HOG CHAINS AND MARK TWAINS: A STUDY OF LABOR HISTORY, ARCHAEOLOGY, AND INDUSTRIAL ETHNOGRAPHY OF THE STEAMBOAT ERA OF THE MONONGAHELA VALLEY 1811-1950

This dissertation examines a unique working class in the United States, the men and women who worked on the steamboats from the Industrial Revolution until the demise of steam-powered boats in the mid-20th century. The steamboat was the beginning of a technological system that was developed in America and used in such great numbers that it made the rapid population of the Trans-Appalachian West possible. The steamboat was forever romanticized by images of the antebellum South or the quick wit of Samuel Clemens and his sentimental book, Life on the Mississippi. The imagination swirls with thoughts of boats, bleach white, slowly churning the calm waters of some Spanish moss covered river. The reality of the boats and the experience of those who worked on them has been lost in this nostalgic vision. This research details the history of the western steamboat in the Monongahela Valley, the birthplace of the commercial steamboat industry. The first part of this dissertation examines the literature of authors in the field of labor history and Industrial Archaeology to place this work into the larger context of published literature. The second builds a framework for understanding the various eras that the steamboat went through both in terms of technological change, but also the change the workers experienced as their identity as a working class was being shaped. The third part details the excavations of two steamboat captains houses, those of Captain James Gormley and Captain Michael A. Cox. Both men represented a time in which the steamboat was in an era of transition. Excavations at their homes yield clues to their class status and how integrated they were in the local community. The fourth part of this study documents the oral histories of steamboat workers, both men and women, and their experience on the boats and on the river. Their rapidly declining population of those who lived and worked on the boats gives urgency for their lives to be documented. Finally, this study concludes with a synthesis of how worker identity solidified in the face of technological, socio-economic, and ideological change especially during their push for unionization and the introduction of the diesel towboat.

Aromatic l-amino acid decarboxylase : histochemical localization in rat kidney and lack of effect of dietary potassium or sodium loading on enzyme distribution

Utilizing a mono-specific antiserum produced in rabbits to hog kidney aromatic L-amino acid decarboxylase (AADC), the enzyme was localized in rat kidney by immunoperoxidase staining. AADC was located predominantly in the proximal convoluted tubules; there was also weak staining in the distal convoluted tubules and collecting ducts. An increase in dietary potassium or sodium intake produced no change in density or distribution of AADC staining in kidney. An assay of AADC enzyme activity showed no difference in cortex or medulla with chronic potassium loading. A change in distribution or activity of renal AADC does not explain the postulated dopaminergic modulation of renal function that occurs with potassium or sodium loading.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

V1-inspired features induce a weighted margin in SVMs

Image representations derived from simplified models of the primary visual cortex (V1), such as HOG and SIFT, elicit good performance in a myriad of visual classification tasks including object recognition/detection, pedestrian detection and facial expression classification. A central question in the vision, learning and neuroscience communities regards why these architectures perform so well. In this paper, we offer a unique perspective to this question by subsuming the role of V1-inspired features directly within a linear support vector machine (SVM). We demonstrate that a specific class of such features in conjunction with a linear SVM can be reinterpreted as inducing a weighted margin on the Kronecker basis expansion of an image. This new viewpoint on the role of V1-inspired features allows us to answer fundamental questions on the uniqueness and redundancies of these features, and offer substantial improvements in terms of computational and storage efficiency.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.

Parts based representation for pedestrian using NMF with robustness to partial occlusion

Computer Vision has seen a resurgence in the parts-based representation for objects over the past few years. The parts are usually annotated beforehand for training. We present an annotation free parts-based representation for the pedestrian using Non-Negative Matrix Factorization (NMF). We show that NMF is able to capture the wide range of pose and clothing of the pedestrians. We use a modified form of NMF i.e. NMF with sparsity constraints on the factored matrices. We also make use of Riemannian distance metric for similarity measurements in NMF space as the basis vectors generated by NMF aren't orthogonal. We show that for 1% drop in accuracy as compared to the Histogram of Oriented Gradients (HOG) representation we can achieve robustness to partial occlusion.

