bluuebunny/crossref_metadata_2025_split huggingface dataset:
Supervised biological network alignment with graph neural networks
Authors: Ding Kerr , Wang Sheng , Luo Yunan 
Published: April 2023

AbstractMotivationDespite the advances in sequencing technology, massive proteins with known sequences remain functionally unannotated. Biological network alignment (NA), which aims to find the node correspondence between species’ protein-protein interaction (PPI) networks, has been a popular strategy to uncover missing annotations by transferring functional knowledge across species. Traditional NA methods assumed that topologically similar proteins in PPIs are functionally similar. However, it was recently reported that functionally unrelated proteins can be as topologically similar as functionally related pairs, and a new data-driven or supervised NA paradigm has been proposed, which uses protein function data to discern which topological features correspond to functional relatedness.ResultsHere, we proposeGraNA, a deep learning framework for the supervised NA paradigm for the pairwise network alignment problem. Employing graph neural networks, GraNA utilizes within-network interactions and across-network anchor links for learning protein representations and predicting functional correspondence between across-species proteins. A major strength of GraNA is its flexibility to integrate multi-faceted non-functional relationship data, such as sequence similarity and ortholog relationships, as anchor links to guide the mapping of functionally related proteins across species. Evaluating GraNA on a benchmark dataset composed of several NA tasks between different pairs of species, we observed that GraNA accurately predicted the functional relatedness of proteins and robustly transferred functional annotations across species, outperforming a number of existing NA methods. When applied to a case study on a humanized yeast network, GraNA also successfully discovered functionally replaceable human-yeast protein pairs that were documented in previous studies.AvailabilityThe code of GraNA is available athttps://github.com/luo-group/GraNA.Contactyunan@gatech.edu
Large scale eDNA database reveals latitudinal diversity gradients with seasonal variations
Authors: Kasada Minoru , Shinohara Naoto , Fukasawa Riku , Tanabe Akifumi S. , Kondoh Michio 
Published: April 2023

Withdrawal StatementThe authors have withdrawn their manuscript owing to conflicts regarding the use of data. Therefore, the authors do not wish this work to be cited as reference for the project. The revised version will be updated once this issue has been resolved. If you have any questions, please contact the corresponding author.
Targeting Transferrin Receptor to Transport Antisense Oligonucleotides Across the Blood-Brain Barrier
Authors: Barker Scarlett J , Thayer Mai B , Kim Chaeyoung , Tatarakis David , Simon Matthew , Dial Rebekah L , Nilewski Lizanne , Wells Robert C , Zhou Yinhan , Afetian Megan , Chappell Alfred , Chew Kylie S , Chow Johann , Clemens Allisa , Discenza Claire B , Dugas Jason , Dwyer Chrissa , Earr Timothy , Ha Connie , Huynh David , Jayaraman Srini , Kwan Wanda , Mahon Cathal , Pizzo Michelle , Roche Elysia , Sanders Laura , Stergioulis Alexander , Tong Raymond , Tran Hai , Zuchero Joy , Estrada Anthony A , Gadkar Kapil , Koth Christopher MM , Sanchez Pascal E , Thorne Robert G , Watts Ryan J , Sandmann Thomas , Kane Lesley , Rigo Frank , Dennis Mark S , Lewcock Joseph W , DeVos Sarah L 
Published: April 2023

AbstractAntisense oligonucleotides (ASO) are promising therapies for neurological disorders, though they are unable to cross the blood-brain barrier (BBB) and must be delivered directly to the central nervous system (CNS). Here, we use a human transferrin receptor (TfR)-binding molecule to transport ASO across the BBB in mice and non-human primates, termed oligonucleotide transport vehicle (OTV). Systemically delivered OTV drives significant, cumulative, and sustained knockdown of the ASO target across multiple CNS regions and all major cell types. Further, systemic OTV delivery enables more uniform ASO biodistribution and knockdown compared to two other clinically relevant ASO delivery routes: a standard, high affinity TfR antibody, or direct ASO delivery to the CSF. Together, our data support systemically delivered OTV as a potential therapeutic platform for neurological disorders.One-Sentence SummarySystemically dosed OTV delivered via TfR1 targeting shows widespread and cumulative target knockdown in the mouse and NHP CNS.
Using sentinel nodes to evaluate changing connectivity in a protected areas network
Authors: O’Brien Paul , Carr Natasha , Bowman Jeff 
Published: April 2023

