Miracle-dz/UniFAR-FTUs huggingface dataset:
Lovastatin decreases coenzyme Q levels in humans.

Lovastatin is clinically used to treat patients with hypercholesterolemia and successfully lowers cholesterol levels. The mechanism of action of lovastatin is inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A reductase, an enzyme involved in the biosynthesis of cholesterol from acetyl-CoA. Inhibition of this enzyme could also inhibit the intrinsic biosynthesis of coenzyme Q10 (CoQ10), but there have not been definitive data on whether lovastatin reduces levels of CoQ10 as it does cholesterol. The clinical use of lovastatin is to reduce a risk of cardiac disease, and if lovastatin were to reduce levels of CoQ10, this reduction would constitute a new risk of cardiac disease, since it is established that CoQ10 is indispensable for cardiac function. We have conducted three related protocols to determine whether lovastatin does indeed inhibit the biosynthesis of CoQ10. One protocol was done on rats, and is reported in the preceding paper [Willis, R. A. , Folkers, K. , Tucker, J. L. , Ye, C. -Q. , Xia, L. -J. & Tamagawa, H. (1990) Proc. Natl. Acad. Sci. USA 87, 8928-8930]. The other two protocols are reported here. One involved patients in a hospital, and the other involved a volunteer who permitted extraordinary monitoring of CoQ10 and cholesterol levels and cardiac function. All data from the three protocols revealed that lovastatin does indeed lower levels of CoQ10. The five hospitalized patients, 43-72 years old, revealed increased cardiac disease from lovastatin, which was life-threatening for patients having class IV cardiomyopathy before lovastatin or after taking lovastatin. Oral administration of CoQ10 increased blood levels of CoQ10 and was generally accompanied by an improvement in cardiac function. Although a successful drug, lovastatin does have side effects, particularly including liver dysfunction, which presumably can be caused by the lovastatin-induced deficiency of CoQ10.
Repression of α-Fetoprotein Gene Expression under Hypoxic Conditions in Human Hepatoma Cells: Characterization of a Negative Hypoxia Response Element That Mediates Opposite Effects of Hypoxia Inducible Factor-1 and c-Myc

Hypoxia is an important component of many pathological processes including cancerogenesis and cirrhosis. We have attempted to identify additional hepatic genes sensitive to hypoxia by postulating that genes with possible binding sites for hypoxia inducible factor-1 (HIF-1) are regulated by hypoxia. A computer analysis identified the oncodevelopmental α-fetoprotein gene ( afp ) as one of them. The amounts of both α-fetoprotein mRNA and protein were decreased under hypoxic conditions in HepG2 hepatoma cells. Stability of afp mRNA was not altered, and de novo synthesis of proteins was required. Transfection experiments in HepG2 cells showed that both hypoxia and overproduction of HIF-1α specifically repressed the transcriptional activity of the rat afp regulatory region through the sequence 5′-CACGTGGG-3′ located at −3625 to −3619. Mutation in this sequence strongly impaired these repressions. Interestingly, this sequence was a functional stimulatory target for c-Myc, suggesting that c-Myc regulates afp gene expression. Lastly, the amounts of c- myc mRNA and protein were reduced when these cells were grown under hypoxic conditions. Taken together, these results suggest the existence of a possible competition between HIF-1 and c-Myc that could modulate the transcriptional activity of the afp gene in response to hypoxia.
Review of Marijuana Use in the Adolescent Population and Implications of its Legalization in the United States

With the changing attitudes towards the legality of marijuana in the United States (US), there has been an abundance of rhetoric surrounding the potential societal effects of decriminalized cannabis use. These statements vary in degree from visions of the downfall of our moral and productive civilization, to the elimination of drug crime and a windfall of economic growth. From either side of this divisive issue, there has been a strongly expressed concern of the unknown consequences of a more readily available cannabis supply to the adolescent population, followed by what measures should be taken to curtail this risk. Given this country's unique relationship with mindaltering substances, a great deal of comparisons have been made with alcohol, as it is also a substance of recreation as well as abuse that is currently legal but has also gone through a period of prohibition. This paper looks to examine the validity of this comparison, as well as reviews the current research on the effects of early cannabis use on the adolescent brain.
THE INFLUENCE OF CORTISONE ON EXPERIMENTAL VIRAL INFECTION

The administration of cortisone to chicken embryos infected with influenza B virus results in (a) an initial inhibition of viral synthesis and (b) an eventual increase in the final yield of virus attained. Increased yields of virus are attained regardless of the number of viral particles in the infecting inoculum or the proportion of particles which are infective. Changes in the distribution ratios of allantoic fluid and intramembrane virus are effected by cortisone only as the secondary result of reduction in viral synthesis. The manifest effect of cortisone on influenza A virus increase is inhibitory unless inocula containing relatively high proportions of inactive viral particles are used.
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to construct PAC confidence sets on ResNet for ImageNet, and on a dynamics model the half-cheetah reinforcement learning problem.
Decomposition of Lagrangian classes on K3 surfaces

We study the decomposability of a Lagrangian homology class on a K3 surface into a sum of classes represented by special Lagrangian submanifolds, and develop criteria for it in terms of lattice theory. As a result, we prove the decomposability on an arbitrary K3 surface with respect to the K\"ahler classes in dense subsets of the K\"ahler cone. Using the same technique, we show that the K\"ahler classes on a K3 surface which admit a special Lagrangian fibration form a dense subset also. This implies that there are infinitely many special Lagrangian 3-tori in any log Calabi-Yau 3-fold.
Sparsity-Driven Moving Target Detection in Distributed Multistatic FMCW Radars

We investigate the problem of sparse target detection from widely distributed multistatic Frequency Modulated Continuous Wave (FMCW) radar systems (using chirp modulation). Unlike previous strategies (e.g., developed for FMCW or distributed multistatic radars), we propose a generic framework that scales well in terms of computational complexity for high-resolution space-velocity grid. Our approach assumes that (i) the target signal is sparse in a discrete space-velocity domain, hence allowing for non-static target detection, and (ii) the resulting multiple baseband radar signals share a common support. By simplifying the representation of the FMCW radar signals, we propose a versatile scheme balancing complexity and detection accuracy. In particular, we design a low-complexity, factorized alternative for the Matching Pursuit algorithm leveraging this simplified model, as well as an iterative methodology to compensate for the errors caused by the model simplifications. Extensive Monte-Carlo simulations of a K-band radar system show that our method achieves a fast estimation of moving target's parameters on dense grids, with controllable accuracy, and reaching state-of-the-art performances compared to previous sparsity-driven approaches.
Brain-to-brain Wireless Communication and Technologies Beyond 5G

During the last few years, intensive research efforts are being done in the field of brain interfaces to extract neuro-information from the signals representing neuronal activities in the human brain. A recent development of these interfaces is capable of direct communication between animals' brains, enabling direct brain-to-brain communication. Although these results are new and the experimental scenario simple, the fast development in neuroscience, and information and communication technologies indicate the potential of new scenarios for wireless communications between brains. Depending of the specific kind of neuro-activity to be communicated, the brain-to-brain link shall follow strict requirements of high data rates, low-latency, and reliable communication. In this paper we highlight key beyond 5G technologies that potentially will support this promising approach.
Numerical analysis of a parabolic variational inequality system modeling biofilm growth at the porescale

In this article, we consider a system of two coupled nonlinear diffusion–reaction partial differential equations (PDEs) which model the growth of biofilm and consumption of the nutrient. At the scale of interest the biofilm density is subject to a pointwise constraint, thus the biofilm PDE is framed as a parabolic variational inequality. We derive rigorous error estimates for a finite element approximation to the coupled nonlinear system and confirm experimentally that the numerical approximation converges at the predicted rate. We also show simulations in which we track the free boundary in the domains which resemble the pore scale geometry and in which we test the different modeling assumptions.
The Algebraic Theory of Fractional Jumps

In this paper we start by briefly surveying the theory of Fractional Jumps and transitive projective maps. Then, we give an efficient construction of a fractional jump of a projective map and we extend the compound generator construction for the Inversive Congruential Generator to Fractional jump sequences. In addition, we provide new results on the absolute jump index, on projectively primitive polynomials, and on the explicit description of fractional jump generators.
Connecting Optical Morphology, Environment, and H i Mass Fraction for Low-redshift Galaxies Using Deep Learning

