Biology subjects

Hunter, L. E.

Publications and source records attributed to Hunter, L. E..

14 recordsLinked to original sources

bioRxiv2026

Salmonid science on the move: Twenty years of research through the lens of early career researchers

Salmonids are among the most studied fish species worldwide, owing to their wide geographic distribution, diversity of life-history strategies, ecological and socioeconomic importance, and vulnerability to global change. In this review, we take advantage of two decades of the International Workshop of PhDs and Post-doctoral Fellows on Anadromous Salmonids (NoWPaS) conference series to track the evolution of salmonid science through the lens of early-career researchers (ECRs). From all abstracts presented at NoWPaS meetings between 2005 and 2025, we extracted structured qualitative and quantitative information to characterize major trends and shifts in the field. Our results show a strong research bias toward Atlantic salmon and brown trout, concentrated in Europe and Atlantic Canada, and a persistent mismatch between the life stages and habitats studied. In particular, the marine phase remains understudied in comparison to fresh and coastal waters. Over the study period, salmonid research shifted markedly from hydro-geomorphological and habitat-based approaches toward genetic and genomic methods, alongside the rise of advanced telemetry methods for reconstructing individual life histories. Research orientation, measured as a gradient from fundamental to applied research, was on average intermediate but varied among countries and disciplines, with the UK and Norway showing shifts to more applied research, as well as the discipline of Ecology becoming more applied. Additionally, we identified a significant increase in Climate change and Aquaculture as research drivers, with some drivers well-represented in some species (e.g., Hydropower and dams for landlocked Atlantic salmon and Climate change for Arctic char). These trends should be interpreted in context of the composition of the NoWPaS network: participation is primarily ECRs in the Northern Hemisphere, with research from Asia, South America, and western North America largely absent from this synthesis. Despite this lens, our analysis identifies clear knowledge gaps, particularly around marine life stages and species other than Atlantic salmon and brown trout, and highlights opportunities for broadening taxonomic, geographic, and life-history coverage in future salmonid research. We also present a roadmap, identifying combinations of life stages, disciplines and taxa which have been under-represented and constitute potential knowledge gaps, while also flagging those combinations that have already been well studied. Overall, we advocate for a continued shift toward integrative and holistic approaches, bridging distinct research disciplines, to better understand, manage, and conserve salmonids under increasingly uncertain future environmental conditions.

ecology↗

bioRxiv2026

Dynamic thermodynamic-informational entropic relationship (TIER) models of selective vulnerability to neurodegeneration

BACKGROUNDNeurodegenerative diseases share selective vulnerability patterns suggesting common physical mechanisms. We apply unified mechanics theory to neural systems, predicting that brain regions accumulate structural damage proportional to computational workload. METHODSWe simulated a hierarchical neural network implementing relationships between mechanical work (W = F x D), proportional thermodynamic entropy accumulation ({Delta}s {propto} W), and structural failure thresholds. Neural architectures at three hierarchical levels employed Hebbian learning across 2000 simulation sets, tracking thermodynamic entropy generation and dynamic stability. A coupled "siphon" model simulated cortical and subcortical support populations under constant cognitive demand. RESULTSHeteromodal integration nodes consistently exhibited elevated work, accelerated entropy accumulation, and dynamic instability across architectures. Support systems reached 50% population loss before cortical systems despite lower absolute work, demonstrating accelerated compensatory failure. DISCUSSIONThese thermodynamic-informational entropic relationship (TIER) models depict mechanisms underlying selective vulnerability across neurodegeneration, reframing neurodegeneration as the physical consequence of evolutionary trade-offs optimizing cognitive performance over longevity.

neuroscience↗

bioRxiv2025

KG-Microbe - Building Modular and Scalable Knowledge Graphs for Microbiome and Microbial Sciences

The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are often thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Some experimental observations only distinguish coarser taxonomic resolutions such as family or phylum-level. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex interactions and helps interpret correlative results. The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a knowledge graph (KG). Starting from a core KG with organismal traits, environments and growth preferences, the framework generates a hierarchy of related KGs targeting specific conceptual use cases, including the human host-associated microbiome in the context of disease. KG-Microbe is a standardized and interoperable framework that integrates microbial organismal and genomic traits, represented ontologically, for biomedical, environmental, and other applications. The framework supports customizable taxa subsets representing microbial lineages or communities of interest. Evaluations of the KG-Microbe knowledge graphs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in inflammatory bowel and Parkinsons diseases. Finally, the predictive and environmental capabilities of the KGs are demonstrated by explaining growth preferences through training a model using graph features. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover mechanistic explanations of microbial associations.

bioinformatics↗

bioRxiv2022

Creating an Ignorance-Base: Exploring Known Unknowns in the Scientific Literature

BackgroundScientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. ResultsWe present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development), which were buried among the many standard enriched concepts, by searching for concepts enriched in ignorance statements. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. ConclusionOur goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/519634v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1edb6e6org.highwire.dtl.DTLVardef@182bf3corg.highwire.dtl.DTLVardef@d90312org.highwire.dtl.DTLVardef@1589433_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe created the first ignorance-base (knowledge-base) to capture goals for scientific knowledge C_LIO_LIOur exploration methods provide analyses, summaries, and visualizations based on a query C_LIO_LIIgnorance enrichment provided fruitful avenues for future research C_LIO_LIExploration by topic in vitamin D found three avenues to explore C_LIO_LIExploration by experimental results for vitamin D and preterm birth found an emerging topic C_LI

bioinformatics↗

bioRxiv2021

Identifying and Classifying Goals For Scientific Knowledge

MotivationScience progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize ignorance statements, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals. ResultsWe present a novel ignorance taxonomy driven by the role ignorance statements play in the research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10,000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. AvailabilityCorpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python. ContactMayla.Boguslav@CUAnshcutz.edu

bioinformatics↗

bioRxiv2021

Linguistic Analysis of the bioRxiv Preprint Landscape

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supplementary sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint-peer reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

bioinformatics↗

bioRxiv2020

Concept Recognition as a Machine Translation Problem

BackgroundAutomated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al. recast the concept recognition problem as a type of machine translation and demonstrated that sequence-to-sequence machine learning models had the potential to outperform multi-class classification approaches. Here we systematically characterize the factors that contribute to the accuracy and efficiency of several approaches to sequence-to-sequence machine learning. ResultsWe report on our extensive studies of alternative methods and hyperparameter selections. The results not only identify the best-performing systems and parameters across a wide variety of ontologies but also illuminate about the widely varying resource requirements and hyperparameter robustness of alternative approaches. Analysis of the strengths and weaknesses of such systems suggest promising avenues for future improvements as well as design choices that can increase computational efficiency with small costs in performance. Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) for span detection (as previously found) along with the Open-source Toolkit for Neural Machine Translation (OpenNMT) for concept normalization achieve state-of-the-art performance for most ontologies in CRAFT Corpus. This approach uses substantially fewer computational resources, including hardware, memory, and time than several alternative approaches. ConclusionsMachine translation is a promising avenue for fully machine-learning-based concept recognition that achieves state-of-the-art results on the CRAFT Corpus, evaluated via a direct comparison to previous results from the 2019 CRAFT Shared Task. Experiments illuminating the reasons for the surprisingly good performance of sequence-to-sequence methods targeting ontology identifiers suggest that further progress may be possible by mapping to alternative target concept representations. All code and models can be found at: https://github.com/UCDenver-ccp/Concept-Recognition-as-Translation.

bioinformatics↗

bioRxiv2020

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

MotivationAlthough knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms. PheKnowLator is available at: https://github.com/callahantiff/PheKnowLator.

bioinformatics↗

bioRxiv2019

Applying knowledge-driven mechanistic inference totoxicogenomics

Government regulators and others concerned about toxic chemicals in the environment hold that a mechanistic, causal explanation of toxicity is strongly preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually-curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This framework can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.\n\nAuthor summarySeveral recent computational methods have displayed excellent performance in predicting toxicity outcomes [1-3] of chemicals. Yet, to our knowledge, there is to date no computational approach to generate mechanistic hypotheses to answer why these chemicals elicit a toxic response. There is great value in understanding the mechanism of toxicity for a chemical that appears to elicit an adverse response. Novel small molecule development is one example, where a chemical that failed initial toxicological screenings could be assessed to evaluate the actual mechanism of toxicity, greatly reducing research time and expenses on subsequent ones. The value of a mechanistic awareness of toxicity also applies to pharmacovigilance, when researching rare adverse effects of a drug in subsets of the population. The development of oncological chemotherapeutics is another example, where certain mechanisms of cytotoxicity can actually be desirable to eliminate different types of tumor cells. More importantly, the costs, time expenditure, and ethical concerns of toxicity animal models, make in vitro and in silico approaches an enticing alternative. We present a solution that uses a combination of gene expression assays and biomedical knowledge to address the gap of answering the why question.

bioinformatics↗

bioRxiv2019

Individual differences in model-based planning are linked to the ability to infer latent structure

Humans appear to represent many forms of knowledge in associative networks whose nodes are multiply connected, including sensory, spatial, and semantic. Recent work has shown that explicitly augmenting artificial agents with such graph-structured representations endows them with more human-like capabilities of compositionality and transfer learning. An open question is how humans acquire these representations. Previously, it has been shown that humans can learn to navigate graph-structured conceptual spaces on the basis of direct experience with trajectories that intentionally draw the network contours (Schapiro et al., 2012;2016), or through direct experience with rewards that covary with the underlying associative distance (Wu et al., 2018). Here, we provide initial evidence that this capability is more general, extending to learning to reason about shortest-path distances across a graph structure acquired across disjoint experiences with randomized edges of the graph - a form of latent learning. In other words, we show that humans can infer graph structures, assembling them from disordered experiences. We further show that the degree to which individuals learn to reason correctly and with reference to the structure of the graph corresponds to their propensity, in a separate task, to use model-based reinforcement learning to achieve rewards. This connection suggests that the correct acquisition of graph-structured relationships is a central ability underlying forward planning and reasoning, and may be a core computation across the many domains in which graph-based reasoning is advantageous.

