Stochastic Desertification

The process of desertification is usually modeled as a first order transition, where a change of an external parameter (e.g. precipitation) leads to a catastrophic bifurcation followed by an ecological regime shift. However, vegetation elements like shrubs and trees undergo a stochastic birth-death process with an absorbing state; such a process supports a second order continuous transition with no hysteresis. We present a numerical study of a minimal model that supports bistability and catastrophic shift on spatial domain with demographic noise and an absorbing state. When the external parameter varies adiabatically the transition is continuous and the front velocity renormalizes to zero at the extinction transition. Below the transition one may identify three modes of desertification: accumulation of local catastrophes, desert invasion and global collapse. A catastrophic regime shift occurs as a dynamical hysteresis, when the pace of environmental variations is too fast. We present some empirical evidence, suggesting that the mid-holocene desertification of the Sahara was, indeed, continuous

Empirical analysis of vegetation dynamics and the possibility of a catastrophic desertification transition

The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that admits alternative steady states. Some support to this idea comes from the analysis of static patterns, where peaks of the vegetation density histogram were associated with these alternative states. Here we present a large-scale empirical study of vegetation dynamics, aimed at identifying and quantifying directly the effects of positive feedback. To do that, we have analyzed vegetation density across $~2.5 \times 10^6 \ \rm{km}^2$ of the African Sahel region, with spatial resolution of $30 \times 30$ meters, using three consecutive snapshots. The results are mixed. The local vegetation density (measured at a single pixel) moves towards the average of the corresponding rainfall line, indicating a purely negative feedback. On the other hand, the chance of spatial clusters (of many "green" pixels) to expand in the next census is growing with their size, suggesting some positive feedback. We show that these apparently contradicting results emerge naturally in a model with positive feedback and strong demographic stochasticity, a model that allows for a catastrophic shift only in a certain range of parameters. Static patterns, like the double peak in the histogram of vegetation density, are shown to vary between censuses, with no apparent correlation with the actual dynamical features

Sacred Rhythms and Political Frequencies: Reading Lefebvre in an Urban House of Prayer

In recent years, Lefebvre’s concept of rhythm analysis has been implied in various ways to critically examine how rhythms are formed, disrupted, and reformed through different urban venues. One theme that this body of knowledge has yet to comprehensively examine, however, is how changes in the urban sphere impact the spatial rhythms of religious institutions in cities, which can be pivotal for understanding how religious institutions are formed as urban public spaces. This article addresses this issue with a rhythm analysis of a particular religious urban locus: a synagogue in the mixed Palestinian and Jewish city of Acre in northern Israel. Based on ethnographic fieldwork and an urban survey, the article discusses how different forms of rhythm making undergo a process of contested synchronization with linear and cyclical rhythms of the city. More specifically, how the ability to forge a space hinges on the ability to maintain a rhythmic cycle of attendance, which, in turn, is not only dependent on the ability to achieve synchronization amongst the needs of the different participants but is also intertwined with the larger linear cycle of urban life as a rhythmic equation that fuses the personal with the political, the linear with the cyclical, and the religious with the urban

Task specific image text recognition

This thesis addresses the problem of reading image text, which we define here as a digital image of machine printed text. Images of license plates, signs, and scanned documents fall into this category, whereas images of handwriting do not. Automatically reading image text is a very well researched problem, which falls into the broader category of Optical Character Recognition (OCR). Virtually all work in this domain begins by segmenting characters from the image and proceeds with a classification stage to identify each character. This conventional approach is not best suited for task specific recognition such as reading license plates, scanned documents, or freeway signs, which can often be blurry and poor quality. In this thesis, we apply a boosting framework to the character recognition problem, which allows us to avoid character segmentation altogether. This approach allows us to read blurry, poor quality images that are difficult to segment. When there is a constrained domain, there is generally a large amount of training images available. Our approach benefits from this since it is entirely based on machine learning. We perform experiments on hand labeled datasets of low resolution license plate images and demonstrate highly encouraging results. In addition, we show that if enough domain knowledge is available, we can avoid the arduous task of hand-labeling examples by automatically synthesizing training dat

Task specific image text recognition

This thesis addresses the problem of reading image text, which we define here as a digital image of machine printed text. Images of license plates, signs, and scanned documents fall into this category, whereas images of handwriting do not. Automatically reading image text is a very well researched problem, which falls into the broader category of Optical Character Recognition (OCR). Virtually all work in this domain begins by segmenting characters from the image and proceeds with a classification stage to identify each character. This conventional approach is not best suited for task specific recognition such as reading license plates, scanned documents, or freeway signs, which can often be blurry and poor quality. In this thesis, we apply a boosting framework to the character recognition problem, which allows us to avoid character segmentation altogether. This approach allows us to read blurry, poor quality images that are difficult to segment. When there is a constrained domain, there is generally a large amount of training images available. Our approach benefits from this since it is entirely based on machine learning. We perform experiments on hand labeled datasets of low resolution license plate images and demonstrate highly encouraging results. In addition, we show that if enough domain knowledge is available, we can avoid the arduous task of hand-labeling examples by automatically synthesizing training dat

Nitric Oxide Interaction with the Eye

Nitric oxide (NO) is acknowledged as a vital intercellular messenger in multiple systems in the body. Medicine has focused on its functions and therapeutic applications for decades, especially in cardiovascular and nervous systems, and its role in immunological responses. This review was composed to demonstrate the prevalence of NO in components of the ocular system, including corneal cells and multiple cells in the retina. It discussed NO’s assistance during the immune, inflammation and wound-healing processes. NO is identified as a vascular endothelial relaxant that can alter the choroidal blood flow and prompt or suppress vascular changes in age-related macular degeneration and diabetes, as well as the blood supply to the optic nerve, possibly influencing the progression of glaucoma. It will provide a deeper understanding of the role of NO in ocular homeostasis, the delicate balance between overproduction or underproduction and the effect on the processes from aqueous outflow and subsequent intraocular pressure to axial elongation and the development of myopia. This review also recognized the research and investigation of therapies being developed to target the NO complex and treat various ocular diseases

Neisseria meningitidis and cytomegalovirus simultaneous detection in the filmarray meningitis/encephalitis panel and its clinical relevance

A BioFire FilmArray Meningitis/Encephalitis test was performed on a 7-month old child suspected for bacterial meningitis. Two pathogens were detected, Neisseria meningitidis and cytomegalovirus (CMV). We verified the filmarray meningitis/encephalitis test by pan-bacterial assay to test for Neisseria meningitidis and CMV viral load test for the CMV detection. Pan-bacterial confirmed presence of N. meningitidis, but CMV was not detected by the CMV viral load test. Together with the manifestations of high fever, vomiting, diffuse petechial rash, bulging fontanel, and leukocytosis, it is a clear case of meningococcal meningitis, while CMV detection had no clinical relevance