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This is a textbook on modelling, data analysis and numerical techniques for advanced students and researchers in chemical, biological, geological and physical oceanography.

Background Lysosomal cysteine protease cathepsin V has previously been shown to exhibit elevated expression in breast cancer tissue and be associated with distant metastasis. Research has also identified that cathepsin V expression is elevated in tumour tissues from numerous other malignancies, but despite this, there has been limited examination of the function of this protease in cancer. Here we investigate the role of cathepsin V in breast cancer in order to delineate the molecular mechanisms by which this protease contributes to tumourigenesis. Methods Lentiviral transductions were used to generate shRNA cell line models, with cell line validation undertaken using RQ-PCR and Western blotting. Phenotypic changes of tumour cell biology were examined using clonogenic and invasion assays. The relationship between GATA3 expression and cathepsin V was primarily analysed using Western blotting. Site-directed mutagenesis was used to generate catalytic mutant and shRNA-resistant constructs to confirm the role of cathepsin V in regulating GATA3 expression. Results We have identified that elevated cathepsin V expression is associated with reduced survival in ER-positive breast cancers. Cathepsin V regulates the expression of GATA3 in ER-positive breast cancers, through promoting its degradation via the proteasome. We have determined that depletion of cathepsin V results in elevated pAkt-1 and reduced GSK-3β expression, which rescues GATA3 from proteasomal degradation. Conclusions In this study, we have identified that cysteine protease cathepsin V can suppress GATA3 expression in ER-positive breast cancers by facilitating its turnover via the proteasome. Therefore, targeting cathepsin V may represent a potential therapeutic strategy in ER-positive breast cancers, by restoring GATA3 protein expression, which is associated with a more favourable clinical outcome.

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Enlarged perivascular spaces (PVS) are imaging markers of cerebral small vessel disease (CSVD) that are associated with age, disease phenotypes, and overall health. Quantification of PVS is challenging but necessary to expand an understanding of their role in cerebrovascular pathology. Accurate and automated segmentation of PVS on T1‐weighted images would be valuable given the widespread use of T1‐weighted imaging protocols in multisite clinical and research datasets. We introduce segcsvdPVS, a convolutional neural network (CNN)‐based tool for automated PVS segmentation on T1‐weighted images. segcsvdPVS was developed using a novel hierarchical approach that builds on existing tools and incorporates robust training strategies to enhance the accuracy and consistency of PVS segmentation. Performance was evaluated using a comprehensive evaluation strategy that included comparison to existing benchmark methods, ablation‐based validation, accuracy validation against manual ground truth annotations, correlation with age‐related PVS burden as a biological benchmark, and extensive robustness testing. segcsvdPVS achieved strong object‐level performance for basal ganglia PVS (DSC = 0.78), exhibiting both high sensitivity (SNS = 0.80) and precision (PRC = 0.78). Although voxel‐level precision was lower (PRC = 0.57), manual correction improved this by only 3%, indicating that the additional voxels reflected primary boundary‐ or extent‐related differences rather than correctable false positive error. For non‐basal ganglia PVS, segcsvdPVS outperformed benchmark methods, exhibiting higher voxel‐level performance across several metrics (DSC = 0.60, SNS = 0.67, PRC = 0.57, NSD = 0.77), despite overall lower performance relative to basal ganglia PVS. Additionally, the association between age and segmentation‐derived measures of PVS burden was consistently stronger and more reliable for segcsvdPVS compared to benchmark methods across three cohorts (test6, ADNI, CAHHM), providing further evidence of the accuracy and consistency of its segmentation output. segcsvdPVS demonstrates robust performance across diverse imaging conditions and improved sensitivity to biologically meaningful associations, supporting its utility as a T1‐based PVS segmentation tool. segcsvdPVS is a novel convolutional neural network (CNN)‐based perivascular spaces (PVS) segmentation tool for T1‐weighted images based on a hierarchical segmentation framework developed with strategically designed inputs and robust training. It demonstrates high accuracy, consistent performance, and improved detection of age‐related changes in PVS burden, supporting its utility in studying cerebrovascular pathology.

