Generalizable Framework Research Articles

A Framework for Applying Global Learning to Improve Primary Health Care in the United States.

Lessons from global health have long informed efforts to improve primary health care (PHC) in the United States (US). Despite this history, no generalizable framework exists to guide US stakeholders in the identification and application of ideas from abroad related to the key PHC components of community engagement and service delivery. We sought to develop such a framework. We reviewed the experience of Global to Local, a community-based organization (CBO) founded with a mission to apply global health strategies to improve the health in vulnerable populations in the US, and examined the experience of care delivery organizations in the US that have successfully implemented global-to-local solutions. Based on that experience, and supported by the advice of an expert panel, we developed a framework for applying global learning to improve US PHC. The framework includes six change concepts under three broad categories. The first category focuses on the need to actively and intentionally incorporate a global perspective in organizational program design and improvement activities. The second category addresses approaches to identifying global solutions related to community engagement and to health service delivery. The third category focuses on adaptation and implementation of lessons from global health in domestic contexts by applying relevant insights from dissemination and implementation science and diffusion of innovation theory. In the absence of a robust literature providing implementation guidance to US health systems and CBOs open to adopting or adapting PHC strategies and practices from other countries, the proposed framework synthesizing the experience of organizations that have done so can inform efforts to apply lessons from global health to improve PHC in the US.

Relative uncertainty-based Bayesian interlaboratory consensus building

An interlaboratory comparison is typically conducted among the laboratories for the purpose of providing quality assurance and control. To solve the interlaboratory agreement problem, a distinct type of metrological challenge, a new uncertainty-based Bayesian strategy was developed and tested among environmental laboratories. A holistic algorithm with the key phases of sampling, outlier analysis, recognition, and simulation-based structure identification was developed and is being addressed in place of conventional indices and plots. Computer simulations showed that the proposed hybrid approach has no discernible sensitivity to outliers and that the agreement structure is transparent and robust. Some meta-data is also generated by the analysis based on relative uncertainty. To measure the performance and capability of Bayesian consensus building algorithm, the uncertainty intervals were established and comparative evaluations have been carried out using the conventional techniques. As a result, the suggested algorithm can explore both the laboratory performances (harmony) and the conformity between two independent samples. The algorithmic procedure features a generalizable framework that may be adapted in other fields to obtain a consensus among the laboratories.

Selecting Counterions to Improve Ionized Hydrophilic Drug Encapsulation in Polymeric Nanoparticles.

Hydrophobic ion pairing (HIP) can successfully increase the drug loading and control the release kinetics of ionizable hydrophilic drugs, addressing challenges that prevent these molecules from reaching the clinic. Nevertheless, polymeric nanoparticle (PNP) formulation development requires trial-and-error experimentation to meet the target product profile, which is laborious and costly. Herein, we design a preformulation framework (solid-state screening, computational approach, and solubility in PNP-forming emulsion) to understand counterion-drug-polymer interactions and accelerate the PNP formulation development for HIP systems. The HIP interactions between a small hydrophilic molecule, AZD2811, and counterions with different molecular structures were investigated. Cyclic counterions formed amorphous ion pairs with AZD2811; the 0.7 pamoic acid/1.0 AZD2811 complex had the highest glass transition temperature (Tg; 162 °C) and the greatest drug loading (22%) and remained as phase-separated amorphous nanosized domains inside the polymer matrix. Palmitic acid (linear counterion) showed negligible interactions with AZD2811 (crystalline-free drug/counterion forms), leading to a significantly lower drug loading despite having similar log P and pKa with pamoic acid. Computational calculations illustrated that cyclic counterions interact more strongly with AZD2811 than linear counterions through dispersive interactions (offset π-π interactions). Solubility data indicated that the pamoic acid/AZD2811 complex has a lower organic phase solubility than AZD2811-free base; hence, it may be expected to precipitate more rapidly in the nanodroplets, thus increasing drug loading. Our work provides a generalizable preformulation framework, complementing traditional performance-indicating parameters, to identify optimal counterions rapidly and accelerate the development of hydrophilic drug PNP formulations while achieving high drug loading without laborious trial-and-error experimentation.

