We report an objective examination of nosocomial transmission events derived from long-term (10-year) data from a single medical centre. Cluster analysis, based on the temporal proximity of genetically identical isolates of the respiratory pathogen Moraxella catarrhalis, identified 40 transmission events involving 33 of the 52 genotypes represented by multiple isolates. There was no evidence of highly transmissible or outbreak-prone genotypes. Although most clusters were small (mean size 3·6 isolates) and of short duration (median duration 25 days), clustering accounted for 38·7% of all isolates. Significant risk factors for clustering were multi-bed wards, and winter and spring season, but bacterial antibiotic resistance, manifested as the ability to produce a β-lactamase was not a risk factor. The use of cluster analysis to identify transmission events and its application to long-term data demonstrate an approach to pathogen transmission that should find wide application beyond hospital populations.

Background: Little is known about the associations of social experiences with mental health service use. Aim: This study aimed to classify social experiences variables in the past year and examine the associations of selected variables in social experiences with mental health service use among US adolescents. Methods: A total of 13,038 adolescents (aged 12 to 17), of which 2208 received mental health services, were from the 2018 National Survey on Drug Use and Health. Multivariate logistic regression (MLR) analysis was conducted. Results: The overall prevalence of mental health service use was 16.1%. 44 variables on social experiences were grouped into 10 disjoint clusters and one variable from each cluster was selected for MLR analysis. Being female, African American, Hispanics, insured and having depression in the past year were associated with increased odds of mental health service use. Negative feelings about going to school, having a serious fight at school/work, active involvement in substance use help programs, knowledge of drug prevention, negative perceptions about the role of religious beliefs on life decisions were positively associated with mental health service use. Conclusion: Mental health service use is associated with feelings about school and peers, perceptions about drug use, and involvement in activities.

Nitrogen (N) is a key nutrient input to crops and one of the major pollutants to the environment from agriculture in the United States. Recent developments in site-specific management (SSM) technology have the potential to reduce both N overapplication and underapplication and increase farmers' net returns. In Virginia, due to the high variability of within-field yield-limiting factors such as soil physical properties and fertility, the adoption of SSM is hindered by high gridsampling cost. Many Virginia corn-wheat-soybean farms have practiced generating yield maps using yield monitors for several years even though few variable applications based on yield maps were reported. It is unknown if the information generated by yield monitors under actual production situations can be used to direct N management for increased net returns in this area. The overall objective of the study is to analyze the economic and environmental impact of alternative management strategies for N in corn and wheat production based on site-specific information in eastern Virginia. Specifically, evaluations were made of three levels of site-specific information regarding crop N requirements combined with variable and uniform N application. The three levels of information are information about the yield potential of the predominant soil type within the field, information about yield potentials of all soils within the field (soil zones), information about yield potentials of smaller sub-field units which are aggregated into functional zones. Effects of information on expected net returns and net N (applied N that is not removed by the crop) were evaluated for corn-wheat-soybean fields in eastern Virginia. Ex post and ex ante evaluations of information were carried out. Historical weather data and farm-level yield data were used to generate yield sequences for individual fields. A Markov chain model was used to describe both temporal and spatial yield variation. Soil maps were used to divide a field into several soil management units. Cluster analysis was used to group subfield units into functional zones based on yield monitor data. Yield monitor data were used to evaluate ex post information and variable application values for 1995-1999, and ex ante information and variable application values for 1999. Ex post analysis results show that soil zone information increased N input but decreased net return, while functional zone information decreased N input and increased net returns. Variable application decreased N input compared with uniform application. Variable application based on soil zone information reduced net return due to cost of overapplication or underapplication. Variable application based on functional information increased net return. Ex ante results show that information on spatial variability was not able to increase farmers?net return due to the cost of variable N application and information. Variable rate application decreases N input relative to uniform application. However, imprecision in the spatial predictor makes the variable application unprofitable due to an imbalance between costs of under- and over-application of N. Sensitivity analysis showed that value of information was positive when temporal uncertainty was eliminated. The ex post results of this study suggest there is potential to improve efficiency of N use and farmers?net returns with site specific management techniques. The ex ante results suggest that site specific management improvements should be tested under conditions faced by farmers including imperfect information about temporal and spatial yield variability. / Ph. D.

Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.

Genetically modified food is a reality for many modern-day consumers around the world. With the introduction of GM food to the food market, consumers were faced with a number of new products and also familiar products containing new ingredients. The introduction of genetically modified food products to food markets around the world, led to a lot of controversy. In many cases consumer attitudes and perceptions of GM food products were revealed as fears, concern for, and avoidance of the new technology. Consumer attitudes, perceptions and acceptance towards the use of genetically modified foods or -food ingredients are currently highly relevant issues for role-player such as researchers, government, food companies, biotechnology companies, retailers and farmers all over the world.The importance of genetically modified food products in South Africa is increasing, even though the debate surrounding genetically modified food products lags behind many other (often more developed) parts of the world. Genetically modified white maize is among the agricultural crops approved for commercial production in South Africa. The production of genetically modified white maize in South Africa increased dramatically from its introduction in the 2001/2002-production season. White maize, especially in the form of super- and special maize meal, is an extremely important staple food source for consumers of all age groups in South Africa. The implication of the significant increase in the cultivation of genetically modified white maize is that the product is entering the South African food market at an increasing rate. In reality South African consumers are increasingly exposed to food products containing genetically modified white maize. This goes hand in hand with increasing consumer awareness regarding genetically modified food issues.The general objective of the dissertation is to develop an understanding of the perceptions, attitudes, acceptance and knowledge of South African urban consumers, regarding GM white maize as a staple food product within South Africa. The specific objectives are to identify trade-offs between selected attributes of maize meal and to determine the relative importance of selected GM characteristics within the trade-offs by means of a conjoint experiment, to construct market segments based on the outcomes of a conjoint experiment, to determine the effect of consumer perceptions on the sensory experience of white maize porridge and to determine the knowledge, perceptions and GM food acceptance of the different market segments.Quota sampling was applied to obtain a random sample of 80 urban white-maize consumers, based on the LSM (Living Standard Measures) market segmentation tool. The respondents participated in sensory evaluation of maize porridge. This was followed by a conjoint experiment designed around three selected product characteristic variables describing a 2.5kg packet of super white maize meal: “Brand variable”, “Genetic modification variable” and “Price variable”. Market segmentation was done through Ward’s hierarchical cluster analysis based on the conjoint results. The final phase of the experimental analysis involved the profiling of the identified clusters based on demographic variables, respondents’ knowledge of genetic modification and respondents perceptions, attitudes and acceptance towards genetically modified food.The limited sample size (80 respondents) could influence the ability of the results to reflect on the population of urban white maize consumers given the presence of GM food in the market. However, the experimental results should be seen in view of general trends in South Africa and available anecdotal evidence supporting the results of the study. The results of this study could go a long way in representing the results of a more representative sample of urban white maize consumers given the presence of GM food in the market.The cluster analysis revealed that the sample of urban, white maize consumers could be grouped into three meaningful and distinct market segments, based on their preferences for branded- versus non-branded white-grained maize meal, as well as their preferences for non-GM white maize meal versus GM white maize meal with various types of genetic manipulations. The “Anti-GM” segment (35% of the sample) is particularly negative towards GM food irrelevant of the type of genetic modification applied to the food. The “Pro-GM farmer sympathetic” segment (20% of the sample) is positive towards genetically modified food in cases where the farmer receives the benefit of the genetic modification. The “Pro-GM” segment (45% of the sample) is generally positive towards GM food, but especially when the consumer receives the benefit of the genetic modification. The results indicated that the differences among the cluster groups were more prominent than the differences among the LSM groups. Thus, the clusters were most effective to distinguish between sub-groups in the experimental sample.The results of the respondents’ knowledge of genetic modification indicated that there is some degree of confusion among respondents regarding the meaning of genetic modification, as well as discrepancies between perceived and actual knowledge levels of genetic modification. In general, the respondents’ knowledge of GM food is relatively low.A strong positive correlation was observed between the sample respondents’ exposure to GM food related terms and their perceived understanding of these issues, implying that the exposure caused the respondents to learn more about GM food related terms. The balanced GM food information presented to the respondents during the experimental procedure probably influenced their knowledge levels and opinions about GM food as the experiment evolved. Despite these observations the research methodology was still deemed as appropriate. The GM food knowledge gained by the respondents during the experiment could be seen as a simulation of situations where they could receive GM food information from external sources such as television, radio, magazines or newspapers. The cluster profiling revealed that urban white-grain maize consumers’ perceptions and attitudes towards GM food were the strongest distinguishing factors between the various market segments, especially the preferences of the various cluster groups for non-GM maize or maize that was genetically modified for consumer benefit or maize that was genetically modified for producer benefit. Demographic factors and GM knowledge aspects did not really contribute towards distinguishing between the clusters.The dissertation determined that there is a need for a better understanding of consumer perceptions, attitudes towards and acceptance of GM food products, which could enable producers and scientists to engage in more consumer driven product development and marketing activities. Consumer acceptance is the most critical factor for the success of GM food products within the South African food market place and could shape the future of the agricultural modern biotechnology industry and the agricultural sector in South Africa. / Dissertation (MSc (Agric) Agricultural Economics)--University of Pretoria, 2004. / Agricultural Economics, Extension and Rural Development / unrestricted

