Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Advances in Immunonutrition and Atherosclerosis: A Review

Atherosclerosis is a chronic low-grade inflammatory disease that affects large and medium-sized arteries and is considered to be a major underlying cause of cardiovascular disease (CVD). The high risk of mortality by atherosclerosis has led to the development of new strategies for disease prevention and management, including immunonutrition. Plant-based dietary patterns, functional foods, dietary supplements, and bioactive compounds such as the Mediterranean Diet, berries, polyunsaturated fatty acids, ω-3 and ω-6, vitamins E, A, C, and D, coenzyme Q10, as well as phytochemicals including isoflavones, stilbenes, and sterols have been associated with improvement in atheroma plaque at an inflammatory level. However, many of these correlations have been obtained in vitro and in experimental animals' models. On one hand, the present review focuses on the evidence obtained from epidemiological, dietary intervention and supplementation studies in humans supporting the role of immunonutrient supplementation and its effect on anti-inflammatory response in atherosclerotic disease. On the other hand, this review also analyzes the possible molecular mechanisms underlying the protective action of these supplements, which may lead a novel therapeutic approach to prevent or attenuate diet-related disease, such as atherosclerosis.
Natural Deduction and Normalization Proofs for the Intersection Type Discipline

Refining and extending previous work by Retore, we develop a systematic approach to intersection types via natural deduction. We show how a step of beta reduction can be seen as performing, at the level of typing derivations, Prawitz reductions in parallel. Then we derive as immediate consequences of Subject Reduction the main theorems about normalization for intersection types: for system D, strong normalization, for system Omega, the leftmost reduction termination for terms typable without Omega.
Soluble Epoxide Hydrolase Inhibitor: A Novel Potential Therapeutic or Prophylactic Drug for Psychiatric Disorders

Psychiatric disorders, including depression and schizophrenia, affect millions of individuals worldwide. However, the precise neurobiology of psychiatric disorders remains unclear. Accumulating evidence suggests that various inflammatory processes play a key role in depression and schizophrenia, and that anti-inflammatory drugs exert a therapeutic effect in patients with psychiatric disorders. Epoxyeicosatrienoic acids (EETs) and epoxydocosapentaenoic acids (EDPs) have potent anti-inflammatory properties. These mediators are broken down into their corresponding diols by soluble epoxide hydrolase (sEH), and inhibition of sEH enhances the anti-inflammatory effects of EETs. Therefore, sEH may play a key role in inflammation, which is involved in psychiatric disorders. Recent studies have shown that abnormal levels of sEH may be involved in the pathogenesis of certain psychiatric diseases, and that sEH inhibitors exhibit antidepressant and antipsychotic activity. The present review discusses the extensive evidence supporting sEH as a therapeutic target for psychiatric diseases, and the clinical value of sEH inhibitors as therapeutic or prophylactic drugs.
A Survey on Practical Applications of Multi-Armed and Contextual Bandits

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.
Docosahexaenoic Acid Increases the Potency of Soluble Epoxide Hydrolase Inhibitor in Alleviating Streptozotocin-Induced Alzheimer’s Disease-Like Complications of Diabetes

Diabetes is a risk factor for Alzheimer’s disease and it is associated with significant memory loss. In the present study, we hypothesized that the soluble epoxide hydrolase (sEH) inhibitor N-[1-(1-oxopropyl)-4-piperidinyl]-N’-[4-(trifluoromethoxy)phenyl)-urea (also known as TPPU) could alleviate diabetes-aggravated Alzheimer’s disease-like symptoms by improving memory and cognition, and reducing the oxidative stress and inflammation associated with this condition. Also, we evaluated the effect of edaravone, an antioxidant on diabetes-induced Alzheimer’s-like complications and the additive effect of docosahexaenoic acid (DHA) on the efficacy of TPPU. Diabetes was induced in male Sprague-Dawley rats by intraperitoneally administering streptozotocin (STZ). Six weeks after induction of diabetes, animals were either treated with vehicle, edaravone (3 or 10 mg/kg), TPPU (1 mg/kg) or TPPU (1 mg/kg) + DHA (100 mg/kg) for 2 weeks. The results demonstrate that the treatments increased the memory response of diabetic rats, in comparison to untreated diabetic rats. Indeed, DHA + TPPU were more effective than TPPU alone in reducing the symptoms monitored. All drug treatments reduced oxidative stress and minimized inflammation in the brain of diabetic rats. Expression of the amyloid precursor protein (APP) was increased in the brain of diabetic rats. Treatment with edaravone (10 mg/kg), TPPU or TPPU + DHA minimized the level of APP. The activity of acetylcholinesterase (AChE) which metabolizes acetylcholine was increased in the brain of diabetic rats. All the treatments except edaravone (3 mg/kg) were effective in decreasing the activity of AChE and TPPU + DHA was more efficacious than TPPU alone. Intriguingly, the histological changes in hippocampus after treatment with TPPU + DHA showed significant protection of neurons against STZ-induced neuronal damage. Overall, we found that DHA improved the efficacy of TPPU in increasing neuronal survival and memory, decreasing oxidative stress and inflammation possibly by stabilizing anti-inflammatory and neuroprotective epoxides of DHA. In the future, further evaluating the detailed mechanisms of action of sEH inhibitor and DHA could help to develop a strategy for the management of Alzheimer’s-like complications in diabetes.
Para-Halogenation Affects Monoamine Transporter Inhibition Properties and Hepatocellular Toxicity of Amphetamines and Methcathinones

