bluuebunny/crossref_metadata_2025_split huggingface dataset:
Carrying oversized loads may create pheromone “blind spots” in leafcutter ants
Authors: Porras-Brenes Katherine , Amador-Vargas Sabrina 
Published: January 2025

AbstractUnderstanding animals’ sensorial abilities and cognitive processes while foraging helps explain why animals depart from theoretical optimal foraging. Here, we studied foraging decisions in leafcutter ants (Atta colombica), which form and follow pheromone foraging trails that workers smell by tapping the substrate with the antennae. Carrying oversized items transports more plant material in a single trip, but workers walk more slowly and can delay the nestmates walking behind them. We tested the hypothesis that balancing an oversized load limits the ability to tap the ground with the antennae, therefore reducing the ability to smell the foraging trail. As expected, we found that the number of antennae taps per step was (1) fewer in laden vs. unladen workers, (2) fewer as loads increased in area, but only for larger ants, and (3) unrelated to the load shape. Second, workers increased the antennae taps and speed after experimentally reducing standardized loads. Last, we evaluated the allometric relation between the antennae length and worker size, and found that it showed negative allometry. Hence, larger ants had proportionally shorter antennae, which could explain why larger workers are more impacted by oversized loads in the number of antennae taps. Overall, our results support that carrying an oversized load limits the ability of workers to tap the ground, analogous to the blind spots in a large truck. Our findings reveal a sensory limitation to foraging and reflect how foraging decisions in a social organism are adjusted to the individual capacities and the colony needs.
Causal disentanglement for single-cell representations and controllable counterfactual generation
Authors: Gao Yicheng , Dong Kejing , Shan Caihua , Li Dongsheng , Liu Qi 
Published: January 2025

AbstractConducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell, which incorporates the causal relationships among disentangled concepts within a diffusion model to perform disentanglement learning, with the aim of increasing the explainability, generalizability and controllability of single-cell data, including spatial and temporal omics data, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios, i.e., disentanglement and reconstruction, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.
miRScore: a rapid and precise microRNA validation tool
Authors: Vanek Allison , Griffiths-Jones Sam , Meyers Blake C. , Shahid Saima , Axtell Michael J. 
Published: January 2025

AbstractMicroRNAs (miRNAs) are small non-protein-coding RNAs that regulate gene expression in many eukaryotes. Next-generation sequencing of small RNAs (small RNA-seq) has accelerated discovery and annotation of novel miRNAs. Newly discovered miRNAs are typically submitted to databases such as the miRBase microRNA registry following the publication of a peer-reviewed study. However, genome-wide scans using small RNA-seq data often yield high rates of false-positive miRNA annotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating miRNA annotations using sRNA-seq data. miRScore combines structural and expression-based analyses to provide rapid and reliable validation of miRNA annotations. By providing users with detailed metrics and visualization, miRScore enhances the ability to assess confidence in novel and existing miRNA annotations. miRScore has the potential to advance the overall quality of miRNA annotations by improving accuracy of new submissions to miRNA databases and serving as a resource for re-evaluating existing annotations.
PepMSND: Integrating Multi-level Feature Engineering and Comprehensive Databases to Enhance in vivo/in vitro Peptide Blood Stability Prediction
Authors: Haomeng Hu , Zhang Chengyun , Zhenyu Xu , Duan Hongliang 
Published: January 2025

AbstractDeep learning technology has revolutionized the field of peptides, but key questions such as how to predict the blood stability of peptides remain. While such a task can be accomplished by experiments, it requires much time and cost. Here, to address this challenge, we collect extensive experimental data on peptide stability in blood from public databases and literature and construct a database of peptide blood stability that includes 635 samples. Based on this database, we develop a novel model called PepMSND, integrating KAN, Transformer, GAT and SE(3)-Transformer to make multi-level feature engineering to make peptide stability prediction. Our model can achieve the ACC of 0.8672 and the AUC of 0.9118 on average and outperforms the baseline models. This work can facilitate the development of novel peptides with strong stability, which is crucial for their therapeutic use in clinical applications.
A questionnaire for a conceptual framework and interdisciplinary public health research using the Delphi technique – development and validation
Authors: Alaze Anita , Finne Emily , Batram-Zaantvoort Stephanie , Razum Oliver , Miani Céline 
Published: January 2025

