Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase

There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.

The effect of pseudoknot base pairing on cotranscriptional structural switching of the fluoride riboswitch

A central question in biology is how RNA sequence changes influence dynamic conformational changes during cotranscriptional folding. Here we investigated this question through the study of transcriptional fluoride riboswitches, non-coding RNAs that sense the fluoride anion through the coordinated folding and rearrangement of a pseudoknotted aptamer domain and a downstream intrinsic terminator expression platform. Using a combination of Escherichia coli RNA polymerase in vitro transcription and cellular gene expression assays, we characterized the function of mesophilic and thermophilic fluoride riboswitch variants. We showed that only variants containing the mesophilic pseudoknot function at 37°C. We next systematically varied the pseudoknot sequence and found that a single wobble base pair is critical for function. Characterizing thermophilic variants at 65°C through Thermus aquaticus RNA polymerase in vitro transcription showed the importance of this wobble pair for function even at elevated temperatures. Finally, we performed all-atom molecular dynamics simulations which supported the experimental findings, visualized the RNA structure switching process, and provided insight into the important role of magnesium ions. Together these studies provide deeper insights into the role of riboswitch sequence in influencing folding and function that will be important for understanding of RNA-based gene regulation and for synthetic biology applications.

Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease

BACKGROUND

The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult.

RESULTS

In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states.

CONCLUSIONS

We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

Monitoring Changes to Alkenone Biosynthesis in Commercial Tisochrysis lutea Microalgae

Alkenones are unique lipids produced by certain species of microalgae, well-known for use in paleoclimatology, and more recently pursued to advance sustainability across multiple industries. Beginning in 2018, the biosynthesis of alkenones by commercially grown Tisochrysis lutea (T-Iso) microalgae from one of the world's most established producers, Necton S.A., changed dramatically from structures containing 37 and 38 carbons, to unusual shorter-chain C35 and C36 diunsaturated alkenones (C35:2 and C36:2 alkenones). While the exact reasons for this change remain unknown, analysis of alkenones isolated from T-Iso grown in 2021 and 2023 revealed that this change has persisted. The structure of these rare shorter-chain alkenones, including double bond position, produced by Necton T-Iso remained the same over the last five years, which was determined using a new and optimized cross-metathesis derivatization approach with analysis by comprehensive two-dimensional gas chromatography and NMR. However, noticeable differences in the alkenone profiles among the different batches were observed. Combined with fatty acid compositional analysis, the data suggest a connection between these lipid classes (e.g., increased DHA corresponds to lower amounts of shorter-chain alkenones) and the ability to manipulate their biosynthesis in T-Iso with changes to cultivation conditions.

Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling

Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.

Chromosome-level assembly of the gray fox (Urocyon cinereoargenteus) confirms the basal loss of PRDM9 in Canidae

Reference genome assemblies have been created from multiple lineages within the Canidae family; however, despite its phylogenetic relevance as a basal genus within the clade, there is currently no reference genome for the gray fox (Urocyon cinereoargenteus). Here, we present a chromosome-level assembly for the gray fox (U. cinereoargenteus), which represents the most contiguous, non-domestic canid reference genome available to date, with 90% of the genome contained in just 34 scaffolds and a contig N50 and scaffold N50 of 59.4 and 72.9 Megabases, respectively. Repeat analyses identified an increased number of simple repeats relative to other canids. Based on mitochondrial DNA, our Vermont sample clusters with other gray fox samples from the northeastern United States and contains slightly lower levels of heterozygosity than gray foxes on the west coast of California. This new assembly lays the groundwork for future studies to describe past and present population dynamics, including the delineation of evolutionarily significant units of management relevance. Importantly, the phylogenetic position of Urocyon allows us to verify the loss of PRDM9 functionality in the basal canid lineage, confirming that pseudogenization occurred at least 10 million years ago.

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

MOTIVATION

Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies-e.g. live cell imaging, scRNAseq, agent-based models-requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties.

RESULTS

Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced.

AVAILABILITY AND IMPLEMENTATION

All source codes for the agent-based model, emulation, and analyses are publicly available at the corresponding DOIs: 10.5281/zenodo.10622155, 10.5281/zenodo.10611675, 10.5281/zenodo.10621244, respectively.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

First-order ultrasensitivity in phosphorylation cycles

Cellular signal transduction takes place through a network of phosphorylation cycles. These pathways take the form of a multi-layered cascade of cycles. This work focuses on the sensitivity of single, double and n length cycles. Cycles that operate in the zero-order regime can become sensitive to changes in signal, resulting in zero-order ultrasensitivity (ZOU). Using frequency analysis, we confirm previous efforts that cascades can act as noise filters by computing the bandwidth. We show that n length cycles display what we term first-order ultrasensitivity which occurs even when the cycles are not operating in the zero-order regime. The magnitude of the sensitivity, however, has an upper bound equal to the number of cycles. It is known that ZOU can be significantly reduced in the presence of retroactivity. We show that the first-order ultrasensitivity is immune to retroactivity and that the ZOU and first-order ultrasensitivity can be blended to create systems with constant sensitivity over a wider range of signal. We show that the ZOU in a double cycle is only modestly higher compared with a single cycle. We therefore speculate that the double cycle has evolved to enable amplification even in the face of retroactivity.

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models

MOTIVATION

Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features.

RESULTS

We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML).