Halogenated derivatives of amphetamine-type stimulants are appearing on the drug market, often with altered pharmacological profile and sometimes different legal status compared to the non-halogenated substances. The aim of the present study was to investigate the pharmacological profile and hepatocellular toxicity of para-halogenated amphetamines and cathinones. The potential of amphetamine, 4-fluoroamphetamine, 4-chloroamphetamine, methcathinone, 4-fluoromethcathinone, and 4-chloromethcathinone to inhibit the monoamine transporters for norepinephrine, dopamine, and serotonin was determined in transporter-transfected human embryonic kidney 293 cells. Cell membrane integrity, ATP content, oxygen consumption rate, and superoxide levels were measured in human hepatoma HepG2 cells after exposure to the substances for 24 h. All compounds inhibited the norepinephrine transporter at submicromolar concentrations and the dopamine transporter at low micromolar concentrations. The selectivity of the compounds to inhibit the dopamine versus serotonin transporter decreased with increasing size of the para-substituent, resulting in potent serotonin uptake inhibition for the halogenated derivatives. All substances depleted the cellular ATP content at lower concentrations (0.25–2 mM) than cell membrane integrity loss occurred (≥0.5 mM), suggesting mitochondrial toxicity. The amphetamines and 4-chloromethcathinone additionally impaired the mitochondrial respiratory chain, confirming mitochondrial toxicity. The following toxicity rank order for the para-substituents was observed: chloride > fluoride > hydrogen. In conclusion, para-halogenation of stimulants increases the risk for serotonergic neurotoxicity. Furthermore, para-halogenation may increase hepatic toxicity mediated by mitochondrial impairment in susceptible users.
One‐Step Construction of Hydrophobic MOFs@COFs Core–Shell Composites for Heterogeneous Selective Catalysis

The exploration of novel porous core–shell materials is of great significance because of their prospectively improved performance and extensive applications in separation, energy conversion, and catalysis. Here, mesoporous metal–organic frameworks (MOFs) NH2‐MIL‐101(Fe) as a core generate a shell with mesoporous covalent organic frameworks (COFs) NUT‐COF‐1(NTU) by a covalent linking process, the composite NH2‐MIL‐101(Fe)@NTU keeping retentive crystallinity with hierarchical porosity well. Importantly, the NH2‐MIL‐101(Fe)@NTU composite shows significantly enhanced catalytic conversion and selectivity during styrene oxidation. It is mainly due to the hydrophilic MOF nanocrystals readily gathering the hydrophobic reactants styrene and boosting the radical mechanism path after combining the hydrophobic COFs shell. The synthetic strategy in this systematic study develops a new rational design for the synthesis of other core–shell MOF/COF‐based hybrid materials, which can expand the promising applications.
Guaranteed and robust L2-norm a posteriori error estimates for 1D linear advection problems

We propose a reconstruction-based a posteriori error estimate for linear advection problems in one space dimension. In our framework, a stable variational ultra-weak formulation is adopted, and the equivalence of the L2-norm of the error with the dual graph norm of the residual is established. This dual norm is showed to be localizable over vertex-based patch subdomains of the computational domain under the condition of the orthogonality of the residual to the piecewise affine hat functions. We show that this condition is valid for some well-known numerical methods including continuous/discontinuous Petrov–Galerkin and discontinuous Galerkin methods. Consequently, a well-posed local problem on each patch is identified, which leads to a global conforming reconstruction of the discrete solution. We prove that this reconstruction provides a guaranteed upper bound on the L2 error. Moreover, up to a generic constant, it also gives local lower bounds on the L2 error, where the constant only depends on the mesh shape-regularity. This, in particular, leads to robustness of our estimates with respect to the polynomial degree. All the above properties are verified in a series of numerical experiments, additionally leading to asymptotic exactness. Motivated by these results, we finally propose a heuristic extension of our methodology to any space dimension, achieved by solving local least-squares problems on vertex-based patches. Though not anymore guaranteed, the resulting error indicator is still numerically robust with respect to both advection velocity and polynomial degree in our collection of two-dimensional test cases including discontinuous solutions aligned and not aligned with the computational mesh.
Impaired STAT3-Dependent Upregulation of IL2Rα in B Cells of a Patient With a STAT1 Gain-of-Function Mutation

