Miracle-dz/UniFAR-FTUs huggingface dataset:
Respectful community engagement in health research with diverse im/migrant communities

Introduction Global migration and immigration are increasing, and migrants and immigrants (im/migrants) have specific health needs and healthcare experiences. Yet, im/migrant involvement in immigration and health research in Canada is inconsistent. Heretofore, involvement has primarily been in research planning, data collection and analysis, with little community involvement during knowledge exchange or through training and colearning opportunities. Community engagement has been especially uncommon in mixed-method and quantitative research in Canada. Objective This article describes lessons learnt from the Evaluating Inequities in Refugee & Immigrants’ Health Access (IRIS) project from 2018 to 2023, an ongoing mixed-method, community-based research project in British Columbia, Canada. Specifically, we share our core community engagement project structures, Commitments to Community and our Community Engagement Backbone, both collaboratively developed with im/migrant community memebers. Participants People with varied experiences of im/migration and connections to multiple, specific im/migrant communities participate in the project as participants, community researchers, community advisory board members, faculty members and students. Core research activities are supported in English, Farsi, Spanish and Tigrinya. We engage community members throughout the research process, from identifying research topics to knowledge exchange. Conclusion We found that these structures offer an accessible visual representation of the project’s commitments to community engagement, and the ways these commitments are demonstrated through values and action. Our training opportunities, colearning activities and knowledge exchange efforts also confirmed the accuracy of interpretation, prompted additional analysis to clarify or add depth to findings, and helped us identify additional research topics. We hope these learnings can be used to expand engagement with diverse im/migrant communities in health and immigration research.
Forecasting Volatility of Bank Deposits of Individuals Using Hybrid Arcing -ARIMA Approach: Forecasting Volatility of Bank Deposits

To forecast successfully financial time series data is not a trivial task. This type of data is characterized by internal complexity, high volatility and hidden stochastic dependencies. The factors that determines the values of the considered time series are unknown at future times and also must be forecasted. In this study, a univariate time series is modeled with data from bank deposits. A detailed analysis of the lagged variables allows to identify the predictor series to which a first-order trend and two temporal variables are added. Using the powerful machine learning algorithm Arcing from the class of gradient boosting methods the models for predicting of the examined data were built and analyzed. Aiming to avoid the presence of serial correlation in the residuals, an appropriate ARIMA error procedure is conducted to obtain the final hybrid Arcing-ARIMA models concerning the full sample and also for reduced 70% sample.
On the compliance with ethical principles in AI

In recent years, there has been a lot of discussion around ethics in IT and AI. Researchers and organizations have proposed guidelines to address privacy, fairness, and explainability challenges for creating trustworthy AI. In this work, we outline the importance of compliance with the above-mentioned ethical principles and their influence on the quality of AI systems. We map the relationship between available approaches for compliance with privacy, fairness, explainability principles and the accuracy of AI system decisions. Additionally, we introduce the difference between ensuring fairness for phenomena presented with tabular data and text. Tabular data may contain protected attributes such as gender, age, or race as well as the decision made historically in relation to the people concerned. Data presented in text is not structured and requires sense perception by AI systems to detect bias or unfairness. In the poster, we compare available approaches and present experiments for measuring bias in text data.
Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes

Selectivity estimation aims to estimate the size of query results size accurately and efficiently. Despite being a well-researched area for decades, most existing estimators are designed to handle comparison predicates over numeric and categorical data. Nevertheless, Set-valued data are ubiquitous in many applications such as information retrieval and recommendation systems. However, these estimators may not be effective for handling predicates over set-valued data. In this work, we presents novel techniques for selectivity estimation on queries involving predicates over set-valued attributes. We first propose the set-valued column factorization problem, whereby each each set-valued column is converted to multiple numeric subcolumns, and set containment predicates are converted to numeric comparison predicates. This enables us to leverage any existing estimator to perform selectivity estimation. We then develop two methods for column factorization and query conversion, namely ST and STH. We integrate ST and STH into three estimators, Postgres, Neurocard, and DeepDB. We then conduct a comprehensive empirical analysis by comparing our approach against three baselines across three different datasets. The experimental results demonstrate that our methods exhibit superior estimation accuracy while maintaining high efficiency compared to the baseline techniques.
Immersive Neural Network Exploration: A VR Approach to Human-Centered AI Understanding

