Short Timeframe Prediction of Kidney Failure among Patients with Advanced Chronic Kidney Disease.

BACKGROUND: Development of a short timeframe (6-12 months) kidney failure risk prediction model may serve to improve transitions from advanced chronic kidney disease (CKD) to kidney failure and reduce rates of unplanned dialysis. The optimal model for short timeframe kidney failure risk prediction remains unknown. METHODS: This retrospective study included 1757 consecutive patients with advanced CKD (mean age 66 years, estimated glomerular filtration rate 18 mL/min/1.73 m2). We compared the performance of Cox regression models using (a) baseline variables alone, (b) time-varying variables and machine learning models, (c) random survival forest, (d) random forest classifier in the prediction of kidney failure over 6/12/24 months. Performance metrics included area under the receiver operating characteristic curve (AUC-ROC) and maximum precision at 70% recall (PrRe70). Top-performing models were applied to 2 independent external cohorts. RESULTS: Compared to the baseline Cox model, the machine learning and time-varying Cox models demonstrated higher 6-month performance [Cox baseline: AUC-ROC 0.85 (95% CI 0.84-0.86), PrRe70 0.53 (95% CI 0.51-0.55); Cox time-varying: AUC-ROC 0.88 (95% CI 0.87-0.89), PrRe70 0.62 (95% CI 0.60-0.64); random survival forest: AUC-ROC 0.87 (95% CI 0.86-0.88), PrRe70 0.61 (95% CI 0.57-0.64); random forest classifier AUC-ROC 0.88 (95% CI 0.87-0.89), PrRe70 0.62 (95% CI 0.59-0.65)]. These trends persisted, but were less pronounced, at 12 months. The random forest classifier was the highest performing model at 6 and 12 months. At 24 months, all models performed similarly. Model performance did not significantly degrade upon external validation. CONCLUSIONS: When predicting kidney failure over short timeframes among patients with advanced CKD, machine learning incorporating time-updated data provides enhanced performance compared with traditional Cox models.

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds.

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.

Alkynedicobalt mediated vinylogous Nazarov reactions.

The SnCl4 mediated reactions of cross conjugated aryl enynone-Co2(CO)6 and dienynone-Co2(CO)6 complexes afford benzocycloheptynone complexes or cycloheptenynone complexes in a thermal vinylogous Nazarov process. The rate of ring closure is strongly dependent on the ketone α-substituent.

Generation and Reactions of ε-Carbonyl Cations via Group 13 Catalysis.

The generation of ε-carbonyl cations and their reactions with nucleophiles is accomplished readily without transition metal cation stabilization, using the ε-bromide dienoate or dienone starting materials and GaCl3 or InCl3 catalysis. Arene nucleophiles are somewhat more straightforward than allyltrimethylsilane, but allyltrimethylsilane and propiophenone trimethysilyl enol ether each react successfully with InCl3 catalysis. The viability of these cations is supported by DFT calculations.

Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction.

The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.

Reaction of Alkynyl- and Alkenyltrifluoroborates with Propargyldicobalt Cations: Alkynylation, Alkenylation, and Cyclopropanation Product Pathways.

The Lewis acid-mediated Nicholas reactions of propargyl acetate-Co2(CO)6 complexes with a series of potassium alkynyltrifluoroborates and potassium alkenyltrifluoroborates are described. Alkynyltrifluoroborates directly alkynylate the intermediate propargyldicobalt cations. In contrast, alkenyltrifluoroborates proceed through one of the three modes of dominant reactivity: C-2-substituted alkenyltrifluorobrates directly alkenylate, predominantly with the retention of stereochemistry. C-1-substituted alkenyltrifluoroborates alkenylate at C-2. Potassium vinyltrifluoroborate incorporates a cyclopropane at the site propargyl to alkynedicobalt. Computational analysis of these systems explains the differential modes of reactivity of alkenyltrifluoroborates and outlines the probable mechanisms for the formation of each product.

The formation of benzoxacin-3-ones via intramolecular Nicholas reactions and synthesis of 8-membered heliannuols.

The γ-carbonyl cations generated from propargyl ether-Co2(CO)6 complexes undergo intramolecular Nicholas reactions to give dehydrobenzoxacin-3-one-Co2(CO)6 complexes in good yields. Reductive decomplexation and subsequent manipulation allows the synthesis of (±)-heliannuol K methyl ether and the formal syntheses of (±)-heliannuol K, (±)-heliannuol A, and (-)-heliannuol L.

A review of network-based approaches to drug repositioning.

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

A semi-supervised machine learning framework for microRNA classification.

BACKGROUND: MicroRNAs (miRNAs) are a family of short, non-coding RNAs that have been linked to critical cellular activities, most notably regulation of gene expression. The identification of miRNA is a cross-disciplinary approach that requires both computational identification methods and wet-lab validation experiments, making it a resource-intensive procedure. While numerous machine learning methods have been developed to increase classification accuracy and thus reduce validation costs, most methods use supervised learning and thus require large labeled training data sets, often not feasible for less-sequenced species. On the other hand, there is now an abundance of unlabeled RNA sequence data due to the emergence of high-throughput wet-lab experimental procedures, such as next-generation sequencing. RESULTS: This paper explores the application of semi-supervised machine learning for miRNA classification in order to maximize the utility of both labeled and unlabeled data. We here present the novel combination of two semi-supervised approaches: active learning and multi-view co-training. Results across six diverse species show that this multi-stage semi-supervised approach is able to improve classification performance using very small numbers of labeled instances, effectively leveraging the available unlabeled data. CONCLUSIONS: The proposed semi-supervised miRNA classification pipeline holds the potential to identify novel miRNA with high recall and precision while requiring very small numbers of previously known miRNA. Such a method could be highly beneficial when studying miRNA in newly sequenced genomes of niche species with few known examples of miRNA.

Extension of the 5-alkynyluridine side chain via C-C-bond formation in modified organometallic nucleosides using the Nicholas reaction.

Dicobalt hexacarbonyl nucleoside complexes of propargyl ether or esters of 5-substituted uridines react with diverse C-nucleophiles. Synthetic outcomes confirmed that the Nicholas reaction can be carried out in a nucleoside presence, leading to a divergent synthesis of novel metallo-nucleosides enriched with alkene, arene, arylketo, and heterocyclic functions, in the deoxy and ribo series.

Long-Lasting Complex Reaction Behavior in a Closed Ferroin-Bromate-Hydroxybenzenesulfonate System.

The bromate-phenolsulfonate reaction was found to exhibit spontaneous oscillations in a batch reactor, where the addition of small amounts of ferroin would result in nonoscillatory behavior. As the ferroin concentration was increased, the system produced very rich nonlinear behavior, including three isolated oscillatory regimes that were separated by as long as 48 h nonoscillatory period. The long-lasting nonlinear behavior may be attributed to the slow desulfonation of phenolsulfonate in an acidic solution, forming phenol-like intermediates. However, unlike the bromate-phenol oscillator, oxygen was found to greatly influence the reaction, and various complex oscillations could be observed by tuning the oxygen concentration. Mechanistic studies performed through employing 1H NMR spectroscopy and mass spectrometry to measure intermediate species at different stages of the reaction were able to identify 1,4-benzoquinone, 2-bromo-1,4-benzoquinone, 2,6-dibromo-1,4-benzoquinone, and 2,4,6-tribromophenol as major components during the reaction.

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning.

Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm.

A framework for improving microRNA prediction in non-human genomes.

The prediction of novel pre-microRNA (miRNA) from genomic sequence has received considerable attention recently. However, the majority of studies have focused on the human genome. Previous studies have demonstrated that sensitivity (correctly detecting true miRNA) is sustained when human-trained methods are applied to other species, however they have failed to report the dramatic drop in specificity (the ability to correctly reject non-miRNA sequences) in non-human genomes. Considering the ratio of true miRNA sequences to pseudo-miRNA sequences is on the order of 1:1000, such low specificity prevents the application of most existing tools to non-human genomes, as the number of false positives overwhelms the true predictions. We here introduce a framework (SMIRP) for creating species-specific miRNA prediction systems, leveraging sequence conservation and phylogenetic distance information. Substantial improvements in specificity and precision are obtained for four non-human test species when our framework is applied to three different prediction systems representing two types of classifiers (support vector machine and Random Forest), based on three different feature sets, with both human-specific and taxon-wide training data. The SMIRP framework is potentially applicable to all miRNA prediction systems and we expect substantial improvement in precision and specificity, while sustaining sensitivity, independent of the machine learning technique chosen.

