Exploiting protein language models for the precise classification of ion channels and ion transporters.

This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion transporters (ITs) from MPs, and differentiating ICs from ITs. Drawing upon the strengths of six Protein Language Models (PLMs)-ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters), TooT-PLM-ionCT employs a combination of traditional classifiers and deep learning models for nuanced protein classification. Originally validated on an existing dataset by previous researchers, our systems demonstrated superior performance in identifying ITs from MPs and distinguishing ICs from ITs, with the IC-MP discrimination achieving state-of-the-art results. In light of recommendations for additional validation, we introduced a new dataset, significantly enhancing the robustness and generalization of our models across bioinformatics challenges. This new evaluation underscored the effectiveness of TooT-PLM-ionCT in adapting to novel data while maintaining high classification accuracy. Furthermore, this study explores critical factors affecting classification accuracy, such as dataset balancing, the impact of using frozen versus fine-tuned PLM representations, and the variance between half and full precision in floating-point computations. To facilitate broader application and accessibility, a web server (https://tootsuite.encs.concordia.ca/service/TooT-PLM-ionCT) has been developed, allowing users to evaluate unknown protein sequences through our specialized systems for IC-MP, IT-MP, and IC-IT classification tasks.

Physical activity, organized sport participation and active transportation to school among Canadian youth by gender identity and sexual attraction. / Activité physique, participation à des sports organisés et utilisation du transport actif pour se rendre à l'école chez les adolescents canadiens par identité de genre et par orientation sexuelle.

INTRODUCTION: Regular physical activity is associated with a wide range of health benefits in youth. While previous studies have identified disparities in physical activity among youth by gender identity and sexual attraction, these have seldom been explored in Canadian youth. METHODS: Data from the 2019 Canadian Health Survey on Children and Youth were used to assess prevalence of and time spent in organized sports participation, total physical activity and active transportation by gender identity (non-cisgender vs. cisgender) among youth aged 12 to 17, and by sexual attraction (nonheterosexual attraction vs. heterosexual attraction) among youth aged 15 to 17. RESULTS: There was no difference in average minutes of total physical activity per week between non-cisgender and cisgender Canadian youth. Non-cisgender youth (which represent 0.5% of the population) averaged significantly fewer minutes of organized sports per week than their cisgender counterparts. There was some evidence of increased active transportation to school among non-cisgender youth, but insufficient power to detect significant differences. Canadian youth reporting any nonheterosexual attraction (which represent 21.2% of the population, including mostly heterosexual youth) were less likely to be regularly physically active and participate in organized sports than youth reporting exclusive heterosexual attraction. Differences were larger among males than females. Males reporting nonheterosexual attraction were more likely to use active transportation to get to school than their heterosexual counterparts. CONCLUSION: Non-cisgender youth and youth reporting nonheterosexual attraction tended to participate less in organized sports than their counterparts, but may have engaged in more active transportation. Mitigating the barriers associated with sport participation could increase physical activity among these groups.

Prioritizing a research agenda on built environments and physical activity: a twin panel Delphi consensus process with researchers and knowledge users.

BACKGROUND: The growth of urban dwelling populations globally has led to rapid increases of research and policy initiatives addressing associations between the built environment and physical activity (PA). Given this rapid proliferation, it is important to identify priority areas and research questions for moving the field forward. The objective of this study was to identify and compare research priorities on the built environment and PA among researchers and knowledge users (e.g., policy makers, practitioners). METHODS: Between September 2022 and April 2023, a three-round, modified Delphi survey was conducted among two independent panels of international researchers (n = 38) and knowledge users (n = 23) to identify similarities and differences in perceived research priorities on the built environment and PA and generate twin 'top 10' lists of the most important research needs. RESULTS: From a broad range of self-identified issues, both panels ranked in common the most pressing research priorities including stronger study designs such as natural experiments, research that examines inequalities and inequities, establishing the cost effectiveness of interventions, safety and injuries related to engagement in active transportation (AT), and considerations for climate change and climate adaptation. Additional priorities identified by researchers included: implementation science, research that incorporates Indigenous perspectives, land-use policies, built environments that support active aging, and participatory research. Additional priorities identified by knowledge users included: built environments and PA among people living with disabilities and a need for national data on trip chaining, multi-modal travel, and non-work or school-related AT. CONCLUSIONS: Five common research priorities between the two groups emerged, including (1) to better understand causality, (2) interactions with the natural environment, (3) economic evaluations, (4) social disparities, and (5) preventable AT-related injuries. The findings may help set directions for future research, interdisciplinary and intersectoral collaborations, and funding opportunities.

