bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Proposed Stereoelectroencephalography Electrode Nomenclature and Call for Standardization
Authors: Calley Clifford S. , Ho Winson , Babajani-Feremi Abbas , Bodden Carla , Tyler-Kabara Elizabeth , Clarke Dave F. 
Published: June 2024

Introduction:
Between 20 and 40% of patients with epilepsy are considered pharmacoresistant. Stereoelectroencephalography (sEEG) is frequently used as an invasive method for localizing seizures in patients with pharmacoresistant epilepsy who are surgical candidates; however, electrode nomenclature varies widely across institutions. This lack of standardization can have many downstream consequences, including difficulty with intercenter or intracenter interpretation, communication, and reliability.

Methods:
The authors propose a novel sEEG nomenclature that is both intuitive and comprehensive. Considerations include clear/precise entry and target anatomical locations, laterality, distinction of superficial and deep structures, functional mapping, and relative labeling of electrodes in close proximity if needed. Special consideration was also given to electrodes approximating radiographically distinct lesions. The accuracy of electrode identification and the use of correct entry-target labels were assessed by neurosurgeons and epileptologists, not directly involved in each case.

Results:
The authors' nomenclature was used in 41 consecutive sEEG cases (497 electrodes total) within their institution. After reconstruction was complete, the accuracy of electrode identification was 100%, and the correct use of entry-target labels was 98%. The last 30 sEEG cases had 100% correct use of entry-target labels.

Conclusions:
The proposed sEEG nomenclature demonstrated both high accuracy in electrode identification and consistent use of entry-target labeling. The authors submit this nomenclature as a model for standardization across epilepsy surgery centers. They intend to improve practicability, ease of use, and specificity of this nomenclature through collaboration with other surgical epilepsy centers.
Validation of the Spanish version of the multifaceted empathy test: comparison between cannabis use effects and controls in social cognition
Authors: Sainz-Cort Alberto , Martín-Islas Marta , Jimenez-Garrido Daniel , López-Navarro Miriam , Oña Genís , Muñoz-Marron Elena , Heredia Luis , Gil-Pérez Mercè , Torrente Margarita , Vicens Paloma , Bouso José Carlos 
Published: June 2024

Objective
While social cognition is shown to be impaired in several mental disorders, the effects of cannabis on social cognition are still not clear. Past studies have used the multifaceted empathy test (MET) to study social cognition. This study aims to test the validity of the MET Spanish version and to evaluate the effects of cannabis use on social cognition.

Methods
In total 116 participants from a Cannabis Social Club (CSC) completed the MET and the reading the mind in the eyes test (RMET) under the effects of cannabis and were compared to 86 university students (control group). Internal consistency and convergent validity were assessed. Cognitive empathy (CE) and emotional empathy (EE) were tested in both groups.

Results
The MET CE scale shows low internal consistency, while the EE scale shows high internal consistency. Items showed similar difficulty for both groups. Cannabis users showed deficient overall emotional recognition, with reduced scores associated with positive stimuli. Overall scores for EE were similar for both groups, but the experimental group scored lower with negative stimuli when compared to controls.

Conclusion
This study validates the MET Spanish version for its use in future studies. Results confirmed deficient emotional recognition in cannabis users and a dampened reaction to negative stimuli for the first time.
RTF: An R package for modelling time course data
Authors: Brombacher Eva , Kreutz Clemens 
Published: June 2024

AbstractSummaryThe retarded transient function (RTF) approach has been introduced as a complementary approach to employing ordinary differential equations (ODEs) for modelling dynamics typically observed for cellular signalling processes. Here, we introduce the R package to the RTF approach, which has originally been implemented within the MATLAB-basedData2Dynamicsmodelling framework. The package enables modelling of time and dose dependencies and includes model reduction to minimize overfitting. It can be applied not only to experimental data but also to trajectories of ODE models, to characterize the dynamics or to identify major targets of experimental perturbations in the low-dimensional representations calculated by the package.Availability and ImplementationThe R package RTF is available athttps://github.com/kreutz-lab/RTF.Contactclemens.kreutz@uniklinik-freiburg.de
Sweet and sticky: increased cell adhesion through click-mediated functionalization of regenerative liver progenitor cells
Authors: Alipio Amaziah R. , Vieira Melissa R. , Haefeli Tamara , Hoelting Lisa , Frey Olivier , Haj Alicia J. El , Arno Maria C. 
Published: June 2024

