Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymer-coated hexagonal upconverting nanoparticles: chemical stability and cytotoxicity

Large (120 nm) hexagonal NaYF4:Yb, Er nanoparticles (UCNPs) were synthesized by high-temperature coprecipitation method and coated with poly(ethylene glycol)-alendronate (PEG-Ale), poly (N,N-dimethylacrylamide-co-2-aminoethylacrylamide)-alendronate (PDMA-Ale) or poly(methyl vinyl ether-co-maleic acid) (PMVEMA). The colloidal stability of polymer-coated UCNPs in water, PBS and DMEM medium was investigated by dynamic light scattering; UCNP@PMVEMA particles showed the best stability in PBS. Dissolution of the particles in water, PBS, DMEM and artificial lysosomal fluid (ALF) determined by potentiometric measurements showed that all particles were relatively chemically stable in DMEM. The UCNP@Ale-PEG and UCNP@Ale-PDMA particles were the least soluble in water and ALF, while the UCNP@PMVEMA particles were the most chemically stable in PBS. Green fluorescence of FITC-Ale-modified UCNPs was observed inside the cells, demonstrating successful internalization of particles into cells. The highest uptake was observed for neat UCNPs, followed by UCNP@Ale-PDMA and UCNP@PMVEMA. Viability of C6 cells and rat mesenchymal stem cells (rMSCs) growing in the presence of UCNPs was monitored by Alamar Blue assay. Culturing with UCNPs for 24 h did not affect cell viability. Prolonged incubation with particles for 72 h reduced cell viability to 40%–85% depending on the type of coating and nanoparticle concentration. The greatest decrease in cell viability was observed in cells cultured with neat UCNPs and UCNP@PMVEMA particles. Thanks to high upconversion luminescence, high cellular uptake and low toxicity, PDMA-coated hexagonal UCNPs may find future applications in cancer therapy.
Novel loss of function mutation in TUBA1A gene compromises tubulin stability and proteostasis causing spastic paraplegia and ataxia

Microtubules are dynamic cytoskeletal structures involved in several cellular functions, such as intracellular trafficking, cell division and motility. More than other cell types, neurons rely on the proper functioning of microtubules to conduct their activities and achieve complex morphologies. Pathogenic variants in genes encoding for α and β-tubulins, the structural subunits of microtubules, give rise to a wide class of neurological disorders collectively known as “tubulinopathies” and mainly involving a wide and overlapping range of brain malformations resulting from defective neuronal proliferation, migration, differentiation and axon guidance. Although tubulin mutations have been classically linked to neurodevelopmental defects, growing evidence demonstrates that perturbations of tubulin functions and activities may also drive neurodegeneration. In this study, we causally link the previously unreported missense mutation p.I384N in TUBA1A, one of the neuron-specific α-tubulin isotype I, to a neurodegenerative disorder characterized by progressive spastic paraplegia and ataxia. We demonstrate that, in contrast to the p.R402H substitution, which is one of the most recurrent TUBA1A pathogenic variants associated to lissencephaly, the present mutation impairs TUBA1A stability, reducing the abundance of TUBA1A available in the cell and preventing its incorporation into microtubules. We also show that the isoleucine at position 384 is an amino acid residue, which is critical for α-tubulin stability, since the introduction of the p.I384N substitution in three different tubulin paralogs reduces their protein level and assembly into microtubules, increasing their propensity to aggregation. Moreover, we demonstrate that the inhibition of the proteasome degradative systems increases the protein levels of TUBA1A mutant, promoting the formation of tubulin aggregates that, as their size increases, coalesce into inclusions that precipitate within the insoluble cellular fraction. Overall, our data describe a novel pathogenic effect of p.I384N mutation that differs from the previously described substitutions in TUBA1A, and expand both phenotypic and mutational spectrum related to this gene.
Linear Mixed Effects Models with Measurement Error: Bayesian Approach

Measurement error usually occurs in practice whenever we can not exactly observe the variables in a model. It has been long recognized that measurement error will bias the estimates if we do not correct it. Thus, it is significant for us to take into account measurement error in our analysis in order to obtain valuable results. The Bayesian method is one of approaches for correcting measurement error in covariates in both linear models and linear mixed effects models. Bayesian approach became feasible and straightforward for many problems due to the availability of modern computers and computational tools such as the Markov chain Monte Carlo (MCMC) methods and WinBUGS. In this paper, the first goal is to assess the effects of measurement error on naive analysis which ignore it in both linear models and linear mixed effects models. Then we focus on correcting measurement error through utilizing regression calibration methods and Bayesian methods, and comparing their performance in different situations. Estimating the regression coefficients using regression calibration methods and Bayesian methods in a linear mixed effects model with measurement error in time-varying covariates is mainly considered. We illustrate with real data analysis investigating the relationship between true dietary intake of beta-carotene and serum beta-carotene, and analyze the estimation results of naive methods, regression calibration methods and Bayesian methods.
Sex-specific associations between daytime sleepiness, chronic diseases and mortality in obstructive sleep apnea

Objective Excessive daytime sleepiness (EDS) is common in obstructive sleep apnea (OSA) and has been linked to adverse outcomes, albeit inconsistently. Furthermore, whether the prognostic impact of EDS differs as a function of sex is unclear. We aimed to assess the associations between EDS and chronic diseases and mortality in men and women with OSA. Methods Newly-diagnosed adult OSA patients who underwent sleep evaluation at Mayo Clinic between November 2009 and April 2017 and completed the Epworth Sleepiness Scale (ESS) for assessment of perceived sleepiness (N = 14,823) were included. Multivariable-adjusted regression models were used to investigate the relationships between sleepiness, with ESS modeled as a binary (ESS > 10) and as a continuous variable, and chronic diseases and all-cause mortality. Results In cross-sectional analysis, ESS > 10 was independently associated with lower risk of hypertension in male OSA patients (odds ratio [OR], 95% confidence interval [CI]: 0.76, 0.69–0.83) and with higher risk of diabetes mellitus in both OSA men (OR, 1.17, 95% CI 1.05–1.31) and women (OR 1.26, 95% CI 1.10–1.45). Sex-specific curvilinear relations between ESS score and depression and cancer were noted. After a median 6.2 (4.5–8.1) years of follow-up, the hazard ratio for all-cause death in OSA women with ESS > 10 compared to those with ESS ≤ 10 was 1.24 (95% CI 1.05–1.47), after adjusting for demographics, sleep characteristics and comorbidities at baseline. In men, sleepiness was not associated with mortality. Conclusion The implications of EDS for morbidity and mortality risk in OSA are sex-dependent, with hypersomnolence being independently associated with greater vulnerability to premature death only in female patients. Efforts to mitigate mortality risk and restore daytime vigilance in women with OSA should be prioritized.
Peroxisome proliferator-activated receptors, farnesoid X receptor, and dual modulating drugs in hypertension

Hypertension characterized by an elevated blood pressure is a cardiovascular disease that afflicts greater than one in every three adults worldwide. Nuclear receptors are large superfamily of DNA-binding transcription factors that target genes to regulate metabolic and cardiovascular function. Drugs have been developed for nuclear receptors such as peroxisome proliferator-activated receptors (PPARα and PPARγ) and farnesoid X receptor (FXR). PPARα, PPARγ, and FXR agonists are used clinically to treat lipid disorders and metabolic diseases. Evidence from clinical studies and animal hypertension models have demonstrated that PPARα, PPARγ, and FXR agonism can lower blood pressure and decrease end organ damage which could be useful for the treatment of hypertension in patients with metabolic diseases. Unfortunately, PPAR and FXR agonists have unwanted clinical side effects. There have been recent developments to limit side effects for PPAR and FXR agonists. Combining PPAR and FXR agonism with soluble epoxide hydrolase (sEH) inhibition or Takeda G protein receptor 5 (TGR5) agonism has been demonstrated in preclinical studies to have actions that would decrease clinical side effects. In addition, these dual modulating drugs have been demonstrated in preclinical studies to have blood pressure lowering, anti-fibrotic, and anti-inflammatory actions. There is now an opportunity to thoroughly test these novel dual modulators in animal models of hypertension associated with metabolic diseases. In particular, these newly developed dual modulating PPAR and FXR drugs could be beneficial for the treatment of metabolic diseases, organ fibrosis, and hypertension.
A two-branch trade-off neural network for balanced scoring sleep stages on multiple cohorts

