Mixed Model Techniques Research Articles

Neurocognitive and neurobehavioral mechanisms of change following psychological treatment for alcohol use disorder

BackgroundAlthough modestly effective treatments exist for alcohol use disorder (AUD), many individuals return to heavy drinking after treatment, suggesting the need for better understanding of factors that contribute to maintaining abstinence or drinking reductions. Whereas past studies identified what treatments work for AUD, recent studies focus more on why particular treatments work, and the mechanisms by which treatment leads to change. This focus on mechanisms of behavior change (MOBC) may inform the process by which treatment leads to better outcomes, and also may lead to new treatments or modifications of existing treatments that target empirically supported mechanisms known to lead to change. There is a paucity of studies examining MOBC from a neurocognitive perspective. MethodTo address this gap in knowledge, the study described here is examining emotional reactivity, alcohol cue reactivity, and cognitive control as potential MOBC at three levels of analysis - self-report, behavior, and neural. ResultsOne hundred ten treatment-seeking individuals with an AUD are being randomized to receive 8 sessions of either Cognitive Behavioral Treatment (CBT) or Mindfulness Based Treatment (MBT) after up to 4 sessions of a platform treatment focused on enhancing motivation to change. To establish the temporal relationship between changes in drinking and changes in MOBC, patients are assessed at baseline, during and immediately after treatment, and 9- and 15-months post-baseline. Relationships between changes in drinking and changes in the proposed MOBC will be examined using advanced mixed modeling techniques. ConclusionsResults should advance AUD treatment by targeting treatments to neurocognitive MOBC.

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers’ head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

PSIII-11 Effects of Inoculation of Alfalfa Hay with Pediococcus Pentosaceus and α-Amylase on Hay Temperature, Microbial Ecology, Nutrient Composition, and Feed Value

Abstract Haying in the Midwest is challenging, with excessive spring and early summer precipitation followed by unpredictable rain events throughout the growing months, forcing farmers to be innovative and resort to novel haying practices. High moisture haying with lactic acid bacterial (LAB) inoculants allows farmers to harvest earlier with less expenditure. This study examined how inoculating high-moisture alfalfa hay with Pediococcus pentosaceus and α-amylase (Magniva for Hay; Lallemand Animal Nutrition, Milwaukee, WI) affected hay temperature, microbial ecology, nutritional content, and feed value. Alfalfa was harvested at the late flowering stage and allowed to wilt in windrows for two days until the moisture level dropped to 20-25%, at which point half of the field was baled (20 bales) with the inoculant at 1.25 L ton-1, providing 1.0 × 106 cfu g-1 fresh forage of Pediococcus pentosaceus (NCIMB 12675) and 2.5 × 106 units of α-amylase (INOC). Uninoculated alfalfa baled at 15–18% moisture (20 bales) served as control (CON). The hay bales were stored under cover and sampled using a hay corer on days 0, 60, 120, and 180 to evaluate nutrient composition, microbial ecology, and feed value. Dallas Thermochron iButton sensors (Embedded Data Systems, Lawrenceburg, KY) were inserted into the center of hay bales during day 0 sampling (20 sensors per treatment) to measure the temperature every 4 h over 180 days. The core holes were air-tightened with alfalfa hay after installing the iButton sensors. SAS (SAS Institute Inc., 2012) mixed model technique was used to assess repeated measurements with treatment (T), day (D), and T × D effects. CON had greater LAB counts (log10 cfu g-1 DM) than INOC (7.45 vs. 7.11; P = 0.03) during storage. On days 0, 120, and 180 of storage, INOC had lower yeast counts (P < 0.02) than CON (T × D interaction). INOC exhibited higher core temperatures (P < 0.03) than CON on days 14, 21, and 28 of storage, peaking at 46.7°C on day 14. INOC reported lower DM than CON on days 60 and 120 of storage (P < 0.03). INOC had a higher CP concentration (18.0 vs. 16.9; P < 0.0001) than CON throughout storage. On days 60, 120, and 180 of storage, INOC had increased ADICP and NDICP (% DM) concentrations than CON (P < 0.05). Results indicated that inoculation of high moisture alfalfa hay with LAB inoculants is a viable alternative for farmers in the Midwest severely impacted by unpredictable weather events due to global warming. Further studies will provide detailed information on nutrient degradability and utilization for livestock production.

