Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating ketone bodies and cardiovascular outcomes: the MESA study.

AIMS Ketone bodies (KB) are an important alternative metabolic fuel source for the myocardium. Experimental and human investigations suggest that KB may have protective effects in patients with heart failure. This study aimed to examine the association between KB and cardiovascular outcomes and mortality in an ethnically diverse population free from cardiovascular disease (CVD). METHODS AND RESULTS This analysis included 6796 participants (mean age 62 ± 10 years, 53% women) from the Multi-Ethnic Study of Atherosclerosis. Total KB was measured by nuclear magnetic resonance spectroscopy. Multivariable-adjusted Cox proportional hazard models were used to examine the association of total KB with cardiovascular outcomes. At a mean follow-up of 13.6 years, after adjusting for traditional CVD risk factors, increasing total KB was associated with a higher rate of hard CVD, defined as a composite of myocardial infarction, resuscitated cardiac arrest, stroke, and cardiovascular death, and all CVD (additionally included adjudicated angina) [hazard ratio, HR (95% confidence interval, CI): 1.54 (1.12-2.12) and 1.37 (1.04-1.80) per 10-fold increase in total KB, respectively]. Participants also experienced an 87% (95% CI: 1.17-2.97) increased rate of CVD mortality and an 81% (1.45-2.23) increased rate of all-cause mortality per 10-fold increase in total KB. Moreover, a higher rate of incident heart failure was observed with increasing total KB [1.68 (1.07-2.65), per 10-fold increase in total KB]. CONCLUSION The study found that elevated endogenous KB in a healthy community-based population is associated with a higher rate of CVD and mortality. Ketone bodies could serve as a potential biomarker for cardiovascular risk assessment.
Anomalous diffusion among two-dimensional scatterers

In this study, we introduce a new model to study the particle diffusion among 2D scatterers. Different from previous models, the potential between the particle and scatterers consists of an attractive interaction as well as a repulsive one. The geometric arrangement of the scatterers has important effects on the diffusion behavior. In the case of periodic scatterers, the low-energy particles may show superdiffusive motion while the high-energy ones diffuse normally. In the case of random scatterers, the global subdiffusive motion may be observed in an energy region slightly above the localization threshold. The subdiffusion phenomenon is explored for the first time in Hamiltonian systems with deterministic scatterers. The mechanism of the observed diffusion behavior is linked to the stickiness effect of chaotic Hamiltonian systems.
Strong laws for local quantile processes

We show that increments of size h n from the uniform quantile and uniform empirical processes in the neighborhood of a fixed point t 0 ∈ (0, 1) may have different rates of almost sure convergence to 0 in the range where h n → 0 and nh n /log n → ∞. In particular, when h n = n -λ with 0 0, the best possible uniform almost sure rate of approximation of the uniform quantile process by a normed Kiefer process is not better than O(n -1/4 (log n) -e ).
Omnidirectional planar dipole array antenna

A planar dipole array antenna having good omnidirectional radiation in the azimuthal plane is presented. The planar dipole array antenna comprises two 1 /spl times/ 2 dipole arrays arranged back to back and separated by a narrow rectangular ground plane, and is easily constructed by printing on both sides of a dielectric substrate. Prototypes of the proposed antenna for WLAN operation in the 2.4 GHz band (2400-2484 MHz) were constructed and studied. Good omnidirectional radiation with gain variations less than 2 dBi in the azimuthal plane has been obtained, and the measured maximum antenna gain for operating frequencies across the 2.4 GHz band is about 5.0-6.1 dBi. Effects of the ground-plane width and the array spacing on the performance of the proposed antenna are also studied.
Magnetic-field re-connexion in a highly conducting incompressible fluid

A theoretical model is proposed for the process of re-connexion of frozen-in magnetic-field lines at an X-type null point in the field. The model involves a diffusion region, immediately adjacent to the null point and an outer wave- dominated region. For the latter an exact solution to the magneto-hydrodynamic equations is obtained; for the former an approximate relationship is derived between the field-re-connexion rate, measured by the Alfvén number of the incident flow, and the magnetic Reynolds number, based on the width of the diffusion region. The maximum field-re-connexion rate is determined entirely by conditions near the null point and may under suitable conditions become large. A condition for maximum conversion of magnetic energy is derived.
Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Abstract Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.
The Role of Gingival Fibroblasts in the Pathogenesis of Periodontitis

