Miracle-dz/UniFAR-FTUs huggingface dataset:
Red Yeast Rice for the Improvement of Lipid Profiles in Mild-to-Moderate Hypercholesterolemia: A Narrative Review

Reducing low-density lipoprotein cholesterol (LDL-C) levels is a key target for lowering cardiovascular risk and preventing atherosclerotic cardiovascular disease (ASCVD). Red yeast rice (RYR) is a nutraceutical widely used as a lipid-lowering dietary supplement. The main cholesterol-lowering components of RYR are monacolins, particularly monacolin K, which is structurally identical to lovastatin and targets the same key enzyme of cholesterol biosynthesis. RYR supplementation reduces LDL-C levels by approximately 15–34% versus placebo, with a similar effect to low-dose, first-generation statins in subjects with mild-to-moderate dyslipidemia. RYR has also demonstrated beneficial reductions of up to 45% versus placebo in the risk of ASCVD events in secondary prevention studies. RYR at a dose that provides about 3 mg/d of monacolin K is well tolerated, with an adverse event profile similar to that of low-dose statins. RYR is therefore a treatment option for lowering LDL-C levels and ASCVD risk for people with mild-to-moderate hypercholesterolemia who are ineligible for statin therapy, particularly those who are unable to implement lifestyle modifications, and also for people who are eligible for statin therapy but who are unwilling to take a pharmacologic therapy.
TEMAS EMERGENTES DEL ANÁLISIS DE INTERPRETACIONES DEL PROFESORADO SOBRE LA ARGUMENTACIÓN EN CLASE

In this paper we present a study on recurrent themes that have been identified in dis- courses concerning argumentation in the mathematics classroom written by secondary teachers (12- to 16-year-old students). A discursive approach is adopted to examine the notion of argumentative prac- tice through a tridimensional analysis that takes into account structural, epistemic and communicative issues. To develop themes, common methods of constant comparison are applied from the perspective of the Grounded Theory. The results point to the teachers' attention to reformulations, connectors and structural marks in the identification of argumentative practices. Teachers, however, tend not to mention the epistemic value of arguments in their explanations.
Effects of Long-Term Cold Stress on Growth Performance, Behavior, Physiological Parameters, and Energy Metabolism in Growing Beef Cattle

Simple Summary Winter cold stress is a major environmental factor affecting cattle industry production in northern China. Determining the effects of prolonged cold stress on the relevant biological mechanisms in cattle can help develop effective mitigation measures. Therefore, this study evaluated the effects of long-term cold stress on growth performance, physiological mechanisms, blood biochemical, and stress hormones of Simmental crossbred bulls. Results indicated that the physiological behavior, digestive function, and contents of enzymes and hormones in the body changed in Simmental cattle during winter, which enhanced the body’s heat production to maintain the body temperature constant and ultimately led to impaired growth and development. Abstract This study aimed to evaluate the effects of a long-term cold environment on growth performance, physiological behavior, biochemical blood indexes, and hormone levels in Simmental cattle. Thirty Simmental crossbred bulls (weight = 350 ± 17 kg, 13–14 months old) were selected for two trials at autumn suitable temperatures (A-ST) and winter cold temperatures (W-CT) (15 cattle per season). The results showed that compared with the A-ST group, dry matter intake (p < 0.05) and feed:gain (p < 0.01) of the W-CT group increased, while body weight (p < 0.01) and average daily gain (p < 0.01) significantly decreased. Long-term cold stress also increased lying time (p < 0.01), feeding time (p < 0.05), and pulse rate (p < 0.01) in the W-CT group, while the rumen volatile fatty acids content (p < 0.01) and apparent digestibility of nutrients (p < 0.05) were significantly decreased. In terms of blood indicators, long-term cold stress increased the concentrations of glucose, glucose metabolic enzymes, glucocorticoids, triiodothyronine, and tetraiodothyronine in the plasma of the W-CT group (p < 0.05), but the levels of triglycerides, β-hydroxybutyrate, propionate, insulin, and growth hormone were decreased (p < 0.01). In summary, long-term cold stress may inhibit the digestive function of Simmental cattle and enhance the body’s energy metabolism and stress hormone imbalance, ultimately damaging the normal growth and development of the body.
Personalizing the Pressure Reactivity Index for Neurocritical Care Decision Support

