bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative analysis of spike-sorters in large-scale brainstem recordings
Authors: De Preter Caitlynn C , Leimer Elizabeth M , Sonneborn Alex , Heinricher Mary M 
Published: November 2024

AbstractRecent technological advancements in high-density multi-channel electrodes have made it possible to record large numbers of neurons from previously inaccessible regions. While the performance of automated spike-sorters has been assessed in recordings from cortex, dentate gyrus, and thalamus, the most effective and efficient approach for spike-sorting can depend on the target region due to differing morphological and physiological characteristics. We therefore assessed the performance of five commonly used sorting packages, Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust, in recordings from the rostral ventromedial medulla, a region that has been characterized using single-electrode recordings but that is essentially unexplored at the high-density network level. As demonstrated in other brain regions, each sorter produced unique results. Manual curation preferentially eliminated units detected by only one sorter. Kilosort3 and IronClust required the least curation while maintaining the largest number of units, whereas SpyKING CIRCUS and MountainSort5 required substantial curation. Tridesclous consistently identified the smallest number of units. Nonetheless, all sorters successfully identified classically defined RVM physiological cell types. These findings suggest that while the level of manual curation needed may vary across sorters, each can extract meaningful data from this deep brainstem site.Significance StatementHigh-density multichannel recording probes that can access deep brainstem structures have only recently become commercially available, but the performance of open-source spike-sorting packages applied to recordings from these regions has not yet been evaluated. The present findings demonstrate that Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust can all be reasonably used to identify units in a deep brainstem structure, the rostral ventromedial medulla (RVM). However, manual curation of the output was essential for all sorters. Importantly, all sorters identified the known, physiologically defined RVM cell classes, confirming their utility for deep brainstem recordings. Our findings provide suggestions for processing parameters to use for brainstem recordings and highlight considerations when using high-density silicon probes in the brainstem.
Orchestrating the Acquisition of Oligodendrocyte Precursor Cell versus Olfactory Bulb Interneuron Fates throughOlig1/2during Mammalian Cortical Gliogenesis and Gliomagenesis
Authors: Tian Yu , Wang Ziwu , Yang Feihong , Zhang Wen , Li Jialin , Yang Lin , Fu Tongye , Zheng Wenhui , Xu Zhejun , Ma Tong , You Yan , Li Xiaosu , Song Jiangang , Xie Yunli , Yang Zhengang , Zhang Zhuangzhi 
Published: November 2024

AbstractThe hijacking of developmental gliogenesis programs is a hallmark of glioblastoma (GBM), in which glial precursor cells (GPCs) typically differentiate into both neurons and glial cells. In GBM, this process is disrupted, leading to the overproduction of proliferative glial-like cells. Our study demonstrates that the knockout ofOlig1/2in both normal development and gliomagenesis causes GPCs to shift from generating highly proliferative oligodendrocyte precursor cells to producing non-proliferative olfactory bulb interneurons. Mechanistically,Olig1/2play dual roles by orchestrating distinct transcriptional programs in GPCs, particularly inhibiting the expression ofGsx2through direct binding to its multiple enhancers. Additionally, we provide compelling evidence that human H3.3G34R/V-mutant tumors, a subtype of high-grade gliomas, originate from dorsal cortical-derived GPCs rather than from the previously assumed progenitors in the ventral basal ganglia. Collectively, our findings reveal a previously unrecognized role ofOlig1/2in both gliogenesis and gliomagenesis, offering deeper insights into the connections between normal neural development and tumorigenesis.Graphical Abstract
Astroglial Dysfunction, Demyelination and Nodular inflammation in Necrotizing Meningoencephalitis
Authors: Hernandez Sara I , Assenmacher Charles A , Church Molly E , Alvarez Jorge I 
Published: November 2024

