bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interpretable and explainable predictive machine learning models for data-driven protein engineering
Authors: Medina-Ortiz David , Khalifeh Ashkan , Anvari-Kazemabad Hoda , Davari Mehdi D. 
Published: February 2024

AbstractProtein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in data-driven strategies. However, the lack of explainability and interpretability in these models poses challenges. Explainable Artificial Intelligence addresses the interpretability and explainability of machine learning models, providing transparency and insights into predictive processes. Nonetheless, there is a growing need to incorporate explainable techniques in predicting protein properties in machine learning-assisted protein engineering. This work explores incorporating explainable artificial intelligence in predicting protein properties, emphasizing its role in trustworthiness and interpretability. It assesses different machine learning approaches, introduces diverse explainable methodologies, and proposes strategies for seamless integration, improving trust-worthiness. Practical cases demonstrate the explainable model’s effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness. The study highlights the utility of explainable artificial intelligence in advancing computationally assisted protein design, fostering confidence in model reliability.
Alcohol drinking is associated with greater calcium activity in mouse central amygdala dynorphin-expressing neurons
Authors: Lebonville Christina L. , Rinker Jennifer A. , O’Hara Krysten , McMahan Christopher S. , Hoffman Michaela , Becker Howard C. , Mulholland Patrick J. 
Published: February 2024

AbstractAlcohol use disorder (AUD) is a chronic disease that poses significant economic burden and health risks. It is pivotal to better understand brain mechanisms engaged by alcohol that promote misuse. The central amygdala (CeA) has emerged as a key mediator of excessive preclinical alcohol consumption. A dynorphin-expressing subpopulation within the CeA (CeADyn) has been implicated in excessive alcohol drinking, yet how cellular activity of CeADynneurons relates to ongoing alcohol drinking is not well-understood. The current study interrogated the engagement of CeADynneurons in male and female mice during voluntary alcohol consumption using fiber photometry and compared this cellular response with that of other solutions having similar motivational and/or taste characteristics. Activity of a calcium sensor, GCaMP7f, expressed in mouse CeADynneurons was recorded and time-locked to bouts of drinking. Multilevel linear mixed modeling was applied to better resolve focal effects from complex data. These analyses revealed a relatively large increase in CeADynneuron calcium transients after bouts of alcohol drinking compared to water or sucrose drinking, indicating these neurons are uniquely engaged during alcohol consumption. Drinking behavior unique to alcohol (i.e., longer bout durations) did not fully explain signal differences between alcohol and other solutions nor did the relatively increased alcohol response diminish over time. No other conditions or solutions tested reproduced the pronounced change in CeADynactivity associated with alcohol drinking. These findings, collectively, support the presence of a unique functional signature for alcohol in a cell population known to control excessive alcohol drinking.HighlightsCentral amygdala dynorphin cells (CeADyn) are firmly implicated in alcohol misuse.CeADynneuron activity was higher when mice drank alcohol versus other solutions.Neither how mice drank alcohol nor motivational states could explain this activity.CeADynneurons having uniquely high alcohol responses may underlie AUD development.
New, biomechanically sound tendon tissue after injection of uncultured, autologous, adipose derived regenerative cells in partial Achilles tendon defects in rabbits
Authors: Schmitz Christoph , Alt Christopher , Würfel Tobias , Milz Stefan , Dinzey Jacqueline , Hill Ashley , Sikes Katie J. , Burton Lindsey , Easley Jeremiah , Stewart Holly L. , Puttlitz Christian M. , Gadomski Benjamin C. , Labus Kevin , Pearce David A. , Maffulli Nicola , Alt Eckhard U. 
Published: February 2024

ABSTRACTBackgroundCurrent management options for partial tendon tears may not offer future potential to heal tissue and improve clinical results. This study tested the hypothesis that treatment of a partial rabbit common calcaneus tendon (CCT) defect with uncultured, autologous, adipose derived regenerative cells (UA-ADRCs) enables regenerative healing without scar formation, as recently observed in a biopsy of a human supraspinatus tendon.MethodsA full-thickness hole (diameter, 3 mm) was punched into the midsubstance of the right gastrocnemius tendon (GT; which is a part of the CCT) of adult, female New Zealand white rabbits. Immediately thereafter the rabbits were treated by application of an averaged 28.3×106UA-ADRCs in 0.5 ml lactated Ringer’s solution (RLS) into the GT defect and surrounding tendon tissue, or underwent sham treatment. Rabbits were sacrificed either four weeks (W4) or twelve weeks (W12) post-treatment, and the CCTs were investigated using histology, immunohistochemistry and non-destructive biomechanical testing.ResultsNewly formed connective tissue was consistent with the formation of new tendon tissue after treatment with UA-ADRCs, and with the formation of scar tissue after sham treatment, at both W4 and W12 post-treatment. Biomechanical testing demonstrated a significantly higher mean percent relaxation after treatment with UA-ADRCs than after sham treatment (p < 0.05), and significant, negative correlations between the peak stress as well as the equilibrium stress and the cross-sectional area of the CCT (p < 0.05) after treatment with UA-ADRCs but not after sham treatment.ConclusionsManagement of partial tendon tears with UA-ADRCs has the potential to be truly “structure-modifying”.
From local seafloor imagery to global patterns in benthic habitat states: contribution of citizen science to habitat classification across latitudes
Authors: Violet Clément , Boyé Aurélien , Dubois Stanislas , Edgar Graham J. , Oh Elizabeth S. , Stuart-Smith Rick D. , Marzloff Martin P. 
Published: February 2024