AVAILABILITY AND IMPLEMENTATION

VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

Development of a centrifugal bioreactor for rapid expansion of CD8 cytotoxic T cells for use in cancer immunotherapy

One of the current difficulties limiting the use of adoptive cell therapy (ACT) for cancer treatment is the lack of methods for rapidly expanding T cells. As described in the present report, we developed a centrifugal bioreactor (CBR) that may resolve this manufacturing bottleneck. The CBR operates in perfusion by balancing centrifugal forces with a continuous feed of fresh medium, preventing cells from leaving the expansion culture chamber while maintaining nutrients for growth. A bovine CD8 cytotoxic T lymphocyte (CTL) cell line specific for an autologous target cell infected with a protozoan parasite, Theileria parva, was used to determine the efficacy of the CBR for ACT purposes. Batch culture experiments were conducted to predict how CTLs respond to environmental changes associated with consumption of nutrients and production of toxic metabolites, such as ammonium and lactate. Data from these studies were used to develop a kinetic growth model, allowing us to predict CTL growth in the CBR and determine the optimal operating parameters. The model predicts the maximum cell density the CBR can sustain is 5.5 × 107  cells/mL in a single 11-mL conical chamber with oxygen being the limiting factor. Experimental results expanding CTLs in the CBR are in 95% agreement with the kinetic model. The prototype CBR described in this report can be used to develop a CBR for use in cancer immunotherapy.

Task-dependent optimal representations for cerebellar learning

The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

Manipulating nest architecture reveals three-dimensional building strategies and colony resilience in honeybees

Form follows function throughout the development of an organism. This principle should apply beyond the organism to the nests they build, but empirical studies are lacking. Honeybees provide a uniquely suited system to study nest form and function throughout development because we can image the three-dimensional structure repeatedly and non-destructively. Here, we tracked nest-wide comb growth in six colonies over 45 days (control colonies) and found that colonies have a stereotypical process of development that maintains a spheroid nest shape. To experimentally test if nest structure is important for colony function, we shuffled the nests of an additional six colonies, weekly rearranging the comb positions and orientations (shuffled colonies). Surprisingly, we found no differences between control and shuffled colonies in multiple colony performance metrics-worker population, comb area, hive weight and nest temperature. However, using predictive modelling to examine how workers allocate comb to expand their nests, we show that shuffled colonies compensate for these disruptions by accounting for the three-dimensional structure to reconnect their nest. This suggests that nest architecture is more flexible than previously thought, and that superorganisms have mechanisms to compensate for drastic architectural perturbations and maintain colony function.

Nectar feeding beyond the tongue: hummingbirds drink using phase-shifted bill opening, flexible tongue flaps and wringing at the tips

Hummingbirds are the most speciose group of vertebrate nectarivores and exhibit striking bill variation in association with their floral food sources. To explicitly link comparative feeding biomechanics to hummingbird ecology, deciphering how they move nectar from the tongue to the throat is as important as understanding how this liquid is collected. We employed synced, orthogonally positioned, high-speed cameras to describe the bill movements, and backlight filming to track tongue and nectar displacements intraorally. We reveal that the tongue base plays a central role in fluid handling, and that the bill is neither just a passive vehicle taking the tongue inside the flower nor a static tube for the nectar to flow into the throat. Instead, we show that the bill is actually a dynamic device with an unexpected pattern of opening and closing of its tip and base. We describe three complementary mechanisms: (1) distal wringing: the tongue is wrung out as soon as it is retracted and upon protrusion, near the bill tip where the intraoral capacity is decreased when the bill tips are closed; (2) tongue raking: the nectar filling the intraoral cavity is moved mouthwards by the tongue base, leveraging flexible flaps, upon retraction; (3) basal expansion: as more nectar is released into the oral cavity, the bill base is open (phase-shifted from the tip opening), increasing the intraoral capacity to facilitate nectar flow towards the throat.

Zebrafish cutaneous injury models reveal that Langerhans cells engulf axonal debris in adult epidermis

Somatosensory neurons extend enormous peripheral axons to the skin, where they detect diverse environmental stimuli. Somatosensory peripheral axons are easily damaged due to their small caliber and superficial location. Axonal damage results in Wallerian degeneration, creating vast quantities of cellular debris that phagocytes must remove to maintain organ homeostasis. The cellular mechanisms that ensure efficient clearance of axon debris from stratified adult skin are unknown. Here, we established zebrafish scales as a tractable model to study axon degeneration in the adult epidermis. Using this system, we demonstrated that skin-resident immune cells known as Langerhans cells engulf the majority of axon debris. In contrast to immature skin, adult keratinocytes did not significantly contribute to debris removal, even in animals lacking Langerhans cells. Our study establishes a powerful new model for studying Wallerian degeneration and identifies a new function for Langerhans cells in maintenance of adult skin homeostasis following injury. These findings have important implications for pathologies that trigger somatosensory axon degeneration.

The evolution of sexually dimorphic traits in ecological gradients: an interplay between natural and sexual selection in hummingbirds

Traits that exhibit differences between the sexes have been of special interest in the study of phenotypic evolution. Classic hypotheses explain sexually dimorphic traits via intra-sexual competition and mate selection, yet natural selection may also act differentially on the sexes to produce dimorphism. Natural selection can act either through physiological and ecological constraints on one of the sexes, or by modulating the strength of sexual/social selection. This predicts an association between the degree of dimorphism and variation in ecological environments. Here, we characterize the variation in hummingbird dimorphism across ecological gradients using rich databases of morphology, colouration and song. We show that morphological dimorphism decreases with elevation in the understorey and increases with elevation in mixed habitats, that dichromatism increases at high altitudes in open and mixed habitats, and that song is less complex in mixed habitats. Our results are consistent with flight constraints, lower predation pressure at high elevations and with habitat effects on song transmission. We also show that dichromatism and song complexity are positively associated, while tail dimorphism and song complexity are negatively associated. Our results suggest that key ecological factors shape sexually dimorphic traits, and that different communication modalities do not always evolve in tandem.

