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Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method.

Coronary arteries have high curvatures, and hence, flow through them causes disturbed flow patterns, resulting in stenosis and atherosclerosis. This in turn decreases the myocardial flow perfusion, causing myocardial ischemia and infarction. Therefore, in order to understand the mechanisms of these phenomena caused by high curvatures and branching of coronary arteries, we have conducted elaborate hemodynamic analysis for both (i) idealized coronary arteries with geometrical parameters representing realistic curvatures and stenosis and (ii) patient-specific coronary arteries with stenoses. Firstly, in idealized coronary arteries with approximated realistic arterial geometry representative of their curvedness and stenosis, we have computed the hemodynamic parameters of pressure drop, wall shear stress (WSS) and wall pressure gradient (WPG), and their association with the geometrical parameters of curvedness and stenosis. Secondly, we have similarly determined the wall shear stress and wall pressure gradient distributions in four patient-specific curved stenotic right coronary arteries (RCAs), which were reconstructed from medical images of patients diagnosed with atherosclerosis and stenosis; our results show high WSS and WPG regions at the stenoses and inner wall of the arterial curves. This paper provides useful insights into the causative mechanisms of the high incidence of atherosclerosis in coronary arteries. It also provides guidelines for how simulation of blood flow in patient’s coronary arteries and determination of the hemodynamic parameters of WSS and WPG can provide a medical assessment of the risk of development of atherosclerosis and plaque formation, leading to myocardial ischemia and infarction. The novelty of our paper is in our showing how in actual coronary arteries (based on their CT imaging) curvilinearity and narrowing complications affect the computed WSS and WPG, associated with risk of atherosclerosis. This is very important for cardiologists to be able to properly take care of their patients and provide remedial measures before coronary complications lead to myocardial infarctions and necessitate stenting or coronary bypass surgery. We want to go one step further and provide clinical application of our research work. For that, we are offering to cardiologists worldwide to carry out hemodynamic analysis of the medically imaged coronary arteries of their patients and compute the values of the hemodynamic parameters of WSS and WPG, so as to provide them an assessment of the risk of atherosclerosis for their patients.

Intracranial blood vessel segmentation plays an essential role in the diagnosis and surgical planning of cerebrovascular diseases. Recently, deep convolutional neural networks have shown increasingly outstanding performance in image classification and also in the field of image segmentation. However, cerebrovascular segmentation is a challenging task as it requires the processing of more information compared to natural images. In this paper, we propose a novel network for intracranial vessel segmentation in computed tomography angiography, which is termed as global channel attention network (GCA-Net). GCA-Net combines a four-branch at the shallow feature that captures global context information efficiently that focuses on preserving more feature details. To achieve this, we formulate an UpSampling Module (USM) by introducing the channel attention mechanism when aggregating high-level features and shallow features, leading to learning the global feature information better. This novel design is developed into different branches to learn feature information at different levels. Furthermore, we introduce Atrous Spatial Pyramid Pooling (ASPP) for capturing more details in feature maps with different resolutions. Comprehensive experimental results demonstrate the superiority of our proposed method, whereby it can achieve a dice coefficient score of 96.51% and a Mean IoU score of 92.73%, outperforming the state-of-the-art methods.

Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood's temperature model during transportation, the UAVs' scheduling and routes' planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood's temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

This paper presents a three-dimensional and compressible biological cell model based on discrete element method using multiple interacting agent that represent cellular structures within a simulated environment. The cytoplasm and nucleoplasm fluid behavior in the cell is time dependent. When taking this approach, it is important to calibrate protoplasmic flow behaviors through simulation techniques such as compressing the cell and examining the agents representing the cell cytoplasm seeping between the ones representing the confining cell membrane. This type of modelling may motivate future work on simulating simultaneous operations and interactions of multiple cellular agents in an attempt to re-create and predict the appearance of complex phenomena such as protoplasmic seepage that is caused by the force actuations of neighboring cells. Seepage occurs when a cytoplasm agent passes between three membrane particles connected in a triangular network. Based on the force–deformation response of spheres having variable size and stiffness, semi-analytic expressions are developed for the force required to cause seepage and solved numerically to find the maximum resistance offered by the membrane against cytoplasm seepage. The equations are based on force equilibrium and the constitutive relations for particle contact and membrane stiffness. In multi-particle representations of an individual cell undergoing deformation, different modes of cytoplasm seepage through confining cell membranes can occur. This can be avoided if simple criteria are satisfied. These findings can lead to certain fundamental laws for the improvement of novel cell-to-organ simulation techniques based on discrete element method.

