bluuebunny/crossref_metadata_2025_split huggingface dataset:
Changes in Muscle Synergy Structure and Activation Patterns Underlie Force Field Adaptation, Retention, and Generalization
Authors: Herzog Michael , Berger Denise J. , Russo Marta , d’Avella Andrea , Stein Thorsten 
Published: December 2024

1AbstractHumans can adapt their motor commands in response to errors when they perform reaching movements in new dynamic conditions, a process called motor adaptation. They acquire knowledge about the new dynamics, which they can use when they are re-exposed and, limitedly, generalize to untrained reaching directions. While force field adaptation, retention, and generalization have been thoroughly investigated at a kinematic and kinetic task level, the underlying coordination at a muscular level remains unclear. Many studies propose that the central nervous system uses low- dimensional control, i.e., coordinates muscles in functional groups: so-called muscle synergies. Accordingly, we hypothesized that changes in muscle synergy structure and activation patterns represent the acquired knowledge underlying force field adaptation, retention, and generalization. To test this, 36 male humans practiced reaching to a single target in a viscous force field and were tested for retention and generalization to new directions, while we simultaneously measured muscle activity from 13 upper-body muscles. We found that muscle synergies used for unperturbed reaching cannot explain the muscle patterns when adapted. Instead, muscle synergies specific to this adapted state were necessary, alongside a novel four-phasic pattern of muscle synergy activation. Furthermore, these structural changes and patterns were also evident during retention and generalization. Our results suggest that reaching in an environment with altered dynamics requires structural changes to muscle synergies compared to unperturbed reaching, and that these changes facilitate retention and generalization. These findings provide new insights into how the central nervous system coordinates the muscles underlying motor adaptation, retention, and generalization.2Significance StatementHumans can adapt their reaching movements to new dynamic conditions. They acquire knowledge about the new dynamics and can use it not only when re-exposed to these conditions but also, in part, to generalize unpracticed reaching directions. While adaptation, retention, and generalization in a force field with new dynamics have been thoroughly investigated at a kinematic and kinetic task level, coordination of the underlying muscles remains elusive. Our results show how muscle synergies - functional groupings of co-activated muscles - underlie adaptation, retention, and generalization. In particular, we observed structural changes in the muscle synergies after adaptation compared to unperturbed reaching. These changes facilitate retention and spatial generalization. Thus, muscle synergies provide new insights into human motor adaptation.
Natural speech re-synthesis from direct cortical recordings using a pre-trained encoder-decoder framework
Authors: Li Jiawei , Guo Chunxu , Chang Edward F. , Li Yuanning 
Published: December 2024

AbstractReconstructing perceived speech stimuli from neural recordings is not only advancing the understanding of the neural coding underlying speech processing but also an important building block for brain-computer interfaces and neuroprosthetics. However, previous attempts to directly re-synthesize speech from neural decoding suffer from low re-synthesis quality. With the limited neural data and complex speech representation space, it is hard to build decoding model that directly map neural signal into high-fidelity speech. In this work, we proposed a pre-trained encoder-decoder framework to address these problems. We recorded high-density electrocorticography (ECoG) signals when participants listening to natural speech. We built a pre-trained speech re-synthesizing network that consists of a context-dependent speech encoding network and a generative adversarial network (GAN) for high-fidelity speech synthesis. This model was pre-trained on a large naturalistic speech corpus and can extract critical features for speech re-synthesize. We then built a light-weight neural decoding network that mapped the ECoG signal into the latent space of the pre-trained network, and used the GAN decoder to synthesize natural speech. Using only 20 minutes of intracranial neural data, our neural-driven speech re-synthesis model demonstrated promising performance, with phoneme error rate (PER) at 28.6%, and human listeners were able to recognize 71.6% of the words in the re-synthesized speech. This work demonstrates the feasibility of using pre-trained self-supervised model and feature alignment to build efficient neural-to-speech decoding model.
GenePioneer: A Comprehensive Python Package for Identification of Essential Genes and Modules in Cancer
Authors: Taheri Golnaz , Haerianardakani Amirhossein 
Published: December 2024

AbstractSummaryWe propose a network-based unsupervised learning model to identify essential cancer genes and modules for 12 different cancer types, supported by a Python package for practical application. The model constructs a gene network from frequently mutated genes and biological processes, ranks genes using topological features, and detects critical modules. Evaluation across cancer types confirms its effectiveness in prioritizing cancer-related genes and uncovering relevant modules. The Python package allows users to input gene lists, retrieve rankings, and identify associated modules. This work providing a robust method for gene prioritization and module detection, along with a user-friendly package to support research and clinical decision-making in cancer genomics.AvailabilityGenePioneeris released as an open-source software under the MIT license. The source code is available on GitHub athttps://github.com/Golnazthr/ModuleDetectionContactgolnazt@kth.seandhaerian.amirhossein@gmail.com
Distinct representational properties of cues and contexts shape fear learning and extinction
Authors: Bouyeure Antoine , Pacheco Daniel , Fellner Marie-Christin , Jacob George , Kobelt Malte , Rose Jonas , Axmacher Nikolai 
Published: December 2024

AbstractExtinction learning does not erase previously established memories but inhibits the expression of fear by the formation of new memory traces that are strongly context-dependent. Previous human neuroimaging studies using representational similarity analysis revealed several core properties of memory traces during fear learning, including their tendency to generalize beyond the initial context – a process described as “cue generalization” – and their reliance on sensory rather than conceptual representational formats. How fear memories are altered during extinction learning, however, remains largely unknown. To address this question, we used a novel experimental paradigm involving multiple cues and contexts in each experimental phase, which allowed us to disentangle the effect of contingency changes (i.e., reversal learning) from the disappearance of unconditioned stimuli during extinction learning. Our data show that contingency changes during reversal induce memory traces with distinct representational geometries characterized by stable activity patterns across repetitions in the precuneus, which interact with specific context representations in medial and lateral prefrontal cortex. The representational geometries of these traces differ strikingly from the generalized patterns established during initial fear learning and persist in the absence of an unconditioned stimulus during extinction. Interestingly, increased levels of prefrontal context specificity predict the subsequent reinstatement of fear memory traces, providing a possible mechanistic explanation for the clinical phenomenon of fear renewal. Our findings show that contingency changes induce novel memory traces with distinct representational properties that are reminiscent to those observed during episodic memory formation and contrast with the generalized representations of initial fear memories. These results shed new light on the neural mechanisms underlying the malleability of memories that support cognitive flexibility, and contribute to conceptual frameworks of extinction learning during the treatment of anxiety disorders.
WITHDRAWN: CPP Translocation in Haptophyte Species
Authors: Gorrie David 
Published: December 2024

Withdrawal StatementThe authors have withdrawn this manuscript because it requires extensive revision before submission (see "shortcomings" section). Additionally, some of those acknowledged insist on authorship. Therefore, the authors do not wish this work to be cited as a reference for the project. If you have any questions, don’t hesitate to contact the corresponding author.
In silico discovery of potential inhibitors targeting the MEIG1-PACRG complex for male contraceptive development
Authors: Hasse Timothy , Zhang Zhibing , Huang Yu-ming M. 
Published: December 2024