Heterozygous STAT1 gain-of-function (GOF) mutations form the most common genetic cause of chronic mucocutaneous candidiasis (CMC). In such patients, increased STAT1 function leads to impaired STAT3-dependent activation of IL-17A and IL-17F in T cells, thereby causing impaired Th17 responses to Candida. In spite of the critical role of STAT3 in IL-21 signaling in B cells, nearly all STAT1 GOF patients have normal or high serum IgG. We here present a 44 year-old male with childhood onset of CMC and antibody deficiency since early adulthood. Sequence analysis of STAT1 revealed a heterozygous missense mutation in the coiled-coil domain (p.D168E), which resulted in increased STAT1 phosphorylation of B-cells activated with IFNα and IFNγ. IL-21 induced STAT3 phosphorylation and nuclear localization were normal, but resulted in impaired upregulation of IL2Rα. This newly identified B-cell intrinsic impairment of STAT3 function could underlie the progressive development of hypogammaglobulinemia. Considering the high risk of bronchiectasis and irreversible organ damage, this case illustrates the need for monitoring of IgG levels and/or function in adult patients with STAT1 GOF mutations.
Graphs that are Critical for the Packing Chromatic Number

Abstract Given a graph G, a coloring c : V (G) → {1, …, k} such that c(u) = c(v) = i implies that vertices u and v are at distance greater than i, is called a packing coloring of G. The minimum number of colors in a packing coloring of G is called the packing chromatic number of G, and is denoted by χρ(G). In this paper, we propose the study of χρ-critical graphs, which are the graphs G such that for any proper subgraph H of G, χρ(H) < χρ(G). We characterize χρ-critical graphs with diameter 2, and χρ-critical block graphs with diameter 3. Furthermore, we characterize χρ-critical graphs with small packing chromatic number, and we also consider χρ-critical trees. In addition, we prove that for any graph G and every edge e ∈ E(G), we have (χρ(G)+1)/2 ≤ χρ(G−e) ≤ χρ(G), and provide a corresponding realization result, which shows that χρ(G − e) can achieve any of the integers between these bounds.
Physical Mapping of Peroxidase Genes and Development of Functional Markers for TaPod-D1 on Bread Wheat Chromosome 7D

Peroxidase (POD) activity in wheat (Triticum aestivum L.) grain influences natural carotenoid pigment content and is associated with the color of flour, and processing and product quality. Here, we report the molecular characterization and physical mapping of POD genes in bread wheat. The complete genomic DNA (gDNA) sequence of two POD genes (TaPod-A2 and TaPod-D1), and the partial gDNA sequence of two additional POD genes (TaPod-A3 and TaPod-B1) from wheat were characterized using in silico cloning and validated through laboratory experiments. Using a set of 21 nullisomic-tetrasomic (NT) lines, six group-7 ditelosomic (Dt) lines, and 38 group-7 deletion (Del) lines of Chinese Spring (CS), TaPod-A2 and TaPod-D1 were found to be physically located on 0.73–0.83 and on the most distal 0.39 fraction arm length (FL) of 7AS and 7DS in cv. CS, respectively; whereas, TaPod-A3 and TaPod-B1 were assigned to the 0.40–0.49 and 0.40–0.48 FL of 7AL and 7BL, respectively. Based on single nucleotide polymorphisms (SNPs) of two alleles at the TaPod-D1 locus, two functional markers POD-7D1 and POD-7D6 were developed, amplifying 540- and 640-bp, fragments in varieties with higher and lower POD activities, respectively. A total of 224 wheat varieties were analyzed and showed a significant association between the polymorphic fragments and POD activity using POD-7D1 and POD-7D6 markers. The analysis of variance (ANOVA) indicated the average POD activities of 115 varieties with TaPod-D1a were significantly lower than 109 varieties with TaPod-D1b (P < 0.01). This study provides useful information of the POD genes in bread wheat, insight into wheat genome synteny and structure, gene-specific markers, and contributes a valuable resource for quality improvement in wheat breeding programs.
Non-Stationary Markov Decision Processes a Worst-Case Approach using Model-Based Reinforcement Learning

