Recurrent neural network based hybrid model for reconstructing gene regulatory network

One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.     

Spectrofluorimetric determination of tetracycline and anhydrotetracycline in serum and urine.

A spectrofluorimetric method, involving alkaline degradation and formation of a magnesium complex, is described for the determination of tetracycline (TC) and anhydrotetracycline (ATC) in their mixed solution. Tetracycline is degraded and determined in alkaline solution. This treatment of ATC produces almost no fluorescence, but a fluorescent magnesium complex forms at pH 7.5. Several synthetic samples of TC and ATC, with TC:ATC ratios ranging from 50:1 to 1:50, were analysed. The recoveries of TC and ATC are about 71-76 and 61-63% in serum, respectively, and are all about 100% in urine.     

Impact of the exchange-correlation functional on the structure of glassy GeSe2

The structural properties of glassy GeSe₂ were studied by using first-principles molecular dynamics with the Becke, Lee, Yang and Parr (BLYP) expression for the exchange-correlation energy within density functional theory. A comparison is made with the results previously obtained for this material by using first-principles molecular dynamics with the Perdew and Wang (PW) exchange-correlation functional. Overall, the structures of the BLYP-GeSe₂ and PW-GeSe₂ networks are quite similar, the BLYP approach favoring a larger number of Ge–Ge homopolar bonds, in better agreement with the experimental results. The BLYP network does, however, feature a smaller fraction of corner-sharing motifs by comparison with the PW network but the fraction of edge-sharing motifs is the same for both structures, at least within the confines of an approach based on a single temporal trajectory. Further studies are required to determine whether agreement between the BLYP structure and experiment can be improved by taking the average over a larger number of temporal trajectories or whether additional developments are required for the exchange-correlation part of the energy functional.     

声速匹配技术对甲状腺癌的诊断价值

本研究的目的是分析声速匹配技术对甲状腺癌的诊断价值。选取了159例甲状腺病变患者设为甲状腺病变组,健康志愿者40例设为甲状腺正常组,行声速匹配技术检查,分析特征并对比ZSI值,评估了声速匹配技术对甲状腺癌的诊断价值。结果显示,甲状腺癌组ZSI值较高(P<0.05)。ZSI值预测甲状腺癌的ROC曲线下面积(AUC)为0.867(0.729~1.004),灵敏度为86.67%,特异度为60.00%,似然比为2.167,ZSI值临界值为50.42 m/s。预后不良组甲状腺癌患者ZSI值高于预后良好组(P<0.05)。由本文结果可知声速匹配技术对不同类型甲状腺癌的诊断准确性均较高,可准确测出患者甲状腺声速值,稳定性良好,可用于临床甲状腺癌的诊断和预后预测。     

Spectrofluorimetric assay of tetracycline and anhydrotetracycline in combination

Spectrofluorimetric methods are described for the assay of tetracycline (TC) and anhydrotetracycline (ATC) in combination, without prior separation. The interference from ATC in the TC assay has been corrected for by forming the aluminium complexes of both drugs and measuring the difference in fluorescence at 475 and 418 nm, with excitation at 393 nm. Similarly, measurement of the fluorescence of the magnesium complexes at 525 and 470 nm (excitation at 440 nm) nullifies TC interference in the ATC assay.     

Fluorimetric determination of tetracycline and anhydrotetracycline

Summary A rapid fluorimetric method employing the complexes of Al³⁺ with tetracycline (TC) and anhydrotetracycline (ATC) has been used for the measurement of the concentrations of TC and ATC in about 10^–7–10^–6 mol/l. The assay offers simplicity and rapidly compared with other methods. The recoveries in the determination of synthetic samples are in the range of 95–103% for TC and 101–120% for ATC. Several determinations of TC and ATC in serum and urine have been carried out with satisfactory results.     

Tetracycline speciation during molecular imprinting in xerogels results in class-selective binding

The creation of tetracycline (TC) responsive molecularly imprinted xerogels (MIXs) was investigated using electronic absorbance, liquid chromatography-ion-trap mass spectrometry (LC-ITMS), and first-principles theory. Experimental results show that the template molecule converts to its epimer, 4-epitetracycline (ETC), during the imprinting process. Additionally, end capping of the MIX surface silanols transforms TC into anhydrotetracycline (ATC) and 4-epianhydrotetracycline (EATC). Hence, despite aiming to imprint for a single analyte (TC), one simultaneously imprints for up to four analogs (TC, ETC, EATC and ATC) within a MIX. Binding studies using LC-MS showed the binding of the prepared xerogels with the four analogs. In some formulations, preferential uptake of ETC, EATC and ATC relative to the template molecule (TC) was observed. Computations of the interaction energies between silane monomers and the four analogs reveal that ETC, EATC and ATC have higher interaction energies and are more likely to be imprinted in comparison to TC.     

Supramolecular Self-Sorting Networks using Hydrogen-Bonding Motifs

A current objective in supramolecular chemistry is to mimic the transitions between complex self-sorted systems that represent a hallmark of regulatory function in nature. In this work, a self-sorting network, comprising linear hydrogen motifs, was created. Selecting six hydrogen-bonding motifs capable of both high-fidelity and promiscuous molecular recognition gave rise to a complex self-sorting system, which included motifs capable of both narcissistic and social self-sorting. Examination of the interactions between individual components, experimentally and computationally, provided a rationale for the product distribution during each phase of a cascade. This reasoning holds through up to five sequential additions of six building blocks, resulting in the construction of a biomimetic network in which the presence or absence of different components provides multiple unique pathways to distinct self-sorted configurations.     

Homomeric Inclusion and Hydrogen‐Bonding Cooperatively Directed Crystalline Assembly of a Cyclotriguaiacylene Derivative with Different Aromatic Acids

Five organic complexes are prepared by cocrystallization of tri‐[(N‐methyl‐2‐imidazoly)methoxy]cyclotriguiacylene (TMIM‐CTG) with corresponding acids. While hydrogen bonds are the basic interactions in the five complexes, the homo‐included motifs with C? H···π interactions also control the molecular arrangement except for complex 5 . In complex 1 , the two dimensional networks contain 1D zigzag chains formed by hydrogen bonding and 1D chains of homo‐included CTG motifs. Complex 2 has 2D 4·8² hydrogen bonding networks, and the chains of homomeric inclusion extend the 2D structure to a 3D framework. Complex 3 contains four‐membered rings through hydrogen bonds, and the chains of homo‐inclusion link the rings to a novel 2D network. In complex 4 , hydrogen bonds construct a novel double‐layer 2D network, which is extended to a 3D framework by "hand‐shake" dimeric homomeric inclusion interactions. Interestingly, there is no homomeric inclusion phenomenon in complex 5 and hydrogen bonds solely connect two components to a chiral (6,3) 2D network.     

Development and utilization of non-coding RNA-small molecule interactions

RNA plays a crucial role in cellular biology as a carrier of genetic information. However, beyond this passive role, RNA has been shown to regulate various cellular processes in a form that is not translated into protein. Non-coding RNA (ncRNA) has been shown to be important in gene regulation, and its aberrant activity has been associated with several disease states. As such, ncRNAs represent a novel target for small molecule regulation and recently, significant advances have been made towards elucidating small molecule regulators of ncRNAs. Herein, we provide an overview of miRNA, siRNA, RNA aptamers, riboswitches, and ribozymes, within the context of recent findings regarding the exogenous regulation of these ncRNAs by small molecules. The development of these small molecule tools has far-reaching applications in the advancement of molecular therapeutics.     

A closer look at "social" boundary genes reveals knowledge to gene expression profiles

As social network analysis is gaining popularity in modeling real world problems, the task of applying the social network model concepts and notions to biological data is still one of the most attractive research problems to be addressed. According, our work described in this paper focuses on a particular set of genes that reside on the community boundaries in gene co-expression networks. Stemmed from community mining problem in social networks, peripheries of communities (i.e., boundaries) can be used to aid certain biological analysis. The proposed method consists of three parts: 1) Finding communities of gene co-expression networks through clustering. 2) Analyzing stability of community structures by Monte Carlo method. 3) Designing of dynamic adoption of boundaries using geometric convexity. We validated our findings using breast cancer gene expression data from various studies. Our approach contributes to the new branch of applying social network mechanisms in biological data analysis, leading to new data mining strategies implied by witnessing social behaviors in gene expression analysis.