49 Leveraging Big Data Analytics to Improve Disease Surveillance and Welfare in Pig Production

Abstract Large amounts of data are routinely collected in pig production, including operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Furthermore, additional sources of information, such as demographic, economic, and weather variables, can be combined with farm data. Integrating and analyzing such data can generate valuable insights and data-driven decision-making tools for disease surveillance, animal welfare, and production system optimization. This presentation discusses two examples of big data integration and analysis in the context of optimizing management practices in pig production. The first example pertains to a data analytics project related to total transportation losses (TTL) of market-weight pigs, an important animal welfare concern that economically impacts producers and abattoirs. After merging and checking data for errors, the final dataset included 4.5M+ pigs from 420 farms loaded in 26,819 shipments delivered to two abattoirs. Results from a generalized additive mixed model (GAMM) technique showed that the risk of loss per shipment was constant for temperature-humidity index (THI) values between −10 and 10. Additionally, a strong interaction between wind speed and precipitation was associated with TTL. TTL were strongly associated with the type of driver and the distance traveled, in addition to weather variables. The second example pertains to swine disease surveillance, in which data from a multi-site system, where pigs are transported between farms after each particular production phase, were investigated. Reports of 76,566 shipments across sites were obtained and used to characterize pig movement flow, conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and assess the potential impact of each farm type on disease dissemination across the system. Topological properties of networks were investigated, such as the number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Overall, the results indicated that gilt development units, nursery, and sow farms have a more central role in the pig production hierarchical structure. As such, they are potential major factors in the introduction and spread of diseases in the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to infection. Additionally, the characteristics of a disease should have strong implications for biosecurity practices across production sites.

Longitudinal trajectories of maternal TSH in healthy pregnant women in Catalonia.

Longitudinal evaluation of thyroid function throughout pregnancy in the same subject could offer precise information about its dynamics as a physiological mechanism of adaption to the requirements. In this study, we evaluated longitudinal trajectories of maternal thyroid function during pregnancy by a latent class growth analysis and explored their association with maternal-fetal outcomes. A prospective observational study was carried out, including 414 healthy pregnant women, from the first trimester to delivery. Thyroid function and autoimmunity were measured in the three trimesters. Clinical data during pregnancy were obtained. Longitudinal mixed model techniques were performed to explore trajectories of gestational thyroid function. Three different longitudinal trajectories were obtained from maternal thyrotropin (TSH) levels: low-increasing TSH (class 1) in 86% of cases, high-increasing TSH (class 2) in 9.7%, and decreasing TSH (class 3) in 4.3%. No statistical differences in free thyroxine levels were found among the three classes. Differences in maternal age (P = 0.027) and initial maternal weight (P = 0.043) were observed among the groups. In logistic regression analysis, maternal age correlated with longitudinal trajectories. The three longitudinal classes remain when women with thyroid autoimmunity (TAI) are excluded. Multinomial logistic regression showed maternal age correlated with longitudinal trajectories independently of TAI status. Three differentiated TSH trajectories were found in healthy pregnant women living in Catalonia, as previously described. No association with obstetric outcomes was observed in these different chronological thyroid pathways, but maternal age might condition the longitudinal mechanism of thyroid function regulation throughout pregnancy.

Differences in Religious Framing of Muslims and Islam in American Media Coverage Before and After the Trump Campaign

ABSTRACT Over the last 20 years Muslims have received a disproportionate amount of news media attention. In 2017 they were again thrust into the spotlight when the Trump administration issued a travel ban blocking entry from seven predominately-Muslim countries. With our hand coding of over 900 newspaper articles and mixed modeling techniques, we examine the newspaper rhetoric surrounding Muslims before and during the Trump administration and across US states. We find that after President Trump’s election, fewer newspapers mentioned religion or referenced Muslim claimsmakers. We also reveal that higher state-level religious salience was associated with more newspapers referencing religious claimsmakers in Islam-related discussions. Other state-level political and religious characteristics had no effect on the use of religious elements or Muslims claimsmakers. These results provide insight into the position of Muslims and Islam in the United States, as well as the impact of political rhetoric on media portrayals.

