bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bispecific antibodies against the hepatitis C virus E1E2 envelope glycoprotein
Authors: Radić Laura , Offersgaard Anna , Kadavá Tereza , Zon Ian , Capella-Pujol Joan , Mulder Fabian , Koekkoek Sylvie , Spek Vera , Chumbe Ana , Bukh Jens , van Gils Marit J , Sanders Rogier W , Yin Victor C , Heck Albert J R , Gottwein Judith M , Sliepen Kwinten , Schinkel Janke 
Published: October 2024

AbstractHepatitis C virus (HCV) currently causes about one million infections and 240,000 deaths worldwide each year. To reach the goal set by the World Health Organization (WHO) of global HCV elimination by 2030, it is critical to develop a prophylactic vaccine. Broadly neutralizing antibodies (bNAbs) target the E1E2 envelope glycoproteins on the viral surface, can neutralize a broad range of the highly diverse circulating HCV strains and are essential tools to inform vaccine design. However, bNAbs targeting a single E1E2 epitope might be limited in neutralization breadth, which can be enhanced by using combinations of bNAbs that target different envelope epitopes. We have generated 60 IgG-like bispecific antibodies (bsAbs) that can simultaneously target two distinct epitopes on E1E2. We combine non-overlapping E1E2 specificities into three types of bsAbs, each containing a different hinge length. The bsAbs show retained or increased potency and breadth against a diverse panel of HCV pseudoparticles (HCVpp) and HCV produced in cell culture (HCVcc) compared to monospecific and cocktail controls. Additionally, we demonstrate that changes in the hinge length of bsAbs can alter the binding stoichiometry to E1E2. These results provide insights into the binding modes and the role of avidity in bivalent targeting of diverse E1E2 epitopes, and suggest structural differences between HCVpp and HCVcc. This study illustrates how potential cooperative effects of HCV bNAbs can be utilized by strategically designing bispecific constructs. These new HCV bsAbs can guide vaccine development and unlock novel therapeutic and prophylactic strategies against HCV and other (flavi)viruses.
Motor Imagery Enhances Performance Beyond the Imagined Action
Authors: Gippert M. , Shih P.-C. , Heed T. , Howard I. S. , Jamshidi Idaji M. , Villringer A. , Sehm B. , Nikulin V. V. 
Published: October 2024

AbstractMotor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagery’s benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides new evidence for the functional equivalence of imagined and overt movements and suggests new applications for imagery in sports and rehabilitation.
Scaling laws of plasmids across the microbial tree of life
Authors: Maddamsetti Rohan , Wilson Maggie L. , Son Hye-In , Zhou Zhengqing , Lu Jia , You Lingchong 
Published: October 2024

AbstractPlasmids play a critical role in shaping the dynamics and evolution of microbial communities. The capacity of a plasmid to express genes is constrained by two parameters: length and copy number. However, the interplay between these parameters and their constraints on plasmid evolution have remained elusive due to the absence of comprehensive quantitative analyses. To address this gap, we developed Probabilistic Iterative Read Assignment (PIRA), a new computational method that overcomes previous computational bottlenecks, enabling rapid and accurate determination of plasmid copy numbers at an unprecedented scale. Applying PIRA to all microbial genomes in the NCBI RefSeq database with linked short-read sequencing data in the Sequencing Read Archive (SRA), we analyzed 4,317 bacterial and archaeal genomes encompassing 11,338 plasmids, spanning the microbial tree of life. Our analysis reveals three scaling laws of plasmids: first, an inverse power-law correlation between plasmid copy number and plasmid length; second, a positive linear correlation between protein-coding genes and plasmid length; and third, a positive correlation between metabolic genes per plasmid and plasmid length, particularly for large plasmids. These scaling laws imply fundamental constraints on plasmid evolution and functional organization, indicating that as plasmids increase in length, they converge toward chromosomal characteristics in copy number and functional content. Our findings not only advance the understanding of plasmid dynamics but also have implications for microbial evolution, biotechnology, and the design of synthetic plasmids.SignificanceBy discovering universal scaling laws and developing a new computational method to compute plasmid copy numbers across the microbial tree of life, we show that as plasmids increase in length, they converge to chromosomes in their copy number and their coding and metabolic properties. This insight reveals fundamental principles governing plasmid evolution and has implications for biotechnology and medicine.
Archaeal signalling networks - new insights into the structure and function of histidine kinases and response regulators of the methanogenic archaeonMethanosarcina acetivorans
Authors: Georgiev Nora FK , Andersson Anne L , Ruppe Zoe , Kattwinkel Loriana , Frankenberg-Dinkel Nicole 
Published: October 2024

