Predicting functional disability : One year results from the Scottish Early Rheumatoid Arthritis Inception Cohort

Peer reviewed

Important factors in predicting mortality outcome from stroke : findings from the Anglia Stroke Clinical Network Evaluation Study

The Anglia Stroke Clinical Network Evaluation Study (ASCNES) is funded by the National Institute for Health Research (NIHR) Research for Patient Benefit Programme (PB-PG-1208-18240). This paper presents independent research funded by the NIHR under its Research for Patient Benefit (RfPB) programme (Grant Reference Number PB-PG-1208-18240). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. EAW receives funding support from the NIHR Biomedical Research Centre award to Cambridge

Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

Peer reviewed

Bidding For Parking: The Impact of University Affiliation on Predicting Bid Values in Dutch Auctions of On-Campus Parking Permits

Parking is often underpriced and expanding its capacity is expensive; universities need a better way of reducing congestion outside of building costly parking garages. Demand based pricing mechanisms, such as auctions, offer a possible solution to the problem by promising to reduce parking at peak times. However, faculty, students, and staff at universities have systematically different parking needs, leading to different parking valuations. In this study, I determine the impact university affiliation has on predicting bid values cast in three Dutch Auctions of on-campus parking permits sold at Chapman University in Fall 2010. Using clustering techniques crosschecked with university demographic information to detect affiliation groups, I ran a log-linear regression, finding that university affiliation had a larger effect on bid amount than on lot location and fraction of auction duration. Generally, faculty were predicted to have higher bids whereas students were predicted to have lower bids.

Predicting Forest Responses to Changing Environmental Conditions

Forests change with changes in their environment based on the physiological responses of individual trees. These short-term reactions have cumulative impacts on long-term demographic performance. For a tree in a forest community, success depends on biomass growth to capture above- and belowground resources and reproductive output to establish future generations. Here we examine aspects of how forests respond to changes in moisture and light availability and how these responses are related to tree demography and physiology.

First we address the long-term pattern of tree decline before death and its connection with drought. Increasing drought stress and chronic morbidity could have pervasive impacts on forest composition in many regions. We use long-term, whole-stand inventory data from southeastern U.S. forests to show that trees exposed to drought experience multiyear declines in growth prior to mortality. Following a severe, multiyear drought, 72% of trees that did not recover their pre-drought growth rates died within 10 years. This pattern was mediated by local moisture availability. As an index of morbidity prior to death, we calculated the difference in cumulative growth after drought relative to surviving conspecifics. The strength of drought-induced morbidity varied among species and was correlated with species drought tolerance.

Next, we investigate differences among tree species in reproductive output relative to biomass growth with changes in light availability. Previous studies reach conflicting conclusions about the constraints on reproductive allocation relative to growth and how they vary through time, across species, and between environments. We test the hypothesis that canopy exposure to light, a critical resource, limits reproductive allocation by comparing long-term relationships between reproduction and growth for trees from 21 species in forests throughout the southeastern U.S. We found that species had divergent responses to light availability, with shade-intolerant species experiencing an alleviation of trade-offs between growth and reproduction at high light. Shade-tolerant species showed no changes in reproductive output across light environments.

Given that the above patterns depend on the maintenance of transpiration, we next developed an approach for predicting whole-tree water use from sap flux observations. Accurately scaling these observations to tree- or stand-levels requires accounting for variation in sap flux between wood types and with depth into the tree. We compared different models with sap flux data to test the hypotheses that radial sap flux profiles differ by wood type and tree size. We show that radial variation in sap flux is dependent on wood type but independent of tree size for a range of temperate trees. The best-fitting model predicted out-of-sample sap flux observations and independent estimates of sapwood area with small errors, suggesting robustness in new settings. We outline a method for predicting whole-tree water use with this model and include computer code for simple implementation in other studies.

Finally, we estimated tree water balances during drought with a statistical time-series analysis. Moisture limitation in forest stands comes predominantly from water use by the trees themselves, a drought-stand feedback. We show that drought impacts on tree fitness and forest composition can be predicted by tracking the moisture reservoir available to each tree in a mass balance. We apply this model to multiple seasonal droughts in a temperate forest with measurements of tree water use to demonstrate how species and size differences modulate moisture availability across landscapes. As trees deplete their soil moisture reservoir during droughts, a transpiration deficit develops, leading to reduced biomass growth and reproductive output.

This dissertation draws connections between the physiological condition of individual trees and their behavior in crowded, diverse, and continually-changing forest stands. The analyses take advantage of growing data sets on both the physiology and demography of trees as well as novel statistical techniques that allow us to link these observations to realistic quantitative models. The results can be used to scale up tree measurements to entire stands and address questions about the future composition of forests and the land’s balance of water and carbon.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.

