bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Magic Impacts of Sounds on Consumer’s Brain: How Do Natural Sounds Nudge Green Product Purchase?
Authors: Liang Geying , Wang Yiwen 
Published: March 2024

AbstractDespite extensive research on nudging techniques aimed at promoting green consumption, the influence of natural sounds on encouraging environmentally friendly behavior remains relatively understudied. It is valuable to examine how sounds matter in green marketing, as more and more firms are using background sounds in video advertisements. Drawing on the Stimulus-Organism-Response model, we propose that natural sound increase early attentional congruency associated with green products, thereby promoting customers’ purchase of green products. To test on our theory, we conducted an experiment using an event-related potentials (ERP) method and employed a hierarchical drift-diffusion model (HDDM). The results show that compared with unnatural sounds, natural sounds not only increased the purchase rates for green products but also enhanced decision-making efficiency (drift rate) in favor of purchasing green products. Additionally, consumers also exhibited a reduced frontal early P2 wave (150-230ms) in response to green products under natural sounds, indicating that natural sounds increased the early attentional congruency associated with green products. More importantly, the early attentional congruency provides an explanation for how natural sounds nudge the efficiency of purchasing decisions for green products. This study thus contributes to the discovery of a new psychological path that helps deepen our understanding of how natural sounds can nudge green consumption. It also provides actionable implications for green consumption market managers.
Functional Mapping of Movement and Speech Using Task-Based Electrophysiological Changes in Stereoelectroencephalography
Authors: Jensen Michael A , Fine Anthony , Kerezoudis Panagiotis , Wong Kisiel Lily , Alden Eva , Hermes Dora , Miller Kai J 
Published: March 2024

ABSTRACTIntroductionStereoelectroencephalography (sEEG) has become the predominant method for intracranial seizure localization. When imaging, semiology, and scalp EEG are not in full agreement or definitively localizing, implanted sEEG recordings are used to test candidate seizure onset zones (SOZs). Discovered SOZs may then be targeted for resection, laser ablation, or neurostimulation. If a SOZ is eloquent, resection and ablation are both contraindicated, so identifying functional representation is crucial for therapeutic decision making.ObjectiveWe present a novel functional brain mapping technique that utilizes task-based electrophysiological changes in sEEG during behavioral tasks and test this in pediatric and adult patients.MethodssEEG was recorded in twenty patients with epilepsy, aged 6-39 (12 female, 18 of 20 patients < 21 years old), who underwent implanted monitoring to identify seizure onset. Each performed 1) visually cued simple repetitive movements of the hand, foot, or tongue while electromyography was recorded, and 2) simple picture naming or verb generation speech tasks while audio was recorded. Broadband changes in the power spectrum of the sEEG were compared between behavior and rest.ResultsElectrophysiological functional mapping of movement and/or speech areas was completed in all 20 patients. Eloquent representation was identified in both cortex and white matter, and generally corresponded to classically described functional anatomic organization as well as other clinical mapping results. Robust maps of brain activity were identified in healthy brain, regions of developmental or acquired structural abnormality, and SOZs.ConclusionTask based electrophysiological mapping using broadband changes in the sEEG signal reliably identifies movement and speech representation in pediatric and adult epilepsy patients.
Filopodial Mechanotransduction is regulated by Angiotensin-Converting Enzyme 2 (ACE2) and by SARS-CoV-2 spike protein
Authors: He Wei , Wu Chien-Ting , Jackson Peter K. 
Published: March 2024

AbstractFilopodia are dynamic, actin-rich cellular protrusions, increasingly linked to cellular mechanotransduction. However, how dynamic filopodia translate external mechanical cues remains poorly understood. Recent studies show that the SARS-CoV-2 spike (S) protein binds the ACE2 receptor on airway multicilia and that cilia are required for viral infection(1) and sufficient to induce filopodial extension and viral binding. To test if spike protein is sufficient to induce filopodial expansion, we employed live-cell single-particle tracking with quantum dots targeting ACE2, to reveal a robust filopodia extension and virus binding mechanism requiring the enzymatic activity of ACE2. Using time-lapse imaging, we reveal that spike protein binding to filopodia is associated with intracellular actin remodeling, alterations in bulk cell stiffness, and an elevation in intracellular calcium levels linked to actin-rearrangement, filopodia initiation, and persistence. We propose the activation of ACE2 creates an active signaling and mechanosensory environment within adherent cells and airway epithelial cells that allows the remodeling of actin in filopodia to trap virus and potentially organize viral exit from cells. [164 words]One-Sentence SummarySARS-CoV-2 spike protein activates calcium and actin dynamics to enable filopodial extension and virus binding
Multicore-fiber microendoscopy for functional cellular in-organ imaging
Authors: Dancker Tobias A. , Elhawy Mohamed Ibrahem , Rittershauß Ramona , Tian Qinghai , Schwarz Yvonne , Hoffmann Markus D. A. , Carlein Christopher , Wyatt Amanda , Wahl Vanessa , Speyerer Daniel , Kandah Alaa , Boehm Ulrich , Prates Roma Leticia , Bruns Dieter , Lipp Peter , Krasteva-Christ Gabriela , Lauterbach Marcel A. 
Published: March 2024

