Self-assembled biotinylated disulfide derivative monolayer on gold electrode for immobilizing enzymes

Based on self-assembled biotinylated disulfide derivative monolayer on gold electrode, the sensors immobilized monolayer or multilayer membranes composed of avidin and biotinlabeled glucose oxidase (B.GOD) or of avidin-B.GOD complex (ABC) and B.COD were prepared. The present technique may be useful for controlling the enzyme content of the sensors in molecular level by repeating the deposition of enzyme layers. The sensors have the characteristics of shorter response time, higher sensitivity. The linear range is from 6.0 x 10(-6) - 5.0 x 10(-3) M. The sensor can be used for more than 1 month and can be reactivated. The sensor was used to determine glucose in human blood serum, and the results are satisfactory.     

The extended negative factor counting method for tridiagonal block matrices with cross links

The NFC (negative factor counting) method ws extended to solve the eigenvalue problem of tridiagonal block matrices with elements corresponding to cross links which may be derived from the quantum-chemical calculation on a native protein molecule. The mathematical proof of the necessary theorem is given in detail.     

Theoretical formalism and experimental verification of line shapes of NMR intermolecular multiple-quantum coherence spectra

Although the theories and potential applications of intermolecular multiple-quantum coherences (iMQCs) have been under active investigations for over a decade, discussion of iMQC NMR signal formation was mainly confined in the time domain. In this paper, a full line-shape theory was developed to describe iMQC signals in the frequency domain. Relevant features of the line shape, such as peak height, linewidth, and phase, were investigated in detail. Predictions based on the theory agree well with experimental and simulated results. Since radiation-damping effects always couple with iMQCs in highly polarized liquid-state NMR systems, and strongly radiation-damped signals have many spectral characteristics similar to those of iMQCs, a detailed comparison was also made between them from different spectral aspects. With detailed comparison of peak height, linewidth, and phase, this work demonstrates that the iMQC and radiation-damping phenomena result from two completely different physical mechanisms despite that both present similar signal features and coexist in highly polarized liquid-state NMR systems.     

Comparison of the column performance of narrow-bore and standard-bore columns for the chromatographic determination of alpha-, beta-, gamma-, and delta-tocopherol

A comparison of the performance of narrow-bore (2.1-mm i.d.) and standard-bore (4.6-mm i.d.) analytical silica columns having the same length is completed for the resolution of alpha-, beta-, gamma-, and delta-tocopherol. The studies are performed on high-performance liquid chromatographic equipment with minimum extracolumn contribution. Column permeabilities are 1.16 x 10(-9) and 2.48 x 10(-9) cm2 for narrow and standard bore, respectively. The narrow-bore column gives up to a 7 times increase in sensitivity compared with a standard-bore column at equivalent running times for the analytes. Approximately one-third solvent savings can be achieved with the narrow-bore column. Theoretical plates of the standard-bore column are higher than that of the narrow-bore column.     

DNAzyme amplification of molecular beacon signal

This paper reports an improved catalytic molecular beacon. Addition of the target oligonucleotide activates a DNA enzyme (DNAzyme), which, in turn, activates multiple copies of molecular beacons (MB) and gives rise to a strong fluorescence signal. In a previous design, the activated DNAzyme could oligomerize, especially dimerize, and result in inactivation of the DNAzyme. The current design avoids this problem, upon activated by the target DNA, the DNAzyme will stay constantly active. With the improved method, a detection of 10 pM DNA has been demonstrated, which is 1000 times more sensitive than the method previously reported.     

Liquid chromatography/electrospray ionization mass spectrometry for the characterization of twenty-three flavonoids in the extract of Dalbergia odorifera

A method incorporating high-performance liquid chromatography (HPLC) with electrospray ionization and tandem mass spectrometry, with parallel analysis by HPLC with UV detection using a diode-array detector, was developed for the qualitative characterization of flavonoids in D. odorifera. Twenty-three flavonoids, including six isoflavones, six neoflavones, four isoflavanones, three flavanones, two chalcones, one isoflavanonol and one pterocarpan, were unambiguously identified by comparing their retention times, UV and MS spectra with those of authentic compounds. Furthermore, the collision-induced dissociations of the [M-H]- ions were studied to clarify the MS behavior of the different types of flavonoids. In negative ion ESI-MS all the flavonoids yielded prominent [M-H]- ions in the first order mass spectra. Fragments involving losses of CH3*, H2O, CO, C2H2O, and CO2 were observed in the MS/MS spectra. Each of the seven types of flavonoid showed characteristic MS/MS fragmentation patterns. The isoflavanones, flavanones and chalcones were observed to undergo retro-Diels-Alder fragmentations. The spectra of almost all the neoflavonoids unexpectedly exhibited only [M-H-CH3]-* radical anions as base peaks without any further fragmentation. Substitution positions also remarkably influenced the fragmentation behavior, which could assist in distinction among the flavonoid isomers. The fragmentation rules deduced here could aid in the characterization of other flavonoids of these types.     

Organometallic reagents in organic synthesis—V: An organocuprate conjugate addition—Aldol condensation synthetic procedure for sequential formation of two carbon-carbon bonds

The enolates produced from the conjugate addition reaction of lithium dialkylcuprates with α,β- unsaturated ketones react with aldehydes in the presence of zinc chloride to give overall β-alkyl α-hydroxyalkyl addition to the original alkene. Reaction of these enolates with carbon dioxide and ethyl formate is also reported.     

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.