bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short- and long-term experience-dependent neuroplasticity interact during the perceptual learning of concurrent speech
Authors: MacLean Jessica , Stirn Jack , Sisson Alexandria , Bidelman Gavin M. 
Published: September 2023

AbstractPlasticity from auditory experiences shapes brain encoding and perception of sound. However, whether such long-term plasticity alters the trajectory of short-term plasticity during speech processing has yet to be investigated. Here, we explored the neural mechanisms and interplay between short- and long-term neuroplasticity for rapid auditory perceptual learning of concurrent speech sounds in young, normal-hearing musicians and nonmusicians. Participants learned to identify double-vowel mixtures during ∼45 minute training sessions recorded simultaneously with high-density EEG. We analyzed frequency-following responses (FFRs) and event-related potentials (ERPs) to investigate neural correlates of learning at subcortical and cortical levels, respectively. While both groups showed rapid perceptual learning, musicians showed faster behavioral decisions than nonmusicians overall. Learning-related changes were not apparent in brainstem FFRs. However, plasticity was highly evident in cortex, where ERPs revealed unique hemispheric asymmetries between groups suggestive of different neural strategies (musicians: right hemisphere bias; nonmusicians: left hemisphere). Source reconstruction and the early (150-200 ms) time course of these effects localized learning-induced cortical plasticity to auditory-sensory brain areas. Our findings confirm domain-general benefits for musicianship but reveal successful speech sound learning is driven by a critical interplay between long- and short-term mechanisms of auditory plasticity that first emerge at a cortical level.
ElegansBot: Development of equation of motion deciphering locomotion including omega turns ofCaenorhabditis elegans
Authors: Chung Taegon , Chang Iksoo , Kim Sangyeol 
Published: September 2023

AbstractLocomotion is a fundamental behavior ofCaenorhabditis elegans(C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood howC. elegansutilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion ofC. elegansby developing Newtonian equations of motion for each body segment ofC. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants ofC. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion ofC. eleganssuch as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment ofC. elegansto allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.
Blood-based multivariate methylation risk score for cognitive impairment and dementia
Authors: Koetsier Jarno , Cavill Rachel , Reijnders Rick , Harvey Joshua , Deckers Kay , Köhler Sebastian , Eijssen Lars , Smith Rebecca G. , Smith Adam R. , Burrage Joe , Walker Emma M. , Shireby Gemma , Hannon Eilis , Dempster Emma , Frayling Tim , Mill Jonathan , Dobricic Valerija , Sommerer Yasmine , Johannsen Peter , Wittig Michael , Franke Andre , Vandenberghe Rik , Schaeverbeke Jolien , Freund-Levi Yvonne , Frölich Lutz , Scheltens Philip , Teunissen Charlotte , Frisoni Giovanni , Blin Olivier , Richardson Jill , Bordet Régis , Engelborghs Sebastiaan , de Roeck Ellen , Martinez-Lage Pablo , Tainta Mikel , Lleó Alberto , Sala Isabel , Popp Julius , Peyratout Gwedoline , Verhey Frans , Tsolaki Magda , Andreasson Ulf , Blennow Kaj , Zetterberg Henrik , Streffer Johannes , Vos Stephanie J. B. , Lovestone Simon , Visser Pieter-Jelle , Bertram Lars , Lunnon Katie , Pishva Ehsan 
Published: September 2023

ABSTRACTINTRODUCTIONGiven the established association between DNA methylation and the pathophysiology of dementia and its plausible role as a molecular mediator of lifestyle and environment, blood-derived DNA methylation data could enable early detection of dementia risk.METHODSIn conjunction with an extensive array of machine learning techniques, we employed whole blood genome-wide DNA methylation data as a surrogate for 14 modifiable and non-modifiable factors in the assessment of dementia risk in two independent cohorts of Alzheimer’s disease (AD) and Parkinson’s disease (PD).RESULTSWe established a multivariate methylation risk score (MMRS) to identify the status of mild cognitive impairment (MCI) cross-sectionally, independent of age and sex. We further demonstrated significant predictive capability of this score for the prospective onset of cognitive decline in AD and PD.DISCUSSIONOur work shows the potential of employing blood-derived DNA methylation data in the assessment of dementia risk.
Associations of Combined Phenotypic Aging and Genetic Risk with Incident Cancer: A Prospective Cohort Study
Authors: Bian Lijun , Ma Zhimin , Fu Xiangjin , Ji Chen , Wang Tianpei , Yan Caiwang , Dai Juncheng , Ma Hongxia , Hu Zhibin , Shen Hongbing , Wang Lu , Zhu Meng , Jin Guangfu 
Published: September 2023

