Biology subjects

Curran, T.

Publications and source records attributed to Curran, T..

8 recordsLinked to original sources

bioRxiv2026

Association of object mnemonic discrimination deficit with age and AD biomarkers in older adults

Background Blood-based Alzheimers disease (AD) biomarkers provide scalable detection of AD pathology, yet their relationships with hippocampal-dependent mnemonic discrimination remain unclear. This study examined how object mnemonic discrimination relates to age, hippocampal structure, and plasma AD biomarkers in cognitively normal (CN) older adults and individuals with amnestic mild cognitive impairment (aMCI). Methods Forty-six CN and forty-one aMCI participants completed an object mnemonic similarity task (MST). Mnemonic discrimination was quantified using the overall lure discrimination index (LDI) and similarity-specific LDIs. Plasma pTau181, pTau217, pTau231, and GFAP were assayed, and hippocampal volumes were derived from 7T MR scanner. Multiple linear regression models assessed associations between LDI and biomarkers, controlling for age, sex, education, APOE4 status, and diagnosis. Repeated-measures ANCOVA and linear mixed-effects models evaluated similarity-dependent age effects. ROC analyses and stepwise logistic regression quantified diagnostic classification performance. Results LDI was significantly reduced in aMCI compared to CN. Total hippocampal volume and plasma pTau217 showed the strongest associations with LDI, exceeding those of pTau181, pTau231, and GFAP. LDI discriminated aMCI from CN with high accuracy (AUC = 0.836), comparable to hippocampal volume and plasma biomarkers. A combined model including LDI, pTau181, pTau217, and hippocampal volume achieved the highest diagnostic accuracy (AUC = 0.931). Age effects were similarity-dependent: discrimination declined with age only for low-similarity lures, independent of diagnosis. Sex differences emerged within aMCI, with females showing lower LDI than males. Conclusions Object mnemonic discrimination provides a sensitive behavioral marker linked to hippocampal volume and plasma pTau217, showing discriminative power comparable to established AD biomarkers in separating aMCI from CN participants. Similarity-dependent age effects highlight age-related deficits are not universal for lures with varying similarity levels.

neuroscience↗

bioRxiv2026

From fluke to fragment: a multifaceted method for molecular sex identification and mitochondrial haplotyping from environmental DNA samples

O_LIEnvironmental DNA (eDNA) analyses have become a powerful tool for non-invasive biodiversity monitoring, yet the applicability of population genetic approaches to environmental samples remains largely unexplored. Even when genetic traces originate from a single individual, low target DNA concentrations and amplification or sequencing artefacts can compromise downstream genetic inferences. Here, we present a novel approach for obtaining demographic insights and lineage-level mitogenomic information from aquatic eDNA samples collected near vertebrate individuals. C_LIO_LIPaired eDNA and tissue samples were collected during sperm whale (Physeter macrocephalus) encounters in the Azores. Samples were screened for the presence of vertebrate eDNA and analyzed with a novel molecular sex identification assay. Additionally, long-range PCR was used to amplify up to five mitochondrial DNA fragments ([~]3-4k bp) before subsequent sequencing on an Oxford Nanopore Technologies platform. A stringent three-tier filtering framework capable of identifying true mitogenomic variation across eDNA samples was developed for maximum recovery of genetic diversity at the haplogroup level. By benchmarking eDNA samples via their paired tissues, parameter values were optimized to maximize concordance and minimize spurious variant calls. C_LIO_LISexing was successful for 50% of eDNA samples, with 96% concordance to paired tissues, and marine vertebrate DNA concentration significantly predicted sexing success. Further, Medaka polishing produced high identity mitochondrial consensus sequences (>16 kb) from eDNA samples. Across filtering regimes in the framework, curated SNP panels comprising up to 453 high-confidence mitochondrial SNPs resolved 19 haplogroups, with 93% concordance between eDNA and tissue samples. An intermediate bioinformatics filtering strategy maximized biologically accurate haplogroup recovery while minimizing sequencing artefacts, providing the most reliable lineage-level inferences. C_LIO_LIThis integrative approach demonstrates that targeted nuclear assays combined with long-range mitochondrial sequencing can recover individual-level genetic information from aquatic eDNA. By defining analytical thresholds governing success, the framework advances non-invasive genetic monitoring of populations via eDNA and enables population-level monitoring and conservation of endangered and genetically-vulnerable species. C_LI

genomics↗

bioRxiv2025

Design and optimisation of rapid real-time PCR assays for the detection of key Culicoides species

