Enantioselective Rhodium-Catalyzed Pauson–Khand Reactions of 1,6-Chloroenynes with 1,1-Disubstituted Olefins

An enantioselective rhodium(I)-catalyzed Pauson–Khand reaction (PKR) using 1,6-chloroenynes that contain challenging 1,1-disubstituted olefins is described. In contrast to the previous studies with these types of substrates, which are only suitable for a single type of tether and alkyne substituent, the new approach results in a more expansive substrate scope, including carbon and heteroatom tethers with polar and non-polar substituents on the alkene. DFT calculations provide critical insight into the role of the halide, which pre-polarizes the alkyne to lower the barrier for metallacycle formation and provides the proper steric profile to promote a favorable enantiodetermining interaction between substrate and chiral diphosphine ligand. Hence, the chloroalkyne enables the efficient and enantioselective PKR with 1,6-enynes that contain challenging 1,1-disubstituted olefins, thereby representing a new paradigm for enantioselective reactions involving 1,6-enynes.     

Easily Scalable Shell-Structured Copper Catalyst with High Activity and Durability for Carbon Dioxide Hydrogenation

Inducing strong metal-support interaction (SMSI) has been a useful way to control the structure of surface active sites. The SMSI often causes the encapsulation of metal particles with an oxide layer. Herein, an amorphous ceria shell was formed on Cu nanoparticles under a mild gas condition with high activity and durability for surface reaction. Cu−Ce solid solution promoted the transfer of surface oxygen species, which induced the ceria shell formation on Cu nanoparticles. This catalyst was used for CO₂ hydrogenation, selectively producing CO with high low-temperature activity and good durability for operation at high temperature. CO₂ activation and H₂ spillover could occur at low temperatures, enhancing the activity. The shell prevented the sintering, assuring durability. This catalyst was applied to a bench-scale reactor without loss in performance, resulting in high CO productivity in all temperature ranges.     

Kinetically Controlled Layer-by-Layer Stacking of Metal Oxide 2D Nanosheets

An efficient chemical way to finely control the layer-by-layer stacking of inorganic nanosheets (NS) is developed by tuning the type and composition of intercalant ion, and the reaction temperature for restacking process. The finely controlled stacking of NS relies on a kinetic control of the self-assembly of NS in the presence of coordinating organic cations. A critical role of organic cations in this assembly highlights the importance of the appropriate activation energy. Of prime importance is that a fine-control of the interstratification of 2D NS is highly effective not only in tailoring its pore structure but also in enhancing its electrode activity. The present study clearly demonstrates that the kinetically controlled restacking of NS provides a facile and powerful method to tailor their stacking number and functionality.     

Synthesis and characterization of Rosuvastatin calcium impurity A; a HMG-CoA reductase inhibitor

During the process development for multistep synthesis of Rosuvastatin calcium several impurities were obtained along with the final Rosuvastatin calcium. Out of this; synthesis of impurity A (acetone adduct) a minor impurity of Rosuvastatin calcium (3R,5S,6E)-7-[4-(4-fluorophenyl)-2-[[(2-hydroxy-2-methylpropyl)sulfonyl(methyl)amino]-6-(1-methylethyl)-pyrimidin-5-yl]-3,5-dihydroxyheptenoicacid hemicalcium salt, is described. The synthesis of impurity A has been accomplished in 6 steps; starting from formation of β-hydroxy sulfonamide as the key intermediate and followed by using convenient routes with overall yield of 13.5%. The target compound can be used as the reference substance of impurity of the Rosuvastatin calcium.     

Cobalt-catalyzed regioselective [3+2] annulation of ortho-formyl and acetyl substituted phenylboronic acids with alkynes

Treatment of alkynes with ortho-formyl and acetyl phenylboronic acids in the presence of a cobalt catalyst resulted in the formation of 2,3-disubstituted indenols in good yields. When aryl silyl alkynes were used, 2-aryl-3-silyl indenols were obtained regioselectively in good yields.     

High-Resolution Infrared-Vacuum Ultraviolet Photoion and Pulsed Field Ionization-Photoelectron Methods for Spectroscopic Studies of Neutrals and Cations

We show that by scanning the frequency of a single mode infrared (IR) optical parametric oscillator (IR-OPO) laser to excite the molecular species of interest and fixing the frequency of a vacuum ultraviolet (VUV) laser to photoionize the IR excited species, high-resolution IR spectra of polyatomic neutrals can be obtained with high sensitivity. The fact that this IR-VUV-photoion (IR-VUV-PI) method is based on VUV photoionization probe, and thus, allows the identification of the neutral IR absorber, makes it applicable for IR spectroscopy measurements of isotopemers, radicals, and clusters, which usually exist as impure samples. The highly resolved IR-VUV-PI measurements achieved using the single mode IR-OPO laser have made possible the selection of single rovibrational states of CH3X (X=Br and I), C2H4, and C3H4 for VUV-pulsed field ionization-photoelectron (VUV-PFI-PE) measurements, resulting in rovibrationally resolved photoelectron spectra for these polyatomic molecules. These experiments show that the signal-to-noise ratios of the IR-VUV-PI and IR-VUV-PFI-PE spectra obtained by employing the high-resolution IR-OPO laser are significantly higher than those observed in previous IR-VUV-PI and IR-VUV-PFI-PE studies using a low-resolution IR-OPO laser. Further improvement in sensitivity of IR-VUV-PI and IR-VUV-PFI-PE measurements by using the collinear arrangement of IR-VUV lasers and molecular beam is discussed.     

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms

Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of drone swarms by monitoring their movements. We focus on interference, a phenomenon that is not initially anticipated by drones but results in complicated operations due to its significant impact on performance and its challenging nature. We infer interference from predictability by first applying various machine learning methods, including deep learning, and then computing entropy to compare against interference. Our computational framework begins by building a set of computational models called double transition models from the drone movements and revealing reward distributions using inverse reinforcement learning. These reward distributions are then used to compute the entropy and interference across a variety of drone scenarios specified by combining multiple combat strategies and command styles. Our analysis confirmed that drone scenarios experienced more interference, higher performance, and higher entropy as they became more heterogeneous. However, the direction of interference (positive vs. negative) was more dependent on combinations of combat strategies and command styles than homogeneity.     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

Antimicrobial Activity of Smilax china L. Root Extracts against the Acne-Causing Bacterium,Cutibacterium acnes,and Its Active Compounds

The root of Smilax china L. is used in traditional Korean medicine. We found that the Smilax china L. root extract has strong antimicrobial activity against two Cutibacterium acnes strains (KCTC 3314 and KCTC 3320). The aim of this study was to identify the beneficial properties of Smilax china L. extracts for their potential use as active ingredients in cosmetics for the treatment of human skin acne. The high-performance liquid chromatography (HPLC) and liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC/QTOF/MS) methods were used to obtain the profile of secondary metabolites from the ethyl acetate-soluble fraction of the crude extract. Agar diffusion and resazurin-based broth microdilution assays were used to evaluate antimicrobial activity and minimum inhibitory concentrations (MIC), respectively. Among the 24 metabolites, quercetin, resveratrol, and oxyresveratrol were the most potent compounds against Cutibacterium acnes. Minimum inhibitory concentrations of quercetin, resveratrol, and oxyresveratrol were 31.25, 125, and 250 μg/mL, respectively.