AbstractNecrotizing Meningoencephalitis (NME), a form of Meningoencephalitis of Unknown Origin (MUO), is a progressive neuroinflammatory disease that primarily affects young, small-breed dogs. Due to limited understanding of its pathophysiology, early detection and the development of targeted therapies remain challenging. Definitive ante-mortem diagnosis is often unfeasible, and dogs with NME are frequently grouped under the broader MUO category. Our long-term objective is to identify distinct disease mechanisms within each MUO subtype to improve diagnostic accuracy, therapeutic approaches, and prognostic outcomes. To establish unique inflammatory patterns as they relate to neuropathologic changes in NME, we studied we studied the degree of immune cell infiltration, astrogliosis, demyelination, and microglial activation, comparing these factors with granulomatous meningoencephalomyelitis (GME), a closely related MUO subtype. We found that in the leptomeninges, NME is characterized by mild immune cell infiltration, in contrast to the prominent, B cell-rich aggregates seen in GME. In the neuroparenchyma, both diseases exhibit a comparable degree of lymphocyte infiltration; however, demyelination is more pronounced in NME, particularly within the subcortical white matter. Notably, areas of the brain affected by NME display a reduction in astrogliosis, which is associated with a marked decrease in the expression of the water channel protein aquaporin-4 (AQP4), a reduction not observed in GME. Additionally, we found that AQP4 expression levels correlate with the extent of microglial and macrophage activation. These findings suggest that astrocyte dysfunction in regions of microglial inflammation is a driver of NME and with adaptive immune responses likely playing a supportive role.
Information Theory of Composite Sequence Motifs: Mutational and Biophysical Determinants of Complex Molecular Recognition
Authors: Mascolo Elia , Erill Ivan 
Published: November 2024

AbstractThe recognition of nucleotide sequence patterns is a fundamental biological process that controls the start sites of replication, transcription and translation, as well as transcriptional and translational regulation. Foundational work on the evolution of biological information showed that the amount of information encoded in the target nucleotide sequence patterns, a quantity namedRsequence, evolves by natural selection to match a predictable quantity calledRfrequency. In this work, we propose a generalization of this canonical framework that can describe composite sequence motifs: motifs composed of a series of sequence patterns at some variable (not necessarily conserved) distance from each other. We find that some information can be encoded through the conservation of the distance between sequence patterns, a quantity we namedRspacer, and that - to be functional - biological systems require the sum ofRsequenceandRspacerto be constant. We empirically validate our mathematical results through evolutionary simulations. We apply this general framework to demonstrate that the pre-recruitment of regulatory complexes to target sites has intrinsic advantages over in situ recruitment in terms of energy dissipation and search efficiency, and that realistic values of protein flexibility co-evolve with the target composite motifs to match their spacer size variability. Lastly, we show that the relative advantage of encoding information in sequence patterns or in spacers depends on the balance between nucleotide substitutions and insertions/deletions, with known estimates for the rates of these mutation types favoring the evolution of composite motifs with highly conserved spacer length.
Therapeutic benefits of maintaining CDK4/6 inhibitors and incorporating CDK2 inhibitors beyond progression in breast cancer
Authors: Armand Jessica , Kim Sungsoo , Kim Kibum , Son Eugene , Kim Minah , Yang Hee Won 
Published: November 2024

AbstractThe combination of CDK4/6 inhibitors (CDK4/6i) and endocrine therapy has revolutionized treatment for hormone receptor-positive (HR+) metastatic breast cancer. However, the emergence of resistance in most patients often leads to treatment discontinuation with no consensus on effective second-line therapies. The therapeutic benefits of maintaining CDK4/6i or incorporating CDK2 inhibitors (CDK2i) after disease progression remain unclear. Here, we demonstrate that sustained CDK4/6i therapy, either alone or combined with CDK2i, significantly suppresses the growth of drug-resistant HR+breast cancer. Continued CDK4/6i treatment induces a non-canonical pathway for retinoblastoma protein (Rb) inactivation via post-translational degradation, resulting in diminished E2F activity and delayed G1 progression. Importantly, our data highlight that CDK2i should be combined with CDK4/6i to effectively suppress CDK2 activity and overcome resistance. We also identify cyclin E overexpression as a key driver of resistance to CDK4/6 and CDK2 inhibition. These findings provide crucial insights into overcoming resistance in HR+breast cancer, supporting the continued use of CDK4/6i and the strategic incorporation of CDK2i to improve therapeutic outcomes.
Decoding mesolimbic dopamine transmission in the olfactory tubercle and its contribution to methamphetamine responses through neurochemical sensing and chemogenetics
Authors: Bhimani Rohan V. , Pauly Ryan C. , Bass Caroline E. , Park Jinwoo 
Published: November 2024