Public perceptions of governance and tax evasion: insights from developed and developing economies

Purpose This paper aims to explore how the role of the perception of good public governance reduces tax evasion (TE). Besides, this study investigates whether the nexus of public governance and TE differs between developed and developing economies. Design/methodology/approach Apart from the ordinary least squares (OLS) model, this study uses the linear mixed modeling technique. The World Governance Indicators and the multiple causes estimation (MIMIC) method are used to measure public governance. The shadow economy is used as a proxy for TE. Findings The results show that people's perceptions of public governance and the quality of government institutions are core elements that influence tax-evasion behavior. Besides, the rule of law (RoL) and political stability (PS) significantly impact tax-evasion behavior in developing countries. Nevertheless, the RoL, the control of corruption and PS are the most critical tax-evasion determinants among public governance indicators for developed countries. Regulatory quality shows a substantial positive relationship with TE in developed but not developing countries. Practical implications This paper provides a guide for policymakers on reducing tax-evasion behavior by paying more attention to maintaining the RoL and PS and fighting corruption. Additionally, this study highlights the importance of people's perceptions of the government's pursuit of the above policy-related improvements, which, in turn, affect their tax behavior. Originality/value To the best of the authors’ knowledge, this study is the first to explore the role of people's perceptions of improvements in public governance and how this can reduce TE behavior in developed and developing economies. Unlike prior studies, this study used the linear mixed model method, which is more advantageous than OLS and produces robust estimators.

Mixed linear and non-linear tree volume models with regional parameters to main tree species in Finland

AbstractThe volume models that have been used in Finland for the last 40 years, while generally well thought-out, exhibit an illogical behaviour for small trees. In recent studies, tree stem form was observed to have changed in time and also involve spatial variation attributable to environmental factors. It is yet unclear how the stem taper has actually changed. To overcome these problems, we fitted a completely new set of volume and taper curve models and examined whether this change is attributable to the changes in management and environmental factors rather than to measurement errors in the previously used datasets. For the latter, we added a dataset into the analysis, which was smaller but of higher quality due to the destructive nature of the stem taper measurements. We aim at (1) developing a new non-linear variable form factor volume function that works with trees of all sizes, (2) improving the description of the variation of the stem form in time and space by including temperature sum and soil type as predictors, (3) understanding the changes in the stem form by fitting new taper curve models and (4) improving the statistical properties of the predictions by using mixed model techniques and by addressing the effect of parameter uncertainty. To assess the impact of renewing the models, we (5) predicted the mean volume and its confidence interval with each model for forest inventory data at country level. The results show that the tree stem form has a spatial trend that can be described with the temperature sum. Moreover, the changes in stem form also have a spatial trend, with largest changes in Lapland. The difference is mostly observable in the lowest part of the stem, and it is especially large in the largest pines. We conclude that environmental variables can help to improve national stem taper functions in countries with pronounced environmental gradients.

Are the Police Primarily Responsible for Influencing Place-Level Perceptions of Procedural Justice and Effectiveness? A Longitudinal Study of Street Segments

While there has been significant study of the relationship between police legitimacy and its key antecedents - procedural justice (PJ) and police effectiveness (PE) at the individual level, little attention has been paid to what impacts general evaluations of PJ and PE. Our paper focuses on these perceptions at places. Our analyzes utilize survey data collected on 447 street segments in Baltimore City, MD, in two waves. We first used EFA to determine the latent structure of PJ and PE measures. We then used mixed effects OLS regression modeling techniques to examine the antecedents of a “scorecard” of perceptions of the police. The results of the EFA show a single latent structure that we term the scorecard for PJ and PE. While we find that experiences with the police and street conditions that the police are presumed to impact influence the scorecard, street conditions that are less likely to be influenced by police (collective efficacy and concentrated disadvantage) also have strong influence. Both the research and policy-oriented literature often view the police as primarily responsible for their public image. Our data suggest that at the place level, such perceptions are also strongly impacted by factors primarily outside police influence.

