Synthesis and X-ray structural investigations of 2-amino-7-methyl-4-(1-naphthyl)-5-oxo4H,5H-pyrano[4,3-b]pyran-3-carbonitrile and 2-amino-4-(1-naphthyl)-5-oxo-4H,5Hpyrano[3,2-c]chromene-3-carbonitrile

Synthesis and X-ray structural investigations have been carried out for the two title compounds C₂₀H₁₄N₂O₃ (4) and C₂₃H₁₄N₂O₃⋅C₂H₃N (5). Compound 4 crystallizes in the monoclinic space group P2₁/n, with a = 7.0542(5), b = 8.822(1), c = 24.833(2) ?, β = 94.30(4)^∘, V = 1541.0(4) ?³, and Z = 4. Compound 5 crystallizes with an acetonitrile solvent molecule in the monoclinic space group P2₁/n, with a = 11.075(1), b = 7.854(1), c = 22.703(2) ?, β = 90.67(1)^∘, V = 1974.5(3) ?³, and Z = 4. In both molecules, the 4H-pyran ring adopts a flattened-boat conformation. The naphthalene substituent occupies a pseudo-axial position and the dihedral angle with the flat part of the pyran ring is equal to 94.6(3) in 4 and 76.8(3)^∘ in 5. The mutual orientation of these fragments and the flatness of the heterocyclic rings lead to H⋅sH intramolecular steric interactions: H4A⋅sH18A 1.98 ? in 4 and 2.11 ? in 5. In the crystal of 4, intermolecular hydrogen bonds N–H⋅sO and C–H⋅sN link molecules into infinite tapes along the b axis. In the crystals of 5, intermolecular hydrogen bonds N–H⋅sO and C–H⋅sN link molecules into infinite layers parallel to the bc plane. In each case, the C–H⋅sN interaction can be considered to be a weak hydrogen bond. The acetonitrile molecules link via intermolecular weak C–H⋅sN hydrogen bonds to form infinite chains along the b axes.     

Zinc Detection in Serum by Anodic Stripping Voltammetry on Microfabricated Bismuth Electrodes

Zinc (Zn) homeostasis is required for a functional immune system. Critically ill patients often exhibit decreased Zn serum concentrations and could potentially benefit from Zn supplementation as a therapeutic strategy. However, the conventional approaches to monitoring Zn are time consuming and costly. This work reports on detection of Zn by anodic stripping voltammetry (ASV) on bismuth electrodes in a microfabricated electrochemical cell. The working potential window of the electrodeposited bismuth film electrode was investigated by cyclic voltammetry, while square wave ASV was used for measuring Zn in acetate buffer and blood serum. Conditions critical to sensing, such as preconcentration potential, preconcentration time, and buffer pH, were optimized for Zn detection. The sensor was successfully calibrated with pH 6 acetate buffer in the physiologically‐relevant range of 5 µM to 50 µM Zn and exhibited well‐defined and highly repeatable peaks. The sensor was used to demonstrate measurement of Zn in blood serum digested in HCl. The results of this work show that Zn detection in serum is possible with smaller sample volumes (µL vs. mL) and faster turnaround time (hours vs. days) as compared with the conventional spectroscopic methods.     

The development of a Selectfluor®-mediated oxidative Mannich reaction

Selectfluor® is an easy to handle, air-stable and powerful electrophilic fluorination agent that can be used in a novel oxidative Mannich-type carbon-carbon bond forming process to allylate electron deficient tertiary amines in moderate to good yields. This process is rapid, operationally simple, insensitive to atmospheric conditions and of potentially broad use, especially in the construction of homoallylamines.     

Explanation of exceptional values in multi-dimensional business databases

In this paper, we describe an extension of the OnLine Analytical Processing (OLAP) framework with causal explanation, offering the possibility to automatically generate explanations for exceptional cell values. This functionality can be built into conventional OLAP databases using a generic explanation formalism, which supports the work of managers in diagnostic processes. The central goal is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from multi-dimensional data and business models. The methodology was tested on a case study involving the comparison of financial figures of a firm’s business units. The findings suggest improved decision-making by managers because the current tedious and error-prone manual analysis process is enhanced by automated problem identification and explanation generation. It is also noted that this novel methodology has general utility for decision-support systems, for example, for automated diagnosis in the financial and accountancy domain.     

Label-free electrical detection of pyrophosphate generated from DNA polymerase reactions on field-effect devices

We introduce a label-free approach for sensing polymerase reactions on deoxyribonucleic acid (DNA) using a chelator-modified silicon-on-insulator field-effect transistor (SOI-FET) that exhibits selective and reversible electrical response to pyrophosphate anions. The chemical modification of the sensor surface was designed to include rolling-circle amplification (RCA) DNA colonies for locally enhanced pyrophosphate (PPi) signal generation and sensors with immobilized chelators for capture and surface-sensitive detection of diffusible reaction by-products. While detecting arrays of enzymatic base incorporation reactions is typically accomplished using optical fluorescence or chemiluminescence techniques, our results suggest that it is possible to develop scalable and portable PPi-specific sensors and platforms for broad biomedical applications such as DNA sequencing and microbe detection using surface-sensitive electrical readout techniques.     

Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering

Multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) wireless systems use link adaptation to exploit the dynamic nature of wireless environments. Link adaptation maximizes throughput while maintaining target reliability by adaptively selecting the modulation order and coding rate. Link adaptation is extremely challenging, however, due to the difficulty in predicting error rates in OFDM with binary convolutional codes, bit interleaving, MIMO processing, and real channel impairments. This paper proposes a new machine-learning framework that exploits past observations of the error rate and the associated channel-state information to predict the best modulation order and coding rate for new realizations of the channel state without modeling the input–output relationship of the wireless transceiver. Our approach is enabled through our new error-rate expression that is only parameterized by postprocessing signal-to-noise ratios (SNRs), ordered over subcarriers and spatial streams. Using ordered SNRs, we propose a low-dimensional feature set that enables machine learning to increase the accuracy of link adaptation. An IEEE 802.11n simulation study validates the application of this machine-learning framework in real channels and demonstrates the improved performance of SNR ordering as it compares with competing link-quality metrics.