AbstractBackgroundAge is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer.MethodsUsing Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and 9 biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs).ResultsCompared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men,P< 0.001; Beta = -0.98 in women,P< 0.001).ConclusionsWithin and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle.
Multi-task analysis of gene expression data on cancer public datasets
Authors: Martins Yasmmin 
Published: September 2023

AbstractBackgroundThere is an availability of omics and often multi-omics cancer datasets on public databases such as Gene Expression Omnibus (GEO), International Cancer Genome Consortium and The Cancer Genome Atlas Program. Most of these databases provide at least the gene expression data for the samples contained in the project. Multi-omics has been an advantageous strategy to leverage personalized medicine, but few works explore strategies to extract knowledge relying only on gene expression level for decisions on tasks such as disease outcome prediction and drug response simulation. The models and information acquired on projects based only on expression data could provide decision making background for future projects that have other level of omics data such as DNA methylation or miRNAs.ResultsWe extended previous methodologies to predict disease outcome from the combination of protein interaction networks and gene expression profiling by proposing an automated pipeline to perform the graph feature encoding and further patient networks outcome classification derived from RNA-Seq. We integrated biological networks from protein interactions and gene expression profiling to assess patient specificity combining the treatment/control ratio with the patient normalized counts of the deferentially expressed genes. We also tackled the disease outcome prediction from the gene set enrichment perspective, combining gene expression with pathway gene sets information as features source for this task. We also explored the drug response outcome perspective of the cancer disease still evaluating the relationship among gene expression profiling with single sample gene set enrichment analysis (ssGSEA), proposing a workflow to perform drug response screening according to the patient enriched pathways.ConclusionWe showed the importance of the patient network modeling for the clinical task of disease outcome prediction using graph kernel matrices strategy and showed how ssGSEA improved the prediction only using transcriptomic data combined with pathway scores. We also demonstrated a detailed screening analysis showing the impact of pathway-based gene sets and normalization types for the drug response simulation. We deployed two fully automatized Screening workflows following the FAIR principles for the disease outcome prediction and drug response simulation tasks.AvailabilityThe ScreenDOP code is available athttps://github.com/yascoma/screendopwhile the DReCaS is available athttps://github.com/YasCoMa/caliscoma_pipeline/
An extended catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2
Authors: Lombard Belinda , Cohen Cheryl , von Gottberg Anne , Dushoff Jonathan , van Schalkwyk Cari , Pulliam Juliet R.C. 
Published: September 2023

AbstractBackgroundThe SARS-CoV-2 pandemic has illustrated that monitoring trends in multiple infections can provide insight into the biological characteristics of new variants. Following several pandemic waves, many people have already been infected and reinfected by SARS-CoV-2 and therefore methods are needed to understand the risk of multiple reinfections.ObjectivesIn this paper, we extended an existing catalytic model designed to detect increases in the risk of reinfection by SARS-CoV-2 to detect increases in the population-level risk of multiple reinfections.MethodsThe catalytic model assumes the risk of reinfection is proportional to observed infections and uses a Bayesian approach to fit model parameters to the number ofnthinfections among individuals whose (n− 1)thinfection was observed at least 90 days before. Using a posterior draw from the fitted model parameters, a 95% projection interval of dailynthinfections is calculated under the assumption of a constantnthinfection hazard coefficient. An additional model parameter was introduced to consider the increased risk of reinfection detected during the Omicron wave. Validation was performed to assess the model’s ability to detect increases in the risk of third infections.Key FindingsThe model parameters converged when applying the model’s fitting and projection procedure to the number of observed third SARS-COV-2 infections in South Africa. No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. The validation of the third infections method showed that the model can successfully detect increases in the risk of third infections under different scenarios.LimitationsEven though the extended model is intended to detect the risk ofnthinfections, the method was only validated for detecting increases in the risk of third infections and not for four or more infections. The method is very sensitive to low numbers ofnthinfections, so it might not be usable in settings with small epidemics, low coverage of testing or early in an outbreak.ConclusionsThe catalytic model to detect increases in the risk of reinfections was successfully extended to detect increases in the risk ofnthinfections and could contribute to future detection of increases in the risk ofnthinfections by SARS-CoV-2 or other similar pathogens.
Temporal Trends in Antibiotic Resistance in Europe, 1998-2019
Authors: Emons Martin , Blanquart François , Lehtinen Sonja 
Published: September 2023

