Maximizing Product Formation and Minimizing Degradation: A Look at Buffering Conditions and Post-Reaction Degradation for the In-Line Jaffe Reaction with Capillary Electrophoresis

Creatinine levels in blood serum are typically used to assess renal function. Clinical determination of creatinine is often based on the Jaffe reaction, in which creatinine in the serum reacts with sodium picrate, resulting in a spectrophotometrically quantifiable product. Previous work from our lab has introduced an electrophoretically mediated initiation of this reaction, in which nanoliter plugs of individual reagent solutions can be added to the capillary and then mixed and reacted. Following electrophoretic separation of the product from excess reactant(s), the product can be directly determined on column. This work aims to gain a detailed understanding of the in-capillary reagent mixing dynamics, in-line reaction yield, and product degradation during electrophoresis, with an overall goal of improving assay sensitivity. One set of experiments focuses on maximizing product formation through manipulation of various conditions such as pH, voltage applied, and timing of the applied voltage, in addition to manipulations in the identity, concentration, and pH of the background electrolyte. Through this work, it was determined that dramatic changes in local voltage fields within the various reagent zones lead to ineffective reagent overlapping. Use of the software simulation program Simul 5 enabled visualization of the reaction dynamics within the capillary, specifically the wide variance between the electric field intensities within the creatinine and picrate zones. Because of this simulation work, the experimental method was modified to increase the ionic strength of the creatinine reagent zone to lower the local voltage field, thus producing more predictable and effective overlap conditions for the reagents and allowing the formation of more Jaffe product. As second set of experiments focuses on controlling the post-reaction product degradation. In that vein, we have systematically explored the importance of the identity, concentration, and pH of the background electrolyte on the post-reaction degradation rate of the product. Although prior work with borate background electrolytes indicated that product degradation was probably a function of the ionic strength of the background electrolyte, this work with a glycine background electrolyte demonstrates that degradation is in fact not a function of ionic strength of the background electrolyte. As the concentration and pH of the glycine background increased, the rate of degradation of product did not change dramatically, whereas in borate-buffered systems, the rate of Jaffe product degradation increased linearly with background electrolyte concentration above 100.0 mM borate. Similarly, increasing pH of the glycine background electrolyte did not result in a corresponding increase in product degradation, as it had with the borate background electrolyte. Other general trends that were observed include: increasing background electrolyte concentration increases peak efficiency and higher pH favors product formation; thus, it appears that use of a background electrolyte other than borate, such as glycine, the rate of degradation of the Jaffe product can be slowed, increasing the sensitivity of this in-line assay.

Selecting, optimizing and fusing ‘salient’ Gabor features for facial expression recognition

This paper describes a novel framework for facial expression recognition from still images by selecting, optimizing and fusing ‘salient’ Gabor feature layers to recognize six universal facial expressions using the K nearest neighbor classifier. The recognition comparisons with all layer approach using JAFFE and Cohn-Kanade (CK) databases confirm that using ‘salient’ Gabor feature layers with optimized sizes can achieve better recognition performance and dramatically reduce computational time. Moreover, comparisons with the state of the art performances demonstrate the effectiveness of our approach.

Comprehensive standardized data definitions for acute coronary syndrome research in emergency departments in Australasia

Patients with chest discomfort or other symptoms suggestive of acute coronary syndrome (ACS) are one of the most common categories seen in many Emergency Departments (EDs). While the recognition of patients at high-risk of ACS has improved steadily, identifying the majority of chest pain presentations who fall into the low-risk group remains a challenge. Research in this area needs to be transparent, robust, applicable to all hospitals from large tertiary centres to rural and remote sites, and to allow direct comparison between different studies with minimum patient spectrum bias. A standardised approach to the research framework using a common language for data definitions must be adopted to achieve this. The aim was to create a common framework for a standardised data definitions set that would allow maximum value when extrapolating research findings both within Australasian ED practice, and across similar populations worldwide. Therefore a comprehensive data definitions set for the investigation of non-traumatic chest pain patients with possible ACS was developed, specifically for use in the ED setting. This standardised data definitions set will facilitate ‘knowledge translation’ by allowing extrapolation of useful findings into the real-life practice of emergency medicine.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

On the application of the probabilistic linear discriminant analysis to face recognition across expression

Facial expression is one of the main issues of face recognition in uncontrolled environments. In this paper, we apply the probabilistic linear discriminant analysis (PLDA) method to recognize faces across expressions. Several PLDA approaches are tested and cross-evaluated on the Cohn-Kanade and JAFFE databases. With less samples per gallery subject, high recognition rates comparable to previous works have been achieved indicating the robustness of the approaches. Among the approaches, the mixture of PLDAs has demonstrated better performances. The experimental results also indicate that facial regions around the cheeks, eyes, and eyebrows are more discriminative than regions around the mouth, jaw, chin, and nose.

Hodgkin lymphoma and immunodeficiency in persons with HIV/AIDS

In persons with HIV/AIDS (PWHAs), Hodgkin lymphoma (HL) risk is increased. However, HL incidence in PWHAs has unexpectedly increased since highly active antiretroviral therapy (HAART) was introduced. We linked nationwide HIV/AIDS and cancer registry data from 1980 through 2002. Immunity was assessed by CD4 T-lymphocyte counts at AIDS onset. Annual HL incidence rates were calculated for 4 through 27 months after AIDS onset. During 477 368 person years (py's) of follow-up in 317 428 persons with AIDS (PWAs), 173 HL cases occurred (36.2 per 105 py's). Incidence was significantly higher in 1996 to 2002 than earlier. Incidence in PWAs with 150 to 199 CD4 cells/μL was 53.7 per 105 py's, whereas in PWAs with fewer than 50 CD4 cells/μL, it was 20.7 per 105 py's (Ptrend = .002). For each HL subtype, incidence decreased with declining CD4 counts, but nodular sclerosing decreased more precipitously than mixed cellularity, thereby increasing the proportion of mixed cellularity HL seen in PWAs. We conclude that HL incidence is lower with severe immunosuppression than with moderate immunosuppression, and HAART-related improvements in CD4 counts likely explain the increasing HL incidence in PWHAS observed since 1996. With more severe immunosuppression, nodular sclerosing HL becomes infrequent, explaining the higher proportion of mixed cellularity HL found in PWAs. Pathogenesis implications are discussed.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.

Random Gabor based templates for facial expression recognition in images with facial occlusion

Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of local texture information and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. The results on the parameter sensitivity demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusions ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth.