bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low Dose Radiation by Radiopharmaceutical Therapy Enhances GD2TRAC-CAR T Cells Efficacy in Localized Neuroblastoma
Authors: Sodji Quaovi H. , Shea Amanda , Cappabianca Dan , Forsberg Matthew H. , Eickhoff Jens C. , Idrissou Malick Bio , Ollendorff Andy S. , Kwon Ohyun , Ong Irene M. , Hernandez Reinier , Weichert Jamey , Bednarz Bryan P. , Saha Krishanu , Sondel Paul M. , Capitini Christian M. , Morris Zachary S. 
Published: November 2024

ABSTRACTBackgroundWhile chimeric antigen receptor (CAR) T cells have achieved significant success against hematological malignancies, efficacy against neuroblastoma has been limited. Virus-free CRISPR-edited GD2TRAC-CAR T cells have been developed as a potential means of improving CAR T efficacy but are not curative. Radiopharmaceutical therapy (RPT) is a promising approach to enhance the effectiveness of immunotherapies, including immune checkpoint inhibitors. However, it remains unclear whether RPT can synergize with GD2TRAC-CAR T cells to improve outcomes in neuroblastoma.MethodsDosimetry studies were conducted to measure the absorbed radiation dose delivered by lutetium-177 (177Lu) in bothin vitroandin vivomodels. Tumor-bearing mice were treated sequentially with low dose radiation by177Lu-NM600, an alkylphosphocholine mimetic radiopharmaceutical agent, followed 9 days later by GD2TRAC-CAR T cells generated in a virus-free manner by CRISPR/Cas9. Tumor burden was monitored through bioluminescence imaging and tumor size measurements. Mechanistic studies were performed using flow cytometry, multiplex assay and single-cell proteomic analysis.ResultsLow dose radiation delivered by177Lu-NM600 synergized with GD2TRAC-CAR T cells in a localized neuroblastoma model, resulting in complete tumor regression in all mice. The optimal combination was dependent on both the radiation dose and timing to minimize the negative impact of radiation on CAR T cell viability. Irradiation of neuroblastoma cells by low-dose RPT before GD2TRAC-CAR T cells enhanced the release by CAR T cells of perforin, granzyme B and cytokines like TNF-α and IL-7 while abrogating TGF-β1 secretion. Additionally, low-dose RPT upregulated Fas on neuroblastoma cells, potentially enabling a CAR-independent killing.ConclusionsThis study demonstrates that low-dose RPT can enhance CAR T cell efficacy to treat a solid tumor. Findings suggest that optimization of radiation dose and timing may be needed for each patient and RPT to account for effects of varied tumor radiosensitivity and dosimetry.Graphical Abstract
Impact of prenatal delta-9-tetrahydrocannabinol exposure on mouse brain development: a fetal-to-adulthood magnetic resonance imaging study
Authors: Cupo Lani , Vecchiarelli Haley A. , Gallino Daniel , VanderZwaag Jared , Bradshaw Katerina , Phan Annie , Khakpour Mohammadparsa , Ben-Azu Benneth , Guma Elisa , Fouquet Jérémie , Spring Shoshana , Nieman Brian J. , Devenyi Gabriel A. , Tremblay Marie-Eve , Chakravarty M. Mallar 
Published: November 2024

AbstractWhile cannabis use during pregnancy is often perceived as harmless, little is known about its consequences on offspring neurodevelopment. There is an urgent need to map the effects of prenatal cannabis exposure on the brain through the course of the lifespan. We used magnetic resonance imaging spanning nine timepoints, behavioral assays, and electron microscopy to build a trajectory from gestation to adulthood in mice exposed prenatally to delta-9-tetrahydrocannabinol (THC). Our results demonstrate a spatio-temporal patterning, with ventriculomegaly in THC-exposed embryos followed by a deceleration of brain growth in neonates that is sustained until adulthood, especially in females. We observed consistently impacted regions in both the cortex and subcortex, aligned with sex-dependent changes to social behavior in neonates and increased anxiety-like behavior in adolescents. Our results suggest prenatal THC exposure has a sustained sex-dependent impact on neurodevelopment that may persist into early adulthood.
Pyramidal cell types and 5-HT2Areceptors are essential for psilocybin’s lasting drug action
Authors: Shao Ling-Xiao , Liao Clara , Davoudian Pasha A. , Savalia Neil K. , Jiang Quan , Wojtasiewicz Cassandra , Tan Diran , Nothnagel Jack D. , Liu Rong-Jian , Woodburn Samuel C. , Bilash Olesia M. , Kim Hail , Che Alicia , Kwan Alex C. 
Published: November 2024