AbstractAimThe aim of this study was to define reef benthic habitat states and explore their spatial and temporal variability at a global scale using an innovative clustering pipeline.LocationThe study uses data on the transects surveyed on shallow (< 20m) reef ecosystems across the globe. Time period: Transects sampled between 2008 and 2021. Major taxa studied: Macroalgae, sessile invertebrates, hydrozoans, seagrass, corals.MethodsPercentage cover was estimated for 24 functional groups of sessile biota and substratum from annotated underwater photoquadrats taken along 6,554 transects by scuba divers contributing to the Reef Life Survey dataset. A clustering pipeline combining a non-linear dimension-reduction technique (UMAP), with a density-based clustering approach (HDBSCAN), was used to identify benthic habitat states. Spatial and temporal variation in habitat distribution was then explored across ecoregions.ResultsTheUMAP-HDBSCANpipeline identified 17 distinct clusters representing different benthic habitats and gradients of ecological state. Certain habitat states displayed clear biogeographic patterns, predominantly occurring in temperate regions or tropical waters. Notably, some reefs dominated by turf algae were ubiquitous across latitudinal zones. Transition zones between temperate and tropical waters emerged as spatial hotspots of habitat state diversity. Temporal analyses revealed changes in the proportion of certain states over time, notably an increase in turf algae occurrence.Main ConclusionsTheUMAP-HDBSCANclustering pipeline effectively characterised fine-scale benthic habitat states at a global scale, confirming known broader biogeographic patterns, including the importance of temperate-tropical transition zones as hotspots of habitat state diversity. This fine-scale, yet broadly-scalable habitat classification could be applied as a standardised template for tracking benthic habitat change across space and time at a global scale. TheUMAP-HDBSCANpipeline has proven to be a powerful and versatile approach for analysing complex biological datasets and can be applied in various ecological domains.
Multi-trait multi-environment genomic prediction of preliminary yield trials in pulse crops
Authors: Saludares Rica Amor , Atanda Sikiru Adeniyi , Piche Lisa , Worral Hannah , Dariva Francoise , McPhee Kevin , Bandillo Nonoy 
Published: February 2024

ABSTRACTPhenotypic selection in preliminary yield trials (PYT) is challenged by limited seeds, resulting in trials with few replications and environments. The emergence of multi-trait multi-environment enabled genomic prediction (MTME-GP) offers opportunity for enhancing prediction accuracy and genetic gain across multiple traits and diverse environments. Using a set of 300 advanced breeding lines in the North Dakota State University (NDSU) pulse crop breeding program, we assessed the efficiency of a MTME-GP model for improving seed yield and protein content in field peas in stress and non-stress environments. MTME-GP significantly improved predictive ability, improving up to 2.5-fold, particularly when a significant number of genotypes overlapped across environments. Heritability of the training environments contributed significantly to the overall prediction of the model. Average predictive ability ranged from 3 to 7-folds when environments with low heritability were excluded from the training set. Overall, the Reproducing Kernel Hilbert Spaces (RKHS) model consistently resulted in improved predictive ability across all breeding scenarios considered in our study. Our results lay the groundwork for further exploration, including integration of diverse traits, incorporation of deep learning techniques, and the utilization of multi-omics data in predictive modeling.Core ideasPhenotypic selection in PYT is challenged by limited seeds, resulting to few replications and environments.MTME-GP offers opportunity for enhancing prediction accuracy of multi-trait and diverse environments in PYT.MTME-GP enhances prediction by up to 2.5-fold, especially with numerous overlapping genotypes in various tested environments.RKHS MTME-GP models, excels in low-heritability, negatively correlated traits, like drought-affected conditions.
Adaptive Mating Among Natural Strains ofS. cerevisiae
Authors: Strauss Sivan Kaminski , Liti Gianni , Dahan Orna , Pilpel Yitzhak 
Published: February 2024

