Physicochemical properties, pharmacokinetic studies, DFT approach, and antioxidant activity of nitro and chloro indolinone derivatives

The process of developing of new drugs is greatly hampered by their inadequate physicochemical, pharmacokinetic, and intrinsic characteristics. In this regard, the selected chloro indolinone, (Z)-6-chloro-3-(2-chlorobenzylidene)indolin-2-one (C1), and nitro indolinone, (Z)-6-chloro-3-(2-nitrobenzylidene)indolin-2-one (C2), were subjected to SwissADME and density function theory (DFT) analysis. For compounds C1 and C2, the BOILED-Egg pharmacokinetic model predicted intestinal absorption, blood–brain barrier (BBB) penetration, and p-glycoprotein interaction. According to the physicochemical analysis, C1 has exceptional drug-like characteristics suitable for oral absorption. Despite only being substrates for some of the major CYP 450 isoforms, compounds C1 and C2 were anticipated to have strong plasma protein binding and efficient distribution and block these isoforms. The DFT study using the B3LYP/6-311G(d,p) approach with implicit water effects was performed to assess the structural features, electronic properties, and global reactivity parameters (GRP) of C1 and C2. The DFT results provided further support for other studies, implying that C2 is more water-soluble than C1 and that both compounds can form hydrogen bonds and (weak) dispersion interactions with other molecules, such as solvents and biomolecules. Furthermore, the GRP study suggested that C1 should be more stable and less reactive than C2. A concentration-dependent 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical scavenging activity was shown by both C1 and C2. In brief, this finding has provided a strong foundation to explore further the therapeutic potential of these molecules against a variety of human disorders

An intersection game-theory-based traffic control algorithm in a connected vehicle environment

Urban traffic congestion is a growing problem that we experience every day. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic control methods, such as traffic signal and stop sign control are not optimal for all demand levels as demonstrated in the literature. Recently, numerous research efforts proposed Intelligent Transportation System (ITS) applications to enhance intersection capacity and hence reduce congestion. In this paper we propose a game-theory-based algorithm for controlling autonomous vehicle movements equipped with Cooperative Adaptive Cruise Control (CACC) systems at uncontrolled intersections. The goal of this research effort is to develop an algorithm capable of using the future autonomous/automated vehicle capabilities to replace the usual state-of-the-practice control systems at intersections (e.g. stop signs, traffic signals, etc.). The proposed algorithm is chicken-game inspired and is efficient for application in real-time. It assumes vehicles can communicate with a central agent at the intersection to provide their instantaneous speeds and locations. The proposed algorithm assumes that vehicles obey the Nash equilibrium solution of the game. The simulation results demonstrated reductions in vehicle travel time and delay relative to an all-way stop sign control in the range of 49 and 89 percent on average respectively.</p

Preparation, Antimicrobial Activity and Docking Study of Vanadium Mixed Ligand Complexes Containing 4-Amino-5-hydrazinyl-4H-1,2,4-triazole-3-thiol and Aminophenol Derivatives

The synthesis of mixed-ligand complexes is considered an important strategy for developing new metal complexes of enhanced biological activity. This paper presents the synthesis, characterization, in vitro antimicrobial assessment, and theoretical molecular docking evaluation for synthesized oxidovanadium (V) complexes. The proposed structures of the synthesized compounds were proved using elemental and different spectroscopic analysis. The antimicrobial tests showed moderate activity of the compounds against the Gram-positive bacterial strains and the fungal yeast, whereas no activity was observed against the Gram-negative bacterial strains. The performance of density functional theory (DFT) was conducted to study the interaction mode of the targeted compounds with the biological system. Calculating the quantitative structure-activity relationship (QSPR) was performed depending on optimization geometries, frontier molecular orbitals (FMOs), and chemical reactivities for synthesized compounds. The molecular electrostatic potentials (MEPs) that were plotted link the interaction manner of synthesized compounds with the receptor. The molecular docking evaluation revealed that the examined compounds may possess potential antibacterial activity

Synthesis of Novel Nano-Sulfonamide Metal-Based Corrosion Inhibitor Surfactants

The synthesis of novel corrosion inhibitors and biocide metal complex nanoparticle surfactants was achieved through the reaction of sulfonamide with selenious acid to produce a quaternary ammonium salt. Platinum and cobalt surfactants were then formed by complexing the first products with platinum (II) or cobalt (II) ions. The surface properties of these surfactants were then investigated, and the free energy of form micelles (&Delta;Gomic) and adsorption (&Delta;Goads) was determined. The obtained cationic compounds were evaluated as corrosion inhibitors for carbon steel dissolution in 1N HCl medium. The results of gravimetric and electrochemical measurements showed that the obtained inhibitors were excellent corrosion inhibitors. The anti-sulfate-reducing bacteria activity known to cause corrosion of oil pipes was obtained by the inhibition zone diameter method for the prepared compounds, which were measured against sulfate-reducing bacteria. FTIR spectra, elemental analysis, H1 NMR spectrum, and 13C labeling were performed to ensure the purity of the prepared compounds