Reprogramming Müller glia to regenerate ganglion-like cells in adult mouse retina with developmental transcription factors

Many neurodegenerative diseases cause degeneration of specific types of neurons. For example, glaucoma leads to death of retinal ganglion cells, leaving other neurons intact. Neurons are not regenerated in the adult mammalian central nervous system. However, in nonmammalian vertebrates, glial cells spontaneously reprogram into neural progenitors and replace neurons after injury. We have recently developed strategies to stimulate regeneration of functional neurons in the adult mouse retina by overexpressing the proneural factor Ascl1 in Müller glia. Here, we test additional transcription factors (TFs) for their ability to direct regeneration to particular types of retinal neurons. We engineered mice to express different combinations of TFs in Müller glia, including Ascl1, Pou4f2, Islet1, and Atoh1. Using immunohistochemistry, single-cell RNA sequencing, single-cell assay for transposase-accessible chromatin sequencing, and electrophysiology, we find that retinal ganglion-like cells can be regenerated in the damaged adult mouse retina in vivo with targeted overexpression of developmental retinal ganglion cell TFs.

Intersexual social dominance mimicry drives female hummingbird polymorphism

Female-limited polymorphisms, where females have multiple forms but males have only one, have been described in a variety of animals, yet are difficult to explain because selection typically is expected to decrease rather than maintain diversity. In the white-necked jacobin (Florisuga mellivora), all males and approximately 20% of females express an ornamented plumage type (androchromic), while other females are non-ornamented (heterochromic). Androchrome females benefit from reduced social harassment, but it remains unclear why both morphs persist. Female morphs may represent balanced alternative behavioural strategies, but an alternative hypothesis is that androchrome females are mimicking males. Here, we test a critical prediction of these hypotheses by measuring morphological, physiological and behavioural traits that relate to resource-holding potential (RHP), or competitive ability. In all these traits, we find little difference between female types, but higher RHP in males. These results, together with previous findings in this species, indicate that androchrome females increase access to food resources through mimicry of more aggressive males. Importantly, the mimicry hypothesis provides a clear theoretical pathway for polymorphism maintenance through frequency-dependent selection. Social dominance mimicry, long suspected to operate between species, can therefore also operate within species, leading to polymorphism and perhaps similarities between sexes more generally.

Mobile Smartphone-Based Digital Pupillometry Curves in the Diagnosis of Traumatic Brain Injury

Objective

The pupillary light reflex (PLR) and the pupillary diameter over time (the PLR curve) is an important biomarker of neurological disease, especially in the diagnosis of traumatic brain injury (TBI). We investigated whether PLR curves generated by a novel smartphone pupillometer application could be easily and accurately interpreted to aid in the diagnosis of TBI.

Methods

A total of 120 PLR curves from 42 healthy subjects and six patients with TBI were generated by PupilScreen. Eleven clinician raters, including one group of physicians and one group of neurocritical care nurses, classified 48 randomly selected normal and abnormal PLR curves without prior training or instruction. Rater accuracy, sensitivity, specificity, and interrater reliability were calculated.

Results

Clinician raters demonstrated 93% accuracy, 94% sensitivity, 92% specificity, 92% positive predictive value, and 93% negative predictive value in identifying normal and abnormal PLR curves. There was high within-group reliability (k = 0.85) and high interrater reliability (K = 0.75).

Conclusion

The PupilScreen smartphone application-based pupillometer produced PLR curves for clinical provider interpretation that led to accurate classification of normal and abnormal PLR data. Interrater reliability was greater than previous studies of manual pupillometry. This technology may be a good alternative to the use of subjective manual penlight pupillometry or digital pupillometry.

Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions

Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumer-facing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions.

A Dichotomous Role for FABP7 in Sleep and Alzheimer's Disease Pathogenesis: A Hypothesis

Fatty acid binding proteins (FABPs) are a family of intracellular lipid chaperone proteins known to play critical roles in the regulation of fatty acid uptake and transport as well as gene expression. Brain-type fatty acid binding protein (FABP7) is enriched in astrocytes and has been implicated in sleep/wake regulation and neurodegenerative diseases; however, the precise mechanisms underlying the role of FABP7 in these biological processes remain unclear. FABP7 binds to both arachidonic acid (AA) and docosahexaenoic acid (DHA), resulting in discrete physiological responses. Here, we propose a dichotomous role for FABP7 in which ligand type determines the subcellular translocation of fatty acids, either promoting wakefulness aligned with Alzheimer's pathogenesis or promoting sleep with concomitant activation of anti-inflammatory pathways and neuroprotection. We hypothesize that FABP7-mediated translocation of AA to the endoplasmic reticulum of astrocytes increases astrogliosis, impedes glutamatergic uptake, and enhances wakefulness and inflammatory pathways via COX-2 dependent generation of pro-inflammatory prostaglandins. Conversely, we propose that FABP7-mediated translocation of DHA to the nucleus stabilizes astrocyte-neuron lactate shuttle dynamics, preserves glutamatergic uptake, and promotes sleep by activating anti-inflammatory pathways through the peroxisome proliferator-activated receptor-γ transcriptional cascade. Importantly, this model generates several testable hypotheses applicable to other neurodegenerative diseases, including amyotrophic lateral sclerosis and Parkinson's disease.

Parasite communities in English Sole (Parophrys vetulus) have changed in composition but not richness in the Salish Sea, Washington, USA since 1930

Earth is rapidly losing free-living species. Is the same true for parasitic species? To reveal temporal trends in biodiversity, historical data are needed, but often such data do not exist for parasites. Here, parasite communities of the past were reconstructed by identifying parasites in fluid-preserved specimens held in natural history collections. Approximately 2500 macroparasites were counted from 109 English Sole (Parophrys vetulus ) collected between 1930 and 2019 in the Salish Sea, Washington, USA. Alpha and beta diversity were measured to determine if and how diversity changed over time. Species richness of parasite infracommunities and community dispersion did not vary over time, but community composition of decadal component communities varied significantly over the study period. Community dissimilarity also varied: prior to the mid-20th century, parasites shifted in abundance in a seemingly stochastic manner and, after this time period, a canalization of community change was observed, where species' abundances began to shift in consistent directions. Further work is needed to elucidate potential drivers of these changes and to determine if these patterns are present in the parasite communities of other fishes of the Salish Sea.