Gingival fibroblasts (GFs) are essential components of the periodontium, which are responsible for the maintenance of tissue structure and integrity. However, the physiological role of GFs is not restricted to the production and remodeling of the extracellular matrix. GFs also act as sentinel cells that modulate the immune response to oral pathogens invading the gingival tissue. As an important “nonclassical” component of the innate immune system, GFs respond to bacteria and damage-related signals by producing cytokines, chemokines, and other inflammatory mediators. Although the activation of GFs supports the elimination of invading bacteria and the resolution of inflammation, their uncontrolled or excessive activation may promote inflammation and bone destruction. This occurs in periodontitis, a chronic inflammatory disease of the periodontium initiated and sustained by dysbiosis. In the inflamed gingival tissue, GFs acquire imprinted proinflammatory phenotypes that promote the growth of inflammophilic pathogens, stimulate osteoclastogenesis, and contribute to the chronicity of inflammation. In this review, we discuss the biological functions of GFs in healthy and inflamed gingival tissue, highlighting recent studies that provide insight into their role in the pathogenesis of periodontal diseases. We also draw parallels with the recently discovered fibroblast populations identified in other tissues and their roles in health and disease. This knowledge should be used in future studies to discover more about the role of GFs in periodontal diseases, especially chronic periodontitis, and to identify therapeutic strategies targeting their pathological interactions with oral pathogens and the immune system.
Long-term results of a new posterior chamber phakic intraocular lens in patients with high myopia: 5-year results

To the authors' knowledge, this study is the first to evaluate the long-term outcomes of the Eyecryl posterior chamber pIOL for high myopia. According to 5-year results, it is safe. Purpose: To evaluate the long-term refractive outcomes of Eyecryl posterior chamber spherical phakic intraocular lens (pIOL) implantation in high myopia and endothelial cell density (ECD) change. Setting: Beyoglu Eye Training and Research Hospital, Istanbul, Turkey. Design: Retrospective. Methods: Eyes that were not suitable for corneal refractive surgery, had high myopia between −6.00 diopters (D) and −20.00 D, had Eyecryl posterior chamber spherical pIOL implantation, and had at least 5 years of follow-up were included. Preoperative ECD was ≥2300 cells/mm2 and cylindrical value was ≤2.0 D in all cases. Preoperative and postoperative first, third, and fifth years of refraction, uncorrected and corrected distance visual acuity (UDVA/CDVA), and ECD were recorded. Results: 36 eyes of 18 patients were examined. The mean UDVA and CDVA in postoperative fifth years were 0.24 ± 0.19 logMAR and 0.12 ± 0.18 logMAR, respectively. The safety and efficacy indices were 1.52 ± 0.54 and 1.14 ± 0.38, respectively. At 5 years, the spherical equivalent was ±0.50 D in 75% of eyes and ±1.00 D in 92% of eyes. After 5 years, the mean cumulative ECD loss was 6.91% (P = .07). The annual ECD loss was 1.57% in the first year, 0.26% between 1 year and 3 years, and 2.38% between 3 years and 5 years. Asymptomatic anterior capsule opacity developed in 1 eye 4 years after surgery. Rhegmatogenous retinal detachment developed in 1, and myopic choroidal neovascular membrane occurred in 1 eye. Conclusions: Eyecryl posterior chamber spherical pIOL implantation is one of the effective and safe refractive surgical methods in correcting high myopia with predictable and stable refractive results over a 5-year period. Longer-term studies are needed for complications such as decreased ECD, retinal complications, and lens opacity.
In vitro data for fire pollutants: contribution of studies using human cell models towards firefighters’ occupational

ABSTRACT Firefighters are the principal line of defense against fires, being at elevated risk of exposure to health-relevant pollutants released during fires and burning processes. Although many biomonitoring studies exist, only a limited number of human in vitro investigations in fire risk assessment are currently available. In vitro studies stand out as valuable tools to assess the toxicity mechanisms involved following exposure to fire pollutants at a cellular level. The aim of the present review was to contextualize existing in vitro studies using human cell models exposed to chemicals emitted from fire emissions and wood smoke and discuss the implications of the observed toxic outcomes on adverse health effects detected in firefighters. Most of the reported in vitro investigations focused on monocultures respiratory models and exposure to particulate matter (PM) extracts collected from fire effluents. Overall, (1) a decrease in cellular viability, (2) enhanced oxidative stress, (3) increased pro-inflammatory cytokines levels and (4) elevated cell death frequencies were noted. However, limited information remains regarding the toxicity mechanisms initiated by firefighting activities. Hence, more studies employing advanced in vitro models and exposure systems using human cell lines are urgently needed taking into consideration different routes of exposure and health-related pollutants released from fires. Data are needed to establish and define firefighters’ occupational exposure limits and to propose mitigation strategies to promote beneficial human health.
A new 2-D adaptive filter using affine projection algorithm

