NBS-mediated elimination of ß-keto sulfides to access enones and dienones.

A novel transformation of ß-keto sulfides into enones has been developed. The new method facilitates an NBS-mediated elimination of sulfides to access both enones and dienones. 22 enone products were obtained in moderate to high yields. 4 different dienones were also prepared in 73%-93% yields. 7 different alkylthio motifs have been removed efficiently from ß-keto sulfides. We also found that our transformation proceeds well in gram-scale reactions showing no decrease in yield. This methodology is significant in the research and application of sulfides, giving a new pathway to transform ß-keto sulfides into enones.

NbCl5-catalyzed sulfa-Michael addition for constructing quaternary centers in enones.

A novel NbCl5-catalyzed sulfa-conjugate addition has been developed to construct quaternary centers in various enones. This new method enables a range of functionalized thiols to access different ß-sulfido carbonyl compounds bearing a quaternary center. 27 novel ß-sulfido ketones have been obtained with moderate to excellent yields. The preparative scale reactions also proceed well, showing no decrease in yield. We further studied the mechanism by DFT calculations. This methodology is significant in sulfur chemistry, especially in sulfa-conjugate addition, giving a new pathway to add thiols to tri-substituted enones.

An omics data analysis method based on feature linear relationship and graph convolutional network.

Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification.

Data analysis methods for defining biomarkers from omics data.

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

The risks associated with the use of lamotrigine during pregnancy.

OBJECTIVE: This paper reviewed the relevant literature on the effects of lamotrigine on pregnancy outcomes to provide useful information regarding lamotrigine use in pregnant women with bipolar disorder. METHODS: A systematic search of electronic databases and other original sources was conducted that examined the effects of lamotrigine on pregnancy outcomes. RESULTS: It is not clear that foetuses of lamotrigine-exposed pregnant women are at higher risk of malformation or neurodevelopmental delay. When treating pregnant women with bipolar disorder, the risks associated with lamotrigine use have to be balanced with the risks of uncontrolled maternal symptoms. The information obtained from our review of psychotropic medications will assist clinicians in managing pregnant women with bipolar disorder. CONCLUSIONS: Although lamotrigine has emerged as the safest mood stabiliser for use during pregnancy based on the clinical evidence thus far, further studies are needed to inform the best clinical practice when treating bipolar disorder in pregnant women.

Analyzing omics data based on sample network.

Identifying valuable features from complex omics data is of great significance for disease diagnosis study. This paper proposes a new feature selection algorithm based on sample network (FS-SN) to mine important information from omics data. The sample network is constructed according to the sample neighbor relationship at the molecular (feature) expression level, and the distinguishing ability of the feature is evaluated based on the topology of the sample network. The sample network established on a feature with a strong discriminating ability tends to have many edges between the same group samples and few edges between the different group samples. At the same time, FS-SN removes redundant features according to the gravitational interaction between features. To show the validation of FS-SN, it was compared on ten public datasets with ERGS, mRMR, ReliefF, ATSD-DN, and INDEED which are efficient in omics data analysis. Experimental results show that FS-SN performed better than the compared methods in accuracy, sensitivity and specificity in most cases. Hence, FS-SN making use of the topology of the sample network is effective for analyzing omics data, it can identify key features that reflect the occurrence and development of diseases, and reveal the underlying biological mechanism.

Research on the bearing creep characteristics and constitutive model of gangue filling body.

The creep characteristics and potential deformation patterns of gangue backfill material are crucial in backfill mining operations. This study utilizes crushed gangue from the Gangue Yard in Fuxin City as the research material. An in-house designed, large-scale, triaxial gangue compaction test system was used. Triaxial compaction creep tests were conducted on gangue materials with varying particle size distributions. Analysis was performed based on different particle sizes, stresses, and confinement pressures. The study investigates the creep characteristics of the gangue under different conditions and explores the underlying causes. It reveals the relationship between the creep deformation of gangue materials and the passage of time. Mathematical methods are applied to develop a triaxial compaction creep power law model for gangue backfill materials. Finally, the creep results are fitted using an empirical formula approach.

Water-Soluble Dual-Channel Fluorescent Probe for Sensitive Detection of Biothiols In Vitro and In Vivo.