Associations between school-level environment and individual-level factors of walking and cycling to school in Canadian youth.

Identifying individual-level and school-level correlates of walking and cycling to school remains a public health priority as only one in four Canadian youth actively travels to school. This study aimed to estimate the prevalence of Canadian youth in grades 6 to 10 who walk, cycle, or use motorised transport to go to school, and to examine if school neighbourhood walkability, neighbourhood-level and individual-level correlates are associated with mode of transportation to school. Data come from the 2017/2018 Health Behaviour in School-aged Children study. The walkability of the schools' neighbourhood was measured using the Canadian Active Living Environments (Can-ALE) index. We observed that only 22.4% and 4.2% of youth walked and cycled to school, respectively. Most (73.4%) used motorised transport to school, including 53.2% of youth who lived less than 5 minutes from school. Schools located in neighbourhoods with higher Can-ALE classes (i.e., higher walkability) were positively associated with walking to school. No statistically significant association between school walkability and cycling to school was observed. Individual-level socioeconomic status (SES) was associated with walking, but not cycling, to school. Conversely, neighbourhood-level SES was associated with cycling, but not with walking, to school. Correlates of walking to school differed from those for cycling to school, suggesting that different approaches to promoting active transportation are needed.

Enhanced identification of membrane transport proteins: a hybrid approach combining ProtBERT-BFD and convolutional neural networks.

Transmembrane transport proteins (transporters) play a crucial role in the fundamental cellular processes of all organisms by facilitating the transport of hydrophilic substrates across hydrophobic membranes. Despite the availability of numerous membrane protein sequences, their structures and functions remain largely elusive. Recently, natural language processing (NLP) techniques have shown promise in the analysis of protein sequences. Bidirectional Encoder Representations from Transformers (BERT) is an NLP technique adapted for proteins to learn contextual embeddings of individual amino acids within a protein sequence. Our previous strategy, TooT-BERT-T, differentiated transporters from non-transporters by employing a logistic regression classifier with fine-tuned representations from ProtBERT-BFD. In this study, we expand upon this approach by utilizing representations from ProtBERT, ProtBERT-BFD, and MembraneBERT in combination with classical classifiers. Additionally, we introduce TooT-BERT-CNN-T, a novel method that fine-tunes ProtBERT-BFD and discriminates transporters using a Convolutional Neural Network (CNN). Our experimental results reveal that CNN surpasses traditional classifiers in discriminating transporters from non-transporters, achieving an MCC of 0.89 and an accuracy of 95.1 % on the independent test set. This represents an improvement of 0.03 and 1.11 percentage points compared to TooT-BERT-T, respectively.

Gender identity and sexual attraction among Canadian youth: findings from the 2019 Canadian Health Survey on Children and Youth. / Identité de genre et attirance sexuelle chez les jeunes Canadiens : résultats de l'Enquête canadienne sur la santé des enfants et des jeunes de 2019.

Gender identity and sexual attraction are important determinants of health. This study reports distributions of gender identity and sexual attraction among Canadian youth using data from the 2019 Canadian Health Survey on Children and Youth. Among youth aged 12 to 17, 0.2% are nonbinary and 0.2% are transgender. Among youth aged 15 to 17, 21.0%, comprising more females than males, report attraction not exclusive to the opposite gender. Given known associations between health and gender and sexual attraction, oversampling of sexual minority groups is recommended in future studies to obtain reliable estimates for identifying inequities and informing policy.

Gender and sexual attraction as a dimension of sexual orientation are important determinants of health among youth. Collecting gender and sexual attraction information as a routine part of public health surveillance is important for identifying inequities and informing policy. This study provides nationally representative estimates for the distribution of gender and sexual attraction among Canadian youth. This study identifies populations (nonbinary, transgender and same gender­attracted youth) that require oversampling or other approaches to ensure that reliable estimates can be obtained in public health surveillance.

