A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition

We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization. Our proposed CNN architecture is fully compatible with the end-to-end learning mechanism of typical CNN architectures and learns the subband decomposition from the input dataset. We show that the proposed CNN architecture has attractive properties, such as robustness to input and weight-and-bias quantization noise, compared to regular full-band CNN architectures. Importantly, the proposed architecture significantly reduces computational costs, while maintaining state-of-the-art classification accuracy. Experiments on image classification tasks using the MNIST, CIFAR-10/100, Caltech-101, and ImageNet-2012 datasets show that the proposed architecture allows accuracy surpassing state-of-the-art results. On the ImageNet-2012 dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and 69.73%, respectively. Notably, the proposed architecture offers over 90% reduction in computation cost in the inference path and approximately 75% reduction in back-propagation (per iteration) with just a single-layer subband decomposition. With a 2-layer subband decomposition, the computational gains are even more significant with comparable accuracy results to the single-layer decomposition

Algorithm and architecture for simultaneous diagonalization of matrices applied to subspace-based speech enhancement

This thesis presents algorithm and architecture for simultaneous diagonalization of matrices. As an example, a subspace-based speech enhancement problem is considered, where in the covariance matrices of the speech and noise are diagonalized simultaneously. In order to compare the system performance of the proposed algorithm, objective measurements of speech enhancement is shown in terms of the signal to noise ratio and mean bark spectral distortion at various noise levels. In addition, an innovative subband analysis technique for subspace-based time-domain constrained speech enhancement technique is proposed. The proposed technique analyses the signal in its subbands to build accurate estimates of the covariance matrices of speech and noise, exploiting the inherent low varying characteristics of speech and noise signals in narrow bands. The subband approach also decreases the computation time by reducing the order of the matrices to be simultaneously diagonalized. Simulation results indicate that the proposed technique performs well under extreme low signal-to-noise-ratio conditions. Further, an architecture is proposed to implement the simultaneous diagonalization scheme. The architecture is implemented on an FPGA primarily to compare the performance measures on hardware and the feasibility of the speech enhancement algorithm in terms of resource utilization, throughput, etc. A Xilinx FPGA is targeted for implementation. FPGA resource utilization re-enforces on the practicability of the design. Also a projection of the design feasibility for an ASIC implementation in terms of transistor count only is include

Bcl-2 21 and Ac-DEVD-CHO inhibit death of wheat microspores

Microspore cell death and low green plant production efficiency are an integral obstacle in the development of doubled haploid production in wheat. The aim of the current study was to determine the effect of anti-apoptotic recombinant human B-cell lymphoma-2 (Bcl-2△21) and caspase-3-inhibitor (Ac-DEVD-CHO) in microspore cell death in bread wheat cultivars AC Fielder and AC Andrew. Induction medium containing Bcl-2△21 and Ac-DEVD-CHO yielded a significantly higher number of viable microspores, embryo-like structures and total green plants in wheat cultivars AC Fielder and AC Andrew. Total peroxidase activity was lower in Bcl-2△21 treated microspore cultures at 96 h of treatment compared to control and Ac-DEVD-CHO. Electron paramagnetic resonance study of total microspore protein showed a different scavenging activity for Bcl-2△21 and Ac-DEVD-CHO. Bcl-2△21 scavenged approximately 50% hydroxyl radical (HO•) formed, whereas Ac-DEVD-CHO scavenged approximately 20% of HO•. Conversely, reduced caspase-3-like activities were detected in the presence of Bcl-2△21 and Ac-DEVD-CHO, supporting the involvement of Bcl-2△21 and Ac-DEVD-CHO in increasing microspore viability by reducing oxidative stress and caspase-3-like activity. Our results indicate that Bcl-2△21 and Ac-DEVD-CHO protects cells from cell death following different pathways. Bcl-2△21 prevents cell damage by detoxifying HO• and suppressing caspase-3-like activity, while Ac-DEVD-CHO inhibits the cell death pathways by modulating caspase-like activit

Comparison of LC and LC/MS Methods for Quantifying N-Glycosylation in Recombinant IgGs

High-performance liquid chromatography (LC) and liquid chromatography/electrospray ionization time-of-flight mass spectrometry (LC/ESI-MS) methods with various sample preparation schemes were compared for their ability to identify and quantify glycoforms in two different production lots of a recombinant monoclonal IgG1 antibody. IgG1s contain a conserved N-glycosylation site in the fragment crystallizable (Fc) subunit. Six methods were compared: (1) LC/ESI-MS analysis of intact IgG, (2) LC/ESI-MS analysis of the Fc fragment produced by limited proteolysis with Lys-C, (3) LC/ESI-MS analysis of the IgG heavy chain produced by reduction, (4) LC/ESI-MS analysis of Fc/2 fragment produced by limited proteolysis and reduction, (5) LC/MS analysis of the glycosylated tryptic fragment (293EEQYNSTYR301) using extracted ion chromatograms, and (6) normal phase HPLC analysis of N-glycans cleaved from the IgG using PNGase F. The results suggest that MS quantitation based on the analysis of Fc/2 (4) is accurate and gives results that are comparable to normal phase HPLC analysis of N-glycans (6)

Multianalyte Sensing Of Addictive Over-the-counter (otc) Drugs

A supramolecular sensor array composed of two fluorescent cucurbit[n]uril-type receptors (probe 1 and probe 2) displaying complementary selectivities was tested for its ability to detect and quantify drug-related amines. The fluorimetric titration of the individual probes showed highly variable and cross-reactive analyte-dependent changes in fluorescence. An excellent ability to recognize a variety of analytes was demonstrated in qualitative as well as quantitative assays. Importantly, a successful quantitative analysis of several analytes of interest was achieved in mixtures and in human urine. The throughput and sensitivity surpass those of the current state-of-the-art methods that usually require analyte solid-phase extraction (SPE). These results open up the opportunity for new applications of cucurbit[n]uril-type receptors in sensing and pave the way for the development of simple high-throughput assays for various drugs in the near future

A Methodological Framework for the Reconstruction of Contiguous Regions of Ancestral Genomes and Its Application to Mammalian Genomes

The reconstruction of ancestral genome architectures and gene orders from homologies between extant species is a long-standing problem, considered by both cytogeneticists and bioinformaticians. A comparison of the two approaches was recently investigated and discussed in a series of papers, sometimes with diverging points of view regarding the performance of these two approaches. We describe a general methodological framework for reconstructing ancestral genome segments from conserved syntenies in extant genomes. We show that this problem, from a computational point of view, is naturally related to physical mapping of chromosomes and benefits from using combinatorial tools developed in this scope. We develop this framework into a new reconstruction method considering conserved gene clusters with similar gene content, mimicking principles used in most cytogenetic studies, although on a different kind of data. We implement and apply it to datasets of mammalian genomes. We perform intensive theoretical and experimental comparisons with other bioinformatics methods for ancestral genome segments reconstruction. We show that the method that we propose is stable and reliable: it gives convergent results using several kinds of data at different levels of resolution, and all predicted ancestral regions are well supported. The results come eventually very close to cytogenetics studies. It suggests that the comparison of methods for ancestral genome reconstruction should include the algorithmic aspects of the methods as well as the disciplinary differences in data aquisition