The Potential of Lamiaceae Herbs for Mitigation of Overweight,Obesity, and Fatty Liver: Studies and Perspectives

Numerous plants, plant extracts, and plant-derived compounds are being explored for their beneficial effects against overweight and liver diseases. Obesity is associated with the increased prevalence of non-alcoholic fatty liver disease (NAFLD), becoming the most common liver disease in Western countries. Obesity and NAFLD are closely associated with many other metabolic alternations such as insulin resistance, diabetes mellitus, and cardiovascular diseases. Many herbs of the Lamiaceae family are widely employed as food and spices in the Mediterranean area, but also in folk medicine, and their use for the management of metabolic disorders is well documented. Hereby, we summarized the scientific results of the medicinal and nutraceutical potential of plants from the Lamiaceae family for prevention and mitigation of overweight and fatty liver. The evidence indicates that Lamiaceae plants may be a cost-effective source of nutraceuticals and/or phytochemicals to be used in the management of metabolic-related conditions such as obesity and NAFLD. PubMed, Google Scholar, Scopus, and SciFinder were accessed to collect data on traditional medicinal plants, compounds derived from plants, their reported anti-obesity mechanisms, and therapeutic targets.     

Copper Binding and Oligomerization Studies of the Metal Resistance Determinant CrdA from Helicobacter pylori

Within this research, the CrdA protein from Helicobacter pylori (HpCrdA), a putative copper-binding protein important for the survival of bacterium, was biophysically characterized in a solution, and its binding affinity toward copper was experimentally determined. Incubation of HpCrdA with Cu(II) ions favors the formation of the monomeric species in the solution. The modeled HpCrdA structure shows a conserved methionine-rich region, a potential binding site for Cu(I), as in the structures of similar copper-binding proteins, CopC and PcoC, from Pseudomonas syringae and from Escherichia coli, respectively. Within the conserved amino acid motif, HpCrdA contains two additional methionines and two glutamic acid residues (MMXEMPGMXXMXEM) in comparison to CopC and PcoC but lacks the canonical Cu(II) binding site (two His) since the sequence has no His residues. The methionine-rich site is in a flexible loop and can adopt different geometries for the two copper oxidation states. It could bind copper in both oxidation states (I and II), but with different binding affinities, micromolar was found for Cu(II), and less than nanomolar is proposed for Cu(I). Considering that CrdA is a periplasmic protein involved in chaperoning copper export and delivery in the H. pylori cell and that the affinity of the interaction corresponds to a middle or strong metal–protein interaction depending on the copper oxidation state, we conclude that the interaction also occurs in vivo and is physiologically relevant for H. pylori.     

Gaussian Amplitude Amplification for Quantum Pathfinding

We study an oracle operation, along with its circuit design, which combined with the Grover diffusion operator boosts the probability of finding the minimum or maximum solutions on a weighted directed graph. We focus on the geometry of sequentially connected bipartite graphs, which naturally gives rise to solution spaces describable by Gaussian distributions. We then demonstrate how an oracle that encodes these distributions can be used to solve for the optimal path via amplitude amplification. And finally, we explore the degree to which this algorithm is capable of solving cases that are generated using randomized weights, as well as a theoretical application for solving the Traveling Salesman problem.     

Cancer-Associated Mutations of the Adenosine A2A Receptor Have Diverse Influences on Ligand Binding and Receptor Functions

The adenosine A_2A receptor (A_2AAR) is a class A G-protein-coupled receptor (GPCR). It is an immune checkpoint in the tumor micro-environment and has become an emerging target for cancer treatment. In this study, we aimed to explore the effects of cancer-patient-derived A_2AAR mutations on ligand binding and receptor functions. The wild-type A_2AAR and 15 mutants identified by Genomic Data Commons (GDC) in human cancers were expressed in HEK293T cells. Firstly, we found that the binding affinity for agonist NECA was decreased in six mutants but increased for the V275A mutant. Mutations A165V and A265V decreased the binding affinity for antagonist ZM241385. Secondly, we found that the potency of NECA (EC₅₀) in an impedance-based cell-morphology assay was mostly correlated with the binding affinity for the different mutants. Moreover, S132L and H278N were found to shift the A_2AAR towards the inactive state. Importantly, we found that ZM241385 could not inhibit the activation of V275A and P285L stimulated by NECA. Taken together, the cancer-associated mutations of A_2AAR modulated ligand binding and receptor functions. This study provides fundamental insights into the structure–activity relationship of the A_2AAR and provides insights for A_2AAR-related personalized treatment in cancer.     

A Lie symmetry approach for the solution of the inverse kinematics problem

In this paper, it is considered the inverse kinematics problem, which is faced from a differential point of view. In particular, it is shown that an asymptotic inverse kinematics can be interpreted as a Lie symmetry of the direct kinematics. A parameterization of all Lie symmetries of the direct kinematics is proposed, and the classical Newton and gradient method are obtained as particular cases.     

Symmetries of BLT-Sets

We do the tentative beginnings of a study of BLT-sets of generalised quadrangles via their symmetries. In particular, the study of whorls about a line leads us to hyperbolic reflections preserving a BLT-set of Q(4, q).     

Ensemble Approach for Detection of Depression Using EEG Features

Depression is a public health issue that severely affects one’s well being and can cause negative social and economic effects to society. To raise awareness of these problems, this research aims at determining whether the long-lasting effects of depression can be determined from electroencephalographic (EEG) signals. The article contains an accuracy comparison for SVM, LDA, NB, kNN, and D3 binary classifiers, which were trained using linear (relative band power, alpha power variability, spectral asymmetry index) and nonlinear (Higuchi fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis) EEG features. The age- and gender-matched dataset consisted of 10 healthy subjects and 10 subjects diagnosed with depression at some point in their lifetime. Most of the proposed feature selection and classifier combinations achieved accuracy in the range of 80% to 95%, and all the models were evaluated using a 10-fold cross-validation. The results showed that the motioned EEG features used in classifying ongoing depression also work for classifying the long-lasting effects of depression.     

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.     

Disentangling the Information in Species Interaction Networks

Shannon’s entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl.