Low complexity frequency monitoring filter for fast exon prediction sequence analysis

Over the last few years, the application of Digital Signal Processing (DSP) techniques for genomic sequence analysis has received great interest. Indeed, among its applications in genomic analysis, it has been demonstrated that DSP can be used to detect protein coding regions (exons) among non-coding regions in a DNA sequence. The period-3 behavior exhibited by exons is one of its features that has been exploited in several developed algorithms for exon prediction. Identification of this periodicity in genomic sequences can be done by using different methods such as the well-known Fast Fourier Transform (FFT) and the Goertzel algorithm for complexity reduction in which the reduction of computational time is a great challenge in genomic analysis. Therefore, this paper presents a novel one frequency analysis by using half of the arithmetic complexity of the Goertzel algorithm for gene prediction. Compared to the Intel®’s FFT (MKL) optimized function, the Goertzel’s (IPP) and the dedicated Goertzel compiled function with ICC on Xeon CPU (24 cores), the proposed method conserves the same accuracy provided by the referenced methods which will manifest a speedup of 3000, 10 and 2 compared to MKL FFT, IPP Goertzel and the dedicated Goertzel with ICC, respectively

Decadal changes in Arctic Ocean Chlorophyll a: Bridging ocean color observations from the 1980s to present time

Remotely-sensed Ocean color data offer a unique opportunity for studying variations of bio-optical properties which is especially valuable in the Arctic Ocean (AO) where in situ data are sparse. In this study, we re-processed the raw data from the Sea-viewing Wide Field-of-View (SeaWiFS, 1998–2010) and the MODerate resolution Imaging Spectroradiometer (MODIS, 2003–2016) ocean-color sensors to ensure compatibility with the first ocean color sensor, namely, the Coastal Zone Color Scanner (CZCS, 1979–1986). Based on a bio-regional approach, this study assesses the quality of this new homogeneous pan-Arctic Chl a dataset, which provides the longest (but non-continuous) ocean color time-series ever produced for the AO (37 years long between 1979 and 2016). We show that despite the temporal gaps between 1986 and 1998 due to the absence of ocean color satellite, the time series is suitable to establish a baseline of phytoplankton biomass for the early 1980s, before sea-ice loss accelerated in the AO. More importantly, it provides the opportunity to quantify decadal changes over the AO revealing for instance the continuous Chl a increase in the inflow shelves such as the Barents Sea since the CZCS era

Altered Fungal Morphogenesis During Early Stages of Ectomycorrhiza Formation in Eucalyptus Pilularis

Scanning electron microscopy (SEM) of Eucalyptus pilularis roots inoculated with Pisolithus tinctorius, under controlled conditions, revealed altered morphogenesis of fungal hyphae in contact with the root surface. These changes occurred prior to the formation of a full fungal mantle and resulted in the formation of a compact fungal layer as a consequence of fusion of proliferating, branching hyphae. Although similar growth patterns have been observed in the inner mantle of fully developed ectomycorrhizae using contrast interference microscopy, this is the first time this feature has been observed during early mantle formation using SEM. Changes in fungal morphology during early stages of colonization may be correlated with recognition between the symbionts, and the subsequent establishment of a symbiotic relationship between compatible partners

Shift in the chemical composition of dissolved organic matter in the Congo River network

The processing of terrestrially derived dissolved organic matter (DOM) during downstream transport in fluvial networks is poorly understood. Here, we report a dataset of dissolved organic carbon (DOC) concentrations and DOM composition (stable carbon isotope ratios, absorption and fluorescence properties) acquired along a 1700 km transect in the middle reach of the Congo River Basin. Samples were collected in the mainstem and its tributaries during high water (HW) and falling water (FW) periods. DOC concentrations and DOM composition along the mainstem were found to differ between the two periods, because of a reduced lateral mixing between the central water masses of the Congo River and DOM-rich waters from tributaries and also likely because of a greater photodegradation during FW as water residence time (WRT) increased. Although the Cuvette Centrale wetland (one of the world’s largest flooded forest) continuously releases highly aromatic DOM in streams and rivers of the Congo Basin, the downstream transport of DOM was found to result in an along stream gradient from aromatic to aliphatic compounds. The characterization of DOM through parallel factor analysis (PARAFAC) suggests that this transition results from (1) the losses of aromatic compounds by photodegradation and (2) the production of aliphatic compounds by biological reworking of terrestrial DOM. Finally, this study highlights the critical importance of the river-floodplain connectivity in tropical rivers in controlling DOM biogeochemistry at large spatial scale and suggests that the degree of DOM processing during downstream transport is a function of landscape characteristics and WRTAFRIVA

S-Convnet: A shallow convolutional neural network architecture for neuromuscular activity recognition using instantaneous high-density surface EMG images

The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep convolutional neural network (ConvNet) architecture and complex training schemes for HD-sEMG image recognition, which requires learning of ˃5.63 million(M) training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training dataset, as a result, it makes high-end resource-bounded and computationally expensive. To overcome this problem, we propose S-ConvNet models, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch using random-initialization. Without using any pre-trained models, our proposed S-ConvNet demonstrate very competitive recognition accuracy to the more complex state of the art, while reducing learning parameters to only ≈ 2M and using ≈ 12 × smaller dataset. The experimental results proved that the proposed S-ConvNet is highly effective for learning discriminative features for instantaneous HD-sEMG image recognition, especially in the data and high-end resource-constrained scenarios

Nonlinearity-robust linear acoustic echo canceller using the maximum Correntropy criterion