AbstractCentral dopamine (DA) innervation of the olfactory tubercle (OT) from the ventral tegmental area (VTA) plays a critical role in encoding multisensory information and generating behavioral outputs necessary for survival. However, due to anatomical restrictions and the neurochemical heterogeneity of the VTA and OT, very little is known about the functional link between mesolimbic VTA-DA transmission in the OT and its role in mediating reward and drug seeking. In this study, we integratedin vivofast-scan cyclic voltammetry with chemogenetics to (1) characterize the effects of chemogenetic modulation (excitation and inhibition) of mesolimbic DA transmission in the OT of both anesthetized and awake-behaving wild-type rats and (2) demonstrate that inhibition of VTA-DA neurons is sufficient to suppress methamphetamine-induced DA transmission as well as its locomotor and rewarding effects. These results offer novel insights into mesolimbic DA transmission in the OT and its contribution to substance use disorders.
Activity-based selection for enhanced base editor mutational scanning
Authors: Kaplan Eleanor G , Steger Ryan J , Shah Spencer T , Drepanos Laura M , Griffith Audrey L , Reint Ganna , Doench John G 
Published: November 2024

ABSTRACTBase editing is a CRISPR-based technology that enables high-throughput, nucleotide-level functional interrogation of the genome, which is essential for understanding the genetic basis of human disease and informing therapeutic development. Base editing screens have emerged as a powerful experimental approach, yet significant cell-to-cell variability in editing efficiency introduces noise that may obscure meaningful results. Here, we develop a co-selection method that enriches for cells with high base editing activity, substantially increasing editing efficiency at a target locus. We evaluate this activity-based selection method against a traditional screening approach by tiling guide RNAs acrossTP53, demonstrating its enhanced capacity to pinpoint specific mutations and protein regions of functional importance. We anticipate that this modular selection method will enhance the resolution of base editing screens across many applications.
Astrocytic connexin43 phosphorylation contributes to seizure susceptibility after mild Traumatic Brain Injury
Authors: Muñoz-Ballester Carmen , Leitzel Owen , Golf Samantha , Phillips Chelsea M , Zeitz Michael J , Pandit Rahul , Smyth James W. , Lamouille Samy , Robel Stefanie 
Published: November 2024

