Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Analysis of Macrophage Uptake and Retention of Fluorescent Organosilica Nanoparticles

This study investigates the uptake and retention of stable fluorescent organosilica nanoparticles by macrophages, which play a vital role in scavenging environmental nanoparticles and nanomedicine within the body. We used rhodamine 6G-loaded fluorescent organosilica nanoparticles (SiNP-R6G) synthesized from a thiol-functionalized organosilane precursor. Our primary objective was to establish a quantitative relationship between fluorescent measurements and nanoparticle tracking analysis, enabling the precise “counting” of nanoparticles taken up by macrophages under kinetic measurement conditions. Our kinetic study demonstrated a concentration-dependent, saturable internalization of nanoparticles in a model macrophage (RAW 264.7 cells), with a maximum uptake rate (Vmax) of 7.9 × 104 nanoparticles per hour per cell. The estimated number concentration of nanoparticles for half-maximum uptake was approximately 0.8 trillion nanoparticles per milliliter, and a significant portion (∼80%) of internalized SiNP-R6G remained entrapped within the cells for 48 hours, indicating the sustained particle retention capacity of macrophages. These findings highlight the successful development of a methodology to accurately “count” the cellular uptake of nanoparticles in macrophages, providing valuable insights into the kinetics and retention capabilities of macrophages for nanoparticles.
Structure-based virtual screening identifies small molecule inhibitors of O-fucosyltransferase SPINDLY

Protein O-glycosylation is a nutrient-signaling mechanism that plays essential roles in maintaining cellular homeostasis across different species. In plants, SPINDLY (SPY) and SECRET AGENT (SEC) catalyze posttranslational modifications of hundreds of intracellular proteins by O-fucose and O-linked N-acetylglucosamine, respectively. SPY and SEC play overlapping roles in cellular regulation and loss of both SPY and SEC causes embryo lethality in Arabidopsis. Using structure-based virtual screening of chemical libraries followed by in vitro and in planta assays, we identified a SPY O-fucosyltransferase inhibitor (SOFTI). Computational analyses predicted that SOFTI binds to the GDP-fucose-binding pocket of SPY and competitively inhibits GDP-fucose binding. In vitro assays confirmed that SOFTI interacts with SPY and inhibits its O-fucosyltransferase activity. Docking analysis identified additional SOFTI analogs that showed stronger inhibitory activities. SOFTI treatment of Arabidopsis seedlings decreased protein O-fucosylation and caused phenotypes similar to the spy mutants, including early seed germination, increased root hair density, and defect in sugar-dependent growth. By contrast, SOFTI had no visible effect on the spy mutant. Similarly, SOFTI inhibited sugar-dependent growth of tomato seedlings. These results demonstrate that SOFTI is a specific SPY O-fucosyltransferase inhibitor and a useful chemical tool for functional studies of O-fucosylation and potentially for agricultural management.
DF-Sense: Multi-user Acoustic Sensing for Heartbeat Monitoring with Dualforming

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS2IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.
Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data

Biological macromolecules in solution are surrounded by a hydration shell, whose structure differs from the structure of bulk solvent. While the importance of the hydration shell for numerous biological functions is widely acknowledged, it remains unknown how the hydration shell is regulated by macromolecular shape and surface composition, mainly because a quantitative probe of the hydration shell structure has been missing. We show that small-angle scattering in solution using X-rays (SAXS) or neutrons (SANS) provide a protein-specific probe of the protein hydration shell that enables quantitative comparison with molecular simulations. Using explicit-solvent SAXS/SANS predictions, we derived the effect of the hydration shell on the radii of gyration R _g of five proteins using 18 combinations of protein force field and water model. By comparing computed R _g values from SAXS relative to SANS in D_2O with consensus SAXS/SANS data from a recent worldwide community effort, we found that several but not all force fields yield a hydration shell contrast in remarkable agreement with experiments. The hydration shell contrast captured by R _g values depends strongly on protein charge and geometric shape, thus providing a protein-specific footprint of protein–water interactions and a novel observable for scrutinizing atomistic hydration shell models against experimental data. Protein hydration shells play an important role in protein function, however, regulation of the hydration shell remains underexplored. Here, the authors use small-angle scattering (SAS) data in solution as a protein-specific probe, quantitatively compare SAS data with explicit-solvent SAS prediction by molecular simulations, and reveal the effect of protein charge and geometric shape on the hydration shell.
Longitudinal characterization of the captive adult and tadpole Wyoming toad (Anaxyrus baxteri) microbiome