AbstractMicroendoscopy enables minimally invasive investigations of organs even within small cavities. Conventional microendoscopy is limited by probe size and often restricted to a single excitation wavelength. We developed and characterized a multichannel microendoscope as thin as 360 µm and recorded functional cellular signals in-situ using custom written software for image processing. The endoscope had an effective resolution of 4.64 µm and resolved subcellular structures of neurons. The system enabled analysis of in-situ calcium responses in murine tracheal brush cells and kidney podocytes. Additionally, ratiometric redox responses were recorded in whole, explanted organs and pancreatic islet culture. The flexibility and simplicity of our approach for imaging a variety of tissues and organs paves the way for in-vivo, longitudinal studies with cellular resolution.
Assessing the impact of Brd2 depletion on chromatin compartmentalization
Authors: Athreya Advait , Xie Liangqi , Tjian Robert , Zhang Bin , Liu Zhe J. 
Published: March 2024

AbstractRecent insights into genome organization have emphasized the importance of A/B chromatin compartments. While our previous research showed that Brd2 depletion weakens compartment boundaries and promotes A/B mixing1, Hinojosa-Gonzalez et al.2were unable to replicate the findings. In response, we revisited our Micro-C data and successfully replicated the original results using the default parameters in the cooltools software package. We show that, after correcting inconsistencies with the selection and phasing of the compartment profiles, the decrease in B compartment strength persists but the change in compartment identity is to a much lesser extent than originally reported. To further assess the regulatory role of Brd2, we used saddle plots to determine the strength of compartmentalization and observed a consistent decrease of compartment strength especially at B compartments upon Brd2 depletion. This study highlights the importance of selecting appropriate parameters and analytical tools for compartment analysis and carefully interpreting the results.
Accurate prediction of site- and amino-acid substitution rates with a mutation-selection model
Authors: André Ingemar 
Published: March 2024

AbstractThe pattern of substitutions at sites in proteins provides invaluable information about their biophysical and functional importance and what selection pressures are acting at individual sites. Amino acid site rates are typically estimated using phenomenological models in which the sequence variability is described by rate factors that scale the overall substitution rate in a protein to sites. In this study, we demonstrate that site rates can be calculated accurately from amino acid sequences using a mutation-selection model in combination with a simple nucleotide substitution model. The method performs better than the standard phylogenetic approach on sequences generated by structure-based evolutionary dynamics simulations, robustly estimates rates for shallow multiple sequence alignments, and can be rapidly calculated also on larger sequence alignments. On natural sequences, site rates from the mutation-selection model are strongly correlated to rates calculated with the empirical Bayes methods. The model provides a link between amino acid substitution rates and equilibrium frequency distributions at sites in proteins. We show how an ensemble of equilibrium frequency vectors can be used to represent the rate variation encoded in empirical amino acid substitution matrices. This study demonstrates that a rapid and simple method can be developed from the mutation-selection model to predict substitution rates from amino acid data, complementing the standard phylogenetic approach.
Prediction of Ca2+binding site in proteins with a fast and accurate method based on statistical mechanics and analysis of crystal structures
Authors: Basit Abdul , Choudhury Devapriya , Bandyopadhyay Pradipta 
Published: March 2024

AbstractPredicting the precise locations of metal binding sites within metalloproteins is a crucial challenge in biophysics. A fast, accurate, and interpretable computational prediction method can complement the experimental studies. With this endeavor, In the current work, we have developed a method to predict the location of Ca2+ions in calcium-binding proteins using a physics-based method with an all-atom description of the proteins, which is substantially faster than the molecular dynamics simulation-based methods with accuracy as good as data-driven approaches. Our methodology uses the three-dimensional reference interaction site model (3D-RISM), a statistical mechanical theory, to calculate Ca2+ion density around protein structures, and the locations of the Ca2+ions are obtained from the density. We have taken previously used datasets to assess the efficacy of our method as compared to previous works. Our accuracy is found to be 88%, comparable with the FEATURE program, one of the well-known data-driven methods. Moreover, our method being physical, the reasons for failures can be ascertained in most cases. We have thoroughly examined the failed cases using different structural and crystallographic measures, such as B-factor, R-factor, electron density map, and geometry at the binding site. It has been found that X-ray structures have issues in many of the failed cases, such as geometric irregularities and dubious assignment of ion positions. Our algorithm, along with the checks for structural accuracy, is a major step in predicting calcium ion positions in metalloproteins.
Deep Learning Models for Atypical Serotoninergic Cells Recognition
Authors: Corradetti Daniele , Bernardi Alessandro , Corradetti Renato 
Published: March 2024

