Crystallographic report: catena‐Aqua(2,2′‐bipyrimidine)lithium(I) perchlorate

A lithium(I) coordination polymer has been formed from LiClO₄ and the 2,2′‐bipyrimidine (bpym) ligand in which each square pyramidal lithium(I) atom is coordinated in the basal plane by four nitrogen donor atoms derived from two bpym ligands and one water molecule at the apical position. These are connected into a layer structure via hydrogen‐bonding interactions involving the perchlorate anions. Copyright © 2004 John Wiley & Sons, Ltd.     

Crystallographic report: Crystal structure of tetra(4‐methyl‐5‐imidazole‐carboxyaldehyde)zinc(II) diperchlorate

The central zinc(II) atom in the title complex is tetrahedrally coordinated by four nitrogen atoms derived from 4‐methyl‐5‐imidazolecarboxyaldehyde ligands with Zn? N in the range 2.007(3) to 2.026(4) Å. Copyright © 2003 John Wiley & Sons, Ltd.     

Novel routes to 1-aryl-1,4-dihydro-3(2H)-isoquinolinones and 2-substituted or 2,3-disubstituted benzofurans by intramolecular cyclizations

N-(α-Benzotriazolylalkyl)arylacetamides, readily available from an arylacetamide, an aldehyde and benzotriazole, undergo intramolecular cyclization under acidic conditions to give 1-aryl-1,4-dihydro-3(2H)-isoquinolinones in good to excellent yields. Similarly, 2-(benzotriazol-1-yl)-2-(o-hydroxyphenyl)ethanols, obtained by lithiation of 2-(benzotriazol-1-ylmethyl)phenols followed by quenching with aldehydes or ketones, eliminate a molecule of water and a molecule of benzotriazole yielding 2-substituted and 2,3-disubstituted benzofurans.     

聚苯乙烯稀溶液的小角激光弹性和准弹性光散射

随着激光技术的发展,近年来出现了与散射光频加宽相应的光子相干光谱即准弹性光散射。于是,利用光散射(也称弹性光散射)法不仅能获得高聚物的各种物性参数如分子量(?),第二维利系数A_2和均方旋转半径(),而且还能得到大分子在溶液中的动态参数,即扩散系数D_0。由D_0求得流体力学半径R_H。本文只涉及小角度准弹性光散射(1°<θ<7°)对稀溶液范围的平动扩散系数的测定。与文献比较,表明方法是成功的。     

A zwitterion of 1,5‐bis­(2‐hydroxy­benzamido)‐3‐aza­pentane: a two‐dimensional hydrogen‐bonding network involving dimers

The title compound, 2‐{N‐[2‐(2‐hydroxy­benzamido)ethyl­ammonio­ethyl]amino­carbon­yl}phenolate, C₁₈H₂₁N₃O₄, crystallizes in a zwitterionic form as a result of inter­molecular proton transfer and possesses a negatively charged phenolate group and a protonated amino group. The 2‐hydroxy­benzamide and 2‐(amino­carbonyl)­phenolate moieties attached to the two ends of the C—C—N—C—C backbone adopt a cis conformation in relation to this backbone. All N‐ and O‐bound H atoms are involved in hydrogen‐bond formation; the zwitterions are first linked into head‐to‐tail dimers, which are further organized into a two‐dimensional network parallel to the crystallographic bc plane.     

Co-C bond dissociation energy and reaction volume change of 2',5'-dideoxyadenosylcobalamin studied by laser-induced time-resolved photoacoustic calorimetry

Time resolved photoacoustic calorimetry (PAC) was applied to a study of the photolysis of a coenzyme B(12) analog 2',5'-dideoxyadenosylcobalamin, which lacks an -OH group at the 2' position of ribofuranose ring. In aqueous solution, we report for the first time the quantum yield Phi(d) (0.25+/-0.02), Co-C bond dissociation energy (BDE; 31.8+/-2.5 kcal mol(-1)) and reaction volume change deltaV(R) (6.5+/-0.5 ml mol(-1)) due to conformation changes of the corrin ring and its side chains accompanying the cleavage of the Co-C bond. These values for the analog are very similar to those for the natural cofactor. Based our results and previous studies, a possible explanation for the similarity in their structure and properties versus the large difference in their enzymatic activity is discussed.     

Rethinking ramoplanin: the role of substrate binding in inhibition of peptidoglycan biosynthesis

Ramoplanin is a cyclicdepsipeptide antibiotic that inhibits peptidoglycan biosynthesis. It was proposed in 1990 to block the MurG step of peptidoglycan synthesis by binding to the substrate of MurG, Lipid I. The proposed mechanism of MurG inhibition has become widely accepted even though it was never directly tested. In this paper, we disprove the accepted mechanism for how ramoplanin functions, and we present an alternative mechanism. This work has implications for the design of ramoplanin derivatives and may influence how other proposed substrate binding antibiotics are studied.     

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.     

Deep Multilabel Multilingual Document Learning for Cross-Lingual Document Retrieval

Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods.