Response Surface Methodology for the production of endopolygalacturonase by a novel Bacillus licheniformis

Background: Polygalacturonase is one of the most important commercial pectinase. The production cost and the mesophilic nature of the present polygalacturonase is a big problem in its application in the juice industry. A lot of work is going on for the isolation of thermophilic bacterial strains which can utilize pectin as the only carbon source.Methods: Bacterial strains were isolated from rotten fruits and vegetables and cultured at 50 – 70oC. The strains were than screened for endopolygalacturonase activity and identified on the basis of 16S rRNA sequence. Different growth parameters for the production of endopolygalacturonase by Bacillus licheniformis IEB-8 were optimized using Response Surface Methodology under Center Composite Design using JMP-12 software. Endopolygalacturonase was purified in two steps; ammonium sulfate precipitation and then by size exclusion column chromatography.Results: Only four strains, IEB-8, IEB-11, IEB-12 and IEB-13 showed growth above 60oC. Among these four, only IEB-8 was found to be endopolygalacturonase positive, which was identified as Bacillus licheniformis by 16S rRNA gene sequence. Purification fold of 2.57 and 7.48 in the specific activity were achieved using ammonium sulfate precipitation and gel filtration chromatography respectively. Molecular weight of the purified endopolygalacturonase was found to be 42 kDa. The purified endopolygalacturonase showed an optimum pH of 7 and optimum temperature of 55oC.Conclusion: Bacillus licheniformis IEB-8 is a novel bacteria which can efficiently be utilized in the industry for the production of endopolygalacturonase very cheaply. Furthermore, the high optimum working temperature of endopolygalacturonase, increases its significance for its industrial applications.Keywords: Endopolygalacturonase; Bacillus licheniformis; Thermophilic; Response Surface Methodology; Ammonium sulfate precipitatio

Multimodal biometrics based on identification and verification system

The need for an increase of reliability and security in a biometric system is motivated by the fact that there is no single technology that can realize multi-purpose scenarios. Experimental results showed that the recognition rate of Heart Sound Identification (HSI) model is 81.9%, while the rate for Speaker Identification (SI) model is 99.3% from 20 clients and 70 impostors. Heart Sound-Verification (HSV) provides an average Equal Error Rate (EER) of 13.8%, while the average EER for the Speaker Verification model (SV) is 2.1%. Electrocardiogram Identification (ECGI), on the other hand, provides an accuracy of 98.5% and ECG Verification (ECGV) EER of 4.5%. In order to reach a higher security level, an alternative multimodal and a fusion technique were implemented into the system. Through the performance analysis of the three biometric system and their combination using two multimodal biometric score level fusion, this paper found the optimal combination of those systems. The best performance of the work is based on simple-sum score fusion, with a piecewise-linear normalization technique which provides an EER of 0.7%

Multimodal biometrics based on identification and varification system

The need for an increase of reliability and security in a biometric system is motivated by the fact that there is no single technology that can realize multi-purpose scenarios. Experimental results showed that the recognition rate of Heart Sound Identification (HSI) model is 81.9%, while the rate for Speaker Identification (SI) model is 99.3% from 20 clients and 70 impostors. Heart Sound-Verification (HSV) provides an average Equal Error Rate (EER) of 13.8%, while the average EER for the Speaker Verification model (SV) is 2.1%. Electrocardiogram Identification (ECGI), on the other hand, provides an accuracy of 98.5% and ECG Verification (ECGV) EER of 4.5%. In order to reach a higher security level, an alternative multimodal and a fusion technique were implemented into the system. Through the performance analysis of the three biometric system and their combination using two multimodal biometric score level fusion, this paper found the optimal combination of those systems. The best performance of the work is based on simple-sum score fusion, with a piecewise-linear normalization technique which provides an EER of 0.7%

Identifying individuals using EEG-based brain connectivity patterns

Considering the recent rapid advancements in digital technology, electroencephalogram (EEG) signal is a potential candidate for a robust human biometric authentication system. In this paper the focus of investigation is the use of brain activity as a new modality for identification. Univariate model biometrics such as speech, heart sound and electrocardiogram (ECG) require high-resolution computer system with special devices. The heart sound is obtained by placing the digital stethoscope on the chest, the ECG signals at the hands or chest of the client and speaks into a microphone for speaker recognition. It is challenging task when adapting these technologies to human beings. This paper proposed a series of tasks in a single paradigm rather than having users perform several tasks one by one. The advantage of using brain electrical activity as suggested in this work is its uniqueness; the recorded brain response cannot be duplicated, and a person’s identity is therefore unlikely to be forged or stolen. The disadvantage of applying univariate is that the process only includes correlation in time precedence of a signal, while the correlation between regions is ignored. The inter-regional could not be assessed directly from univariate models. The alternative to this problem is the generalization of univariate model to multivariate modeling, hypothesized that the inter-regional correlations could give additional information to discriminate between brain conditions where the models or methods can measure the synchronization between coupling regions and the coherency among them on brain biometrics. The key issue is to handle the single task paradigm proposed in this paper with multivariate signal EEG classification using Multivariate Autoregressive (MVAR) rather than univariate model. The brain biometric systems obtained a significant result of 95.33% for dynamic Vector autoregressive (VAR) time series and 94.59% for Partial Directed Coherence (PDC) and Coherence (COH) frequency domain features