This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithms in this setting. We make two hypotheses: 1) the environment evolves continuously with a bounded evolution rate; 2) a current model is known at each decision epoch but not its evolution. Our contribution can be presented in four points. 1) we define a specific class of MDPs that we call Non-Stationary MDPs (NSMDPs). We introduce the notion of regular evolution by making an hypothesis of Lipschitz-Continuity on the transition and reward functions w.r. t. time; 2) we consider a planning agent using the current model of the environment but unaware of its future evolution. This leads us to consider a worst-case method where the environment is seen as an adversarial agent; 3) following this approach, we propose the Risk-Averse Tree-Search (RATS) algorithm, a zero-shot Model-Based method similar to Minimax search; 4) we illustrate the benefits brought by RATS empirically and compare its performance with reference Model-Based algorithms.
Autonomous microfluidic actuators for periodic sequential flow generation

We report a microfluidic system that generates sequential periodic multiflows only with a constant water head pressure. Control of periodic sequential flows of multisolutions is invaluable in a variety of technology and science applications, but it requires complex and expensive external controllers. Here, we present microfluidic systems that autonomously regulate periodic sequential flows without any user instructions or dynamic external controllers. The systems consist of astable and monostable actuators that mimic the functions of analog electronic circuits. With a constant water head pressure of the input solution acting as the sole driving force, these systems generate periodic sequential flows in a predetermined and sophisticated manner. We validate our technology with the applications that have been previously addressed only by dynamic external controllers: dynamic staining of cell nuclei and playing a touchscreen piano. Our approach provides a useful and effective alternative to dynamic external controllers.
Twins Can Help Us Understand How Genes and the Environment Shape Us

AGE: 14–15 Have you ever wondered why you look di erent from other people? Di erences, such as height and hair color can come from di erences in your genes called mutations. Understanding how mutations shape us can be a bit tricky, because we are also a ected by the world around us. Twins and siblings can help us understand whether people are di erent because of genetic di erences or because they live in di erent environments. Identical twins share the same genes, while non-identical twins share, on average, half of their genes with the other twin. We can measure and compare the degree of similarity in a trait between pairs of identical and non-identical twins, to understand how much of the di erence in a trait between people is caused by genes and how much is caused by the environment.
Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping

Neural networks are now actively being used for computer vision tasks in security critical areas such as robotics, face recognition, autonomous vehicles yet their safety is under question after the discovery of adversarial attacks. In this paper we develop simplified adversarial attack algorithms based on a scoping idea, which enables execution of fast adversarial attacks that minimize structural image quality (SSIM) loss, allows performing efficient transfer attacks with low target inference network call count and opens a possibility of an attack using pen-only drawings on a paper for the MNIST handwritten digit dataset. The presented adversarial attack analysis and the idea of attack scoping can be easily expanded to different datasets, thus making the paper's results applicable to a wide range of practical tasks.
Higher order WKB formula for quasinormal modes and grey-body factors: recipes for quick and accurate calculations

The WKB approach for finding quasinormal modes of black holes, suggested in Schutz and Will (1985 Astrophys. J. Lett. 291 L33–6) by Schutz and Will at the first order and later developed to higher orders (Iyer and Will 1987 Phys. Rev. D 35 3621; Konoplya 2003 Phys. Rev. D 68 024018; Matyjasek and Opala 2017 Phys. Rev. D 96 024011), became popular during the past decades, because, unlike more sophisticated numerical approaches, it is automatic for different effective potentials and mostly provides sufficient accuracy. At the same time, the seeming simplicity of the WKB approach resulted in appearance of a big number of partially misleading papers, where the WKB formula was used beyond its scope of applicability. Here we review various situations in which the WKB formula can or cannot bring us to reliable conclusions. As the WKB series converges only asymptotically, there is no mathematically strict criterium for evaluation of an error. Therefore, here we are trying to introduce a number of practical recipes instead and summarize cases in which higher WKB orders improve accuracy. We show that averaging of the Padé approximations, suggested first by Matyjasek and Opala (2017 Phys. Rev. D 96 024011), leads to much higher accuracy of the WKB approach, estimate the error and present the automatic code (The Mathematica® package with the WKB formula of 13th order and Padé approximations ready for calculation of the quasinormal modes and grey-body factors, as well as examples of such calculations for the Schwarzschild black hole are publicly available to download from https://goo.gl/nykYGL) which computes quasinormal modes and grey-body factors.
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose this second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, participants compete under a limited environment sample-complexity budget to develop systems which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform where they are trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. In this follow-up iteration to the NeurIPS 2019 MineRL Competition, we implement new features to expand the scale and reach of the competition. In response to the feedback of the previous participants, we introduce a second minor track focusing on solutions without access to environment interactions of any kind except during test-time. Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.