A Comprehensive Study of Accounting Principles and Applications through Analysis of Case Studies

The following thesis is a compilation of case studies that provide a detailed analysis of various financial accounting standards in agreement with Generally Accepted Accounting Principles as governed by the Financial Accounting Standards Board. In concurrence with the topics learned in the Patterson School of Accountancy program, each case focuses on a separate subject of financial reporting through application within specific corporations and dissertations. This thesis explores understanding of accounting principles, financial statement preparation and analysis, current accounting topics, and real-life application for a career in public accounting. Additionally, the thesis work incorporated knowledge from studies in other coursework, such as economics and finance. The case studies were completed under the direction of Dr. Victoria Dickinson in fulfillment of the requirements for the University of Mississippi Sally McDonnell Barksdale Honors College, Patterson School of Accountancy, and Professional Research and Development Thesis Program

Assessing changes in the agricultural productivity of upland systems in the light of peatland restoration

Human activity has had a profound negative impact on the structure and function of the earth’s ecosystems. However, with a growing awareness of the value of the services provided by intact ecosystems, restoration of degraded land is increasingly used as a means of reviving ecosystem function. Upland landscapes offer an excellent example of an environment heavily modified by human land use. Agriculture has been the key driver of ecosystem change, but as upland habitats such as peatlands can provide a number of highly valuable services, future change may focus on restoration in order to regain key ecosystem processes. However, as pastoral farming continues to dominate upland areas, ecosystem restoration has the potential to conflict with existing land use. This thesis attempts to assess differences in the agricultural productivity of the different habitat types present in upland pastures. Past and present land use have shaped the distribution of different upland habitat types, and future changes associated with ecosystem restoration are likely to lead to further change in vegetation communities. Three key contributors to agricultural productivity are examined. Firstly, variation in the nutritional quality of different upland habitats is assessed, in order to understand their value for grazing animals. Secondly, levels of livestock use in different habitats are compared in order to identify areas of particular importance for grazing. Finally, parasite populations are measured in different habitats in order provide an indication of which habitats pose the greatest potential risk of infection.It is shown that these factors appear to differ between habitats, meaning that agricultural productivity may show spatial variation in upland pastures. However, it appears that peatland restoration might have a negligible impact on farming in upland pastures due to apparent minor differences in the agricultural productivity of the habitats most likely to be affected

Learning and predicting with chain event graphs

Graphical models provide a very promising avenue for making sense of large, complex datasets. The most popular graphical models in use at the moment are Bayesian networks (BNs). This thesis shows, however, they are not always ideal factorisations of a system. Instead, I advocate for the use of a relatively new graphical model, the chain event graph (CEG), that is based on event trees. Event trees directly represent graphically the event space of a system. Chain event graphs reduce their potentially huge dimensionality by taking into account identical probability distributions on some of the event tree’s subtrees, with the added benefits of showing the conditional independence relationships of the system — one of the advantages of the Bayesian network representation that event trees lack — and implementation of causal hypotheses that is just as easy, and arguably more natural, than is the case with Bayesian networks, with a larger domain of implementation using purely graphical means. The trade-off for this greater expressive power, however, is that model specification and selection are much more difficult to undertake with the larger set of possible models for a given set of variables. My thesis is the first exposition of how to learn CEGs. I demonstrate that not only is conjugate (and hence quick) learning of CEGs possible, but I characterise priors that imply conjugate updating based on very reasonable assumptions that also have direct Bayesian network analogues. By re-casting CEGs as partition models, I show how established partition learning algorithms can be adapted for the task of learning CEGs. I then develop a robust yet flexible prediction machine based on CEGs for any discrete multivariate time series — the dynamic CEG model — which combines the power of CEGs, multi-process and steady modelling, lattice theory and Occam’s razor. This is also an exact method that produces reliable predictions without requiring much a priori modelling. I then demonstrate how easily causal analysis can be implemented with this model class that can express a wide variety of causal hypotheses. I end with an application of these techniques to real educational data, drawing inferences that would not have been possible simply using BNs