Vinylogous Nicholas reactions in the synthesis of bi- and tricyclic cycloheptynedicobalt complexes.

The Lewis acid mediated intramolecular Nicholas reactions of allylic acetate enyne-Co2(CO)6 complexes afford cycloheptenyne-Co2(CO)6 complexes in three manifestations. Electron rich aryl substituted alkyne complexes give tricyclic 6,7,x-benzocycloheptenyne complexes, with x = 5, 6, or 7. Allylsilane substituted complexes afford exo methylene bicyclic x,7-cycloheptenyne complexes (x = 6,7). The allyl acetate function may also be replaced by a benzylic acetate, to afford dibenzocycloheptyne-Co2(CO)6 complexes. Following reductive complexation, the methodology may be applied to the synthesis of the icetexane diterpene carbon framework.

Efficient prediction of human protein-protein interactions at a global scale.

BACKGROUND: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. RESULTS: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. CONCLUSIONS: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

Complex reaction dynamics in the cerium-bromate-2-methyl-1,4-hydroquinone photoreaction.

Spontaneous oscillations with a long induction time were observed in the bromate-2-methyl-1,4-hydroquinone photoreaction in a batch reactor, where removal of illumination effectively quenched any reactivity. A substantial lengthening of the oscillatory window and a dramatic increase in the complexity of the reaction behavior arose upon the addition of cerium ions, in which separate bifurcation regions and mixed mode oscillations were present. The complexity has a strong dependence on the intensity of illumination supplied to the system and on the initial concentrations of the reactants. (1)H NMR spectroscopy measurements show that the photoreduction of 2-methyl-1,4-benzoquinone leads to the formation of 2-methyl-1,4-hydroquinone and the compound 2-hydroxy-3-methyl-1,4-benzoquinone. Spectroscopic investigation also indicates that the presence of methyl group hinders the bromination of the studied organic substrate 2-methyl-1,4-hydroquinone, resulting in the formation of 2-methyl-1,4-benzoquinone.

Simulating whole-body vibration for neonatal patients on a tire-coupled road simulator.

Exposure to excessive whole-body vibration is linked to health issues and may result in increased rates of mortality and morbidity in infants. Newborn infants requiring specialized treatment at neonatal intensive care units often require transportation by road ambulance to specialized care centers, exposing the infants to potentially harmful vibration and noise. A standardized Neonatal Patient Transport System (NPTS) has been deployed in Ontario, Canada, that provides life saving equipment to patients and safe operation for the clinical care staff. However, there is evidence that suggests patients may experience a higher amplitude of vibration at certain frequencies when compared with the vehicle vibration. In a multi-year collaborative project, we seek to create a standardized test procedure to evaluate the levels of vibration and the effectiveness of mitigation strategies. Previous studies have looked at laboratory vibration testing of a transport system or transport incubator and were limited to single degree of freedom excitation, neglecting the combined effects of rotational motion. This study considers laboratory testing of a full vehicle and patient transport system on an MTS Model 320 Tire-Coupled Road Simulator. The simulation of road profiles and discrete events on a tire-coupled road simulator allows for the evaluation of the vibration levels of the transport system and the exploration of mitigation strategies in a controlled setting. The tire-coupled simulator can excite six degrees-of-freedom motion of the transport system for vibration evaluation in three orthogonal directions including the contributions of the three rotational degrees of freedom. The vibration data measured on the transport system during the tire-coupled testing are compared to corresponding road test data to assess the accuracy of the vibration environment replication. Three runs of the same drive file were conducted during the laboratory testing, allowing the identification of anomalies and evaluation of the repeatability. The tire-coupled full vehicle testing revealed a high level of accuracy in re-creating the road sections and synthesized random profiles. The simulation of high amplitude discrete events, such as speed hump traverses, were highly repeatable, yet yielded less accurate results with respect to the peak amplitudes at the patient. The resulting accelerations collected at the input to the manikin (sensor located under the mattress) matched well between the real-world and road simulator. The sensors used during testing included series 3741B uni-axial and series 356A01 tri-axial accelerometers by PCB Piezotronics. These results indicate a tire-coupled road simulator can be used to accurately evaluate vibration levels and assess the benefits of future mitigation strategies in a controlled setting with a high level of repeatability.