A galaxy’s morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to learn a galaxy’s optical morphological information in order to estimate its neutral atomic hydrogen (H i) content directly from Sloan Digital Sky Survey (SDSS) gri image cutouts. We are able to accurately predict a galaxy’s logarithmic H i mass fraction, , by training a CNN on galaxies in the Arecibo Legacy Fast ALFA Survey (ALFALFA) 40% sample. Using pattern recognition, we remove galaxies with unreliable estimates. We test CNN predictions on the ALFALFA 100%, extended Galaxy Evolution Explorer Arecibo SDSS Survey, and Nançay Interstellar Baryons Legacy Extragalactic Survey catalogs, and find that the CNN consistently outperforms previous estimators. The H i–morphology connection learned by the CNN appears to be constant in low- to intermediate-density galaxy environments, but it breaks down in the highest-density environments. We also use a visualization algorithm, Gradient-weighted Class Activation Maps, to determine which morphological features are associated with low or high gas content. These results demonstrate that CNNs are powerful tools for understanding the connections between optical morphology and other properties, as well as for probing other variables, in a quantitative and interpretable manner.
Out of equilibrium higher-order topological insulator: Floquet engineering and quench dynamics

Higher-order topological~(HOT) states,~hosting topologically protected modes on lower-dimensional boundaries,~such as hinges and corners, have recently extended the realm of the static topological phases. ~Here we demonstrate the possibility of realizing a two-dimensional \emph{Floquet} second-order topological insulator, featuring corner-localized zero quasienergy modes and characterized by quantized Floquet qudrupolar moment $Q^{\rm Flq}_{xy}=0.5$, by periodically kicking a quantum spin Hall insulator (QSHI) with a discrete fourfold ($C_4$) and time-reversal (${\mathcal T}$) symmetry breaking Dirac mass perturbation. ~Furthermore, we show that $Q^{\rm Flq}_{xy}$ becomes independent of the choice of origin as the system approaches the thermodynamic limit. ~We also analyze the dynamics of a corner mode after a sudden quench, when the $C_4$ and ${\mathcal T}$ symmetry breaking perturbation is switched off, and find that the corresponding survival probability displays periodic appearances of complete, partial and no revival for long time, encoding the signature of corner modes in a QSHI. ~Our protocol is sufficiently general to explore the territory of dynamical HOT phases in insulators and gapless systems.
Permutations, Moments, Measures

Which combinatorial sequences correspond to moments of probability measures on the real line? We present a generating function, in the form of a continued fraction, for a fourteen-parameter family of such sequences and interpret these in terms of combinatorial statistics on the symmetric groups. Special cases include several classical and noncommutative probability laws, along with a substantial subset of the orthogonalizing measures in the Askey-Wilson scheme, now given a new combinatorial interpretation in terms of elementary permutation statistics. This framework further captures a variety of interesting combinatorial sequences including, notably, the moment sequences associated to distributions of the numbers of occurrences of (classical and vincular) permutation patterns of length three. This connection between pattern avoidance and broader ideas in classical and noncommutative probability is among several intriguing new corollaries, which generalize and unify results previously appearing in the literature, while opening up new lines of inquiry. The fourteen combinatorial statistics further generalize to signed and colored permutations, and, as an infinite family of statistics, to the k-arrangements: permutations with $k$-colored fixed points, introduced here along with several related results and conjectures.
On typical triangulations of a convex $n$-gon

Let $f_n$ be a function assigning weight to each possible triangle whose vertices are chosen from vertices of a convex polygon $P_n$ of $n$ sides. Suppose ${\mathcal T}_n$ is a random triangulation, sampled uniformly out of all possible triangulations of $P_n$. We study the sum of weights of triangles in ${\mathcal T}_n$ and give a general formula for average and variance of this random variable. In addition, we look at several interesting special cases of $f_n$ in which we obtain explicit forms of generating functions for the sum of the weights. For example, among other things, we give new proofs for already known results such as the degree of a fixed vertex and the number of ears in ${\mathcal T}_n,$ as well as, provide new results on the number of "blue" angles and refined information on the distribution of angles at a fixed vertex. We note that our approach is systematic and can be applied to many other new examples while generalizing the existing results.
Prophylactic use of implanted cardiac defibrillators in patients at high risk for ventricular arrhythmias after coronary-artery bypass graft surgery

Background Patients with coronary heart disease, left ventricular dysfunction, and abnormalities on signal-averaged electrocardiograms have an increased risk of sudden death. We evaluated the effect on survival of the prophylactic implantation of cardioverter–defibrillators in such patients at the time of coronary-artery bypass surgery. Methods Over the course of five years, 37 clinical centers screened all patients who were scheduled for elective coronary bypass surgery. Patients were eligible for the trial if they were less than 80 years old, had a left ventricular ejection fraction of less than 0.36, and had abnormalities on signal-averaged electrocardiograms. We identified 1422 eligible patients, enrolled 1055, and randomly assigned 900 to therapy with an implantable cardioverter–defibrillator (446 patients) or to the control group (454 patients). The primary end point of the study was overall mortality, and the two groups were compared in an intention-to-treat analysis. Results The base-line characte.
Visual Evaluation of Generative Adversarial Networks for Time Series Data