AbstractIt has been recognized that well-connected networks of protected areas are needed to halt the continued loss of global biodiversity. The recently signed Kunming-Montreal biodiversity agreement commits countries to protecting 30% of terrestrial lands in well-connected networks of protected areas by 2030. To meet these ambitious targets, land-use planners and conservation practitioners will require tools to identify areas important for connectivity and track future changes. In this study we present methods using circuit theoretic models with a subset of sentinel park nodes to evaluate connectivity for a protected areas network. We assigned a lower cost to natural areas within protected areas, under the assumption that animal movement within parks should be less costly given the regulation of activities. We found that by using mean pairwise effective resistance (MPER) as an indicator of overall network connectivity, we were able to detect changes in a parks network in response to simulated land-use changes. As expected, MPER increased with the addition of high-cost developments and decreased with the addition of new, low-cost protected areas. We tested our sentinel node method by evaluating connectivity for the protected areas network in the province of Ontario, Canada. Our method can help provide protected areas ecologists and planners with baseline estimates of connectivity for a given protected areas network and an indicator that can be used to track changes in connectivity in the future.
Phase-specific stimulation reveals consistent sinusoidal modulation of human corticospinal excitability along the oscillatory beta cycle
Authors: Keute Marius , Gebühr Julian-Samuel , Guggenberger Robert , Trunk Bettina Hanna , Gharabaghi Alireza 
Published: April 2023

AbstractThe responsiveness of neuronal populations to incoming information fluctuates. Retrospective analyses of randomly applied stimuli reveal a neural input-output relationship along the intrinsic oscillatory cycle. Prospectively harnessing this biological mechanism would necessitate frequency- and phase-specificity, intra- and inter-individual consistency, and instantaneous access to the oscillatory cycle.We used a novel real-time approach to electroencephalography-triggered transcranial magnetic stimulation to precisely target 8 equidistant phases of the oscillatory cycle in the human motor cortex of male and female healthy participants. The phase-dependency of corticospinal excitability was investigated in ten different intrinsic frequencies (4, 8, 12, 16, 20, 24, 28, 32, 36, and 40Hz) and indexed by motor-evoked potentials (MEP) in the corresponding forearm muscle.On both the individual and group level, we detected a consistent sinusoidal MEP modulation along the oscillatory cycle at 24Hz (χ22=9.2, p=.01), but not at any other target frequency (all χ22<5, all p>.08). Moreover, cross-validations showed also at 24Hz the highest consistency of the optimal phase between prospective (real-time) and retrospective (out-of-sample) testing (r=.605, p<.001), and across experimental sessions on three different days (r≥.45). The optimal corticospinal signal transmission was at the transition from the trough to the rising flank of the oscillatory 24Hz cycle.Integrating real-time measurement and brain stimulation revealed that the sinusoidal input-output relationship of corticospinal signal transmission is frequency- and phase specific, and consistent within and across individuals and sessions. In future, this approach allows to selectively and repetitively target windows of increased responsiveness, and to thereby investigate potential cumulative effects on plasticity induction.
STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data
Authors: Kwee Bjørn P. Y. , Messemaker Marius , Marcus Eric , Oliveira Giacomo , Scheper Wouter , Wu Catherine J. , Teuwen Jonas , Schumacher Ton N. 
Published: April 2023

AbstractThe prediction of peptide-MHC (pMHC) recognition by αβ T-cell receptors (TCRs) remains a major biomedical challenge. Here, we develop STAPLER (Shared TCR And Peptide Language bidirectional Encoder Representations from transformers), a transformer language model that uses a joint TCRαβ- peptide input to allow the learning of patterns within and between TCRαβ and peptide sequences that encode recognition. First, we demonstrate how data leakage during negative data generation can confound performance estimates of neural network-based models in predicting TCR – pMHC specificity. We then demonstrate that, because of its pre-training and fine-tuning masked language modeling tasks, STAPLER outperforms both neural network-based and distance-based ML models in predicting the recognition of known antigens in an independent dataset, in particular for antigens for which little related data is available. Based on this ability to efficiently learn from limited labeled TCR- peptide data, STAPLER is well-suited to utilize growing TCR – pMHC datasets to achieve accurate prediction of TCR – pMHC specificity.
Propulsive cell entry diverts pathogens from immune degradation by remodeling the phagocytic synapse
Authors: Zhang Zihan , Gaetjens Thomas K. , Yu Yanqi , Mallory D. Paul , Abel Steven M. , Yu Yan 
Published: April 2023