AbstractBackgroundThe Delphi technique has become established in public health research, yet there is a lack of methodological standards in questionnaire development. We here demonstrate how the Delphi technique can be used in an interdisciplinary public health topic for framework development, and we highlight methodological challenges and possible solutions.MethodsWe developed the questionnaire through a comprehensive literature review and the generation of an item pool based on the rules of item construction. We used cognitive interviews, Delphi experts’ assessment and group discussions to refine the questionnaire and to ensure content validity. Finally, we pre-tested the online questionnaire on LimeSurvey.ResultsThe questionnaire consists of three main sections, namely gender (norms), the social environment and the mental health of adolescents, and another section on the expert’s characteristics. The questionnaire comprises a total of 32 questions and includes rating and ranking questions, contentrelated and comment questions, open and closed questions as well as questions on personal characteristics and evaluation questions.ConclusionInterdisciplinary researchers need to be involved in the development process of the questionnaire to address challenges related to interdisciplinarity. Researchers using the Delphi technique should consider certain aspects of the development process, such as comprehensively preprocessing the content, disentangling the theoretical concepts, reducing the complexity, providing more structure and clarity, and ensuring that the length of the questionnaire is manageable. Future research should focus on developing methodological guidelines and testing their applicability for different objectives of the Delphi technique (e.g. framework development).
Immunohistochemical Profiling of Histone Modification Biomarkers Identifies Subtype-Specific Epigenetic Signatures and Potential Drug Targets in Breast Cancer
Authors: Huo Zirong , Zhang Sitong , Su Guodong , Cai Yu , Chen Rui , Jiang Mengju , Yang Dongyan , Zhang Shengchao , Xiong Yuyan , Zhang Xi 
Published: January 2025

AbstractBackground:Breast cancer (BC) subtypes exhibit distinct epigenetic landscapes, with triple-negative breast cancer (TNBC) lacking effective targeted therapies. This study investigates histone biomarkers and therapeutic vulnerabilities across BC subtypes.Methods:Immunohistochemical profiling of >20 histone biomarkers, including histone modifications, modifiers and oncohistone mutations was conducted on a discovery cohort and a validation cohort of BC tissues, healthy controls and cell line models. Transcriptomic and cell growth analyses were conducted to evaluate the effects of the small-molecule G9a inhibitor in diverse BC models.Results:Key histone biomarkers, including H3K9me2, H3K36me2, and H3K79me, were differentially expressed across BC subtypes. H3K9me2 emerged as an independent predictor for distinguishing TNBC from other less aggressive BC subtypes, with elevated expression correlating with higher tumor grade and stage. G9a inhibition impaired cell proliferation and modulated epithelial-mesenchymal transition pathways, with the strongest impact in basal-like TNBC. Disruption of oncogene and tumor suppressor regulation (e.g., TP53, SATB1) was observed in TNBC.Conclusion:This study highlights G9a’s context-dependent roles in BC, supporting its potential as a therapeutic target. Findings provide a foundation for subtype-specific epigenetic therapies to improve outcomes in aggressive BC subtypes.Clinical PerspectiveThis study was undertaken to explore the epigenetic landscape of breast cancer subtypes, focusing on histone modifications and their therapeutic potential, particularly through targeting G9a, a histone methyltransferase.The study identified subtype-specific histone biomarkers, with H3K9me2 emerging as a key marker distinguishing TNBC from other less aggressive subtypes. G9a inhibition demonstrated robust anti-cancer effects, including cell proliferation impairment and disruption of oncogenic pathways, particularly in TNBC models.These findings provide compelling evidence for the development of subtype-specific epigenetic therapies targeting G9a and similar regulators, highlighting their potential to reshape the treatment landscape for TNBC and improve patient outcomes.
Insular cortex encodes task alignment
Authors: Niediek Johannes , Jankowski Maciej M. , Polterovich Ana , Kazakov Alexander , Nelken Israel 
Published: January 2025