Introduction: We previously identified that Cathepsin V (CTSV) expression is associated with poor prognosis in ER+ breast cancer, particularly within the Luminal A subtype. Examination of the molecular role of the protease within Luminal A tumours, revealed that CTSV promotes tumour cell invasion and proliferation, in addition to degradation of the luminal transcription factor, GATA3, via the proteasome. Methods: Cell line models expressing CTSV shRNA or transfected to overexpress CTSV were used to examine the impact of CTSV on cell proliferation by MTT assay and flow cytometry. Western blotting analysis was used to identify the impact of CTSV on histone and chaperone protein expression. Cell fractionation and confocal microscopy was used to illustrate the presence of CTSV in the nuclear compartment. Results: In this work we have identified that CTSV has an impact on breast cancer cell proliferation, with CTSV depleted cells exhibiting delayed progression through the G2/M phase of the cell cycle. Further investigation has revealed that CTSV can control nuclear expression levels of histones H3 and H4 via regulating protein expression of their chaperone sNASP. We have discovered that CTSV is localised to the nuclear compartment in breast tumour cells, mediated by a bipartite nuclear localisation signal (NLS) within the CTSV sequence and that nuclear CTSV is required for cell cycle progression and histone stability in breast tumour cells. Discussion: Collectively these findings support the hypothesis that targeting CTSV may have utility as a novel therapeutic target in ER+ breast cancer by impairing cell cycle progression via manipulating histone stabilisation.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)‐based biomarker of cerebral small vessel disease (CSVD). WMH are associated with cognitive decline and increased risk of stroke and dementia, and are commonly observed in aging, vascular cognitive impairment, and neurodegenerative diseases. The reliable and rapid measurement of WMH in large‐scale multisite clinical studies with heterogeneous patient populations remains challenging, where the diversity of imaging characteristics across studies adds additional complexity to this task. We present segcsvdWMH, a convolutional neural network‐based tool developed to provide reliable and accurate WMH quantification across diverse clinical datasets. segcsvdWMH was developed using a large dataset consisting of over 700 fluid‐attenuated inversion recovery MRI scans from seven multisite studies, spanning a wide range of clinical populations, WMH burdens, and imaging protocols. Model training incorporated anatomical information through a novel hierarchical segmentation approach, together with extensive data augmentation techniques to improve performance across varied imaging conditions. Benchmarked against three widely available segmentation tools, segcsvdWMH demonstrated superior accuracy, achieving mean Dice score improvements of 7.8% ± 9.7% over HyperMapp3r, 21.8% ± 8.6% over SAMSEG, and 43.5% ± 7.1% over WMH‐SynthSeg across four diverse test datasets. segcsvdWMH also maintained consistently high Dice scores across these test datasets (mean DSC = 0.86 ± 0.08), and exhibited strong, stable correlations with periventricular, deep, and total WMH ground truth volumes (mean r = 0.99 ± 0.01). Additionally, segcsvdWMH was robust to low and moderate levels of simulated MRI spike noise artifacts and maintained strong performance across a range of binary segmentation thresholds and WMH burden levels. These findings suggest that segcsvdWMH may provide more accurate and robust WMH segmentation performance for heterogeneous clinical datasets characterized by varying degrees of CSVD severity. segcsvdWMH is an innovative convolutional neural network‐based white matter hyperintensities segmentation tool that leverages highly accurate ground truth labels and strategically designed inputs within a hierarchical segmentation framework. This approach delivers significantly enhanced performance in heterogeneous clinical datasets characterized by varying levels of cerebral small vessel disease burden.

Plant biophenols have been shown to be effective in the modulation of Alzheimer’s disease (AD) pathology resulting from free radical-induced oxidative stress and imbalance of the redox chemistry of transition metal ions (e.g., iron and copper). On the basis of earlier reported pharmacological activities, olive biophenols would also be expected to have anti-Alzheimer’s activity. In the present study, the antioxidant activity of individual olive biophenols (viz. caffeic acid, hydroxytyrosol, oleuropein, verbascoside, quercetin, rutin and luteolin) were evaluated using superoxide radical scavenging activity (SOR), hydrogen peroxide (H2O2) scavenging activity, and ferric reducing ability of plasma (FRAP) assays. The identification and antioxidant activities in four commercial olive extracts—Olive leaf extractTM (OLE), Olive fruit extractTM (OFE), Hydroxytyrosol ExtremeTM (HTE), and Olivenol plusTM (OLP)—were evaluated using an on-line HPLC-ABTS•+ assay, and HPLC-DAD-MS analysis. Oleuropein and hydroxytyrosol were the predominant biophenols in all the extracts. Among the single compounds examined, quercetin (EC50: 93.97 μM) and verbascoside (EC50: 0.66 mM) were the most potent SOR and H2O2 scavengers respectively. However, OLE and HTE were the highest SOR (EC50: 1.89 μg/mL) and H2O2 (EC50: 115.8 μg/mL) scavengers among the biophenol extracts. The neuroprotection of the biophenols was evaluated against H2O2-induced oxidative stress and copper (Cu)-induced toxicity in neuroblastoma (SH-SY5Y) cells. The highest neuroprotection values (98% and 92%) against H2O2-induced and Cu-induced toxicities were shown by the commercial extract HTETM. These were followed by the individual biophenols, caffeic acid (77% and 64%) and verbascoside (71% and 72%). Our results suggest that olive biophenols potentially serve as agents for the prevention of neurodegenerative diseases such as AD, and other neurodegenerative ailments that are caused by oxidative stress.

