This paper presents a novel secure and robust steganographic technique in the compressed video domain namely embedding-based byte differencing (EBBD). Unlike most of the current video steganographic techniques which take into account only the intra frames for data embedding, the proposed EBBD technique aims to hide information in both intra and inter frames. The information is embedded into a compressed video by simultaneously manipulating the quantized AC coefficients (AC-QTCs) of luminance components of the frames during MPEG-2 encoding process. Later, during the decoding process, the embedded information can be detected and extracted completely. Furthermore, the EBBD basically deals with two security concepts: data encryption and data concealing. Hence, during the embedding process, secret data is encrypted using the simplified data encryption standard (S-DES) algorithm to provide better security to the implemented system. The security of the method lies in selecting candidate AC-QTCs within each non-overlapping 8 × 8 sub-block using a pseudo random key. Basic performance of this steganographic technique verified through experiments on various existing MPEG-2 encoded videos over a wide range of embedded payload rates. Overall, the experimental results verify the excellent performance of the proposed EBBD with a better trade-off in terms of imperceptibility and payload, as compared with previous techniques while at the same time ensuring minimal bitrate increase and negligible degradation of PSNR values.
Matched MeSH terms: Video Recording/methods*; Data Compression/methods*
MAIN CONCLUSION: Bambara groundnut has the potential to be used to contribute more the climate change ready agriculture. The requirement for nitrogen fixing, stress tolerant legumes is clear, particularly in low input agriculture. However, ensuring that existing negative traits are tackled and demand is stimulated through the development of markets and products still represents a challenge to making greater use of this legume. World agriculture is currently based on very limited numbers of crops, representing a significant risk to food supplies, particularly in the face of climate change which is expected to increase the frequency of extreme events. Minor and underutilised crops can help to develop a more resilient and nutritionally dense future agriculture. Bambara groundnut [Vigna subterranea (L.) Verdc.[, as a drought resistant, nitrogen-fixing, legume has a role to play. However, as with most underutilised crops, there are significant gaps in knowledge and also negative traits such as 'hard-to-cook' and 'photoperiod sensitivity to pod filling' associated with the crop which future breeding programmes and processing methods need to tackle, to allow it to make a significant contribution to the well-being of future generations. The current review assesses these factors and also considers what are the next steps towards realising the potential of this crop.
In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.
Malignant pleural effusion with persistent air leak (PAL) is a rare manifestation of lung malignancy. We present a woman in her 50s with postpleuroscopy PAL. Pleural biopsy confirmed adenocarcinoma with detection of epidermal growth factor receptor mutation in exon 19. An indwelling pleural catheter (IPC) was inserted and connected to an Atrium Express Mini ambulatory drain. This procedure reduced the length of hospital stay. Autopleurodesis with resolution of PAL occurred at week 3 of IPC insertion.
Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.
Matched MeSH terms: Biotechnology/methods; Protein Engineering/methods
A wide interest has been observed to find a low power and area efficient hardware design of discrete cosine transform (DCT) algorithm. This research work proposed a novel Common Subexpression Elimination (CSE) based pipelined architecture for DCT, aimed at reproducing the cost metrics of power and area while maintaining high speed and accuracy in DCT applications. The proposed design combines the techniques of Canonical Signed Digit (CSD) representation and CSE to implement the multiplier-less method for fixed constant multiplication of DCT coefficients. Furthermore, symmetry in the DCT coefficient matrix is used with CSE to further decrease the number of arithmetic operations. This architecture needs a single-port memory to feed the inputs instead of multiport memory, which leads to reduction of the hardware cost and area. From the analysis of experimental results and performance comparisons, it is observed that the proposed scheme uses minimum logic utilizing mere 340 slices and 22 adders. Moreover, this design meets the real time constraints of different video/image coders and peak-signal-to-noise-ratio (PSNR) requirements. Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively.
Matched MeSH terms: Photography/methods; Video Recording/methods; Data Compression/methods*
The volume of patient monitoring video acquired in hospitals is very huge and hence there is a need for better compression of the same for effective storage and transmission. This paper presents a new motion segmentation technique, which improves the compression of patient monitoring video. The proposed motion segmentation technique makes use of a binary mask, which is obtained by thresholding the standard deviation values of the pixels along the temporal axis. Two compression methods, which make use of the proposed motion segmentation technique, are presented. The first method uses MPEG-4 coder and 9/7-biorthogonal wavelet for compressing the moving and stationary portions of the video respectively. The second method uses 5/3-biorthogonal wavelet for compressing both the moving and the stationary portions of the video. The performances of these compression algorithms are evaluated in terms of PSNR and bitrate. From the experimental results, it is found that the proposed motion technique improves the performance of the MPEG-4 coder. Among the two compression methods presented, the MPEG-4 based method performs better for bitrates less than 767 Kbps whereas for bitrates above 767 Kbps the performance of the wavelet based method is found superior.
Matched MeSH terms: Monitoring, Physiologic/methods*; Videotape Recording/methods*; Data Compression/methods*
This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR's, it becomes virtually impossible to distinguish between the original and compressed-decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.