AbstractPhagocytosis is a critical immune function for infection control and tissue homeostasis. This process is typically described as non-moving pathogens being internalized and degraded in phagolysosomes. For pathogens that evade immune degradation, the prevailing view is that virulence factors that biochemically disrupt the biogenesis of phagoslysosomes are required. In contrast, here we report that physical forces exerted by pathogens during cell entry divert them away from the canonical phagolysosomal degradation pathway, and this altered intracellular fate is determined at the time of phagocytic synapse formation. We used the eukaryotic parasiteToxoplasma gondiias a model because liveToxoplasmauses gliding motility to actively invade into host cells. To differentiate the effect of physical forces from that of virulence factors in phagocytosis, we developed a strategy that used magnetic forces to induce propulsive entry of inactivatedToxoplasmainto macrophage cells. Experiments and computer simulations collectively reveal that large propulsive forces suppress productive activation of receptors by hindering their spatial segregation from phosphatases at the phagocytic synapse. Consequently, the inactivated parasites, instead of being degraded in phagolysosomes, are engulfed into vacuoles that fail to mature into degradative units, following an intracellular pathway strikingly similar to that of the live motile parasite. Using opsonized beads, we further confirmed that this mechanism is general, not specific to the parasite used. These results reveal previously unknown aspects of immune evasion by demonstrating how physical forces exerted during active cell entry, independent of virulence factors, can help pathogens circumvent phagolysosomal degradation.
Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data
Authors: Lu Shan , Keleş Sündüz 
Published: April 2023

AbstractPopulation-scale single cell RNA-seq (scRNA-seq) datasets create unique opportunities for quantifying expression variation across individuals at the gene co-expression network level. Estimation of co-expression networks is well-established for bulk RNA-seq; however, single-cell measurements pose novel challenges due to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased towards zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq datasets and accurately quantify network level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the datasets. Compared to alternatives, Dozer results in fewer false positive edges in the co-expression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the datasets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Co-expression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer disease and controls uniquely reveals co-expression modules of innate immune response with markedly different co-expression levels between the diagnoses. Dozer represents an important advance in estimating personalized co-expression networks from scRNA-seq data.
Organization of orbitofrontal-auditory pathways in the Mongolian gerbil
Authors: Ying Rose , Hamlette Lashaka , Nikoobakht Laudan , Balaji Rakshita , Miko Nicole , Caras Melissa L. 
Published: April 2023

AbstractSound perception is highly malleable, rapidly adjusting to the acoustic environment and behavioral demands. This flexibility is the result of ongoing changes in auditory cortical activity driven by fluctuations in attention, arousal, or prior expectations. Recent work suggests that the orbitofrontal cortex (OFC) may mediate some of these rapid changes, but the anatomical connections between the OFC and the auditory system are not well-characterized. Here, we used virally-mediated fluorescent tracers to map the projection from OFC to the auditory midbrain, thalamus, and cortex in a classic animal model for auditory research, the Mongolian gerbil (Meriones unguiculatus). We observed no connectivity between the OFC and the auditory midbrain, and an extremely sparse connection between the dorsolateral OFC and higher-order auditory thalamic regions. In contrast, we observed a robust connection between the ventral and medial subdivisions of the OFC and the auditory cortex, with a clear bias for secondary auditory cortical regions. OFC axon terminals were found in all auditory cortical lamina but were significantly more concentrated in the infragranular layers. Tissue-clearing and lightsheet microscopy further revealed that auditory cortical-projecting OFC neurons send extensive axon collaterals throughout the brain, targeting both sensory and non-sensory regions involved in learning, decision-making, and memory. These findings provide a more detailed map of orbitofrontal-auditory connections and shed light on the possible role of the OFC in supporting auditory cognition.
SeqImprove: Machine Learning Assisted Creation of Machine Readable Sequence Information
Authors: Mante Jeanet , Sents Zach , Myers Chris J. 
Published: April 2023

AbstractThe progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post-hoc curation is highly noisy and error-prone. To combat this, author participation during the curation process is crucial. To encour-age author participation without overburdening them, an ML-assisted curation tool called SeqImprove has been developed. Using named entity recognition, named entity normalization, and sequence matching, SeqImprove creates machine-readable sequence data and metadata annotations, which authors can then review and edit before sub-mitting a final sequence file. SeqImprove makes it easier for authors to submit FAIR sequence data that is findable, accessible, interoperable, and reusable.
Connectome embedding in multidimensional graph-invariant spaces
Authors: Mach Mathieu , Amico Enrico , Liégeois Raphaël , Preti Maria Giulia , Griffa Alessandra , Van De Ville Dimitri , Pedersen Mangor 
Published: April 2023