AbstractThe methanogenic archaeon Methanosarcinaacetivoranshas one of the largest known archaeal genomes. With 53 histidine kinases (HK), it also has the largest set of signal transduction systems. To gain insight into the hitherto not very well understood signal transduction in archaea andM. acetivoransin particular, we have categorized the predicted HK into four types based on their H-box using anin silicoanalysis. Representatives of three types were recombinantly produced inEscherichia coliand purified by affinity chromatography. All investigated kinases showed ATP binding and hydrolysis. The MA_type 2 kinase, which lacks the classical H-box, showed no autokinase activity. Furthermore, we could show thatM. acetivoranspossesses an above-average number of response regulators (RR), many of them consisting of only a REC domain (REC-only). Using the hybrid kinase MA4377 as an example we show that both intra-and intermolecular transphosphorylation to REC domains occur. These experiments are furthermore indicative of complex phosphorelay systems inM. acetivoransand suggest that REC-only proteins act as a central hub in signal transduction inM. acetivorans.
Brain-like neural dynamics for behavioral control develop through reinforcement learning
Authors: Codol Olivier , Krishna Nanda H. , Lajoie Guillaume , Perich Matthew G. 
Published: October 2024

AbstractDuring development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produce neural dynamics that enable the rich repertoire of behaviors we perform with our limbs. What begins as a series of “babbles” coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories—supervised learning (SL) and reinforcement learning (RL)—that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that the similarity between RL-trained networks and biological brains depends critically on matching biomechanical properties of the limb. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.
Subcortical Hubs of Brain Networks Sustaining Human Consciousness
Authors: Cambareri Morgan K. , Horn Andreas , Lewis Laura D. , Li Jian , Edlow Brian L. 
Published: October 2024

AbstractNeuromodulation of subcortical network hubs by pharmacologic, electrical, or ultrasonic stimulation is a promising therapeutic strategy for patients with disorders of consciousness (DoC). However, optimal subcortical targets for therapeutic stimulation are not well established. Here, we leveraged 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy subjects from the Human Connectome Project to map the subcortical connectivity of six canonical cortical networks that modulate higher-order cognition and function: the default mode, executive control, salience, dorsal attention, visual, and somatomotor networks. Based on spatiotemporally overlapped networks generated by the Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method, our goal was to identify subcortical hubs that are functionally connected to multiple cortical networks. We found that the ventral tegmental area (VTA) in the midbrain and the central lateral (CL) and parafascicular (Pf) nuclei of the thalamus – regions that have historically been targeted by neuromodulatory therapies to restore consciousness – are subcortical hubs widely connected to multiple cortical networks. Further, we identified a subcortical hub in the pontomesencephalic tegmentum that overlapped with multiple reticular and extrareticular arousal nuclei and that encompassed a well-established “hot spot” for coma-causing brainstem lesions. Multiple hubs within the brainstem arousal nuclei and thalamic intralaminar nuclei were functionally connected to both the DMN and SN, emphasizing the importance of these cortical networks in integrative subcortico-cortical signaling. Additional subcortical connectivity hubs were observed within the caudate head, putamen, amygdala, hippocampus, and bed nucleus of the stria terminalis, regions classically associated with modulation of cognition, behavior, and sensorimotor function. Collectively, these results suggest that multiple subcortical hubs in the brainstem tegmentum, thalamus, basal ganglia, and medial temporal lobe modulate cortical function in the human brain. Our findings strengthen the evidence for targeting subcortical hubs in the ventral tegmental area, central lateral thalamus, and pontomesencephalic tegmentum to restore consciousness in patients with DoC. We release all subcortical connectivity maps to support ongoing efforts at therapeutic neuromodulation of consciousness.
A preprocessing toolbox for 2-photon subcellular calcium imaging
Authors: Jiang Anqi , Zhao Chong , Sheffield Mark 
Published: October 2024