ABSTRACTThe burgeoning field of cell therapies is rapidly expanding, offering the promise to tackle complex and unsolved healthcare problems. One prominent example is represented by CAR T-cells, which have been introduced into the clinic for treating a variety of cancers. Promising cell-based candidates have also been developed to promote tissue regeneration, showing high potencies for the treatment of damaged liver. Nevertheless, in the remit of regenerative medicine, cell-therapy efficacies remain suboptimal as a consequence of the low engraftment of injected cells to the existing surrounding tissue. Herein, we present a facile approach to enhance the adhesion and engraftment of therapeutic hepatic progenitor cells (HPCs) through specific and homogeneous cell surface modification with exogenous polysaccharides, without requiring genetic modification. Coated HPCs exhibited significantly increased markers of adhesion and cell spreading, demonstrating preferential interactions with certain extra-cellular matrix proteins. Moreover, they displayed enhanced binding to endothelial cells and 3D liver microtissues. This translatable methodology shows promise for improving therapeutic cell engraftment, offering a potential alternative to liver transplantation in end-stage liver disease.
Diversification of gene expression across extremophytes and stress-sensitive species in the Brassicaceae
Authors: Wang Guannan , Ryu Kook Hui , Dinneny Andrea , Carlson Joseph , Goodstein David , Lee Jiyoung , Oh Dong-Ha , Oliva Marina , Lister Ryan , Dinneny José R. , Schiefelbein John , Dassanayake Maheshi 
Published: June 2024

SUMMARYStress-sensitive and stress-adapted plants respond differently to environmental stresses. To explore the cellular-level stress adaptations, we built root single-cell transcriptome atlases for diverse Brassicaceae species: stress-sensitive plants (Arabidopsis thalianaandSisymbrium irio), extremophytes (Eutrema salsugineumandSchrenkiella parvula) and a polyploid crop (Camelina sativa), under control, NaCl, and abscisic acid treatments. Approximately half of Arabidopsis cell-type markers lacked expression conservation across species. We identified new conserved cell-type markers, along with orthologs showing divergent expressions. We experimentally mapped distinct cortex sub-populations to different cortex layers across species. We found distinct cell-type-specific transcriptomic responses between species and treatments. Lineage-specific losses of stress responses were less prevalent but evolutionarily more favored than gains. InC. sativa, sub-genomes contributed equally to stress responses and homeologs with divergent stress responses typically did not exhibit high coding sequence or expression divergence. Our study provides a foundational root atlas and an analytical framework for multi-species single-cell transcriptomics.
ProBASS – a language model with sequence and structural features for predicting the effect of mutations on binding affinity
Authors: Gurusinghe Sagara N.S. , Wu Yibing , DeGrado William , Shifman Julia M. 
Published: June 2024

AbstractProtein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbindvalues. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbindvalues of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbindvalues across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbindprediction, greatly facilitating future protein engineering and design studies.
Exon inclusion signatures enable accurate estimation of splicing factor activity
Authors: Anglada-Girotto Miquel , Moakley Daniel F. , Zhang Chaolin , Miravet-Verde Samuel , Califano Andrea , Serrano Luis 
Published: June 2024

ABSTRACTSplicing factors control exon inclusion in messenger RNA, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on their own fall short in identifying which splicing factors underlie a phenotype. Here, we propose splicing factor activity can be estimated by interpreting changes in exon inclusion. We benchmark methods to construct splicing factor→exon networks and calculate activity. Combining RNA-seq perturbation-based networks with VIPER (virtual inference of protein activity by enriched regulon analysis) accurately captures splicing factor activation modulated by different regulatory layers. This approach consolidates splicing factor regulation into a single score derived solely from exon inclusion signatures, allowing functional interpretation of heterogeneous conditions. As a proof of concept, we identify recurrent cancer splicing programs, revealing oncogenic- and tumor suppressor-like splicing factors missed by conventional methods. These programs correlate with patient survival and key cancer hallmarks: initiation, proliferation, and immune evasion. Altogether, we show splicing factor activity can be accurately estimated from exon inclusion changes, enabling comprehensive analyses of splicing regulation with minimal data requirements.
The evolution of GC-biased gene conversion by means of natural selection
Authors: Clessin Augustin , Joseph Julien , Lartillot Nicolas 
Published: June 2024

AbstractGC-biased gene conversion (gBGC) is a recombination-associated evolutionary process that biases the segregation ratio of AT:GC polymorphisms in the gametes of heterozygotes, in favour of GC alleles. This process is the major determinant of variation in base composition across the human genome and can be the cause of a substantial burden of GC deleterious alleles. While the importance of GC-biased gene conversion in molecular evolution is increasingly recognised, the reasons for its existence and its variation between species remain largely unknown. Using simulations and semi-analytical approximations, we investigated the evolution of gBGC as a quantitative trait evolving by mutation, drift and natural selection. We show that in a finite population where most mutations are deleterious, gBGC is under weak stabilising selection around a positive value that mainly depends on the intensity of the mutation bias and on the intensity of selective constraints exerted on the genome. Importantly, the levels of gBGC that evolve by natural selection do not minimize the load in the population, and even increase it substantially in regions of high recombination rate. Therefore, despite reducing the population’s fitness, levels of gBGC that are currently observed in humans could in fact have been (weakly) positively selected.
Genotype-environment association across precipitation regimens reveals the mechanism of plant adaptation to rainy environments
Authors: Castellana Simone , Triozzi Paolo Maria , Dell’Acqua Matteo , Loreti Elena , Perata Pierdomenico 
Published: June 2024

