Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

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Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

Lower critical solution temperature (LCST) phase transition of glycol ether (GE)-water mixtures induces an abrupt change in osmotic pressure driven by a mild temperature change. The temperature-controlled osmotic change was applied for the forward osmosis (FO) desalination. Among three GEs evaluated, di(ethylene glycol) n-hexyl ether (DEH) was selected as a potential FO draw solute. A DEH-water mixture with a high osmotic pressure could draw fresh water from a high-salt feed solution such as seawater through a semipermeable membrane at around 10 °C. The water-drawn DEH-water mixture was phase-separated into a water-rich phase and a DEH-rich phase at around 30 °C. The water-rich phase with a much reduced osmotic pressure released water into a low-salt solution, and the DEH-rich phase was recovered into the initial DEH-water mixture. The phase separation behaviour, the residual GE concentration in the water-rich phase, the osmotic pressure of the DEH-water mixture, and the osmotic flux between the DEH-water mixture and salt solutions were carefully analysed for FO desalination. The liquid-liquid phase separation of the GE-water mixture driven by the mild temperature change between 10 °C and 30 °C is very attractive for the development of an ideal draw solute for future practical FO desalination.

ObjectiveTo evaluate the accuracy of a computer-aided evaluation program (CAE) of breast MRI for the assessment of residual tumor extent and response monitoring in breast cancer patients receiving neoadjuvant chemotherapy.Materials and MethodsFifty-seven patients with breast cancers who underwent neoadjuvant chemotherapy before surgery and dynamic contrast enhanced MRI before and after chemotherapy were included as part of this study. For the assessment of residual tumor extent after completion of chemotherapy, the mean tumor diameters measured by radiologists and CAE were compared to those on histopathology using a paired student t-test. Moreover, the agreement between unidimensional (1D) measurement by radiologist and histopathological size or 1D measurement by CAE and histopathological size was assessed using the Bland-Altman method. For chemotherapy monitoring, we evaluated tumor response through the change in the 1D diameter by a radiologist and CAE and three-dimensional (3D) volumetric change by CAE based on Response Evaluation Criteria in Solid Tumors (RECIST). Agreement between the 1D response by the radiologist versus the 1D response by CAE as well as by the 3D response by CAE were evaluated using weighted kappa (k) statistics.ResultsFor the assessment of residual tumor extent after chemotherapy, the mean tumor diameter measured by radiologists (2.0 ± 1.7 cm) was significantly smaller than the mean histological diameter (2.6 ± 2.3 cm) (p = 0.01), whereas, no significant difference was found between the CAE measurements (mean = 2.2 ± 2.0 cm) and histological diameter (p = 0.19). The mean difference between the 1D measurement by the radiologist and histopathology was 0.6 cm (95% confidence interval: -3.0, 4.3), whereas the difference between CAE and histopathology was 0.4 cm (95% confidence interval: -3.9, 4.7). For the monitoring of response to chemotherapy, the 1D measurement by the radiologist and CAE showed a fair agreement (k = 0.358), while the 1D measurement by the radiologist and 3D measurement by CAE showed poor agreement (k = 0.106).ConclusionCAE for breast MRI is sufficiently accurate for the assessment of residual tumor extent in breast cancer patients receiving neoadjuvant chemotherapy. However, for the assessment of response to chemotherapy, the assessment by the radiologist and CAE showed a fair to poor agreement.