bluuebunny/crossref_metadata_2025_split huggingface dataset:
Co-production of a Neurodiversity-Affirmative Anxiety Intervention for Autistic Children
Authors: Cullingham Tasha , Rennard Una , Creswell Cathy , Milton Damian , Buckle Karen Leneh , Godber Lucie , Gordon Kate , Larkin Michael , Green Jonathan 
Published: July 2023

BackgroundMental health difficulties are common for autistic people; however, few interventions have been co-produced with the autistic community. Mental health interventions constructed with a ‘non-autistic lens’ likely miss key understandings from autistic experiences and priorities of the autistic community. Additionally, the style and aims of intervention may be prone to unconscious bias around the pathologisation of autism. Currently, there are limited methodological and practical examples of how to rigorously co-produce mental health interventions with autistic people. This paper details the methodology and processes of co-adapting an intervention for autistic children with anxiety problems. Providing a worked example of co-producing a neurodiversity-affirmative mental health intervention which reflects autistic, parental, academic, and clinical, experience and expertise.MethodsWe co-adapted content from a brief, parent-led, CBT approach for non-autistic children with anxiety problems to meet the needs of autistic children and their parents. The adaptation for autistic children was co-constructed using processes and strategies adopted from Experience-Based Co-Design (EBCD). The research team, comprising autistic and non-autistic members, worked alongside an expert reference group (ERG). The ERG comprised parents (autistic and non-autistic) of autistic children with anxiety problems, autistic adults with experience of anxiety problems, and clinicians with experience supporting autistic children with mental health difficulties. Data were obtained from qualitative research interviews with autistic children with anxiety problems and parents. These data were considered reciprocally by the research team and the ERG.ResultsThe resulting intervention includes a neurodiversity-affirmative perspective that considers how anxieties for autistic children can emerge from being neurodivergent in a neurotypical world integrated with traditional CBT techniques and understandings of anxiety.ConclusionSuccessful co-production can help to integrate multiple theoretical backgrounds and result in the creation of interventions that are potentially acceptable to clinicians, autistic people and their family members.
Feasibility and reliability of online vs in-person cognitive testing in healthy older people
Authors: Morrissey Sol , Gillings Rachel , Hornberger Michael 
Published: July 2023

AbstractBackgroundEarly evidence in using online cognitive assessments show that they potentially offer a feasible and resource efficient alternative to in-person clinical assessments in evaluating cognitive performance, yet there is currently little understanding about how these assessments relate to traditional, in-person cognitive tests.ObjectivesWe assess the feasibility and reliability of NeurOn, a novel online cognitive battery, measuring processing speed, executive functioning, spatial working memory, episodic memory, attentional control, visuospatial functioning and spatial orientation.Design32 participants (mean age: 70.19) completed two testing sessions, unsupervised online and in-person, one-week apart. Participants were randomised in the order of testing appointments. For both sessions, participants completed questionnaires prior to a cognitive assessment. Test-retest reliability and concurrent validity of the online cognitive battery was assessed by comparing performance in repeated tasks across testing sessions as well as with traditional in-person cognitive tests.ResultsGlobal cognition in the NeurOn battery moderately validated against MoCA performance. The battery demonstrated moderate test-retest reliability as performance across repeated tasks did not show a significant difference. Concurrent validity was found only between the online and paper versions of the Trail Making Test -A, as well as global cognitive performance between online and in-person testing sessions.ConclusionsThe NeurOn cognitive battery provides a promising tool for measuring cognitive performance online both longitudinally and across short retesting intervals within healthy older adults. When considering cost-effectiveness, flexible administration, and improved accessibility for wider populations, online cognitive assessments have the potential to improve future screening for neurodegenerative diseases.
Structure-based network analysis predicts mutations associated with inherited retinal disease
Authors: Hauser Blake M. , Luo Yuyang , Nathan Anusha , Gaiha Gaurav D. , Vavvas Demetrios , Comander Jason , Pierce Eric A. , Place Emily M. , Bujakowska Kinga M. , Rossin Elizabeth J. 
Published: July 2023