Predicting 10-year risk of fatal cardiovascular disease in Germany: an update based on the SCORE-Deutschland Risk Charts

Estimation of absolute risk of cardiovascular disease (CVD), preferably with population-specific risk charts, has become a cornerstone of CVD primary prevention. Regular recalibration of risk charts may be necessary due to decreasing CVD rates and CVD risk factor levels. The SCORE risk charts for fatal CVD risk assessment were first calibrated for Germany with 1998 risk factor level data and 1999 mortality statistics. We present an update of these risk charts based on the SCORE methodology including estimates of relative risks from SCORE, risk factor levels from the German Health Interview and Examination Survey for Adults 2008-11 (DEGS1) and official mortality statistics from 2012. Competing risks methods were applied and estimates were independently validated. Updated risk charts were calculated based on cholesterol, smoking, systolic blood pressure risk factor levels, sex and 5-year age-groups. The absolute 10-year risk estimates of fatal CVD were lower according to the updated risk charts compared to the first calibration for Germany. In a nationwide sample of 3062 adults aged 40-65 years free of major CVD from DEGS1, the mean 10-year risk of fatal CVD estimated by the updated charts was lower by 29% and the estimated proportion of high risk people (10-year risk > = 5%) by 50% compared to the older risk charts. This recalibration shows a need for regular updates of risk charts according to changes in mortality and risk factor levels in order to sustain the identification of people with a high CVD risk.

The Validity of Broad and Narrow Personality Traits For Predicting Job Performance: The Differential Effects of Time

Research into the dynamicity of job performance criteria has found evidence suggesting the presence of rank-order changes to job performance scores across time as well as intraindividual trajectories in job performance scores across time. These findings have influenced a large body of research into (a) the dynamicity of validities of individual differences predictors of job performance and (b) the relationship between individual differences predictors of job performance and intraindividual trajectories of job performance. In the present dissertation, I addressed these issues within the context of the Five Factor Model of personality. The Five Factor Model is arranged hierarchically, with five broad higher-order factors subsuming a number of more narrowly tailored personality facets. Research has debated the relative merits of broad versus narrow traits for predicting job performance, but the entire body of research has addressed the issue from a static perspective -- by examining the relative magnitude of validities of global factors versus their facets. While research along these lines has been enlightening, theoretical perspectives suggest that the validities of global factors versus their facets may differ in their stability across time. Thus, research is needed to not only compare the relative magnitude of validities of global factors versus their facets at a single point in time, but also to compare the relative stability of validities of global factors versus their facets across time. Also necessary to advance cumulative knowledge concerning intraindividual performance trajectories is research into broad vs. narrow traits for predicting such trajectories. In the present dissertation, I addressed these issues using a four-year longitudinal design. The results indicated that the validities of global conscientiousness were stable across time, while the validities of conscientiousness facets were more likely to fluctuate. However, the validities of emotional stability and extraversion facets were no more likely to fluctuate across time than those of the factors. Finally, while some personality factors and facets predicted performance intercepts (i.e., performance at the first measurement occasion), my results failed to indicate a significant effect of any personality variable on performance growth. Implications for research and practice are discussed.

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income.

Predicting the Dispersal Potential of an Invasive Polychaete Pest along a Complex Coastal Biome

Boccardia proboscidea is a recently introduced polychaete in South Africa where it is a notorious pest of commercially reared abalone. Populations were originally restricted to abalone farms but a recent exodus into the wild at some localities has raised conservation concerns due to the species’ invasive status in other parts of the world. Here, we assessed the dispersal potential of B. proboscidea by using a population genetic and oceanographic modeling approach. Since the worm is in its incipient stages of a potential invasion, we used the closely related Polydora hoplura as a proxy due its similar reproductive strategy and its status as a pest of commercially reared oysters in the country. Populations of P. hoplura were sampled from seven different localities and a section of the mtDNA gene, Cyt b and the intron ATPSa was amplified. A high resolution model of the coastal waters around southern Africa was constructed using the Regional Ocean Modeling System. Larvae were represented by passive drifters that were deployed at specific points along the coast and dispersal was quantified after a 12-month integration period. Our results showed discordance between the genetic and modeling data. There was low genetic structure (Φ = 0.04 for both markers) and no geographic patterning of mtDNA and nDNA haplotypes. However, the dispersal model found limited connectivity around Cape Point—a major phylogeographic barrier on the southern African coast. This discordance was attributed to anthropogenic movement of larvae and adult worms due to vectors such as aquaculture and shipping. As such, we hypothesized that cryptic dispersal could be overestimating genetic connectivity. Though wild populations of B. proboscidea could become isolated due to the Cape Point barrier, anthropogenic movement may play the critical role in facilitating the dispersal and spread of this species on the southern African coast.