AbstractThe interaction between meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) is a critical determinant of spermiogenesis, the process by which round spermatids mature into functional spermatozoa. Disruption of the MEIG1-PACRG complex can impair sperm development, highlighting its potential as a therapeutic target for addressing male infertility or for the development of non-hormonal contraceptive methods. This study used virtual screening, molecular docking, and molecular dynamics (MD) simulations to identify small molecule inhibitors targeting the MEIG1-PACRG interface. MD simulations provided representative protein conformations, which were used to virtually screen a library of over 800,000 compounds, resulting in 48 high-ranking candidates for each protein. PACRG emerged as a favorable target due to its flexible binding pockets and better docking scores compared to MEIG1. Key binding residues with compounds included W50, Y68, N70, and E74 on MEIG1, and K93, W96, E101, and H137 on PACRG. MD simulations revealed that compound stability in MEIG1 complexes is primarily maintained by hydrogen bonding with E74 and π-π stacking interactions with W50 and Y68. In PACRG complexes, compound stabilization is facilitated by hydrogen bonding with E101 and π-π interactions involving W96 and H137. These findings highlight distinct molecular determinants of ligand binding for each protein. Our work provides mechanistic insights and identifies promising compounds for further experimental validation, establishing a foundation for developing MEIG1-PACRG interaction inhibitors as male contraceptives.
Formulation, Characterization, andin vivoImmunogenicity of Heat-Stabilized Dissolvable Microneedles Containing a Novel VLP Vaccine
Authors: Leyba Aidan , Francian Alexandra , Razjmoo Mohammad , Bierle Amelia , Janardhana Ranjith , Jackson Nathan , Chackerian Bryce , Muttil Pavan 
Published: December 2024

AbstractSince its introduction, vaccination has heavily improved health outcomes. However, implementing vaccination efforts can be challenging, particularly in low and middle-income countries with warmer climates. Microneedle technology has been developed for its simple and relatively painless applications of vaccines. However, no microneedle vaccine has yet been approved by the FDA. A few hurdles must be overcome, including the need to evaluate the safety and biocompatibility of the polymer used to fabricate these microneedles. Additionally, it is important to demonstrate reliable immune responses comparable to or better than those achieved through traditional administration routes. Scalability in manufacturing and the ability to maintain vaccine potency during storage and transportation are also critical factors. In this study, we developed vaccine-loaded dissolvable microneedles that showed preclinical immunogenicity after storage in extreme conditions. We developed our microneedles using the conventional micromolding technique with polyacrylic acid (PAA) polymer, incorporating a novel virus-like particle (VLP) vaccine targeting arboviruses. We performed characterization studies on these microneedles to assess needle sharpness, skin insertion force, and VLP integrity. We also investigated the thermostability of the vaccine after storing the microneedles at elevated temperatures for approximately 140 days. Finally, we evaluated the immunogenicity of this vaccine in mice, comparing transdermal (microneedle) with intramuscular (hypodermic needle) administration. We successfully fabricated and characterized VLP-loaded microneedles that could penetrate the skin and maintain vaccine integrity even after exposure to extreme storage conditions. These microneedles also elicited robust and long-lasting antibody responses similar to those achieved with intramuscular administration.
Genome sequence of the ornamental plantAquilegia vulgarisreveals the flavonoid biosynthesis gene repertoire
Authors: Friedhoff Ronja , Wolff Katharina , Pucker Boas 
Published: December 2024

AbstractAquilegia vulgarisis a widespread ornamental plant. The species is well known for a huge variation of different flower colors and can even be considered as a model species for the evolution of flower morphology. Anthocyanins are a major pigment group responsible for pigmentation of flowers inA. vulgarisand many plant species. Here, we report a highly continuous genome sequence of a EuropeanAquilegia vulgarisplant displaying purple flowers. The corresponding annotation facilitates research on flower color and morphology evolution. Candidate genes for all steps in the core anthocyanidin biosynthesis were identified and investigated with respect to their expression in different seedling parts. The discovery of a flavonoid 3’5’ hydroxylase (F3’5’H), a gene required for the biosynthesis bluish delphinidin derivatives, matches previous reports about metabolites and transcripts inA. vulgaris.
Capturing the Conformational Heterogeneity of HSPB1 Chaperone Oligomers at Atomic Resolution
Authors: Berkeley Raymond F. , Plonski Alexander P. , Phan Tien , Grohe Kristof , Becker Lukas , Wegner Sebastian , Herzik Mark A. , Mittal Jeetain , Debelouchina Galia T. 
Published: December 2024