A crucial factor to trust Machine Learning (ML) algorithm decisions is a good representation of its application field by the training dataset. This is particularly true when parts of the training data have been artificially generated to overcome common training problems such as lack of data or imbalanced dataset. Over the last few years, Generative Adversarial Networks (GANs) have shown remarkable results in generating realistic data. However, this ML approach lacks an objective function to evaluate the quality of the generated data. Numerous GAN applications focus on generating image data mostly because they can be easily evaluated by a human eye. Less efforts have been made to generate time series data. Assessing their quality is more complicated, particularly for technical data. In this paper, we propose a human-centered approach supporting a ML or domain expert to accomplish this task using Visual Analytics (VA) techniques. The presented approach consists of two views, namely a GAN Iteration View showing similarity metrics between real and generated data over the iterations of the generation process and a Detailed Comparative View equipped with different time series visualizations such as TimeHistograms, to compare the generated data at different iteration steps. Starting from the GAN Iteration View, the user can choose suitable iteration steps for detailed inspection. We evaluate our approach with a usage scenario that enabled an efficient comparison of two different GAN models.
Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling

Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use the Gaussian heat map to locate the vertebrae's centroid. However, the process of obtaining the vertebrae's centroid coordinates using heat maps is non-differentiable, so it is impossible to train the network to label the vertebrae directly. Therefore, for end-to-end differential training of vertebra coordinates on CT scans, a robust and accurate automatic vertebral labeling algorithm is proposed in this study. Firstly, a novel residual-based multi-label classification and localization network is developed, which can capture multi-scale features, but also utilize the residual module and skip connection to fuse the multi-level features. Secondly, to solve the problem that the process of finding coordinates is non-differentiable and the spatial structure is not destructible, integral regression module is used in the localization network. It combines the advantages of heat map representation and direct regression coordinates to achieve end-to-end training, and can be compatible with any key point detection methods of medical image based on heat map. Finally, multi-label classification of vertebrae is carried out, which use bidirectional long short term memory (Bi-LSTM) to enhance the learning of long contextual information to improve the classification performance. The proposed method is evaluated on a challenging dataset and the results are significantly better than the state-of-the-art methods (mean localization error <3mm).
Fast Perception, Planning, and Execution for a Robotic Butler: Wheeled Humanoid M-Hubo

As the aging population grows at a rapid rate, there is an ever growing need for service robot platforms that can provide daily assistance at practical speed with reliable performance. In order to assist with daily tasks such as fetching a beverage, a service robot must be able to perceive its environment and generate corresponding motion trajectories. This becomes a challenging and computationally complex problem when the environment is unknown and thus the path planner must sample numerous trajectories that often are sub-optimal, extending the execution time. To address this issue, we propose a unique strategy of integrating a 3D object detection pipeline with a kinematically optimal manipulation planner to significantly increase speed performance at run-time. In addition, we develop a new robotic butler system for a wheeled humanoid that is capable of fetching requested objects at 24% of the speed a human needs to fulfill the same task. The proposed system was evaluated and demonstrated in a real-world environment setup as well as in public exhibition.
Graph Signal Processing - Part III: Machine Learning on Graphs, from Graph Topology to Applications

Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution. Part III of this monograph starts by addressing ways to learn graph topology, from the case where the physics of the problem already suggest a possible topology, through to most general cases where the graph topology is learned from the data. A particular emphasis is on graph topology definition based on the correlation and precision matrices of the observed data, combined with additional prior knowledge and structural conditions, such as the smoothness or sparsity of graph connections. For learning sparse graphs (with small number of edges), the least absolute shrinkage and selection operator, known as LASSO is employed, along with its graph specific variant, graphical LASSO. For completeness, both variants of LASSO are derived in an intuitive way, and explained. An in-depth elaboration of the graph topology learning paradigm is provided through several examples on physically well defined graphs, such as electric circuits, linear heat transfer, social and computer networks, and spring-mass systems. As many graph neural networks (GNN) and convolutional graph networks (GCN) are emerging, we have also reviewed the main trends in GNNs and GCNs, from the perspective of graph signal filtering. Tensor representation of lattice-structured graphs is next considered, and it is shown that tensors (multidimensional data arrays) are a special class of graph signals, whereby the graph vertices reside on a high-dimensional regular lattice structure. This part of monograph concludes with two emerging applications in financial data processing and underground transportation networks modeling.