At one time thought to be extinct in the wild, the Wyoming toad (Anaxyrus baxteri) is one of the most critically endangered North American amphibian species. Despite approximately 20 years of ex situ breeding and reintroduction programs, these animals remain functionally extinct in the wild. There is concern among those working in these programs that individuals bred in captivity fail to develop the proper microbiome to withstand the stressors of their native habitat following release. In related species, the skin microbiome has been shown to have a defensive function against common pathogens affecting these animals. However, the early-life microbiome of developing tadpoles in this species remains unknown and therefore this defensive function is unexplored in the Wyoming toad. This study employed 16S rRNA amplicon sequencing to document the baseline microbiome of tadpoles bred for release and captive adult breeder populations. To characterize microbiome development, multiple rounds of skin mucosal and cloacal swabs were obtained concurrently from adult Wyoming toads bred at Omaha’s Henry Doorly Zoo and Aquarium. Our results revealed significant differences between tadpole and adult microbiomes, as well as significant sex-dependent differences within the adult Wyoming toads, in terms of richness and composition. Thus, these findings have identified the baseline microbiome of this endangered species, and variables significantly influencing its composition. Ongoing studies of the only extant wild population are expected to identify taxa not present in captive toads, and potentially help design husbandry modifications to maximize survivability following reintroduction to the wild.
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems

In this study, we partition users by rating disposition - looking first at their percentage of negative ratings, and then at the general use of the rating scale. We hypothesize that users with different rating dispositions may use the recommender system differently and therefore the agreement with their past ratings may be less predictive of the future agreement. We use data from a large movie rating website to explore whether users should be grouped by disposition, focusing on identifying their various rating distributions that may hurt recommender effectiveness. We find that such partitioning not only improves computational efficiency but also improves top-k performance and predictive accuracy. Though such effects are largest for the user-based KNN CF, smaller for item-based KNN CF, and smallest for latent factor algorithms such as SVD.
Sensory context for coding of natural sounds in auditory cortex

6 Accurate sound perception can require integrating information over hundreds of milliseconds or even 7 seconds. Spectro-temporal models of sound coding by single neurons in auditory cortex indicate that the 8 majority of sound-evoked activity can be attributed to stimuli with a few tens of milliseconds. It remains 9 uncertain how the auditory system integrates information about sensory context on a longer timescale. Here 10 we characterized long-lasting contextual effects in auditory cortex (AC) using a diverse set of natural sound 11 stimuli. We measured context effects as the difference in a neuron’s response to a single probe sound 12 following two different context sounds. Many AC neurons showed context effects lasting longer than the 13 temporal window of a traditional spectro-temporal receptive field. The duration and magnitude of context 14 effects varied substantially across neurons and stimuli. This diversity of context effects formed a sparse 15 code across the neural population that encoded a wider range of contexts than any constituent neuron. 16 Encoding model analysis indicates that context effects can be explained by activity in the local neural 17 population, suggesting that recurrent local circuits support a long-lasting representation of sensory context 18 in auditory cortex.
H3.3-G34R Mutation-Mediated Epigenetic Reprogramming Leads to Enhanced Efficacy of Immune Stimulatory Gene Therapy in Pediatric High-Grade Gliomas

Pediatric high-grade gliomas (pHGGs) are diffuse and highly aggressive CNS tumors which remain incurable, with a 5-year overall survival of less than 20%. Within glioma, mutations in the genes encoding the histones H3.1 and H3.3 have been discovered to be age-restricted and specific of pHGGs. This work focuses on the study of pHGGs harboring the H3.3-G34R mutation. H3.3-G34R tumors represent the 9-15% of pHGGs, are restricted to the cerebral hemispheres, and are found predominantly in the adolescent population (median 15.0 years). We have utilized a genetically engineered immunocompetent mouse model for this subtype of pHGG generated via the Sleeping Beauty-transposon system. The analysis of H3.3-G34R genetically engineered brain tumors by RNA-Sequencing and ChIP-Sequencing revealed alterations in the molecular landscape associated to H3.3-G34R expression. In particular, the expression of H3.3-G34R modifies the histone marks deposited at the regulatory elements of genes belonging to the JAK/STAT pathway, leading to an increased activation of this pathway. This histone G34R-mediated epigenetic modifications lead to changes in the tumor immune microenvironment of these tumors, towards an immune-permissive phenotype, making these gliomas susceptible to TK/Flt3L immune-stimulatory gene therapy. The application of this therapeutic approach increased median survival of H3.3-G34R tumor bearing animals, while stimulating the development of anti-tumor immune response and immunological memory. Our data suggests that the proposed immune-mediated gene therapy has potential for clinical translation for the treatment of patients harboring H3.3-G34R high grade gliomas.
BioScatter: Low-Power Sweat Sensing with Backscatter