Real-world research and its importance in respiratory medicine

Peer reviewedPublisher PD

A NEW SPECIES OF \u3ci\u3eRHYNCHOMYS\u3c/i\u3e (MURIDAE) FROM THE PHILIPPINES

. . . Mr. Whitehead has made a most wonderful and unexpected discovery, that of a new and peculiar Mammal-fauna inhabiting the Luzon highlands, and, so far as is yet known, mostly isolated on a small plateau on the top of Monte Data, in the centre of Northern Luzon, at an altitude of from 7000 to 8000 feet. With this dramatic announcement, Oldfield Thomas (1898:377) introduced to naturalists the spectacular rats found by Whitehead: Crateromys schadenbergi, Carpomys melanurus, C. phaeurus, Batomys granti, Chrotomys whiteheadi, Celaenomys silaceus, and Rhynchomys soricoides. One of the most interesting of these to Thomas was R. soricoides, which he had previously named and briefly described in 1895. This shrew-rat is medium-sized with a very long muzzle, small eyes, short dense fur, dark brown upperparts, gray underparts, a short brown tail, and brown feet. Its very long and slender rostrum, small zygomatic plates, small white incisors, and tiny basined teeth indicated adaptations for special foods, possibly soft-bodied invertebrates. Nothing quite like Rhynchomys had been discovered before, either in the Philippines or elsewhere. Thomas based his description of R. soricoides on five individuals collected by Whitehead, all from Mount Data. Two others were obtained from Mount Data during 1946 and subsequently reported by Sanborn (1952). Rhynchomys was represented by these specimens until 1961, when an example was caught on Mount Isarog in the southeastern part of Luzon. That specimen, which forms the nucleus of our report, is significant because it indicates that Rhynchomys occurs on at least one other highland in Luzon, and because some of its morphological features fall outside the range of variation of the series taken on Mount Data. The differences in external and cranial characters between the two samples suggest to us that the specimen from Mount Isarog was drawn from a population that is reproductively isolated from that on Mount Data. We hypothesize that the specimen represents a new species, which we name Rhynchomys isarogensis (new species), and describe below

Learning and predicting with chain event graphs

Graphical models provide a very promising avenue for making sense of large, complex datasets. The most popular graphical models in use at the moment are Bayesian networks (BNs). This thesis shows, however, they are not always ideal factorisations of a system. Instead, I advocate for the use of a relatively new graphical model, the chain event graph (CEG), that is based on event trees. Event trees directly represent graphically the event space of a system. Chain event graphs reduce their potentially huge dimensionality by taking into account identical probability distributions on some of the event tree’s subtrees, with the added benefits of showing the conditional independence relationships of the system — one of the advantages of the Bayesian network representation that event trees lack — and implementation of causal hypotheses that is just as easy, and arguably more natural, than is the case with Bayesian networks, with a larger domain of implementation using purely graphical means. The trade-off for this greater expressive power, however, is that model specification and selection are much more difficult to undertake with the larger set of possible models for a given set of variables. My thesis is the first exposition of how to learn CEGs. I demonstrate that not only is conjugate (and hence quick) learning of CEGs possible, but I characterise priors that imply conjugate updating based on very reasonable assumptions that also have direct Bayesian network analogues. By re-casting CEGs as partition models, I show how established partition learning algorithms can be adapted for the task of learning CEGs. I then develop a robust yet flexible prediction machine based on CEGs for any discrete multivariate time series — the dynamic CEG model — which combines the power of CEGs, multi-process and steady modelling, lattice theory and Occam’s razor. This is also an exact method that produces reliable predictions without requiring much a priori modelling. I then demonstrate how easily causal analysis can be implemented with this model class that can express a wide variety of causal hypotheses. I end with an application of these techniques to real educational data, drawing inferences that would not have been possible simply using BNs.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Shaping the formation of university-industry research collaborations: what type of proximity does really matter?

Research collaborations between universities and industry (U-I) are considered to be one important channel of potential localized knowledge spillovers (LKS). These collaborations favour both intended and unintended flows of knowledge and facilitate learning processes between partners from different organizations. Despite the copious literature on LKS, still little is known about the factors driving the formation of U-I research collaborations and, in particular, about the role that geographical proximity plays in the establishment of such relationships. Using collaborative research grants between universities and business firms awarded by the UK Engineering and Physical Sciences Research Council (EPSRC), in this article we disentangle some of the conditions under which different kinds of proximity contribute to the formation of U-I research collaborations, focusing in particular on clustering and technological complementarity among the firms participating in such partnerships

Testosterone therapy and cardiovascular events among men: a systematic review and meta-analysis of placebo-controlled randomized trials

Background Testosterone therapy is increasingly promoted. No randomized placebo-controlled trial has been implemented to assess the effect of testosterone therapy on cardiovascular events, although very high levels of androgens are thought to promote cardiovascular disease. Methods A systematic review and meta-analysis was conducted of placebo-controlled randomized trials of testosterone therapy among men lasting 12+ weeks reporting cardiovascular-related events. We searched PubMed through the end of 2012 using “(“testosterone” or “androgen”) and trial and (“random*”)” with the selection limited to studies of men in English, supplemented by a bibliographic search of the World Health Organization trial registry. Two reviewers independently searched, selected and assessed study quality with differences resolved by consensus. Two statisticians independently abstracted and analyzed data, using random or fixed effects models, as appropriate, with inverse variance weighting. Results Of 1,882 studies identified 27 trials were eligible including 2,994, mainly older, men who experienced 180 cardiovascular-related events. Testosterone therapy increased the risk of a cardiovascular-related event (odds ratio (OR) 1.54, 95% confidence interval (CI) 1.09 to 2.18). The effect of testosterone therapy varied with source of funding (P-value for interaction 0.03), but not with baseline testosterone level (P-value for interaction 0.70). In trials not funded by the pharmaceutical industry the risk of a cardiovascular-related event on testosterone therapy was greater (OR 2.06, 95% CI 1.34 to 3.17) than in pharmaceutical industry funded trials (OR 0.89, 95% CI 0.50 to 1.60). Conclusions The effects of testosterone on cardiovascular-related events varied with source of funding. Nevertheless, overall and particularly in trials not funded by the pharmaceutical industry, exogenous testosterone increased the risk of cardiovascular-related events, with corresponding implications for the use of testosterone therapy

Molecular antimicrobial resistance surveillance for neisseria gonorrhoeae, Northern Territory, Australia

Neisseria gonorrhoeae antimicrobial resistance (AMR) is a globally recognized health threat; new strategies are needed to enhance AMR surveillance. The Northern Territory of Australia is unique in that 2 different first-line therapies, based primarily on geographic location, are used for gonorrhea treatment. We tested 1,629 N. gonorrhoeae nucleic acid amplification test–positive clinical samples, collected from regions where ceftriaxone plus azithromycin or amoxicillin plus azithromycin are recommended first-line treatments, by using 8 N. gonorrhoeae AMR PCR assays. We compared results with those from routine culture-based surveillance data. PCR data confirmed an absence of ceftriaxone resistance and a low level of azithromycin resistance (0.2%), and that penicillin resistance was \u3c5% in amoxicillin plus azithromycin regions. Rates of ciprofloxacin resistance and penicillinase-producing N. gonorrhoeae were lower when molecular methods were used. Molecular methods to detect N. gonorrhoeae AMR can increase the evidence base for treatment guidelines, particularly in settings where culture-based surveillance is limited

Porosity Controls Spread of Excitation in Tectorial Membrane Traveling Waves

Cochlear frequency selectivity plays a key role in our ability to understand speech, and is widely believed to be associated with cochlear amplification. However, genetic studies targeting the tectorial membrane (TM) have demonstrated both sharper and broader tuning with no obvious changes in hair bundle or somatic motility mechanisms. For example, cochlear tuning of Tectb[superscript –/–] mice is significantly sharper than that of Tecta[superscript Y1870C/+] mice, even though TM stiffnesses are similarly reduced relative to wild-type TMs. Here we show that differences in TM viscosity can account for these differences in tuning. In the basal cochlear turn, nanoscale pores of Tecta[superscript Y1870C/+] TMs are significantly larger than those of Tectb[superscript –/–] TMs. The larger pore size reduces shear viscosity (by ∼70%), thereby reducing traveling wave speed and increasing spread of excitation. These results demonstrate the previously unrecognized importance of TM porosity in cochlear and neural tuning.National Institutes of Health (U.S.) (Grant R01-DC00238)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374)National Institutes of Health (U.S.) (Training Grant