Symptom prevalence and clustering of symptoms in people living with chronic hepatitis C infection

Quality of life has been shown to be poor among people living with chronic hepatitis C However, it is not clear how this relates to the presence of symptoms and their severity. The aim of this study was to describe the typology of a broad array of symptoms that were attributed to hepatitis C virus (HCV) infection. Phase I used qualitative methods to identify symptoms. In Phase 2, 188 treatment-naive people living with HCV participated in a quantitative survey. The most prevalent symptom was physical tiredness (86%) followed by irritability (75%), depression (70%), mental tiredness (70%), and abdominal pain (68%). Temporal clustering of symptoms was reported in 62% of participants. Principal components analysis identified four symptom clusters: neuropsychiatric (mental tiredness, poor concentration, forgetfulness, depression, irritability, physical tiredness, and sleep problems); gastrointestinal (day sweats, nausea, food intolerance, night sweats, abdominal pain, poor appetite, and diarrhea); algesic (joint pain, muscle pain, and general body pain); and dysesthetic (noise sensitivity, light sensitivity, skin. problems, and headaches). These data demonstrate that symptoms are prevalent in treatment-naive people with HCV and support the hypothesis that symptom clustering occurs.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.

Understanding supply chain disruption risk with the aid of social networks and information flows analysis

Supply Chain Risk Management (SCRM) has become a popular area of research and study in recent years. This can be highlighted by the number of peer reviewed articles that have appeared in academic literature. This coupled with the realisation by companies that SCRM strategies are required to mitigate the risks that they face, makes for challenging research questions in the field of risk management. The challenge that companies face today is not only to identify the types of risks that they face, but also to assess the indicators of risk that face them. This will allow them to mitigate that risk before any disruption to the supply chain occurs. The use of social network theory can aid in the identification of disruption risk. This thesis proposes the combination of social networks, behavioural risk indicators and information management, to uniquely identify disruption risk. The propositions that were developed from the literature review and exploratory case study in the aerospace OEM, in this thesis are:- By improving information flows, through the use of social networks, we can identify supply chain disruption risk. - The management of information to identify supply chain disruption risk can be explored using push and pull concepts. The propositions were further explored through four focus group sessions, two within the OEM and two within an academic setting. The literature review conducted by the researcher did not find any studies that have evaluated supply chain disruption risk management in terms of social network analysis or information management studies. The evaluation of SCRM using these methods is thought to be a unique way of understanding the issues in SCRM that practitioners face today in the aerospace industry.

The development and growth of tissues derived from cranial neural crest and primitive mesoderm is dependent on the ligation status of retinoic acid receptor γ:evidence that retinoic acid receptor γ functions to maintain stem/progenitor cells in the absence of retinoic acid

Retinoic acid (RA) signaling is important to normal development. However, the function of the different RA receptors (RARs)-RARα, RARβ, and RARγ-is as yet unclear. We have used wild-type and transgenic zebrafish to examine the role of RARγ. Treatment of zebrafish embryos with an RARγ-specific agonist reduced somite formation and axial length, which was associated with a loss of hoxb13a expression and less-clear alterations in hoxc11a or myoD expression. Treatment with the RARγ agonist also disrupted formation of tissues arising from cranial neural crest, including cranial bones and anterior neural ganglia. There was a loss of Sox 9-immunopositive neural crest stem/progenitor cells in the same anterior regions. Pectoral fin outgrowth was blocked by RARγ agonist treatment. However, there was no loss of Tbx-5-immunopositive lateral plate mesodermal stem/progenitor cells and the block was reversed by agonist washout or by cotreatment with an RARγ antagonist. Regeneration of the caudal fin was also blocked by RARγ agonist treatment, which was associated with a loss of canonical Wnt signaling. This regenerative response was restored by agonist washout or cotreatment with the RARγ antagonist. These findings suggest that RARγ plays an essential role in maintaining stem/progenitor cells during embryonic development and tissue regeneration when the receptor is in its nonligated state.