AbstractThe emergence of resistant phenotypes following the introduction of new antibiotics is well documented. However, the subsequent dynamics of resistance frequencies over long time periods are less well understood: the extent to which resistance frequencies increase, the rate at which resistance frequencies change, and how this depends on antibiotic consumption remain open questions. Here, we systematically analyse the resistance trajectories emerging from 3,375,774 hospital bacterial isolates sampled from infections in Europe over 1998-2019, representing 887 bug-drug-country combinations. Our analyses support a model in which, after an initial increase, resistance frequencies reach a stable intermediate equilibrium. The plurality (37%) of analysed trajectories were best described as ‘stable’ (neither increasing nor decreasing). The second largest category of trajectories (21%) was those best described as ‘stabilising’ – i.e. showing a transition from increasing frequency to a stable plateau. The antibiotic consumption in a country predicts both the equilibrium frequency of the corresponding resistance and the speed at which this equilibrium is reached. Moreover, we find weak evidence that temporal fluctuations in resistance frequency are driven by temporal fluctuations in hospital antibiotic consumption. Overall, our results indicate that ever increasing antibiotic resistance frequencies are not inevitable and that antibiotic management limits resistance spread. A large fraction of the variability in the speed of increase and the equilibrium level of resistance remains unexplained by antibiotic use, suggesting other factors also drive resistance dynamics.
Mitochondrial Dysfunction and Neuronal Anomalies inPOLGMutant Midbrain Organoids
Authors: Chen Anbin , Yangzom Tsering , Sullivan Gareth John , Liang Kristina Xiao 
Published: September 2023

AbstractHuman pluripotent stem cell-derived midbrain organoids offer transformative potential for elucidating brain development, disease representation, and therapeutic innovations. We introduce a novel methodology to generate midbrain-specific organoids from both embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). By employing tailored differentiation cues, notably dual-SMAD inhibition combined with FGF-8b and Sonic hedgehog agonist purmorphamine, we direct pluripotent stem cells towards a midbrain lineage. These organoids, growing beyond 2mm in diameter, house diverse neuroepithelial cell populations. Their midbrain character is affirmed by the pronounced expression of midbrain-specific markers and the absence of forebrain and hindbrain indicators. Critically, these organoids differentiate into dopaminergic neurons characteristic of the midbrain, displaying both morphological sophistication and electrophysiological vigor. Additionally, our experiments with POLG iPSC-derived midbrain organoids revealed a marked loss of dopaminergic neurons and diminished expression of genes governing mitochondrial pathways. This evidence underscores the model’s potential in simulating mitochondrial diseases and neurodegenerative conditions, notably Parkinson’s disease. Our protocol thus emerges as a pivotal instrument for crafting functionally adept, midbrain-centric organoids, paving avenues for advanced studies in midbrain evolution, disorders like Parkinson’s disease, and their interplay with mitochondrial dysfunction.
Listening to classical music influences brain connectivity in post-stroke aphasia: a pilot study
Authors: Chea Maryane , Ben Salah Amina , Toba Monica N. , Zeineldin Ryan , Kaufmann Brigitte , Weill-Chounlamountry Agnès , Naccache Lionel , Bayen Eléonore , Bartolomeo Paolo 
Published: September 2023

Stroke-induced aphasia is a leading cause of cognitive disability. The healthy right hemisphere may play a role in aphasia compensation. Music-based therapy, known to enhance cognitive functions after stroke, offers a potential intervention due to its impact on brain connectivity, engaging both hemispheres.In this proof-of-concept study, we aimed to assess the feasibility and effectiveness of music-assisted therapy for language disorders following left hemisphere strokes. We also sought preliminary insights into the impact of music on post-stroke brain connectivity. We enrolled four right-handed patients (one female and three males, mean age 57.75 years, SD 7.63) who experienced their first left-hemisphere stroke inducing chronic aphasia symptoms three months post-stroke. Patients were randomly assigned to receive either two weeks of music therapy, involving daily 2-hour sessions of listening to instrumental music by Haydn, Mozart, and Beethoven, in addition to standard rehabilitation, or two weeks of standard care, using a crossover design. Patients underwent cognitive and neuroimaging assessments at baseline, crossover, and study end, with preliminary evaluation one week before the study to confirm stable deficits.All patients listened to the same curated playlist, accompanied by narrations situating the music in its with historical context. Cognitive assessments included the Core Assessment of Language Processing (CALAP) and a French aphasia battery (Bilan Informatise d’Aphasie, BIA). EEG and MRI data were analyzed, focusing on functional connectivity metrics (weighted symbolic mutual information, permutation entropy) and white matter tractography. Preliminary findings indicate that music therapy may be a feasible and promising approach to aphasia rehabilitation, with improvements observed in language tests and brain connectivity metrics.Despite limitations such as a small sample size and patient heterogeneity, these preliminary results suggest that intensive classical music listening may enhance language abilities in post-stroke aphasia patients. Additionally, using EEG connectivity metrics originally designed for non-communicative patients provides a novel avenue for monitoring brain plasticity during rehabilitation.
Correlation of calcium multiphoton with ultrastructural STED imaging of the slit diaphragm in the same glomerulus
Authors: Wiesner Eva , Binz-Lotter Julia , Hackl Agnes , Unnersjö-Jess David , Rutkowski Nelli , Benzing Thomas , Hackl Matthias J. 
Published: September 2023

