No general coordinated organizing strategy to deal with the expanding for-profit business model in the higher education industry has emerged from any of the major faculty unions at the national or state levels. Nonetheless, some attempts to organize have taken place. In most cases, they were single employer based and lacked a regional base of support. Most did not get very far. But the continuing existence of two local unions in the Art Institutes chain and the recent election victory in Kaplan International Centers in Manhattan after a number of defeats of organizing attempts in recent years suggests two things: first, organizing in this sector of this industry is difficult but not impossible, and second, there is a persistent, steady desire for organization among the faculty, as evidenced by the repeated attempts."Industry" is a term not often used for higher education, but in this case, none is better. In the next section, we describe the three sectors of this industry in order to highlight the transformation that has taken place in the last thirty years, as the for-profit corporate model has both expanded as a subsector and has greatly influenced traditional non-profit higher education. However, this article is not primarily about the for-profits as a new and challenging phenomenon. Instead, it is about faculty responses to working in the for-profits and the technically nonprofits that increasingly model themselves on for-profit business strategies. Therefore, we follow that section with examples of organizing efforts within the for-profit model sector and conclude with an argument for a regional, workforce-based organizing strategy. The Three Subsectors of Higher Education TodayHigher education in the U.S., which enrolls over fourteen million students, 1 breaks out into three subsectors based on how each subsector is financed and bs_bs_banner
The olfactory bulb (OB) is one of the first regions of the brain affected by Parkinson’s disease (PD) as measured by both dysfunction and presence of α-synuclein aggregation. Better understanding of how PD affects OB function could lead to earlier diagnosis and potential treatment. By simulating damage to the OB in a computational model, it may be possible to identify regions of interest or markers of early disease. We modified a simple rate-based computational model of the olfactory bulb and simulated damage to various components of the network. This was done for several configurations of the network, at different sizes and with 1D and 2D connectivity structures. We found that, in almost every case, activity of 2D networks were more robust to damage than 1D networks, leading us to conclude that a connection scheme of at least 2D is vital to computational modeling of the OB. We also found that certain types of damage (namely, seeded damage to the granule cell layer and to the synapses between mitral and granule cells) resulted in a peak of the oscillatory power of the network as a function of damage. This result is testable experimentally and bears further investigation utilizing more sophisticated computational models. If proven accurate, this rise in oscillatory power in the OB has the potential to be an early marker of PD.Author summaryOne of the first symptoms of Parkinson’s disease is the degradation of the sense of smell. The olfactory bulb is the first region of the brain to process odor information and is affected by Parkinson’s disease at early stages. We simulated neural activity in a computational model of the olfactory bulb in the presence of damage and compared it to simulations of undamaged activity. We found that 2D model networks were more robust to damage than their 1D counterparts. We also found that 2D networks displayed increased oscillatory activity when damage was applied to certain parts of the network. This last result, if proven correct, would potentially be a marker of early-stage Parkinson’s disease, and if so, could aid in early diagnosis and treatment of the disease.
Several neurodegenerative diseases impact the olfactory system, and in particular the olfactory bulb, early in disease progression. One mechanism by which damage occurs is via synaptic dysfunction. Here, we implement a computational model of the olfactory bulb and investigate the effect of weakened connection weights on network oscillatory behavior. Olfactory bulb network activity can be modeled by a system of equations that describes a set of coupled nonlinear oscillators. In this modeling framework, we propagate damage to synaptic weights using several strategies, varying from localized to global. Damage propagated in a dispersed or spreading manner leads to greater oscillatory power at moderate levels of damage. This increase arises from a higher average level of mitral cell activity due to a shift in the balance between excitation and inhibition. That this shift leads to greater oscillations critically depends on the nonlinearity of the activation function.Linearized analysis of the network dynamics predicts when this shift leads to loss of oscillatory activity. We thus demonstrate one potential mechanism involved in the increased gamma oscillations seen in some animal models of Alzheimer's disease and highlight the potential that pathological olfactory bulb behavior presents as an early biomarker of disease. I. INTRODUCTIONThe olfactory system, and in particular the olfactory bulb (OB), is implicated in early stages of a number of neurodegenerative diseases, including two of the most prevalent, Alzheimer's disease (AD) and Parkinson's disease (PD) [1][2][3]. In both AD and PD, olfactory deficits occur years before diagnosis and often before other symptoms [2,[4][5][6][7][8][9]. Furthermore, the OB is a site of early pathology in both diseases [7,[10][11][12][13][14][15], with resulting aberrant neural activity [16][17][18][19][20][21]. We hope that computationally modeling olfactory bulb activity in diseaselike conditions can help to further shed light on mechanisms of dysfunction, identify markers of disease, and bring attention to the opportunity the OB presents for earlier diagnosis of neurodegenerative illnesses.The OB is the first processing area for incoming odor information [22], but exactly how it is represented is an ongoing question for which there are various theories, mainly revolving around combinatorics of principal neuron activity [23][24][25][26]. Oscillations in neural activity in
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