Introduction Automatic sleep staging is a classification process with severe class imbalance and suffers from instability of scoring stage N1. Decreased accuracy in classifying stage N1 significantly impacts the staging of individuals with sleep disorders. We aim to achieve automatic sleep staging with expert-level performance in both N1 stage and overall scoring. Methods A neural network model combines an attention-based convolutional neural network and a classifier with two branches is developed. A transitive training strategy is employed to balance universal feature learning and contextual referencing. Parameter optimization and benchmark comparisons are conducted using a large-scale dataset, followed by evaluation on seven datasets in five cohorts. Results The proposed model achieves an accuracy of 88.16%, Cohen’s kappa of 0.836, and MF1 score of 0.818 on the SHHS1 test set, also with comparable performance to human scorers in scoring stage N1. Incorporating multiple cohort data improves its performance. Notably, the model maintains high performance when applied to unseen datasets and patients with neurological or psychiatric disorders. Discussion The proposed algorithm demonstrates strong performance and generalizablility, and its direct transferability is noteworthy among similar studies on automated sleep staging. It is publicly available, which is conducive to expanding access to sleep-related analysis, especially those associated with neurological or psychiatric disorders.
TMEM123 a key player in immune surveillance of colorectal cancer

Colorectal cancer (CRC) is a leading cause of cancer-associated death. In the tumor site, the interplay between effector immune cells and cancer cells determines the balance between tumor elimination or outgrowth. We discovered that the protein TMEM123 is over-expressed in tumour-infiltrating CD4 and CD8 T lymphocytes and it contributes to their effector phenotype. The presence of infiltrating TMEM123+ CD8+ T cells is associated with better overall and metastasis-free survival. TMEM123 localizes in the protrusions of infiltrating T cells, it contributes to lymphocyte migration and cytoskeleton organization. TMEM123 silencing modulates the underlying signaling pathways dependent on the cytoskeletal regulator WASP and the Arp2/3 actin nucleation complex, which are required for synaptic force exertion. Using tumoroid-lymphocyte co-culture assays, we found that lymphocytes form clusters through TMEM123, anchoring to cancer cells and contributing to their killing. We propose an active role for TMEM123 in the anti-cancer activity of T cells within tumour microenvironment.
Large and in charge: cortisol levels vary with sex, diet, and body mass in an Antarctic predator, the leopard seal

Evaluating physiological responses in the context of a species’ life history, demographics, and ecology is essential to understanding the health of individuals and populations. Here, we measured the main mammalian glucocorticoid, cortisol, in an elusive Antarctic apex predator, the leopard seal (Hydrurga leptonyx). We also examined intraspecific variation in cortisol based on life history (sex), morphometrics (body mass, body condition), and ecological traits (δ 15N, δ 13C). To do this, blood samples, life history traits, and morphometric data were collected from 19 individual leopard seals off the Western Antarctic Peninsula. We found that adult leopard seals have remarkably high cortisol concentrations (100.35 ± 16.72 μg/dL), showing the highest circulating cortisol concentration ever reported for a pinniped: 147 μg/dL in an adult male. Leopard seal cortisol concentrations varied with sex, body mass, and diet. Large adult females had significantly lower cortisol (94.49 ± 10.12 μg/dL) than adult males (120.85 ± 6.20 μg/dL). Similarly, leopard seals with higher isotope values (i.e., adult females, δ15N: 11.35 ± 0.69‰) had lower cortisol concentrations than seals with lower isotope values (i.e., adult males, δ15N: 10.14 ± 1.65‰). Furthermore, we compared cortisol concentrations across 26 closely related Arctoid taxa (i.e., mustelids, bears, and pinnipeds) with comparable data. Leopard seals had the highest mean cortisol concentrations that were 1.25 to 50 times higher than other Arctoids. More broadly, Antarctic ice seals (Lobodontini: leopard seal, Ross seal, Weddell seal, crabeater seal) had higher cortisol concentrations compared to other pinnipeds and Arctoid species. Therefore, high cortisol is a characteristic of all lobodontines and may be a specialized adaptation within this Antarctic-dwelling clade. Together, our results highlight exceptionally high cortisol concentrations in leopard seals (and across lobodontines) and reveal high variability in cortisol concentrations among individuals from a single location. This information provides the context for understanding how leopard seal physiology changes with life history, ecology, and morphology and sets the foundation for assessing their physiology in the context of a rapidly changing Antarctic environment.
Fast Compression of Deterministic Finite Automata

We revisit the popular \emph{delayed deterministic finite automaton} (\ddfa{}) compression algorithm introduced by Kumar~et~al.~[SIGCOMM 2006] for compressing deterministic finite automata (DFAs) used in intrusion detection systems. This compression scheme exploits similarities in the outgoing sets of transitions among states to achieve strong compression while maintaining high throughput for matching. The \ddfa{} algorithm and later variants of it, unfortunately, require at least quadratic compression time since they compare all pairs of states to compute an optimal compression. This is too slow and, in some cases, even infeasible for collections of regular expression in modern intrusion detection systems that produce DFAs of millions of states. Our main result is a simple, general framework for constructing \ddfa{} based on locality-sensitive hashing that constructs an approximation of the optimal \ddfa{} in near-linear time. We apply our approach to the original \ddfa{} compression algorithm and two important variants, and we experimentally evaluate our algorithms on DFAs from widely used modern intrusion detection systems. Overall, our new algorithms compress up to an order of magnitude faster than existing solutions with either no or little loss of compression size. Consequently, our algorithms are significantly more scalable and can handle larger collections of regular expressions than previous solutions.
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning

Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users’ preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-the-art baselines (for example, 3.4% improvement in prediction and 15.8% improvement in explanation for TripAdvisor).
Convergence of Dirichlet Forms for MCMC Optimal Scaling with Dependent Target Distributions on Large Graphs

Markov chain Monte Carlo (MCMC) algorithms have played a significant role in statistics, physics, machine learning and others, and they are the only known general and efficient approach for some high-dimensional problems. The random walk Metropolis (RWM) algorithm as the most classical MCMC algorithm, has had a great influence on the development and practice of science and engineering. The behavior of the RWM algorithm in high-dimensional problems is typically investigated through a weak convergence result of diffusion processes. In this paper, we utilize the Mosco convergence of Dirichlet forms in analyzing the RWM algorithm on large graphs, whose target distribution is the Gibbs measure that includes any probability measure satisfying a Markov property. The abstract and powerful theory of Dirichlet forms allows us to work directly and naturally on the infinite-dimensional space, and our notion of Mosco convergence allows Dirichlet forms associated with the RWM chains to lie on changing Hilbert spaces. Through the optimal scaling problem, we demonstrate the impressive strengths of the Dirichlet form approach over the standard diffusion approach.
A Taxonomy of Snow Crystal Growth Behaviors: 2. Quantifying the Nakaya Diagram