BJS Prize 1 Postoperative electrical muscle stimulation attenuates loss of muscle mass and function following major abdominal surgery: A split body randomised control trial

Abstract Aims Significant loss of muscle mass and function occurs after major abdominal surgery. Neuromuscular electrical stimulation (NMES) has been shown to reduce muscle atrophy in some patient groups, but evidence in postoperative patients is limited. This study assesses the efficacy of NMES in attenuating muscle atrophy following major abdominal surgery. Methods Fifteen patients undergoing open colorectal resection were recruited to a split body randomised control trial and their lower limbs randomised to control (CON) or NMES (STIM). The STIM limb underwent 15 minutes of quadriceps NMES twice daily on postoperative day (POD) 1 to 4. Ultrasound measurement of Vastus Lateralis (VL) cross sectional area (CSA) and muscle thickness (MT) was made preoperatively and on POD 5, as was dynamometer measurement of knee extensor strength (KES). All outcomes were analysed using linear mixed model techniques. The study was approved by NHS research ethics committee (ref 20/EM/069). Results NMES significantly reduced the loss of CSA (-2.52% vs -9.16%, p<0.001), MT (-2.76% vs -8.145, p=0.001) and KES (-10.35% vs -19.69%, p=0.03). No adverse events occurred, and patients reported that NMES caused minimal or no discomfort. Conclusions NMES reduces loss of muscle mass and function following major abdominal surgery and may be an important tool in aiding recovery to normal activity levels. This will be especially important in preventing postoperative loss of independence in the increasingly physiologically frail patients undergoing major abdominal surgery. Further studies should establish the efficacy of bilateral whole-leg NMES for improving patient-centred outcomes.

Use of Repeated Measures Data Analysis for Field Trials with Annual and Perennial Crops.

Field studies conducted over time to collect any type of plant response to a set of treatments are often not treated as repeated measures data. The most used approaches for statistical analyses of this type of longitudinal data are based on separate analyses such as ANOVA, regression, or time contrasts. In many instances, during the review of manuscripts, reviewers have asked researchers to treat year, for example, as a random effect and ignore the interactions between year and other main effects. One drawback of this approach is that the correlation between measurements taken on the same subject over time is ignored. Here, we show that avoiding the covariance between measurements can induce erroneous (e.g., no differences reported when they exist, or differences reported when they actually do not exist) inference of treatment effects. Another issue that has received little attention for statistical inference of multi-year field experiments is the combination of fixed, random, and repeated measurement effects in the same statistical model. This type of analysis requires a more in-depth understanding of modeling error terms and how the statistical software used translates the statistical language of the given command into mathematical computations. Ignoring possible significant interactions among repeated, fixed, and random effects might lead to an erroneous interpretation of the data set. In this manuscript, we use data from two field experiments that were repeated during two and three consecutive years on the same plots to illustrate different modeling strategies and graphical tools with an emphasis on the use of mixed modeling techniques with repeated measures.

A Meta-analysis of Fiber Ratio Effects on Growth Performance, Gastrointestinal Traits, and Nutrient Digestibility of Broiler Chickens