ABSTRACTRecording the spiking activity from subcellular compartments of neurons such as axons and dendrites during behavior with 2-photon calcium imaging is increasingly common yet remains challenging due to low signal-to-noise, inaccurate region-of-interest (ROI) identification, movement artifacts, and difficulty in grouping ROIs from the same neuron. To address these issues, we present a computationally efficient pre-processing pipeline for subcellular signal detection, movement artifact identification, and ROI grouping. For subcellular signal detection, we capture the frequency profile of calcium transient dynamics by applying Fast Fourier Transform (FFT) on smoothed time-series calcium traces collected from axon ROIs. We then apply band-pass filtering methods (e.g. 0.05 to 0.12 Hz) to select ROIs that contain frequencies that match the power band of transients. To remove motion artifacts from z-plane movement, we apply Principal Component Analysis on all calcium traces and use a Bottom-Up Segmentation change-point detection model on the first principal component. After removing movement artifacts, we further identify calcium transients from noise by analyzing their prominence and duration. Finally, ROIs with high activity correlation are grouped using hierarchical or k-means clustering. Using axon ROIs in the CA1 region, we confirm that both clustering methods effectively determine the optimal number of clusters in pairwise correlation matrices, yielding similar groupings to “ground truth” data. Our approach provides a guideline for standardizing the extraction of physiological signals from subcellular compartments during behavior with 2-photon calcium imaging.
Dynamic Models of Neural Population Dynamics
Authors: Hao Han , Zhang Kai , Xiong Momiao 
Published: October 2024

AbstractThe recent developments in artificial intelligence (AI) increase the hope that AI can provide a powerful tool to facilitate scientific discovery and to generate and validate new ideas for scientific research autonomously. Large Language Models (LLMs), such as ChatGPT4 have demonstrated remarkable capabilities in understanding and generating human-like text. Their potential extends beyond simple language tasks, offering transformative possibilities in scientific research of all fields. By leveraging vast amounts of data and advanced computational power, LLMs can assist researchers in generating novel ideas, automating routine tasks, and fostering interdisciplinary collaborations. On September 12, 2024, OpenAI released their updated generative artificial intelligence system called ChatGPTo1. This new AI system, built upon chain-of-thought and reinforcement learning, has greatly enhanced logical reasoning abilities and can effectively solve various complex problems from elementary-level mathematical problems to modern scientific research issues in physics, chemistry, and biology. Unlike previous LLMs in which logical reasoning and data analysis abilities are developed through training on actual data, ChatGPTo1 logical reasoning ability and capacity to generate new scientific ideas are primarily acquired through chain-of-thought processes and reinforcement learning rather than pre-training. To examine this, we specifically tested ChatGPTo1 current reasoning and scientific discovery capabilities by developing theoretically complex and quantitatively challenging scientific equations in various fields of neuroscience, such as dynamical systems, nonlinear dynamical systems, dynamical systems on differential manifolds, neural field theory, nonlinear divergence theorems, nonlinear heat conduction equations and Laplace equations and their extensions on differential manifolds, nonlinear statistical analysis methods, deep learning, and other topics involving multiple fields. The current large language models may illustrate a certain degree of general intelligence, even if fundamentally it may be different from human intelligence. However, it does not mean the current LLMs can fully apply such ability in practical applications or that their reasoning potential can be fully tapped. It is essential to explore specific pathways and methods to cultivate their potential for scientific discovery. To accomplish this, we consider how to integrate them with common search engines (such as Google) capabilities and ChatGPT4o cross-modal abilities to better understand new disciplines and scientific discoveries. To this point, the major shortcoming of ChatGPTo1 is that it is not an end-to-end scientific discovery method and lacks the ability to achieve full automation. It also lacks methods for image analysis and full-scale data analysis, making it difficult to use simulation and data analysis to evaluate and test proposed new theories and methods.
An investigation into factors influencing consumer buying behaviour towards online shopping in Ghana
Authors: Turkson Patrick Joel , Amoah Felix , Amoah Laura Novienyo Abla 
Published: October 2024

PurposeThe study aims to investigate the factors influencing consumer buying behaviour towards online shopping in Ghana.Design/methodology/approachA web survey was employed to test the concept mode. Perceived ease of use (PEOU), perceived usefulness (PU), perceived hedonic value (PHV) and perceived trust (PT) served as the independent variables, while consumer online buying behaviour (ConOBB) served as the dependent variable. The convenience sampling method was adopted to solicit data from 437 respondents. SPSS 26 and STATA 17 were the analytical tools used for the study. The analysis of the data includes a structural equation model (SEM) to assess the measurement and the influence of PEOU, PU, PHV and PT on ConOBB.FindingsThe study revealed that PEOU (Beta = 0.262), PU (Beta = 0.216), PHV (Beta = 0.354) and PT (Beta = 0.630) all had a positive relationship with ConOBB. The study also established that there are significant relationships between the factors (PEOU, PU, PHV and PT) and ConOBB in Ghana.Research limitations/implicationsThe study focuses on Ghanaian consumers and emphasises mainly online shopping factors, which are PEOU, PU, PHV and PT. E-commerce businesses face fierce competition due to the increased availability of websites and other online platforms. To attract potential customers, companies must streamline processes, ensure user-friendliness and create a user-friendly experience.Originality/valueThe uptake of online shopping in Ghana is relatively slow compared with other countries, particularly in Africa. Online shoppers and service providers in Ghana are keen to sustain the industry. However, there is currently a scarcity of empirical studies in Ghana that focus on specific factors that influence consumer online buying behaviour. This study offers a new model that offers novel insights into the specific factors that aid in consumer online shopping behaviour in Ghana. The measuring instrument, which was found to be reliable and valid, also serves as an added value that this study offers. Both the model and measuring instrument can assist managers offering online shopping opportunities to be able to measure and formulate strategies that would enhance consumer online shopping experiences. By adding perceived hedonic value and trust to the model, this study offers a unique extension of the technology acceptance model. Thus, the findings add to the body of knowledge in the field of online shopping, particularly in the context of Ghana.
Unlocking the potential of digital innovations on BPM: a bibliometric and systematic literature review
Authors: Moreira Sérgio Adriany Santos , Dallavalle Silvia 
Published: October 2024

PurposeThis study aims to perform a bibliometric and systematic literature review (SLR) to elucidate the current landscape and to propose a framework that defines and explores research strands to guide the formulation of business process management (BPM) strategies anchored in digital innovations.Design/methodology/approachA bibliometric analysis of 238 papers from the last 10 years (2014–2023) that were correlated to BPM strategies and digital innovations was conducted. Additionally, a SLR was performed on 15 selected papers, making it possible to propose a framework that explored research strands through the content analysis.FindingsA framework comprising eight research strands (value creation; ambidexterity; governance; agility in processes; strategic process management; external context and culture and integrated systems) was developed. It proposes a research agenda and provides valuable insights as a reference for formulating BPM organizational strategies anchored in digital innovations.Research limitations/implicationsThis study acknowledges the limitation of utilizing a single software for bibliometric analysis. The framework developed in this study has not yet been validated.Practical implicationsThis paper provides insights to assist managers in comprehending BPM strategies anchored in digital innovations and calls for organizations to prepare for future digital needs.Social implicationsThis paper contributes to advancing our understanding of BPM aligned with the demands of a digital age society.Originality/valueThe developed framework sheds light on the current landscape and research strands that explore the association between digital innovations and BPM strategies. This provides a valuable opportunity for future qualitative and quantitative studies to explore the eight research strands presented in the framework.
Motivating Filipino Generation Z employees at work: enablers and outcomes
Authors: Talamayan Mikee C. , Teng-Calleja Mendiola , Caringal-Go Jaimee Felice 
Published: October 2024

PurposeThis research focused on exploring factors that engender the work motivation of Gen Z employees and determined whether these factors affected their job satisfaction, engagement and affective commitment to their organizations.Design/methodology/approachThe study employed a sequential mixed-methods approach, starting with 40 qualitative interviews to determine the work motivation factors of Gen Z employees. Findings of the qualitative study were then validated through an online survey that gathered data from 132 Gen Z employees. The quantitative study tested a model that relates factors that contribute to the motivation of Gen Z and positive employee outcomes – affective commitment, job satisfaction and work engagement.FindingsSeveral factors were found as contributors to the work motivation of Gen Z employees: learning and development, family, impact on others and society, passion and enjoyment, financial security, ability to provide for oneself, personal achievement and self-rewards. The result of the structural equation modeling showed that the presence of motivating factors at work is a significant predictor of the outcome variables. Moreover, affective commitment was also found to partially mediate the relationship between the motivating factors and outcome variables – job satisfaction and work engagement.Originality/valueThis study addresses the dearth of empirical studies on factors that motivate Gen Z employees (the youngest employee cohort that recently joined the workforce) and how these contribute to positive work outcomes. Findings may help organizations curate their human resources management programs to attract, engage and motivate their Gen Z employees.