Matched MeSH terms: Magnetic Resonance Imaging/methods*; Radiology/methods*; Data Compression/methods*
Electrical properties of living cells have been proven to play significant roles in understanding of various biological activities including disease progression both at the cellular and molecular levels. Since two decades ago, many researchers have developed tools to analyze the cell's electrical states especially in single cell analysis (SCA). In depth analysis and more fully described activities of cell differentiation and cancer can only be accomplished with single cell analysis. This growing interest was supported by the emergence of various microfluidic techniques to fulfill high precisions screening, reduced equipment cost and low analysis time for characterization of the single cell's electrical properties, as compared to classical bulky technique. This paper presents a historical review of single cell electrical properties analysis development from classical techniques to recent advances in microfluidic techniques. Technical details of the different microfluidic techniques are highlighted, and the advantages and limitations of various microfluidic devices are discussed.
Worldwide healthcare delivery trends are undergoing a subtle paradigm shift--patient centered services as opposed to provider centered services and wellness maintenance as opposed to illness management. In this paper we present a Tele-Healthcare project TIDE--Tele-Healthcare Information and Diagnostic Environment. TIDE manifests an 'intelligent' healthcare environment that aims to ensure lifelong coverage of person-specific health maintenance decision-support services--i.e., both wellness maintenance and illness management services--ubiquitously available via the Internet/WWW. Taking on an all-encompassing health maintenance role--spanning from wellness to illness issues--the functionality of TIDE involves the generation and delivery of (a) Personalized, Pro-active, Persistent, Perpetual, and Present wellness maintenance services, and (b) remote diagnostic services for managing noncritical illnesses. Technically, TIDE is an amalgamation of diverse computer technologies--Artificial Intelligence, Internet, Multimedia, Databases, and Medical Informatics--to implement a sophisticated healthcare delivery infostructure.
Matched MeSH terms: Health Promotion/methods*; Telemedicine/methods*; Patient-Centered Care/methods*
In recent years, the number of interdisciplinary research works related to the development of miniaturized systems with integrated chemical and biological analyses is increasing. Digital microfluidic biochips (DMFBs) are one kind of miniaturized systems designed for conducting inexpensive, fast, convenient and reliable biochemical assay procedures focusing on basic scientific research and medical diagnostics. The role of a dielectric layer in the digital microfluidic biochips is prominent as it helps in actuating microliter droplets based on the electrowetting-on-dielectric (EWOD) technique. The advantages of using three different material layers of dielectric such as parafilm, polytetrafluoroethylene (PTFE) and ethylene tetrafluoroethylene (ETFE) were reported in the current work. A simple fabrication process of a digital microfluidic device was performed and good results were obtained. The threshold of the actuation voltage was determined for all dielectric materials of varying thicknesses. Additionally, the OpenDrop device was tested by utilizing a single-plate system to transport microliter droplets for a bioassay operation. With the newly proposed fabrication methods, these dielectric materials showed changes in contact angle and droplet velocity when the actuation voltage was applied. The threshold actuation voltage for the dielectric layers of 10⁻13 μm was 190 V for the open plate DMFBs.
Entamoeba histolytica, the causative agent for human amoebiasis, is among the most deadly parasites, accounting for the second highest mortality rate among parasitic diseases. Because this parasite dwells in low oxygen tension, for its cultivation, microaerophilic conditions are required to mimick the human gut environment. Several methods developed for optimal growth environment are commercially available and some are conventionally modified in-house which include the Anaerocult A and oil blocking preparation methods. This study was undertaken to compare the reliability of the Anaerocult A and the oil blocking methods in generating anaerobic environment for cultivation of E. histolytica. The trophozoites of E. histolytica HM1: IMSS strains were axenically cultivated in TYI-S-33 medium in culture incubated anaerobically by using Anaerocult A (Merck) and mineral oil blocking method. The outcomes of both methods were determined by the minimum inhibitory concentration (MIC) of metronidazole against E. histolytica by giving a score to the growth pattern of the trophozoites. The reliability of both methods was assessed based on susceptibility testing of E. histolytica to metronidazole. The MIC obtained by both anaerobic condition methods was 6.25 ug/ ml, thus showing that oil-blocking method is comparable to the Anaerocult A method and therefore, considered as a reliable method for generating an anaerobic environment for the cultivation of E. histolytica.
Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR=1 dB, 84% when SNR=5 dB, and 88% when SNR=10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.
Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth.
A low-power wideband mixer is designed and implemented in 0.13 µm standard CMOS technology based on resistive feedback current-reuse (RFCR) configuration for the application of cognitive radio receiver. The proposed RFCR architecture incorporates an inductive peaking technique to compensate for gain roll-off at high frequency while enhancing the bandwidth. A complementary current-reuse technique is used between transconductance and IF stages to boost the conversion gain without additional power consumption by reusing the DC bias current of the LO stage. This downconversion double-balanced mixer exhibits a high and flat conversion gain (CG) of 14.9 ± 1.4 dB and a noise figure (NF) better than 12.8 dB. The maximum input 1-dB compression point (P1dB) and maximum input third-order intercept point (IIP3) are -13.6 dBm and -4.5 dBm, respectively, over the desired frequency ranging from 50 MHz to 10 GHz. The proposed circuit operates down to a supply headroom of 1 V with a low-power consumption of 3.5 mW.
An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.