Use of multiple regression analysis to summarize and interpret linear programming shadow prices in an economic planning model /

Issued May 1980.

Multiple classification analysis ; a report on a computer program for multiple regression using categorical predictors /

Bibliographical footnotes.

Mathematical modelling of patient risks in a hospital environment, using multiple regression analysis

The aim of this research work was primarily to examine the relevance of patient parameters, ward structures, procedures and practices, in respect of the potential hazards of wound cross-infection and nasal colonisation with multiple resistant strains of Staphylococcus aureus, which it is thought might provide a useful indication of a patient's general susceptibility to wound infection. Information from a large cross-sectional survey involving 12,000 patients from some 41 hospitals and 375 wards was collected over a five-year period from 1967-72, and its validity checked before any subsequent analysis was carried out. Many environmental factors and procedures which had previously been thought (but never conclusively proved) to have an influence on wound infection or nasal colonisation rates, were assessed, and subsequently dismissed as not being significant, provided that the standard of the current range of practices and procedures is maintained and not allowed to deteriorate. Retrospective analysis revealed that the probability of wound infection was influenced by the patient's age, duration of pre-operative hospitalisation, sex, type of wound, presence and type of drain, number of patients in ward, and other special risk factors, whilst nasal colonisation was found to be influenced by the patient's age, total duration of hospitalisation, sex, antibiotics, proportion of occupied beds in the ward, average distance between bed centres and special risk factors. A multi-variate regression analysis technique was used to develop statistical models, consisting of variable patient and environmental factors which were found to have a significant influence on the risks pertaining to wound infection and nasal colonisation. A relationship between wound infection and nasal colonisation was then established and this led to the development of a more advanced model for predicting wound infections, taking advantage of the additional knowledge of the patient's state of nasal colonisation prior to operation.

A Method for Solving a Class of Multiple-Criteria Analysis Problems

AMS subject classification: 90C29.

Mathematical competence for information visualization by correspondence analysis methods

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

[climacteric Syndrome In A Northeastern Brazilian City: A Household Survey].

To assess the prevalence of Climacteric Syndrome (CS) in women from a municipality of Northeastern Brazil which is less developed socioeconomically. A prospective household survey was performed in São Luís, Maranhão, Brazil with 1,210 climacteric women aged 45 to 60 years. Interviews were applied using previously tested standard questionnaires from April to July 2008. The severity of climacteric symptoms was analyzed by circulatory and psychological indexes and the latter were associated with menopausal status. Multiple correspondence analysis was used to assess the relation among climacteric symptoms. Most patients were 55 to 60 years old (35.3%), mulatto (37.9%), with 9-11 years of schooling (39.8%), with a partner (56%), Catholic (73.9%) and belonged to the socioeconomic class C (51.1%). The prevalence of CS was 85.9%, and hot flashes (56.4%) and sweating (50.4%) were the most prevalent symptoms. The most frequent psychological symptoms were nervousness (45%) and emotional liability (44.8%). The severity of vasomotor and psychological symptoms was significantly higher during the peri and postmenopausal period (p<0.05). Vaginal dryness (62.7%) was the most prevalent urogenital complaint. The prevalence of CS was high among women from São Luís, Maranhão, Brazil.

Avaliação da satisfação de alunos em bLearning

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Municipalities of higher vulnerability to Sylvatic Yellow Fever occurrence in the São Paulo State, Brazil

Until 1999 the endemic cases of Sylvatic Yellow Fever were located in the states of northern, midwestern and pre-Amazon regions. Since then, the disease progressively expanded its territory of occurrence, cases being registered beyond the traditional boundaries of endemism. The São Paulo State is considered to be part of this context, since after decades without registration of autochthonous cases of the disease, it reported, in 2000 and 2008-2009, epizootic occurrence in non-human primates and 30 cases in humans. Facts like these, added to the increase in incidences of serious adverse effects resulting from the Yellow Fever vaccination, have highlighted the importance of defining priority municipalities for vaccination against the disease in the state. Two groups of municipalities, some affected and some non-affected by YF, were compared for environmental variables related to the eco-epidemiology of the disease according to literature. The Multiple Correspondence Analysis (MCA) was used to pinpoint the factor able to differentiate the two groups of municipalities and define the levels of risk. The southeast region of the São Paulo State was considered to be the area with a higher number of municipalities classified as high risk and should be considered a priority for the application of prevention measures against Yellow Fever.

Preferential binding of the methyl-CpG binding domain protein 2 at methylated transcriptional start site regions.

Methyl-CpG Binding Domain (MBD) proteins are thought to be key molecules in the interpretation of DNA methylation signals leading to gene silencing through recruitment of chromatin remodeling complexes. In cancer, the MBD-family member, MBD2, may be primarily involved in the repression of genes exhibiting methylated CpG at their 5' end. Here we ask whether MBD2 randomly associates methylated sequences, producing chance effects on transcription, or exhibits a more specific recognition of some methylated regions. Using chromatin and DNA immunoprecipitation, we analyzed MBD2 and RNA polymerase II deposition and DNA methylation in HeLa cells on arrays representing 25,500 promoter regions. This first whole-genome mapping revealed the preferential localization of MBD2 near transcription start sites (TSSs), within the region analyzed, 7.5 kb upstream through 2.45 kb downstream of 5' transcription start sites. Probe by probe analysis correlated MBD2 deposition and DNA methylation. Motif analysis did not reveal specific sequence motifs; however, CCG and CGC sequences seem to be overrepresented. Nonrandom association (multiple correspondence analysis, p < 0.0001) between silent genes, DNA methylation and MBD2 binding was observed. The association between MBD2 binding and transcriptional repression weakened as the distance between binding site and TSS increased, suggesting that MBD2 represses transcriptional initiation. This hypothesis may represent a functional explanation for the preferential binding of MBD2 at methyl-CpG in TSS regions.