In today’s rapidly evolving artificial intelligence (AI) landscape, the complexity of neural networks, especially in deep learning, presents significant challenges for intuitive human understanding. Compelling visualization methods have become crucial with AI systems integrating into everyday experiences and environments. Virtual Reality (VR), as an immersive and interactive technology, offers a novel approach to visualizing intricate AI processes. This work introduces transformative updates to the DeepVisionVR platform, a pioneering tool for 3D visualization of Convolutional Neural Networks (CNNs) in VR. A cornerstone of our enhancements is the Sensitivity Analysis module, which offers real-time interactivity, allowing users to adjust pixels within the VR space, shedding light on the intricacies of model outcomes. In addition, advanced model interpretation methodologies have been integrated, including Integrated Gradients, GradientShap, and Occlusion, enriching the depth of insight into model rationale. Our Adversarial Analysis, utilizing the Fast Gradient Sign Method (FGSM), unveils potential weak points in models, emphasizing their vulnerability to minor input alterations. The new features aim to provide a deeper understanding of how neural networks interpret and react to various inputs, thereby bridging the gap between complex machine learning models and human interpretability.
Latent Dirichlet Allocation Mixture Models for Nucleotide Sequence Analysis

Strings of nucleotides that carry biological information are typically described using sequence motifs that can be represented by weight matrices or consensus sequences. However, many biological signals in DNA or RNA are recognized by multiple factors in temporal sequence, consist of a mixture of sometimes dissimilar alternative motifs, or may be described better by base composition. Here we apply the Latent Dirichlet Allocation (LDA) mixture model to nucleotide sequences, using k-mers as features, in three related approaches. First, positions in aligned sequences are used as samples. Alternatively, whole sequences are used as samples, either with positional k-mers or with bulk k-mers occurring throughout the sequence. LDA readily identifies motifs, including such elusive cases as the intron branch site. LDA can also identify subtypes of sequence, such as splice site subtypes enriched in long vs. short introns, and can reliably distinguish such properties as reading frame or species of origin. Our results show that LDA is a useful model for describing heterogeneous signals, for assigning individual sequences to subtypes, and for identifying and characterizing sequences that do not fit recognized subtypes. Because LDA topic models are interpretable, they also aid the discovery of new motifs, even those present in a small fraction of samples, allowing a user to hypothesize potential regulatory factors. In summary, LDA can identify and characterize signals in nucleotide sequences and is useful for identifying candidate regulatory factors involved in complex biological processes.
Direct disassembly of α-syn preformed fibrils into native α-syn monomers by an all-D-peptide

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder worldwide. One of its central features is the neurodegeneration that starts in the substantia nigra and progressively tends to involve other brain regions. α-Synuclein (α-syn) and its aggregation during pathogenesis have been drawn into the center of attention, where especially soluble oligomeric and fibrillar structures are thought to play a key role in cell-to-cell transmission and induction of toxic effects. Here, we report the development of all-D-enantiomeric peptide ligands that bind monomeric α-syn with high affinity, thereby stabilizing the physiological intrinsically disordered structure and preventing initiation of aggregation, and more important, disassembling already existing aggregates. This “anti prionic” mode of action (MoA) has the advantage over other MoAs that it eliminates the particles responsible for disease propagation directly and independently of the immune system, thereby restoring the physiological monomer. Based on mirror image phage display on the D-enantiomeric full-length α-syn target, we identified SVD-1 and SVD-1a by next generation sequencing, Thioflavin-T screens and rational design. The compounds were analyzed with regard to their anti-aggregation potential and both compounds showed aggregation delaying as well as seed capacity reducing effects in de novo and seeded environments, respectively. High affinity towards the monomeric α-syn, in the low nano- to picomolar KD range was identified by surface plasmon resonance (SPR). SVD-1a reduced toxic effects as well as intracellular seeding capacity of α-syn pre-fromed fibrils (PFF) in cell culture. SVD-1a disassembled α-syn PFF into monomers as identified by atomic force microscopy (AFM), time dependent dynamic light scattering (DLS) and size exclusion chromatography (SEC) analysis. The present work provides promising results on the development of lead compounds with this anti-prionic mode of action for treatment of Parkinson’s disease and other synucleinopathies.
Proactive and reactive construction of memory-based preferences