AbstractIn evolutionary biology, sexual mating plays a pivotal role in facilitating the combination of beneficial alleles among individuals. Cross-species data suggest that organisms selectively mate with partners based on factors such as genetic distance and partner fitness. Understanding the determinants of pair-specific mating affinity is crucial for unraveling the impact of sex on evolution. However, despite the significance of this phenomenon, the availability of large and consistent datasets is limited, leading to inconsistent conclusions.To address this gap, we present a comprehensive mating assay enabling the simultaneous quantification of mating affinity among approximately 100 naturalSaccharomyces cerevisiaestrains. Our study demonstrates that mating in anen massemanner, allowing mating based on affinity, enhances the overall fitness of the hybrids offspring population compared to mating that enforces one specific partner at a time. By employing a DNA barcode recombination system integrated into natural isolates’ genomes, we sequence recombined barcode pairs and revealed mating frequencies and affinities among all strains in different environments.Our findings unveil strain-specific mating affinity among natural yeast strains, with certain parental pairs exhibiting a heightened affinity for each other over other strains, whereas certain strains combinations are avoided. Notably, among the pairs with the highest affinity, there is a preference for lower genetic distances. Intriguingly, multiple strains show a propensity for mating with partners that yield higher-fitness hybrids on average. Collectively, our results provide compelling evidence that yeast actively engages in adaptive mate affinity.
Nodulation Trio inMedicago truncatula:Unveiling the Overlapping Roles of MtLYK2, MtLYK3, and MtLYK2bis
Authors: Li Yaohua , Zhao Yanwen , Yan Ziang , Dong Ru , Yu Haixiang , Zhu Hui , Cao Yangrong 
Published: February 2024

AbstractThe perception of rhizobial nodulation factors (NF) by plant NF receptors is crucial for mediating legume nodulation. In Medicago truncatula nodulation, a proposed two-receptor model designates MtLYK3 as an entry receptor, while other LYK proteins, with unidentified functions, may serve as signaling receptors. Here, we generated the single, double, and triple mutants for M. truncatula MtLYK2, MtLYK3, and MtLYK2bis using CRISPR/Cas technology and examined their roles in nodulation. Our findings suggest that all three LYKs possess redundant functions in Medicago nodulation, with their distinct contributions attributed to varying transcription patterns.Our findings also suggest that a one-receptor model with multiple specificities that mediates both signaling and entry responses is more suitable in M. truncatula.
Modeling Neuromotor Adaptation to Pulsed Torque Assistance During Walking
Authors: Kim GilHwan , Sergi Fabrizio 
Published: February 2024

AbstractMultiple mechanisms of motor learning contribute to the response of individuals to robot-aided gait training, including error-based learning and use-dependent learning. Previous models described either of these mechanisms, but not both, and their relevance to gait training is unknown. In this paper, we establish the validity of existing models to describe the response of healthy individuals to robot-aided training of propulsion via a robotic exoskeleton, and propose a new model that accounts for both use-dependent and error-based learning.We formulated five state-space models to describe the stride-by-stride evolution of metrics of propulsion mechanics during and after robot-assisted training, applied by a hip/knee robotic exoskeleton for 200 consecutive strides. The five models included a single-state, a two-state, a two-state fast and slow, a use-dependent learning (UDL), and a newly-developed modified UDL model, requiring 4, 9, 5, 3, and 4 parameters, respectively. The coefficient of determination (R2) and Akaike information criterion (AIC) values were calculated to quantify the goodness of fit of each model. Model fit was conducted both at the group and at the individual participant level.At the group level, the modified UDL model shows the best goodness-of-fit compared to other models in AIC values in 15/16 conditions. At the participant level, both the modified UDL model and the two-state model have significantly better goodness-of-fit compared to the other models. In summary, the modified UDL model is a simple 4-parameter model that achieves similar goodness-of-fit compared to a two-state model requiring 9 parameters. As such, the modified UDL model is a promising model to describe the effects of robot-aided gait training on propulsion mechanics.
Mating-compatibility genes employed as diagnostic markers to identify novel incursions of the myrtle rust pathogenAustropuccinia psidii
Authors: Feng Jinghang , Bird Austin , Luo Zhenyan , Tam Rita , Shepherd Luc , Murphy Lydia , Singh Lavi , Graetz Abigail , Moeller Mareike , Amorim Lilian , Massola Nelson Sidnei , Prodhan M. Asaduzzaman , Shuey Louise , Beattie Douglas , Gonzalez Alejandro Trujillo , Tobias Peri A. , Padovan Amanda , Kimber Rohan , McTaggart Alistair , Kehoe Monica , Schwessinger Benjamin , Boufleur Thaís R. 
Published: February 2024

