Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell-Specific Gene Networks and Drivers in Rheumatoid Arthritis Synovial Tissues

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18,16,19,11 key regulators of fibroblast-like synoviocyte (FLS), T cells, B cells, and monocyte signatures and networks, respectively, in RA synovial tissues. Interestingly, FLS and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (synovial B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of NKT cell and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected KDG, TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.
All three MutL complexes are required for repeat expansion in a human stem cell model of CAG-repeat expansion mediated glutaminase deficiency

The Repeat Expansion Diseases (REDs) arise from expansion of a disease-specific short tandem repeat (STR). Different REDs differ with respect to the repeat involved, the cells that are most expansion prone and the extent of expansion and whether these diseases share a common expansion mechanism is unclear. To date, expansion has only been studied in a limited number of REDs. Here we report the first studies of the expansion mechanism in induced pluripotent stem cells derived from a patient with a form of the glutaminase deficiency disorder known as Global Developmental Delay, Progressive Ataxia, And Elevated Glutamine (GDPAG; OMIM# 618412) caused by the expansion of a CAG-STR in the 5’ UTR of the glutaminase (GLS) gene. We show that alleles with as few as ∼100 repeats show detectable expansions in culture despite relatively low levels of R-looped formed at this locus. Additionally, using a CRISPR-cas9 knockout approach we show that PMS2 and MLH3, the constituents of MutLα and MutLψ, the 2 mammalian MutL complexes known to be involved in mismatch repair (MMR), are essential for expansion. Furthermore, PMS1, a component of a less well understood MutL complex, MutLβ, is also important, if not essential, for repeat expansion in these cells. Our results provide insights into the factors important for expansion and lend weight to the idea that, despite some differences, many, if not all, REDs likely expand via in very similar ways.
Coupling Cell Size Regulation and Proliferation Dynamics of C. glutamicum Reveals Cell Division Based on Surface Area

Single cells actively coordinate growth and division to regulate their size, yet how this size homeostasis at the single-cell level propagates over multiple generations to impact clonal expansion remains fundamentally unexplored. Classical timer models for cell proliferation (where the duration of the cell cycle is an independent variable) predict that the stochastic variation in colony size will increase monotonically over time. In stark contrast, implementing size control according to adder strategy (where on average a fixed size added from cell birth to division) leads to colony size variations that eventually decay to zero. While these results assume a fixed size of the colony-initiating progenitor cell, further analysis reveals that the magnitude of the intercolony variation in population number is sensitive to heterogeneity in the initial cell size. We validate these predictions by tracking the growth of isogenic microcolonies of Corynebacterium glutamicum in microfluidic chambers. Approximating their cell shape to a capsule, we observe that the degree of random variability in cell size is different depending on whether the cell size is quantified as per length, surface area, or volume, but size control remains an adder regardless of these size metrics. A comparison of the observed variability in the colony population with the predictions suggests that proliferation matches better with a cell division based on the cell surface. In summary, our integrated mathematical-experimental approach bridges the paradigms of single-cell size regulation and clonal expansion at the population levels. This innovative approach provides elucidation of the mechanisms of size homeostasis from the stochastic dynamics of colony size for rod-shaped microbes.
Is there a neural common factor for visual illusions?

It is tempting to map interindividual variability in human perception to variability in brain structure or neural activity. Indeed, it has been shown that susceptibility to size illusions correlates with the size of primary visual cortex V1. Yet contrary to common belief, illusions correlate only weakly at the perceptual level, raising the question of how they can correlate with a localized neural measure. In addition, mounting evidence suggests that there is substantial interindividual variability not only in neural function and anatomy but also in the mapping between the two, which further challenges the findings of a neural common factor for illusions. To better understand these questions, here, we re-evaluated previous studies by correlating illusion strengths in a battery of 13 illusions with the size of visual areas and population receptive field sizes. We did not find significant correlations either at the perceptual level or between illusion susceptibility and visual functional neuroanatomy.
In-utero exposure to estrogen-mimicking bisphenols alters bone mineralization in the offspring