Bike share travel time modeling: San Francisco bay area case study

Bike Share Systems (BSSs) are emerging in many US cities as a new sustainable transportation mode that provides a last-mile solution for short-distance transfers between different private and public transportation modes. In order to encourage the increased use of bikes as a mode of transportation, tools, measures, and planning techniques similar to those used for other transportation modes need to be developed. With precise information on the trip travel time, route planner systems can suggest optimal alternative routes, and manage and control traffic congestion. Although there is a growing body of literature dealing with BSSs, bike travel time has been studied sparingly up to this point. In this paper, we addressed this issue by developing different bike travel time models using random forest (RF), least square boosting (LSBoost) and artificial neural network (ANN) techniques. We studied 33 different predictors affecting bike travel time, including such predictors as travel distance, biker experience, time-of-day, and weather conditions. The RF model produced a reasonable prediction rate with a mean absolute error (MAE) of 84.01 sec and a mean absolute percentage error (MAPE) of 16.92%. We further improved the bike travel time prediction model by using RF and forward stepwise regression to select the best subset of predictors to explain the bike travel time variability. The resulting model, with only seven predictors, reduced the MAE to 82.04 sec and the MAPE to 16.2%.</p

Structural and Spectroscopic Characteristics of NiII and CuII Complexes with Poly (Vinyl Alcohol-Nicotinic Acid) Copolymers for Photocatalytic Degradation of Indigo Carmine Dye

Poly-vinyl-alcohol (PVA) has been cross-linked chemically with nicotinic-acid (NA) in an aqueous medium. The copolymers were complexed with NiII and CuII ions. The complexes and copolymers were analyzed using FT-IR and UV–Visible spectroscopy, XRD and TGA, but copolymers were extra analyzed with nuclear magnetic resonance (1H NMR). FT-IR spectra of copolymer revealed the presence of C=O &amp; C–N groups due to the esterification of PVA-NA. The Cu/NA-PVA formed via bidentate interaction of the pyridinyl and carboxyl of NA. EPR/UV-vis data shows the square-planar geometry for NiII and CuII complexes. The adsorption of IC dye onto CuII/NA-PVA complex was noticeably greater (90%) in 35 min than NiII/NA-PVA. The DFTB3LYP with 6- 311G* quantum chemical calculations were carried out for tested compounds. The DFT was conducted to examine an interaction mode of the target compounds with the reaction system. The QSPR was calculated as: optimization geometries, (FMOs), chemical-reactivities and NLO for the copolymers. The (MEPs) were figured to predict the interaction behavior of the ligand and its complexes

Network-wide bike availability clustering using the college admission algorithm: A case study of San Francisco Bay area

The significant increase in the use of bike sharing systems (BSSs) causes imbalances in the distribution of bikes, creating logistical challenges and discouraging bike riders who find it difficult to pick up or drop off a bike at their desired location. We investigated this issue by finding the network-wide availability patterns and how these patterns evolve temporally using a novel supervised clustering algorithm based on the College Admission and the K-median algorithms. The proposed approach models the clustering problem as a matching problem between two disjoint sets of agents: centroids and data points. This new view of the clustering problem makes our algorithm a multi-objective algorithm where the impurity and distance in each cluster are minimized simultaneously. The proposed algorithm showed promising performance when applied to BSS data for the San Francisco Bay area. The resultant network-wide availability patterns were used to identify imbalances in the BSS. Using a spatial analysis of these imbalances, we propose potential solutions for decision makers and agencies to improve BSS operations and make it more stable.</p

Laparoendoscopic single-site surgery for the treatment of different urological pathologies: Defining the learning curve of an experienced laparoscopist

Objectives: To define the learning curve of laparoendoscopic single-site surgery (LESS) of an experienced laparoscopist. Patients and methods: Patients who had LESS, since its implementation in December 2009 until December 2014, were retrospectively analysed. Procedures were divided into groups of 10 and scored according to the European Scoring System for Laparoscopic Operations in Urology. Different LESS indications were done by one experienced laparoscopist. Technical feasibility, surgical safety, outcome, as well as the number of patients required to achieve professional competence were assessed. Results: In all, 179 patients were included, with mean (SD) age of 36.3 (17.5) years and 25.4% of the patients had had previous surgeries. Upper urinary tract procedures were done in 65.9% of patients and 54.7% of the procedures were extirpative. Both transperitoneal and retroperitoneal LESS were performed in 92.8% and 7.2% of the patients, respectively. The intraoperative and postoperative complication rates were 2.2% and 5.6% (Clavien–Dindo Grade II 3.9% and IIIa 1.7%), respectively. In all, 75% of intraoperative complications and all conversions were reported during the first 30 LESS procedures; despite the significantly higher difficulty score in the subsequent LESS procedures. One 5-mm extra port, conversion to conventional laparoscopy and open surgery was reported in 14%, 1.7%, and 1.1% of the cases, respectively. At mean (SD) follow-up of 39.7 (11.4) months, all the patients that underwent reconstructive LESS procedures but one were successful. Conclusion: In experienced hands, at least 30 LESS procedures are required to achieve professional competence. Although difficult, both conversion and complication rates of LESS are low in experienced hands

Modeling bike availability in a bike-sharing system using machine learning

This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.</p