We are often faced with decisions we have never encountered before, requiring us to infer possible outcomes before making a choice. Computational theories suggest that one way to make these types of decisions is by accessing and linking related experiences stored in memory. Past work has shown that such memory-based preference construction can occur at a number of different timepoints relative to the moment a decision is made. Some studies have found that memories are integrated at the time a decision is faced (reactively) while others found that memory integration happens earlier, when memories are encoded (proactively). Here we offer a resolution to this inconsistency. We demonstrate behavioral and neural evidence for both strategies and for how they tradeoff rationally depending on the associative structure of memory. Using fMRI to decode patterns of brain responses unique to categories of images in memory, we found that proactive memory access is more common and allows more efficient inference. However, participants also use reactive access when choice options are linked to more numerous memory associations. Together, these results indicate that the brain judiciously conducts proactive inference by accessing memories ahead of time in conditions when this strategy is most favorable.
Lifestyle and brain health determinants of word-finding failures in healthy ageing

Cognitive decline associated with healthy ageing is complex and multifactorial: vascular and lifestyle factors uniquely and jointly contribute to distinct neurocognitive trajectories of ageing. To evaluate existing accounts of neurocognitive ageing that propose mechanisms of compensation, maintenance or reserve, studies should explore how various known brain-based and lifestyle factors intersect to better understand cognitive decline. Here, we bring together brain function, structure, perfusion, and cardiorespiratory fitness to investigate a well-documented, prominent cognitive challenge for older adults: word-finding failures. Commonality analysis on 73 neurologically healthy older adults revealed that functional activation of language networks associated with tip-of-the-tongue states is in part determined by age and, interestingly, cardiorespiratory fitness levels. Age-associated atrophy and perfusion in regions other than those showing functional differences accounted for variance in tip-of-the-tongue states. Our findings can be interpreted in the context of the classic models of neurocognitive ageing, with mechanisms of compensation and reserve interacting with each other. Highlights The incidence of word-finding failures is determined by brain health and lifestyle factors Language network activation associated with word-finding failures is determined by age and cardiorespiratory fitness levels Distinct contribution of brain structure and perfusion also predict this decline Brain health indices in concordance with lifestyle measures provide a holistic explanation of individual differences in age-related cognitive decline
Sex-specific behavioral features of juvenile and adult haploinsufficient Scn2a+/− female mice, model of Autism Spectrum Disorder

Variants of the SCN2A gene, encoding the NaV1.2 sodium channel, cause a spectrum of neurodevelopmental and epileptic disorders, and are among those that show the strongest association with Autism Spectrum Disorder (ASD). ASD has a male-bias prevalence, but several studies have proposed that female prevalence may be underestimated due to different symptomatic expression compared with males. However, it is unclear whether this is related to actual different pathological features or to greater masking abilities in females. Studies on Scn2a+/− mice, a model of SCN2A haploinsufficiency and ASD, have shown an age-dependent ASD-like phenotype attenuated at adulthood in males. However, little is known about the behavioral features of Scn2a+/− female mice. We performed a battery of behavioral tests that are relevant for assessing ASD-like features, investigating juvenile and adult Scn2a+/− female mice. Our results demonstrate that female Scn2a+/− mice exhibit an overall milder phenotype than males, showing increased sociability and increased risk taking in juveniles, hyper-reactivity to cold stimuli in adults, altered decision-making related behaviors in both. Thus, this is consistent with the male-bias prevalence of ASD and the existence of different ASD phenotypic features in males and females. Both genders should be investigated in studies of mouse models of ASD.
‘Pscore’ - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics

BACKGROUND Quantitative MRI metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSE To confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, ‘Pscore’ to address this issue. STUDY TYPE Retrospective cohort POPULATION 961 healthy young-adults (age:22-35 years, Females:53%) from the Human Connectome Project FIELD STRENGTH/SEQUENCE 3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE ASSESSMENT The dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU-atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate ‘Pscores’– which normalized the difference between the median and a participant’s value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTS ROI-wise distributions were assessed using Log transformations, Zscore, and the ‘Pscore’ methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%),PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (n=100) using 100 iterations. RESULTS The dMRI metric distributions were systematically non-Gaussian, including positively skewed (e.g., mean and radial diffusivity) and negatively skewed (e.g., fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95≠5%,PEV<5≠5%) whereas ‘Pscore’ distributions were symmetric and balanced (PEV>95=PEV<5=5%); even for small bootstrapped samples (average . DATA CONCLUSION The inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed ‘Pscore’ method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.
LncRNA Bigheart trans-activates gene expression in a feed forward mechanism that facilitates calcineurin-NFAT signaling in myocardial hypertrophy

Terminally differentiated cardiomyocytes exhibit hypertrophy as a default response to injury by translating biomechanical stress into a complex network of intracellular signaling events. The molecular intricacies how calcium-dependent signaling engage molecular circuits and epigenetic modifications to activate deleterious gene programs remain enigmatic. Here we report on the re-activation of the evolutionarily conserved lncRNA “Bigheart”, which is repressed in the postnatal myocardium and quickly re-activated in a calcineurin-NFAT-dependent fashion in the diseased myocardium in man and mouse. In line, AAV9-mediated overexpression of lncRNA Bigheart in otherwise healthy primary cardiomyocytes or mouse hearts suffices to drive maladapative hypertrophy. Conversely, mice receiving a “Gapmer” antisense oligonucleotide designed to specifically silence endogenous lncRNA Bigheart display resistance to biomechanical stress-induced myocardial remodeling, indicating its requirement in left ventricular hypertrophy. Mechanistically, lncRNA Bigheart recruits the RNA binding proteins hnRNP-F1 and HMGB1 to modulate the local chromatin environment and trans-activate Bigheart target genes including Rcan1 to stimulate calcineurin-NFAT coupling. Our observations confirm that human heart failure arises from specific susceptibilities in gene regulatory circuits that are amenable for therapeutic intervention using RNA-based therapeutics.
Model-Based Inference of Synaptic Plasticity Rules

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja’s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from Drosophila during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.
Cluster Editing with Overlapping Communities

Cluster Editing , also known as correlation clustering, is a well-studied graph modification problem. In this problem, one is given a graph and allowed to perform up to k edge additions and deletions to transform it into a cluster graph, i.e., a graph consisting of a disjoint union of cliques. However, in real-world networks, clusters are often overlapping. For example, in social networks, a person might belong to several communities – e.g. those corresponding to work, school, or neighborhood. Another strong motivation comes from language networks where trying to cluster words with similar usage can be confounded by homonyms, that is, words with multiple meanings like “bat”. The recently introduced operation of vertex splitting is one natural approach to incorporating such overlap into Cluster Editing . First used in the context of graph drawing, this operation allows a vertex v to be replaced by two vertices whose combined neighborhood is the neighborhood of v (and thus v can belong to more than one cluster). The problem of transforming a graph into a cluster graph using at most k edge additions, edge deletions, or vertex splits is called Cluster Editing with Vertex Splitting and is known to admit a polynomial kernel with respect to k and an O (9 k 2 + n + m )-time (parameterized) algorithm. However, it was not known whether the problem is NP -hard, a question which was originally asked by Abu-Khzam et al. [Combinatorial Optimization, 2018]. We answer this in the affirmative. We further give an improved algorithm running in O (2 7 k log k + n + m ) time.
The Routledge Companion to Accounting and Risk

To date, there has been little consideration of the many different ways in which accounting and risk intersect, despite organisations being more determined than ever to build resilience against potential risks. This comprehensive volume overcomes this gap by providing an overview of the field, drawing together current knowledge of risk in a wide range of different accounting contexts.