animal behavior and cognition↗

bioRxiv2019

Biomedical Concept Recognition Using Deep Neural Sequence Models

Backgroundthe automated identification of mentions of ontological concepts in natural language texts is a central task in biomedical information extraction. Despite more than a decade of effort, performance in this task remains below the level necessary for many applications. Resultsrecently, applications of deep learning in natural language processing have demonstrated striking improvements over previously state-of-the-art performance in many related natural language processing tasks. Here we demonstrate similarly striking performance improvements in recognizing biomedical ontology concepts in full text journal articles using deep learning techniques originally developed for machine translation. For example, our best performing system improves the performance of the previous state-of-the-art in recognizing terms in the Gene Ontology Biological Process hierarchy, from a previous best F1 score of 0.40 to an F1 of 0.70, nearly halving the error rate. Nearly all other ontologies show similar performance improvements. ConclusionsA two-stage concept recognition system, which is a conditional random field model for span detection followed by a deep neural sequence model for normalization, improves the state-of-the-art performance for biomedical concept recognition. Treating the biomedical concept normalization task as a sequence-to-sequence mapping task similar to neural machine translation improves performance.

bioinformatics↗

bioRxiv2019

Excessive deliberation in social anxiety

A goal of computational psychiatry is to ground symptoms in more fundamental computational mechanisms. Theory suggests that rumination and other symptoms in mood disorders reflect dysregulated mental simulation, a process that normally serves to evaluate candidate actions. If so, these covert symptoms should have observable consequences: excessively deliberative choices, specifically about options related to the content of rumination. In two large general population samples, we examined how symptoms of social anxiety disorder (SAD) predict choices in a socially framed reinforcement learning task, the Patent Race game. Using a computational learning model to assess learning strategy, we found that self-reported social anxiety was indeed associated with an increase in deliberative evaluation. The effect was specific to learning from a particular (\"upward counterfactual\") subset of feedback, broadly matching the biased content of rumination in SAD. It was also robust to controlling for other psychiatric symptoms. These results ground the symptoms of SAD, such as overthinking and paralysis in social interactions, in well characterized neuro-computational mechanisms and offer a rare example of enhanced function in disease

animal behavior and cognition↗

bioRxiv2018

A common deliberative process underlies model-based planning and patient intertemporal choice

Humans and animals consistently forego, or \"discount\" future rewards in favor of more proximal, but less valuable, options. This behavior is often thought of in terms of a failure of \"self-control\", a lack of inhibition when considering the possibility of immediate gratification. However, rather than overweighting the near-term reward, the same behavior can result from failing to properly consider the far-off reward. The capacity to plan for future gains is a core construct in Reinforcement Learning (RL), known as \"model-based\" planning. Both discounting and model-based planning have been shown to track everyday behaviors from diet to exercise habits to drug abuse. Here, we show that these two capacities are related via a common mechanism - people who are more likely to deliberate about future reward in an intertemporal choice task, as indicated by the time they spend considering the choice, are also more likely to make multi-step plans for reward in a sequential reinforcement learning task. In contrast, the degree to which peoples intertemporal choices were driven by a more automatic bias did not correspond to their planning tendency, and neither did the more standard measure of discounting behavior. These results suggest that the standard behavioral economic measure of discounting is more fruitfully understood by decomposing it into constituent parts, and that only one of these parts corresponds to the sort of multi-step thinking needed to make plans for the future.

animal behavior and cognition↗

bioRxiv2018

Reference-dependent preferences arise from structure learning

Modern theories of decision making emphasize the reference-dependency of decision making under risk. In particular, people tend to be risk-averse for outcomes greater than their reference point, and risk-seeking for outcomes less than their reference point. A key question is where reference points come from. A common assumption is that reference points correspond to expectations about outcomes, but it is unclear whether people rely on a single global expectation, or multiple local expectations. If the latter, how do people determine which expectation to apply in a particular situation? We argue that people discover reference points using a form of Bayesian structure learning, which partitions outcomes into distinct contexts, each with its own reference point corresponding to the expected outcome in that context. Consistent with this theory, we show experimentally that dramatic change in the distribution of outcomes can induce the discovery of a new reference point, with systematic effects on risk preferences. By contrast, when changes are gradual, a single reference point is continuously updated.

animal behavior and cognition↗