Osteoblasts pattern endothelium and somatosensory axons during zebrafish caudal fin organogenesis

Skeletal elements frequently associate with vasculature and somatosensory nerves, which regulate bone development and homeostasis. However, the deep, internal location of bones in many vertebrates has limited in vivo exploration of the neurovascular-bone relationship. Here, we use the zebrafish caudal fin, an optically accessible organ formed of repeating bony ray skeletal units, to determine the cellular relationship between nerves, bones and endothelium. In adult zebrafish, we establish the presence of somatosensory axons running through the inside of the bony fin rays, juxtaposed with osteoblasts on the inner hemiray surface. During development we show that the caudal fin progresses through sequential stages of endothelial plexus formation, bony ray addition, ray innervation and endothelial remodeling. Surprisingly, the initial stages of fin morphogenesis proceed normally in animals lacking either fin endothelium or somatosensory nerves. Instead, we find that sp7+ osteoblasts are required for endothelial remodeling and somatosensory axon innervation in the developing fin. Overall, this study demonstrates that the proximal neurovascular-bone relationship in the adult caudal fin is established during fin organogenesis and suggests that ray-associated osteoblasts pattern axons and endothelium.

Machine learning and feature engineering for predicting pulse presence during chest compressions

Current resuscitation protocols require pausing chest compressions during cardiopulmonary resuscitation (CPR) to check for a pulse. However, pausing CPR when a patient is pulseless can worsen patient outcomes. Our objective was to design and evaluate an ECG-based algorithm that predicts pulse presence with or without CPR. We evaluated 383 patients being treated for out-of-hospital cardiac arrest with real-time ECG, impedance and audio recordings. Paired ECG segments having an organized rhythm immediately preceding a pulse check (during CPR) and during the pulse check (without CPR) were extracted. Patients were randomly divided into 60% training and 40% test groups. From training data, we developed an algorithm to predict the clinical pulse presence based on the wavelet transform of the bandpass-filtered ECG. Principal component analysis was used to reduce dimensionality, and we then trained a linear discriminant model using three principal component modes as input features. Overall, 38% (351/912) of checks had a spontaneous pulse. AUCs for predicting pulse presence with and without CPR on test data were 0.84 (95% CI (0.80, 0.88)) and 0.89 (95% CI (0.86, 0.92)), respectively. This ECG-based algorithm demonstrates potential to improve resuscitation by predicting the presence of a spontaneous pulse without pausing CPR with moderate accuracy.

Structured time-delay models for dynamical systems with connections to Frenet-Serret frame

Time-delay embedding and dimensionality reduction are powerful techniques for discovering effective coordinate systems to represent the dynamics of physical systems. Recently, it has been shown that models identified by dynamic mode decomposition on time-delay coordinates provide linear representations of strongly nonlinear systems, in the so-called Hankel alternative view of Koopman (HAVOK) approach. Curiously, the resulting linear model has a matrix representation that is approximately antisymmetric and tridiagonal; for chaotic systems, there is an additional forcing term in the last component. In this paper, we establish a new theoretical connection between HAVOK and the Frenet-Serret frame from differential geometry, and also develop an improved algorithm to identify more stable and accurate models from less data. In particular, we show that the sub- and super-diagonal entries of the linear model correspond to the intrinsic curvatures in the Frenet-Serret frame. Based on this connection, we modify the algorithm to promote this antisymmetric structure, even in the noisy, low-data limit. We demonstrate this improved modelling procedure on data from several nonlinear synthetic and real-world examples.

Mechanoethology: The Physical Mechanisms of Behavior

Research that integrates animal behavior theory with mechanics-including biomechanics, physiology, and functional morphology-can reveal how organisms accomplish tasks crucial to their fitness. Despite the insights that can be gained from this interdisciplinary approach, biomechanics commonly neglects a behavioral context and behavioral research generally does not consider mechanics. Here, we aim to encourage the study of "mechanoethology," an area of investigation intended to encompass integrative studies of mechanics and behavior. Using examples from the literature, including papers in this issue, we show how these fields can influence each other in three ways: (1) the energy required to execute behaviors is driven by the kinematics of movement, and mechanistic studies of movement can benefit from consideration of its behavioral context; (2) mechanics sets physical limits on what behaviors organisms execute, while behavior influences ecological and evolutionary limits on mechanical systems; and (3) sensory behavior is underlain by the mechanics of sensory structures, and sensory systems guide whole-organism movement. These core concepts offer a foundation for mechanoethology research. However, future studies focused on merging behavior and mechanics may reveal other ways by which these fields are linked, leading to further insights in integrative organismal biology.

Wing structure and neural encoding jointly determine sensing strategies in insect flight

Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that is critical for stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing are largely unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively on a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, enable accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of understanding each in the context of their joint evolution.

In addition to generating forces for flight, insect wings also serve an important role as sensory structures, providing rapid feedback about wing bending that is used to stabilize flight. While much is known about how wing structure affects aerodynamic performance, the effects of wing structure on sensing remain unexplored. Using a computational model of a flapping wing, we examine how sensing strategies depend on wing stiffness and sensor properties. We show that body rotations can be accurately detected with a small number of sensors on the wing across a wide range of conditions. Optimal sensor locations are clustered at either the wing base or wing tip, depending on a combination of wing stiffness and sensor properties. Moreover, sensing performance is robust to multiple kinds of perturbations. Our work provides a basis for understanding how wing structure impacts incoming sensory information during flight.

