Susana K. Lai-Yuen

Virtual prototyping and manufacturing planning by using tri-dexel models and haptic force feedback

This paper presents a new method of using the tri-dexel volumetric models and a haptics force feedback for virtual prototyping and manufacturing planning. In the proposed method, the initial polyhedral surface model is converted to a tri-dexel volumetric model by using a depth-peeling dexelization algorithm. In the virtual prototyping process, the tri-dexel volumetric model is updated by the swept volume of a moving cutter via a haptic force feedback interface device. A collision detection algorithm is proposed for the virtual sculpting and the pencil-cut planning with real-time haptic force feedback to the users. Tool paths are generated for machining the virtual sculpted parts via the simulation and verification on a virtual CNC machine tool before they are actually machined. Computer implementation and practical examples are also presented in this paper. The proposed method enables the haptic-aided virtual prototyping and manufacturing planning of complex surface parts.

Interactive Computer-Aided Design for Molecular Docking and Assembly

AbstractThis paper presents a computer-aided design system for molecular docking and nanoscale assembly. A lab-built 5-DOF (degree of freedom) haptic device and the driving computational engine have been developed to provide force-torque feedback to the users for computer-aided molecular design (CAMD). The developed haptic force-torque feedback will enable researchers to visualize, touch, manipulate and assemble molecules in a virtual environment. The presented techniques can be used in the computer-aided molecular design to provide the researchers a realtime tool to better understand molecular interactions and to evaluate possible pharmaceutical drugs and nanoscale devices. Computer implementation and illustrative examples are also presented in this paper.

Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets

A new oversampling method for imbalanced dataset classification is presented.It clusters the minority class and identifies borderline minority instances.Considering majority class during minority class clustering improves oversampling.Cluster size after oversampling should be dependent on its misclassification error.Generated synthetic instances improved subsequent classification. In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to one of the classes (majority class), while only a few instances are from the other class (minority class). Conventional classifiers will strongly favor the majority class and ignore the minority instances. In this paper, we present a new oversampling method called Adaptive Semi-Unsupervised Weighted Oversampling (A-SUWO) for imbalanced binary dataset classification. The proposed method clusters the minority instances using a semi-unsupervised hierarchical clustering approach and adaptively determines the size to oversample each sub-cluster using its classification complexity and cross validation. Then, the minority instances are oversampled depending on their Euclidean distance to the majority class. A-SUWO aims to identify hard-to-learn instances by considering minority instances from each sub-cluster that are closer to the borderline. It also avoids generating synthetic minority instances that overlap with the majority class by considering the majority class in the clustering and oversampling stages. Results demonstrate that the proposed method achieves significantly better results in most datasets compared with other sampling methods.

Synergetic effects of doxycycline-loaded chitosan nanoparticles for improving drug delivery and efficacy

Introduction Doxycycline, a broad-spectrum antibiotic, is the most commonly prescribed antibiotic worldwide for treating infectious diseases. It may be delivered orally or intravenously but can lead to gastrointestinal irritation and local inflammation. For treatment of uterine infections, transcervical administration of doxycycline encapsulated in nanoparticles made of biodegradable chitosan may improve sustained delivery of the drug, thereby minimizing adverse effects and improving drug efficacy. Methods and materials As a first step toward assessing this potential, we used an ionic gelation method to synthesize blank and doxycycline-loaded chitosan nanoparticles (DCNPs), which we then characterized in terms of several properties relevant to clinical efficacy: particle size, shape, encapsulation efficiency, antibacterial activity, and in vitro cytotoxicity. Two particle formulations were examined, with one (named DCNP6) containing approximately 1.5 times the crosslinker concentration of the other (DCNP4). Results The two formulations produced spherically shaped drug-loaded nanoparticles. The spheres ranged in size from 30 to 220 nm diameter for DCNP4 and 200 to 320 nm diameter for DCNP6. Average encapsulation yield was 53% for DCNP4 and 56% for DCNP6. In terms of drug release, both formulations showed a burst effect within the first 4 to 5 hours, followed by a slow, sustained release for the remainder of the 24-hour monitoring period. The in vitro antibacterial activity against Escherichia coli was high, with both formulations achieving more than 90% inhibition of 4-hour bacterial growth. Cytotoxic effects of the DCNPs on normal human ovarian surface epithelial cells were significantly lower than those of unencapsulated doxycycline. After 5 days, cultures exposed to the unencapsulated antibiotic showed a 61% decrease in cell viability, while cultures exposed to the DCNPs exhibited less than a 10% decrease. Conclusion These laboratory results suggest that DCNPs show preliminary promise for possible eventual use in transcervical drug delivery and improved efficacy in the treatment of bacterial uterine infections.

