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A special case of the range of invariant problem for AF type actions of Z₂
No full textElliott and Su gave a classification of inductive Limit type Z2 actions on AF algebras
using a K-theoretic invariant. In this paper, we consider the range of invariant problem,
and give a sufficient condition for an object to be one of their invariants for such a system.
In addition, we defines some structures that will be useful for further investigation of this
problem
Petrogenesis of the Sunday Lake Intrusion, Jacques Township, Ontario, Canada
Full text linkThe Sunday Lake Intrusion (SLI) is an early-phase (1109.0±1.3 Ma) mafic-ultramafic intrusion associated with the Midcontinent Rift System. It was emplaced along the Crock Lake Fault, a splay of the regional Quetico fault. The intrusion is a layered funnel/tabular-shaped intrusion divided into Gabbro, Upper Ultramafic, Lower Ultramafic and Marginal zones. The intrusion hosts significant Ni-Cu-Platinum group-elements (PGE) mineralization within the Marginal Zone, which contains up to 2.11 g/t platinum, 0.95 g/t palladium, 0.16 g/t gold, 0.26% copper and 0.11% nickel. In total, this contact-type deposit hosts an estimated 20.4 Mt at an average grade of 2.5 g/t combined Pt+Pd+Au. Mineralization reflects late-stage exsolution of PGM from sulfide melt, including maslovite, michenerite, sperrylite and native silver, platinum and palladium.
The SLI comprises wehrlite, olivine clinopyroxenite, feldspathic olivine clinopyroxenite, melagabbro, gabbro, leucogabbro, quartz monzonite and quartz gabbro. Trace element and radiogenic isotope data support a mantle plume origin, with patterns resembling ocean island basalts (OIB) and likely tied to the Keweenaw Plume. Mass-balance calculations yield a calculated parental magma composition of ~11.15 wt. % FeO and ~19.5 wt. % MgO, consistent with a high-Mg tholeiitic basaltic magma. Compositional variations in olivine and whole-rock MgO (wt. %) suggest the SLI was formed from two discrete magma injections: the first formed the Lower Ultramafic Zone and Marginal Zone, and the second formed the Upper Ultramafic Zone.
Radiogenic isotopes values (ɛNd and 87Sr/86Sr) are mostly mantle-like, though early-pulse samples show negative Nb anomalies from limited interaction with the subcontinental lithospheric mantle beneath the lithosphere. A later injection of purely primitive, plume-derived magma appears to have flushed the staging chamber, depleting the subcontinental lithosphere mantle (SCLM) signature and resetting the sulfur isotope system to near-mantle values. This two-stage model explains the combination of mantle-like isotopes with localized Nb depletion.
Some radiogenic samples from the Gabbro and Marginal zones record isotopic and trace element evidence for assimilation of Quetico metasedimentary rocks. Negative ɛNd values and Nb-Th anomalies, together with decreased Fo in olivine at the Lower Ultramafic Zone-Marginal Zone contact, suggest localized interaction of magma with country rocks. Although this process may have introduced a crustal sulfur signature into the system, it was largely diluted or reset by the later primitive recharge, leaving the overall sulfur isotope system dominated by mantle values
Two-dimensional molecular assembly and surface-confined reaction of pyrene-based bifunctional molecules on silver (111) and gold (111)
No full textTwo-dimensional (2D) materials have attracted significant attention owing to their remarkable properties stemming from their reduced dimensionality. The bottom-up approach is one of the most promising methods to design single layered 2D materials with engineered functionalities or properties. The factors impacting the creation process and the products include the effect of precursor symmetry/shape, the unique nature of bifunctional systems, and the competition between kinetics and thermodynamics of on-surface phenomena. This research reports the creation and characterization of 2D self-assembled molecular networks and their effects on the subsequent on-surface reactions.