Enzymatic synthesis of flavors in supercritical carbon dioxide

Commercially important flavor esters of isoamyl alcohol, catalyzed by crude hog pancreas lipase (HPL), were synthesized under solvent-free conditions and in supercritical carbon dioxide. The esters synthesized were isoamyl acetate, isoamyl propionate, isoamyl butyrate, and isoamyl octanoate. Very low yields (3-4%) of isoamyl acetate were obtained, but high yields for the other three esters were obtained under both supercritical and solvent-free conditions. The yields of esters of the even-carbon acids, isoamyl acetate, butyrate, and octanoate, increased with increasing chain length, whereas the yield of isoamyl propionate was higher than that of isoamyl butyrate. The optimum temperature of the reaction was higher under supercritical conditions (45 degreesC) than under solvent-free conditions (35-40 degreesC). The effects of other parameters such as alcohol concentration, water concentration, and enzyme loading were investigated. An increase in the water concentration decreased the conversion significantly in supercritical carbon dioxide but not under solvent-free conditions. The optimum ratio of alcohol to acid was dependent on the extent of inhibition by the acid. Although providing a higher apparent yield by being run in a highly concentrated medium, the overall conversion under solvent-free conditions was lower than that under supercritical conditions for similar enzyme concentrations, indicating that the synthesis of esters in supercritical carbon dioxide might be a viable option.

Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers

Vision based tracking can provide the spatial location of construction entities such as equipment, workers, and materials in large scale, congested construction sites. It tracks entities in video streams by inferring their locations based on the entities’ visual features and motion histories. To initiate the process, it is necessary to determine the pixel areas corresponding to the construction entities to be tracked in the following consecutive video frames. In order to fully automate the process, an automated way of initialization is needed. This paper presents the method for construction worker detection which can automatically recognize and localize construction workers in video frames. The method first finds the foreground areas of moving objects using a background subtraction method. Within these foreground areas, construction workers are recognized based on the histogram of oriented gradients (HOG) and histogram of the HSV colors. HOG’s have proved to work effectively for detection of people, and the histogram of HSV colors helps differentiate between pedestrians and construction workers wearing safety vests. Preliminary experiments show that the proposed method has the potential to automate the initialization process of vision based tracking.

Construction Worker Detection in Video Frames for Initializing Vision Trackers

Monitoring the location of resources on large scale, congested, outdoor sites can be performed more efficiently with vision tracking, as this approach does not require any pre-tagging of resources. However, the greatest impediment to the use of vision tracking in this case is the lack of detection methods that are needed to automatically mark the resources of interest and initiate the tracking. This paper presents such a novel method for construction worker detection that localizes construction workers in video frames. The proposed method exploits motion, shape, and color cues to narrow down the detection regions to moving objects, people, and finally construction workers, respectively. The three cues are characterized by using background subtraction, the histogram of oriented gradients (HOG), and the HSV color histogram. The method has been tested on videos taken in various environments. The results demonstrate its suitability for automatic initialization of vision trackers.

An alignment based similarity measure for hand detection in cluttered sign language video

Locating hands in sign language video is challenging due to a number of factors. Hand appearance varies widely across signers due to anthropometric variations and varying levels of signer proficiency. Video can be captured under varying illumination, camera resolutions, and levels of scene clutter, e.g., high-res video captured in a studio vs. low-res video gathered by a web cam in a user’s home. Moreover, the signers’ clothing varies, e.g., skin-toned clothing vs. contrasting clothing, short-sleeved vs. long-sleeved shirts, etc. In this work, the hand detection problem is addressed in an appearance matching framework. The Histogram of Oriented Gradient (HOG) based matching score function is reformulated to allow non-rigid alignment between pairs of images to account for hand shape variation. The resulting alignment score is used within a Support Vector Machine hand/not-hand classifier for hand detection. The new matching score function yields improved performance (in ROC area and hand detection rate) over the Vocabulary Guided Pyramid Match Kernel (VGPMK) and the traditional, rigid HOG distance on American Sign Language video gestured by expert signers. The proposed match score function is computationally less expensive (for training and testing), has fewer parameters and is less sensitive to parameter settings than VGPMK. The proposed detector works well on test sequences from an inexpert signer in a non-studio setting with cluttered background.

Phosphoramidates of 2'-beta-D-arabinouridine (AraU) as phosphate prodrugs; design, synthesis, in vitro activity and metabolism

2'-Beta-D-arabinouridine (AraU), the uridine analogue of the anticancer agent AraC, was synthesized and evaluated for antiviral activity and cytotoxicity. In addition, a series of AraU monophosphate prodrugs in the form of triester phosphoramidates (ProTides) were also synthesized and tested against a range of viruses, leukaemia and solid tumour cell lines. Unfortunately, neither the parent compound (AraU) nor any of its ProTides showed antiviral activity, nor potent inhibitory activity against any of the cancer cell lines. Therefore, the metabolism of AraU phosphoramidates to release AraU monophosphate was investigated. The results showed carboxypeptidase Y, hog liver esterase and crude CEM tumor cell extracts to hydrolyse the ester motif of phosphoramidates with subsequent loss of the aryl group, while molecular modelling studies suggested that the AraU l-alanine aminoacyl phosphate derivative might not be a good substrate for the phosphoramidase enzyme Hint-1. These findings are in agreement with the observed disappearance of intact prodrug and concomitant appearance of the corresponding phosphoramidate intermediate derivative in CEM cell extracts without measurable formation of araU monophosphate. These findings may explain the poor antiviral/cytostatic potential of the prodrugs.

Effects of modified LDL and HDL on retinal pigment epithelial cells: a role in diabetic retinopathy?

Aims/hypothesis: Blood–retina barrier leakage in diabetes results in extravasation of plasma lipoproteins. Intra-retinal modified LDLs have been implicated in diabetic retinopathy (DR), but their effects on retinal pigment epithelial (RPE) cells and the added effects of extravasated modified HDLs are unknown.

Methods: In human retinas from individuals with and without diabetes and DR, immunohistochemistry was used to detect ApoB, ApoA1 and endoplasmic reticulum (ER) stress markers. In cell culture, human RPE cells were treated with native LDL (N-LDL) or heavily-oxidised glycated LDL (HOG-LDL) with or without pretreatment with native HDL (N-HDL) or heavilyoxidised glycated HDL (HOG-HDL). Cell viability, oxidative stress, ER stress, apoptosis and autophagy were assessed by Cell Counting Kit-8 assay, dichlorofluorescein assay, western blotting, immunofluorescence and TUNEL assay. In separate
experiments, RPE cells were treated with lipid oxidation products, 7-ketocholesterol (7-KC, 5–40 µmol/l) or 4-hydroxynonenal (4-HNE, 5–80 µmol/l), with or without pretreatment with N-HDL or HOG-HDL.

Results: ApoB, ApoA1 staining and RPE ER stress were increased in the presence of DR. HOG-LDL but not N-LDL significantly decreased RPE cell viability and increased reactive oxygen species generation, ER stress, apoptosis and autophagy. Similarly, 4-HNE and 7-KC decreased viability and induced ER stress. Pretreatment with N-HDL mitigated these effects, whereas HOG-HDL was less effective by most, but not all, measures.

Conclusions/interpretation: In DR, extravascular modified LDL may promote RPE injury through oxidative stress, ER stress, autophagy and apoptosis. N-HDL has protective effects, but HOG-HDL is less effective. Extravasation and modification of HDL may modulate the injurious effects of extravasated modified LDL on the retinal pigment epithelium.