AbstractBackgroundThe serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of “typical” features of their activity, i.e. slow regular firing and relatively long duration of spikes. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with “atypical” features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied.New MethodsWe propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials.ResultsThe models were trained on a dataset comprising 43,327 original spike samples, alongside an extensive set of 6.7 million synthetic spike samples, designed to mitigate the risk of overfitting the background noise in the recordings, a potential source of bias. Results show that the models achieved high accuracy and were further validated on “non-homogeneous” data, i.e., data not used for constructing the model, to confirm their robustness and reliability in real-world experimental conditions.Comparison with existing methodsConventional methods for identifying serotonergic neurons allow recognition of serotonergic neurons defined as typical. Our model based on the analysis of the sole spike reliably recognizes over 92 features of spike and activity.ConclusionsThe model is ready for use in experiments conducted with the here described recording parameters. We release the codes and procedures allowing to adapt the model to different acquisition parameters or for identification of other classes of spontaneously active neurons.PACS87.19.L, 87.19.lv, 87.85.dm, 07.05.Mh, 87.85.Tu
AMP-Diffusion: Integrating Latent Diffusion with Protein Language Models for Antimicrobial Peptide Generation
Authors: Chen Tianlai , Vure Pranay , Pulugurta Rishab , Chatterjee Pranam 
Published: March 2024

AbstractDenoising Diffusion Probabilistic Models (DDPMs) have emerged as a potent class of generative models, demonstrating exemplary performance across diverse AI domains such as computer vision and natural language processing. In the realm of protein design, while there have been advances in structure-based, graph-based, and discrete sequence-based diffusion, the exploration of continuous latent space diffusion within protein language models (pLMs) remains nascent. In this work, we introduce AMP-Diffusion, a latent space diffusion model tailored for antimicrobial peptide (AMP) design, harnessing the capabilities of the state-of-the-art pLM, ESM-2, tode novogenerate functional AMPs for downstream experimental application. Our evaluations reveal that peptides generated by AMP-Diffusion align closely in both pseudo-perplexity and amino acid diversity when benchmarked against experimentally-validated AMPs, and further exhibit relevant physicochemical properties similar to these naturally-occurring sequences. Overall, these findings underscore the biological plausibility of our generated sequences and pave the way for their empirical validation. In total, our framework motivates future exploration of pLM-based diffusion models for peptide and protein design.
PDE4 inhibitor Rolipram represses hedgehog signaling via ubiquitin-mediated proteolysis of GLI transcription factors to regress breast cancer
Authors: Bagchi Arka , Bhattacharya Anuran , Chatterji Urmi , Biswas Arunima 
Published: March 2024

AbstractAberrant activation of the hedgehog signaling pathway positively correlates with progression, invasion and metastasis of several cancers, including breast cancer. Although numerous inhibitors of the Hedgehog signaling pathway are available, several oncogenic mutations of key components of the pathway, including Smoothened (Smo), have limited their capability to be developed as putative anti-cancer drugs. In this study, we have modulated the Hedgehog signaling pathway in breast cancer cell lines and tumor-bearing BALB/c mice, using a specific phosphodiesterase 4 (PDE4) inhibitor rolipram. The results indicated that increased levels of cyclic adenosine monophosphate (cAMP)-dependent protein kinase A (PKA), due to the treatment with rolipram on MCF-7 and MDA-MB-231 cells, induced PKA-mediated ubiquitination of Glioma-associated oncogene homolog 2 full length (GLI2FL) and GLI3FL, leading to their transformation to respective repressor forms. This in turn reduced the level of Glioma-associated oncogene homolog 1 (GLI1) transcription factor in a time-dependent manner. We have also shown that elevated level of PKA reduced the level of phosphorylated glycogen synthase kinase 3β (GSK3β), which is known to augment PKA-mediated phosphorylation of GLI2FL and GLI3FL. Rolipram also significantly altered the wound healing capability as well as key markers of wound healing in MCF-7 and MDA-MB-231 cells. Moreover, rolipram caused significant reduction in tumor weight and volume in tumor-bearing mice model. The histological analysis revealed significant reduction in the number of multi-nucleated cells in tumor tissue and substantially reduced levels of cellular infiltration in the lung of rolipram treated tumor-bearing mice. Rolipram also reduced the levels of GLI1 in tumor-bearing mice by enhancing the PKA levels. Therefore, it may be concluded that rolipram can be used as an effective inhibitor of Hedgehog signaling pathway downstream of Smo to control breast cancer progression and metastasis in both hormone-responsive and triple negative breast cancers.
IPPK-1 and IP6 contribute to ventral nerve cord assembly inC. elegans
Authors: Noblett Nathaniel , Roenspies Tony , Flibotte Stephane , Colavita Antonio 
Published: March 2024

ABSTRACTInositol phosphates (IPs) play important roles in nervous system development and function. One of these roles uncovered by loss-of-function studies, is that IP isomers are essential for proper neural tube formation. In this study, we show that inositol pentakisphosphate 2-kinase (IPPK-1), the kinase that phosphorylates IP5 to generate IP6, is involved in assembling the ventral nerve cord (VNC) inC. elegans. We show that mutations inippk-1lead to the mispositioning of motor neurons along the VNC of newly hatched larvae. These positioning defects reflect disruption of VNC assembly during embryogenesis, asippk-1embryos display improper alignment of VNC neuroblasts and delays in rosette-mediated convergent extension (CE). We further show that injection of exogenous IP6 into the gonads ofippk-1mutants can rescue both embryonic and neuron positioning defects. Our findings indicate that inositol metabolism is important for regulating CE inC. elegansand suggest that IP isomers play a conserved role in the formation of central nerve cords.Highlights–ipmk-1andippk-1mutants display neuron position defects in the ventral nerve cord (VNC).–ippk-1 mutants display disorganization in VNC neuronal precursors during midline convergence.–IPPK-1 is involved in convergent extension during VNC formation.–Exogenous IP6 rescues larval and embryonic defects inippk-1mutants.
Subtomogram averaging of helical filaments in cryo-electron tomography
Authors: Zhang Xiaojie , Mahamid Julia 
Published: March 2024

SummaryHelical filaments are essential macromolecular elements in cellular organization and dynamics. Recent advances in cryo-electron tomography allow faithful imaging of isolated or in-cell helical filaments. Here, we present a protocol to generate density maps at sub-nanometer resolution of helical filaments by subtomogram averaging, exemplified with isolated mumps virus nucleocapsids and their in-cell form as an extension of the method. We detail procedures from pre-processing of tilt series frames to refinement of reconstructed averages for streamlined data processing of helical filaments.
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Authors: Zheng Kangjie , Long Siyu , Lu Tianyu , Yang Junwei , Dai Xinyu , Zhang Ming , Nie Zaiqing , Ma Wei-Ying , Zhou Hao 
Published: March 2024

AbstractProtein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in proteinmolecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins.1
A novel sulfatase for acesulfame degradation in wastewater treatment plants as evidenced fromShinellastrains
Authors: Liu Yu , Rohwerder Thore , Bonatelli Maria L. , von Postel Theda , Kleinsteuber Sabine , Adrian Lorenz , Ding Chang 
Published: March 2024

AbstractThe artificial sweetener acesulfame is a persistent pollutant in wastewater worldwide. So far, only a few bacterial isolates were recently found to degrade acesulfame efficiently. InBoseaandChelatococcusstrains, a Mn2+-dependent metallo-β-lactamase-type sulfatase and an amidase signature family enzyme catalyze acesulfame hydrolysis via acetoacetamide-N-sulfonate (ANSA) to acetoacetate. Here, we describe a new acesulfame sulfatase inShinellastrains isolated from German wastewater treatment plants. Their genomes do not encode the Mn2+-dependent sulfatase. Instead, a formylglycine-dependent sulfatase gene was found, together with the ANSA amidase gene on a plasmid shared by all known acesulfame-degradingShinellastrains. Heterologous expression, shotgun proteomics and size exclusion chromatography corroborated the physiological function of theShinellaenzyme as a Mn2+-independent acesulfame sulfatase. Since both theBosea/Chelatococcussulfatase and the novelShinellasulfatase are absent in other bacterial genomes or metagenome assembled genomes, we surveyed 60 tera base pairs of wastewater-associated metagenome raw datasets. TheBosea/Chelatococcussulfatase gene was regularly found from 2014 on, particularly in North America, Europe and East Asia, whereas theShinellasulfatase gene was first detected in 2020. The completeShinellapathway is only present in five datasets from China, Finland and Mexico, suggesting that it emerged quite recently in wastewater treatment facilities.SynopsisA novel sulfatase was identified that hydrolyzes the once recalcitrant xenobiotic acesulfame. Surveying metagenome datasets revealed the recent emergence of gene homologs encoding this sulfatase in wastewater treatment systems worldwide.