Low-temperature scanning-tunneling microscopy (STM) at 77.5K was used to explore the molecular arrangements created, and to identify structural changes as a result of on-surface reactions, of the bifunctional molecule 4,4’-(3,8-Bis(4-aminophenyl)pyrene-1,6-diyl)dibenzaldehyde (APPDB), which is a pyrene-based precursor containing two amines and two aldehyde functional groups. The APPDB molecule exhibits a distinct windmill-like arrangement on Ag(111) caused by the grouping of four aldehydes and four amine functional groups into separate centres. The arrangement was additionally exposed to a sequential post-annealing to invoke a reaction. At 100 °C, enantiomeric domains were resolved. At 150 °C, atomically resolved silver atoms appeared in the molecular arrangement, one atom decorated with four functional groups provided by four molecules, indicative of metal-organic structure. Additionally at this temperature, reacted APPDB monomers were observed on the edges and defects of molecular domains. Finally, after annealing to 225 °C, the STM images recording the domains of APPDB no longer resolved the silver atom. In order to highlight the difference between the APPDB-Ag network and the reaction of APPDB monomers, a heated substrate deposition at 200 °C was performed which successfully introduced reacted monomer to the domain centres although the network was overall disordered. The APPDB molecule was also deposited on Au(111) which did not result in an organized molecular arrangement, instead disordered rope-like structures were formed. The structure remained disordered after post-annealing although reacted molecular features were formed.
A second bifunctional molecule, 4,4’-(3,8-bis((4-aminophenyl)ethynyl)pyrene-1,6-diyl)dibenzaldehyde (APDB), was studied; it is nearly identical to APPDB but contains a carbon-carbon triple bond between the pyrene core and the aniline substituent. Adsorption of APDB on Au(111) gave disordered small clusters of molecules rather than the rope-like structures of APPDB. Finally, a star-shaped molecule containing three aldehyde groups, 1,3,5-tris(p-formylphenyl)benzene (TFPB), was also studied on Au(111). The molecular structures were disordered but contained some local assemblies such as dog bone-like structures, windmill-like structures, and hexagonal structures. The adsorbed TFPB was post-annealed, displaying no structural changes up to 150 °C
Advancing Black Spruce (Picea Mariana) breeding through genomic selection: a comparative analysis of models using pedigree and genomic marker information.
Full text linkLong generation times in forest trees constrain the pace of genetic improvement necessary to sustain productivity under climate change. Genomic selection offers a promising approach to accelerate breeding gains in long-lived species like black spruce (Picea mariana). In this study, we evaluated genomic selection models using data from a long-term half-sib progeny trial in the Lake Nipigon West breeding zone of northern Ontario. A subset of 1194 trees from 70 families was genotyped using two platforms: a SNP array (16,217 SNPs) and a genotyping-by-sequencing approach based on RADseq (10,626 SNPs). Growth traits—including height, diameter at breast height (DBH), growth rate, and volume—were measured at multiple time points.
We compared three animal models differing in their relationship matrices: pedigree-based (ABLUP), genomic-based (GBLUP), and a hybrid model integrating both pedigree and genomic information (HBLUP). The HBLUP model consistently produced the most accurate heritability estimates and the smallest prediction errors for key growth traits such as volume and DBH, likely due to its ability to incorporate both genotyped and ungenotyped individuals. Genomic models (GBLUP and HBLUP) outperformed pedigree-based models, highlighting the value of genomic information for improving selection efficiency.
While early height has traditionally served as a proxy for long-term growth, its low heritability in this study suggests caution in its use as a sole selection criterion. Instead, height may be better incorporated as part of multi-trait selection indices to capture its environmental responsiveness, particularly during early testing stages.
Among genotyping platforms, SNP chips consistently outperformed RADseq, indicating their preference when budget allows, though RADseq remains a cost-effective alternative that could benefit from complementary strategies such as imputation or hybrid integration.
Overall, our findings support the practical integration of genomic selection into black spruce breeding programs. By aligning genotyping strategies and model choice with specific trait characteristics and breeding goals, programs can accelerate genetic gain, reduce breeding cycle time, and enhance forest adaptability under future environmental challenges
Advancing wind load assessment of low-rise buildings: CFD and wind tunnel approaches
No full textUnderstanding wind hazards is essential for designing low-rise buildings that are resilient over time. These structures located within the turbulent Atmospheric Boundary Layer (ABL) are particularly vulnerable to wind-induced damage. In more realistic scenarios where, low-rise buildings are surrounded by similar structures, the flow and, consequently, the pressure distribution can be significantly altered. The characteristics of incident wind flow significantly influence pressure patterns and magnitudes on building façades. During windstorms, the cladding of low-rise buildings often suffers damage due to uplift forces, compromising their structural integrity. Roof damage is typically triggered by high suction regions caused by flow separation at edges and corners, forming conical and separation bubble vortices. Most previous studies focused on tall buildings for accurately evaluating and aerodynamically mitigating the wind load experimentally and numerically. Therefore, the main objective of this research is to develop a framework to accurately evaluate and effectively mitigate the wind load on low-rise buildings to enhance safety and structural integrity. The first objective of this research is to investigate the impact of discontinuous corner and ridgeline parapets on stand-alone low-rise buildings with complex roof geometry located in suburban terrain in reducing wind load by displacing the flow separation zones from the corners and edges using parapets. As for the second objective, it aims to estimate and correlate the effective parameters controlling the accuracy of the numerical wind pressure evaluation using Large Eddy Simulations (LES) on a low-rise building based on comparing wind pressures (i.e., mean and RMS) to wind tunnel results. In the third objective, the thesis aims to systematically define the required computational fluid dynamics (CFD) details to produce accurate ABL flows with LES models, particularly including a discussion aimed to efficiently select turbulence maximum frequency () employed as an input in the turbulence flow generator concerning grid size in the refinement zones that can accurately capture the pressure fluctuation induced on the building façade. The fourth objective is to experimentally evaluate the effectiveness of parapets in reducing wind pressures on low-rise buildings under two different terrain roughness conditions and with two parapet configurations added to the benchmark model. To address the challenges faced during the experimental testing, the fifth objective is to optimally reduce the number of pressure sensors needed while maintaining accurate wind load evaluations, ultimately enhancing the resilience of buildings against wind-induced damage. This research uses multi-resolution Dynamic Mode Decomposition (mrDMD) to decompose multiscale wind pressure data into modes representing different timescales. QR-Pivoting then identifies key dynamic modes that best capture the pressure field’s dynamics. Together, these techniques can help identify sensor locations that minimize the required sensors while ensuring accurate pressure field reconstruction. This research provides a comprehensive framework for enhancing the resilience of low-rise buildings against wind-induced damage by addressing numerical and experimental challenges in wind load evaluation and mitigation
Geochemistry and geochronology of the Shebandowan greenstone belt in the vicinity of the Moss Lake deposit, NW Ontario
No full textThis thesis presents a detailed investigation of the geology, geochemistry, and
geochronology of the Archean Shebandowan Greenstone Belt (SGB) in the vicinity of the Moss
Lake gold deposit in Northwestern Ontario. The Moss Lake property is located in the western part
of the SGB and consists primarily of rocks belonging to the Greenwater and Burchell assemblages.
The study characterizes the geological attributes, tectonic setting, and timing of magmatism within
the belt and provides regional context for ongoing mineral exploration.
The research involved extensive fieldwork, including detailed lithological descriptions and
structural analysis, supported by petrographic analysis of thin sections. Whole-rock geochemical
analysis was performed on 56 samples to classify rock types, determine magmatic affinities, and
evaluate element mobility, revealing distinct geochemical signatures between the Greenwater and
Burchell assemblages and various intrusive bodies.
The Greenwater assemblage mafic rocks exhibit tholeiitic affinities with flat HREE,
enriched LREE, and negative Nb anomalies. The Burchell assemblage mafic rocks are calcalkaline,
with moderately enriched LREE, flat HREE, and negative Nb-Ti anomalies. Similarly,
the felsic and intermediate metavolcanic rocks show distinct geochemical signatures between
assemblages. Greenwater rocks are more enriched in LREE and display stronger negative Ti
anomalies compared to the less enriched LREE and weaker Ti anomalies observed in Burchell
rocks.
U-Pb zircon geochronology on four key samples yielded new ages: 2716.0 ± 0.45 Ma for
the Moss Lake syenogranite stock, 2718.34 ± 0.14 Ma for the Obadinaw quartz syenite stock,
2711.80 ± 0.14 Ma for an intermediate metavolcanic rock, and 2707.35 ± 0.14 Ma for the Greenwater Lake quartz monzonite stock. These ages complement and refine the existing
geochronological framework of the SGB.
Neodymium isotope analysis of thirteen samples provided insights into the mantle source
and crustal contamination processes. The Greenwater assemblage exhibits consistently positive
εNd(t) values in both mafic and felsic-intermediate rocks (+1.6 to +2.7), indicating a dominantly
juvenile mantle source with limited crustal involvement. In contrast, the Burchell assemblage
shows a broader εNd(t) range (+0.01 to +3.2), suggesting a more heterogeneous source and greater
influence from crustal assimilation, despite an overall juvenile magmatic character.
The integration of geological, geochemical, and geochronological data supports models of
a complex Neoarchean tectonic evolution involving distinct magmatic pulses and settings for the
Greenwater and Burchell assemblages. Geochemical evidence suggested the Greenwater
assemblage originated from an oceanic plateau evolving to a primitive arc, whereas the Burchell
assemblage formed in a primitive arc environment. Intrusive bodies were classified as tonalite
trondjhemite granodiorite, sanukitoids, and Archean hybrid granites, reflecting diverse sources and
conditions of formation. The results confirm independent magmatic histories for the two
assemblages and highlight a protracted crustal evolution involving juvenile mantle input and
crustal assimilation
Integrating multi-omics data via latent space construction for breast and bladder cancer analysis
No full textCancer remains one of the most complex and heterogeneous diseases, driven by intricate
interactions across genetic, epigenetic, and transcriptional landscapes. Accurately
understanding and predicting tumor characteristics, such as Tumor Mutational Burden
(TMB), is critical for effective diagnosis, prognosis, and personalized treatment strategies.
This research aims to address inherent challenges in integrating high-dimensional,
heterogeneous multi-omics datasets—including DNA methylation, gene expression, and
Copy Number Alteration (CNA)—specifically for bladder and breast cancer analysis, by
building a shared latent space that captures and preserves meaningful cross-omics representations.
Some of these challenges include data imbalance, dimensionality, modalityspecific
noise, and complex non-linear biological interactions.
To overcome these obstacles, this thesis proposes constructing a shared latent space
through advanced deep-learning approaches by utilizing Deep Multiset Canonical Correlation
Analysis (DMCCA) and Graph Attention Networks (GATs). The shared latent
space methodology provides a unified representation capturing crucial and intricate biological
interactions across various omics modalities, as a result giving improved predictive
accuracy for TMB classification. Attention mechanisms further refine this integration by
dynamically focusing on the most relevant relational patterns within multiomics data,
enhancing the model’s ability to capture biological interactions between genes, pathways,
and patient profiles. In addition, this study utilizes oversampling techniques—mainly
the Synthetic Minority Oversampling Technique (SMOTE)—to offset data imbalance
among TMB classes and menopausal status groups. As compared to baseline supervised
machine learning models such as Logistic Regression (LR), Artificial Neural Network
(ANN), and Tabular Transformer, the new GAT model with shared latent space training
performed better by achieving an AUC of 0.76 and accuracy of 76.1% for BRCA,
whereas that of BLCA was 0.73 with an accuracy of 65.3%, thereby establishing the
usefulness of multi-omics integration through shared latent space learning
Ecological drivers of fish metacommunity structure in boreal shield lakes
Full text linkFish community composition in freshwater lakes is shaped by a range of biotic and abiotic factors, including environmental conditions, species interactions, and spatial connectivity between waterbodies. While aquatic community ecology studies historically treated lakes as isolated systems, recent research has increasingly embraced a metacommunity framework, integrating spatial connectivity with environmental and biological predictors of community composition. Despite this shift, few studies have thoroughly examined the relative roles of spatial connectivity, environmental factors, and species interactions in shaping lake fish communities. To address this gap, I conducted a study across 81 lakes distributed within two quaternary watersheds at the IISD Experimental Lakes Area in northwest Ontario. Using Joint Species Distribution Modeling (JSDM) alongside spatial eigenvector mapping techniques—Asymmetric Eigenvector Mapping (AEM) and Moran’s Eigenvector Mapping (MEM)—drivers of fish community composition were investigated. Results indicate that spatial variables—specifically lake connectivity, stream flow direction, and the maximum gradient along connecting streams—are primary drivers of fish metacommunity composition. In presence-absence models, these spatial factors explained more variation than environmental variables and species co-occurrence patterns (potentially reflecting species interactions). Conversely, relative abundance models (conditional on presence) performed poorly across all ecological models evaluated. These findings provide valuable insights into the role of spatial connectivity relative to other factors in shaping fish community structure on a presence-absence basis, emphasizing the importance of applying a metacommunity approach in community analyses
GraphSAGE-based approach for age-specific multi-omics biomarker identification in bladder cancer
Full text linkBladder cancer is a highly prevalent malignancy with substantial morbidity and mor-
tality, emphasizing the urgent need for early detection and personalized treatment
strategies. Although recent advances in cancer genomics have enhanced our under-
standing of tumor biology, the role of age-related genomic variations in bladder cancer
progression remains largely unexplored. In this study, we present a novel framework
that combines multi-omics data integration with Graph Neural Networks (GNNs)
to identify age-specific biomarkers associated with bladder cancer prognosis. We in-
tegrate copy number alterations (CNA), DNA methylation, and mRNA expression
profiles into graph-based representations, where nodes denote genomic features and
edges encode molecular interactions. Unlike conventional statistical or machine learn-
ing approaches, our method incorporates age both as a stratification factor and as a
graph-level feature, enabling the model to learn distinct molecular signatures across
different patient age groups. Using survival outcomes, we determined 64 years as the
optimal threshold for age stratification, revealing significant differences in mortality
between patients aged ≤64 years (30.46%) and those > 64 years (51.74%), thereby
highlighting the prognostic value of age in bladder cancer. To enhance model in-
terpretability and performance, we implemented a robust feature selection pipeline
involving variance thresholding, ANOVA F-scores, L1 regularization, and Recursive
Feature Elimination with Cross-Validation (RFECV). Among several models tested,
GraphSAGE consistently achieved the highest accuracy, F1-score, and AUC, demon-
strating the effectiveness of graph-based learning in capturing complex biological re-
lationships. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed
key age-associated biomarkers such as SNRPN, LINC01091, and DHX36, which are
strongly implicated in patient survival and may inform future therapeutic target-
ing. This study introduces a comprehensive, age-aware graph learning framework for
biomarker discovery in bladder cancer, offering a powerful tool for advancing per-
sonalized diagnosis, prognosis, and treatment planning. Beyond bladder cancer, this
methodology has the potential to be generalized to other cancer types where age sig-
nificantly influences disease trajectory, thereby contributing to the broader field of
precision oncology. By bridging age-specific genomic variation with multi-modal data
and explainable machine learning, our approach opens new avenues for developing
clinically actionable insights and enhancing patient-specific management strategies in
oncology
Interactive effects of photoperiod, soil moisture and carbon dioxide of ecophysiological traits of yellow birch (betula alleghaniensis)
Full text linkClimate change is expected to drive tree migration, a phenomenon of significant ecological
importance. The projected northward migration of boreal and temperate trees by 10°N in the next
century, due to increasing atmospheric carbon dioxide [(CO2)] concentration, will likely expose
them to new set of environmental conditions. These conditions, such as photoperiod regime and
soil moisture availability, will likely influence eco-physiological traits in trees. The ability of these
migrating trees to acclimate to these new conditions will be essential in determining the limit and
success of their migration. In this study, I investigated the interactive effects of (1) elevated carbon
dioxide and photoperiod and (2) elevated carbon dioxide and soil moisture regime on the eco-
physiological response of yellow birch (Betula alleghaniensis) seedlings, research that holds
significant implications for our understanding of tree species migration and climate change.
Seedlings were exposed to factorial combination of two levels of [CO2] (400 vs. 1000 μmol mol-
1
) and two soil moisture regimes (well-watered (WW) vs. drought stress (DS)), and factorial
combinations of two levels of CO2 and three photoperiods (45° (seed origin) 50° and 55°N
latitudes)). In the first set of experiments, we observed that elevated carbon dioxide and longer
photoperiod significantly decreased electron transport rate (Jmax) and ratio of electron transport
rate to carboxylation rate (Jmax/Vcmax). Total leaf area (TLA), specific leaf area (SLA), leaf dry
mass (LEAFDM) and total seedling dry mass (TSDM) were significantly increased under elevated
carbon dioxide and longer photoperiod. Furthermore, the phenological study of yellow birch
revealed that the timing of bud set and leaf senescence was delayed at (P50) and advanced at the
longest photoperiod of 10°N (P55). Yellow birch might not tolerate freezing temperatures when
exposed to (-45°C). This could reduce the cold hardiness performance of yellow birch in response
to a changing climate. These findings open new avenues for future research in understanding the
complex interplay between climate change, tree physiology, and species migration.
In the second experiment, a significant interaction effect was observed between soil moisture and
carbon dioxide on height growth and specific leaf area (SLA). Drought stress significantly
decreased tree height and SLA under elevated carbon dioxide. Also, drought stress and elevated
carbon dioxide significantly increased root dry mass (ROOTDM), root mass ratio (RMR),
maximum electron transport rate (Jmax) and ratio of electron transport rate to carboxylation rate
(Jmax/Vcmax). These findings underscore the potential implications of climate change on tree
species survival, advocating immediate action to mitigate its effects