SummaryAnimals can learn complex behaviors. Animal behavior in the lab has traditionally been studied via summary statistics such as trial-based success rates. However, animal behavior is much more fine-grained: a trial in an experiment often consists of multiple actions, and more than one strategy can lead to a successful completion of a trial. To understand how the brain controls behavior, a fine-grained yet compact description of behavior is necessary.We describe here an approach for estimating the strategies animals use for a large family of tasks from first principles. Using reinforcement learning with informational constraints on policies, we compute a rich set of candidate policies with a small number of meaningful parameters, and match observed behavior to these policies. In a sample rat task, our approach revealed ongoing learning for more than 100 days after the saturation of success rates. Moreover, we showed that many neurons in the insular cortex of rats track the instantaneous task engagement of the rats with a resolution of a few minutes.Due to its generic formulation in reinforcement learning terminology, our work is directly applicable to the majority of animal tasks in use today.
Bees flexibly adjust learning strategies to information content in a foraging task
Authors: Spaethe Johannes , Hutzenthaler Selma , Dietz Alexander , Gehrig Karl , Foster James , Stöckl Anna 
Published: January 2025

AbstractWhen making decisions, humans and animals often rely on multiple sensory cues simultaneously. These provide complementary sources of information, which can help overcome ambiguity and noise, and increase the accuracy of decisions. While most studies have focused on the benefits of multimodal and within-mode integration for learning and decision making, their costs have received less attention. Processing, learning and memorizing multiple inputs requires more neural resources than for single cues, and might also require more time. In this study, we tested whether insects trade off the costs and benefits of learning multiple cues in a foraging task, using the buff-tailed bumblebeeBombus terrestris. To maximize comparability between cues, we presented combinations of visual-only features, as found on natural flowers: colours of varying discriminability, paired with shapes or patterns. We found that the bees relied exclusively on colours when these were easy to discriminate, and did not learn pattern or shape features presented simultaneously. With harder to discriminate colours, the bees learned both colour and shape or pattern features. Our results demonstrate that bumblebees flexibly adjust their learning strategies when presented with visual features of varying discriminability, to balance the costs and benefits of multi-cue learning. Our analysis of the learning rates of the bees with multi- and single attribute stimuli suggests that blocking could serve as a mechanism to implement this strategy switch. These results shed light on trade-offs in learning and decision making with multiple cues, and can directly be compared to studies in other insects, animals and humans.Significance StatementWhen making decisions, animals often rely on multiple sensory cues simultaneously. These complementary sources of information can increase decision accuracy. Unlike the benefits of cue integration, their costs have received less attention. In this study, we tested whether insects trade off the costs and benefits of learning multiple cues in a foraging task. We trained buff-tailed bumblebees,Bombus terrestris, to feature combinations of natural flowers. Bees flexibly switched from linear integration to winner-takes-all decision strategies depending on the sensory information available, thereby balancing accuracy and time investment in the task. We find that this switch can be explained by the cognitive phenomenon of blocking; providing the ground for future investigations into the underlying neural mechanisms of flexible decision strategies.
Notch signaling maintains a progenitor-like subclass of hepatocellular carcinoma
Authors: Seidel Kerstin , Piskol Robert , Thao Nguyen Thi Thu , Shelton Amy , Cottonham Charisa , de la Cruz Cecile C. , Castillo Joseph , Garcia Jesse , Segal Udi , Merchant Mark , Yang Yeqing Angela , Chen Jasmine , Ahmed Musa , Scherl Alexis , Vij Rajesh , Mosteiro Lluc , Wu Yan , Modrusan Zora , Metcalfe Ciara , Siebel Chris 
Published: January 2025

