Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Two Communities: Privacy of Third Party App Users in Crowdsourcing - The Case of Receipt Transcription

Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers.
Mathematical modelling of solute transport in a heterogeneous aquifer

This study provides a contribution to the understanding of parsimony and predictive uncertainty in the context of groundwater solute transport modelling. The study is unique because the modelling was undertaken using tracer test data from a heterogeneous artificial aquifer whose structure was known to a very high level of detail. The aquifer structure was based on a ‘real life’ Canterbury Plains alluvial aquifer (in New Zealand). Parsimonious principles were applied by starting with a simple analytical model that assumed homogeneity then progressively adding heterogeneity using numerical models with varying degrees of parameterisation complexity. The results show that increased complexity did not necessarily make the model better at replicating the tracer test data. For example, the outputs from a numerical model that represented heterogeneity using a zone based approach based on the recorded distribution of all 2,907 blocks that comprised the artificial aquifer was little different to a simple numerical model that adopted a homogenous distribution and included a single value of dispersion. Parameterisation of numerical models using ‘pilot points’ provided the most complex representation of heterogeneity and resulted in the best replication of the tracer test data. However, increasing model complexity had its disadvantages such as decreasing parameterisation uniqueness. The contribution to predictive uncertainty from model parameters and observations was assessed using a linear approach based on Bayes theorem. This approach has been applied to other groundwater modelling studies, but not to solute transport modelling within Canterbury Plains alluvial aquifers or to an artificial aquifer. A unique finding was the reduction in predictive uncertainty along the groundwater flow path. This finding correlated well with the numerical model outputs which showed closer fits to the observation data near the end of the aquifer compared to those near the top of the aquifer where the tracer was injected. Physical solute transport processes were identified and described as part of the modelling. These included the increase in dispersivity with travel distance and the spatial distribution of the aquifer hydraulic properties. Analytical modelling was a useful tool in identifying physical processes, aquifer characteristics and the variation in aquifer hydraulic properties both spatially and with depth. An important finding was the value of undertaking multiple modelling approaches. This is because each approach has its own advantages and disadvantageous and by comparing the results of different approaches, the true facts about the aquifer system are made clearer.
Differential reorganization of episodic and semantic memory systems in epilepsy-related mesiotemporal pathology

Declarative memory encompasses episodic and semantic divisions. Episodic memory captures singular events with specific spatiotemporal relationships, while semantic memory houses context-independent knowledge. Behavioural and functional neuroimaging studies have revealed common and distinct neural substrates of both memory systems, implicating mesiotemporal lobe (MTL) regions such as the hippocampus and distributed neocortices. Here, we explored declarative memory system reorganization in patients with unilateral temporal lobe epilepsy (TLE), as a human disease model to test the impact of variable degrees of MTL pathology on memory function. Our cohort included 31 patients with TLE as well as 60 age and sex-matched healthy controls and all participants underwent episodic and semantic retrieval tasks during a multimodal MRI session. The functional MRI tasks were closely matched in terms of stimuli and trial design. Capitalizing on non-linear connectome gradient mapping techniques, we derived task-based functional topographies during episodic and semantic memory states, both in the MTL and in neocortical networks. Comparing neocortical and hippocampal functional gradients between TLE patients and healthy controls, we observed a marked topographic reorganization of both neocortical and MTL systems during episodic memory states. In the neocortex, alterations were characterized by reduced functional differentiation in patients in lateral temporal and midline parietal cortices in both hemispheres. In the MTL, on the other hand, patients presented with a more marked functional differentiation of posterior and anterior hippocampal segments ipsilateral to the seizure focus and pathological core, indicating perturbed intrahippocampal connectivity. Semantic memory reorganization was also found in bilateral lateral temporal and ipsilateral angular regions, while hippocampal functional topographies were unaffected. Leveraging MRI proxies of MTL pathology, we furthermore observed alterations in hippocampal microstructure and morphology are associated with TLE-related functional reorganization during episodic memory. Moreover, correlation analysis and statistical mediation models revealed that these functional alterations contributed to behavioural deficits in episodic, but again not semantic memory in patients. Altogether, our findings suggest that semantic processes rely on distributed neocortical networks, while episodic processes are supported by a network involving both the hippocampus and neocortex. Alterations of such networks can provide a compact signature of state-dependent reorganization in conditions associated with MTL damage, such as TLE.
Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases

Background COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. Aim To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. Method We leveraged the correlation network approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization (WHO) Ordinal Scale (WOS) was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state omics-specific graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Results Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed more shared biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. We further identified both biosignatures that discriminate between the disease states and interactions between biosignatures that are either common between or associated with COVID-19 disease phases. Interestingly, cross-layer interactions between different omics profiles increased with disease severity. Conclusion This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions that are either common between or unique to mild, moderate, and severe COVID-19. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. In addition, the approach implemented here can be used for other diseases. Key findings ⍰ Integrative multi-omics analysis revealed biosignatures and biosignature interactions associated with COVID-19 disease states. ⍰ Disease severity increases with biosignature interactions across different multi-omics data. ⍰ The harmonization approach proposed and implemented here can be applied to other diseases
Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumors globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones with the same microenvironment. In this work, the effects of interclonal interactions between two cell populations - one sensitive to treatment and another resistant to treatment - on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool in understanding the behaviors of heterogeneous tumors and in identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations. Importance The cellular makeup of tumors can play a vital role in its growth and cancer development. In this work, two different types of cell populations of diffuse large B-cell lymphoma (DLBCL) were studied together to understand how they interact with each other in cultures. In mixed cultures, both types of cells cooperated with each other and increased their growth in complex manners. A mathematical model was created to simulate the growth behavior of mixed cultures. The model can potentially be used to predict future cell behavior and help in identifying more effective therapy regimens to maximize tumor cell reduction.
The electrical conductivity of the oceanic upper mantle

SUMMARY Previous inversions of sea-floor magnetotelluric (MT) sounding data have predicted upper mantle electrical conductivities which are more than an order of magnitude higher than laboratory measurements of the conductivity of olivine would suggest, and controlled source-field electromagnetic (CSEM) soundings require a lithospheric mantle conductivity of 3 x lop5 Sm-’, which is so low that the electromagnetic (EM) coast effect would produce more anisotropy in MT soundings than is observed. We address these issues by constructing an olivine mantle model for conductivity and examining the inversion of MT data from three sea-floor sites, and show that the incompatibilities can be largely resolved if the effects of oceans and coastlines are considered. Our mantle model is based on recent measurements of olivine conductivity, the conductivity of tholeiite melt, a thermal model for the upper mantle based on lithospheric cooling and the temperature of a+ a + /3 olivine transition, the pyrolite model of mantle petrology, and conductivities derived from CSEM sounding. We propose Archie’s Law with exponent 2 and interconnected tubes as realistic lower and upper bounds for the effect of partial melt on rock conductivity, and Archie’s Law with exponent 1.5 as the preferred estimate. The 1-D forward response of this model is not compatible with observed sea-floor MT data. Three data sets presented by Oldenburg (1981) pass a test for one-dimensionality based on the size of the residuals when fit with Parker’s D+ algorithm, but two of the three soundings fail a test for independence of residuals. We also find that the presence of the upper mantle ‘high-conductivity zone’ previously inferred from these data is highly dependent on data misfit and not required when the misfit criterion is relaxed a reasonable amount. Re-inversions of the MT data produce models which are incompatible with our petrological model of mantle conductivity. However, by adding an ocean with various coastlines of simple geometry to our petrological model and solving the forward 3-D problem using thin-sheet analysis we predict MT responses which are distorted in a manner that is remarkably similar to the observed data.
Attention and microsaccades: do attention shifts trigger new microsaccades or only bias ongoing microsaccades?

Brain circuitry that controls where we look also contributes to attentional focusing of visual contents outside of current fixation or contents held within the spatial layout of working memory. A behavioural manifestation of the contribution of oculomotor brain circuitry to selective attention comes from modulations in microsaccade direction that accompany attention shifts. Here, we address whether such modulations come about because attention itself triggers new microsaccades or whether, instead, shifts in attention only bias the direction of ongoing microsaccades – i.e., naturally occurring microsaccades that would have been made whether or not attention was also shifted. We utilised an internal-selective-attention task that has recently been shown to yield clear spatial microsaccade modulations and compared microsaccade rates following colour retrocues that were matched for sensory input, but differed in whether they invited an attention shift or not. If shifts in attention trigger new microsaccades then we would expect more microsaccades following attention-directing cues than following neutral cues. In contrast, we found no evidence for an increase in overall microsaccade rate following attention-directing cues, despite observing robust modulations in microsaccade direction. This implies that shifting attention biases the direction of ongoing microsaccades without changing the probability that a microsaccade will occur. These findings provide relevant context for complementary and future work delineating the links between attention, microsaccades, and upstream oculomotor brain circuitry, such as by helping to explain why microsaccades and attention shifts are often correlated but not obligatorily linked.
Anti-prion drugs do not improve survival in knock-in models of inherited prion disease