AbstractPsilocybin is a serotonergic psychedelic with therapeutic potential for treating mental illnesses1–4. At the cellular level, psychedelics induce structural neural plasticity5,6, exemplified by the drug-evoked growth and remodeling of dendritic spines in cortical pyramidal cells7–9. A key question is how these cellular modifications map onto cell type-specific circuits to produce psychedelics’ behavioral actions10. Here, we usein vivooptical imaging, chemogenetic perturbation, and cell type-specific electrophysiology to investigate the impact of psilocybin on the two main types of pyramidal cells in the mouse medial frontal cortex. We find that a single dose of psilocybin increased the density of dendritic spines in both the subcortical-projecting, pyramidal tract (PT) and intratelencephalic (IT) cell types. Behaviorally, silencing the PT neurons eliminates psilocybin’s ability to ameliorate stress-related phenotypes, whereas silencing IT neurons has no detectable effect. In PT neurons only, psilocybin boosts synaptic calcium transients and elevates firing rates acutely after administration. Targeted knockout of 5-HT2Areceptors abolishes psilocybin’s effects on stress-related behavior and structural plasticity. Collectively these results identify a pyramidal cell type and the 5-HT2Areceptor in the medial frontal cortex as playing essential roles for psilocybin’s long-term drug action.
DSA-DeepFM: A Dual-Stage Attention-Enhanced DeepFM Model for Predicting Anticancer Synergistic Drug Combinations
Authors: Gu Yuexi , Sun Yongheng , Zhang Louxin , Zu Jian 
Published: November 2024

AbstractMotivationDrug combinations are crucial in combating drug resistance, reducing toxicity, and improving therapeutic outcomes in the treatment of complex diseases. As the number of available drugs grows, the potential combinations increase exponentially, making it impractical to rely solely on biological experiments to identify synergistic drug pairs. Consequently, machine learning methods are increasingly used to systematically screen for synergistic drug combinations. However, most current approaches prioritize predictive performance by integrating auxiliary information or increasing model complexity. By overlooking the biological mechanisms behind feature interactions, their effectiveness in predicting drug synergy can be limited.ResultsWe present DSA-DeepFM, a deep learning model that integrates a dual-stage attention (DSA) mechanism with Factorization Machines (FMs) to improve drug synergy prediction by addressing complex biological feature interactions. The model incorporates categorical and auxiliary numerical inputs, embedding them into high-dimensional spaces and then fusing them through the DSA mechanism to capture both field-aware and embedding-aware patterns. These patterns are then processed by a DeepFM module, which captures low-order and high-order feature interactions before making final predictions. Validation testing demonstrates that DSA-DeepFM significantly outperforms traditional machine learning and state-of-the-art deep learning models. Additionally, t-SNE visualizations confirm the model’s discriminative power at various stages. As a case study, we use our model to identify eight novel synergistic drug combinations, three of which are well-supported by existing wet-lab experiments, underscoring its practical utility and potential for future applications.
Synaptic and Somatic Targeting of ArcLight, a Genetically Encoded Voltage Indicator
Authors: Zhong Sheng , Cohen Lawrence B. 
Published: November 2024

ABSTRACTVoltage signals in neurons are highly compartmentalized, which can influence their specific functions within neuronal circuits. Targeting of a genetically encoded voltage indicator (GEVI) to specific subcellular compartments can enhance the signal-to-noise ratio and provide more precise information about the location and timing of synaptic firing across different neuronal regions, reducing spatiotemporal signal convolution. To achieve subcellular targeting of the GEVI, ArcLight, we utilized five different postsynaptic targeting sequences (ShakerK+channel C-terminus, stargazin C-terminus, rat Neuroligin-1 C-terminus, and anti-homer1 nanobodies HC20 & HC87) to direct ArcLight expression to the excitatory postsynaptic density. Additionally, we assessed a presynaptic-targeting tag (rat Neurexin-1β C-terminus) and a somatodendritic targeting tag (Kv2.1-Lk-Tlcn C-terminus). Patch clamp experiments in HEK293 cells showed that the targeting tags used in this study did not significantly alter ArcLight’s voltage sensitivity compared to controls. AAV infection in the mouse olfactory bulb demonstrated that the subcellular targeting sequences effectively localized GEVI expression to specific compartments of mitral/tufted cells, including postsynaptic densities, presynaptic terminals, and somatodendritic regions. Furthermore, in vivovoltage imaging in mice expressing targeting-enhanced ArcLight variants revealed odorant-evoked responses similar to those observed with the original ArcLight. This indicates that subcellular targeting did not significantly impact the voltage sensing capability of ArcLight in mitral/tufted cells.
Structural variations contribute to genetic diversity and heterosis in rice
Authors: Dan Zhiwu , Chen Yunping , Huang Wenchao 
Published: November 2024

AbstractThe contributions of structural variants (SVs) to rice heterosis, which has significantly boosted crop yield, remain largely unexplored. In this study, we assembled 12 high-quality rice genomes and revealed that SVs are pivotal in the evolution and variation ofindicaandjaponica. Our findings indicate that SVs trigger gene expression changes which explain the variance in heterosis of 17 traits, highlighting the importance of SVs in harnessing rice heterosis.
Conversion of somatic sex identity in the testis induces female-specific cellular behaviors in the soma and early oocyte specification in the germline
Authors: Roach Tiffany V. , Harsh Sneh , Sainath Rajiv , Bach Erika A. , Lenhart Kari F. 
Published: November 2024

AbstractEstablishment and maintenance of cellular sex identity is essential for reproduction. Critically, the sex identity of somatic and germline cells must correspond for sperm or oocytes to be produced, with mismatched identity causing infertility in all organisms from flies to humans. In theDrosophilatestis, Chronologically inappropriate morphogenesis (Chinmo) is required for maintenance of adult male somatic identity. Loss ofchinmoleads to progressive feminization of the male soma, including adoption of female-specific cell morphologies, tissue organization and gene expression. However, the degree to which this feminized soma in the male engages female-specific cellular behaviors or influences the associated XY germline is unknown. Using extended live imaging, we have visualized the process of male-to-female somatic sex conversion uponchinmoloss of function. We find that feminized soma in the testis engage cell behaviors characteristic of ovarian follicle cells (FCs), including female-specific incomplete cytokinesis, as early as one day ofchinmoinhibition. In the ovary, FCs collectively migrate around the underlying germ cells to establish the elongated shape of the oocyte. Surprisingly, we find that FC-like soma inchinmo-depleted testes also engage this female-specific collective cell migration. Critically, migration of FC-like cells in the testis has the same molecular requirements as FC migration in the ovary. Depletion of the basement membrane protein Perlecan or adhesion protein Ecadherin significantly disrupts rotational migration in both the ovary andchinmo-depleted testes. Finally, we find that feminized soma non-autonomously alters sex identity of the associated XY germ cells, inducing expression of a protein required for early oocyte specification. Taken together, our work reveals a dramatic transformation of somatic cell behavior during the process of sex conversion and provides a powerful model to study soma-derived induction of oocyte identity.
The distribution of clade size in a coalescent diversification model
Authors: Song Yexuan , Colijn Caroline , MacPherson Ailene 
Published: November 2024

