Potential tuberculostatic agents. Topliss application on benzoic acid [(5-nitro-thiophen-2-yl)-methylene]-hydrazide series

Nitroaromatic compounds such as nifuroxazide are used in many human enteropathogenic bacteria infections without causing an increase in the plasmidial antibiotic resistance of the aerobic Gram-negative intestinal Enterobacteriaceae. For these reasons, these compounds have been synthesized using the rational approach of Topliss' decision tree. Generally, this approach allows us to obtain the most active derivative from the series in a few steps. These compounds were tested against Mycobacterium tuberculosis in vitro and the most active of the series identified. A new lead for potential tuberculostatic activity has been predicted and will be used in further QSAR studies.     

DNA molecule classification using feature primitives

BACKGROUND: We present a novel strategy for classification of DNA molecules using measurements from an alpha-Hemolysin channel detector. The proposed approach provides excellent classification performance for five different DNA hairpins that differ in only one base-pair. For multi-class DNA classification problems, practitioners usually adopt approaches that use decision trees consisting of binary classifiers. Finding the best tree topology requires exploring all possible tree topologies and is computationally prohibitive. We propose a computational framework based on feature primitives that eliminates the need of a decision tree of binary classifiers. In the first phase, we generate a pool of weak features from nanopore blockade current measurements by using HMM analysis, principal component analysis and various wavelet filters. In the next phase, feature selection is performed using AdaBoost. AdaBoost provides an ensemble of weak learners of various types learned from feature primitives. RESULTS AND CONCLUSION: We show that our technique, despite its inherent simplicity, provides a performance comparable to recent multi-class DNA molecule classification results. Unlike the approach presented by Winters-Hilt et al., where weaker data is dropped to obtain better classification, the proposed approach provides comparable classification accuracy without any need for rejection of weak data. A weakness of this approach, on the other hand, is the very "hands-on" tuning and feature selection that is required to obtain good generalization. Simply put, this method obtains a more informed set of features and provides better results for that reason. The strength of this approach appears to be in its ability to identify strong features, an area where further results are actively being sought.     

Classifying Cinnamomums using rough sets classifier based on interval-discretization

Classification, which is the task of assigning objects to one of several predefined categories, is a pervasive problem that encompasses many diverse applications. Decision tree classifier, which is a simple yet widely used classification technique, employs training data to yield decision rules; moreover, it can create thresholds and then split the list of continuous attributes into descrete intervals for handling continuous attributes (Quinlan in Journal of Artificial Intelligence Research 4:77–90, 1996). Rough set theory (Pawlak in International Journal of Computer and Information Sciences 11:341–356, 1982; International Journal of Man-Machine Studies 20:469–483, 1984; Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht, 1991) has been applied to a wide variety of decision analysis problems for the extraction of rules from databases. This paper proposes a hybrid approach that takes advantage of combining decision tree and rough sets classifier and applies it to plant classification. The introduced approach starts with decision tree classifier (C4.5) as preprocessing technique to make interval-discretization, subsequently, and uses rough set method for extracting rules. The proposed approach aims at finding out classification rules via analyzing lamina attributes (leaf stalk, leaf width, leaf length, length/width ratio) of Cinnamomum, which are gathered and measured by plant specialists in the field of Taiwan. A comparison with the widely used algorithms (e.g., decision tree, multilayer perceptrons, naïve Bayes, and rough sets classifier) is carried out to show numerous advantages of the proposed approach. Finally, employing with test data in which species are unknown, results of classification are approved by consulting the relative plant specialists.     

Personalized Modeling for Prediction with Decision-Path Models

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach.     