In extensive surveillance programmes of Culicoides biting midges (Diptera: Ceratopogonidae), morphological identification can be time-consuming and difficult, while DNA barcoding, although highly accurate, may not be cost-effective or suitable for rapid analysis, as it requires individual specimen processing. To address these limitations, we developed a rapid screening method using real-time PCR assays with either SYBR Green or hydrolysis probe-based detection chemistries. Species-specific primers and, where necessary, hydrolysis probes were designed based on the updated sequences of the ITS2 region of seven Culicoides species. The specificity and efficiency of these assays were validated both in silico and through real-time PCR testing on target and non-target Culicoides species, tested individually and as mixed-species samples. The new real-time PCR assays detect vector species including C. obsoletus, C. scoticus, C. chiopterus, C. dewulfi, C. pulicaris, C. punctatus, and C. impunctatus in pools of individual specimens, with single-specimen sensitivity. The molecular techniques developed in this study provide a valuable tool for accurate and high-throughput Culicoides surveillance, which can be used for year-round monitoring of adult midges in traps and larvae in environmental samples, potentially revealing novel insights into the spatial and temporal turnover of Culicoides species. These methods can be applied to large-scale vector screening programmes involving pooled samples, addressing the limitations of previously described methods used in midge surveillance.

molecular biology↗

bioRxiv2025

Molecular characterisation of common Culicoides Biting Midges (Diptera: Ceratopogonidae) in Ireland

BackgroundBiting midges in the genus Culicoides (Diptera: Ceratopogonidae) act as vectors for several arboviruses, including Bluetongue virus (BTV) and Schmallenberg virus (SBV), which affect livestock health and productivity. In Ireland, limited genetic data are available regarding the diversity of Culicoides species. This study represents the first attempt to characterise Culicoides in this region using molecular techniques. MethodsAdult Culicoides samples were captured using Onderstepoort Veterinary Institute (OVI) traps across six locations in Ireland. Subsequent molecular analyses involved polymerase chain reaction (PCR) and sequencing of the cytochrome oxidase subunit 1 (CO1) and the internal transcriber spacer (ITS) barcoding regions to obtain species identities. Additionally, using both markers, we inferred the population genetic structure and potential colonisation pathways of Culicoides obsoletus sensu stricto (s. str.), the major vector species in Ireland. ResultsDNA barcoding facilitated identification of 177 specimens. Eight common Culicoides species were identified through DNA barcoding of CO1 and ITS gene regions. The presence of putative vectors of Bluetongue virus (BTV) and Schmallenberg virus (SBV) were also confirmed, including species in the subgenus Avaritia (C. obsoletus s. str., C. scoticus, C. chiopterus, and C. dewulfi) and subgenus Culicoides s. str. (C. pulicaris and C. punctatus). Phylogenetic analysis confirmed the relationship between these vector species and facilitated the placement of Culicoides sp. which could not be identified to species level through DNA barcoding. Haplotype network analysis of C. obsoletus showed that some haplotypes of these species are shared between Continental Europe, the UK, and Ireland, suggesting a possible incursion pathway for this vector. ConclusionDNA barcoding employing a combination of two barcodes, CO1 and ITS, proved effective in identifying Culicoides, especially species within the obsoletus complex, which are difficult to morphologically distinguish. Our findings also suggest that investigation of the population genetic structure of Culicoides spp. could be used to model the potential introduction routes of midge-borne pathogens into the country.

molecular biology↗

bioRxiv2024

Thyroid Hormone Suppresses Medulloblastoma Progression Through Promoting Terminal Differentiation of Tumor Cells

Hypothyroidism is commonly detected in patients with medulloblastoma (MB). A possible link between thyroid hormone (TH) signaling and MB pathogenicity has not been reported. Here, we find that TH plays a critical role in promoting tumor cell differentiation. Reduction in TH levels frees the TH receptor, TR1, to bind to EZH2 and repress expression of NeuroD1, a transcription factor that drives tumor cell differentiation. Increased TH reverses EZH2-mediated repression of NeuroD1 by abrogating the binding of EZH2 and TR1, thereby stimulating tumor cell differentiation and reducing MB growth. Importantly, TH-induced differentiation of tumor cells is not restricted by the molecular subgroup of MB. These findings establish an unprecedented association between TH signaling and MB pathogenicity, providing solid evidence for TH as a promising modality for MB treatment.

cancer biology↗

bioRxiv2020

Single-cell transcriptomic profile reveals macrophage heterogeneity in medulloblastoma and their treatment-dependent recruitment

The role of macrophages in medulloblastoma, the most common malignant pediatric brain tumor, is unclear. Using single-cell RNA sequencing in a mouse model of sonic hedgehog medulloblastoma and analysis of bulk RNA sequencing of human medulloblastoma, we investigated macrophage heterogeneity. Our findings reveal differential recruitment of macrophages with molecular-targeted versus radiation therapy and identify an immunosuppressive monocyte-derived macrophages following radiation treatment of mouse medulloblastoma, uncovering potential strategies for immunomodulation as adjunctive therapy.

cancer biology↗

bioRxiv2019

A robust deep neural network for denoising task-based fMRI data: An application to working memory and episodic memory

In this study, a deep neural network (DNN) is proposed to reduce the noise in task-based fMRI data without explicitly modeling noise. The DNN artificial neural network consists of one temporal convolutional layer, one long short-term memory (LSTM) layer, one time-distributed fully-connected layer, and one unconventional selection layer in sequential order. The LSTM layer takes not only the current time point but also what was perceived in a previous time point as its input to characterize the temporal autocorrelation of fMRI data. The fully-connected layer weights the output of the LSTM layer, and the output denoised fMRI time series is selected by the selection layer. Assuming that task-related neural response is limited to gray matter, the model parameters in the DNN network are optimized by maximizing the correlation difference between gray matter voxels and white matter or ventricular cerebrospinal fluid voxels. Instead of targeting a particular noise source, the proposed neural network takes advantage of the task design matrix to better extract task-related signal in fMRI data. The DNN network, along with other traditional denoising techniques, has been applied on simulated data, working memory task fMRI data acquired from a cohort of healthy subjects and episodic memory task fMRI data acquired from a small set of healthy elderly subjects. Qualitative and quantitative measurements were used to evaluate the performance of different denoising techniques. In the simulation, DNN improves fMRI activation detection and also adapts to varying hemodynamic response functions across different brain regions. DNN efficiently reduces physiological noise and generates more homogeneous task-response correlation maps in real data.

neuroscience↗

bioRxiv2019

Frequency specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators

Various processes occur in memory retrieval in recognition memory and it is necessary to investigate memory brain function. Most of the research in past decades have focused on particular brain region function, but the interaction between these has a major role in human cognition. In this study, we used the memory retrieval task to investigate the underlying mechanism of recognition memory. The connectivity between brain regions is estimated from scalp electroencephalography signals that were recorded from twenty-three healthy subject participated in recognition memory task to correctly classify old/new words. Multivariate autoregressive models (MVAR) are used for the determination of Granger causality to estimate the effective connectivity in the time-frequency domain. We use GPDC and dDTF methods because they have almost resolved the previous problems in estimations. Results show that brain regions in the old condition have greater global connectivity in the theta and gamma band compared to the new words retrieval. Connectivity within and between the brains hemisphere may be related to correct rejection. The left frontal has a crucial role in recollection. theta and gamma specific connectivity pattern between temporal, parietal and frontal cortex may disclose the retrieval mechanism. old/new comparison resulted in the different patterns of network connection. These results and other evidence emphasize the role of frequency of causal network interactions in the memory process.

bioengineering↗