Risk acceptance and offensive war: The case of Russia under the Putin regime

ABSTRACT Russia’s decision to invade Ukraine in early 2022 was seemingly driven by an unprecedented willingness to accept risks for the Russian regime, state, and society. Developing a generalizable framework, this article analyzes the development of Russian risk acceptance in offensive war initiation. Drawing on policy documents, speeches, expert literature, and various interviews with Russian, Ukrainian, and Western policymakers, the article finds that risk acceptance has continuously risen since the mid-2000s, although the 2022 invasion still evinces some risk aversion. These results are robust when accounting for miscalculation and caution against attributing the 2022 invasion solely to short-term and leader-centric factors. They also provide cues for understanding the crisis behavior of Russia and other major powers, corroborate prospect theory models on cognitive biases in elite decision-making, and indicate the need to revise the theoretical assumption that risk acceptance is an empirically rare and drastic aberration from a risk-neutral or risk-averse normality.

Formal Verification of Robot Rotary Kinematics

With the widespread application of robots in aerospace, medicine, automation, and other fields, their motion safety is essential for the well-being of humans and the accomplishment of vital socially beneficial programs. Conventional robot hardware and software designs mainly rely on experiential knowledge and manual testing to ensure safety, but this fails to cover all possible testing paths and adds risks. Alternatively, formal, mathematically rigorous verifications can provide predictable and reliable guarantees of robot motion safety. To demonstrate the feasibility of this approach, we formalize the mathematical coordinate transformation of a robot’s rigid-body kinematics using the Coq Proof Assistant to verify the correctness of its theoretical design. First, based on record-type matrix formalization, we define and verify a robot’s spatial geometry by constructing formal expressions of the matrix’ Frobenius norm, trace, and inner product. Second, we divide rotary motion into revolution and rotation construct and provide their formal definitions. Next, we formally verify the rotational matrices of angle conventions (e.g., roll–pitch–yaw and Euler), and we complete the formal verification of the Rodriguez formula to formally verify the correctness of the motion theory in specific rotating kinematics problems. The formal work of this paper has a variety of essential applications and provides a generalizable kinematics analysis framework for robot control system verification. Moreover, it paves the way for automatic programming capabilities.

A Cross-Species Neuroimaging Study of Sex Chromosome Dosage Effects on Human and Mouse Brain Anatomy.

All eutherian mammals show chromosomal sex determination with contrasting sex chromosome dosages (SCDs) between males (XY) and females (XX). Studies in transgenic mice and humans with sex chromosome trisomy (SCT) have revealed direct SCD effects on regional mammalian brain anatomy, but we lack a formal test for cross-species conservation of these effects. Here, we develop a harmonized framework for comparative structural neuroimaging and apply this to systematically profile SCD effects on regional brain anatomy in both humans and mice by contrasting groups with SCT (XXY and XYY) versus XY controls. Total brain size was substantially altered by SCT in humans (significantly decreased by XXY and increased by XYY), but not in mice. Robust and spatially convergent effects of XXY and XYY on regional brain volume were observed in humans, but not mice, when controlling for global volume differences. However, mice do show subtle effects of XXY and XYY on regional volume, although there is not a general spatial convergence in these effects within mice or between species. Notwithstanding this general lack of conservation in SCT effects, we detect several brain regions that show overlapping effects of XXY and XYY both within and between species (cerebellar, parietal, and orbitofrontal cortex), thereby nominating high priority targets for future translational dissection of SCD effects on the mammalian brain. Our study introduces a generalizable framework for comparative neuroimaging in humans and mice and applies this to achieve a cross-species comparison of SCD effects on the mammalian brain through the lens of SCT.SIGNIFICANCE STATEMENT Sex chromosome dosage (SCD) affects neuroanatomy and risk for psychopathology in humans. Performing mechanistic studies in the human brain is challenging but possible in mouse models. Here, we develop a framework for cross-species neuroimaging analysis and use this to show that an added X- or Y-chromosome significantly alters human brain anatomy but has muted effects in the mouse brain. However, we do find evidence for conserved cross-species impact of an added chromosome in the fronto-parietal cortices and cerebellum, which point to regions for future mechanistic dissection of sex chromosome dosage effects on brain development.

Scaling migrations to communities: An empirical case of migration network in the Arctic

Seasonal migrants transport energy, nutrients, contaminants, parasites and diseases, while also connecting distant food webs between communities and ecosystems, which contributes to structuring meta-communities and meta-ecosystems. However, we currently lack a framework to characterize the structure of the spatial connections maintained by all migratory species reproducing or wintering in a given community. Here, we use a network approach to represent and characterize migratory pathways at the community level and provide an empirical description of this pattern from a High-Arctic terrestrial community. We define community migration networks as multipartite networks representing different biogeographic regions connected with a focal community through the seasonal movements of its migratory species. We focus on the Bylot Island High-Arctic terrestrial community, a summer breeding ground for several migratory species. We define the non-breeding range of each species using tracking devices, or range maps refined by flyways and habitat types. We show that the migratory species breeding on Bylot Island are found across hundreds of ecoregions on several continents during the non-breeding period and present a low spatial overlap. The migratory species are divided into groups associated with different sets of ecoregions. The non-random structure observed in our empirical community migration network suggests evolutionary and geographic constraints as well as ecological factors act to shape migrations at the community level. Overall, our study provides a simple and generalizable framework as a starting point to better integrate migrations at the community level. Our framework is a far-reaching tool that could be adapted to address the seasonal transport of energy, contaminants, parasites and diseases in ecosystems, as well as trophic interactions in communities with migratory species.