Go with the FLOW: visualizing spatiotemporal dynamics in optical widefield calcium imaging

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporally coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wavefronts that may be correlated with behavioural events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits. These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent technique developed to analyse time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.

COVID-19 vaccines that reduce symptoms but do not block infection need higher coverage and faster rollout to achieve population impact

Trial results for two COVID-19 vaccines suggest at least 90% efficacy against symptomatic disease (VEDIS). It remains unknown whether this efficacy is mediated by lowering SARS-CoV-2 infection susceptibility (VESUSC) or development of symptoms after infection (VESYMP). We aim to assess and compare the population impact of vaccines with different efficacy profiles (VESYMP and VESUSC) satisfying licensure criteria. We developed a mathematical model of SARS-CoV-2 transmission, calibrated to data from King County, Washington. Rollout scenarios starting December 2020 were simulated with combinations of VESUSC and VESYMP resulting in up to 100% VEDIS. We assumed no reduction of infectivity upon infection conditional on presence of symptoms. Proportions of cumulative infections, hospitalizations and deaths prevented over 1 year from vaccination start are reported. Rollouts of 1 M vaccinations (5000 daily) using vaccines with 50% VEDIS are projected to prevent 23-46% of infections and 31-46% of deaths over 1 year. In comparison, vaccines with 90% VEDIS are projected to prevent 37-64% of infections and 46-64% of deaths over 1 year. In both cases, there is a greater reduction if VEDIS is mediated mostly by VESUSC. The use of a "symptom reducing" vaccine will require twice as many people vaccinated than a "susceptibility reducing" vaccine with the same 90% VEDIS to prevent 50% of the infections and death over 1 year. Delaying the start of the vaccination by 3 months decreases the expected population impact by more than 50%. Vaccines which prevent COVID-19 disease but not SARS-CoV-2 infection, and thereby shift symptomatic infections to asymptomatic infections, will prevent fewer infections and require larger and faster vaccination rollouts to have population impact, compared to vaccines that reduce susceptibility to infection. If uncontrolled transmission across the U.S. continues, then expected vaccination in Spring 2021 will provide only limited benefit.

The bioactivity of soluble Fas ligand is modulated by key amino acids of its stalk region

We have previously reported that the 26-amino acid N-terminus stalk region of soluble Fas ligand (sFasL), which is separate from its binding site, is required for its biological function. Here we investigate the mechanisms that link the structure of the sFasL stalk region with its function. Using site-directed mutagenesis we cloned a mutant form of sFasL in which all the charged amino acids of the stalk region were changed to neutral alanines (mut-sFasL). We used the Fas-sensitive Jurkat T-cell line and mouse and human alveolar epithelial cells to test the bioactivity of sFasL complexes, using caspase-3 activity and Annexin-V externalization as readouts. Finally, we tested the effects of mut-sFasL on lipopolysaccharide-induced lung injury in mice. We found that mutation of all the 8 charged amino acids of the stalk region into the non-charged amino acid alanine (mut-sFasL) resulted in reduced apoptotic activity compared to wild type sFasL (WT-sFasL). The mut-sFasL attenuated WT-sFasL function on the Fas-sensitive human T-cell line Jurkat and on primary human small airway epithelial cells. The inhibitory mechanism was associated with the formation of complexes of mut-sFasL with the WT protein. Intratracheal administration of the mut-sFasL to mice 24 hours after intratracheal Escherichia coli lipopolysaccharide resulted in attenuation of the inflammatory response 24 hours later. Therefore, the stalk region of sFasL has a critical role on bioactivity, and changes in the structure of the stalk region can result in mutant variants that interfere with the wild type protein function in vitro and in vivo.

Optimization of AAV vectors to target persistent viral reservoirs

Gene delivery of antiviral therapeutics to anatomical sites where viruses accumulate and persist is a promising approach for the next generation of antiviral therapies. Recombinant adeno-associated viruses (AAV) are one of the leading vectors for gene therapy applications that deliver gene-editing enzymes, antibodies, and RNA interference molecules to eliminate viral reservoirs that fuel persistent infections. As long-lived viral DNA within specific cellular reservoirs is responsible for persistent hepatitis B virus, Herpes simplex virus, and human immunodeficiency virus infections, the discovery of AAV vectors with strong tropism for hepatocytes, sensory neurons and T cells, respectively, is of particular interest. Identification of natural isolates from various tissues in humans and non-human primates has generated an extensive catalog of AAV vectors with diverse tropisms and transduction efficiencies, which has been further expanded through molecular genetic approaches. The AAV capsid protein, which forms the virions' outer shell, is the primary determinant of tissue tropism, transduction efficiency, and immunogenicity. Thus, over the past few decades, extensive efforts to optimize AAV vectors for gene therapy applications have focused on capsid engineering with approaches such as directed evolution and rational design. These approaches are being used to identify variants with improved transduction efficiencies, alternate tropisms, reduced sequestration in non-target organs, and reduced immunogenicity, and have produced AAV capsids that are currently under evaluation in pre-clinical and clinical trials. This review will summarize the most recent strategies to identify AAV vectors with enhanced tropism and transduction in cell types that harbor viral reservoirs.