Le genre et l'attirance sexuelle en tant que dimension de l'orientation sexuelle sont des déterminants importants de la santé chez les jeunes. La collecte de renseignements sur le genre et l'attirance sexuelle dans le cadre des activités habituelles de surveillance de la santé publique est importante pour relever les iniquités et orienter les politiques. Cette étude fait état d'estimations représentatives à l'échelle nationale de la répartition des genres et de l'attirance sexuelle chez les jeunes Canadiens. Cette étude répertorie les populations (non binaires, transgenres et jeunes ayant une attirance envers des personnes du même genre) devant faire l'objet d'un suréchantillonnage ou d'autres approches afin de garantir que des estimations fiables puissent être obtenues dans le cadre de la surveillance de la santé publique.

Strength-training and balance activities in Canada: historical trends and current prevalence. / Activités de musculation et d'amélioration de l'équilibre au Canada : historique des tendances et prévalence actuelle.

INTRODUCTION: Muscle-strengthening and balance activities are associated with the prevention of illness and injury. Age-specific Canadian 24-Hour Movement Guidelines include recommendations for muscle/bone-strengthening and balance activities. From 2000-2014, the Canadian Community Health Survey (CCHS) included a module that assessed frequency in 22 physical activities. In 2020, a healthy living rapid response module (HLV-RR) on the CCHS asked new questions on the frequency of muscle/bonestrengthening and balance activities. The objectives of the study were to (1) estimate and characterize adherence to meeting the muscle/bone-strengthening and balance recommendations; (2) examine associations between muscle/bone-strengthening and balance activities with physical and mental health; and (3) examine trends (2000-2014) in adherence to recommendations. METHODS: Using data from the 2020 CCHS HLV-RR, we estimated age-specific prevalence of meeting recommendations. Multivariate logistic regressions examined associations with physical and mental health. Using data from the 2000-2014 CCHS, sex-specific temporal trends in recommendation adherence were explored using logistic regression. RESULTS: Youth aged 12 to 17 years (56.6%, 95% CI: 52.4-60.8) and adults aged 18 to 64 years (54.9%, 95% CI: 53.1-56.8) had significantly greater adherence to the muscle/ bone-strengthening recommendation than adults aged 65 years and older (41.7%, 95% CI: 38.9-44.5). Only 16% of older adults met the balance recommendation. Meeting the recommendations was associated with better physical and mental health. The proportion of Canadians who met the recommendations increased between 2000 and 2014. CONCLUSION: Approximately half of Canadians met their age-specific muscle/bonestrengthening recommendations. Reporting on the muscle/bone-strengthening and balance recommendations elevates their importance alongside the already recognized aerobic recommendation.

Definition of agronomic circular economy metrics and use for assessment for a nanofertilizer case study.

Circular economy has become global priority, and fertigation make large contribution. Modern circular methodologies base their definitions, besides on waste minimisation and recovery, on the product usage U and lifetime L. We have modified a commonly used equation for the mass circularity indicator (MCI) to permit MCI determination for agricultural cultivation. We defined U as intensity for diverse investigated parameters of plant growth and L as the bioavailability period. In this way, we compute circularity metrics for the plantgrowth performance when exposed to three nanofertilizers and one biostimulant, as compared to no-use of micronutrients (control 1), and micronutrients supplied via conventional fertilizers (control 2). We determined an MCI of 0.839 for best nanofertilizer performance (1.000 denotes full circularity), while the MCI of conventional fertilizer was 0.364. Normalised to control 1, U was determined as 1.196, 1.121 and 1.149 for manganese, copper and iron-based nanofertilizers, respectively, while U was 1.709, 1.432, 1.424 and 1.259 for manganese, copper, iron nanofertilizers and gold biostimulant when normalised to control 2, respectively. Based on the learning of the plant growth experiments, a tailored process design is proposed for the use of nanoparticles with pre-conditioning, post-processing and recycling steps. A life cycle assessment shows that the additional use of pumps for this process design does not increase energy costs, while preserving environmental advantages related to the lower water usage of the nanofertilizers. Moreover, the impact of the losses of conventional fertilisers by missing absorption of plant roots, which is presumed to be lower for the nanofertilizers.