AbstractAstrocytes play a crucial role in maintaining brain homeostasis through functional gap junctions (GJs) primarily formed by connexin43 (Cx43). These GJs facilitate electrical and metabolic coupling between astrocytes, allowing the passage of ions, glucose, and metabolites. Dysregulation of Cx43 has been implicated in various pathologies, including traumatic brain injury (TBI) and acquired epilepsy. We previously identified a subset of atypical astrocytes after mild TBI that exhibit reduced Cx43 expression and coupling and are correlated with the development of spontaneous seizures. Given that mild TBI affects millions globally and can lead to long-term complications, including post-traumatic epilepsy, understanding the molecular events post-TBI is critical for developing therapeutic strategies.In the present study, we assessed the heterogeneity of Cx43 protein expression after mild TBI. In accordance with our previous findings, a subset of astrocytes lost Cx43 expression. As previously reported after TBI, we also found a significant increase in total Cx43 protein expression after mild TBI, predominantly in the soluble form, suggesting that while junctional Cx43 protein levels remained stable, hemichannels and cytoplasmic Cx43 were increased. We then investigated the phosphorylation of Cx43 at serine 368 after TBI, which is known to influence GJ assembly and function. Phosphorylation of Cx43 at serine 368 is elevated following TBI and Cx43S368Amutant mice, lacking this phosphorylation, exhibited reduced susceptibility to seizures induced by pentylenetetrazol (PTZ). These findings suggest that TBI-induced Cx43 phosphorylation enhances seizure susceptibility, while inhibiting this modification presents a potential therapeutic avenue for mitigating neuronal hyperexcitability and seizure development.Significance statementConnexin43 (Cx43) is the main protein of astrocyte gap junctions, mediating astrocyte coupling of astrocytes into cellular networks, but has also other non-junctional functions. Many pathologies present with altered Cx43 regulation. In this study, we studied Cx43 alterations after mild traumatic brain injury (TBI) in a mouse model. We found that while some astrocyte lost Cx43 expression, a subset of astrocytes experienced an increase in cytoplasmic and hemichannel Cx43. This increase correlated with an increase in phosphorylated Cx43 at serine 368. Cx43S368Amutant mice, lacking this phosphorylation, exhibited reduced susceptibility to seizures induced by pentylenetetrazol (PTZ). These findings suggest that TBI-induced Cx43 phosphorylation enhances seizure susceptibility.
Machine Learning Models for Predicting Multiple Myeloma Staging and MGUS Progression Using Gene Expression Data
Authors: Karathanasis Nestoras , Spyrou George M. 
Published: November 2024

AbstractIn this study, we developed and evaluated Machine Learning (ML) models aimed at predicting the stage of multiple myeloma (MM) and the progression of monoclonal gammopathy of undetermined significance (MGUS) to MM. Accurate staging of MM is critical for determining appropriate treatment strategies, and our models, employing algorithms such as ElasticNet, Random Forest, Boosting, and Support Vector Machines, demonstrated high efficacy in capturing the biological differences across disease stages. Among these, the ElasticNet model exhibited strong generalizability, achieving consistent multiclass AUC values across various datasets and data transformations.Predicting MGUS progression to MM presents a significant challenge due to the scarcity of MGUS cases that have progressed. We employed a two-pronged approach to address this: developing models using a limited dataset containing progressing MGUS patients and training models on combined MGUS and MM datasets. The models achieved AUC values slightly above 0.8, particularly with ElasticNet, Boosting and Support Vector Machines, indicating their potential in stratifying MGUS patients by progression risk. This study is original in integrating MM data with MGUS cases to enhance the predictive accuracy of MGUS progression, offering a novel methodology with potential clinical applications in patient monitoring and early intervention.Our feature selection and enrichment analyses further revealed that the identified genes are involved in key signaling pathways, including PI3K-Akt, MAPK, Wnt, and mTOR, all of which play crucial roles in MM pathogenesis. These findings align with established biological knowledge, suggest possible therapeutic targets and increase the explainability of our models.
Single-cell transcriptomic landscape of the neuroimmune compartment in amyotrophic lateral sclerosis brain and spinal cord
Authors: Tuddenham John F. , Fujita Masashi , Khairallah Anthony , Harbison Claire , Flowers Xena E. , Coronas-Samano Guillermo , Maniatis Silas , Daly Aidan , Schneider Julie A. , Teich Andrew F. , Vonsattel Jean Paul G. , Sims Peter A. , Elyaman Wassim , Bradshaw Elizabeth M. , Phatnani Hemali , Shneider Neil , Bennett David A. , De Jager Philip L. , Przedborski Serge , Menon Vilas , Olah Marta 
Published: November 2024