The purpose of this study was to develop a typology of small apparel manufacturers (SAMs), firms classified between SIC 2310 to 2389 and less than 50 employees. The objectives were to (a) determine if distinct manufacturing strategies existed among SAMs, (b) develop a profile of these groups using environmental factors known to affect the apparel industry and small businesses (i.e., customer service, operations, barriers, assistance, customer size, customer location, competitor size, competitor location), (c) develop a profile of SAMs based on demographics (i.e., SIC, end-use for products, manufacturing process, type of firm, fashion position, employee size, manufacturing strategy, marketing strategy, annual gross revenue), and (d) determine the existence of a relationship between SAMs use of market strategies and manufacturing strategies. Data were collected from 146 SAMs, which represented 15 states with the highest number of SAMs. Factor analysis was used to identify manufacturing strategy factors (i.e., flexibility, environmental consciousness, product attributes, lot sizes), which were used to cluster respondents; and environmental factors (i.e., customer service, education/industry awareness, flexibility, timing, unit costs, production resources, technology/automation, consistency in sales, investment capital, import reductions). Four clusters of manufacturing strategies emerged and were profiled according to environmental factors and demographic variables (i.e., products, product classification, manufacturing processes, type of firm, type of fashion, manufacturing strategies, marketing strategies, firm's employee size, annual gross revenues). Significant differences occurred among the four manufacturing strategy groups and environmental factors. Significant differences occurred among the four manufacturing strategy groups and demographic variables. No relationship existed between manufacturing strategy groups and their marketing strategy. / Ph. D.

Given the prevalence and cost of low back pain, particularly among workers' compensation patients, it is advantageous to understand how various psychological constructs may be related to prolonged disability and failure to return to work. Malingering is a psychological construct that is clearly relevant for worker compensation populations and is a construct that is well suited for experimental control within an analog study. Malingering is the intentional exaggeration of physical or psychological symptoms that are motivated by external incentives such as time away from work. The ability to detect malingering in such a population with psychological assessments is unclear. An analog study was conducted in which we instructed college students to portray themselves as injured workers who received a back injury that required them to be off work while they recovered. Students were then told that they would be seeing a psychologist who would attempt to ascertain their abilities to return to work via the MMPI-2. Students were then randomly instructed to respond to the MMPI-2 in three different ways: a control condition was instructed to respond as if they suffered a workplace back injury that resulted in significant pain; a subtle fake-bad condition who received the control instruction plus were informed they did not enjoy their work and their back injury allowed them to enjoy personal and family time more; and a fake-bad condition that received the control instruction plus were asked to deliberately portray themselves as experiencing physical symptoms severe enough to keep them off work longer. Currently, no assessment of malingering exists within a compensated low back pain population. The purpose of this study was to determine if the MMPI-2 can be used to differentially identify "patients" who are instructed to report symptoms veridically versus "patients" instructed to consciously feign and magnify symptoms in an effort to avoid returning to work. Malingering and non-malingering patients' scores on the MMPI-2 validity and clinical scales were subjected to a cluster analysis to determine if a malingering profile could be accurately identified. A 5-cluster validity solution and 4-cluster clinical (both with K correction) solution were accepted. Substantially lower scores on L and K, elevated scores on F on the 5-cluster validity solutions, distinguished the "malingering" profile. The 4-cluster clinical solution was characterized by elevated scores on the clinical scales of hypochondriasis, depression, paranoia, and schizophrenia, which distinguished the "malingering" profile. The results indicate that the MMPI-2 could be useful in detecting malingering in compensated back pain patients. Results are discussed in the context of pain studies.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18583号 / 工博第3944号 / 新制||工||1606(附属図書館) / 31483 / 京都大学大学院工学研究科建築学専攻 / (主査)教授 門内 輝行, 教授 髙田 光雄, 教授 神吉 紀世子 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Recommendation systems have significantly influenced user experiences across various industries, yet their application in the insurance sector remains relatively unexplored. This thesis focuses on developing a car insurance recommendation system that implements a `consumers like you' feature. The study initially employs a clustering-based recommendation system due to missing labels in an offline environment. However, challenges emerge, such as determining the optimal number of clusters and managing complex data. Additionally, the inability to effectively update based on feedback and lower predictive performance compared to supervised methods necessitated exploring supervised alternatives. In response, this thesis proposes a methodology where the unsupervised approach simulates consumer behavior in an offline environment. Supervised alternatives are pre-trained on the clustering-based system to replicate it and come with the ability to be fine-tuned based on live traffic. Three supervised alternatives — KNN, XGBoost, and a neural network — are developed and compared. Given the supervised recommendation system adaptability based on feedback, supervised methods can provide more accurate, personalized recommendations in the insurance domain. The XGBoost and neural network-based recommendation systems were able to replicate the unsupervised approach, and their expressive power makes them valid candidate models to further evaluate on live traffic. The thesis concludes with the potential to both improve and adapt these recommendation systems to other insurance types, marking a significant step toward more personalized, user-friendly insurance services.