Benefiting from high spatiotemporal resolution, deep tissue penetration, and excellent sensitivity, fluorescence imaging technology has been widely applied in cancer diagnosis and treatment. In recent years, a large number of fluorescent probes for monitoring the levels of endogenous biothiols have been reported, which have significant implications for cancer diagnosis and treatment. However, most probes still suffer from poor biological compatibility and easy attachment by the environment. This work presents the development of a water-soluble dual-channel fluorescent probe, named MAL-NBD, for sensitively detecting biothiols. Nonfluorescent MAL-NBD is transformed into fluorescent groups MAL and NBD-SR/NR through nucleophilic substitution by biologically active thiols, producing dual-channel fluorescence signals for precise detection of biologically active thiols. Taking advantage of the excellent biocompatibility and low biotoxicity, MAL-NBD is successfully used for imaging HeLa cancer cells and zebrafish larvae, promoting its potential application for the precise detection of biological thiols involved in physiological and pathological processes.

A novel method for feature selection based on molecular interactive effect network.

Analyzing the biological data by considering the molecule interactions may induce a more accurate identification of disease-related biomarkers. In this study, a novel feature selection method based on molecule (feature) interactive effect network is proposed, denoted as Distance Correlation Gain-Network (DCG-Net). In DCG-Net, DCG is defined to measure the interactive effects between pairwise features with respect to the process of physiological and pathological changes and infer the molecule interactive effect network. DCG index is suitable for discrete random variables and continuous random variables. Then a greedy searching strategy is developed to search the informational modules of the interactive features with high statistical dependence on disease outcome. To evaluate the performance of DCG-Net, it was compared with eight representative feature selection techniques including t-test, ReliefF, SVM-RFE, mRMR, IG-RFE, INDEED, MN-PCC and Dcor-SFS on ten public datasets. The experiment results showed the superior performance of DCG-Net in classification accuracy rate, sensitivity, and specificity for three different classifiers. Subsequently, DCG-Net was employed to analyze a lung adenocarcinoma metabolomics dataset, and the metabolites selected involved in the important pathway and had a better discrimination ability. The experiments demonstrate that DCG can effectively detect the molecular interactions, and incorporation of the molecule interactions is helpful to identify informational biomarkers reflecting the occurrence and development of complex diseases.

Copper-catalyzed thioketalization of enones featuring trifluoromethyl groups.

Synthetic methods for the preparation of thioketals featuring CF3 groups are rare. Here, we have developed a copper-catalyzed thioketalization of enones bearing CF3 groups and various mercaptans. 24 thioketal molecules have been obtained with moderate to excellent yield. Meanwhile, a preparative scale experiment has been performed giving over 95% yield. This work allows the straightforward formation of thioketals containing CF3 groups and unsaturated double bonds.

A new feature selection algorithm based on relevance, redundancy and complementarity.

Defining important information from biological data is critical for the study of disease diagnosis, drug efficacy and individualized treatment. Hence, the feature selection technique is widely applied. Many feature selection methods measure features based on relevance, redundancy and complementarity. Feature complementarity means that two features' cooperation can provide more information than the simple summation of their individual information. In this paper, we studied the feature selection technique and proposed a new feature selection algorithm based on relevance, redundancy and complementarity (FS-RRC). On selecting the feature subset, FS-RRC not only evaluates the feature relevance with the class label and the redundancy among the features but also evaluates the feature complementarity. If complementary features exist for a selected relevant feature, FS-RRC retains the feature with the largest complementarity to the selected feature subset. To show the performance of FS-RRC, it was compared with eleven efficient feature selection methods, MIFS, mRMR, CMIM, ReliefF, FCBF, PGVNS, MCRMCR, MCRMICR, RCDFS, SAFE and SVM-RFE on two synthetic datasets and fifteen public biological datasets. The experimental results showed the superiority of FS-RRC in accuracy, sensitivity, specificity, stability and time complexity. Hence, integrating feature individual discriminative ability, redundancy and complementarity can define more powerful feature subset for biological data analysis, and feature complementarity can help to study the biomedical phenomena more accurately.

Highly Selective and Switchable Access to Tetrasubstituted Alkenyl Sulfones and Naphthyl Sulfones: 1,4-Aryl Migration versus Cyclization.

A highly efficient, chemo-, and regioselective approach has been developed for the switchable synthesis of tetrasubstituted alkenyl sulfones and naphthyl sulfones from homopropargylic alcohols via sulfonylation/1,4-aryl migration and sulfonylation/cyclization. The present switchable processes are characterized by mild and metal-free conditions, high selectivities, good functional group tolerance, the use of inexpensive and easily handled sulfonyl hydrazides as sulfonyl sources, and the release of the nontoxic byproducts H2O and N2.