Integrated intracellular organization and its variations in human iPS cells

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour3,4—at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

Mechanisms of individual variation in large herbivore diets: Roles of spatial heterogeneity and state-dependent foraging.

Many populations of consumers consist of relatively specialized individuals that eat only a subset of the foods consumed by the population at large. Although the ecological significance of individual-level diet variation is recognized, such variation is difficult to document, and its underlying mechanisms are poorly understood. Optimal foraging theory provides a useful framework for predicting how individuals might select different diets, positing that animals balance the "opportunity cost" of stopping to eat an available food item against the cost of searching for something more nutritious; diet composition should be contingent on the distribution of food, and individual foragers should be more selective when they have greater energy reserves to invest in searching for high-quality foods. We tested these predicted mechanisms of individual niche differentiation by quantifying environmental (resource heterogeneity) and organismal (nutritional condition) determinants of diet in a widespread browsing antelope (bushbuck, Tragelaphus sylvaticus) in an African floodplain-savanna ecosystem. We quantified individuals' realized dietary niches (taxonomic richness and composition) using DNA metabarcoding of fecal samples collected repeatedly from 15 GPS-collared animals (range 6-14 samples per individual, median 12). Bushbuck diets were structured by spatial heterogeneity and constrained by individual condition. We observed significant individual-level partitioning of food plants by bushbuck both within and between two adjacent habitat types (floodplain and woodland). Individuals with home ranges that were closer together and/or had similar vegetation structure (measured using LiDAR) ate more similar diets, supporting the prediction that heterogeneous resource distribution promotes individual differentiation. Individuals in good nutritional condition had significantly narrower diets (fewer plant taxa), searched their home ranges more intensively (intensity-of-use index), and had higher-quality diets (percent digestible protein) than those in poor condition, supporting the prediction that animals with greater endogenous reserves have narrower realized niches because they can invest more time in searching for nutritious foods. Our results support predictions from optimal foraging theory about the energetic basis of individual-level dietary variation and provide a potentially generalizable framework for understanding how individuals' realized niche width is governed by animal behavior and physiology in heterogeneous landscapes.

Queering the City: Understanding Gravitational Forces

This paper uses examples drawn from US cities (Midtown in Atlanta, Georgia, and Ybor City in Tampa, Florida) to develop a more generalizable framework within which to analyze the process of queering a neighborhood and identifying policy measures that might keep it queer. This kind of comparative approach enables a more careful reading of queer urban spaces based on the behaviors of LGBTQ people in and around those spaces. The strategic aim of these comparisons is to expand our understanding of queer spaces and to elaborate a more fluid model of queering cities. The paper uses a gravitational framework that identifies the centering (centripetal) and decentering (centrifugal) forces and the interplay between these forces. Each case provides different lessons for planners regarding the importance of either mitigating or exacerbating the centrifugal tendencies that so often threaten queer spaces. Because these forces are unique to each LGBTQ space, the analytic strategy provides for greater generalizability across different sizes of cities, across various cultural and ethnic areas, and a wider array of geographies.

SPICEMIX enables integrative single-cell spatial modeling of cell identity.

Spatial transcriptomics can reveal spatially resolved gene expression of diverse cells in complex tissues. However, the development of computational methods that can use the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here we introduce SPICEMIX, an interpretable method based on probabilistic, latent variable modeling for joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SPICEMIX markedly improves on the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap and Visium, we show that SPICEMIX can enhance the inference of complex cell identities, reveal interpretable spatial metagenes and uncover differentiation trajectories. SPICEMIX is a generalizable analysis framework for spatial transcriptome data to investigate cell-type composition and spatial organization of cells in complex tissues.