How sedentary are Canadian adults? It depends on the measure.

Introduction: The new Canadian 24-Hour Movement Guidelines for Adults aged 18-64 years and Adults aged 65 years and older recommend that adults limit daily sedentary time to eight hours or less, including three hours or less of recreational screen time. The eight-hour recommendation was centred between the evidence from research using self-reported sitting time (threshold: seven hours or less per day) and accelerometer-measured sedentary time (threshold: nine hours or less per day). The purpose of this study is to compare the percentages of Canadians meeting three different sedentary thresholds (three hours or less per day of screen time, seven hours or less per day of self-reported sitting time and nine hours or less per day of accelerometer-measured sedentary time). Methods: This analysis is based on 2,511 adults (aged 18 to 79 years) from Cycle 3 of the Canadian Health Measures Survey, in 2012 and 2013. Screen time and sitting time were assessed via self-report, and average daily sedentary time was assessed using a hip-worn Actical accelerometer. Results: Adults self-reported an average daily screen time of 3.2 hours (95% confidence interval [CI]: 3.0 to 3.5) and an average daily sitting time of 5.7 hours (95% CI: 5.4 to 6.0). According to accelerometry data, adults accumulated an average of 9.8 hours per day (95% CI: 9.7 to 9.9) of sedentary time. Adherence varied, with 57.7% meeting the self-reported recreational screen time threshold of three hours or less per day, 71.7% meeting the self-reported sitting time threshold of seven hours or less per day and 26.5% meeting the accelerometer-measured sedentary time threshold of nine hours or less per day. Interpretation: The percentage of Canadian adults meeting the three different sedentary behaviour thresholds varied widely. The findings in this article highlight the difference in sedentary time between what Canadians report versus what is measured by an accelerometer.

The influence of removing the 10-minute bout requirement on the demographic, behaviour and health profiles of Canadian adults who meet the physical activity recommendations.

Background: Recently, the Canadian 24-Hour Movement Guidelines for Adults were released, and included a revised physical activity (PA) recommendation. The recommendation of 150 minutes per week of moderate-to-vigorous intensity PA (MVPA) was revised, from requiring that MVPA be accrued in bouts of 10 minutes or more (bouted) to having no bout requirement (non-bouted). The objective of this study was to assess whether there were differences in sociodemographic, health and fitness characteristics of Canadians who met the bouted and non-bouted PA recommendations. Data and methods: Using adult (aged 18 to 79 years) accelerometer data from three combined cycles of the nationally representative Canadian Health Measures Survey (N = 7,102), this study compared adherence to the bouted and non-bouted recommendations. Differences in sociodemographic, health and fitness measures were assessed using independent t-tests and chi-squares. Multivariate linear and logistic regressions controlling for age, sex, household education and smoking examined associations with health and fitness measures. Results: More adults met the PA recommendation using the non-bouted versus bouted (45.3% vs. 18.5%) requirement. Characteristics of those who met the bouted and only the non-bouted recommendations were similar. Exceptions among those who met only the non-bouted recommendation compared with meeting the bouted recommendation included fewer adults aged 65 years and older; lower MVPA, recreation PA and transport PA; and higher sedentary time, light PA and grip strength. Interpretation: Although the removal of the 10-minute bout requirement increased the proportion of Canadian adults who met the PA recommendation, there were no substantial differences in the sociodemographic and health characteristics of the populations captured by the bouted and non-bouted definitions. Results help to inform the transition in reporting for PA surveillance.

Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research.

BACKGROUND: Advances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public's trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage. OBJECTIVE: The main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata. METHODS: We collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set. RESULTS: LPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet. CONCLUSIONS: Publicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners.

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk.

BACKGROUND: Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems. OBJECTIVE: This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems. METHODS: We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning-based classification model, and a hybrid convolutional neural network model. RESULTS: Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data. CONCLUSIONS: Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models.

Integrative approach for detecting membrane proteins.