Variability in digestive and respiratory tract Ace2 expression is associated with the microbiome

COVID-19 (coronavirus disease 2019) patients exhibiting gastrointestinal symptoms are reported to have worse prognosis. Ace2 (angiotensin-converting enzyme 2), the gene encoding the host protein to which SARS-CoV-2 spike proteins bind, is expressed in the gut and therefore may be a target for preventing or reducing severity of COVID-19. Here we test the hypothesis that Ace2 expression in the gastrointestinal and respiratory tracts is modulated by the microbiome. We used quantitative PCR to profile Ace2 expression in germ-free mice, conventional raised specific pathogen-free mice, and gnotobiotic mice colonized with different microbiota. Intestinal Ace2 expression levels were significantly higher in germ-free mice compared to conventional mice. A similar trend was observed in the respiratory tract. Intriguingly, microbiota depletion via antibiotics partially recapitulated the germ-free phenotype, suggesting potential for microbiome-mediated regulation of Ace2 expression. Variability in intestinal Ace2 expression was observed in gnotobiotic mice colonized with different microbiota, partially attributable to differences in microbiome-encoded proteases and peptidases. Together, these data suggest that the microbiome may be one modifiable factor determining COVID-19 infection risk and disease severity.

Improved protein structure refinement guided by deep learning based accuracy estimation

We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.

Lettuce (Lactuca sativa) productivity influenced by microbial inocula under nitrogen-limited conditions in aquaponics

The demand for food will outpace productivity of conventional agriculture due to projected growth of the human population, concomitant with shrinkage of arable land, increasing scarcity of freshwater, and a rapidly changing climate. While aquaponics has potential to sustainably supplement food production with minimal environmental impact, there is a need to better characterize the complex interplay between the various components (fish, plant, microbiome) of these systems to optimize scale up and productivity. Here, we investigated how the commonly-implemented practice of continued microbial community transfer from pre-existing systems might promote or impede productivity of aquaponics. Specifically, we monitored plant growth phenotypes, water chemistry, and microbiome composition of rhizospheres, biofilters, and fish feces over 61-days of lettuce (Lactuca sativa var. crispa) growth in nitrogen-limited aquaponic systems inoculated with bacteria that were either commercially sourced or originating from a pre-existing aquaponic system. Lettuce above- and below-ground growth were significantly reduced across replicates treated with a pre-existing aquaponic system inoculum when compared to replicates treated with a commercial inoculum. Reduced productivity was associated with enrichment in specific bacterial genera in plant roots, including Pseudomonas, following inoculum transfer from pre-existing systems. Increased productivity was associated with enrichment of nitrogen-fixing Rahnella in roots of plants treated with the commercial inoculum. Thus, we show that inoculation from a pre-existing system, rather than from a commercial inoculum, is associated with lower yields. Further work will be necessary to test the putative mechanisms involved.

Osmolarity-independent electrical cues guide rapid response to injury in zebrafish epidermis

The ability of epithelial tissues to heal after injury is essential for animal life, yet the mechanisms by which epithelial cells sense tissue damage are incompletely understood. In aquatic organisms such as zebrafish, osmotic shock following injury is believed to be an early and potent activator of a wound response. We find that, in addition to sensing osmolarity, basal skin cells in zebrafish larvae are also sensitive to changes in the particular ionic composition of their surroundings after wounding, specifically the concentration of sodium chloride in the immediate vicinity of the wound. This sodium chloride-specific wound detection mechanism is independent of cell swelling, and instead is suggestive of a mechanism by which cells sense changes in the transepithelial electrical potential generated by the transport of sodium and chloride ions across the skin. Consistent with this hypothesis, we show that electric fields directly applied within the skin are sufficient to initiate actin polarization and migration of basal cells in their native epithelial context in vivo, even overriding endogenous wound signaling. This suggests that, in order to mount a robust wound response, skin cells respond to both osmotic and electrical perturbations arising from tissue injury.

Thermofluidic heat exchangers for actuation of transcription in artificial tissues

Spatial patterns of gene expression in living organisms orchestrate cell decisions in development, homeostasis, and disease. However, most methods for reconstructing gene patterning in 3D cell culture and artificial tissues are restricted by patterning depth and scale. We introduce a depth- and scale-flexible method to direct volumetric gene expression patterning in 3D artificial tissues, which we call "heat exchangers for actuation of transcription" (HEAT). This approach leverages fluid-based heat transfer from printed networks in the tissues to activate heat-inducible transgenes expressed by embedded cells. We show that gene expression patterning can be tuned both spatially and dynamically by varying channel network architecture, fluid temperature, fluid flow direction, and stimulation timing in a user-defined manner and maintained in vivo. We apply this approach to activate the 3D positional expression of Wnt ligands and Wnt/β-catenin pathway regulators, which are major regulators of development, homeostasis, regeneration, and cancer throughout the animal kingdom.

An Alphavirus-derived replicon RNA vaccine induces SARS-CoV-2 neutralizing antibody and T cell responses in mice and nonhuman primates

The coronavirus disease 2019 (COVID-19) pandemic, caused by infection with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is having a deleterious impact on health services and the global economy, highlighting the urgent need for an effective vaccine. Such a vaccine would need to rapidly confer protection after one or two doses and would need to be manufactured using components suitable for scale up. Here, we developed an Alphavirus-derived replicon RNA vaccine candidate, repRNA-CoV2S, encoding the SARS-CoV-2 spike (S) protein. The RNA replicons were formulated with lipid inorganic nanoparticles (LIONs) that were designed to enhance vaccine stability, delivery, and immunogenicity. We show that a single intramuscular injection of the LION/repRNA-CoV2S vaccine in mice elicited robust production of anti-SARS-CoV-2 S protein IgG antibody isotypes indicative of a type 1 T helper cell response. A prime/boost regimen induced potent T cell responses in mice including antigen-specific responses in the lung and spleen. Prime-only immunization of aged (17 months old) mice induced smaller immune responses compared to young mice, but this difference was abrogated by booster immunization. In nonhuman primates, prime-only immunization in one intramuscular injection site or prime/boost immunizations in five intramuscular injection sites elicited modest T cell responses and robust antibody responses. The antibody responses persisted for at least 70 days and neutralized SARS-CoV-2 at titers comparable to those in human serum samples collected from individuals convalescing from COVID-19. These data support further development of LION/repRNA-CoV2S as a vaccine candidate for prophylactic protection against SARS-CoV-2 infection.

