Sensitive, indirect photometric detector for high-performance liquid chromatography using a light-emitting diode

A low-noise detector for indirect photometric detection has been constructed using a highly stable source--a light-emitting diode (LED). Use of the detector is demonstrated for reversed-phase liquid chromatography by adding methylene blue to the mobile phase to make a background signal. The indirect determination of alcohols by their effect on methylene blue concentration distribution is demonstrated, and an investigation is made into the conditions for high sensitivity. Because the source exhibits low noise, the detection limits for alcohols are as low as more complex and expensive detection methods, despite the lower radiant power of the LED. Detection limits for nine alcohols are below micrograms injected amounts.     

Noise at large RF amplitudes in IMPATT oscillators †

The non-stationary noise generator of a large-signal IMPATT oscillator is studied. It is shown that only the zeroth-order term of the series expansion of the autocorrelation for the noise voltage is of importance for the oscillator noise spectrum. The zeroth-order term is determined by an integral of the square of the Green's function for the total voltage from a generation of a pair of carriers weighted by the mean generation rate

A PIN diode model with a fixed rectangular voltage is studied numerically. The short-circuit noise current is shown to be inversely proportional to the reverse saturation current,J_s. The possibility of improving the noise properties by increasing J_s is discussed.     

Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers

High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.     

Decision support for hospital evacuation and emergency response

Evacuation planning is an important part of a hospital’s emergency management plan. In an evacuation the safety and health of patients is the fundamental success parameter. Thus, in this paper we introduce an evacuation model, appropriate for planning and operations, that has the objective of minimizing expected risk, both the threat risk that is forcing the evacuation, and the risk inherent in transporting patients, some in critical condition. Specifically, we study the allocation of patients, categorized by criticality and care requirements, to a limited fleet of vehicles of various capacities and medical capabilities, to be transported to appropriate receiving hospitals considering the current available space in each hospital for each category of patient. The model is an integer program, where the non-linear expected risks are calculated a-priori. This model has a structure that has excellent solution characteristics that permit us to solve large problems in a reasonable time, enabling the model to potentially be used for both planning and operations. To illustrate the solvability of this model and demonstrate its characteristics, we apply it to a realistic case study based on the evacuation of a large regional hospital.