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Ageing represents a major risk factor for many pathologies that limit human lifespan, including cardiovascular diseases. Biological ageing is a good biomarker to assess early individual risk for CVD. However, finding good measurements of biological ageing is an ongoing quest. This study aims to assess the use retinal microvascular function, separate or in combination with telomere length, as a predictor for age and systemic blood pressure in individuals with low cardiovascular risk. In all, 123 healthy participants with low cardiovascular risk were recruited and divided into three groups: group 1 (less than 30 years old), group 2 (31–50 years old) and group 3 (over 50 years old). Relative telomere length (RTL), parameters of retinal microvascular function, CVD circulatory markers and blood pressure (BP) were measured in all individuals. Symbolic regression- analysis was used to infer chronological age and systemic BP measurements using either RTL or a combination of RTL and parameters for retinal microvascular function. RTL decreased significantly with age (p = 0.010). There were also age-related differences between the study groups in retinal arterial time to maximum dilation (p = 0.005), maximum constriction (p = 0.007) and maximum constriction percentage (p = 0.010). In the youngest participants, the error between predicted versus actual values for the chronological age were smallest in the case of using both retinal vascular functions only (p = 0.039) or the combination of this parameter with RTL (p = 0.0045). Systolic BP was better predicted by RTL also only in younger individuals (p = 0.043). The assessment of retinal arterial vascular function is a better predictor than RTL for non-modifiable variables such as age, and only in younger individuals. In the same age group, RTL is better than microvascular function when inferring modifiable risk factors for CVDs. In older individuals, the accumulation of physiological and structural biological changes makes such predictions unreliable.

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. The research presented here therefore focused on defining and developing a GEO label – a decision support mechanism to assist data users in efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for use. This thesis thus presents six phases of research and development conducted to: (a) identify the informational aspects upon which users rely when assessing geospatial dataset quality and trustworthiness; (2) elicit initial user views on the GEO label role in supporting dataset comparison and selection; (3) evaluate prototype label visualisations; (4) develop a Web service to support GEO label generation; (5) develop a prototype GEO label-based dataset discovery and intercomparison decision support tool; and (6) evaluate the prototype tool in a controlled human-subject study. The results of the studies revealed, and subsequently confirmed, eight geospatial data informational aspects that were considered important by users when evaluating geospatial dataset quality and trustworthiness, namely: producer information, producer comments, lineage information, compliance with standards, quantitative quality information, user feedback, expert reviews, and citations information. Following an iterative user-centred design (UCD) approach, it was established that the GEO label should visually summarise availability and allow interrogation of these key informational aspects. A Web service was developed to support generation of dynamic GEO label representations and integrated into a number of real-world GIS applications. The service was also utilised in the development of the GEO LINC tool – a GEO label-based dataset discovery and intercomparison decision support tool. The results of the final evaluation study indicated that (a) the GEO label effectively communicates the availability of dataset quality and trustworthiness information and (b) GEO LINC successfully facilitates ‘at a glance’ dataset intercomparison and fitness for purpose-based dataset selection.

The continuous expansion of the urban traffic sensing infrastructure has led to a surge in the volume of widely available road related data. Consequently, increasing effort is being dedicated to the creation of intelligent transportation systems, where decisions on issues ranging from city-wide road maintenance planning to improving the commuting experience are informed by computational models of urban traffic instead of being left entirely to humans. The automation of traffic management has received substantial attention from the research community, however, most approaches target highways, produce predictions valid for a limited time window or require expensive retraining of available models in order to accurately forecast traffic at a new location. In this article, we propose a novel and accurate traffic flow prediction method based on symbolic regression enhanced with a lag operator. Our approach produces robust models suitable for the intricacies of urban roads, much more difficult to predict than highways. Additionally, there is no need to retrain the model for a period of up to 9 weeks. Furthermore, the proposed method generates models that are transferable to other segments of the road network, similar to, yet geographically distinct from the ones they were initially trained on. We demonstrate the achievement of these claims by conducting extensive experiments on data collected from the Darmstadt urban infrastructure.

Pollen tubes navigate the route from stigma to ovule with great accuracy, but the cues that guide them along this route are not known. We reproduced the environment on the stigma of Nicotiana alata by immersing pollen in stigma exudate or oil close to an interface with an aqueous medium. The growth of pollen in this culture system mimicked growth on stigmas: pollen grains hydrated and germinated, and pollen tubes grew toward the aqueous medium. The rate-limiting step in pollen germination was the movement of water through the surrounding exudate or oil. By elimination of other potential guidance cues, we conclude that the directional supply of water probably determined the axis of polarity of pollen tubes and resulted in growth toward the interface. We propose that a gradient of water in exudate is a guidance cue for pollen tubes on the stigma and that the composition of the exudate must be such that it is permeable enough for pollen hydration to occur but not so permeable that the supply of water becomes nondirectional. Pollen tube penetration of the stigma may be the most frequently occurring hydrotropic response of higher plants

OBJECTIVE: To examine changes in eating behaviors and physical activity, as well as predictors of weight loss success, in obese adults who participated in a 2-year behavioral weight loss intervention conducted in a primary care setting. DESIGN: A longitudinal, randomized controlled, multisite trial. SUBJECTS: Three hundred ninety obese (body mass index, 30–50 kg m –2 ) adults, ⩾21 years, in the Philadelphia region. METHODS: Participants were assigned to one of three interventions: (1) Usual Care (quarterly primary care provider (PCP) visits that included education on diet and exercise); (2) Brief Lifestyle Counseling (quarterly PCP visits plus monthly lifestyle counseling (LC) sessions about behavioral weight control); or (3) Enhanced Brief LC (the previous intervention with a choice of meal replacements or weight loss medication). RESULTS: At month 24, participants in both Brief LC and Enhanced Brief LC reported significantly greater improvements in mean (±s.e.) dietary restraint than those in Usual Care (4.4±0.5, 4.8±0.5 and 2.8±0.5, respectively; both P -values⩽0.016). The percentage of calories from fat, along with fruit and vegetable consumption, did not differ significantly among the three groups. At month 24, both the Brief LC and Enhanced Brief LC groups reported significantly greater increases than usual care in energy expenditure (kcal per week) from moderately vigorous activity (+593.4±175.9, +415.4±179.6 and −70.4±185.5 kcal per week, respectively; both P -values⩽0.037). The strongest predictor of weight loss at month 6 (partial R 2 =33.4%, P <0.0001) and at month 24 (partial R 2 =19.3%, P <0.001) was food records completed during the first 6 months. Participants who achieved a 5% weight loss at month 6 had 4.7 times greater odds of maintaining a ⩾5% weight loss at month 24. CONCLUSIONS: A behavioral weight loss intervention delivered in a primary care setting can result in significant weight loss, with corresponding improvements in eating restraint and energy expenditure. Moreover, completion of food records, along with weight loss at month 6, is a strong predictor of long-term weight loss.