AbstractSmall heat shock proteins (sHSPs), including HSPB1, are essential regulators of cellular proteostasis that interact with unfolded and partially folded proteins to prevent aberrant misfolding and aggregation. These proteins fulfill a similar role in biological condensates, where they interact with intrinsically disordered proteins to modulate their liquid-liquid and liquid-to-solid phase transitions. Characterizing sHSP structure, dynamics, and client interactions is challenging due to their partially disordered nature, their tendency to form polydisperse oligomers, and their diverse range of clients. In this work, we leverage various biophysical methods, including fast1H-based magic angle spinning (MAS) NMR spectroscopy, molecular dynamics (MD) simulations and modeling, to shed new light on the structure and dynamics of HSPB1 oligomers. Using split-intein mediated segmental labeling, we provide unambiguous evidence that in the oligomer context the N-terminal domain (NTD) of HSPB1 is rigid and adopts an ensemble of heterogenous conformations, the α-crystallin domain (ACD) forms dimers and experiences multiple distinct local environments, while the C-terminal domain (CTD) remains highly dynamic. Our computational models suggest that the NTDs participate in extensive NTD-NTD and NTD-ACD interactions and are sequestered within the oligomer interior. We further demonstrate that HSPB1 higher order oligomers disassemble into smaller oligomeric species in the presence of a client protein and that an accessible NTD is essential for HSPB1 partitioning into condensates and interactions with client proteins. Our integrated approach provides a high-resolution view of the complex oligomeric landscape of HSPB1 and sheds light on the elusive network of interactions that underly HSPB1 function in biological condensates.Significance statementHSPB1 is a ubiquitous cellular chaperone that helps prevent the aberrant aggregation of intrinsically disordered proteins involved in biological condensates and neurodegenerative diseases. Despite its central role in this process, many aspects of HSPB1’s structure and interactions with clients are not well understood due to its tendency to form polydisperse oligomeric structures and to function in heterogeneous condensate environments. Here, we present an integrated approach that includes segmental labeling, fast MAS NMR spectroscopy, and computational tools to characterize the structure and dynamics of HSPB1 in its oligomeric form and within client condensates at high resolution. Our approach, which is applicable to other complex biological systems, highlights the important role of HSPB1’s N-terminal domain in oligomeric assembly and interactions with clients.
Confinement stress with movement restriction suppresses male courtship inDrosophilathrough dopamine-dependent neuroplasticity
Authors: Sato Tomohito , Toyama Rana , Kitamoto Toshihiro , Sakai Takaomi 
Published: December 2024

AbstractStress disturbs the physiological and psychological balance in animals, leading to changes in brain function. Here, we show that stress in a small space with movement restriction (SS stress) suppresses male courtship inDrosophilaand that alterations in dopamine signaling induced by SS stress are responsible for the persistence of this suppression after the stress experience. We found that SS stress activates numerous dopamine neurons in the brain. Pharmacological and genetic analyses revealed that dopamine synthesis, release, and reception are essential for the persistence of SS-stress-induced courtship suppression. A specific subset of dopamine neurons projects to the mushroom body (MB), a brain region where various sensory inputs are integrated. We identified that SS-stress-induced dopamine release plastically depresses the activity of a subset of MB neurons, and this neuronal depression contributes to the sustained suppression of male courtship behavior following stress exposure. This novel stress model usingDrosophilaprovides valuable insights into dopamine-mediated stress mechanisms, particularly those related to confined spaces.
Dual roles of IQCG, a novel microtubule nucleation factor rapidly evolving in humans
Authors: Liu Xingtang , Wu Yongzhen , He Xia , Shen Yuan , Fu Wenxiang , Hu Haiyang , Yu Haijing 
Published: December 2024