AbstractHepatocellular carcinomas (HCCs) constitute one of the few cancer indications for which mortality rates continue to rise. While Notch signaling dictates a key progenitor lineage choice during development, its role in HCC has remained controversial. Using therapeutic antibodies targeting Notch ligands and receptors to screen over 40 patient-derived xenograft models, we here identify progenitor-like HCCs that crucially depend on a tumor-intrinsic JAG1-NOTCH2 signal. Inhibiting this signal induces tumor regressions by triggering progenitor-to-hepatocyte differentiation, the same cell fate-switch that Notch controls during development. Transcriptomic analysis places the responsive tumors within the well-characterized progenitor subclass, a poor prognostic group of highly proliferative tumors, providing a diagnostic method to enrich for Notch-dependent HCCs. Furthermore, single-cell RNA sequencing uncovers a heterogeneous population of tumor cells and reveals how Notch inhibition shifts cells from a mixed cholangiocyte-hepatocyte lineage to one resembling mature hepatocytes. Analyzing the underlying transcriptional programs brings molecular detail to this process by showing that Notch inhibition de-represses expression of CEBPA, which enables the activity of HNF4α, a hepatocyte lineage factor that is otherwise quiescent. We thus describe a compelling and targetable dependency in a poor-prognosis class of HCCs.
Accelerometer-derived classifiers for early detection of degenerative joint disease in cats
Authors: Montout A.X. , Maniaki E , Burghardt T. , Hezzell M. J. , Blackwell E. , Dowsey A.W. 
Published: January 2025

AbstractDecreased mobility is a clinical sign of degenerative joint disease (DJD) in cats, which is highly prevalent, with 61% of cats aged six years or older showing radiographic evidence of DJD. Radiographs can reveal morphological changes and assess joint degeneration, but they cannot determine the extent of pain experienced by cats. Additionally, there is no universal objective assessment method for DJD-associated pain in cats. Developing an accurate evaluation model could enable earlier treatment, slow disease progression, and improve cats’ well-being.This study aimed to predict early signs of DJD in cats using accelerometers and machine learning techniques. Cats were restricted to indoors or limited outdoor access, including being walked on a lead or allowed into enclosed areas for short periods. Fifty-six cats were fitted with collar-mounted sensors that collected accelerometry data over 14 days, with data from 51 cats included in the analysis. Cat owners assessed their cats’ mobility and assigned condition scores, validated through clinical orthopaedic examinations. The study group comprised 24 healthy cats (no owner-reported mobility changes) and 27 unhealthy cats (owner-reported mobility changes, suggestive of early DJD). Data were segmented into 60-second windows centred around peaks of high activity. Using a Support Vector Machine (SVM) algorithm, the model achieved 78% (confidence interval: 0.65, 0.88) area under the curve (AUC), with 68% sensitivity (0.64, 0.77) at 75% specificity (0.68, 0.79).These results demonstrate the potential of accelerometry and machine learning to aid early DJD diagnosis and improve management, offering significant advances in non-invasive diagnostic techniques for cats.
Random connectivity generates inhibitory microcircuits that decorrelate adaptation in visual cortex
Authors: Kosiachkin Y. , Hinojosa A. J. , Dominiak S.E. , Evans B. , Lagnado L. 
Published: January 2025

AbstractInhibitory neurons are fundamental to sensory processing in the cortex but the rules governing their connections with excitatory neurons are unclear. Are pyramidal cells with different functions generated through specific or random connections? We used two-photon imaging, optogenetics and modelling to investigate opposing forms of adaptation in layer 2/3 of mouse visual cortex. We find that a slow modulatory drive acts differentially depending on the relative strength of inputs that individual pyramidal cells receive from parvalbumin-positive and somatostatin-positive interneurons. The number of depressing and sensitizing pyramidal cells could be explained quantitatively by the simplest connectivity rule – all inhbitory synapses made randomly. The functional heterogeneity of the pyramidal cell population therefore begins with general statistics of the connectome - interneurons are much sparser and connect with probabilities far less than one. The resulting “patchwork” of inhibitory microcircuits causes modulatory inputs to strongly decorrelate pyramidal cells on the behavioural time-scale of seconds.
Early-life adversity mediates a thalamo-amgydalar circuit dysfunction underlying chronic pain and anxiety
Authors: Borruto Anna Maria , Kohlund Tabea , von Renesse Martin , Rettig Lukas , Leonhardt Nikolas , Calpe-López Claudia , Benn Luca , Friederich Hans-Christoph , Kuner Rohini , Kuner Thomas , Spanagel Rainer , Wieland Sebastian 
Published: January 2025