This paper proposes a realization of two-dimensional (2-D) adaptive finite impulse response (FIR) filters using the affine projection method. The convergence property of the proposed algorithm is superior to that of the 2-D least mean square (LMS) and normalized LMS (NLMS) algorithms. This algorithm has properties that lie between those of NLMS and recursive least squares (RLS) algorithms. The generalization of the proposed algorithm is also discussed. To illustrate the utility of the proposed technique, this filter is applied to the 2-D system identification.
X-Avatar: Expressive Human Avatars

We present X-Avatar, a novel avatar model that captures the full expressiveness of digital humans to bring about life-like experiences in telepresence, AR/VR and beyond. Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data. To achieve this, we propose a part-aware learned forward skinning module that can be driven by the parameter space of SMPL-X, allowing for expressive animation of X-Avatars. To efficiently learn the neural shape and deformation fields, we propose novel part-aware sampling and initialization strategies. This leads to higher fidelity results, especially for smaller body parts while maintaining efficient training despite increased number of articulated bones. To capture the appearance of the avatar with high-frequency details, we extend the geometry and deformation fields with a texture network that is conditioned on pose, facial expression, geometry and the normals of the deformed surface. We show experimentally that our method outperforms strong baselines both quantitatively and qualitatively on the animation task. To facilitate future research on expressive avatars we contribute a new dataset, called X-Humans, containing 233 sequences of high-quality textured scans from 20 participants, totalling 35,500 data frames. Project page: https://ait.ethz. ch/X-Avatar.
Ramsey Achievement Games on Graphs : Algorithms and Bounds

In 1982, Harary introduced the concept of Ramsey achievement game on graphs. Given a graph $F$ with no isolated vertices. Consider the following game played on the complete graph $K_n$ by two players Alice and Bob. First, Alice colors one of the edges of $K_n$ blue, then Bob colors a different edge red, and so on. The first player who can complete the formation of $F$ in his color is the winner. The minimum $n$ for which Alice has a winning strategy is the achievement number of $F$, denoted by $a(F)$. If we replace $K_n$ in the game by the completed bipartite graph $K_{n,n}$, we get the bipartite achievement number, denoted by $\operatorname{ba}(F)$. In his seminal paper, Harary proposed an open problem of determining bipartite achievement numbers for trees. In this paper, we correct $\operatorname{ba}(mK_2)=m+1$ to $m$ and disprove $\operatorname{ba}(K_{1,m})=2m-2$ from Erickson and Harary, and extend their results on bipartite achievement numbers. We also find the exact values of achievement numbers for matchings, and the exact values or upper and lower bounds of bipartite achievement numbers on matchings, stars, and double stars. Our upper bounds are obtained by deriving efficient winning strategies for Alice.
Transonic dislocation propagation in diamond

The motion of line defects (dislocations) has been studied for more than 60 years, but the maximum speed at which they can move is unresolved. Recent models and atomistic simulations predict the existence of a limiting velocity of dislocation motion between the transonic and subsonic ranges at which the self-energy of dislocation diverges, though they do not deny the possibility of the transonic dislocations. We used femtosecond x-ray radiography to track ultrafast dislocation motion in shock-compressed single-crystal diamond. By visualizing stacking faults extending faster than the slowest sound wave speed of diamond, we show the evidence of partial dislocations at their leading edge moving transonically. Understanding the upper limit of dislocation mobility in crystals is essential to accurately model, predict, and control the mechanical properties of materials under extreme conditions. Description Editor’s summary The speed of sound is often a limit of a sort on how fast something can move through a system. For dislocation motions that play a large part in plastic deformation, the limits are very poorly constrained from experiments. Katagiri et al. used x-ray radiography of shocked single-crystal diamond to track dislocation motion during plastic deformation (see the Perspective by Knudson and Seagle). They found that the dislocation speed can be higher than the bulk sound speed of the material. This observation is important for refining deformation models at extreme conditions because these ultrafast dislocation motions have previously only been predicted with theory. —Brent Grocholski Femtosecond x-ray imaging visualized dislocation propagation speed in diamond, exceeding the sound wave barrier.
Estimating a brain network predictive of stress and genotype with supervised autoencoders