This paper presents a matrix of 206 snow crystal growth observations as a function of temperature and water vapor supersaturation in air, each illustrating the morphology and size of a crystal forming on the tip of an isolated c-axis ice needle after a known growth time. Because each complex structure emerged from a simple, well-defined seed crystal under known environmental conditions, this data set is well suited for making comparisons with three-dimensional computational models. These observations thus provide a needed extension of the well-known Nakaya diagram, as they allow a quantitative evaluation of model predictions over a broad range of growth conditions. I also briefly discuss computational methods along with an initial model of the most relevant microphysical processes governing snow crystal growth. My overarching goal with this new data set is to facilitate the development of quantitative computational growth models that can eventually reproduce the remarkable diversity of morphological structures seen in snow crystal formation.
Production of fully-heavy tetraquark states through the double parton scattering mechanism in $pp$ and $pA$ collisions

The production of fully-heavy tetraquark states in proton-proton ($pp$) and proton-nucleus ($pA$) collisions at the center-of-mass energies of the Large Hadron Collider (LHC) and at the Future Circular Collider (FCC) is investigated considering that these states are produced through the double parton scattering mechanism. We estimate the cross sections for the $T_{4c}$, $T_{4b}$ and $T_{2b2c}$ states and present predictions for $pp$, $pCa$ and $pPb$ collisions considering the rapidity ranges covered by central and forward detectors. We demonstrate that the cross sections for $pA$ collisions are enhanced in comparison to the $pp$ predictions scaled by the atomic number. Moreover, our results indicate that a search of these exotic states is, in principle, feasible in the future runs of the LHC and FCC.
Trajectory design for a very-low-thrust lunar mission.

The University of Stuttgart is conducting a research program to build a succession of small satellites. The ultimate goal of this program is to build and launch a craft named Lunar Mission BW-1 (after the federal state that Stuttgart is situated in, Baden-Wurttemberg) into lunar orbit, for eventual impact with the Moon. As with the majority of space missions, launch cost is a severely limiting factor so it is necessary to carefully plan the trajectory before launch, to ensure lunar capture and minimise the amount of fuel needed by the spacecraft. This thesis outlines work conducted to find a robust fuel-optimal trajectory for Lunar Mission BW-1 to reach the Moon. Several unique aspects of this craft require a novel approach to that optimisation. Firstly, the spacecraft uses a new low-cost propulsion system, severely limiting maneouvrability and accessibility of transfer trajectories. Secondly, to reduce the mass and complexity of moving parts, the solar panels are fixed to the body; consequently, the craft must rotate itself to point its solar panels towards the Sun to recharge. No thrusting can occur during this time. This magnifies the effect of the third design decision, which is to restrict the dry mass of the craft by giving it very little on-board power storage. After approximately an hour of accelerating it is expected to need to coast for several hours to recharge its batteries, resulting in a relatively high frequency stop-go-stop thrust profile. Due to these constraints, the trajectory optimisation is one of the most complex ever attempted. Since the craft will be built and launched, many simplifications made in purely theoretical studies could not be utilised, such as neglecting the weaker forces acting on the spacecraft in cis-lunar space.
Hierarchical Neural Simulation-Based Inference Over Event Ensembles

When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where"local"parameters impact individual events and"global"parameters influence the entire dataset. We introduce practical approaches for frequentist and Bayesian dataset-wide probabilistic inference in cases where the likelihood is intractable, but simulations can be realized via a hierarchical forward model. We construct neural estimators for the likelihood(-ratio) or posterior and show that explicitly accounting for the model's hierarchical structure can lead to significantly tighter parameter constraints. We ground our discussion using case studies from the physical sciences, focusing on examples from particle physics and cosmology.
Glued massive particles surfaces

A novel generalization of photon surfaces to the case of massive charged particles is given for spacetimes with at least one isometry, including stationary ones. A related notion of glued massive particle surfaces is also defined. These surfaces join worldlines parametrized by a family of independent conserved quantities and naturally arise in integrable spacetimes. We describe the basic geometric properties of such surfaces and their relationship to slice-reducible Killing tensors, illustrating all concepts with a number of examples. Massive particle surfaces have potential applications in the context of uniqueness theorems, Penrose inequalities, integrability, and the description of black-hole shadows in streams of massive charged particles or photons in a medium with an effective mass and charge.