Sweat contains a wealth of physiologically relevant information and has been used to detect underlying diseases or the sub-health state. However, existing sweat sensors suffer from high energy consumption due to the need for energy-hungry components (i.e., ADC and DAC) and active radio front-ends, making them unable to support continuous and long-term monitoring. This paper introduces BioScatter, a backscatter-based accurate and ultra-low-power sweat sensing wearable sensor that does not need any energy-hungry ADC, DAC, and active radios. The key to eliminating DAC is a novel low-power voltage sweeping circuit design that can perform as well as a 12-bit DAC. To eliminate the ADC, we borrow backscatter technology that can directly transmit the measured analog sensing values to the reader, thus avoiding digital sampling. Extensive results show that BioScatter has a low-power consumption of 313.5 μW and achieves more than 98.5% sensing accuracy for detecting five concentration levels of three types of important bio-fluid in sweat.
Contactless Material Identification with Millimeter Wave Vibrometry

This paper introduces RFVibe, a system that enables contactless material and object identification through the fusion of millimeter wave wireless signals with acoustic signals. In particular, RFVibe plays an audio sound next to the object that generates micro-vibrations in the object. These micro-vibrations can be captured by shining a millimeter wave radar signal on the object and analyzing the phase of the reflected wireless signal. RFVibe can then extract several features including resonance frequencies and vibration modes, damping time of vibrations, and wireless reflection coefficients. These features are then used to enable more accurate identification, with a step towards generalizing towards different setups and locations. We implement RFVibe using an off-the-shelf millimeter-wave radar and an acoustic speaker. We evaluate it on 23 objects of 7 material types (Metal, Wood, Ceramic, Glass, Plastic, Cardboard, and Foam), obtaining 81.3% accuracy for material classification, a 30% improvement over prior work. RFVibe is able to classify with reasonable accuracy in scenarios that it has not encountered before, including different locations, angles, boundary conditions, and objects.
Coding Techniques in JPEG XS for efficient Video Production and Contribution

JPEG XS is a tailored image codec for Video-Over-IP targeting visual lossless quality. The paper describes the requirements for professional use cases as well as tools and constraints to achieve an efficient coding design. The first chapter gives an overview of the requirements, followed by the design concept of JPEG XS. The next few chapters will discuss in detail the JPEG XS design and how it enables high-speed low-latency coding of video streams on CPUs and GPUs. We conclude by providing insights which additional use cases are addressed in the ongoing JPEG XS standardization activities.
Academic staff on their engagement with curriculum internationalisation: an organisational change perspective

ABSTRACT Studies about curriculum internationalisation in higher education frequently report poor academic staff engagement hindering implementation in practice. However, such research does not consider the organisational context in which academics operate. This research applies an organisational change perspective to explore how the context affects the process of engagement and implementation and what to change (content). In a comparative case study of four disciplinary contexts in a West-European university from 2012 to 2020, we disclose the perceptions and experiences of twenty-nine academic staff through in-depth interviews. The academics explain how multiple contextual tensions and inadequate resource management complicate their engagement with curriculum internationalisation. Still, they also reveal evidence of many achievements and strong individual drivers with curriculum internationalisation. Our findings show how disciplinary contextual influences and dynamics create specific perceptions and experiences of curriculum internationalisation in each study programme. This article presents a comprehensive framework of organisational change to explain and facilitate academic staff engagement with curriculum internationalisation in disciplinary communities.
Internationalization of higher education in tumultuous times: transformative powers and problems in embattled Ukraine

ABSTRACT Previous research has conceptualized and investigated internationalization of higher education in relatively stable and peaceful environments. Studies on internationalization in the context of war are largely absent. Using interviews and survey responses from Ukrainian professors and administrators affected by the Russian invasion of 2014–2022, this paper re-examines the premises of internationalization, and outlines key dilemmas facing universities in times of existential crisis. The study reveals that the transformative powers of crisis-driven internationalization redefine ontological and axiological foundations of universities. University stakeholders readjust their responsibilities to reduce human vulnerability, while international solidarity helps them mitigate fragility in the war-affected academia.
WNT4 Regulates Cellular Metabolism via Intracellular Activity at the Mitochondria in Breast and Gynecologic Cancers