Abstract Targeted brain stimulation has the potential to treat mental illnesses. We develop an approach to help design protocols by identifying relevant multi-region electrical dynamics. Our approach models these dynamics as a superposition of latent networks, where the latent variables predict a relevant outcome. We use supervised autoencoders (SAEs) to improve predictive performance in this context, describe the conditions where SAEs improve predictions, and provide modelling constraints to ensure biological relevance. We experimentally validate our approach by finding a network associated with stress that aligns with a previous stimulation protocol and characterizing a genotype associated with bipolar disorder.
Preference-Aware Delivery Planning for Last-Mile Logistics

Optimizing delivery routes for last-mile logistics service is challenging and has attracted the attention of many researchers. These problems are usually modeled and solved as variants of vehicle routing problems (VRPs) with challenging real-world constraints (e.g., time windows, precedence). However, despite many decades of solid research on solving these VRP instances, we still see significant gaps between optimized routes and the routes that are actually preferred by the practitioners. Most of these gaps are due to the difference between what's being optimized, and what the practitioners actually care about, which is hard to be defined exactly in many instances. In this paper, we propose a novel hierarchical route optimizer with learnable parameters that combines the strength of both the optimization and machine learning approaches. Our hierarchical router first solves a zone-level Traveling Salesman Problem with learnable weights on various zone-level features; with the zone visit sequence fixed, we then solve the stop-level vehicle routing problem as a Shortest Hamiltonian Path problem. The Bayesian optimization approach is then introduced to allow us to adjust the weights to be assigned to different zone features used in solving the zone-level Traveling Salesman Problem. By using a real-world delivery dataset provided by the Amazon Last Mile Routing Research Challenge, we demonstrate the importance of having both the optimization and the machine learning components. We also demonstrate how we can use route-related features to identify instances that we might have difficulty with. This paves ways to further research on how we can tackle these difficult instances.
Magnushammer: A Transformer-based Approach to Premise Selection

This paper presents a novel approach to premise selection, a crucial reasoning task in automated theorem proving. Traditionally, symbolic methods that rely on extensive domain knowledge and engineering effort are applied to this task. In contrast, this work demonstrates that contrastive training with the transformer architecture can achieve higher-quality retrieval of relevant premises, without the engineering overhead. Our method, Magnushammer, outperforms the most advanced and widely used automation tool in interactive theorem proving called Sledgehammer. On the PISA and miniF2F benchmarks Magnushammer achieves $59.5\%$ (against $38.3\%$) and $34.0\%$ (against $20.9\%$) success rates, respectively. By combining \method with a language-model-based automated theorem prover, we further improve the state-of-the-art proof success rate from $57.0\%$ to $71.0\%$ on the PISA benchmark using $4$x fewer parameters. Moreover, we develop and open source a novel dataset for premise selection, containing textual representations of (proof state, relevant premise) pairs. To the best of our knowledge, this is the largest available premise selection dataset, and the first one for the Isabelle proof assistant.
Toward Zero-Shot Sim-to-Real Transfer Learning for Pneumatic Soft Robot 3D Proprioceptive Sensing

Pneumatic soft robots present many advantages in manipulation tasks. Notably, their inherent compliance makes them safe and reliable in unstructured and fragile environments. However, full-body shape sensing for pneumatic soft robots is challenging because of their high degrees of freedom and complex deformation behaviors. Vision-based proprioception sensing methods relying on embedded cameras and deep learning provide a good solution to proprioception sensing by extracting the full-body shape information from the high-dimensional sensing data. But the current training data collection process makes it difficult for many applications. To address this challenge, we propose and demonstrate a robust sim-to-real pipeline that allows the collection of the soft robot's shape information in high-fidelity point cloud representation. The model trained on simulated data was evaluated with real internal camera images. The results show that the model performed with averaged Chamfer distance of 8.85 mm and tip position error of 10.12 mm even with external perturbation for a pneumatic soft robot with a length of 100.0 mm. We also demonstrated the sim-to-real pipeline's potential for exploring different configurations of visual patterns to improve vision-based reconstruction results. The code and dataset are available at https://github.com/DeepSoRo/DeepSoRoSim2Real.
Instability conditions for reaction-diffusion-ODE systems

This paper analyzes the stability of a reactiondiffusion equation coupled with a finite-dimensional controller through Dirichlet boundary input and Neumann boundary output. Going against the flow, we intend to propose numerical certificates of instability for such interconnections. From one side, using spectral methods, an analytical condition based on root locus analysis can determine the instability regions in the parameters space and can sometimes be tested. On the other side, using Lyapunov direct and converse approaches, two sufficient conditions of instability are established in terms of linear matrix inequalities. The novelties lie both in the type of system studied and in the methods used. The numerical results demonstrate the performance of the different criteria set up in this paper and allow us to conjecture that these conditions seem to be necessary and sufficient.