A brief review of computation techniques for ECG signal analysis

Automatic detection of life-threatening cardiac arrhythmias has been a subject of interest for many decades. The automatic ECG signal analysis methods are mainly aiming for the interpretation of long-term ECG recordings. In fact, the experienced cardiologists perform the ECG analysis using a strip of ECG graph paper in an event-by-event manner. This manual interpretation becomes more difficult, time-consuming, and more tedious when dealing with long-term ECG recordings. Rather, an automatic computerized ECG analysis system will provide valuable assistance to the cardiologists to deliver fast or remote medical advice and diagnosis to the patient. However, achieving accurate automated arrhythmia diagnosis is a challenging task that has to account for all the ECG characteristics and processing steps. Detecting the P wave, QRS complex, and T wave is crucial to perform automatic analysis of EEG signals. Most of the research in this area uses the QRS complex as it is the easiest symbol to detect in the first stage. The QRS complex represents ventricular depolarization and consists of three consequences waves. However, the main challenge in any algorithm design is the large variation of QRS, P, and T waveform, leading to failure for each method. The QRS complex may only occupy R waves QR (no R), QR (no S), S (no Q), or RSR, depending on the ECG lead. Variations from the normal electrical patterns can indicate damage to the heart, and these variations are manifested as heart attack or heart disease. This paper will discuss the most recent and relevant methods related to each sub-stage, maintaining the related literature to the scope of ECG research

Classification of ECG ventricular beats assisted by Gaussian parameters’ dictionary

Automatic processing and diagnosis of electrocardiogram (ECG) signals remain a very challenging problem, especially with the growth of advanced monitoring technologies. A particular task in ECG processing that has received tremendous attention is to detect and identify pathological heartbeats, e.g., those caused by premature ventricular contraction (PVC). This paper aims to build on the existing methods of heartbeat classification and introduce a new approach to detect ventricular beats using a dictionary of Gaussian-based parameters that model ECG signals. The proposed approach relies on new techniques to segment the stream of ECG signals and automatically cluster the beats for each patient. Two benchmark datasets have been used to evaluate the classification performance, namely, the QTDB and MIT-BIH Arrhythmia databases, based on a single lead short ECG segment. Using the QTDB database, the method achieved the average accuracies of 99.3% ± 0.7 and 99.4% ± 0.6% for lead-1 and lead-2, respectively. On the other hand, identifying ventricular beats in the MIT-BIH Arrhythmia dataset resulted in a sensitivity of 82.8%, a positive predictivity of 62.0%, and F1 score of 70.9%. For non-ventricular beats, the method achieved a sensitivity of 96.0%, a positive predictivity of 98.6%, and F1 score of 97.3%. The proposed technique represents an improvement in the field of ventricular beat classification compared with the conventional methods

Enhanced signal processing using modified cyclic shift tree denoising

The cortical pyramidal neurons in the cerebral cortex, which are positioned perpendicularly to the brain’s surface, are assumed to be the primary source of the electroencephalogram (EEG) reading. The EEG reading generated by the brainstem in response to auditory impulses is known as the Auditory Brainstem Response (ABR). The identification of wave V in ABR is now regarded as the most efficient method for audiology testing. The ABR signal is modest in amplitude and is lost in the background noise. The traditional approach of retrieving the underlying wave V, which employs an averaging methodology, necessitates more attempts. This results in a protracted length of screening time, which causes the subject discomfort. For the detection of wave V, this paper uses Kalman filtering and Cyclic Shift Tree Denoising (CSTD). In state space form, we applied Markov process modeling of ABR dynamics. The Kalman filter, which is optimum in the mean-square sense, is used to estimate the clean ABRs. To save time and effort, discrete wavelet transform (DWT) coefficients are employed as features instead of filtering the raw ABR signal. The results show that even with a smaller number of epochs, the wave is still visible and the morphology of the ABR signal is preserved

Aid and Resource Mobilisation in Sub-Saharan Africa: the Role of Reverse Flows

This article seeks to ascertain the role of 'reverse flows' in explaining the observed limited impact of aid on resource mobilisation in Sub-Saharan Africa. It departs from the previous empirical literature on aid and resource mobilisation by abandoning the pervasive, but untenable, assumption that aid either displaces domestic saving (increases consumption) or increases investment. Some aid is, in fact, used to finance reverse flows (debt servicing, capital flight, and reserve accumulation). The evidence suggests that, for the period covering 1980 to 2006, nearly 50 per cent of aid to Sub-Saharan African countries was used to finance reverse flows.