Predicting the Dispersal Potential of an Invasive Polychaete Pest along a Complex Coastal Biome

Boccardia proboscidea is a recently introduced polychaete in South Africa where it is a notorious pest of commercially reared abalone. Populations were originally restricted to abalone farms but a recent exodus into the wild at some localities has raised conservation concerns due to the species’ invasive status in other parts of the world. Here, we assessed the dispersal potential of B. proboscidea by using a population genetic and oceanographic modeling approach. Since the worm is in its incipient stages of a potential invasion, we used the closely related Polydora hoplura as a proxy due its similar reproductive strategy and its status as a pest of commercially reared oysters in the country. Populations of P. hoplura were sampled from seven different localities and a section of the mtDNA gene, Cyt b and the intron ATPSa was amplified. A high resolution model of the coastal waters around southern Africa was constructed using the Regional Ocean Modeling System. Larvae were represented by passive drifters that were deployed at specific points along the coast and dispersal was quantified after a 12-month integration period. Our results showed discordance between the genetic and modeling data. There was low genetic structure (Φ = 0.04 for both markers) and no geographic patterning of mtDNA and nDNA haplotypes. However, the dispersal model found limited connectivity around Cape Point—a major phylogeographic barrier on the southern African coast. This discordance was attributed to anthropogenic movement of larvae and adult worms due to vectors such as aquaculture and shipping. As such, we hypothesized that cryptic dispersal could be overestimating genetic connectivity. Though wild populations of B. proboscidea could become isolated due to the Cape Point barrier, anthropogenic movement may play the critical role in facilitating the dispersal and spread of this species on the southern African coast.

Predicting Academic Performance Based on Students' Blog and Microblog Posts

This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected from six student cohorts, from six consecutive installments of the Web Applications Design course, comprising of 343 students. A significant model was obtained by relying on the textual complexity and longitudinal analysis indices, applied on the English contributions of 148 students that were actively involved in the undertaken projects.

Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.

PROGNOSTIC FACTORS PREDICTING FUNCTIONAL OUTCOME AT FOUR MONTHS FOLLOWING ACUTE ANKLE SPRAIN

PROGNOSTIC FACTORS PREDICTING FUNCTIONAL OUTCOME AT FOUR MONTHS FOLLOWING ACUTE ANKLE SPRAINBleakley C.M.1, O'Connor S.R.1, Tully M.A.2, Rocke L.G.3, MacAuley D.C.1, Bradbury I.4, Keegan S.4, McDonough S.M.11University of Ulster, Health & Rehabilitation Sciences Research Institute, Newtownabbey, United Kingdom, 2Queen's University, UKCRC Centre of Excellence for Public Health (NI), Belfast, United Kingdom, 3Royal Victoria Hospital, Department of Emergency Medicine, Belfast, United Kingdom, 4Frontier Science (Scotland), Kincraig, Inverness-shire, United KingdomPurpose: To identify clinically relevant factors assessed following acute ankle sprain that predict functional recovery at four months post-injury.Relevance: Ankle sprains are one of the most common musculoskeletal injuries with an estimated 5000 new cases occurring each day in the United Kingdom. In the acute phase, ankle sprains may be associated with pain and loss of function. In the longer-term there is a risk of residual problems including chronic pain or reinjury. Few studies have sought to examine factors associated with a poor long-term prognosis.Participants: 101 patients (Age: Mean (SD) 25.9 (7.9) years; Body Mass Index (BMI): 25.3 (3.5) kg/m2) with an acute grade 1 or 2 ankle sprain attending an accident and emergency department or sports injury clinic. Exclusion criteria included complete (grade 3) rupture of the ankle ligament complex, bony ankle injury or multiple injuries.Methods: Participants were allocated as part of a randomised controlled trial to an accelerated intervention incorporating intermittent ice and early therapeutic exercise or a standard protection, rest, ice, compression, and elevation intervention for one week. Treatment was then standardised in both groups and consisted of ankle rehabilitation exercises focusing on muscle strengthening, neuromuscular training, and sports specific functional exercises for a period of approximately four to six weeks. On initial assessment age, gender, mechanism of injury, presence of an audible pop or snap and the presence of contact during the injury were recorded. The following factors were also recorded at baseline and at one and four weeks post-injury: weight-bearing dorsi-flexion test, lateral hop test, presence of medial pain on palpation and a positive impingement sign. Functional status was assessed using the Karlsson score at baseline, at week four and at four months. Reinjury rates were recorded throughout the intervention phase and at four months.Analysis: A mixed between-within subjects analysis of variance (ANOVA) was used to determine the effect of each factor on functional status at week four and at four months. Significance was set at a Bonferroni adjusted level of 0.0125 (0.05/4).Results: Eighty-five participants (84%) were available at final follow-up assessment. Pain on weight-bearing dorsi-flexion and lateral hop tests at week four were both associated with a lower functional score at four months post-injury (P = 0.011 and P = 0.001). No other significant interactions were observed at any other timepoint (baseline or week one). There were only two reinjuries within the four month follow-up period with a further two reported at approximately six months post-injury. We were therefore unable to determine whether any factors were associated with an increased risk of reinjury.Conclusions: Potential prognostic factors on initial or early examination after acute ankle sprain did not help predict functional recovery at four months post-injury. However, pain on weight-bearing dorsi-flexion and lateral hop tests observed at four weeks were associated with a slower rate of recovery.Implications: Some clinical tests may help identify patients at risk of poor functional recovery after acute ankle sprain. However, further work is required to examine factors which may be predictive on initial assessment.Key-words: 1. Prognostic factors 2. Recovery 3. Ankle sprainFunding acknowledgements: Physiotherapy Research Foundation, Chartered Society of Physiotherapy, Strategic Priority Fund; Department of Employment and Learning, Northern Ireland.Ethics approval: Office for Research Ethics Committee (UK).