Sucking or lapping: facultative feeding mechanisms in honeybees (Apis mellifera)

Nectarivorous insects generally adopt suction or lapping to extract nectar from flowers and it is believed that each species exhibits one specific feeding pattern. In recent literature, large groups of nectarivores are classified as either 'suction feeders', imbibing nectar through their proboscis, or 'lappers', using viscous dipping. Honeybees (Apis mellifera) are the well-known lappers by virtue of their hairy tongues. Surprisingly, we found that honeybees also employ active suction when feeding on nectar with low viscosity, defying their classification as lappers. Further experiments showed that suction yielded higher uptake rates when ingesting low-concentration nectar, while lapping resulted in faster uptake when ingesting nectar with higher sugar content. We found that the optimal concentration of suction mode in honeybees coincided with the one calculated for other typical suction feeders. Moreover, we found behavioural flexibility in the drinking mode: a bee is able to switch between lapping and suction when offered different nectar concentrations. Such volitional switching in bees can enhance their feeding capabilities, allowing them to efficiently exploit the variety of concentrations presented in floral nectars, enhancing their adaptability to a wide range of energy sources.

A survey-based analysis of the academic job market

Many postdoctoral researchers apply for faculty positions knowing relatively little about the hiring process or what is needed to secure a job offer. To address this lack of knowledge about the hiring process we conducted a survey of applicants for faculty positions: the survey ran between May 2018 and May 2019, and received 317 responses. We analyzed the responses to explore the interplay between various scholarly metrics and hiring outcomes. We concluded that, above a certain threshold, the benchmarks traditionally used to measure research success - including funding, number of publications or journals published in - were unable to completely differentiate applicants with and without job offers. Respondents also reported that the hiring process was unnecessarily stressful, time-consuming, and lacking in feedback, irrespective of outcome. Our findings suggest that there is considerable scope to improve the transparency of the hiring process.

Cytosolic delivery of proteins by cholesterol tagging

Protein-based imaging agents and therapeutics are superior in structural and functional diversity compared to small molecules and are much easier to design or screen. Antibodies or antibody fragments can be easily raised against virtually any target. Despite these fundamental advantages, the power and impact of protein-based agents are substantially undermined, only acting on a limited number of extracellular targets because macrobiomolecules cannot spontaneously cross the cell membrane. Conventional protein delivery techniques fail to address this fundamental problem in that protein cargos are predominantly delivered inside cells via endocytosis, a remarkably effective cell defense mechanism developed by Mother Nature to prevent intact biomolecules from entering the cytoplasm. Here, we report a unique concept, noncovalent cholesterol tagging, enabling virtually any compact proteins to permeate through the cell membrane, completely bypassing endocytosis. This simple plug-and-play platform greatly expands the biological target space and has the potential to transform basic biology studies and drug discovery.

Mathematical modeling to reveal breakthrough mechanisms in the HIV Antibody Mediated Prevention (AMP) trials

The ongoing Antibody Mediated Prevention (AMP) trials will uncover whether passive infusion of the broadly neutralizing antibody (bNAb) VRC01 can protect against HIV acquisition. Previous statistical simulations indicate these trials may be partially protective. In that case, it will be crucial to identify the mechanism of breakthrough infections. To that end, we developed a mathematical modeling framework to simulate the AMP trials and infer the breakthrough mechanisms using measurable trial outcomes. This framework combines viral dynamics with antibody pharmacokinetics and pharmacodynamics, and will be generally applicable to forthcoming bNAb prevention trials. We fit our model to human viral load data (RV217). Then, we incorporated VRC01 neutralization using serum pharmacokinetics (HVTN 104) and in vitro pharmacodynamics (LANL CATNAP database). We systematically explored trial outcomes by reducing in vivo potency and varying the distribution of sensitivity to VRC01 in circulating strains. We found trial outcomes could be used in a clinical trial regression model (CTRM) to reveal whether partially protective trials were caused by large fractions of VRC01-resistant (IC50>50 μg/mL) circulating strains or rather a global reduction in VRC01 potency against all strains. The former mechanism suggests the need to enhance neutralizing antibody breadth; the latter suggests the need to enhance VRC01 delivery and/or in vivo binding. We will apply the clinical trial regression model to data from the completed trials to help optimize future approaches for passive delivery of anti-HIV neutralizing antibodies.

Infusions of broadly neutralizing antibodies are currently being tested as a novel HIV prevention modality. To help interpret the results of these antibody mediated prevention (AMP) studies we developed a mathematical modeling framework. The approach combines antibody potency and drug levels with models of HIV viral dynamics, which will be generally applicable to future studies. Through simulating these clinical trials, we found trial outcomes can be used in combination to infer whether breakthrough infections are caused by large fractions of antibody-resistant circulating strains or some reduction in potency against all strains. This distinction helps to focus future trials on enhancing neutralizing antibody breadth or antibody delivery and/or in vivo binding.

Kinematic flexibility allows bumblebees to increase energetic efficiency when carrying heavy loads

Foraging bees fly with heavy loads of nectar and pollen, incurring energetic costs that are typically assumed to depend on load size. Insects can produce more force by increasing stroke amplitude and/or flapping frequency, but the kinematic response of a given species is thought to be consistent. We examined bumblebees (Bombus impatiens) carrying both light and heavy loads and found that stroke amplitude increased in proportion to load size, but did not predict metabolic rate. Rather, metabolic rate was strongly tied to frequency, which was determined not by load size but by the bee's average loading state and loading history, with heavily loaded bees displaying smaller changes in frequency and smaller increases in metabolic rate to support additional loading. This implies that bees can increase force production through alternative mechanisms; yet, they often choose the energetically costly option of elevating frequency, suggesting associated performance benefits that merit further investigation.