Gentamicin is an aminoglycoside antibiotic commonly used for treating Pseudomonas infections, but its use is limited by a relatively short half-life. In this investigation, developed a controlled-release gentamicin formulation using poly(lactide-co-glycolide) (PLGA) nanoparticles. We demonstrate that entrapment of the hydrophilic drug into a hydrophobic PLGA polymer can be improved by increasing the pH of the formulation, reducing the hydrophilicity of the drug and thus enhancing entrapment, achieving levels of up to 22.4 μg/mg PLGA. Under standard incubation conditions, these particles exhibited controlled release of gentamicin for up to 16 days. These particles were tested against both planktonic and biofilm cultures of P. aeruginosa PA01 in vitro, as well as in a 96-hour peritoneal murine infection model. In this model, the particles elicited significantly improved antimicrobial effects as determined by lower plasma and peritoneal lavage colony-forming units and corresponding reductions of the surrogate inflammatory indicators interleukin-6 and myeloperoxidase compared to free drug administration by 96 hours. These data highlight that the controlled release of gentamicin may be applicable for treating Pseudomonas infections.

INTRODUCTION Apolipoprotein E E4 allele (APOE E4) and slow gait are independently associated with cognitive impairment and dementia. However, it is unknown whether their coexistence is associated with poorer cognitive performance and its underlying mechanism in neurodegenerative diseases. METHODS Gait speed, APOE E4, cognition, and neuroimaging were assessed in 480 older adults with neurodegeneration. Participants were grouped by APOE E4 presence and slow gait. Mediation analyses were conducted to determine if brain structures could explain the link between these factors and cognitive performance. RESULTS APOE E4 carriers with slow gait had the lowest global cognitive performance and smaller gray matter volumes compared to non‐APOE E4 carriers with normal gait. Coexistence of APOE E4 and slow gait best predicted global and domain‐specific poorer cognitive performances, mediated by smaller gray matter volume. DISCUSSION Gait slowness in APOE E4 carriers with neurodegenerative diseases may indicate extensive gray matter changes associated with poor cognition. Highlights APOE E4 and slow gait are risk factors for cognitive decline in neurodegenerative diseases. Slow gait and smaller gray matter volumes are associated, independently of APOE E4. Worse cognition in APOE E4 carriers with slow gait is explained by smaller GM volume. Gait slowness in APOE E4 carriers indicates poorer cognition‐related brain changes.

Objectives To investigate whether a history of traumatic brain injury (TBI) is associated with greater long-term grey-matter loss in patients with mild cognitive impairment (MCI). Methods 85 patients with MCI were identified, including 26 with a previous history of traumatic brain injury (MCI[TBI-]) and 59 without (MCI[TBI+]). Cortical thickness was evaluated by segmenting T1-weighted MRI scans acquired longitudinally over a 2-year period. Bayesian multilevel modelling was used to evaluate group differences in baseline cortical thickness and longitudinal change, as well as group differences in neuropsychological measures of executive function. Results At baseline, the MCI[TBI+] group had less grey matter within right entorhinal, left medial orbitofrontal and inferior temporal cortex areas bilaterally. Longitudinally, the MCI[TBI+] group also exhibited greater longitudinal declines in left rostral middle frontal, the left caudal middle frontal and left lateral orbitofrontal areas sover the span of 2 years (median = 1–2%, 90%HDI [−0.01%: −0.001%], probability of direction (PD) = 90–99%). The MCI[TBI+] group also displayed greater longitudinal declines in Trail-Making-Test (TMT)-derived ratio (median: 0.737%, 90%HDI: [0.229%: 1.31%], PD = 98.8%) and differences scores (median: 20.6%, 90%HDI: [−5.17%: 43.2%], PD = 91.7%). Conclusions Our findings support the notion that patients with MCI and a history of TBI are at risk of accelerated neurodegeneration, displaying greatest evidence for cortical atrophy within the left middle frontal and lateral orbitofrontal frontal cortex. Importantly, these results suggest that long-term TBI-mediated atrophy is more pronounced in areas vulnerable to TBI-related mechanical injury, highlighting their potential relevance for diagnostic forms of intervention in TBI.