AbstractCharacterizing the patterns and determinants of biological diversity is a central aim of evolutionary biology. Doing so requires developing expectations for clade size, the relationship between the number of species in a clade and its age, for standard diversification models, such as the coalescent or birth-death process. These expectations are necessary for identifying diversity outliers, specious or depauperate clades in the macroevolutionary context or transmission clusters of particular or little public health concern in the epidemiological context, and for testing alternative diversification hypotheses. Here, we derive a closed-form expression for the distribution of clade sizes under the widely used Kingman coalescent diversification model and extend the results to allow the number of niches (the effective population size) to vary through time. This result complements analogous results for the birth-death model in that it provides expectations for the joint distribution of clade sizes for a case where diversification is strongly density-dependent.
Natural selection of synthetic gene drives for population suppression can favour an intermediate strength of drive
Authors: Beaghton PJ , Burt Austin 
Published: November 2024

AbstractSynthetic gene drives are being investigated as tools to suppress pest populations, and it is important to understand how natural selection will act on variant drivers that may either arise byde novomutation or are intentionally released. In this study we extend previous spatially implicit stochastic models to examine the evolutionary dynamics of synthetic driving Y chromosomes in patchy environments when population size is responding dynamically to the spread of the driver, and derive conditions for the existence of an evolutionarily stable strategy (ESS) for drive strength. Under broad conditions an intermediate drive strength emerges as the ESS, capable of outcompeting both stronger and weaker variants. Additionally, we show how the intentional release of two drivers straddling the ESS can help stabilise population dynamics. Finally, inbreeding depression has the effect of expanding the range of conditions under which no intermediate ESS exists, with ever stronger drive being selected until the population is eliminated. These results provide insights into the expected evolutionary trajectories of gene drive systems, with important implications for the design and release of gene drives for pest and vector control.
Role of Non-Exponential Reversal times in Aggregation Models of Bacterial Populations
Authors: Batista Michael , Murphy Patrick , Igoshin Oleg A. , Perepelitsa Misha , Timofeyev Ilya 
Published: November 2024

AbstractIn this paper, we consider 1D agent-based and kinetic models of aggregation with reversals. In particular, we fit a Gamma distribution to represent the run times in myxobacteria and analyze numerically the importance of non-exponential reversal times. We demonstrate that non-exponential reversal times aid aggregation and result in tighter aggregates. We compare and contrast the behavior of agent-based and kinetic models, and also consider kinetic models with aggregation driven by chemotaxis. Thus, incorporating non-exponential reversal times into models of aggregation can be particularly important for reproducing experimental data, such as aggregate persistence and dispersal.
Drug Delivery Process Simulation - Quantifying the Conformation Dynamics of Paclitaxel and Cremophor EL
Authors: Uddin Mafiz , Coombe Dennis 
Published: November 2024

ABSTRACTPaclitaxel is a highly successful anti-neoplastic cancer drug. The first clinically successful paclitaxl-delivery method is a mixture with cremophor EL and ethanol, here termed the taxol micelle. Until now, molecular dynamics analysis has not been presented to quantify the structural and conformation properties of these drug molecules when interacting with each other to create this nonstandard micelle. Here we apply systematic molecular simulation and statistical analysis of paclitaxel and cremophor EL conformations based on all atom and coarse-grained approaches. The cremophor EL in the taxol micelle showed a clustering network in a 3D landscape where paclitaxel can be loaded at much higher than standard concentration with no aggregation. Paclitaxel particles within the cremophor EL cavities showed some oscillatory behaviour due to a repeated adsorption/desorption with the surrounding network. Paclitaxel conformations at the lowest energy state can be described when the side-chain phenyl rings are closer relative to the immobile core. Cremophor EL molecules reached the highest energy state when wings were fully spread and at the lowest energy state when wings were fully closed. The spiral shapes were observed to be the dominant species in the cremophor EL population. We then established reliable statistical correlations between molecular conformations and the energy states. Our reliable all atom and coarse-grained modelling approach can also be applied for effective drug design analysis using different drug delivery systems.Table of Contents/Abstract Graphic
Navigating the Unstructured by Evaluating AlphaFold’s Efficacy in Predicting Missing Residues and Structural Disorder in Proteins
Authors: Zheng Sen 
Published: November 2024