Support vector machine implementations for classification & clustering

BACKGROUND: We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide noise tolerant solutions for pattern recognition. The SVM approach encapsulates a significant amount of model-fitting information in the choice of its kernel. In work thus far, novel, information-theoretic, kernels have been successfully employed for notably better performance over standard kernels. Currently there are two approaches for implementing multiclass SVMs. One is called external multi-class that arranges several binary classifiers as a decision tree such that they perform a single-class decision making function, with each leaf corresponding to a unique class. The second approach, namely internal-multiclass, involves solving a single optimization problem corresponding to the entire data set (with multiple hyperplanes). RESULTS: Each SVM approach encapsulates a significant amount of model-fitting information in its choice of kernel. In work thus far, novel, information-theoretic, kernels were successfully employed for notably better performance over standard kernels. Two SVM approaches to multiclass discrimination are described: (1) internal multiclass (with a single optimization), and (2) external multiclass (using an optimized decision tree). We describe benefits of the internal-SVM approach, along with further refinements to the internal-multiclass SVM algorithms that offer significant improvement in training time without sacrificing accuracy. In situations where the data isn't clearly separable, making for poor discrimination, signal clustering is used to provide robust and useful information--to this end, novel, SVM-based clustering methods are also described. As with the classification, there are Internal and External SVM Clustering algorithms, both of which are briefly described.     

FragIt: A Tool to Prepare Input Files for Fragment Based Quantum Chemical Calculations

Near linear scaling fragment based quantum chemical calculations are becoming increasingly popular for treating large systems with high accuracy and is an active field of research. However, it remains difficult to set up these calculations without expert knowledge. To facilitate the use of such methods, software tools need to be available to support these methods and help to set up reasonable input files which will lower the barrier of entry for usage by non-experts. Previous tools relies on specific annotations in structure files for automatic and successful fragmentation such as residues in PDB files. We present a general fragmentation methodology and accompanying tools called FragIt to help setup these calculations. FragIt uses the SMARTS language to locate chemically appropriate fragments in large structures and is applicable to fragmentation of any molecular system given suitable SMARTS patterns. We present SMARTS patterns of fragmentation for proteins, DNA and polysaccharides, specifically for D-galactopyranose for use in cyclodextrins. FragIt is used to prepare input files for the Fragment Molecular Orbital method in the GAMESS program package, but can be extended to other computational methods easily.     

Methanethiosulfonate Derivatives Inhibit Current through the Ryanodine Receptor/Channel

To identify regions of the ryanodine receptor (RyR) important for ion conduction we modified the channel with sulfhydryl-reacting compounds. After addition of methanethiosulfonate (MTS) compounds channel conductance was decreased while other channel properties, including channel regulation by ATP, caffeine, or Ca, were unaffected. The site of action was accessible to the MTS compounds from the cytoplasmic, but not the luminal, side of the channel. In addition, the hydrophilic MTS compounds were only effective when the channel was open, suggesting that the compounds covalently modify the channel from within the water-filled ion conducting pathway. The decrease in channel current amplitude occurred in a step-wise fashion and was irreversible and cumulative over time, eventually leading to the complete block of channel current. However, the time required for each consecutive modification during continuous exposure to the MTS compounds increased, suggesting that successive modification by the MTS compounds is not independent. These results are consistent with the hypothesis that the channel forms a wide vestibule on the cytoplasmic side and contains a much smaller opening on the luminal side. Furthermore, our results indicate that the MTS compounds can serve as functional markers for specific residues of the RyR to be identified in molecular studies.     

Interactions between proteins implicated in exocytosis and voltage-gated calcium channels

Neurotransmitter release from synaptic vesicles is triggered by voltage-gated calcium influx through P/Q-type or N-type calcium channels. Purification of N-type channels from rat brain synaptosomes initially suggested molecular interactions between calcium channels and two key proteins implicated in exocytosis: synaptotagmin I and syntaxin 1. Co-immunoprecipitation experiments were consistent with the hypothesis that both N- and P/Q-type calcium channels, but not L-type channels, are associated with the 7S complex containing syntaxin 1, SNAP-25, VAMP and synaptotagmin I or II. Immunofluorescence confocal microscopy at the frog neuromuscular junction confirmed that calcium channels, syntaxin 1 and SNAP-25 are co-localized at active zones of the presynaptic plasma membrane where transmitter release occurs. Experiments with recombinant proteins were performed to map synaptic protein interaction sites on the alpha 1A subunit, which forms the pore of the P/Q-type calcium channel. In vitro-translated 35S-synaptotagmin I bound to a site located on the cytoplasmic loop linking homologous domains II and III of the alpha 1A subunit. This direct link would target synaptotagmin, a putative calcium sensor for exocytosis, to a microdomain of calcium influx close to the channel mouth. Cysteine string proteins (CSPs) contain a J-domain characteristic of molecular chaperones that cooperate with Hsp70. They are located on synaptic vesicles and thought to be involved in modulating the activity of presynaptic calcium channels. CSPs were found to bind to the same domain of the calcium channel as synaptotagmin, and also to associate with VAMP. CSPs may act as molecular chaperones in association with Hsp70 to direct assembly or dissociation of multiprotein complexes at the calcium channel.     