BACKGROUND: Membrane proteins are key gates that control various vital cellular functions. Membrane proteins are often detected using transmembrane topology prediction tools. While transmembrane topology prediction tools can detect integral membrane proteins, they do not address surface-bound proteins. In this study, we focused on finding the best techniques for distinguishing all types of membrane proteins. RESULTS: This research first demonstrates the shortcomings of merely using transmembrane topology prediction tools to detect all types of membrane proteins. Then, the performance of various feature extraction techniques in combination with different machine learning algorithms was explored. The experimental results obtained by cross-validation and independent testing suggest that applying an integrative approach that combines the results of transmembrane topology prediction and position-specific scoring matrix (Pse-PSSM) optimized evidence-theoretic k nearest neighbor (OET-KNN) predictors yields the best performance. CONCLUSION: The integrative approach outperforms the state-of-the-art methods in terms of accuracy and MCC, where the accuracy reached a 92.51% in independent testing, compared to the 89.53% and 79.42% accuracies achieved by the state-of-the-art methods.

The Canadian Bikeway Comfort and Safety (Can-BICS) Classification System: A common naming convention for cycling infrastructure. / Système de classification du confort et de la sécurité des voies cyclables canadiennes (Can-BICS) : convention d'appellation commune des aménagements cyclables.

There is no standard naming convention for cycling infrastructure across cities. Our aim was to develop a common nomenclature for cycling infrastructure in Canada, relevant to the context of public health practice. We drew on transportation engineering design guides and public health guidance to develop a bicycle facility classification system: the Canadian Bikeway Comfort and Safety (Can-BICS) classification system, a three-tiered classification scheme that groups five bicycle facilities based on safety performance and user comfort. Adopting consistent nomenclature as per the Can-BICS system will support regional and national surveillance efforts in public health, planning and sustainability.

A common nomenclature for cycling infrastructure in Canada is needed to further public health surveillance efforts on activetransportation environments. The Can-BICS system is a threetiered cycling infrastructure classification system that reflects the safety performance and user comfort of five bicycle facility types. High-comfort bikeways are lowstress routes. These bikeways include cycle tracks on major streets, local street bikeways and cycle-only off-street paths. Medium-comfort bikeways are low-to-medium stress routes. These bikeways include multi-use paths sited next to a roadway or along independent corridors. Low-comfort bikeways are highstress routes. These bikeways include painted bike lanes along busy roadways.

Il est nécessaire d'établir une nomenclature commune pour les aménagements cyclables au Canada afin d'améliorer les mesures de surveillance en santé publique en matière de milieux de transport actif. Le système Can-BICS est un système de classification des amé- nagements cyclables à trois niveaux qui définit le degré de sécurité et le confort pour les usagers de cinq types d'aménagement cyclable. Les voies cyclables très confortables sont peu stressantes : ce sont principalement les pistes cyclables sur chaussée longeant les rues principales, les voies cyclables dans les rues secondaires (« vélorues ¼) et les pistes en site propre. Les voies cyclables moyennement confortables sont peu ou moyennement stressantes : ce sont principalement les sentiers polyvalents longeant une chaussée ou formant un corridor indépendant. Les voies cyclables peu confortables sont très stressantes : ce sont principalement les bandes cyclables peintes au sol sur des routes achalandées.

BENIN: Biologically enhanced network inference.

Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN: Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference. The method states the network inference as a feature selection problem and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it. BENIN significantly outperforms the state-of-the-art methods on the simulated data from the DREAM 4 challenge when combining genome-wide location data, knockout gene expression data, and time series expression data.

Gender and education differences in sedentary behaviour in Canada: an analysis of national cross-sectional surveys.

BACKGROUND: Canadians spend the majority of their days sedentary. Gender and education are important social determinants of health that impact health behaviours. There is evidence that gender and educational differences in sedentary behaviour exist. In Canada, while general trends suggest that leisure sedentary activities have changed; there has been no comprehensive assessment examining whether historical changes in sedentary behaviour differ by gender and education level. Our objective was to examine whether gender and educational differences in accelerometer-measured sedentary time and self-reported sedentary behaviours exist among Canadians and if differences are consistent across age groups, over time and across multiple survey sources. METHODS: We summarize amounts of total accelerometer-measured sedentary time and self-reported sedentary activities (e.g., passive travel, television, computer, video games, screen, reading) by age (i.e. children: 6-11 years, youth: 12-17 years, adults: 18-34 years, 45-49 years, 50-64 years, and older adults: ≥ 65 years), gender (girls/women, boys/men) and household education level (< post-secondary vs. ≥ post-secondary) over time in the Canadian Community Health Survey, Canadian Health Measures Survey, General Social Survey, and the Health Behaviour in School-Aged Children study. Gender and education level differences are examined using independent sample t-tests or chi-square analyses. RESULTS: While few differences were found for total accelerometer-measured sedentary time, gender and education differences in self-reported, type-specific sedentary behaviour were identified. Among youth, data from all surveys consistently identified that boys engaged in more video/computer game play (e.g., boys: 0.35-2.68 vs. girls: 0.09-2.15 h/day), while girls engaged in more leisure reading (e.g., boys: 0.45-0.65 vs. girls: 0.71-0.99 h/day). Those with a higher education or household education often reported more leisure reading and passive travel. Education level differences in screen time were often age dependent, with leisure computer use greater in higher education groups in adults only and leisure television watching generally higher in lower education groups in children and adults, but not youth. CONCLUSIONS: This information is valuable as it helps to identify segments of the population which may be at greater risk for engaging in higher volumes of sedentary behaviour. In turn, this information can identify target audiences and behaviours for policies and interventions. Future work is needed to further understand factors contributing to these differences (e.g., preferences, occupation, family structure).

TooT-T: discrimination of transport proteins from non-transport proteins.

BACKGROUND: Membrane transport proteins (transporters) play an essential role in every living cell by transporting hydrophilic molecules across the hydrophobic membranes. While the sequences of many membrane proteins are known, their structure and function is still not well characterized and understood, owing to the immense effort needed to characterize them. Therefore, there is a need for advanced computational techniques takes sequence information alone to distinguish membrane transporter proteins; this can then be used to direct new experiments and give a hint about the function of a protein. RESULTS: This work proposes an ensemble classifier TooT-T that is trained to optimally combine the predictions from homology annotation transfer and machine-learning methods to determine the final prediction. Experimental results obtained by cross-validation and independent testing show that combining the two approaches is more beneficial than employing only one. CONCLUSION: The proposed model outperforms all of the state-of-the-art methods that rely on the protein sequence alone, with respect to accuracy and MCC. TooT-T achieved an overall accuracy of 90.07% and 92.22% and an MCC 0.80 and 0.82 with the training and independent datasets, respectively.

Sedentary behaviour surveillance in Canada: trends, challenges and lessons learned.

BACKGROUND: Historical changes in the nature of sedentary activities have been observed in other countries, but it is not clear if similar trends exist in Canada. It is also unclear how changes in the measurement of sedentary behaviour affects national estimates. Our objective is to document all sources and measures of sedentary behaviour from Canadian, nationally representative surveys, and report on selected estimates of time spent in sedentary activities. Lessons learned can benefit the wider international surveillance community. METHODS: We describe and document all data sources of sedentary behaviour at the national level in Canada, and report on selected prevalence data from repeated cross-sectional surveys. We summarize amounts of total device-assessed sedentary time and self-reported sedentary activities (e.g., passive travel, leisure television, computer, video games, screen, and reading) by age group over time. RESULTS: Nineteen national surveys were identified. Changes in questions and/or response categories precluded direct assessment of trends over time for some measures; however, certain trends were observed. Accelerometer-measured sedentary time, leisure reading (among those < 50 years) and television/video viewing in younger age groups have remained relatively stable (with a possible slight decline in television/video viewing). Time spent in passive travel and leisure computer and electronic device use appears to have increased. Television and video viewing appears to have increased in older adults while their leisure reading appears to have fallen. CONCLUSIONS: Changes in measurement of sedentary behaviour can affect estimates and reduce comparability over time. Total leisure screen use appears to have increased over time, reflecting the ways in which Canadians spend their free time and technological advances. The main public health message is the need for continued efforts to reduce leisure screen use, especially among youth and older adults.

TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information.

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.