AbstractThis study explored the difference between predicted structure confidence and disorder detection in protein, focusing on regions with undefined structures detected as missing segments in X-ray crystallography and Cryo-EM data. Recognizing the importance of these ‘unstructured’ regions for protein functionality, we examined the alignment of numerous protein sequences with their resolved or not structures. The research utilized a comprehensive PDB dataset, classifying residues into ‘modeled’, ‘hard missing’ and ‘soft missing’ based on their visibility in structural data. By analysis, key features were firstly determined, including confidence score pLDDT from Al-phaFold2, an advanced AI-based tool, and IUPred, a conventional disorder prediction method. Our analysis reveals that "hard missing" residues often reside in low-confidence regions, but are not exclusively associated with disorder predictions. It was assessed how effectively individual key features can distinguish between structured and unstructured data, as well as the potential benefits of combining these features for advanced machine learning applications. This approach aims to uncover varying correlations across different experimental methodologies in the latest structural data. By analyzing the relationships between predictions and experimental structures, we can more effectively identify structural targets within proteins, guiding experimental designs toward areas of potential functional significance, whether they exhibit high stability or crucial unstructured regions.
How food delivery service can be sustainable: some evidence from the Giusta delivery case
Authors: Prisco Anna , Troise Ciro , Monge Filippo , Giovando Guido 
Published: November 2024

PurposeThe study explores the drivers of the intention to continue using sustainable food delivery services, considering the influence of value, satisfaction and the combined effects of utilitarian and hedonic motives. We employ a comprehensive framework that integrates the norm activation model (NAM) and the theory of planned behavior (TPB), with satisfaction, utilitarian motives and hedonic motives. This approach facilitates a deeper understanding of the factors motivating users to remain loyal to the service.Design/methodology/approachThe study examines the data using a quantitative approach based on the partial least squares approach to structural equation modeling (PLS-SEM).FindingsThe findings suggest that the intention to continue using sustainable food delivery services, such as the Giusta app, is significantly influenced by user satisfaction and attitudes. Attitudes not only influence the intention but also user satisfaction and are shaped by utilitarian motivations associated with sustainable food delivery. Beyond personal expectations and perceived benefits, the study reveals that behavior is also driven by altruistic values and moral convictions. Specifically, attitudes and satisfaction are positively affected by personal norms, which are influenced by ascribed responsibility. This ascribed responsibility is, in turn, shaped by awareness of both environmental and social issues.Originality/valueThis research contributes to the current debate on consumers' ethical behavior in food delivery. Only a few studies have integrated the NAM and TPB models in this context. This article highlights the factors useful in predicting people’s choice of food delivery services, showing the key role of personal norms, attitude, satisfaction and utilitarian motives.
Artificial intelligence models to predict optimal trade-off on construction management
Authors: Pham Vu Hong Son , Ngoc Quynh Khoi Luu 
Published: November 2024

PurposeThis research aims to introduce a novel algorithm, the Chaotic Giant Pacific Octopus Optimizer (CGPOO) and demonstrate framework includes four key aspects: time, cost, quality and safety trade-off (TCQST).Design/methodology/approachArtificial intelligence is causing a big disruption in the construction management. It is being used to building projects to enhance efficiency, safety and decision-making. This research compared the CGPOO method to those of other algorithms, such as the Chaotic Slime Mold Algorithm (CSMA), the Chaotic Salps Swarm Algorithm (CSSA) and the Chaotic Whale Optimization Algorithm (CWOA) and assessed the efficacy of the method using statistical analysis and evaluation indicators such as Hyper-volumn (HV), Spread (Sp), Computational Time (CT) and C-metric.FindingsThe analysis demonstrates that using CGPOO outperforms standalone methods chosen from the literature in terms of outcomes. It is discovered that the CGPOO solution possibilities for each factors are more efficient and beneficial than the comparison algorithms. Moreover, the CGPOO model performs better than the other algorithms with quality indices C-metric, Sp, HV and CT of 0.534, 0.531, 0.891 and 101.Originality/valueThe article presents a novel hybrid CGPOO that permits multi-factor trade-offs in construction management with the goal of surpassing the analyzed models and optimizing the optimal solution in the search space.