牛磺酸镁抗豚鼠尖端扭转型室速的作用研究

目的：研究牛磺酸镁配位化合物（TMCC）抗豚鼠心脏尖端扭转型室速（TdP）的作用。方法：取健康、体重250~300 g的成年雄性豚鼠,随机分为4组：① TdP模型组（n=7）：离体心脏以K-H灌流液灌流20 min,然后使用IKs阻滞剂10μmol/L Chromanol 293B合并低钾（钾离子浓度为1.8 mmol/L）进行灌流,建立TdP模型。②~④ TdP模型+TMCC低中高浓度组（n=6）：正常灌流稳定20 min后,在建立TdP模型的同时分别给予1、2、4 mmol/L TMCC。采用Langendorff逆行主动脉灌流法灌流豚鼠离体心脏,利用Biopac电生理记录仪采集并记录豚鼠离体心脏表面心电图。从心电图第Ⅱ导联图形获取各组豚鼠离体心脏TdP发生率、跨室壁复极离散度、QT间期不稳定性,以观察TMCC对TdP的影响。观测指标量取时间分别为：豚鼠离体心脏正常灌流20 min时、TdP发生前及给药60 min时。结果：TdP模型组的TdP发生率为6/7。1、2、4mmol/L TMCC可降低TdP发生率,三组TdP发生率分别为5/6、1/6、0/6。与给药前相比,TdP模型组中Chromanol 293 B合并低钾可使豚鼠离体心脏校正后的跨室壁复极离散度显著增大（P<0.01）;与TdP模型组相比,TdP模型+1、2、4mmol/L TMCC组可明显减弱Chromanol 293B合并低钾导致的豚鼠离体心脏校正后的跨室壁复极离散度增大（P>0.05）。与模型组相比,2、4mmol/L TMCC明显降低Chromanol 293B合并低钾导致的QT间期不稳定增大（P<0.05）。在TdP模型建立过程中,从心电图中可观察到连续多个心动周期的P波消失,而在TdP模型+TMCC组中,心电图始终拥有独立P波。结论：TMCC可通过降低离体心脏跨室壁复极离散度和QT间期不稳定性以及抑制早后除极的发生而发挥抗TdP作用,降低TdP发生率。     

Advanced Age, Female Gender and Delay in Pacemaker Implantation May Cause TdP in Patients With Complete Atrioventricular Block

Aim

We aimed to report the clinical features related to torsades de pointes (TdP) in patients with complete AV block (CAVB).

Methods

Patients with CAVB who were admitted to our instituition between January 2007 and January 2010 were retrospectively evaluated in terms of the occurence of TdP. The clinical features were compared in patients with and without TdP using the software of SPSS.

Results

Sixty-four patients were determined to have CAVB. Three of them had documented episodes of TdP. All three patients experiencing TdP were females, whereas 48% of the patients with CAVB were females. The mean age of patients with TdP was significantly greater than the mean age of the other patients (85 ±3 vs. 78±7.6, respectively; p<0.05). In our archives, bradycardia exposure time could be determined in only 49 patients without TdP. Among them, just 10 patients had been exposed to bradycardia over 48 hours, whereas all of the 3 patients with TdP had been exposed to bradycardia over 48 hours (p<0.05). Additionally, in two patients with TdP, we demonstrated that QT and QTc prolongation increases as the duration of bradycardia is extended. Furthermore, all patients with TdP had notched T waves in the ECG on the occurrence day of TdP, whereas they did not have any notched T wave in their ECG on the admission day.

Conclusion

Among the patients with CAVB, elderly females are more susceptible to development of TdP. Delay in institution of physiological heart rate leads to further QT prolongation and thereby to TdP. Besides QT prolongation, the finding of T wave notching on ECG may also have a predictive value for TdP.     