Computer-aided molecular design (CAMD) with force-torque feedback

This paper presents a new method for computer-aided molecular design (CAMD) and molecular assembly. A lab-built 5-DOF (degree of freedom) haptic device and the driving computational engine have been developed to provide force-torque feedback to the users for computer-aided molecular design (CAMD). An energy minimization method is proposed for finding collision-free molecular configurations in real-time for molecular docking and assembly. The proposed haptic force-torque feedback provides the users an intuitive tool for understanding the interactions among molecules. The presented techniques can be used in the computer-aided molecular design to provide the scientists or the designers a real-time intuitive guide for manipulating the ligand and understanding of the ligands behavior towards the binding site of a receptor. Computer implementation and illustrative examples are also presented in this paper.

MRI-based segmentation of pubic bone for evaluation of pelvic organ prolapse.

Pelvic organ prolapse (POP) is a major womens health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.

Energy-Field Optimization and Haptic-Based Molecular Docking and Assembly Search System for Computer-Aided Molecular Design (CAMD)

This paper presents a new system using a haptic device with an automatic molecular docking and assembly search method for the problems of molecular docking and molecular assembly in computer-aided molecular design (CAMD). The developed haptic force-torque feedback provides the users an intuitive tool for understanding the interactions among molecules while an automatic docking and assembly search method, NanoDAS, assists the user on determining the docking and assembly feasibility. The proposed system can be used as a tool to screen out candidate molecules that are infeasible, in terms of geometry and energy, to dock or assemble into a larger molecule. This identification of feasible molecules can significantly improve and accelerate the discovery and design of new pharmaceutical drugs and nanoscale devices in CAMD. Computer implementation and illustrative examples are also presented in this paper.

Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord)

A new oversampling method called Cluster-based Weighted Oversampling for Ordinal Regression (CWOS-Ord) is proposed for addressing ordinal regression with imbalanced datasets. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. In many applications, the dataset is highly imbalanced where the instances of some classes (majority classes) occur much more frequently than instances of other classes (minority classes). This significantly degrades the classification performance as classifiers tend to strongly favor the majority classes. Standard oversampling methods can be used to improve the dataset class distribution; however, they do not consider the ordinal relationship between the classes. The proposed CWOS-Ord method aims to address this problem by first clustering minority classes and then oversampling them based on their distances and ordering relationship to other classes instances. The final size to oversample the clusters depends on their complexity and their initial size so that more synthetic instances are generated for more complex and smaller clusters while fewer instances are generated for less complex and larger clusters. As a secondary contribution, existing oversampling methods for two-class classification have been extended for ordinal regression. Results demonstrate that the proposed CWOS-Ord method provides significantly better results compared to other methods based on the performance measures. A new oversampling method for addressing ordinal regression with imbalanced datasets is presented.Minority classes are clustered by considering the instances of other classes.A new measurement is proposed to find the final size of clusters based on their complexity and initial size.Minority instances are oversampled based on their distances and ordering relationship to other classes instances.

Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI

Introduction and hypothesisThe objective of this study was to assess the performance of a semiautomated pelvic floor measurement algorithmic model on dynamic magnetic resonance imaging (MRI) images compared with manual pelvic floor measurements for pelvic organ prolapse (POP) evaluation.MethodsWe examined 15 MRIs along the midsagittal view. Five reference points used for pelvic floor measurements were identified both manually and using our semiautomated measurement model. The two processes were compared in terms of accuracy and precision.ResultsThe semiautomated pelvic floor measurement model provided highly consistent and accurate locations for all reference points on MRI. Results also showed that the model can identify the reference points faster than the manual-point identification process.ConclusionThe semiautomated pelvic floor measurement model can be used to facilitate and improve the process of pelvic floor measurements on MRI. This will enable high throughput analysis of MRI data to improve the correlation analysis with clinical outcomes and potentially improve POP assessment.

MRI-based semi-automatic pelvimetry measurement for pelvic organ prolapse diagnosis

Magnetic resonance imaging (MRI) pelvimetry measurements are useful in the diagnosis of pelvic organ prolapse given the inaccuracy of clinical examination. However, MRI measurements are currently performed manually and can be inconsistent, time-consuming and inaccurate. In this paper, we present a scheme for semi-automatic measurements on MR images based on multi scale wavelet analysis. The experiments on the MR images show that the presented scheme can detect the points of reference on the pelvic bone structure to determine the lines needed for the assessment of pelvic organ prolapse. This may lead towards more accurate and faster pelvic organ prolapse diagnosis on dynamic MR studies, and possible screening procedures for predicting predisposition to pelvic organ prolapse by radiologic evaluation of pelvimetry measurements.