AbstractWith continued advances in gene sequencing technologies comes the need to develop better tools to understand which mutations cause disease. Here we validate structure-based network analysis (SBNA)1, 2in well-studied human proteins and report results of using SBNA to identify critical amino acids that may cause retinal disease if subject to missense mutation. We computed SBNA scores for genes with high-quality structural data, starting with validating the method using 4 well-studied human disease-associated proteins. We then analyzed 47 inherited retinal disease (IRD) genes. We compared SBNA scores to phenotype data from the ClinVar database and found a significant difference between benign and pathogenic mutations with respect to network score. Finally, we applied this approach to 65 patients at Massachusetts Eye and Ear (MEE) who were diagnosed with IRD but for whom no genetic cause was found. Multivariable logistic regression models built using SBNA scores for IRD-associated genes successfully predicted pathogenicity of novel mutations, allowing us to identify likely causative disease variants in 37 patients with IRD from our clinic. In conclusion, SBNA can be meaningfully applied to human proteins and may help predict mutations causative of IRD.
Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services
Authors: Lu Yue , Wang Yaqian , Zhou Bo 
Published: July 2023

AbstractObjectiveWe aimed to establish and authenticate a clinical prognostic nomogram for predicting long-term Major Adverse Cardiovascular Events (MACEs) among high-risk patients who have undergone Percutaneous Coronary Intervention (PCI) in county-level health service.MethodsThis prospective study included Acute Coronary Syndrome (ACS) patients treated with PCI at six county-level hospitals between September 2018 and August 2019, selected from both the original and external validation cohorts. Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques and logistic regression were used to assess potential risk factors and construct a risk predictive nomogram. Additionally, the potential non-linear relationships between continuous variables were tested using Restricted Cubic Splines (RCS). The performance of the nomogram was evaluated based on the Receiver Operating Characteristic (ROC) curve analysis, Calibration Curve, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC).ResultsThe original and external validation cohorts comprised 520 and 1061 patients, respectively. The final nomogram was developed using nine clinical variables: Age, Killip functional classification III-IV, Hypertension, Hyperhomocysteinemia, Heart failure, Number of stents, Multivessel disease, Low-density Lipoprotein Cholesterol, and Left Ventricular Ejection Fraction. The AUC of the nomogram was 0.79 and 0.75 in the original and validation cohorts, respectively. The DCA and CIC validated the clinical value of the constructed prognostic nomogram.ConclusionHerein, we developed and validated a prognostic nomogram for predicting the probability of 3-year MACEs in ACS patients who underwent PCI at county-level hospitals. The nomogram could provide a precise risk assessment for secondary prevention in ACS patients receiving PCI.
Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T
Authors: Needleman Sarah H. , Kim Mina , McClelland Jamie R. , Naish Josephine H. , Tibiletti Marta , O’Connor James P. B. , Parker Geoff J. M. 
Published: July 2023

AbstractPurposeDynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI.MethodsDynamic OE-MRI was performed on healthy participants using a dual-echo multi- slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with non-smokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors, were assessed.ResultsICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and non-smokers. The scan-rescan repeatability, and the ICA pipeline repeatability, were good.ConclusionThe developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and non-smokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies.
Exploring Leishmania linear motifs directing protein secretion, sorting and autophagy
Authors: Zeke Andras , Gibson Toby J. , Dobson Laszlo 
Published: July 2023

ABSTRACTThe pathogenic tropical flagellatesLeishmaniabelong to an early-branching eukaryotic lineage (Kinetoplastida) with several unique features. Here, we explore three ancient protein targeting linear motif systems and their receptors and demonstrate how they resemble or differ from other eukaryotic organisms, including their hosts. Secretory signal peptides, endoplasmic reticulum (ER) retention motifs (KDEL motifs), and autophagy signals (motifs interacting with ATG8 family members) are essential components of cellular life. Although expected to be conserved, we observe that all three systems show a varying degree of divergence from the eukaryotic version observed in animals, plants, or fungi. We not only describe their behavior but also build predictive models that allow the prediction of localization or function for several proteins inLeishmaniaspecies for the first time. Several of these critical protein-protein interactions could serve as targets of selective antimicrobial agents against Leishmaniasis due to their divergence from the host.
Neural signatures of natural behavior in socializing macaques
Authors: Testard Camille , Tremblay Sébastien , Parodi Felipe , DiTullio Ron W. , Acevedo-Ithier Arianna , Gardiner Kristin L. , Kording Konrad , Platt Michael L. 
Published: July 2023

