Lippia alba (Mill.) N. E. Br. (Verbenaceae) is an aromatic shrub whose essential oils have stood out as a promising source for application in several industrial fields. In this study, the essential oils chemical characterization of eight new L. alba genotypes was performed. The selected materials were collected from the Active Germplasm Bank of the Agronomic Institute and the essential oils were extracted by hydrodistillation. Flow-modulated comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was employed for chemical characterization and evaluation of possible co-eluted compounds. In addition, the chemical analyses were submitted to multivariate statistical analyses. From this investigation, 73 metabolites were identified in the essential oils of the genotypes, from which α-pinene, β-myrcene, 1,8-cineole, linalool, neral, geranial, and caryophyllene oxide were the most abundant compounds among the accessions. This is the first report disclosing α-pinene in higher amounts in L. alba (19.69%). In addition, sabinene, trans-verbenol, myrtenol, (E)-caryophyllene, α-guaiene, germacrene D, and α-bulnesene were also found in relevant quantities in some of the genotypes, and myrtenal and myrtenol could be well separated through the second dimension. Such results contributed to the understanding of the chemical composition of those new genotypes, being important to drive a future industrial applicability and studies in genetic breeding.The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster.The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players' data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (≥15 years). Featured variables were taken from the higher correlation coefficients ( less then 0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model's parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93-0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87-0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players.Multi-enzyme cascade reactions for the synthesis of complex products have gained importance in recent decades. Their advantages compared to single biotransformations include the possibility to synthesize complex molecules without purification of reaction intermediates, easier handling of unstable intermediates, and dealing with unfavorable thermodynamics by coupled equilibria. In this study, a four-enzyme cascade consisting of ScADK, AjPPK2, and SmPPK2 for ATP synthesis from adenosine coupled to the cyclic GMP-AMP synthase (cGAS) catalyzing cyclic GMP-AMP (2'3'-cGAMP) formation was successfully developed. The 2'3'-cGAMP synthesis rates were comparable to the maximal reaction rate achieved in single-step reactions. An iterative optimization of substrate, cofactor, and enzyme concentrations led to an overall yield of 0.08 mole 2'3'-cGAMP per mole adenosine, which is comparable to chemical synthesis. The established enzyme cascade enabled the synthesis of 2'3'-cGAMP from GTP and inexpensive adenosine as well as polyphosphate in a biocatalytic one-pot reaction, demonstrating the performance capabilities of multi-enzyme cascades for the synthesis of pharmaceutically relevant products.Geopolymer has been selected as a hydraulic mineral binder for the immobilization of MgZr fuel cladding coming from the dismantling of French Uranium Natural Graphite Gas reactor dedicated to a geological disposal. In this context, the corrosion processes and the nature of the corrosion products formed on MgZr alloy in a geopolymer matrix with and without the corrosion inhibitor NaF have been determined using a multiscale approach combining in situ Grazing Incidence hard X-ray Diffraction, Raman microspectroscopy, Scanning and Transmission Electron Microscopies coupled to Energy Dispersive X-ray Spectroscopy. https://www.selleckchem.com/products/vx803-m4344.html The composition, the morphology, and the porous texture of the corrosion products were characterized, and the effect of the corrosion inhibitor NaF was evidenced. The results highlighted the formation of Mg(OH)2-xFx. In addition, in presence of NaF, NaMgF3 forms leading to a decrease of the thickness and the porosity of the corrosion products layer. Moreover, a precipitation of magnesium silicates within the porosity of the geopolymer was evidenced.