Exposure to plastic-derived estrogen-mimicking endocrine-disrupting bisphenols can have a long-lasting effect on bone health. However, gestational exposure to below tolerable daily intake (TDI) of bisphenol A (BPA) and its substitute, bisphenol S (BPS), on offspring’s bone mineralization is unclear. This study examined the effects of in-utero bisphenol exposure on the growth and bone density of the offspring rats. Pregnant Wistar rats were exposed to BPA and BPS (0.0, 0.4 μg/kg bw) via oral gavage from gestational day 4 to 21. The bone density, IGF-1, osteocalcin, and calcium levels were measured by DEXA, ELISA and AAS, respectively. The bisphenol’s action on canonical BMP signaling was examined in osteoblast SaOS-2 cells. Maternal exposure to bisphenols (BPA and BPS) increased the body weight, bone mineral content, and density in the offspring aged 30 and 90 days (p<0.05). Plasma IGF-1, calcium, osteocalcin, and alkaline phosphatase activities were altered in BPA-exposed offspring (p<0.05). The bisphenols exposure to SaOS-2 cells decreased its viability in a dose-dependent manner and promoted the cell cycle progression of the S/G2-M phase (p<0.05). The expression of BMP1, BMP4, and intracellular signalling mediators SMAD1, SMAD5, and RUNX2 mRNAs was altered upon bisphenol exposure in these cells (p<0.05). The bone mineralization index and expression of extracellular matrix proteins such as ALPL, COL1A1, DMP1, and FN1 were downregulated (p<0.05). Bisphenol co-incubation with noggin decreased TGF-β1 expression, indicating its involvement in bone mineralization. Overall, exposure to bisphenols (BPA and BPS) during gestation altered growth and bone mineralization in the offspring by modulating canonical BMP/ TGF-β1 signalling mediators. Highlights Gestational exposure to low doses of bisphenol increases whole-body BMC and BMD in the offspring. In-utero BPA exposure increased plasma IGF-1 and gla-type osteocalcin, a marker of osteoblast activity in the offspring. Bisphenol exposure modulates Smad-dependent BMP signaling in the SaOS-2 cells.
MmM: a machine learning approach to detect replication states and genomic subpopu-lations for single-cell DNA replication timing disentanglement

We introduce MnM, an efficient tool for characterising single-cell DNA replication states and revealing genomic subpopulations in heterogeneous samples, notably cancers. MnM uses sin-gle-cell copy-number data to accurately perform missing-value imputation, classify cell repli-cation states and detect genomic heterogeneity, which allows to separate somatic copy-num-ber alterations from copy-number variations due to DNA replication. By applying our machine learning methods, our research unveils critical insights into chromosomal aberrations and showcases ubiquitous aneuploidy in tumorigenesis. MnM democratises single-cell subpopu-lation detection which, in hand, enables the extraction of single-cell DNA replication timing (scRT) profiles from genomically-heterogenous subpopulations detected by DNA content and issued from single samples. By analysing over 119,000 human single cells from cultured cell lines, patient tumours as well as patient-derived xenograft samples, the copy-number and rep-lication timing profiles issued in this study lead to the first multi-sample subpopulation-disen-tangled scRT atlas and act as data contribution for further cancer research. Our results high-light the necessity of studying in vivo samples to comprehensively grasp the complexities of DNA replication, given that cell lines, while convenient, lack dynamic environmental factors. This tool offers to advance our understanding of cancer initiation and progression, facilitating further research in the interface of genomic instability and replication stress. GRAPHICAL ABSTRACT
DSISA: A New Neural Machine Translation Combining Dependency Weight and Neighbors

Most of the previous neural machine translations (NMT) rely on parallel corpus. Integrating explicitly prior syntactic structure information can improve the neural machine translation. In this paper, we propose a Syntax Induced Self-Attention (SISA) which explores the influence of dependence relation between words through the attention mechanism and fine-tunes the attention allocation of the sentence through the obtained dependency weight. We present a new model, Double Syntax Induced Self-Attention (DSISA), which fuses the features extracted by SISA and a compact convolution neural network (CNN). SISA can alleviate long dependency in sentence, while CNN captures the limited context based on neighbors. DSISA utilizes two different neural networks to extract different features for richer semantic representation and replaces the first layer of Transformer encoder. DSISA not only makes use of the global feature of tokens in sentences but also the local feature formed with adjacent tokens. Finally, we perform simulation experiments that verify the performance of the new model on standard corpora.
Beyond Apps: Tablet and Micro:bit Devices as Tools for Creative Computing in Primary Schools