Heat decarbonization: Toward a sustainable utility system

Heat source decarbonization has not received the same attention as electricity. A recent Joule paper by Gilbert et al. presents a multi-scale study for the selection of the best source and technology depending on the heat temperature level, showing that hydrogen can become competitive with natural gas by 2050. Heat source decarbonization has not received the same attention as electricity. A recent Joule paper by Gilbert et al. presents a multi-scale study for the selection of the best source and technology depending on the heat temperature level, showing that hydrogen can become competitive with natural gas by 2050. Heat source and application-dependent levelized cost of decarbonized heatGilbert et al.JouleDecember 15, 2022In BriefCost-effective decarbonization of heat is a grand scientific and technological challenge. We develop a generalizable technoeconomic framework to calculate the levelized cost of heat (LCOH) for heat that is carbon neutral. We conduct case studies representative of industrial processes and commercial building heating in the United States. Natural gas is likely the cheapest heat source even after paying for carbon capture, and dramatic reductions in the cost and availability of renewable energy are needed for cost- and emissions-favorable electrification. Full-Text PDF

Towards predicting immersion in surround sound music reproduction from sound field features

When evaluating surround sound loudspeaker reproduction, perceptual effects are commonly analyzed in relationship to different loudspeaker configurations. The presented work contributes to this by modeling perceptual effects based on acoustic properties of various reproduction formats. A model of immersion in music listening is derived from the results of an experimental study analyzing the psychological construct of immersive music experience. The proposed approach is evaluated with respect to the relationship between immersion ratings and sound field features obtained from re-recordings of the stimuli using a spherical microphone array at the listening position. Spatial sound field parameters such as inter-aural cross-correlation (IACC), diffuseness and directivity are found to be of particular relevance. Further, immersion is observed to reach a point of saturation with greater numbers of loudspeakers, which is confirmed to be predictable from the physical properties of the sound field. Although effects related to participants and musical pieces outweigh the impact of sound field features, the proposed approach is found to be suitable for predicting population-average ratings, i.e. immersion experienced by an average listener for unknown content. The proposed method could complement existing research on multichannel loudspeaker reproduction by establishing a more generalizable framework independent of particular speaker setups.

Coordinated control of neuronal differentiation and wiring by sustained transcription factors.

The large diversity of cell types in nervous systems presents a challenge in identifying the genetic mechanisms that encode it. Here, we report that nearly 200 distinct neurons in the Drosophila visual system can each be defined by unique combinations of on average 10 continuously expressed transcription factors. We show that targeted modifications of this terminal selector code induce predictable conversions of neuronal fates that appear morphologically and transcriptionally complete. Cis-regulatory analysis of open chromatin links one of these genes to an upstream patterning factor that specifies neuronal fates in stem cells. Experimentally validated network models describe the synergistic regulation of downstream effectors by terminal selectors and ecdysone signaling during brain wiring. Our results provide a generalizable framework of how specific fates are implemented in postmitotic neurons.

A generalizable framework for assessing the role of emotion during choice.

The study of emotion has been plagued by several challenges that have left the field fractionated. To date, there is no dominant method for measuring the nebulous and often ill-defined experience of emotion. Here, we offer a new way forward, one that marries numerically precise measurements of affect with current models of human behavior, to more deeply understand the role of emotion during choice, and in particular, during social decision-making. This tool can be combined with multiple other measures that capture different features and levels of the emotional experience, making it particularly flexible to be used in any number of contexts. By operationalizing the classic circumplex model of affect so that it can deliver fine-grained, continuous measurements as affect evolves overtime, our goal is to provide a generalizable and flexible framework for computing affect to infer emotions so that we can assess their impact on human behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Power laws and plant trait variation in spatio‐temporally heterogeneous environments

AbstractA challengeVariation is ubiquitous in nature across all spatial and temporal scales and underlies prominent ecological and evolutionary theories. Although understanding the causes and consequences of trait variation is a central goal of trait‐based ecology, the scaling of trait variance across space and time (variance scaling) is unresolved.A solutionWe argue that characterizing trait variance across spatio‐temporal scales using a combination of prominent power laws can elucidate the role of environmental variability in trait variation and potential mechanisms driving trait patterns. In particular, the species–time–area relationship and Taylor's power law help to establish a generalizable framework for developing and testing variance scaling theory. Finally, we outline priority research questions and tractable systems for answering them. Successional forests, long‐term forest monitoring networks and censuses of short‐lived taxa are ideal for coupling high‐resolution environmental data with measurements of trait variance across scales to test the models proposed here.Main conclusionsCharacterizing the behaviour of variance across spatio‐temporal scales is feasible and a prerequisite for developing a predictive theory of trait‐based ecology.

