumor vasculature in the recruitment of T cells and the mechanism of T cell migration through TIME have not been studied comprehensively. In this work, we developed a hybrid discrete-continuum multi-scale model to study TIME. The mathematical model includes angiogenesis and T cell recruitment via tumor vasculature. Moreover, solid tumor growth, vascular growth and remodeling, interstitial fluid flow, hemodynamics, and blood rheology are all considered in the model. In addition, different aspects of T cells, including their migration, proliferation, subtype conversion, and interaction with tumor cells are thoroughly included. The model reproduces spatiotemporal distribution of tumor infiltrating T cells that mimics histopathological patterns. Furthermore, TIME model robustly recapitulates different phases of tumor immunoediting. We also examined a number of biomarkers to predict the outcome of immune checkpoint blockade (ICB) treatment. The results demonstrated that although tumor mutational burden (TMB) may predict non-responders to ICB, a combination of different biomarkers is essential to predict the majority of the responders. Based on our results, the ICB response rate varies significantly from 28 to 89% depending on the values of different parameters, even in the cases with high TMB.

Resilience assessment of FPSO leakage emergency response based on quantitative FRAM

FPSO production process is prone to leakage, and failure to respond promptly and effectively will lead to accident escalation and serious consequences. However, traditional safety assessment methods cannot handle the nonlinear relationships between human, technology, and organization in emergency response process. This study proposes a quantitative FRAM to evaluate the emergency response resilience of FPSO leakage. This method establishes a resilience evaluation framework including three tiers: function, ability, and system, which can quantify system resilience based on the variability of function. First, identify basic functions according to the four stages of monitoring, response, learning and anticipation in the emergency response process, and establish the FRAM model of FPSO leakage emergency response. Then, the quantitative FRAM and Monte Carlo simulation are combined to calculate the variabilities of functions under different operating conditions. Finally, based on the simulation results, the variabilities of basic functions are aggregated and statistically analyzed to quantify system resilience. The implementation process of this method is illustrated by a case study. The influence of different factors on the system resilience is analyzed by setting various operation scenarios, and critical functions are identified by sensitivity analysis, which can provide reference for improving system resilience and ensuring FPSO safety production.

Who gets better after surgery for degenerative cervical myelopathy? A responder analysis from the multicenter Canadian spine outcomes and research network

Degenerative cervical myelopathy (DCM) is the most common cause of acquired non-traumatic spinal cord injury worldwide. Surgery is a common treatment for DCM; however, outcomes often vary across patients. To inform preoperative education and counseling, we performed a responder analysis to identify factors associated with treatment response. An observational cohort study was conducted utilizing prospectively collected data from the Canadian Spine Outcomes Research Network (CSORN) registry collected between 2015-2022. We included all surgically treated DCM patients with complete 12-month follow-up and patient-reported outcomes (PROs) available at 1-year. Treatment response was measured using the minimal clinically important difference (MCID) in PROs including the Neck Disability Index (NDI) and EuroQol-5D (EQ-5D) at 12 months post-surgery. A Least Absolute Shrinkage and Selection Operator (LASSO) machine learning model was used to identify significant associations between 14 pre-operative patient factors and likelihood of treatment response measured by achievement of the MCID in NDI, and EQ-5D. Variable importance was measured using standardized coefficients. To test robustness of findings we trained a separate XGBOOST model, with variable importance measured using SHAP values. Among the 554 DCM patients included, 229 (41.3%) and 330 (59.6%) patients responded to treatment by meeting or surpassing MCID thresholds for NDI and EQ-5D at 1-year, respectively. LASSO regression for likelihood of treatment response measured through NDI found the variable importance rank order to be baseline NDI (OR 1.06 per 1 point increase; 95% CI 1.04 - 1.07), then symptom duration (OR 0.65; 95% CI 0.44-0.97). For EQ-5D, the variable importance rank order was baseline EQ-5D (OR 0.16 per 0.1-point increase; 95% CI 0.03 - 0.78), living independently (OR 2.17; 95% CI 1.22 - 3.85), symptom duration (OR 0.62; 95% CI 0.40 - 0.97), then number of levels affected (OR 0.80 per additional level; 95% CI 0.67 - 0.96). A separate XGBoost model of treatment response measured through NDI, corroborated findings that patients with higher baseline NDI, and shorter symptom duration were more likely to respond to treatment, and additionally found older patients, and those with kyphosis on baseline upright x-ray were less likely to respond. Similarly, an XGBoost model for treatment response measured through EQ-5D corroborated findings that patients with higher baseline EQ-5D, shorter symptom duration, living independently, with fewer affected levels were more likely to respond to treatment, and additionally found older patients were less likely to respond. Our findings suggest patients with shorter symptom duration, higher baseline patient NDI, lower EQ-5D, younger age, living independently, without kyphosis on pre-operative x-ray, and fewer affected levels are more likely to respond to treatment. Timing of surgery with respect to patient symptoms is underscored as a crucial and modifiable patient factor associated with improved surgical outcomes in DCM.