This article shares a practice aimed at enriching primary school computer science education through tablet-centred, hands-on learning in an after school context. A diverse group of primary school students use tablet devices and micro:bits. Unlike conventional methods that heavily rely on basic applications for teaching computing concepts, this practice emphasises the importance of fostering creativity and exploration with physical technology, transcending mere app consumption. Evidence of effectiveness emerges from improved student engagement, a deeper understanding of computer science concepts, and heightened enthusiasm for digital literacy. This innovative idea draws inspiration from constructionist principles and aligns with the evolving landscape of primary education technology. Implementing this practice necessitated adaptability and creative adjustments to the activity. There is a need for further refinement of this approach, complemented by rigorous empirical research methods. This article serves as a resource for educators who are exploring inventive avenues to enhance primary CS education through hands-on tablet-based learning.
Towards Complex-Valued Deep Architectures with Data Model Preservation for Sea Surface Current Estimation from SAR Data

The application of deep learning methods in various fields is rapidly increasing. The development of complex-valued (CV) networks that can process CV data has provided many opportunities for utilizing the immense capabilities of deep networks for CV data, including Synthetic Aperture Radar (SAR). However, the physical model and basic properties of the original SAR data must be preserved in the CV architecture. Without these properties, the physical parameters cannot be accurately retrieved from the SAR data. This study evaluates the competency of CV deep architectures to preserve the properties of the original SAR data and how it affects the retrieval of physical parameters. Ocean Surface Current (OSC) is an important parameter for ocean circulation and plays a vital role globally. In this work, the correlation Doppler estimation (CDE) method is used to estimate the OSC from SAR data before and after reconstruction with the CV autoencoder. The obtained OSCs are compared, and we demonstrate the ability of the CV deep architectures to learn the data model and preserve the original Doppler centroid (fDC) information in the SAR data. This research paves the way for the development of CV deep architectures for physical parameter retrieval and prediction from CV SAR data in future studies.
D4W: Dependable Data-Driven Dynamics for Wheeled Robots

Wheeled robots have gained significant attention due to their wide range of applications in manufacturing, logistics, and service industries. However, due to the difficulty of building a highly accurate dynamics model for wheeled robots, developing and testing control algorithms for them remains challenging and time-consuming, requiring extensive physical experimentation. To address this problem, we propose D4W, i.e., Dependable Data-Driven Dynamics for Wheeled Robots, a simulation framework incorporating data-driven methods to accelerate the development and evaluation of algorithms for wheeled robots. The key contribution of D4W is a solution that utilizes real-world sensor data to learn accurate models of robot dynamics. The learned dynamics can capture complex robot behaviors and interactions with the environment throughout simulations, surpassing the limitations of analytical methods, which only work in simplified scenarios. Experimental results show that D4W achieves the best simulation accuracy compared to traditional approaches, allowing for rapid iteration of wheel robot algorithms with less or no need for fine-tuning in reality. We further verify the usability and practicality of the proposed framework through integration with existing simulators and controllers.
Nanotechnology in the Food Industry

This chapter addresses the potential application of nanotechnology in various areas of the food industry. Nanotechnology is having an impact on several aspects of the food industry, from product development to packaging processes. Nanotechnology is capable of solving the very complex set of engineering and scientific challenges in the food processing industries. This chapter focuses on exploring the role of nanotechnology in enhancing food stability at the various stages of processing. Research has highlighted the prospective role of nanotechnology use in the food sector, including nanoencapsulation, nanopackaging, nanoemulsions, nanonutraceuticals, and nanoadditives. Industries are developing nanomaterials that will make a difference not only in the taste of food but also in food safety and the health benefits that food delivers. While proposed applications of nanotechnologies are wide and varied, developments are met with some caution as progress may be stifled by lack of governance and potential risks.
Active Learning with few shot learning for crisis management

Practical applications of machine learning require fast training of models with few annotations on specific domains. Among them, crisis management is of prime importance to support decisions by extracting useful insights from massive datasets. In particular, social media analysis is an intense topic in this domain. In this paper, we investigate whether this problem can be solved by combining an active learning strategy and the few-shot learning paradigm enabled by recent language models. We find that although Few-Shot learning based on K nearest neighbors and greedy Core-set obtain superior performances at a very low data regime, an active learning strategy based on fine-tuning a Distillbert model performs best as more points are observed, still staying in the regime of less than a hundred of points per class. However, the computation burden of the few-shot based method is typically much lower. These findings are obtained through experiments on Twitter data with the CrisisMMD public dataset.
An evaluation on the effectiveness of virtual tutorials for training emergency professionals