Identification of thyroid hormone receptor binding sites in developing mouse cerebellum.

BACKGROUND: Thyroid hormones play an essential role in early vertebrate development as well as other key processes. One of its modes of action is to bind to the thyroid hormone receptor (TR) which, in turn, binds to thyroid response elements (TREs) in promoter regions of target genes. The sequence motif for TREs remains largely undefined as does the precise chromosomal location of the TR binding sites. A chromatin immunoprecipitation on microarray (ChIP-chip) experiment was conducted using mouse cerebellum post natal day (PND) 4 and PND15 for the thyroid hormone receptor (TR) beta 1 to map its binding sites on over 5000 gene promoter regions. We have performed a detailed computational analysis of these data. RESULTS: By analysing a recent spike-in study, the optimal normalization and peak identification approaches were determined for our dataset. Application of these techniques led to the identification of 211 ChIP-chip peaks enriched for TR binding in cerebellum samples. ChIP-PCR validation of 25 peaks led to the identification of 16 true positive TREs. Following a detailed literature review to identify all known mouse TREs, a position weight matrix (PWM) was created representing the classic TRE sequence motif. Various classes of promoter regions were investigated for the presence of this PWM, including permuted sequences, randomly selected promoter sequences, and genes known to be regulated by TH. We found that while the occurrence of the TRE motif is strongly correlated with gene regulation by TH for some genes, other TH-regulated genes do not exhibit an increased density of TRE half-site motifs. Furthermore, we demonstrate that an increase in the rate of occurrence of the half-site motifs does not always indicate the specific location of the TRE within the promoter region. To account for the fact that TR often operates as a dimer, we introduce a novel dual-threshold PWM scanning approach for identifying TREs with a true positive rate of 0.73 and a false positive rate of 0.2. Application of this approach to ChIP-chip peak regions revealed the presence of 85 putative TREs suitable for further in vitro validation. CONCLUSIONS: This study further elucidates TRß gene regulation in mouse cerebellum, with 211 promoter regions identified to bind to TR. While we have identified 85 putative TREs within these regions, future work will study other mechanisms of action that may mediate the remaining observed TR-binding activity.

Nonlinear dynamical behavior in the photodecomposition of N-bromo-1,4-benzoquinone-4-imine.

Photodecomposition of N-bromo-1,4-benzoquinone-4-imine in water with and without the presence of oxidants was investigated, exhibiting nonlinear kinetic features such as autocatalytic excursion. An acidic environment was found to be essential for the observed photodecomposition. Mechanistic studies through NMR and mass spectrometry show the conversion of N-bromo-1,4-benzoquinone-4-imine into 1,4-benzoquinone. When bromate was introduced to the studied system, transient spontaneous oscillations were uncovered, forming a new photocontrolled chemical oscillator. The system's sensitivity to the intensity of the illumination is great as decreases halt the oscillations from occurring. Phase diagrams show that as the concentration of N-bromo-1,4-benzoquinone-4-imine is increased, broader concentration ranges of both bromate and sulfuric acid allow the system to exhibit spontaneous oscillations. Electrospray-TOF mass spectroscopy and (13)C NMR spectra suggest that 3,4,4-tribromo-2-hydroxycyclohexa-2,5-dienone is a major product in the bromate system.

P-TarPmiR accurately predicts plant-specific miRNA targets.

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.