Neurocritical care patients may benefit from personalized care based on their cerebral autoregulatory function. The pressure reactivity index is an important, prevalent metric used to estimate the state of a patient's cerebral autoregulation and guide clinical decision-making. The pressure reactivity index is calculated by first averaging arterial blood pressure and intracranial pressure signals over a time window and then correlating multiple of these averaged samples. In this manuscript, we use simulated and traumatic brain patient data to evaluate the effect of the size of averaging and correlation windows on the pressure reactivity index estimation. We show that the pressure reactivity index is sensitive to averaging and correlation windows, and the type of sensitivity is heterogeneous across patients. After a comprehensive analysis, we recommend an optimal averaging window of 9-10 seconds and a correlation window from 40-55 samples. Due to the heterogeneous sensitivity across patients, we also suggest that the pressure reactivity index can be made patient-specific by averaging over heartbeats rather than seconds. Implementing this novel heartbeat pressure reactivity index methodology decreases bias, uncertainty, and sensitivity in the pressure reactivity index. Clinical decision-making based on the pressure reactivity index may be a helpful step in personalizing medicine in neurocritical care patients and improving patient outcomes. Here, we suggest two ways to improve the robustness of the pressure reactivity index, allowing it to be more clinically useful and applicable to other populations, such as pediatrics.
Reassessing thermodynamic advantage from indefinite causal order

Indefinite causal order is a key feature involved in the study of quantum higher order transformations. Recently, intense research has been focused on possible advantages related to the lack of definite causal order of quantum processes. Quite often the quantum switch is claimed to provide advantages in information-theoretic and thermodynamic tasks. We address here the question whether indefinite causal order is a resource for quantum thermodynamics. Inspired by previous results in the literature, we show that indefinite causal order is not necessary for the reported increase in free energy and ergotropy. More specifically, we show that a simple causally ordered process, which replaces the system's state with a new one before the final measurement, outperforms the quantum switch in all thermodynamic tasks considered so far. We further show that a similar advantage can be also achieved without completely discarding system, if we allow for non-Markovian interactions between the system and an environment. We extend the analysis to more extreme examples of indefinite causal order, showing that they do not provide an advantage either. Finally, we discuss a possible way to study the advantages that may arise from indefinite causal order in a general scenario.
Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first present MobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we propose SemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method $\texttt {SemMixed}$ to harden the ESC against SemAdv attacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., our MobileSC can even be more robust than classical block-wise communication systems in the low SNR regime.
Proactive Content Caching Scheme in Urban Vehicular Networks

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.
Attention U-net approach in predicting Intensity Modulated Radiation Therapy dose distribution in brain glioma tumor

Today, intensity-modulated radiation therapy (IMRT) is one of the methods used to treat brain tumors. In conventional treatment planning methods, after identifying planning target volume (PTV), and organs at risk (OARs), and determining the limitations for them to receive radiation, the dose distribution is performed based on optimization algorithms, which is usually a time-consuming method. In this article, artificial intelligence is used to acquire the knowledge used in the treatment planning of past patients and to plan for new patients to speed up the process of treatment planning and determination of the appropriate dose distribution. In this paper, using deep learning algorithms, two different approaches are studied to predict dose distribution and compared with actual dose distributions. In the first method, only the images containing PTV and the distribution of the corresponding doses are used to train the convolutional neural network, but in the second one, in addition to PTV, the contours of four OARs are also used to introduce the network. The results show that the performance of both methods on test patients have high accuracy and in comparison with each other almost have the same results and high speed to design the dose distribution. Because the Only-PTV method does not have the process of OARs identifying, applying it in designing the dose distribution will be much faster than using the PTV-OARs method in the whole of treatment planning.
Applications of information geometry to spiking neural network behavior