The use of virtual reality as a tool for professional training enables access to knowledge in a more active way. Applying it in high-risk situations training encourages finding the most effective way to share knowledge. With this objective in mind, video game tutorials as a method of learning in virtual environments are becoming increasingly popular. This study proposes a methodology in which virtual reality tutoring techniques are applied to enhance learning in training for radiation emergencies. A virtual reality experience involving a high-risk situation has been developed. In this experience two groups of professionals involved in such emergencies participated, one group were trained with a tutorial, and the other group faced the simulation without it. The results show that participants who received tutoring were able to solve the test correctly in a more effective way.
Multiobjective quantum circuit compilation (Short Paper)

The compilation of quantum circuits on a given quantum architecture is one of the critical issues when developing quantum algorithms. The related problem is known as the Quantum Circuit Compilation Problem (QCCP). It is known that the current quantum chip technology (Noisy Intermediate-Scale Quantum) is affected by noise that can be originated by different sources and significantly limits the circuit’s usability. Generally, one important source of noise is the onset of decoherence during the quantum circuit’s execution. Decoherence can be described as the difficulty of a quantum state to hold its superposition in time; therefore, quantum circuits should be characterized by a limited depth (i.e., makespan) in order to terminate their execution before qubits start to decohere. The other important source of noise is due to the presence of quantum binary gates (e.g., CNOTs); it is in fact known that the noise produced by binary gates is an order of magnitude greater than the noise produced by unary gates. Hence, trying to minimize the number of the circuit’s binary gates can also positively affect the stability of the circuit. In this work, we analyze how a multiobjective approach can be useful to reduce the circuit’s noise thus improving its stability, focusing on the minimization of both the circuit’s makespan and the number of swap gates that are synthesized as a product of the compilation process, as every swap gate is a binary gate composed of a sequence of three CNOT gates. In particular, we tackle instances of the Quantum Approximate Optimization Algorithm (QAOA) for the resolution of the MaxCut problem, and provide some preliminary results combining an existing genetic algorithm with two well-known multiobjective approaches.
Assessing Project Work for Ontological Inference and Categorization

Project-based learning (PBL) approaches are organized around open-ended challenges that require feedback and revisions to facilitate learning. However, feedback on projects provides a challenge as the varied states of projects, the equivocality of the process, and the varied solutions that students might create present scaffolding challenges to meet the needs of the students. In this paper, we propose an ontology that characterizes activity flow and discourse use during a project. More specifically, we analyze weekly student reports in an interdisciplinary entrepreneurship class to derive a categorization system. The categories within the ontology, in combination, help define a “state” for the project. By identifying the “current state”, human (or intelligent) instructors can provide relevant and timely feedback that can help student teams navigate various procedural knowledge problems associated with project-based learning. This augments the educator’s capacity to meet the specific needs of the student by providing the building blocks for solutions that automate and/or replicate their insightful guidance.
Probando los efectos de la representación no fotorrealista en los videojuegos: Un estudio comparativo (Testing the Effects of Non-Photorealistic Rendering on Video Games: A Comparative Study)

This research article explores the impact of Non-Photorealistic Rendering (NPR) effects on user gaming experiences within the context of video games. Using the video game "John Lemon" as the primary research tool, five distinct NPR effects were applied, each designed to investigate their influence on player immersion, flow, competitiveness, and emotions, assessed through the in-game version of the Game Experience Questionnaire (GEQ). The results reveal that variations in artistic styles generate positive experiences in terms of positive affect, while certain effects, such as color range reduction and limited visibility, decrease immersion and increase tension and negative affect. These findings underscore the importance of considering visual effects in video game design and emphasize the need for further research in this evolving field.
Formulating Video Watch Success Signals for Recommendations on Short Video Platforms

With the rising prominence of short video platforms, the challenge of effective content recommendation has become more pressing, especially given the diverse range of video content and the sparsity of implicit user feedback signals. This research delves into the formulation of "successful video watches", particularly for short video platforms. We introduce various functional formulations to model video watch behavior and use these to define ranking objectives for training recommender models. Our findings reveal that, in contrast to the naive percentage-based thresholds, our proposed formulation — grounded on duration and watch percentile — aligns better with user retention and boosts engagement metrics. Moreover, while the standard approach tends to bias content recommendations towards extremes in video length, our methodology ensures a more balanced content recommendation, greatly impacting user experience on streaming platforms. This study underscores the potential nuances and implications of training recommender systems for video content.