A facile synthesis of (6S,1′S)-(+)-hernandulcin and (6S,1′R)-(+)-epihernandulcin

A facile total synthesis of (+)-hernandulcin (1) was accomplished from (−)-isopulegol in 6 steps with 15% overall yield. Epoxidation of (−)-isopulegol with m-chloroperbenzoic acid followed by opening of the epoxide 3a with prenyl Grignard afforded the tertiary alcohol 4a with correct C-6 and C-1′ stereochemistry as a major product. Oxidation of the secondary alcohol in compound 4a to the ketone 5a was accomplished in high yield by using TPAP and N-methylmorpholine N-oxide. Conversion of the ketone 5a to α,β-unsaturated ketone via organoselenium intermediate gave (+)-hernandulcin (1). This method was also successfully applied to the synthesis of (+)-epihernandulcin (2).     

Mechanism and nanosize products of the sol-gel reaction using diphenylsilanediol and 3-methacryloxypropyltrimethoxysilane as precursors

We use a first-principles calculation and small-angle neutron scattering (SANS) to investigate the mechanism and the nanosize products of the sol-gel reaction with diphenylsilanediol (DPD) and 3-methacryloxypropyltrimethoxysilane (MEMO) precursors in synthesizing a hybrid waveguide material. It is predicted that switching between a DPD hydroxyl and a MEMO methoxy with a reaction rate of 6.8 x 10(-6) s(-1) at 300 K is the fastest process for the first reaction step, thus generating diphenylmethoxysilanol (DPM) and 3-methacryloxypropyldimethoxysilanol (MEDO) as products. However, we determine that this reaction pathway could be modified by the presence of the H2O released from a catalyst such as Ba(OH)2.H2O. Next, switching between the DPM hydroxyl and the MEDO methoxy is followed to generate diphenyldimethoxysilane (DPDM) and 3-methacryloxypropylmethoxysilanediol (MEMDO). However, condensation between a MEMDO hydroxyl and a DPDM methoxy is found to be most favorable for the third reaction step, which generates the DPDM-MEMDO dimer and CH3OH molecule as products. In a similar fashion, a DPDM methoxy of the DPDM-MEMDO dimer can condense with a MEMDO hydroxyl of the second DPDM-MEMDO dimer to increase the chain, but its reaction rate of 2.8 x 10(-11) s(-1) is predicted to be about 5 times smaller than that between a DPDM methoxy and a MEMDO hydroxyl. This implies that the reaction rate for the larger nanostructures becomes smaller. Additionally, our SANS measurements determine that the final products from our sol-gel reaction are on the nanometer scale, at sizes from 1.76 to 2.36 nm.     

Effect of steps on the decomposition of CH3O at PdZn alloy surfaces

The decomposition of methoxide (CH(3)O) on a PdZn alloy is considered to be the rate-limiting step of steam re-forming of methanol over a Pd/ZnO catalyst. Our previous density functional (DF) studies (Langmuir 2004, 20, 8068; Phys. Chem. Chem. Phys. 2004, 6, 4499) revealed only a very low propensity of defect-free flat (111) and (100) PdZn surfaces to promote C-H or C-O bond breaking of CH(3)O. Thus, we applied the same DF periodic slab-model approach to investigate these two routes of CH(3)O decomposition on PdZn(221) surfaces that expose Pd, (221)(Pd), and Zn, (221)(Zn), steps. C-H bond cleavage of CH(3)O is greatly facilitated on (221)(Pd): the calculated activation energy is dramatically reduced, to approximately 50 kJ mol(-1) from approximately 90 kJ mol(-1) on flat PdZn surfaces, increasing the rate constant by a factor of 10(8). The lower barrier is mainly due to a weaker interaction of the reactant CH(3)O and an enhanced interaction of the product CH(2)O with the substrate. The activation energy for C-O bond scission did not decrease on the (221)(Pd) step. On the (221)(Zn) step, the calculated reaction barriers of both decomposition routes are even higher than on flat surfaces, because of the stronger adsorption of CH(3)O. Steps (and other defects) appear to be crucial for methanol steam re-forming on Pd/ZnO catalyst; the stepped surface PdZn(221)(Pd) is a realistic model for studying the reactivity of this catalyst.     