How the ubiquitously expressed splicing factors specifically regulate neural crest (NC) development and enhance their vulnerability to splicing perturbations remain poorly understood. Here, we show that NC-specific DLC1, partnering with SF3B1-PHF5A splicing complex, are crucial for determining avian trunk NC cell fate by regulating the splicing of NC specifiers SOX9 and SNAI2 pre-mRNAs rather than their upstream regulators BMP4 , WNT1 , and PAX7 . Mechanistically, SF3B1-PHF5A binds to the intronic branch site (BS) sequences of all factors, while DLC1 interacts with a specific motif near the BS sequences of SOX9 and SNAI2 , thereby determining their functional specificity in NC specification. Moreover, DLC1 increases NC cells’ vulnerability to splicing modulator pladienolide B (PB) by reducing the binding capacity of the SF3B1-PHF5A splicing complex to the shorter length of both SOX9 intron 2 and SNAI 2 intron 1, which possess weaker polypyrimidine tract 3’ of the BS sequence, resulting in intron retention and loss of NC progenitors. Conversely, somite specific SLU7-SF3B1-PHF5A splicing complex regulates SOX9 and SNAI2 expression and imparts resistance to PB. Our data reveal the cell-type specific splicing complexes with distinct vulnerabilities to PB, highlighting the critical role of the DLC1-SF3B1-PHF5A in determining trunk NC cell fate and enhancing its susceptibility to splicing perturbation. How neural crest development is specifically regulated by splicing factors and its vulnerability to splicing dysregulation remains unclear. Here, the authors identify neural crest-specific and somite-specific splicing complexes that confer distinct susceptibility to splicing perturbation.

There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE(-/-) mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.

This book presents a road map for applying the stages in conceptualization, evaluation, and testing of biomedical devices in a systematic order of approach, leading to solutions for medical problems within a well-deserved safety limit. The issues discussed will pave the way for understanding the preliminary concepts used in modern biomedical device engineering, which include medical imaging, computational fluid dynamics, finite element analysis, particle image velocimetry, and rapid prototyping. This book would undoubtedly be of use to biomedical engineers, medical doctors, radiologists, and any other professionals related to the research and development of devices for health care.

Recognition of the intermuscular spaces within lumbar paraspinal muscles is critically important for using the paramedian muscle-splitting approach to the lumbar spine. As such, it is important to determine the intermuscular spaces within the lumbar paraspinal muscles by utilizing modern medical imaging such as computed tomography (CT) and magnetic resonance imaging (MRI). A total of 30 adult cadavers were studied by sectional anatomic dissection, and 60 patients were examined using CT (16 slices, 3-mm thickness, 3-mm intersection gap, n = 30) and MRI (3.0T, T2-WI, 5-mm thickness, 1-mm intersection gap, n = 30). The distances between the midline and the superficial points of the intermuscular spaces at different intervertebral disc levels were measured. Based on study of our cadavers, the mean distances from the midline to the intermuscular space between multifidus and longissimus, from intervertebral disc levels L1-L2 to L5-S1, were 0.9, 1.1, 1.7, 3.0, and 3.5 cm, respectively. Compared with the upper levels (L1-L3), the superficial location at the lower level (L4-S1) is more laterally to the midline (P<0.05). The intermuscular space between sacrospinalis and quadratus lumborum, and that between longissimus and iliocostalis did not exist at L4-S1. The intermuscular spaces in patients also varied at different levels of the lumbar spine showing a low discontinuous density in CT and a high signal in MRI. There were no significant differences between the observations in cadavers and those made using CT and MRI. The intermuscular spaces within the paraspinal muscles vary at different intervertebral disc levels. Preoperative CT and MRI can facilitate selection of the muscle-splitting approach to the lumbar spine. This paper demonstrates the efficacy of medical imaging techniques in surgical planning.