Atomic structure generation from reconstructing structural fingerprints

Data-driven machine learning methods have the potential to dramatically accelerate the rate of materials design over conventional human-guided approaches. These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory. For crystal structure generation, a key bottleneck lies in developing suitable atomic structure fingerprints or representations for the machine learning model, analogous to the graph-based or SMILES representations used in molecular generation. However, finding data-efficient representations that are invariant to translations, rotations, and permutations, while remaining invertible to the Cartesian atomic coordinates remains an ongoing challenge. Here, we propose an alternative approach to this problem by taking existing non-invertible representations with the desired invariances and developing an algorithm to reconstruct the atomic coordinates through gradient-based optimization using automatic differentiation. This can then be coupled to a generative machine learning model which generates new materials within the representation space, rather than in the data-inefficient Cartesian space. In this work, we implement this end-to-end structure generation approach using atom-centered symmetry functions as the representation and conditional variational autoencoders as the generative model. We are able to successfully generate novel and valid atomic structures of sub-nanometer Pt nanoparticles as a proof of concept. Furthermore, this method can be readily extended to any suitable structural representation, thereby providing a powerful, generalizable framework towards structure-based generation.

GRACE PLUS: A data fusion-based approach to improve GRACE score in the risk assessment of Acute Coronary Syndrome

Cardiovascular Diseases (CVDs) are the world’s leading cause of morbidity and mortality, being responsible for almost 17 million deaths each year. In Europe, let alone it is estimated that 20% of all citizens suffer from one form of CVD, namely cerebrovascular disease or heart failure and Coronary Artery Disease (CAD). Among the latter, Acute Coronary Syndrome (ACS) is of particular importance since it is deadly and, hence, requires a prompt diagnosis and immediate medical attention. Aiming to deal with prognostication and promote consistency in managing patients with ACS, the Global Registry of Acute Coronary Events (GRACE) risk score has been proposed. This tool is based on eight independent risk factors roughly accounting for 89.9% of prognostic information. Nevertheless, as some other risk factors, not included in GRACE, are also known to be important vectors in the stratification of patients, namely haemoglobin at admission, it is expected that by embedding additional risk factors information into GRACE it will lead to a better characterisation of a patient’s risk. Making use of data-fusion techniques, the present work proposes a generalisable framework to improve the classification performance of GRACE in predicting the risk of death in the course of six months after an ACS event, while preserving its interpretability and applicability. Considering haemoglobin concentration at admission, as an additional risk factor, it is shown that the discrimination performance of new GRACE Plus score outperformed that of GRACE in a database of cohorts comprising 1506 patients admitted with ACS, showing a F-1 score of 0.6033 for GRACE Plus against 0.5828 for GRACE, which is corroborated by one-tailed t-test in terms of correct stratification of death and survival endpoints, namely, t=−9.1876 and p<0.001.

Systematic Bayesian posterior analysis guided by Kullback-Leibler divergence facilitates hypothesis formation

Bayesian inference produces a posterior distribution for the parameters of a mathematical model that can be used to guide the formation of hypotheses; specifically, the posterior may be searched for evidence of alternative model hypotheses, which serves as a starting point for hypothesis formation and model refinement. Previous approaches to search for this evidence are largely qualitative and unsystematic; further, demonstrations of these approaches typically stop at hypothesis formation, leaving the questions they raise unanswered. Here, we introduce a Kullback-Leibler (KL) divergence-based ranking to expedite Bayesian hypothesis formation and investigate the hypotheses it generates, ultimately generating novel, biologically significant insights. Our approach uses KL divergence to rank parameters by how much information they gain from experimental data. Subsequently, rather than searching all model parameters at random, we use this ranking to prioritize examining the posteriors of the parameters that gained the most information from the data for evidence of alternative model hypotheses. We test our approach with two examples, which showcase the ability of our approach to systematically uncover different types of alternative hypothesis evidence. First, we test our KL divergence ranking on an established example of Bayesian hypothesis formation. Our top-ranked parameter matches the one previously identified to produce alternative hypotheses. In the second example, we apply our ranking in a novel study of a computational model of prolactin-induced JAK2-STAT5 signaling, a pathway that mediates beta cell proliferation. Within the top 3 ranked parameters (out of 33), we find a bimodal posterior revealing two possible ranges for the prolactin receptor degradation rate. We go on to refine the model, incorporating new data and determining which degradation rate is most plausible. Overall, while the effectiveness of our approach depends on having a properly formulated prior and on the form of the posterior distribution, we demonstrate that our approach offers a novel and generalizable quantitative framework for Bayesian hypothesis formation and use it to produce a novel, biologically-significant insight into beta cell signaling.

Learn to Communicate With Neural Calibration: Scalability and Generalization

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.