Wnt ligand WNT4 is critical in female reproductive tissue development, with WNT4 dysregulation linked to related pathologies including breast cancer (invasive lobular carcinoma, ILC) and gynecologic cancers. WNT4 signaling in these contexts is distinct from canonical Wnt signaling yet inadequately understood. We previously identified atypical intracellular activity of WNT4 (independent of Wnt secretion) regulating mitochondrial function, and herein examine intracellular functions of WNT4. We further examine how convergent mechanisms of WNT4 dysregulation impact cancer metabolism. In ILC, WNT4 is co-opted by estrogen receptor α (ER) via genomic binding in WNT4 intron 1, while in gynecologic cancers, a common genetic polymorphism (rs3820282) at this ER binding site alters WNT4 regulation. Using proximity biotinylation (BioID), we show canonical Wnt ligand WNT3A is trafficked for secretion, but WNT4 is localized to the cytosol and mitochondria. We identified DHRS2, mTOR, and STAT1 as putative WNT4 cytosolic/mitochondrial signaling partners. Whole metabolite profiling, and integrated transcriptomic data, support that WNT4 mediates metabolic reprogramming via fatty acid and amino acid metabolism. Further, ovarian cancer cell lines with rs3820282 variant genotype are WNT4-dependent and have active WNT4 metabolic signaling. In protein array analyses of a cohort of 103 human gynecologic tumors enriched for patient diversity, germline rs3820282 genotype is associated with metabolic remodeling. Variant genotype tumors show increased AMPK activation and downstream signaling, with the highest AMPK signaling activity in variant genotype tumors from non-White patients. Taken together, atypical intracellular WNT4 signaling, in part via genetic dysregulation, regulate the distinct metabolic phenotypes of ILC and gynecologic cancers. Significance WNT4 regulates breast and gynecologic cancer metabolism via a previously unappreciated intracellular signaling mechanism at the mitochondria, with WNT4 mediating metabolic remodeling. Understanding WNT4 dysregulation by estrogen and genetic polymorphism offers new opportunities for defining tumor biology, precision therapeutics, and personalized cancer risk assessment.
Learning Object-Centric Dynamic Modes from Video and Emerging Properties

One of the long-term objectives of Machine Learning is to endow machines with the capacity of structuring and interpreting the world as we do. This is particularly challenging in scenes involving time series, such as video sequences, since seemingly different data can correspond to the same underlying dynamics. Recent approaches seek to decompose video sequences into their composing objects, attributes and dynamics in a self-supervised fashion, thus simplifying the task of learning suitable features that can be used to analyze each component. While existing methods can successfully disentangle dynamics from other components, there have been relatively few efforts in learning parsimonious representations of these underlying dynamics. In this paper, motivated by recent advances in non-linear identification, we propose a method to decompose a video into moving objects, their attributes and the dynamic modes of their trajectories. We model video dynamics as the output of a Koopman operator to be learned from the available data. In this context, the dynamic information contained in the scene is encapsulated in the eigenvalues and eigenvectors of the Koopman operator, providing an interpretable and parsimonious representation. We show that such decomposition can be used for instance to perform video analytics, predict future frames or generate synthetic video. We test our framework in a variety of datasets that encompass different dynamic scenarios, while illustrating the novel features that emerge from our dynamic modes decomposition: Video dynamics interpretation and user manipulation at test-time. We successfully forecast challenging object trajectories from pixels, achieving competitive performance while drawing useful insights.
Operator Learning for Nonlinear Adaptive Control

In this work, we propose an operator learning framework for accelerating nonlinear adaptive con-trol. We define three operator mappings in adaptive control-the parameter identifier operator, the controller gain operator, and the control operator. We introduce neural operators for learning both the parameter identification mapping and the gain function mapping to produce the control action at each step. Through the formalization of neural operators, we are able to learn these mappings for a wide set of different system parameter values without retraining. Empirically, we test our controller on two experiments ranging from an aircraft system (a nonlinear ODE) to a first-order hyperbolic PDE system. We demonstrate that the accuracy of both the gain function and parameter approximation can reach the magnitude of 10 − 4 with speedups around 98% compared to numerical solvers. Furthermore, we empirically demonstrate that despite error propagation, closed-loop stability guarantees are maintained when substituting neural operator approximations.
Contrastive Example-Based Control

While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and specifying reward functions is challenging. Motivated by these challenges, prior work has developed data-driven approaches that learn entirely from samples from the transition dynamics and examples of high-return states. These methods typically learn a reward function from high-return states, use that reward function to label the transitions, and then apply an offline RL algorithm to these transitions. While these methods can achieve good results on many tasks, they can be complex, often requiring regularization and temporal difference updates. In this paper, we propose a method for offline, example-based control that learns an implicit model of multi-step transitions, rather than a reward function. We show that this implicit model can represent the Q-values for the example-based control problem. Across a range of state-based and image-based offline control tasks, our method outperforms baselines that use learned reward functions; additional experiments demonstrate improved robustness and scaling with dataset size.