Evaluation of the secondary use of electronic health records to detect seasonal, holiday-related, and rare events related to traumatic injury and poisoning

BACKGROUND

The increasing adoption of electronic health record (EHR) systems enables automated, large scale, and meaningful analysis of regional population health. We explored how EHR systems could inform surveillance of trauma-related emergency department visits arising from seasonal, holiday-related, and rare environmental events.

METHODS

We analyzed temporal variation in diagnosis codes over 24 years of trauma visit data at the three hospitals in the University of Washington Medicine system in Seattle, Washington, USA. We identified seasons and days in which specific codes and categories of codes were statistically enriched, meaning that a significantly greater than average proportion of trauma visits included a given diagnosis code during that time period.

RESULTS

We confirmed known seasonal patterns in emergency department visits for trauma. As expected, cold weather-related incidents (e.g. frostbite, snowboarding injury) were enriched in the winter, whereas fair weather-related incidents (e.g. bug bites, boating accidents, bicycle accidents) were enriched in the spring and summer. Our analysis of specific days of the year found that holidays were enriched for alcohol poisoning, assaults, and firework accidents. We also detected one time regional events such as the 2001 Nisqually earthquake and the 2006 Hanukkah Eve Windstorm.

CONCLUSIONS

Though EHR systems were developed to prioritize operational rather than analytic priorities and have consequent limitations for surveillance, our EHR enrichment analysis nonetheless re-identified expected temporal population health patterns. EHRs are potentially a valuable source of information to inform public health policy, both in retrospective analysis and in a surveillance capacity.

De novo design of a homo-trimeric amantadine-binding protein

The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the C3 symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems.

Dynamic imaging and quantification of subcellular motion with eigen-decomposition optical coherence tomography-based variance analysis

The dynamic properties of subcellular organism are important biomarkers of the health. Imaging subcellular level dynamics provides effective solutions for evaluating cell metabolism and testing the responses of cells to pathogens and drugs in pharmaceutical engineering. In this paper, we demonstrate an innovative approach to contrast the subcellular motion by using eigen decomposition (ED)-based variance analysis of time-dependent complex optical coherence tomography signals. This method reveals a superior advantage of contrast to noise ratio when compared with the approach that employs intensity decorrelation. Furthermore, the eigen values derived from ED processing are calculated and applied to assess the power ratios of complex signal invariance that decreases exponentially along time dimension. The validation experiments are performed on the patterned samples of yeast powder mixed with gelatin/TiO2 water solution. Additionally, the proposed method is used to image mouse cerebral cortex in normal and pathological conditions, suggesting the practicality of variance power mapping in analyzing cortical neural activities. The technique promises efficient measurement of subcellular motions with high sensitivity and high throughput for in vivo and in situ applications.

Use of the LENA Autism Screen with Children who are Deaf or Hard of Hearing

Background and Objectives: This systematic review reports the evidence from the literature concerning the potential for using an automated vocal analysis, the Language ENvironment Analysis (LENA, LENA Research Foundation, Boulder, CO, USA) in the screening process for children at risk for autism spectrum disorder (ASD) and deaf or hard of hearing (D/HH). ASD and D/HH have increased comorbidity, but current behavioral diagnostic and screening tools have limitations. The LENA Language Autism Screen (LLAS) may offer an additional tool to disambiguate ASD from D/HH in young children. Materials and Methods: We examine empirical reports that use automatic vocal analysis methods to differentiate disordered from typically developing children. Results: Consensus across the sampled scientific literature shows support for use of automatic methods for screening and disambiguation of children with ASD and D/HH. There is some evidence of vocal differentiation between ASD, D/HH, and typically-developing children warranting use of the LLAS, but additional empirical evidence is needed to better understand the strengths and weaknesses of the tool. Conclusions: The findings reported here warrant further, more substantive, methodologically-sound research that is fully powered to show a reliable difference. Findings may be useful for both clinicians and researchers in better identification and understanding of communication disorders.

Cell-Templated Silica Microparticles with Supported Lipid Bilayers as Artificial Antigen-Presenting Cells for T Cell Activation

Biomaterial properties that modulate T cell activation, growth, and differentiation are of significant interest in the field of cellular immunotherapy manufacturing. In this work, a new platform technology that allows for the modulation of various activation particle design parameters important for polyclonal T cell activation is presented. Artificial antigen presenting cells (aAPCs) are successfully created using supported lipid bilayers on various cell-templated silica microparticles with defined membrane fluidity and stimulating antibody density. This panel of aAPCs is used to probe the importance of activation particle shape, size, membrane fluidity, and stimulation antibody density on T cell outgrowth and differentiation. All aAPC formulations are able to stimulate T cell growth, and preferentially promote CD8+ T cell growth over CD4+ T cell growth when compared to commercially available pendant antibody-conjugated particles. T cells cultured with HeLa- and red blood cell-templated aAPCs have a less-differentiated and less-exhausted phenotype than those cultured with spherical aAPCs with matched membrane coatings when cultured for 14 days. These results support continued exploration of silica-supported lipid bilayers as an aAPC platform.

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

High content, high-throughput screening for small molecule inducers of NF-κB translocation

NF-κB is an important mediator of immune activity and its activation is essential in mounting immune response to pathogens. Here, we describe the optimization and implementation of a high-throughput screening platform that utilizes high content imaging and analysis to monitor NF-κB nuclear translocation. We screened 38,991 compounds from three different small molecule libraries and identified 103 compound as hits; 31% of these were active in a dose response assay. Several of the molecules lacked cytotoxicity or had a selectivity index of more than 2-fold. Our image-based approach provides an important first step towards identifying small molecules with immunomodulatory activity.