AbstractThe centrosome serves as the main microtubule-organizing structure, playing crucial roles in animal development. Uncovering new components and elucidating their functions helps to understand the regulation of cell maintenance and division by the centrosome. In this study, through a combination of genetic analyses and cell biological characterizations, we identify IQ Motif Containing G (IQCG) as a novel microtubule nucleation factor with a dual role involved in both centrosomal microtubule organization and mitotic spindle formation. IQCG interacts with GSK3β via its N2 region, enhancing its centrosomal accumulation in interphase; subsequently, IQCG disperses into the spindle to promote microtubule generation during mitosis and ensure robust cell division. Furthermore, our analyses indicate that theIQCGgene has undergone accelerated evolution within the human lineage, with positive selection acting on the variable N2 region. This adaptation may confer advantages to human cells by augmenting central regulatory control over cellular activities through the centrosome. Our findings offer novel insights into the functional mechanisms and evolutionary significance of the human centrosome associated with human evolution.Summary blurbThe cellular functions of the novel microtubule nucleation factor IQCG, which enhances centrosomal microtubule organization and cell division robustness, are implicated in human evolution.
Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models
Authors: Del Campo Alana O’Brien , Lituiev Dmytro , Varma Gowtham , Manoharan Mithun , Ravi Sunil Kumar , Aman Avinash , Kansagra Ankit , Greshock Joel , Venkatakrishnan AJ , Batavia Ashita 
Published: December 2024

AbstractBackgroundReal-world evidence (RWE) is increasingly recognized as a valuable type of oncology research but extracting fit-for-purpose real-world data (RWD) from electronic health records (EHRs) remains challenging. Manual abstraction from free-text clinical documents, although the gold standard for information extraction, is resource-intensive. RWD generation using natural language processing (NLP) has been limited by performance ceilings and annotation requirements, which recent LLMs improve on. We evaluate new NLP workflows in abstracting multiple myeloma (MM) information from de-identified EHRs.MethodsNLP workflows (BERT and Llama-based using various prompt types) were developed for 12 MM-specific data fields and evaluated with manually curated data from 125 clinical notes. The best Llama-based workflow for three data fields was applied to all recent notes in selected charts to generate patient journey timelines.ResultsAverage F1for the best Llama and BERT workflows was 0.82 and 0.65 respectively. Best workflow performance ranged across the data fields (F1= 0.59–0.99). Statistical analysis of the results showed model size, inter-rater reliability (IRR), variable type, and prompt design significantly predicted workflow performance, in descending order of significance (p< 0.05).ConclusionThe overall performance improvements seen with larger LLMs and chain-of-thought prompting was greater in ambiguous data fields. IRR can be used to prioritize NLP resources and increase efficiency of RWD generation without sacrificing data quality.
HieVi: Protein Large Language Model for proteome-based phage clustering
Authors: Panigrahi S. , Ansaldi M. , Ginet N. 
Published: December 2024

AbstractViral taxonomy is a challenging task due to the propensity of viruses for recombination. Recent updates from the ICTV and advancements in proteome-based clustering tools highlight the need for a unified framework to organize bacteriophages (phages) across multiscale taxonomic ranks, extending beyond genome-based clustering. Meanwhile, self-supervised large language models, trained on amino acid sequences, have proven effective in capturing the structural, functional, and evolutionary properties of proteins. Building on these advancements, we introduce HieVi, which uses embeddings from a protein language model to define a vector representation of phages and generate a hierarchical tree of phages. Using the INPHARED dataset of 24,362 complete and annotated viral genomes, we show that in HieVi, a multi-scale taxonomic ranking emerges that aligns well with current ICTV taxonomy. We propose that this method, unique in its integration of protein language models for viral taxonomy, can encode phylogenetic relationships, at least up to the family level. It therefore offers a valuable tool for biologists to discover and define new phage families while unraveling novel evolutionary connections.