Fiber is one of the essential nutrients for broiler chickens. This meta-analysis was carried out to investigate the impacts of fiber fraction ratio on broiler chickens growth performance, digestive characteristics, and nutritional digestibility. The database was compiled from 15 publications reports on the addition of fiber sources in broilers feed. To analyze the effect of acid detergent fiber (ADF) / neutral detergent fiber (NDF) ratio, the mixed model technique was utilized, with ADF/NDF ratio in the feed as a fixed effect and the experiment as a random effect. The ADF/NDF ratio in the feed had no effect on average daily gain, average daily feed intake, and feed per gain ratio in this research. Moreover, a decrease in ADF/NDF ratio in broiler chicken feed increased the relative weight of the gizzard. The relative weight and length of the small intestine and cecum were not affected by the ADF/NDF ratio in the feed. The ADF/NDF ratio in feed enhanced ileal digestibility and total tract apparent retention of most nutrients. The ADF/NDF ratio in the feed had no effect on the jejunal morphology. The minimum ADF/NDF ratio of 0.37 in the feed led to the maximum growth performance, digestive tract development, and optimal nutrient digestibility. In conclusion, controlling the ratio of fiber fraction in broiler chickens feed can improve broiler performance in the non-antibiotic growth promoters era.

Clozapine-related obsessive-compulsive symptoms and their impact on wellbeing: a naturalistic longitudinal study.

Obsessive-compulsive symptoms (OCS) are commonly associated with clozapine treatment but are frequently overlooked by clinicians despite their potential impact on patients' quality of life. In this study, we explored whether OCS severity impacted subjective wellbeing and general functioning, independently of depressive and psychotic symptoms. We used anonymised electronic healthcare records from a large cohort of patients who were treated with clozapine and assessed annually for OCS, wellbeing, general functioning, and psychopathology using standardised scales as part of routine clinical practice. We used statistical mixed linear model techniques to evaluate the longitudinal influence of OCS severity on wellbeing and general functioning. A total of 184 patients were included, with 527 face-to-face assessments and 64.7% evaluated three or more times. Different linear mixed models demonstrated that OCS in patients treated with clozapine were associated with significantly worse wellbeing scores, independently of depression and psychotic symptoms, but OCS did not impair general functioning. Obsessional thinking and hoarding behaviour, but not compulsions, were significantly associated with the impact on wellbeing, which may be attributable to the ego-syntonic nature of the compulsions. Given the frequent occurrence of OCS and their negative impact on wellbeing, we encourage clinicians to routinely assess and treat OCS in patients who are taking clozapine.

APPLICATION INFORMATION MODELING AND MACHINE LEARNING ALGORITHM FOR CLASSIFICATION OF WASTE USING SUPPORT VECTOR MACHINE

The ecological state of the world is deteriorating for the worse every year. One of the main problems is inadequate waste disposal and inadequate sorting by waste type, which has led to inadequate treatment of bulk waste in landfills throughout the world. The issue of improper disposal of municipal solid waste (MSW) in Kazakhstan has been raised since 2013, to solve this problem, the first President of the Republic of Kazakhstan, Nursultan Abishevich Nazarbayev, issued a decree on the transition to a green economy. Under the leadership of the Ministry of Energy, it was planned to reduce the amount of inappropriate waste by 40% in the territory of Kazakhstan by 2030. There are a lot of problems in India like inadequate waste collection, transport, treatment, and disposal. Poorly recyclable garbage has a global impact, fouling oceans, obstructing sewers, and creating flooding, transferring infections, increasing respiratory problems due to burning, injuring animals that inadvertently consume waste, and affecting economic development. To classify garbage, researchers utilized a combination of mixed modeling and machine learning techniques. Using machine learning technology, the data obtained can be used to classify and redistribute garbage for any sector around the world.

Online or in Person? Examining College Decisions to Reopen during the COVID-19 Pandemic in Fall 2020.

When coronavirus disease 2019 (COVID-19) became a major impediment to face-to-face college instruction in spring 2020, most teaching went online. Over the summer, colleges had to make difficult decisions about whether to return to in-person instruction. Although opening campuses could pose a major health risk, keeping instruction online could dissuade students from enrolling. Taking an ecological approach, the authors use mixed modeling techniques and data from 87 percent of two- and four-year public and four-year private U.S. colleges to assess the factors that shaped decisions about fall 2020 instructional modality. Most notably, the authors find that reopening decisions about whether to return to in-person instruction were unrelated to cumulative COVID-19 infection and mortality rates. Politics and budget concerns played the most important roles. Colleges that derived more of their revenue from tuition were more likely to return to classroom instruction, as were institutions in states and counties that supported Donald Trump for president in 2016.