The Relation Between War, Starvation, and Fertility Ideals in Sub-Saharan Africa: A Life History Perspective.

In this article, we examine the relations between extreme environmental harshness during childhood and personal fertility ideals in African students. The study is informed by biological models of predictive adaptive responses (PAR) for individual reproductive schedules in the context of life history theory (LHT). Following theoretical models of external and internal environmental cues, we tested whether war and starvation during childhood differentially predict African students' personal fertility ideals in terms of their desired number of children and their desired age of first parenthood. The data were collected in eight different countries from sub-Saharan Africa with an overall sample size of N = 392. Standardized effect estimates were obtained using a Bayesian approach. The results suggest that war and starvation are predictive of the desired number of children, but not of the desired age of first parenthood. Moreover, the effect estimates varied considerably between females and males, indicating possible interactions between the two independent variables depending on the students' sex. Furthermore, we found a small negative correlation between the desired number of children and the desired age of first parenthood, providing only weak support for a clustering of the two variables on a slow-fast continuum. The results are discussed in light of current models of individual life histories in humans.

Mathematical model of repressive response to collective action and protest waves

The intricate interplay between the state and society may foster opposition and prompt collective action as a mode of protest. When the state responds repressively to such collective action, it aims to undermine it escalating its costs. A mathematical model relating the repressive response to collective action, articulated through differential equations, facilitates a thorough analysis of their dynamic interaction. Modeling outcomes indicate that repressive regimes may exhibit sustained persistence, oscillatory patterns, or destabilization, potentially transitioning into alternative regimes. This modeling framework offers a means to discern the impact of diverse factors on the dynamics of repressive regimes and to provide modelling insight on the emergence of cycles of protest observed in different countries during certain periods of their history.

Behavioural Indonesian disaster data classification in social media using KNN, random forest, and RNN in machine learning

This research focuses on the classification of Indonesian disaster data extracted from social media using three distinct machine learning models: K-Nearest Neighbors (KNN), Random Forest, and Recurrent Neural Network (RNN). The study evaluates and compares the performance of these models based on accuracy metrics. The KNN model demonstrates competency with an 82% accuracy, showcasing its ability to handle data based on nearest neighbors. However, there are indications of potential limitations in handling complex patterns or high-dimensional datasets. In contrast, Random Forest achieves a notable 94% accuracy, highlighting its effectiveness in combining decision trees to enhance performance and mitigate overfitting. The RNN model exhibits the highest performance, achieving 96% accuracy, attributed to its proficiency in understanding temporal relationships and sequential patterns in data, making it a robust choice for sequential datasets such as text or time series. The findings underscore the importance of selecting models tailored to specific dataset characteristics and desired classification analyses. Furthermore, future research directions involve exploring adaptations or optimizations of these models for diverse disaster types and social media data, aiming to develop more sophisticated and responsive models for disaster-related analysis in the Indonesian context.

A systematic review of epidemiological modelling in response to lumpy skin disease outbreaks.

Lumpy skin disease (LSD) is an infectious disease currently spreading worldwide and poses a serious global threat. However, there is limited evidence and understanding to support the use of models to inform decision-making in LSD outbreak responses. This review aimed to identify modelling approaches that can be used before and during an outbreak of LSD, examining their characteristics and priorities, and proposing a structured workflow. We conducted a systematic review and identified 60 relevant publications on LSD outbreak modelling. The review identified six categories of question to be addressed following outbreak detection (origin, entry pathway, outbreak severity, risk factors, spread, and effectiveness of control measures), and five analytical techniques used to address them (descriptive epidemiology, risk factor analysis, spatiotemporal analysis, dynamic transmission modelling, and simulation modelling). We evaluated the questions each analytical technique can address, along with their data requirements and limitations, and accordingly assigned priorities to the modelling. Based on this, we propose a structured workflow for modelling during an LSD outbreak. Additionally, we emphasise the importance of pre-outbreak preparation and continuous updating of modelling post-outbreak for effective decision-making. This study also discusses the inherent limitations and uncertainties in the identified modelling approaches. To support this workflow, high-quality data must be collected in standardised formats, and efforts should be made to reduce inherent uncertainties of the models. The suggested modelling workflow can be used as a process to support rapid response for countries facing their first LSD occurrence and can be adapted to other transboundary diseases.

Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge applications.

Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.

‘Leveraging on CSR as a tool of brand communication based on consumer’s perception with extrapolation from a novel 3-factor model

This study is to establish that consumers’ perceptions can trigger value creation from commitment, and pursuit of CSR by an organization can bring leverage and advantage by adopting the same as a brand, when fused to lifestyle and culture, while an extrapolation has been made from a novel 3-factor model of consumer responses as presented and newly introduced and adapted (draft study, Adewole 2023). Consumer decision-making process reflects the “black box models from cognitions, cognitive pathway, some intrinsically inner hidden pictures, and rational choices”; strategically, a smart and well-communicated brand can impact financial performance, thus emphasizing the significance of the brand from the consumer side. It can be inferred that culture and traditional behavior play significant roles in brand perception, which can generate and create other leads, such as preferences, intentions, and repeats, considering the complexes, unpredictable trends, or patterns associated with consumers’ expressions and behavior in the context of a black box, rational and complex mixes, even justified and vividly affirmed by the result of the ‘hypothesis testing and verification of the composite attributes and evident from the ‘ANOVA and multiple comparison, which gave a p-value exceeding (>0.05).

Behavior of the anti-apoptotic BCL-2 protein in non-small-cell lung cancer cell models in response to treatments with ALK inhibitors and its potential dual BCL2-ALK inhibition

Behavior of the anti-apoptotic BCL-2 protein in non-small-cell lung cancer cell models in response to treatments with ALK inhibitors and its potential dual BCL2-ALK inhibition

New analytical model for multi-layered composite plates with imperfect interfaces under thermomechanical loading

AbstractA new analytical model for thermoelastic responses of a multi-layered composite plate with imperfect interfaces is developed. The composite plate contains an arbitrary number of layers of dissimilar materials and is subjected to general mechanical loads (both distributed internally and applied on edges for each layer) and temperature changes, which can vary from layer to layer and along two in-plane directions. Each layer is regarded as a Kirchhoff plate, and each imperfect interface is described using a spring-layer interface model, which can capture discontinuities in the displacement and stress fields across the interface. Unlike existing models, the governing equations and boundary conditions are simultaneously derived for each layer by using a variational procedure based on the first and second laws of thermodynamics, which are then combined to obtain the global equilibrium equations and boundary conditions for the multi-layered composite plate. A general analytical solution is developed for a symmetrically loaded composite square plate with an arbitrary number of layers and imperfect interfaces by using a new approach that first determines the interfacial normal and shear stress components on one interface. Closed-form solutions for two- and three-layer composite square plates are obtained as examples by directly applying the general analytical solution. Numerical results for two-, three- and five-layer composite plates under different loading and boundary conditions predicted by the current model are provided, which compare well with those obtained from finite element simulations using COMSOL, thereby validating the newly developed analytical model.

Phosphoproteomics predict response to midostaurin plus chemotherapy in independent cohorts of FLT3-mutated acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients. We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n=20). We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n=20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p<7∗10-5, HR=0.005 [95% CI: 0-0.31]). In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology. This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd.

Spatial microenvironments tune immune response dynamics in the Drosophila larval fat body.

Immune responses in tissues display intricate patterns of gene expression that vary across space and time. While such patterns have been increasingly linked to disease outcomes, the mechanisms that generate them and the logic behind them remain poorly understood. As a tractable model of spatial immune responses, we investigated heterogeneous expression of antimicrobial peptides in the larval fly fat body, an organ functionally analogous to the liver. To capture the dynamics of immune response across the full tissue at single-cell resolution, we established live light sheet fluorescence microscopy of whole larvae. We discovered that expression of antimicrobial peptides occurs in a reproducible spatial pattern, with enhanced expression in the anterior and posterior lobes of the fat body. This pattern correlates with microbial localization via blood flow but is not caused by it: loss of heartbeat suppresses microbial transport but leaves the expression pattern unchanged. This result suggests that regions of the tissue most likely to encounter microbes via blood flow are primed to produce antimicrobials. Spatial transcriptomics revealed that these immune microenvironments are defined by genes spanning multiple biological processes, including lipid-binding proteins that regulate host cell death by the immune system. In sum, the larval fly fat body exhibits spatial compartmentalization of immune activity that resembles the strategic positioning of immune cells in mammals, such as in the liver, gut, and lymph nodes. This finding suggests that tissues may share a conserved spatial organization that optimizes immune responses for antimicrobial efficacy while preventing excessive self-damage.

Fixational eye movements enhance the precision of visual information transmitted by the primate retina

Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.