AbstractOur understanding of the neurobiology of primate behavior largely derives from artificial tasks in highly-controlled laboratory settings, overlooking most natural behaviors primate brains evolved to produce1–3. In particular, how primates navigate the multidimensional social relationships that structure daily life4and shape survival and reproductive success5remains largely unexplored at the single neuron level. Here, we combine ethological analysis with new wireless recording technologies to uncover neural signatures of natural behavior in unrestrained, socially interacting pairs of rhesus macaques. Single neuron and population activity in prefrontal and temporal cortex unveiled robust encoding of 24 species-typical behaviors, which was strongly modulated by the presence and identity of surrounding monkeys. Male-female partners demonstrated near-perfect reciprocity in grooming, a key behavioral mechanism supporting friendships and alliances6, and neural activity maintained a running account of these social investments. When confronted with an aggressive intruder, behavioral and neural population responses reflected empathy and were buffered by the presence of a partner. By employing an ethological approach to the study of primate neurobiology, we reveal a highly-distributed neurophysiological ledger of social dynamics, a potential computational foundation supporting communal life in primate societies, including our own.
Improving fMRI-based Autism Spectrum Disorder Classification with Random Walks-informed Feature Extraction and Selection
Authors: Sotero Roberto C. , Sanchez-bornot Jose M. , Iturria-medina Yasser 
Published: July 2023

Functional magnetic resonance imaging (fMRI) is a non-invasive technique measuring brain activity by detecting blood flow changes, enabling the study of cognitive processes and brain states. However, the high dimensionality of resting-state (rs) fMRI data poses challenges for machine learning applications. Feature extraction (FE) and feature selection (FS) are critical for developing efficient machine learning models. Transforming raw data into meaningful features and selecting the most relevant ones, allows models to achieve improved generalization, accuracy, and robustness. Previous studies demonstrated the effectiveness of FE and FS methods for analyzing rs-fMRI data for Autism Spectrum Disorder (ASD) classification. In this study, we apply a random walks technique for correlation-based brain networks to extract features from rs-fMRI data, specifically the number of random walkers on each brain area. We then select significant features, i.e., brain areas with a statistically significant difference in the number of random walkers between neurotypical and ASD subjects. Our random walks-based FE and FS approach reduces the number of brain areas used in the classification and converts the functional connectivity matrix into a manageable vector, enabling faster computation. We examined 16 pipelines and tested support vector machines (SVM) and logistic regression for classification, identifying the optimal pipeline to consist of no filtering, no global signal regression (GSR), and FS, achieving a 76.54% classification accuracy with SVM. Our findings suggest that random walks capture a wide range of interactions and dynamics in brain networks, providing a deeper characterization of their structure and function, ultimately enhancing classification performance.CCS CONCEPTSComputing methodologies→Machine learning
Prolonged activity-deprivation causes pre- and postsynaptic compensatory plasticity at neocortical excitatory synapses
Authors: Wise Derek L. , Escobedo-Lozoya Yasmin , Valakh Vera , Isaac Berith , Gao Emma Y. , Greene Samuel B. , Bhonsle Aishwarya , Lei Qian L. , Cheng Xinyu , Van Hooser Stephen D. , Nelson Sacha B. 
Published: July 2023