AbstractIn recent years functional multiphoton imaging of viable mouse tissue and STED imaging of optically cleared tissues allowed new insights into kidney biology. Here, we present a novel workflow where multiphoton imaging of calcium signals in podocytes is, in the same glomerulus, correlated with super-resolved STED imaging and analysis of the slit diaphragm (SD) morphology. Mice expressing the calcium indicator GCaMP3 exclusively in podocytes served as healthy controls and were challenged with two different doses of nephrotoxic serum (NTS). NTS-induced SD architecture damage increased in a dose-dependent manner, whereas intracellular calcium levels exhibited a wide range of variation and showed no correlation with SD damage in the same glomerulus. Our co-imaging protocol is applicable to a broad range of research questions as it is suitable for other tissues and compatible with a variety of reporters and tissue antigens.Translational statementThe combination of multiphoton and super-resolution microscopy identifies the modulation of signaling pathways and resolves the changes in ultrastructure in disease states in the same glomeruli. Using our new method, we can compare functional and morphological data to investigate the degree of correlation to reveal novel underlying pathomechanism. This knowledge can improve our ability to draw conclusions from human kidney biopsies, in which the ultrastructure will be assessed more-often by routine super-resolution microscopy in the future.
Developmental Ramifications of the Screen Age: Associations between Screen Time and Neural Maturation in a Massive Adolescent Cohort
Authors: Atalay Alexander S. , Newman Benjamin T. , Druzgal T. Jason 
Published: September 2023

AbstractA growing body of literature associates increases in electronic screen time with a vast array of psychological consequences amongst adolescents, but little is known about the neurological underpinnings of this relationship. This longitudinal study examines structural and diffusion brain MRI scans from the Adolescent Brain Cognitive Development (ABCD)Study: a large multi-site study with thousands of participants. By assessing both gray matter density (GMD) and grey matter measurements of diffusion microstructure in the adolescent brain, we describe how the developmental trajectory of the brain changes with screen-based media consumption at the sub-cellular level. Grey matter microstructure was measured across 13 bilateral regions functionally implicated with screen time use, and associated with either the control or reward system. After controlling for age, sex, total brain volume, scanning site, sibling relationships, physical activity, and socioeconomic status, this study finds significant positive correlations between increased screen time and axonal signal across 6 of the 13 regions while also finding significantly decreased intracellular signal in 8 regions. Comparing these associations to normal developmental trajectories suggests adolescent age-related brain development may be accelerated by increased screen time in brain areas associated with reward processing while age-related brain development may be decelerated in regions of the control system. Highlighting the sensitivity of microstructural analysis, no significant cross-sectional or longitudinal relationship with increased screen time was found using GMD, or fractional anisotropy. This work suggests that increased screen usage during adolescent development has a complex association with brain tissue that cannot be completely described by traditional quantifications of tissue microstructure.
Temporal Dynamics and Stoichiometry in Notch Signaling - from Notch Synaptic Complex Formation to NICD Nuclear Entry
Authors: Tveriakhina Lena , Scanavachi Gustavo , Egan Emily D. , Correia Ricardo Bango Da Cunha , Martin Alexandre P. , Rogers Julia M. , Yodh Jeremy S. , Aster Jon C. , Kirchhausen Tom , Blacklow Stephen C. 
Published: September 2023

AbstractMammalian Notch signaling occurs when binding of Delta or Jagged to Notch stimulates proteolytic release of the Notch intracellular domain (NICD), which enters the nucleus to regulate target gene expression. To determine the temporal dynamics of events associated with Notch signaling under native conditions, we fluorescently tagged Notch and Delta at their endogenous genomic loci and visualized them upon pairing of receiver (Notch) and sender (Delta) cells as a function of time after cell contact. At contact sites, Notch and Delta immediately accumulated at 1:1 stoichiometry in synapses, which resolved by 15-20 min after contact. Synapse formation preceded entrance of the Notch extracellular domain into the sender cell and accumulation of NICD in the nucleus of the receiver cell, which approached a maximum after ∼45 min and was prevented by chemical and genetic inhibitors of signaling. These findings directly link Notch-Delta synapse dynamics to NICD production with unprecedented spatiotemporal precision.