ABSTRACTAustropuccinia psidiiis the causal agent of myrtle rust in over 480 species within the family Myrtaceae. Lineages ofA. psidiiare structured by host in its native range, and some have success on new-encounter hosts. For example, the pandemic biotype has spread beyond South America, and proliferation of other lineages is an additional risk to biodiversity and industries. Efforts to manageA. psidiiincursions, including lineage differentiation, relies on variable microsatellite markers. Testing these markers is time-consuming and complex, particularly on a large scale. We designed a novel diagnostic approach targeting the fungal mating-typeHD(homeodomain) transcription factor locus to address these limitations. TheHDlocus (bW1/2-HD1 andbE1/2-HD2)is highly polymorphic, facilitating clear biological predictions about its inheritance from founding populations. To be considered the same lineage, all fourHDalleles must be identical. Our lineage diagnostics relies on PCR amplification of theHDlocus in different genotypes ofA. psidiifollowed by amplicon sequencing using Oxford Nanopore Technologies (ONT) and comparative analysis. The lineage-specific assay was validated on four isolates with existing genomes, uncharacterized isolates, and directly from infected leaf material. We reconstructedHDalleles from amplicons and confirmed their sequence identity relative to their reference. Genealogies usingHDalleles confirmed the variations at theHDloci among lineages/isolates. Our study establishes a robust diagnostic tool, for differentiating known lineages ofA. psidiibased biological predictions. This tool holds promise for detecting new pathogen incursions and can be refined for broader applications, including air-sample detection and mixed-isolate infections.
Phase delays between mouse globus pallidus neurons entrained by common oscillatory drive arise from their intrinsic properties, not their coupling
Authors: Olivares Erick , Wilson Charles J. , Goldberg Joshua A. 
Published: February 2024

AbstractA hallmark of Parkinson’s disease is the appearance of correlated oscillatory discharge throughout the cortico-basal ganglia (BG) circuits. In the primate globus pallidus (GP), where the discharge of GP neurons is normally uncorrelated, pairs of GP neurons exhibit oscillatory spike correlations with a broad distribution of pairwise phase delays in experimental parkinsonism. The transition to oscillatory correlations is thought to indicate the collapse of the normally segregated information channels traversing the BG. The large phase delays are thought to reflect pathological changes in synaptic connectivity in the BG. Here we study the structure and phase delays of spike correlations measured from neurons in the mouse external GP (GPe) subjected to identical 1-100 Hz sinusoidal drive but recorded in separate experiments. First, we find that spectral modes of a GPe neuron’s empirical instantaneous phase response curve (iPRC), elucidate at what phases of the oscillatory drive the GPe neuron locks when it is entrained, and the distribution of phases at which it spikes when it is not. Then, we show that in this case the pairwise spike cross-correlation equals the cross-correlation function of these spike phase distributions. Finally, we show that the distribution of GPe phase delays arises from the diversity of iPRCs, and is broadened when the neurons become entrained. Modeling GPe networks with realistic intranuclear connectivity demonstrates that the connectivity decorrelates GPe neurons without affecting phase delays. Thus, common oscillatory input gives rise to GPe correlations whose structure and pairwise phase delays reflect their intrinsic properties captured by their iPRCs.Significance StatementThe external globus pallidus (GPe) is a hub in the basal ganglia, whose neurons impose a barrage of inhibitory synaptic currents on neurons of the subthalamic nucleus, substantia nigra and internal globus pallidus. GPe neurons normally fire independently, but in experimental parkinsonism, they become correlated in the frequency range associated with the pathological rhythms seen in human Parkinson’s disease, raising the possibility that they may be generators of the pathological oscillation. We drove individual pallidal neurons with an oscillatory input over a wide range of frequencies. Cross-correlations of these neurons reproduced many of the features seen in parkinsonism, suggesting that their correlated oscillations might derive from a shared input rather than internal interconnections.
A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context
Authors: Vijayan Athul , Mody Tejasvinee Atul , Yu Qin , Wolny Adrian , Cerrone Lorenzo , Strauss Soeren , Tsiantis Miltos , Smith Richard S. , Hamprecht Fred A. , Kreshuk Anna , Schneitz Kay 
Published: February 2024

AbstractWe present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We developed a novel approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg, and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading script that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues.Summary StatementWe present computational tools that allow versatile and accurate 3D nuclear segmentation in plant organs, enable the analysis of cell-nucleus geometric relationships, and improve the accuracy of 3D cell segmentation.