AbstractIn an era characterized by rapidly changing and less-predictable weather conditions fueled by the climate crisis, understanding the mechanisms underlying local adaptation in plants is of paramount importance for the conservation of species. As the frequency and intensity of extreme precipitation events increase, so are the flooding events resulting from soil water saturation. The deriving onset of hypoxic stress is one of the leading causes of crop damage and yield loss. By combining genomics and remote sensing data, today it is possible to probe natural plant populations that have evolved in different rainfall regimes and look for molecular adaptation to hypoxia. Here, using an environmental genome-wide association study (eGWAS) on 934 non-redundant georeferenced Arabidopsis ecotypes, we have identified functional variants for the geneMED25 BINDING RING-H2 PROTEIN 1(MBR1). This is a ubiquitin-protein ligase that regulates MEDIATOR25 (MED25), part of a multiprotein complex that interacts with transcription factors which act as key drivers of the hypoxic response in Arabidopsis, namely the RELATED TO AP2 proteins, RAP2.2 and RAP2.12. Through experimental validation, we show that natural variants ofMBR1have a differential impact on the stability of MED25 and, in turn, on hypoxic tolerance. Our study also highlights the pivotal role of the MBR1/MED25 module in establishing a comprehensive hypoxic response. Our findings show that molecular candidates for plant environmental adaptation can be effectively mined from large datasets. This thus supports the need for the integration of forward and reverse genetics with robust molecular physiology validation of the outcomes.
Dietary protein restriction diminishes sucrose reward and reduces sucrose-evoked mesolimbic dopamine signaling
Authors: Wu Chih-Ting , Magaña Diego Gonzalez , Roshgadol Jacob , Tian Lin , Ryan Karen K. 
Published: June 2024

AbstractObjectiveA growing literature suggests manipulating dietary protein status decreases sweet consumption in rodents and in humans. Underlying neurocircuit mechanisms have not yet been determined, but previous work points towards hedonic rather than homeostatic pathways. Here we hypothesized that a history of protein restriction reduces sucrose seeking by altering mesolimbic dopamine signaling.MethodsWe tested this hypothesis using established behavioral tests of palatability and motivation, including the ‘palatability contrast’ and conditioned place preference (CPP) tests. We used modern optical sensors for measuring real-time nucleus accumbens (NAc) dopamine dynamics during sucrose consumption, via fiber photometry, in male C57/Bl6J mice maintained on low-protein high-carbohydrate (LPHC) or control (CON) diet for ∼5 weeks.ResultsA history of protein restriction decreased the consumption of a sucrose ‘dessert’ in sated mice by ∼50% compared to controls [T-test, p< 0.05]. The dopamine release in NAc during sucrose consumption was reduced, also by ∼50%, in LPHC-fed mice compared to CON [T-test, p< 0.01]. Furthermore, LPHC-feeding blocked the sucrose-conditioned place preference we observed in CON-fed mice [paired T-test, p< 0.05], indicating reduced motivation. This was accompanied by a 33% decrease in neuronal activation of the NAc core, as measured by c-Fos immunolabeling from brains collected directly after the CPP test.ConclusionsDespite ongoing efforts to promote healthier dietary habits, adherence to recommendations aimed at reducing the intake of added sugars and processed sweets remains challenging. This study highlights chronic dietary protein restriction as a nutritional intervention that suppresses the motivation for sucrose intake, via blunted sucrose-evoke dopamine release in NAc.
Multi-city analysis of synergies and trade-offs between urban bird diversity and carbon storage to inform decision-making
Authors: Kinnunen R.P. , Ziter C.D. , Frei B. 
Published: June 2024

AbstractCities are particularly vulnerable to the impacts of biodiversity loss and climate change. Urban greenspaces are important ecosystems that can conserve biodiversity and help offset the carbon footprint of urban areas. However, despite large-scale tree planting and restoration initiatives in cities, it is not well known where trees or vegetation should be planted or restored to achieve multiple benefits. We considered urban greenspaces as nature-based solutions for urban climate mitigation and biodiversity conservation planning. Using bivariate mapping, we examined the spatial synergies and trade-offs between bird functional diversity and carbon storage in ten Canadian cities spanning a gradient of geography and population, and modelled the relationships between vegetation attributes and both bird diversity and amount of carbon. We found carbon and biodiversity are weakly positively correlated across the ten cities, however, this relationship varied in strength, direction and significance. Our maps highlight areas within our target cities where greenspaces could be managed, restored, or protected to maximize carbon storage and conserve biodiversity. Nationwide, our results also show that forest management strategies that promote increases in canopy cover and the proportion of needle-leaved species in urban greenspaces are potential win-win strategies for biodiversity and carbon. Our study shows NbS strategies are not always generalizable across regions. National policies should guide municipalities and cities using regional priorities and science advice, since a NbS promoting biodiversity in one region may, in fact, reduce it in another.
Pathlength-selective, interferometric diffuse correlation spectroscopy (PaLS-iDCS)
Authors: Robinson Mitchell B. , Renna Marco , Otic Nikola , Franceschini Maria Angela , Carp Stefan A. 
Published: June 2024

