Contribution of various substrates to total citric acid cycle flux and ]anaplerosis as determined by13C isotopomer analysis and O2 consumption in the heart

A simple relationship between parameters derived from a¹³C NMR isotopomer analysis and O₂ consumption is presented that allows measurement of the absolute rate of acetyl-CoA oxidation and anaplerotic flux in tissues oxidizing a mixture of four substrates. The method was first applied in a study of the effects of work state and -adrenergic stimulation on net acetate oxidation and anaplerosis in the isolated working rat heart. The results demonstrate that the anticipated ratio of 2 between O₂ consumption and TCA cycle flux for hearts oxidizing only acetate holds at low workload when anaplerosis is low, but deviates toward a factor of 3 under high workload conditions when anaplerosis is increased. This analysis was also extended to hearts that oxidize a more physiological mixture of substrates including long-chain fatty acids, acetoacetate, lactate, pyruvate, and glucose. We show that the contribution each substrate makes to total TCA cycle flux can be determined by combined¹³C NMR and O₂ consumption measurements. The present study also demonstrates that stimulation of anaplerosis (by addition of propionate) can significantly alter the relative contribution each substrate makes to total TCA cycle flux. We conclude that if¹³C labeling patterns are selected appropriately, a comprehensive picture of flux through all major metabolic pathways feeding the cycle can be determined in a single experiment even when complex physiological mixtures of substrates are provided.     

Development of quantitative structure-activity relationships for explanatory modeling of fast reacting (meth)acrylate monomers bearing novel functionality

The photoinitiated polymerization of (meth)acrylate monomers bearing novel carbamate functionality exhibits significantly greater reaction rate when compared to more traditional acrylate monomers undergoing similar polymerization. This unusually fast reactivity has been the subject of much investigation. In order to suggest an explanatory mechanism for the enhanced polymerization rates we have conducted quantitative structure-activity relationship investigations of these novel monomers. These studies have resulted in statistically sound models with coefficients of multiple determination of R(2)>0.93. Principal component and k nearest neighbor similarity analysis were also conducted on the multiple regression models. These results are discussed in light of published experimental investigations of the photopolymerization reactivity of the novel monomers.     

Adaptive relaxation for querying heterogeneous XML data sources

Searching XML data with a structured XML query can improve the precision of results compared with a keyword search. However, the structural heterogeneity of the large number of XML data sources makes it difficult to answer the structured query exactly. As such, query relaxation is necessary. Previous work on XML query relaxation poses the problem of unnecessary computation of a big number of unqualified relaxed queries. To address this issue, we propose an adaptive relaxation approach which relaxes a query against different data sources differently based on their conformed schemas. In this paper, we present a set of techniques that supports this approach, which includes schema-aware relaxation rules for relaxing a query adaptively, a weighted model for ranking relaxed queries, and algorithms for adaptive relaxation of a query and top-k query processing. We discuss results from a comprehensive set of experiments that show the effectiveness and the efficiency of our approach.     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.     

Process and learning outcomes from remotely-operated,simulated, and hands-on student laboratories

A large-scale, multi-year, randomized study compared learning activities and outcomes for hands-on, remotely-operated, and simulation-based educational laboratories in an undergraduate engineering course. Students (N = 458) worked in small-group lab teams to perform two experiments involving stress on a cantilever beam. Each team conducted the experiments in one of three lab formats (hands-on, remotely-operated, or simulation-based), collecting data either individually or as a team. Lab format and data-collection mode showed an interaction, such that for the hands-on lab format learning outcomes were higher when the lab team collected data sets working as a group rather than individually collecting data sets to be combined later, while for remotely-operated labs individual data collection was best. The pattern of time spent on various lab-related activities suggests that working with real instead of simulated data may induce higher levels of motivation. The results also suggest that learning with computer-mediated technologies can be improved by careful design and coordination of group and individual activities.     

Insights from a simple expression for linear fisher information in a recurrently connected population of spiking neurons

A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., information-maximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.     

On NFAs where all states are final,initial, or both

We examine questions involving nondeterministic finite automata where all states are final, initial, or both initial and final. First, we prove hardness results for the nonuniversality and inequivalence problems for these NFAs. Next, we characterize the languages accepted. Finally, we discuss some state complexity problems involving such automata.