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1. Patterns and predictors of soil organic carbon storage across a continental-scale network

   https://doi.org/10.1007/s10533-020-00745-9  

   Nave, L. E.; Bowman, M.; Gallo, A.; Hatten, J. A.; Heckman, K. A.; Matosziuk, L.; Possinger, A. R.; SanClements, M.; Sanderman, J.; Strahm, B. D.; et al (October 2021, Biogeochemistry)

   null (Ed.)

   Abstract The rarity of rapid campaigns to characterize soils across scales limits opportunities to investigate variation in soil carbon stocks (SOC) storage simultaneously at large and small scales, with and without site-level replication. We used data from two complementary campaigns at 40 sites in the United States across the National Ecological Observatory Network (NEON), in which one campaign sampled profiles from closely co-located intensive plots and physically composited similar horizons, and the other sampled dozens of pedons across the landscape at each site. We demonstrate some consistencies between these distinct designs, while also revealing that within-site replication reveals patterns and predictors of SOC stocks not detectable with non-replicated designs. Both designs demonstrate that SOC stocks of whole soil profiles vary across continental-scale climate gradients. However, broad climate patterns may mask the importance of localized variation in soil physicochemical properties, as captured by within-site sampling, especially for SOC stocks of discrete genetic horizons. Within-site replication also reveals examples in which expectations based on readily explained continental-scale patterns do not hold. For example, even wide-ranging drainage class sequences within landscapes do not duplicate the clear differences in profile SOC stocks across drainage classes at the continental scale, and physicochemical factors associated with increasing B horizon SOC stocks at continental scales frequently do not follow the same patterns within landscapes. Because inferences from SOC studies are a product of their context (where, when, how), this study provides context—in terms of SOC stocks and the factors that influence them—for others assessing soils and the C cycle at NEON sites. 

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2. Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction

   https://doi.org/10.1145/3610904  

   Tabatabaie, Mahan; He, Suining; Shin, Kang G. (September 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both spoken language (e.g., human textual annotations) and unspoken language (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design. To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken languages, we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants. 

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3. Perceptions of Linguistic Uncertainty by Language Models and Humans

   https://doi.org/10.18653/v1/2024.emnlp-main.483  

   Belém, Catarina G; Kelly, Markelle; Steyvers, Mark; Singh, Sameer; Smyth, Padhraic (January 2024, Association for Computational Linguistics)

   *Uncertainty expressions* such as ‘probably’ or ‘highly unlikely’ are pervasive in human language. While prior work has established that there is population-level agreement in terms of how humans quantitatively interpret these expressions, there has been little inquiry into the abilities of language models in the same context. In this paper, we investigate how language models map linguistic expressions of uncertainty to numerical responses. Our approach assesses whether language models can employ theory of mind in this setting: understanding the uncertainty of another agent about a particular statement, independently of the model’s own certainty about that statement. We find that 7 out of 10 models are able to map uncertainty expressions to probabilistic responses in a human-like manner. However, we observe systematically different behavior depending on whether a statement is actually true or false. This sensitivity indicates that language models are substantially more susceptible to bias based on their prior knowledge (as compared to humans). These findings raise important questions and have broad implications for human-AI and AI-AI communication. 

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4. Multi-Scale Drivers of Immunological Variation and Consequences for Infectious Disease Dynamics

   https://doi.org/10.1093/icb/icz138  

   Becker, Daniel J.; Downs, Cynthia J.; Martin, Lynn B. (July 2019, Integrative and Comparative Biology)

   Abstract The immune system is the primary barrier to parasite infection, replication, and transmission following exposure, and variation in immunity can accordingly manifest in heterogeneity in traits that govern population-level infectious disease dynamics. While much work in ecoimmunology has focused on individual-level determinants of host immune defense (e.g., reproductive status and body condition), an ongoing challenge remains to understand the broader evolutionary and ecological contexts of this variation (e.g., phylogenetic relatedness and landscape heterogeneity) and to connect these differences into epidemiological frameworks. Ultimately, such efforts could illuminate general principles about the drivers of host defense and improve predictions and control of infectious disease. Here, we highlight recent work that synthesizes the complex drivers of immunological variation across biological scales of organization and scales these within-host differences to population-level infection outcomes. Such studies note the limitations involved in making species-level comparisons of immune phenotypes, stress the importance of spatial scale for immunology research, showcase several statistical tools for translating within-host data into epidemiological parameters, and provide theoretical frameworks for linking within- and between-host scales of infection processes. Building from these studies, we highlight several promising avenues for continued work, including the application of machine learning tools and phylogenetically controlled meta-analyses to immunology data and quantifying the joint spatial and temporal dependencies in immune defense using range expansions as model systems. We also emphasize the use of organismal traits (e.g., host tolerance, competence, and resistance) as a way to interlink various scales of analysis. Such continued collaboration and disciplinary cross-talk among ecoimmunology, disease ecology, and mathematical modeling will facilitate an improved understanding of the multi-scale drivers and consequences of variation in host defense. 

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5. Macroimmunology: The drivers and consequences of spatial patterns in wildlife immune defence

   https://doi.org/10.1111/1365-2656.13166  

   Becker, Daniel_J; Albery, Gregory_F; Kessler, Maureen_K; Lunn, Tamika_J; Falvo, Caylee_A; Czirják, Gábor_Á; Martin, Lynn_B; Plowright, Raina_K; Fenton, ed., Andy (January 2020, Journal of Animal Ecology)

   Abstract The prevalence and intensity of parasites in wild hosts varies across space and is a key determinant of infection risk in humans, domestic animals and threatened wildlife. Because the immune system serves as the primary barrier to infection, replication and transmission following exposure, we here consider the environmental drivers of immunity. Spatial variation in parasite pressure, abiotic and biotic conditions, and anthropogenic factors can all shape immunity across spatial scales. Identifying the most important spatial drivers of immunity could help pre‐empt infectious disease risks, especially in the context of how large‐scale factors such as urbanization affect defence by changing environmental conditions.We provide a synthesis of how to apply macroecological approaches to the study of ecoimmunology (i.e. macroimmunology). We first review spatial factors that could generate spatial variation in defence, highlighting the need for large‐scale studies that can differentiate competing environmental predictors of immunity and detailing contexts where this approach might be favoured over small‐scale experimental studies. We next conduct a systematic review of the literature to assess the frequency of spatial studies and to classify them according to taxa, immune measures, spatial replication and extent, and statistical methods.We review 210 ecoimmunology studies sampling multiple host populations. We show that whereas spatial approaches are relatively common, spatial replication is generally low and unlikely to provide sufficient environmental variation or power to differentiate competing spatial hypotheses. We also highlight statistical biases in macroimmunology, in that few studies characterize and account for spatial dependence statistically, potentially affecting inferences for the relationships between environmental conditions and immune defence.We use these findings to describe tools from geostatistics and spatial modelling that can improve inference about the associations between environmental and immunological variation. In particular, we emphasize exploratory tools that can guide spatial sampling and highlight the need for greater use of mixed‐effects models that account for spatial variability while also allowing researchers to account for both individual‐ and habitat‐level covariates.We finally discuss future research priorities for macroimmunology, including focusing on latitudinal gradients, range expansions and urbanization as being especially amenable to large‐scale spatial approaches. Methodologically, we highlight critical opportunities posed by assessing spatial variation in host tolerance, using metagenomics to quantify spatial variation in parasite pressure, coupling large‐scale field studies with small‐scale field experiments and longitudinal approaches, and applying statistical tools from macroecology and meta‐analysis to identify generalizable spatial patterns. Such work will facilitate scaling ecoimmunology from individual‐ to habitat‐level insights about the drivers of immune defence and help predict where environmental change may most alter infectious disease risk. 

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