AbstractDevelopment of therapeutic approaches that target specific microglia responses in amyotrophic lateral sclerosis (ALS) is crucial due to the involvement of microglia in ALS progression. Our study identifies the predominant microglia subset in human ALS primary motor cortex and spinal cord as an undifferentiated phenotype with dysregulated respiratory electron transport. Moreover, we find that the interferon response microglia subset is enriched in donors with aggressive disease progression, while a previously described potentially protective microglia phenotype is depleted in ALS. Additionally, we observe an enrichment of non-microglial immune cell, mainly NK/T cells, in ALS central nervous system, primarily in the spinal cord. These findings pave the way for the development of microglia subset-specific therapeutic interventions to slow or even stop ALS progression.
Visual training induced occipital fast sleep spindle clustering in humans revealed by full-night HD-EEG recordings
Authors: Gerván Patrícia , Bocskai Gábor , Berencsi Andrea , Gombos Ferenc , Kovács Ilona 
Published: November 2024

AbstractThis study investigates the impact of extensive visual procedural training on the temporal organisation of sleep spindles in healthy young adults. We selected 39 participants aged 16-20 and employed high-density electroencephalography to assess spindle characteristics during two full nights of sleep, with daytime practising in a contour integration task in between the two nights. We utilised linear mixed models to comprehensively analyse the effects of age and training on basic, clustering- and rhythmicity-related spindle parameters. Our findings indicate no significant age effects in this age-range, and no significant change between the two nights with respect to slow spindles. Fast spindles demonstrated a significant increase in density after training, and we observed significant changes in spindle clustering and rhythmicity parameters as well. Local spindle density, train density, and the ratio of clustered spindles have increased, and inter-train interval decreased by the second night. These results contribute to the growing literature on sleep-dependent memory consolidation by demonstrating that spindle reorganisation occurs not only in motor tasks but also in visual learning contexts. The absence of age-related differences further highlights the robustness of these mechanisms across developmental stages. Our study emphasises the importance of spindle dynamics in procedural learning and suggests promising possibilities for future research into the neurophysiological basis of memory consolidation. By revealing the relationship between training and sleep spindle characteristics, our findings provide valuable insights into how sleep supports learning and memory processes in young adults, potentially informing interventions aimed at enhancing memory performance through sleep-related strategies.
Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms
Authors: Khan Farhan , Gincley Benjamin , Busch Andrea , Tolofari Dienye L , Norton John W , Varga Emily , Mckay R Michael , Fuentes-Cabrera Miguel , Slawecki Tad , Pinto Ameet J. 
Published: November 2024

AbstractReal-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms so as to enable efficient response by water management agencies. This paper presents an image processing pipeline developed to adapt ARTiMiS, a low-cost automated flow-imaging device, for real-time algal monitoring specifically in freshwater and environmental systems. This pipeline addresses several challenges associated with autonomous imaging of aquatic samples such as flow-imaging artifacts (i.e., out-of-focus and background objects), as well as specific challenges associated with monitoring of open environmental systems (i.e., identification of novel objects). The pipeline leverages a Random Forest model to identify out- of-focus particles with an accuracy of 89% and a custom background particle detection algorithm to identify and remove particles that erroneously appear in consecutive images with >97±2.8% accuracy. Furthermore, a convolutional neural network (CNN), trained to classify distinct classes comprising both taxonomical and morphological categories, achieved 94% accuracy in a closed dataset. Nonetheless, the supervised closed-set classifiers struggled with the accurate classification of objects when challenged with debris and novel particles which are common in complex open environments; this limits real-time monitoring applications by requiring extensive manual oversight. To mitigate this, three methods incorporating classification with rejection were tested to improve model precision by excluding irrelevant or unknown classes. Combined, these advances present a fully integrated, end-to-end solution for real-time HAB monitoring in open environmental systems thus enhancing the scalability of automated detection in dynamic aquatic environments.HighlightsRandom Forest model is more generalizable than Convolutional Neural Networks to remove out-of-focus particles.A two-stage clustering algorithm is effective at removing background particles in flow imaging microscopy.Closed-set CNN classifier performance deteriorates when challenged with unknown particles.Classification with rejection improves both precision and accuracy for environmental samples.