AbstractDiffuse correlation spectroscopy (DCS) is an optical method that offers non-invasive assessment of blood flow in tissue through the analysis of intensity fluctuations in diffusely backscattered coherent light. The non-invasive nature of the technique has enabled several clinical applications for deep tissue blood flow measurements, including cerebral blood flow monitoring as well as tumor blood flow mapping. While a promising technique, in measurement configurations targeting deep tissue hemodynamics, the standard DCS implementations suffer from insufficient signal-to-noise ratio (SNR), depth sensitivity, and sampling rate, limiting their utility. In this work, we present an enhanced DCS method called pathlength-selective, interferometric DCS (PaLS-iDCS), which improves upon both the sensitivity of the measurement to deep tissue hemodynamics and the SNR of the measurement using pathlength-specific coherent gain. Through interferometric detection, PaLS-iDCS can provide time-of-flight (ToF) specific blood flow information without the use of expensive time-tagging electronics and low-jitter detectors. The new technique is compared to time-domain DCS (TD-DCS), another enhanced DCS method able to resolve photon ToF in tissue, through Monte Carlo simulation, phantom experiments, and human subject measurements. PaLS-iDCS consistently demonstrates improvements in SNR (>2x) for similar measurement conditions (same photon ToF), and the SNR improvements allow for measurements at extended photon ToFs, which have increased sensitivity to deep tissue hemodynamics (∼50% increase). Further, like TD-DCS, PaLS-iDCS allows direct estimation of tissue optical properties from the sampled ToF distribution without the need for a separate spectroscopic measurement. This method offers a relatively straightforward way to allow DCS systems to make robust measurements of blood flow with greatly enhanced sensitivity to deep tissue hemodynamics, enabling further applications of this non-invasive technology.
Decoding protein language models: insights from embedding space analysis
Authors: Rissom Pia Francesca , Sarmiento Paulo Yanez , Safer Jordan , Coley Connor W. , Renard Bernhard Y. , Heyne Henrike O. , Iqbal Sumaiya 
Published: June 2024

AbstractFoundation models, which encode patterns in large, high-dimensional data as embeddings, show promise in many machine learning related applications in molecular biology. Embeddings learned by the models provide informative features for downstream prediction tasks, however, the information captured by the model is often not interpretable. One approach to understanding the captured information is through the analysis of their learned embeddings, which in molecular biology so far has mainly focused on visualizing individual embedding spaces. This study introduces a quantitative framework for cross-space comparison, enabling intuitive exploration and comparison of embedding spaces in molecular biology. The framework emphasizes analyzing the distribution of known biological information within embedding space neighborhoods and provides insights into relationships between multiple embedding spaces. Comparison techniques include global pairwise distance measurements as well as local nearest neighbor analyses. By applying our framework to embeddings from protein language models, we demonstrate how embedding space analysis can serve as a valuable pre-filtering step for task-specific supervised machine learning applications and for the recognition of differential patterns in data encoded within and across different embedding spaces. To support a wide usability, we provide a Python library that implements all analysis methods, available athttps://github.com/broadinstitute/EmmaEmb.
Evolutionary branching in multi-level selection models
Authors: Simon Burton , Ispolatov Yaroslav , Doebeli Michael 
Published: June 2024

AbstractWe study a model of group-structured populations featuring individual-level birth and death events, and group-level fission and extinction events. Individuals play games within their groups, while groups play games against other groups. Pay-offs from individual-level games affect birth rates of individuals, and payoffs from group-level games affect group extinction rates. We focus on the the evolutionary dynamics of continuous traits with particular emphasis on the phenomenon of evolutionary diversification. Specifically, we consider two-level processes in which individuals and groups play continuous snowdrift or prisoner’s dilemma games. Individual game strategies evolve due to selection pressure from both the individual and group level interactions. The resulting evolutionary dynamics turns out to be very complex, including branching and type-diversification at one level or the other. We observe that a weaker selection pressure at the individual level results in more adaptable groups and sometimes group-level branching. Stronger individual-level selection leads to more effective adaptation within each group while preventing the groups from optimizing their strategies for group-level games.