Activated sludge system is generally used in wastewater treatment plants for processing domestic influent. Conventionally the activated sludge wastewater treatment is monitored by measuring physico-chemical parameters like total suspended solids (TSSol), sludge volume index (SVI) and chemical oxygen demand (COD) etc. For the measurement, tests are conducted in the laboratory, which take many hours to give the final measurement. Digital image processing and analysis offers a better alternative not only to monitor and characterize the current state of activated sludge but also to predict the future state. The characterization by image processing and analysis is done by correlating the time evolution of parameters extracted by image analysis of floc and filaments with the physico-chemical parameters. This chapter briefly reviews the activated sludge wastewater treatment; and, procedures of image acquisition, preprocessing, segmentation and analysis in the specific context of activated sludge wastewater treatment. In the latter part additional procedures like z-stacking, image stitching are introduced for wastewater image preprocessing, which are not previously used in the context of activated sludge. Different preprocessing and segmentation techniques are proposed, along with the survey of imaging procedures reported in the literature. Finally the image analysis based morphological parameters and correlation of the parameters with regard to monitoring and prediction of activated sludge are discussed. Hence it is observed that image analysis can play a very useful role in the monitoring of activated sludge wastewater treatment plants.
The fundamental step in brain research deals with recording electroencephalogram (EEG) signals and then investigating the recorded signals quantitatively. Topographic EEG (visual spatial representation of EEG signal) is commonly referred to as brain topomaps or brain EEG maps. In this chapter, full search full search block motion estimation algorithm has been employed to track the brain activity in brain topomaps to understand the mechanism of brain wiring. The behavior of EEG topomaps is examined throughout a particular brain activation with respect to time. Motion vectors are used to track the brain activation over the scalp during the activation period. Using motion estimation it is possible to track the path from the starting point of activation to the final point of activation. Thus it is possible to track the path of a signal across various lobes.
Left ventricular motion estimation is very important for diagnosing cardiac abnormality. One of the popular techniques, optical flow technique, promises useful results for motion quantification. However, optical flow technique often failed to provide smooth vector field due to the complexity of cardiac motion and the presence of speckle noise. This chapter proposed a new filtering technique, called quasi-Gaussian discrete cosine transform (QGDCT)-based filter, to enhance the optical flow field for myocardial motion estimation. Even though Gaussian filter and DCT concept have been implemented in other previous researches, this filter introduces a different approach of Gaussian filter model based on high frequency properties of cosine function. The QGDCT is a customized quasi discrete Gaussian filter in which its coefficients are derived from a selected two-dimensional DCT. This filter was implemented before and after the computation of optical flow to reduce the speckle noise and to improve the flow field smoothness, respectively. The algorithm was first validated on synthetic echocardiography image that simulates a contracting myocardium motion. Subsequently, this method was also implemented on clinical echocardiography images. To evaluate the performance of the technique, several quantitative measurements such as magnitude error, angular error, and standard error of measurement are computed and analyzed. The final motion estimation results were in good agreement with the physician manual interpretation.
The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.
A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
In this chapter, the computational biology of cardiac cavity images is proposed. The method uses collinear and triangle equation algorithms to detect and reconstruct the boundary of the cardiac cavity. The first step involves high boost filter to enhance the high frequency component without affecting the low frequency component. Second, the morphological and thresholding operators are applied to the image to eliminate noise and convert the image into a binary image. Next, the edge detection is performed using the negative Laplacian filter and followed by region filtering. Finally, the collinear and triangle equations are used to detect and reconstruct the more precise cavity boundary. Results obtained have proved that this technique is able to perform better segmentation and detection of the boundary of cardiac cavity from echocardiographic images.
Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.
Patients with chronic kidney disease (CKD) are at increased risk for both thrombotic events and bleeding. The early stages of CKD are mainly associated with prothrombotic tendency, whereas in its more advanced stages, beside the prothrombotic state, platelets can become dysfunctional due to uremic-related toxin exposure leading to an increased bleeding tendency. Patients with CKD usually require anticoagulation therapy for treatment or prevention of thromboembolic diseases. However, this benefit could easily be offset by the risk of anticoagulant-induced bleeding. Treatment of patients with CKD should be based on evidence from randomized clinical trials, but usually CKD patients are excluded from these trials. In the past, unfractionated heparins were the anticoagulant of choice for patients with CKD because of its independence of kidney elimination. However, currently low-molecular-weight heparins have largely replaced the use of unfractionated heparins owing to fewer incidences of heparin-induced thrombocytopenia and bleeding. We undertook this review in order to explain the practical considerations for the management of anticoagulation in these high risk population.