The space of possible behaviors complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by a local brain circuit is combinatorially large, though the brain may not make use of all of them. Understanding which of these possible patterns are actually used by the brain, and how those sets of patterns change as properties of neural circuitry change is a major goal in neuroscience. Recently, tools from information geometry have been used to study embeddings of probabilistic models onto a hierarchy of model manifolds that encode how model behaviors change as a function of their parameters, giving a quantitative notion of"distances"between model behaviors. We apply this method to a network model of excitatory and inhibitory neural populations to understand how the competition between membrane and synaptic response timescales shapes the network's information geometry. The hyperbolic embedding allows us to identify the statistical parameters to which the model behavior is most sensitive, and demonstrate how the ranking of these coordinates changes with the balance of excitation and inhibition in the network.
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation

The Schr\"odinger bridge problem (SBP) is gaining increasing attention in generative modeling and showing promising potential even in comparison with the score-based generative models (SGMs). SBP can be interpreted as an entropy-regularized optimal transport problem, which conducts projections onto every other marginal alternatingly. However, in practice, only approximated projections are accessible and their convergence is not well understood. To fill this gap, we present a first convergence analysis of the Schr\"odinger bridge algorithm based on approximated projections. As for its practical applications, we apply SBP to probabilistic time series imputation by generating missing values conditioned on observed data. We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
NevIR: Negation in Neural Information Retrieval

Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking. We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance.
Dielectric elastomer actuator biased by magnetorheological elastomer with permanent magnet

Dielectric elastomer actuators have become one of the most important smart material transducers in recent times. One of the crucial aspects in this field is the application of bias to find the best operating conditions. The basic task is to find the proper bias configuration to obtain a wide range of displacements in the actuator. In the literature, various biases, such as mechanical springs, permanent magnets (PMs), or pneumatic springs, are studied. In our work, the magnetorheological elastomer (MRE) is applied to build a novel bias that ensures a wide range of displacement. Because of the softness and the compliant chemical structure, the MRE can be easily integrated with the dielectric elastomer actuator. The MRE as a bias for a dielectric elastomer actuator is verified in the series of experiments. Finally, the discussion on the advantages and disadvantages of the new bias type is performed.
Spanning trees of $K_{1,4}$-free graphs whose reducible stems have few leaves

Let $T$ be a tree, a vertex of degree one is a \emph{leaf} of $T$ and a vertex of degree at least three is a \emph{branch vertex} of $T$. The {\it reducible stem } of $T$ is the smallest subtree that contains all branch vertices of $T$. In this paper, we give some sharp sufficient conditions for $K_{1,4}$-free graphs to have a spanning tree whose reducible stem having few leaves.
Graph minors and metric spaces

We present problems and results that combine graph-minors and coarse geometry. For example, we ask whether every geodesic metric space (or graph) without a fat $H$ minor is quasi-isometric to a graph with no $H$ minor, for an arbitrary finite graph $H$. We answer this affirmatively for a few small $H$. We also present a metric analogue of Menger's theorem and Konig's ray theorem. We conjecture metric analogues of the Erdos--Posa Theorem and Halin's grid theorem.
Energy estimate up to the boundary for stable solutions to semilinear elliptic problems

We obtain a universal energy estimate up to the boundary for stable solutions of semilinear equations with variable coefficients. Namely, we consider solutions to $- L u = f(u)$, where $L$ is a linear uniformly elliptic operator and $f$ is $C^1$, such that the linearized equation $-L - f'(u)$ has nonnegative principal eigenvalue. Our main result is an estimate for the $L^{2+\gamma}$ norm of the gradient of stable solutions vanishing on the flat part of a half-ball, for any nonnegative and nondecreasing $f$. This bound only requires the elliptic coefficients to be Lipschitz. As a consequence, our estimate continues to hold in general $C^{1,1}$ domains if we further assume the nonlinearity $f$ to be convex. This result is new even for the Laplacian, for which a $C^3$ regularity assumption on the domain was needed.
Analytic aspects of the dilation inequality for symmetric convex sets in Euclidean spaces

We discuss an analytic form of the dilation inequality for symmetric convex sets in Euclidean spaces, which is a counterpart of analytic aspects of Cheeger's isoperimetric inequality. We show that the dilation inequality for symmetric convex sets is equivalent to a certain bound of the relative entropy for symmetric quasi-convex functions, which is close to the logarithmic Sobolev inequality or Cram\'{e}r--Rao inequality. As corollaries, we investigate the reverse Shannon inequality, logarithmic Sobolev inequality, Kahane--Khintchine inequality, deviation inequality and isoperimetry. We also give new probability measures satisfying the dilation inequality for symmetric convex sets via bounded perturbations and tensorization.
Stereographic compactification and affine bi-Lipschitz homeomorphisms

Let $\sigma_q : \mathbb{R}^q \to {\bf S}^q \setminus N_q$ be the inverse of the stereographic projection with centre the north pole $N_q$. Let $W_i$ be a closed subset of $\mathbb{R}^{q_i}$, for $i=1,2$. Let $\Phi:W_1 \to W_2$ be a bi-Lipschitz homeomorphism. The main result states that the homeomorphism $\sigma_{q_2}\circ \Phi \circ \sigma_{q_1}^{-1}$ is a bi-Lipschitz homeomorphism, extending bi-Lipschitz-ly at $N_{q_1}$ with value $N_{q_2}$ whenever $W_1$ is unbounded. As two straightforward applications in the polynomially bounded o-minimal context over the real numbers, we obtain for free a version at infinity of: 1) Sampaio's tangent cone result; 2) Links preserving re-parametrization of definable bi-Lipschitz homeomorphisms of Valette.
A branch cut approach to the probability density and distribution functions of a linear combination of central and non-central Chi-square random variables

The paper considers the distribution of a general linear combination of central and non-central chi-square random variables by exploring the branch cut regions that appear in the standard Laplace inversion process. Due to the original interest from the directional statistics, the focus of this paper is on the density function of such distributions and not on their cumulative distribution function. In fact, our results confirm that the latter is a special case of the former. Our approach provides new insight by generating alternative characterizations of the probability density function in terms of a finite number of feasible univariate integrals. In particular, the central cases seem to allow an interesting representation in terms of the branch cuts, while general degrees of freedom and non-centrality can be easily adopted using recursive differentiation. Numerical results confirm that the proposed approach works well while more transparency and therefore easier control in the accuracy is ensured.
Forward-backward multiplicity and momentum correlations in 
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Correlations and fluctuations between produced particles in an ultra-relativistic nuclear collision remain one of the successor to understand the basics of the particle production mechanism. More differential tools like Forward-Backward (FB) correlations between particles from two different phase-space further strengthened our cognizance. We have studied the strength of FB correlations in terms of charged particle multiplicity and summed transverse momentum for proton-proton ($pp$) and proton-lead ($pPb$) collisions at the centre-of-mass energies $\sqrt{s}$ = 13 TeV and $\sqrt{s_{\rm NN}}$ = 5.02 TeV respectively for the EPOS3 simulated events with hydrodynamical evolution of produced particles. Furthermore, the correlation strengths are separately obtained for the particles coming from the core and the corona. FB correlation strengths are examined as a function of psedorapidity gap ($\eta_{gap}$), psedorapidity window-width ($\delta\eta$), centre-of-mass energy ($\sqrt{s}$), minimum transverse momentum ($p_{Tmin}$) and different multiplicity classes following standard kinematical cuts used by the ALICE and the ATLAS experiments at the LHC for all three EPOS3 event samples. EPOS3 model shows a similar trend of FB multiplicity and momentum correlation strengths for both $pp$ \&$pPb$ systems, though the correlation strengths are found to be larger for $pPb$ system than $pp$ system. Moreover, $\delta\eta$-weighted average of FB correlation strengths as a function of different center-of-mass energies for $pp$ collisions delineates a tendency of saturation at very high energies.