SummaryChildhood adversity increases the risk of developing a vicious cycle of chronic pain and comorbid anxious avoidance, yet the underlying biological mechanisms remain unclear. Here, we investigated the role of a brain circuit from the paraventricular thalamus (PVT) to the central amygdala (CeA) in mediating hyperalgesia and comorbid anxiety following early life stress. Using a vulnerability-stress model, we exposed both male and female mice to early social isolation (vulnerability) followed by nerve injury (stress) and showed increased hyperalgesia and anxious avoidance behavior in nerve-injured female mice following early adversity. Chemogenetic, electrophysiological, and optophysiological analyses revealed a causal contribution of a hyperexcitable PVT-CeA circuit dysfunction to chronic hyperalgesia and anxiety in nerve-injured female mice after early life stress. Our findings reveal a neural mechanism linking childhood adversity to chronic pain and anxiety, and suggest that reprogramming this pathway may reverse the impact of childhood adversity.
Advances in culturing of the sea starPatiria miniata
Authors: Barone Vanessa , Coronado Luisa , Ismail Deka , Fiaz Sareen , Lyons Deirdre C. 
Published: January 2025

AbstractThe use of the sea starPatiria miniataas a model system has produced groundbreaking advances in a disparate set of biomedical research fields, including embryology, immunology, regeneration, cell biology and evolution of development. Nonetheless, the life cycle ofP. miniatahas not yet been closed in the laboratory, precluding the generation of stable transgenic and mutant lines, which would greatly expand the toolset for experimentation with this model system. RearingP. miniatain the laboratory has been challenging due to limited knowledge about metamorphosis cues, feeding habits of juveniles and their relatively long generation time. Here we report protocols to rearP. miniataembryos through sexual maturity in a laboratory setting. We provide detailed staging of early embryonic development at different temperatures, and show that larvae can be raised to competence in as little as 15 days. We find that retinoic acid induces metamorphosis effectively and present methods to rear juveniles on commercially available foods. We show that in a flow-through system, juveniles double in size every 2 months and reach sexual maturity in approximately 2 years. We report the first example ofP. miniataraised through sexual maturity in a laboratory setting, paving the way for the generation of stable mutant sea star lines.
Exploring simultaneous production of polyhydroxybutyrate and exopolysaccharides in cyanobacteria-rich microbiomes
Authors: Altamira-Algarra Beatriz , García Joan , Torres Cristiana A. V. , Reis Maria A.M. , Gonzalez-Flo Eva 
Published: January 2025

AbstractThe aim of this study was to explore the viability of the dual production of polyhydroxybutyrate (PHB) and exopolysaccharides (EPS) by seven microbiomes rich in cyanobacteria. Our initial experiments involved to screen for EPS-producing candidates and examine the impact of salinity and acetate on EPS synthesis. Salinity’s known influence on EPS production and acetate’s role in enhancing PHB production guided our parameter selection. Surprisingly, neither the introduction of an external carbon source (acetate) nor exposure to an abiotic stressor (salt) significantly altered EPS synthesis rates, which ranged from 25 to 150 mg·L-1, or its composition, with glucose being the dominant sugar component. Scaling up to a 3 L photobioreactor, we achieved simultaneous biopolymer production, reaching up to 205 mg·L-1EPS and 87 mg·L-1PHB. Additionally, the presence of uronic acid in the EPS facilitated biomass flocculation, streamlining the separation process, and potentially reducing associated time and costs.