Construction of ß to carbonyl stereogenic centres by asymmetric 1,4-addition of alkylzirconocenes to dienones and ynenones.

Copper-catalyzed asymmetric 1,4-addition of alkylzirconium species to linear α,ß,γ,δ unsaturated dienones and ynenones is reported. A variety of alkyl nucleophiles are introduced with good yields and excellent regio- and enantio-selectivities to give tertiary carbon centres bearing multiple functional groups. The method is also applicable to an ynethioate with ee's over 96%.

Asymmetric conjugate addition of alkylzirconium reagents to α,ß-unsaturated thioesters: access to fragrances and acyclic stereochemical arrays.

Copper-catalyzed asymmetric conjugate addition of alkylzirconium species to α,ß-unsaturated thioesters is reported. A variety of functionalized alkyl nucleophiles were introduced with yields around 70% and ee's over 92%. The method was applied to the straightforward syntheses of the commercially important fragrances phenoxanol (both enantiomers 97% ee), and hydroxycitronellal (98% ee). The 1,4-addition products can be converted to enantiomerically enriched linear building blocks bearing a terminal functional group. Formation of further α,ß-unsaturated thioesters provides an iterative route for the stereocontrolled synthesis of functionalized acyclic arrays and we demonstrate almost complete catalyst control in the formation of additional stereocentres.

Acyclic quaternary centers from asymmetric conjugate addition of alkylzirconium reagents to linear trisubstituted enones.

Synthetic methods for the selective formation of all carbon quaternary centres in non-cyclic systems are rare. Here we report highly enantioselective Cu-catalytic asymmetric conjugate addition of alkylzirconium species to twelve different acyclic trisubstituted enones. A variety of sp3-hybridized nucleophiles generated by in situ hydrozirconation of alkenes with the Schwartz reagent can be introduced, giving linear products bearing quaternary centres with up to 98% ee. The method is tolerant of several important functional groups and 27 total examples are reported. The method uses a new chiral nonracemic phosphoramidite ligand in a complex with copper triflate as the catalyst. This work allows the straightforward stereocontrolled formation of a valuable structural motif using only a catalytic amount of chiral reagent.

Brain areas activated by uncertain reward-based decision-making in healthy volunteers.

Reward-based decision-making has been found to activate several brain areas, including the ventrolateral prefrontal lobe, orbitofrontal cortex, anterior cingulate cortex, ventral striatum, and mesolimbic dopaminergic system. In this study, we observed brain areas activated under three degrees of uncertainty in a reward-based decision-making task (certain, risky, and ambiguous). The tasks were presented using a brain function audiovisual stimulation system. We conducted brain scans of 15 healthy volunteers using a 3.0T magnetic resonance scanner. We used SPM8 to analyze the location and intensity of activation during the reward-based decision-making task, with respect to the three conditions. We found that the orbitofrontal cortex was activated in the certain reward condition, while the prefrontal cortex, precentral gyrus, occipital visual cortex, inferior parietal lobe, cerebellar posterior lobe, middle temporal gyrus, inferior temporal gyrus, limbic lobe, and midbrain were activated during the 'risk' condition. The prefrontal cortex, temporal pole, inferior temporal gyrus, occipital visual cortex, and cerebellar posterior lobe were activated during ambiguous decision-making. The ventrolateral prefrontal lobe, frontal pole of the prefrontal lobe, orbitofrontal cortex, precentral gyrus, inferior temporal gyrus, fusiform gyrus, supramarginal gyrus, inferior parietal lobule, and cerebellar posterior lobe exhibited greater activation in the 'risk' than in the 'certain' condition (P < 0.05). The frontal pole and dorsolateral region of the prefrontal lobe, as well as the cerebellar posterior lobe, showed significantly greater activation in the 'ambiguous' condition compared to the 'risk' condition (P < 0.05). The prefrontal lobe, occipital lobe, parietal lobe, temporal lobe, limbic lobe, midbrain, and posterior lobe of the cerebellum were activated during decision-making about uncertain rewards. Thus, we observed different levels and regions of activation for different types of reward processing during decision-making. Specifically, when the degree of reward uncertainty increased, the number of activated brain areas increased, including greater activation of brain areas associated with loss.