For the problem of acoustic echo cancellation (AEC) with nonlinear distortions, we propose to use a linear adaptive filter that maximizes the Correntropy similarity measure instead of the conventional minimization of the mean squared error (MSE) criterion. The maximum Correntropy criterion (MCC) offers robustness to outliers and impulsive noise, which is interesting for the case of speech signal coupled with nonlinearities. To assess the performance of the algorithm, we consider a hard-clipping memoryless saturation nonlinearity. Our simulation results show very interesting performance of the normalized MCC-based linear adaptive filter for the echo return loss enhancement (ERLE) and misalignment measures compared to the MSE-based normalized least mean squares (NLMS) approach. Furthermore, the NMCC adaptive filter has a similar computational complexity as the NLMS algorithm, which makes it very attractive in practical implementations

Electrodynamic loudspeaker linearization using a low complexity pth-Order inverse nonlinear filter

Nonlinear distortions are very challenging to tackle in electromechanical loudspeakers. They are observed in large signals mode, where high amplitude stimulus drives different components of the transducer to operate in their nonlinear region, resulting in harmonic and intermodulation distortions in the reproduced sounds. Many linearization schemes have been proposed to address this problem, they operate by pre-distorting the input signal before exciting the loudspeaker, in the aim of radiating distortion-free sound waves. In this work, we are interested in the performance evaluation of a low computational complexity feedforward linearization structure which is based on the pth order inverse of a one-dimension Volterra model of the driver. The scheme is designed to compensate for the 2nd and 3rd harmonic distortions. We will study the effect of varying the input voltage amplitude on the harmonic distortions reduction performance. A lumped-parameters model with parameters of a real driver will be used for the evaluation

Can altered production of interleukin-1β, interleukin-6, transforming growth factor-β and prostaglandin E2 by isolated human subchondral osteoblasts identify two subgroups of osteoarthritic patients

AbstractObjective To determine the capacity of human subchondral osteoarthritic osteoblasts (Ob) to produce interleukin (IL)-1β, IL-6, transforming growth factor-β (TGF-β) and prostaglandin E2 (PGE2), and determine if a relationship exists between IL-1β, TGF-β, PGE2 and IL-6 production.Methods We measured the abundance of IL-1β, IL-6, TGF-β and PGE2 using very sensitive ELISA in conditioned-media of human primary subchondral Ob from normal individuals and osteoarthritic patients. Selective inhibition of IL-6 or IL-6 receptor signaling was performed to determine its effect on PGE2 production whereas the inhibiton of PGE2 production was performed to determine its effect on IL-6 production. The expression of bone cell markers and urokinase plasminogen activator (uPA) activity was also determined.Results Osteoarthritic Ob produced all these factors with greater variability than normal cells. Interestingly, the production of IL-6 and PGE2 by osteoarthritic Ob separated patients into two subgroups, those whose Ob produced levels comparable to normal (low producers) and those whose Ob produced higher levels (high producers). In those cells classified as high osteoarthritic Ob, PGE2 and IL-6 levels were increased two- to three-fold and five- to six-fold, respectively, compared with normal. In contrast, while using their IL-6 and PGE2 production to separate osteoarthritic Ob into low and high producers, we found that IL-1β levels were similar in normal and all osteoarthritic Ob. Using the same criteria, TGF-β levels were increased in all osteoarthritic Ob compared with normal. Reducing PGE2 synthesis by Indomethacin [a cyclo-oxygenase (COX) -1 and -2 inhibitor] reduced IL-6 levels in all osteoarthritic Ob, whereas Naproxen (a more selective COX-2 inhbitor) reduced PGE2 and IL-6 levels only in the high osteoarthritic group. Conversely, PGE2 addition to osteoarthritic Ob enhanced IL-6 production in both groups. Moreover, the addition of parathyroid hormone also stimulated IL-6 production to similar normal levels in both osteoarthritic groups. In contrast, using an antibody against IL-6 or IL-6 receptors did not reduce PGE2 levels in either group. The evaluation of alkaline phosphatase activity, osteocalcin release, collagen type I and uPA activity in osteoarthritic Ob failed to show any differences between these cells regardless to which subgroup they were assigned.Conclusions These results indicate that IL-6 and PGE2 production by subchondral Ob can discriminate two subgroups of osteoarthritic patients that cannot otherwise be separated by their expression of cell markers, and that endogenous PGE2 levels influence IL-6 synthesis in osteoarthritic Ob. Copyright 2002 OsteoArthritis Research Society International. Published by Elsevier Science Ltd. All rights reserved

All-ConvNet: A lightweight all CNN for neuromuscular activity recognition using instantaneous high-density surface EMG images

Neuromuscular activity recognition using low-resolution instantaneous high-density surface electromyography (HD-sEMG) images present a great challenge. The recent result shows the high potentiality and hence opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep ConvNet, which requires learning >5.63 million training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training datasets, as a result, it makes high-end resource bounded and computationally expensive. To overcome this problem, we propose a lightweight All-ConvNet model that consists solely of convolutional layers, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch through random initialization. Without using any pre-trained models, our proposed lightweight All-ConvNet demonstrate very competitive or even state of the art performance on a current benchmarks HD-sEMG dataset, while requires learning only ~460k training parameters and using ~12xsmaller dataset. The experimental results proved that the proposed lightweight All-ConvNet is highly effective for learning discriminative features for low-resolution instantaneous HD-sEMG image recognition and low-latency processing especially in the data and high-end resource constrained scenarios