Prion diseases uniquely manifest in three distinct forms: inherited, sporadic, and infectious. Wild-type prions are responsible for the sporadic and infectious versions, while mutant prions cause inherited variants like fatal familial insomnia (FFI) and familial Creutzfeldt-Jakob disease (fCJD). Although some drugs can prolong prion incubation times up to four-fold in rodent models of infectious prion diseases, no effective treatments for FFI and fCJD have been found. In this study, we evaluated the efficacy of various anti-prion drugs on newly-developed knock-in mouse models for FFI and fCJD. These models express bank vole prion protein (PrP) with the pathogenic D178N and E200K mutations. We applied various drug regimens known to be highly effective against wild-type prions in vivo as well as a brain-penetrant compound that inhibits mutant PrPSc propagation in vitro. None of the regimens tested (Anle138b, IND24, Anle138b + IND24, cellulose ether, and PSCMA) significantly extended disease-free survival or prevented mutant PrPSc accumulation in either knock-in mouse model, despite their ability to induce strain adaptation of mutant prions. Paradoxically, the combination of Anle138b and IND24 appeared to accelerate disease by 16% and 26% in kiBVIE200K and kiBVID178N mice, respectively, and accelerated the aggregation of mutant PrP molecules in vitro. Our results show that anti-prion drugs originally developed to treat infectious prion diseases do not necessarily work for inherited prion diseases, and that the recombinant sPMCA is not a reliable platform for identifying compounds that target mutant prions. This work underscores the need to develop therapies and validate screening assays specifically for mutant prions.
Dissolving microneedle array patches containing mesoporous silica nanoparticles of different pore sizes as a tunable sustained release platform

Dissolving microneedle array patches (DMAPs) enable efficient and painless delivery of therapeutic molecules across the stratum corneum and into the upper layers of the skin. Furthermore, this delivery strategy can be combined with the sustained release of nanoparticles to enhance the therapeutic potential in a wide variety of pathological scenarios. Among the different types of nanoparticles that can be included in microneedle formulations, mesoporous silica nanoparticles (MSNs) of tuneable pore sizes constitute a promising tool as drug delivery systems for cargos of a wide range of molecular weights. However, the development of efficient methods to produce DMAP containing large amounts of MSNs of different pore sizes has not been reported. In this work, DMAP containing MSNs with varying pore sizes was prepared and characterized. After synthesizing and characterizing MSNs, the pore size of the nanoparticles (in the range of 3 to 13 nm for S-MSN and XL-MSN, respectively) was observed to influence the loading and release of both small and large molecules, using fluorescein and ovalbumin (OVA) as model cargos. Moreover, a new preparation method was developed to produce DMAP containing large amounts of these MSNs located mainly in the microneedle tips. The successful insertion of these DMAPs was confirmed in vitro (using Parafilm), ex vivo (using excised neonatal porcine skin) and in vivo (in the back of mice) models. The dissolution of the microneedles and deposition of the nanoparticles inside the skin were also confirmed both ex vivo and in vivo using fluorescent nanoparticles, with complete microneedle dissolution after 2 h of insertion in vivo. Through histological studies, the microneedle-delivered MSNs were found to end up inside antigen presenting cells in the skin tissue (either F4/80+ macrophages or CD11c+ dendritic cells). For this reason, the uptake and biological effect of the MSNs was evaluated in vitro in dendritic cells, showing that while smaller pore MSNs were taken up by cells more efficiently (with over 80 % of S-MSN uptake compared to ca. 55 % for XL-MSNs), the dendritic cells treated with OVA- loaded XL-MSNs underwent the largest degree of activation (inducing over 25 % of CD40 expression compared to less than 2 % for OVA-loaded S- MSNs). Finally, the immune response to OVA-loaded XL-MSNs in mice was evaluated after repeated administration either subcutaneously or through DMAP. The results of this experiment showed comparable levels of anti-ovalbumin immunoglobulin generation through both routes of administration (with significant production of OVA-specific IgG1 and IgG2b antibodies), highlighting the good potential of this delivery platform for vaccination or immunotherapy applications.
Relating aspen defoliation to changes in leaf area derived from field and satellite remote sensing data

Trembling aspen is the most important deciduous species in the North American boreal forest for which methods are needed to consistently map and monitor insect defoliation patterns to assess impacts on aspen health and productivity. The loss of foliage due to insect defoliation results in changes to leaf area index (ΔLAI) that were studied from both field and satellite image data for a large aspen tortrix (Choristoneura conflictana (Wlk.)) outbreak in boreal forest aspen stands in northern Alberta. Field estimates of defoliation for stands undergoing various levels of defoliation severity and both allometric and optical LAI-2000 measurements of leaf area index were collected and related to Landsat enhanced thematic mapper plus (ETM+) image data representing before- and after-defoliation events. This study was undertaken as part of the Climate Change Impact on the Productivity and Health of Aspen (CIPHA) research initiative that is taking place in western Canada. Field estimates of ΔLAI followed defoliation trends but with substantial scatter that may be attributed to the use of allometric estimates of predefoliation LAI and the subjective nature of visual defoliation assessment. Change estimates derived from both Landsat ETM+ scenes were statistically correlated (correlation r = 0.84, probability P = 0.001) with percent defoliation. Such an approach may be used in the future to calibrate LAI algorithms to other defoliated areas or to map the temporal intensity of these events as they occur.