Prediction of Antimicrobial Activity of Synthetic Peptides by a Decision Tree Model

Antimicrobial resistance is a persistent problem in the public health sphere. However, recent attempts to find effective substitutes to combat infections have been directed at identifying natural antimicrobial peptides in order to circumvent resistance to commercial antibiotics. This study describes the development of synthetic peptides with antimicrobial activity, created in silico by site-directed mutation modeling using wild-type peptides as scaffolds for these mutations. Fragments of antimicrobial peptides were used for modeling with molecular modeling computational tools. To analyze these peptides, a decision tree model, which indicated the action range of peptides on the types of microorganisms on which they can exercise biological activity, was created. The decision tree model was processed using physicochemistry properties from known antimicrobial peptides available at the Antimicrobial Peptide Database (APD). The two most promising peptides were synthesized, and antimicrobial assays showed inhibitory activity against Gram-positive and Gram-negative bacteria. Colossomin C and colossomin D were the most inhibitory peptides at 5 μg/ml against Staphylococcus aureus and Escherichia coli. The methods described in this work and the results obtained are useful for the identification and development of new compounds with antimicrobial activity through the use of computational tools.     

A standardised approach to calculating floodplain tree condition to support environmental watering decisions

Ground-based visual assessment of crown condition is a cornerstone of tree condition assessment globally, and numerous condition assessment approaches have evolved to address the needs and perspectives of different users. In Australia’s iconic Murray–Darling Basin (MDB), stands of floodplain eucalypts are increasingly vulnerable to a range of interacting stressors related to climate change and over-extraction of water for consumptive and agricultural use. A standardised approach developed in 2008 for assessing floodplain trees within the MDB provides extensive guidance to ensure field data is collected consistently. However, there is minimal instruction on how to interpret data, and consequently a range of evaluation approaches have evolved. The lack of a standardised reporting framework generated by these different approaches makes it difficult for floodplain managers and environmental water holders to make repeatable, robust decisions for prioritising water allocations across competing locations. To provide improved lines of evidence to support decision making, this paper describes a ‘best-practise’ approach to calculating a tree condition score from field data. Within, we document existing approaches in the southern Murray–Darling Basin, and recommend a method that meets the needs of floodplain managers as a pragmatic reporting, communication and decision support tool that does not require statistical analysis. Case studies and a revised conceptual model of tree decline and recovery are provided to demonstrate the validity of the recommended approach.

     

Syntaxin 1A interacts with multiple exocytic proteins to regulate neurotransmitter release in vivo.

Biochemical studies suggest that syntaxin 1A participates in multiple protein-protein interactions in the synaptic terminal, but the in vivo significance of these interactions is poorly understood. We used a targeted mutagenesis approach to eliminate specific syntaxin binding interactions and demonstrate that Drosophila syntaxin 1A plays multiple regulatory roles in neurotransmission in vivo. Syntaxin mutations that eliminate ROP/Munc-18 binding display increased neurotransmitter release, suggesting that ROP inhibits neurosecretion through its interaction with syntaxin. Syntaxin mutations that block Ca2+ channel binding also cause an increase in neurotransmitter release, suggesting that syntaxin normally functions in inhibiting Ca2+ channel opening. Additionally, we identify and characterize a syntaxin Ca2+ effector domain, which may spatially organize the Ca2+ channel, cysteine string protein, and synaptotagmin for effective excitation-secretion coupling in the presynaptic terminal.     

Terrestrial Metals Bioavailability: A Comprehensive Review and Literature-Derived Decision Rule for Ecological Risk Assessment

Interstudy variation among bioavailability studies is a primary deterrent to a universal methodology to assess metals bioavailability to soil-dwelling organisms and is largely the result of specific experimental conditions unique to independent studies. Accordingly, two datasets were established from relevant literature; one includes data from studies related to bioaccumulation (total obs = 520), while the other contains data from studies related to toxicity (total obs = 1264). Experimental factors that affected toxicity and bioaccumulation independent of the effect of soil chemical/physical properties were statistically apportioned from the variation attributed to soil chemical/physical properties for both datasets using a linear mixed model. Residual bioaccumulation data were then used to develop a non-parametric regression tree whereby bootstrap and cross-validation techniques were used to internally validate the resulting decision rule. A similar approach was employed with the toxicity dataset as an independent external validation. A validated decision rule is presented as a quantitative assessment tool that characterizes typical aerobic soils in terms of their potential to sequester common divalent cationic metal contaminants and mitigate their bioavailability to soil-dwelling biota.