AbstractThe topological organization of brain networks, or connectomes, can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to ten node-level graph theoretical invariants. Nodal invariants are intrinsic nodal properties which reflect the topological characteristics of the nodes with respect to the whole network, including segregation (e.g., clustering coefficient) and centrality (e.g., betweenness centrality) measures. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated multiple types of connectomes (structural/functional networks and binary/weighted networks) and embedded the corresponding network nodes (brain regions) into multidimensional graph spaces defined by the invariants. First, we observed that nodal invariants are correlated between them (i.e., they carry similar network information) at a whole-brain and subnetwork level. Second, we conducted a machine learning analysis to test whether brain regions embedded in multidimensional graph spaces can be accurately classified into higher order (association, subcortical and cerebellar) and lower order (visual, somatomotor, attention) areas. Brain regions of higher order and lower order brain circuits were classified with an 80-87% accuracy in a 10-dimensional (10D) space. 10D graph metrics performed better than 2D and 3D graph metrics, and non-linear Gaussian kernels performed better than linear kernels. This suggests a non-linear brain network information gain in a high-dimensional graph space. Finally, we quantified the inter-subject Euclidean distance of each brain region embedded in the multidimensional graph space. The inter-individual distance was largest for regions of the default mode and frontoparietal networks, providing a new avenue for subject-specific network coordinates in a multidimensional space. To conclude, we propose a new framework for quantifying connectome features in multidimensional spaces defined by graph invariants, providing a new avenue for subject-specific network coordinates and inter-individual distance analyses.
Statistical learning in epilepsy: Behavioral, anatomical, and causal mechanisms in the human brain
Authors: Aljishi Ayman , Sherman Brynn E. , Huberdeau David M. , Obaid Sami , Sivaraju Adithya , Turk-Browne Nicholas B. , Damisah Eyiyemisi C. 
Published: April 2023

AbstractStatistical learning, the fundamental cognitive ability of humans to extract regularities across experiences over time, engages the medial temporal lobe in the healthy brain. This leads to the hypothesis that statistical learning may be impaired in epilepsy patients, and that this impairment could contribute to their varied memory deficits. In turn, epilepsy patients provide a platform to advance basic understanding of statistical learning by helping to evaluate the necessity of medial temporal lobe circuitry through disease and causal perturbations. We implemented behavioral testing, volumetric analysis of the medial temporal lobe substructures, and direct electrical brain stimulation to examine statistical learning across a cohort of 61 epilepsy patients and 28 healthy controls. Behavioral performance in a statistical learning task was negatively associated with seizure frequency, irrespective of where seizures originated in the brain. The volume of hippocampal subfields CA1 and CA2/3 correlated with statistical learning performance, suggesting a more specific role of the hippocampus. Indeed, transient direct electrical stimulation of the hippocampus disrupted statistical learning. Furthermore, the relationship between statistical learning and seizure frequency was selective: behavioral performance in an episodic memory task was impacted by structural lesions in the medial temporal lobe and by antiseizure medications, but not by seizure frequency. Overall, these results suggest that statistical learning may be hippocampally dependent and that this task could serve as a clinically useful behavioral assay of seizure frequency distinct from existing neuropsychological tests. Simple and short statistical learning tasks may thus provide patient-centered endpoints for evaluating the efficacy of novel treatments in epilepsy.
Improving the solubility of pseudo-hydrophobic Alzheimer’s Disease medicinal chemicals through co-crystal formulation
Authors: A Tse , I Janilkarn-Urena , Lin J , Chang X , Efthymiou C , Idrissova A , Zhang M , CK Williams , Magaki S , HV Vinters , DL Davies , Gonen T , HJ Gukasyan , PM Seidler 
Published: April 2023

AbstractNatural products are ligands and potential inhibitors of Alzheimer’s disease (AD) tau. Dihydromyricetin (DHM) is a CNS active natural product. Despite having signature polyphenolic character, DHM is ostensibly hydrophobic owing to intermolecular hydrogen bonds that shield hydrophilic phenols. Our research shows DHM becomes ionized at near-neutral pH allowing formulation of salts with transformed solubility. The MicroED co-crystal structure with trolamine reveals DHM salts as metastable solids with unlocked hydrogen bonding and a thermodynamic bent to solubilize in water. All salt formulations show better inhibitory activity against AD tau than the non-salt form, with efficacies correlating to enhanced solubilities. These results underscore the role of structural chemistry in guiding selection of solubilizing agents for chemical formulation. We propose DHM salts are appropriate formulations for research as dietary supplements to promote healthy aging by combating protein misfolding. Additionally, DHM is a suitable lead for medicinal chemistry and possible development of CNS pharmaceuticals.