ABSTRACTHomeostatic plasticity stabilizes firing rates of neurons, but the pressure to restore low activity rates can significantly alter synaptic and cellular properties. Most previous studies of homeostatic readjustment to complete activity silencing in rodent forebrain have examined changes after two days of deprivation, but it is known that longer periods of deprivation can produce adverse effects. To better understand the mechanisms underlying these effects and to address how presynaptic as well as postsynaptic compartments change during homeostatic plasticity, we subjected mouse cortical slice cultures to a more severe five-day deprivation paradigm. We developed and validated a computational framework to measure the number and morphology of presynaptic and postsynaptic compartments from super resolution light microscopy images of dense cortical tissue. Using these tools, combined with electrophysiological miniature excitatory postsynaptic current measurements, and synaptic imaging at the electron microscopy level, we assessed the functional and morphological results of prolonged deprivation. Excitatory synapses were strengthened both presynaptically and postsynaptically. Surprisingly, we also observed a decrement in the density of excitatory synapses, both as measured from colocalized staining of pre- and postsynaptic proteins in tissue, and from the number of dendritic spines. Overall, our results suggest that cortical networks deprived of activity progressively move towards a smaller population of stronger synapses.SIGNIFICANCE STATEMENTBlocking activity in neocortical slice cultures produced coordinated pre and postsynaptic changes at excitatory synapses. Functional and structural assays suggest that deprivation results in fewer excitatory synapses, but each is strengthened both pre- and postsynaptically. This may contribute to the emergence of epileptiform activity.
Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample
Authors: Dunlop Katharine , Grosenick Logan , Downar Jonathan , Vila-Rodriguez Fidel , Gunning Faith M. , Daskalakis Zafiris J. , Blumberger Daniel M. , Liston Conor 
Published: July 2023

AbstractBackgroundRecent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder (MDD), but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with deeper clinical phenotyping. The goals of this work were to develop and evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, deeply phenotyped, single-site sample.MethodsWe used regularized canonical correlation analysis (RCCA) to identify data-driven brain-behavior dimensions explaining individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting state fMRI data for N=328 patients with MDD and N=461 healthy controls. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypesResultsThe optimal RCCA model yielded three robust and generalizable brain-behavior dimensions explaining individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified four depression subtypes, each with distinct clinical symptom profiles, abnormal RSFC patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.ConclusionsOur results define dimensional and categorical solutions to parsing neurobiological heterogeneity in MDD that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that RCCA and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
Terminal regions of a protein are a hotspot for low complexity regions (LCRs) and selection
Authors: Teekas Lokdeep , Sharma Sandhya , Vijay Nagarjun 
Published: July 2023

AbstractA majority of the protein-coding genes consist of low-complexity regions (LCRs) in eukaryotes. Volatile LCRs are a novel source of adaptive variation, functional diversification, and evolutionary novelty. LCRs contribute to a wide range of neurodegenerative disorders. Conversely, these regions also play a pivotal role in critical cellular functions, such as morphogenesis, signaling, and transcriptional regulation. An interplay of selection and mutation governs the composition and length of LCRs. High %GC and mutations provide length variability because of mechanisms like replication slippage. The selection is nearly neutral for expansion/contraction within the normal range and purifying above a critical length. Because of the complex dynamics between selection and mutation, we need a better understanding of the coexistence and mechanisms of the two. Our findings indicate that site-specific positive selection and LCRs prefer the terminal regions of a gene and co-occur in most of the Tetrapoda clades. Interestingly, positively selected sites (PSS) are significantly favored in LCRs in eight of the twelve clades studied. We also observed a significant favor of PSSs in the polyQ region of MAML2 in five clades. We also found that PSSs in a gene have position-specific roles. Terminal-PSS genes are enriched for adenyl nucleotide binding, while central-PSS genes are involved in glycosaminoglycan binding. Moreover, central-PSS genes mainly participate in defense responses, but terminal-PSS genes are non-specific. LCR-containing genes have a significantly higher %GC and lower ω (dN/dS) than genes without repeats across the Tetrapoda clade. A lower ω suggests that even though LCRs provide rapid functional diversity, LCR-containing genes face intense purifying selection.
Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network
Authors: Hatano Narumi , Kamada Mayumi , Kojima Ryosuke , Okuno Yasushi 
Published: July 2023

AbstractBackgroundIn cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part.ResultsNet-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms.ConclusionsWe propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.