It's Not Where You Start, it's How You Finish: Predicting Law School and Bar Success

The AccessLex/LSSSE Bar Exam Success Initiative is the first multi-institutional investigation into the factors that help predict law school academic and first-time bar exam performance. Mixed effects linear and logit modeling techniques are used to analyze pre-admission data; law school transcript data; and bar exam performance data for almost 5,000 Spring 2018 and 2019 graduates from 20 law schools that participated in this study. Law School Survey of Student Engagement (LSSSE) response data were also analyzed for a subset of about 2,000 graduates. Our modeling techniques allowed us to localize the impact of the factors of interest, while also accounting for other factors. For example, our analyses of the impact of various student engagement factors on bar exam performance account for other potentially relevant factors such as law school grades. We find that: • LSAT score and undergraduate GPA (UGPA) are modestly associated with law school GPA (LGPA). LSAT score and first year (1L) LGPA yield the strongest association. Across our sample, a one-point increase in LSAT score is associated with a 0.04 increase in 1L LGPA. A one-tenth point increase in UGPA is associated with a 0.03 increase in LGPA (Figure 2). • LGPA is the strongest predictor of bar exam performance, even at the early stages of matriculation. For example, a one standard deviation increase in 1L LGPA is associated with a 402 percent increase in the odds of bar passage (Figure 3). • Positive growth in LGPA between the end of the first semester and graduation is associated with greater odds of passing the bar exam, particularly among graduates who struggled early on. Graduates with below average first-semester grades who experienced no LGPA growth had a 25 percent chance of passing the bar exam, compared to 43 percent among their peers who experienced average growth of about 0.17 grade points (Figure 5). • Graduates who spent more than 21 hours per week on responsibilities such as caring for dependents or working a non-law-related job had lower third year (3L) LGPAs and bar passage odds than their peers who spent 0 to 5 hours on these activities (Figure 12). • Graduates who worked in law-related jobs while in law school (Figure 9); graduates who felt that their law school experience contributed “very much” to their skills development (Figure 10); and graduates who regularly participated in class (Figure 11) were modestly more likely to pass the bar exam than other graduates. Collectively, our results suggest that academic and bar exam success are driven by what happens in law school, not just early on, but throughout the experience—and the greatest opportunities for impact exist among those who struggle the most early on.

Advancements and critical steps for statistical analyses in blood pressure response to resistance training in hypertensive older women: a methodological approach.

Repeated measures analysis of covariance and three-way analysis of variance with repeated measures are common statistical methods. For a valid interpretation of blood pressure (BP) response to exercise, a variety of additional statistical methods must be implemented. Four additional statistical methods are presented: technical error of measurement (SEM), smallest real difference (SRD), magnitude-based inference and mixed effect modeling technique (MEM). The aim of this perspective article is to demonstrate how to apply already known statistical analyses regarding BP responsiveness in order to improve interpretation and achieve higher reliability for future studies in exercise science. A total of 27 hypertensive older women (aged 68.37 ± 5.55 years) participated in the present study. A whole-body resistance training (RT) program was performed on two nonconsecutive days per week for 10 weeks. BP was monitored during the 10-week RT intervention and after 15 weeks of detraining. First, individuals were classified as high and low responders, then statistical methods to analyze data included the use of SEM, SRD, magnitude-based inference and MEM. When magnitude-based inference was used to classify responsiveness, most participants displayed a trivial response. Decrements in SBP between 1 and 10 mmHg were not clinically meaningful but fell within the measurement error of the SBP measurements. Baseline SBP and time of training predicted post-SBP response. Changes over time and declines in SBP might not be a SRD and fell in the SEM. Moreover, SBP responsiveness was the result of inappropriate control of covariates such as period of training.

Assessing the influence of climate on the growth rate of boreal tree species in northeastern Canada through long-term permanent sample plot data sets

Climate has a considerable influence on tree growth. Forest managers benefit from the empirical study of the historic relationship between climatic variables and tree growth to support forest management frameworks that are to be applied under scenarios of climate change. Through this research, we have utilized long-term permanent sample plot records, historic climate data sets, and linear mixed modelling techniques to evaluate the historic influence of climatic variables on the growth rates of major boreal tree species in Newfoundland and Labrador, Canada. For the commercially significant spruce and fir forests of the province, we found growing degree-days (GDD) to negatively correlate with tree productivity in warmer regions, such as much of Newfoundland (±1350 GDD), but positively correlate with growth in cooler regions, such as those in Labrador (±750 GDD). With respect to precipitation, environmental moisture was not on average a limiting factor to species productivity in the province. These dynamics have implications for the productivity of the spruce–fir forests of the study area when considered alongside contemporary climate projections for the region, which generally entail both a warmer and wetter growing environment.

Pharmacokinetic evaluation of two pirfenidone formulations in patients with idiopathic pulmonary fibrosis and chronic hypersensitivity pneumonitis

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal disease characterized by an abnormal activation of lung epithelium and fibroblasts, as well as an excessive accumulation of extracellular matrix. Pirfenidone was introduced as a therapeutic option for IPF and chronic hypersensitive pneumonitis (cHP), a related disease. However, high plasma concentrations, which can be achieved even at recommended doses, are frequently associated with adverse events. Hence, an extended release formulation (XP), yielding lower peak plasma concentrations, has been developed. The aim of this study was to compare the pharmacokinetic properties of XP with those of the immediate (IR) formulation in patients with IPF or cHP. Data were analyzed using two pharmacokinetic approaches, conventional non compartmental analysis and a population analysis using the nonlinear mixed effects model technique. Results observed with both approaches were consistent. Drug exposure was similar with both formulations. However, XP exhibited less concentration fluctuations and a longer mean resident time. These results suggest that XP could be a feasible option to reduce adverse events associated to pirfenidone elevated concentrations. Nevertheless, efficacy studies are required to fully document the therapeutic potential of XP.

How nursing students\u2019 placement preferences and perceptions of community care develop in a more \u2018community-oriented\u2019 curriculum: a longitudinal cohort study

BackgroundExtramuralisation in healthcare has influenced medical and nursing curricula internationally with the incorporation of themes related to primary/ community care. Despite this, students do not easily change their career preferences. The hospital is still favourite, leading to labour market shortages in extramural care. This study investigates how baccalaureate nursing students’ perceptions of community care and placement preferences develop over time in a more ‘community-care-oriented’ curriculum, to gain insights on which curriculum elements potentially influence career choices.MethodsA nursing student cohort of a University of Applied Sciences in the Netherlands (n = 273) underwent a new four-year curriculum containing extended elements of community care. The primary outcome was assessed with the Scale on Community Care Perceptions (SCOPE). Data were collected each year of study. Descriptive statistics were used to investigate students’ placement preferences and perceptions, and linear mixed model techniques (LMMs) for measuring how students’ perceptions develop over time. Patterns of placement preferences at individual level were visualised.ResultsStudents’ perceptions of community care, as measured with SCOPE, show a slight decrease between year 1 and 4, while items mutually differ substantially. In contrast, the preference of community care for a placement increases from 2.6% in year 1 tot 8.2% in year 4. The hospital is favourite in year 1 (79.8%), and remains most popular. At individual level, students often change placement preferences, although a preference for the hospital is more consistent. The LMMs indicates that, at the four time-points, the estimated marginal means of students’ perceptions fluctuate between 6 and 7 (range 1–10). A placement in community care did not positively influence students’ perceptions, and an intensive 1 week theoretical programme was only temporarily influential.ConclusionsAlthough interest for placement in community care increased substantially, it was not clear which curriculum elements stimulated this, nor did the curriculum positively influence students’ perceptions. As most students do not look forward to the high responsibility of the field, other curricula with educational tracks for more mature students/ nurses with a vocational training may be an alternative contribution to solving the labour market problems in community care.