Since antiquity, ginger or Zingiber officinale, has been used by humans for medicinal purposes and as spice condiments to enhance flavor in cooking. Ginger contains many phenolic compounds such as gingerol, shogaol and paradol that exhibit antioxidant, anti-tumor and anti-inflammatory properties. The role of ginger and its constituents in ameliorating diseases has been the focus of study in the past two decades by many researchers who provide strong scientific evidence of its health benefit. This review discusses research findings and works devoted to gingerols, the major pungent constituent of ginger, in modulating and targeting signaling pathways with subsequent changes that ameliorate, reverse or prevent chronic diseases in human studies and animal models. The physical, chemical and biological properties of gingerols are also described. The use of ginger and especially gingerols as medicinal food derivative appears to be safe in treating or preventing chronic diseases which will benefit the common population, clinicians, patients, researchers, students and industrialists.
In regenerative therapy, in vitro expansion of stem cells is critical to obtain a significantly higher number of cells for successful engraftment after transplantation. However, stem cells lose its regenerative potential and enter senescence during in vitro expansion. In this study, the influence of foetal bovine serum (FBS) and pooled human serum (pHS) on the proliferation, morphology and migration of stem cells from human extracted deciduous teeth (SHED) was compared. SHED (n = 3) was expanded in KnockOut DMEM supplemented with either pHS (pHS-SM) or FBS (FBS-SM). pHS was prepared using peripheral blood serum of six healthy male adults, aged between 21 and 35 years old. The number of live SHED was significantly higher, from passage 5 to 7, when cultured in pHS-SM compared to those cultured in FBS-SM (p
Tocotrienol is a member of vitamin E family and is well-known for its antioxidant and anti-inflammatory properties. It is also a suppressor of mevalonate pathway responsible for cholesterol and prenylated protein synthesis. This review aimed to discuss the health beneficial effects of tocotrienol, specifically in preventing or treating hyperlipidaemia, diabetes mellitus, osteoporosis and cancer with respect to these properties. Evidence from in vitro, in vivo and human studies has been examined. It is revealed that tocotrienol shows promising effects in preventing or treating the health conditions previously mentioned in in vivo and in vitro models. In some cases, alpha-tocopherol attenuates the biological activity of tocotrienol. Except for its cholesterol-lowering effects, data on the health-promoting effects of tocotrienol in human are limited. As a conclusion, the encouraging results on the health beneficial effects of tocotrienol should motivate researchers to explore its potential use in human.
Hypertension is a common but complex human disease, which can lead to a heart attack, stroke, kidney disease or other complications. Since the pathogenesis of hypertension is heterogeneous and multifactorial, it is crucial to establish a comprehensive metabolomic approach to elucidate the molecular mechanism of hypertension. Although there have been limited metabolomic, lipidomic and pharmacometabolomic studies investigating this disease to date, metabolomic studies on hypertension have provided greater insights into the identification of disease-specific biomarkers, predicting treatment outcome and monitor drug safety and efficacy. Therefore, we discuss recent updates on the applications of metabolomics technology in human hypertension with a focus on metabolic biomarker discovery.
Generation of functional hepatocytes from human pluripotent stem cells (hPSCs) is a vital tool to produce large amounts of human hepatocytes, which hold a great promise for biomedical and regenerative medicine applications. Despite a tremendous progress in developing the differentiation protocols recapitulating the developmental signalling and stages, these resulting hepatocytes from hPSCs yet achieve maturation and functionality comparable to those primary hepatocytes. The absence of 3D milieu in the culture and differentiation of these hepatocytes may account for this, at least partly, thus developing an optimal 3D culture could be a step forward to achieve this aim. Hence, review focuses on current development of 3D culture systems for hepatic differentiation and maturation and the future perspectives of its application.
Collagen type I is the most abundant matrix protein in the human body and is highly demanded in tissue engineering, regenerative medicine, and pharmaceutical applications. To meet the uprising demand in biomedical applications, collagen type I has been isolated from mammalians (bovine, porcine, goat and rat) and non-mammalians (fish, amphibian, and sea plant) source using various extraction techniques. Recent advancement enables fabrication of collagen scaffolds in multiple forms such as film, sponge, and hydrogel, with or without other biomaterials. The scaffolds are extensively used to develop tissue substitutes in regenerating or repairing diseased or damaged tissues. The 3D scaffolds are also used to develop in vitro model and as a vehicle for delivering drugs or active compounds.
During low-temperature exposure, temperate plant species increase their freezing tolerance in a process termed cold acclimation. The molecular mechanisms involved in cold acclimation have been mostly investigated in Arabidopsis thaliana. In addition, other Brassicaceae species related to A. thaliana have been employed in recent years to study plant stress responses on a phylogenetically broader basis and in some cases with extremophile species with a much higher stress tolerance. In this paper, we briefly summarize cold acclimation responses in A. thaliana and current knowledge about cold acclimation in A. thaliana relatives with special emphasis on Eutrema salsugineum and two closely related Thellungiella species. We then present a transcriptomic and metabolomic analysis of cold acclimation in five A. thaliana and two E. salsugineum accessions that differ widely in their freezing tolerance. Differences in the cold responses of the two species are discussed.