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.     

An Fc variant with two mutations confers prolonged serum half-life and enhanced effector functions on IgG antibodies

The pH-selective interaction between the immunoglobulin G (IgG) fragment crystallizable region (Fc region) and the neonatal Fc receptor (FcRn) is critical for prolonging the circulating half-lives of IgG molecules through intracellular trafficking and recycling. By using directed evolution, we successfully identified Fc mutations that improve the pH-dependent binding of human FcRn and prolong the serum persistence of a model IgG antibody and an Fc-fusion protein. Strikingly, trastuzumab-PFc29 and aflibercept-PFc29, a model therapeutic IgG antibody and an Fc-fusion protein, respectively, when combined with our engineered Fc (Q311R/M428L), both exhibited significantly higher serum half-lives in human FcRn transgenic mice than their counterparts with wild-type Fc. Moreover, in a cynomolgus monkey model, trastuzumab-PFc29 displayed a superior pharmacokinetic profile to that of both trastuzumab-YTE and trastuzumab-LS, which contain the well-validated serum half-life extension Fcs YTE (M252Y/S254T/T256E) and LS (M428L/N434S), respectively. Furthermore, the introduction of two identified mutations of PFc29 (Q311R/M428L) into the model antibodies enhanced both complement-dependent cytotoxicity and antibody-dependent cell-mediated cytotoxicity activity, which are triggered by the association between IgG Fc and Fc binding ligands and are critical for clearing cancer cells. In addition, the effector functions could be turned off by combining the two mutations of PFc29 with effector function-silencing mutations, but the antibodies maintained their excellent pH-dependent human FcRn binding profile. We expect our Fc variants to be an excellent tool for enhancing the pharmacokinetic profiles and potencies of various therapeutic antibodies and Fc-fusion proteins.Subject terms: Antibody therapy, Molecularly targeted therapy, Drug development     

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.     

Enhanced output voltage of nano energy harvester with diverse textiles

Abstract

We demonstrate the electrical properties of nano energy harvesters (NEHs) with various textiles for smart textiles that can be applied to the next generation wearable electronics. Output voltages and currents of NEHs with various fabrics, such as a cotton, rayon and wool that have different triboelectricity were measured. Cotton, rayon and wool shows the maximum output voltages of 1.250, 3.313 and 4.063 V, respectively. In addition, output currents of those textiles were 0.75, 4.4, 1.063 μA, respectively. Wool, in particular, which has the highest triboelectricity of 350 V exhibits the highest output voltage.     

Chemistry of novolac resins. X. Polymerization studies of HMTA and strategically synthesized model compounds

The reactions between hexamethylenetetramine (HMTA) and compounds which model novolac resins have been studied by ¹³C‐ and ¹⁵N‐NMR techniques. The dimer and tetramer compounds vary in molecular size and structure and react with HMTA to yield benzylamines and benzoxazine as the major initial‐formed intermediates and convert to methylene linked compounds at increased temperatures. The reaction of the compounds with only ortho reactive sites paralleled the 2,4‐xylenol–HMTA case reported by us previously; however, increasing molecular weight favored the formation of benzylamines and not benzoxazines. Those compounds with only para reactive sites paralleled the 2,6‐xylenol–HMTA case. The reactivity of the systems containing both ortho and para reactive sites depends on the ratio of ortho/para sites and various aspects such as the chemical structure and molecular weight of the compound, the HMTA level, and the melting point and pH of the system. These results parallel those obtained from novolac/HMTA systems. The xylenol/HMTA reactions formed similar products but showed quite different relative reaction rates by varying the HMTA ratio and structures of the materials. The importance of careful selection of model systems is also discussed. © 1999 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 37: 1347–1355, 1999     

Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models

African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.