Common Approaches Research Articles

A survey on common biostatistics tools in neuroscience: Machine learning and Bayesian modeling

Machine learning was characterized by building models and finding correlations between data features, while logistic regression, decision trees, support vector machines (SVM), random forest (RF) and neural networks were recognized as common ML approaches. Bayesian modeling model uncertainty, which can estimate the features from the dataset directly instead of from sampling distribution. Their roles were extremely useful for the detection and progression for diseases in neuroscience. This review summarize different approaches in various diseases, hoping to introduce the potential roles of biostatistics tools in neuroscience.

Management of a recurrent third branchial arch fistula using combined endoscopic and open approach

Branchial cleft anomalies are rare, congenital lesions, that occur due to the maldevelopment of the branchial apparatus, during fetal maturation. Anomalies of the third and fourth arch contribute to a very small proportion of these. Most cases are detected in childhood, either as a cyst, sinus or fistula in the neck. Some of the common treatment approaches to third branchial cleft fistulas include endoscopic cauterization, open cervical fistulectomy and, in cases of recurrence, selective neck dissection. Here, we present a case of recurrent branchial fistula of the third arch, wherein we used a combined cervical and intraoral approach, conserving the thyroid cartilage, to achieve a complete excision of the fistula tract.

Comparison of methods to identify individuals prescribed opioid analgesics for pain

IntroductionWhile identifying opioid prescriptions in claims data has been instrumental in informing best practises, studies have not evaluated whether certain methods of identifying opioid prescriptions yield better results. We compared...

Qualitative research in dental traumatology-A narrative review.

This review highlights the recent contributions of qualitative research in advancing understanding of dental trauma injury and the barriers and enablers to guide policy for improved patient-centred care including transitional care. It summarises the common approaches and methods used and outlines the key factors that guide the appraisal of qualitative studies. It highlights the importance of the application of qualitative research methods in dental research to generate rich and detailed data to provide explanations and insights into people's experiences, beliefs and attitudes and the complexity of human decision-making and behaviour. In the past decade while there have been a growing number of publications of qualitative studies in dental journals, qualitative studies remain a small percentage of the published dental traumatology research. This may be because of limited understanding about the background, methods and rigour of qualitative research.

Evaluating density-weighted connectivity of black bears (Ursus americanus) in Glacier National Park with spatial capture–recapture models

BackgroundImproved understanding of wildlife population connectivity among protected area networks can support effective planning for the persistence of wildlife populations in the face of land use and climate change. Common approaches to estimating connectivity often rely on small samples of individuals without considering the spatial structure of populations, leading to limited understanding of how individual movement links to demography and population connectivity. Recently developed spatial capture-recapture (SCR) models provide a framework to formally connect inference about individual movement, connectivity, and population density, but few studies have applied this approach to empirical data to support connectivity planning.MethodsWe used mark-recapture data collected from 924 genetic detections of 598 American black bears (Ursus americanus) in 2004 with SCR ecological distance models to simultaneously estimate density, landscape resistance to movement, and population connectivity in Glacier National Park northwest Montana, USA. We estimated density and movement parameters separately for males and females and used model estimates to calculate predicted density-weighted connectivity surfaces.ResultsModel results indicated that landscape structure influences black bear density and space use in Glacier. The mean density estimate was 16.08 bears/100 km2 (95% CI 12.52–20.6) for females and 9.27 bears/100 km2 (95% CI 7.70–11.14) for males. Density increased with forest cover for both sexes. For male black bears, density decreased at higher grizzly bear (Ursus arctos) densities. Drainages, valley bottoms, and riparian vegetation decreased estimates of landscape resistance to movement for male and female bears. For males, forest cover also decreased estimated resistance to movement, but a transportation corridor bisecting the study area strongly increased resistance to movement presenting a barrier to connectivity.ConclusionsDensity-weighed connectivity surfaces highlighted areas important for population connectivity that were distinct from areas with high potential connectivity. For black bears in Glacier and surrounding landscapes, consideration of both vegetation and valley topography could inform the placement of underpasses along the transportation corridor in areas characterized by both high population density and potential connectivity. Our study demonstrates that the SCR ecological distance model can provide biologically realistic, spatially explicit predictions to support movement connectivity planning across large landscapes.

Environmental and Human Health Impact of Antibiotics Waste Mismanagement: A Review

The discovery of antibiotics and their use in the last century substantially shifted the management of an array of infections. However, its unprecedented irrational usage and inept disposal of their waste exposed the ecosystems to unquantifiable antibiotic deposits, triggering the emergence of antimicrobial resistance (AMR) with its immeasurable critical risk to public health. By adopting a narrative review method and accessing the available literature, the authors described what constitutes antibiotic waste, their classification, and the possible paths to access the ecological system. Further steps were taken to define the appropriate methods of disposing of antibiotic waste alongside the documented common wrong approaches in disposing of these currently undertaken in different parts of the globe, thus creating allowance for antibiotic waste mismanagement. The far-reaching consequences of inappropriate disposal of antibiotic waste into the environment were explored, emphasizing its hazardous impacts on human health and the ecosystem. Emphasis was laid on the pressing need for combined efforts involving policymakers, healthcare professionals, pharmaceutical industries, and the public to implement sustainable antibiotic waste management practices and promote their accountable utilization, and further suggested that proactive measures, such as severe regulatory frameworks, community awareness drives, and the promotion of eco-friendly options will be vital to easing the ecological and human health risks correlated with antibiotic waste mismanagement. By realizing the significance of this issue and approving integrated approaches, significant efforts could be accomplished regarding protecting the ecosystem and securing the effectiveness of antibiotics for the upcoming generation.

How the Support Defines Properties of 2D Metal-Organic Frameworks: Fe-TCNQ on Graphene versus Au(111).

The functionality of 2D metal-organic frameworks (MOFs) is crucially dependent on the local environment of the embedded metal atoms. These atomic-scale details are best ascertained on MOFs supported on well-defined surfaces, but the interaction with the support often changes the MOF properties. We elucidate the extent of this effect by comparing the Fe-TCNQ 2D MOF on two weakly interacting supports: graphene and Au(111). We show that the Fe-TCNQ on graphene is nonplanar with iron in quasi-tetrahedral sites, but on Au(111) it is planarized by stronger van der Waals interaction. The differences in physical and electronic structures result in distinct properties of the supported 2D MOFs. The dz2 center position is shifted by 1.4 eV between Fe sites on the two supports, and dramatic differences in chemical reactivity are experimentally identified using a TCNQ probe molecule. These results outline the limitations of common on-surface approaches using metal supports and show that the intrinsic MOF properties can be partially retained on graphene.

Dysautonomia and activity in the early stroke recovery period.

Maintaining cerebral perfusion in the early stages of recovery after stroke is paramount. Autoregulatory function may be impaired during this period leaving cerebral perfusion directly reliant on intravascular volume and blood pressure (BP) with increased risk for expanding cerebral infarction during periods of low BP and hemorrhagic transformation during BP elevations. We suspected that dysautonomia is common during the acute period related to both pre-existing vascular risk factors and potentially independent of such conditions. Thus, we sought to understand the state of the science specific to dysautonomia and acute stroke. The scoping review search included multiple databases and key terms related to acute stroke and dysautonomia. The team employed a rigorous review process to identify, evaluate, and summarize relevant literature. We additionally summarized common clinical approaches used to detect dysautonomia at the bedside. The purpose of this scoping review is to understand the state of the science for the identification, treatment, and impact of dysautonomia on acute stroke patient outcomes. There is a high prevalence of dysautonomia among persons with stroke, though there is significant variability in the type of measures and definitions used to diagnose dysautonomia. While dysautonomia appears to be associated with poor functional outcome and post-stroke complications, there is a paucity of high-quality evidence, and generalizability is limited by heterogenous approaches to these studies. There is a need to establish common definitions, standard measurement tools, and a roadmap for incorporating these measures into clinical practice so that larger studies can be conducted.

Approaches and game elements used to tailor digital gamification for learning: A systematic literature review

The systematic review examined research on tailored digital gamification for learning based on 43 peer-reviewed articles published between 2013 and 2022. The study aimed to investigate tailored approaches and game elements, contributing to the use of tailored digital gamification in educational settings. The tailored approaches were categorized as personalization, adaptation, and recommendation, with user modeling as their basis. Five clusters of game elements were employed when using these tailored approaches in digital gamified classes. The findings imply that most of the articles in this review were still in the stage of class preparation and focused on what information can be used to tailor. More empirical studies need to be conducted to examine the motivating effects of tailored digital gamifying classes, using the approaches of personalization, adaptation, and recommendation. Additionally, twenty-three game elements were found in this review study, among which reward was the most often used. Then these game elements were grouped into five clusters based on their functions, that is, performance, personal, social, ecological, and fictional cluster. A variety of game element clusters reflect multiple aspects of gamification. The use of them in each tailored approach might contribute to a better understanding and selection of game elements when tailoring digital gamification. These findings provide a holistic picture of common approaches and related game elements in tailored digital gamifying classes. Teachers and curriculum designers can benefit from this study by considering appropriate approaches and game elements.

Bioindication for Ecosystem Regeneration towards Natural conditions: the BERN data base and BERN model

The primary task of the BERN database is to document reference data on typical site parameters for the occurrence of plant communities in which their diagnostic species are in competitive equilibrium with each other and in homeostatic equilibrium with the site factors. Common approaches for the creation of a site-plant database such as ordination or bioindication based on individual species like PROPS or MultiMOVE model are of limited use because it is not possible to determine the potential occurrence of a plant species on the basis of site factors, since the competitive influences cannot be determined in advance according to current knowledge. Therefore, the BERN database takes into account the structure of plant communities with the abundance and dominance of species in the competitive equilibrium of plant communities as a reference for determining anthropogenically induced changes. Qualitative knowledge on the relationship between site types and vegetation communities is widely available, as can be seen from the extensive phytosociological publications. For this purpose, synoptic tables and their location descriptions of around 50,000 relevés were evaluated. The BERN database includes currently 887 central European plant communities and links to their diagnostically defining species composition. The database defines the niche of 2210 central European plant species for the soil properties pH, base saturation, carbon to nitrogen ratio, and wetness index and the climatic properties continentality, length of vegetation period, solar radiation and climatic water balance. The BERN model recombines the realised species niches that mainly form the competitively homeostatic structure of a plant community in order to determine the fundamental multifactorial niche of this community. The BERN database contains mainly historical recordings of more or less undisturbed sites. The BERN model (Bioindication for Ecosystem Regeneration towards Natural conditions) as an application module of the BERN database was developed to integrate ecological cause-effect relationships into studies on environmental status assessment and forecasting. The BERN database now has been published for the first time. The methodology of creating the BERN database and the BERN model are documented and applications are demonstrated with examples. The freely available database should invite you to supplement and modify it.

A Review on Common Approaches Used for Graphene Characterization

A Review on Common Approaches Used for Graphene Characterization

Exploring potential causal associations between autoimmune diseases and colorectal cancer using bidirectional Mendelian randomization

Several observational studies have revealed an association between autoimmune diseases (AIDs) and colorectal cancer (CRC), although their causal association remained controversial. Therefore, our study used a two-sample Mendelian randomization (MR) analysis to verify the causal association between AIDs and CRC. We employed three common MR approaches, including inverse variance weighted (IVW), weighted median, and MR-Egger methods, to assess the causal association between type 1 diabetes (T1D), systemic lupus erythematosus, rheumatoid arthritis, psoriasis, multiple sclerosis, juvenile idiopathic arthritis, celiac disease, and primary sclerosing cholangitis (PSC) and CRC. The reverse MR analysis was performed to assess the possibility of reverse causation. To evaluate the validity of the analysis, we also performed sensitivity analysis, such as the heterogeneity test, the horizontal pleiotropy test, and the leave-one-out sensitivity analysis, and validated the results in the validation cohort. Our results showed that genetically predicted T1D was nominally associated with a lower risk of CRC (IVW OR = 0.965, 95% CI = 0.939–0.992, P = 0.012). However, genetic susceptibility to psoriasis nominally increased the risk of CRC (IVW OR = 1.026, 95% CI = 1.002–1.050, P = 0.037). Genetically predicted PSC had a significant causal effect on the increasing risk of CRC (IVW OR = 1.038, 95% CI = 1.016–1.060, P = 5.85 × 10−4). Furthermore, the MR analysis between PSC and the CRC validation cohort indicated consistent results. We found no causal association between genetically predicted other five AIDs and CRC (P > 0.05). The results of reverse MR analysis showed that genetically predicted CRC had no causal effect on T1D, psoriasis, and PSC (P > 0.05). The sensitivity analysis demonstrated that the results of the MR analysis were reliable. Our findings help to understand the causal association between AIDs and CRC, which deserves further investigation.

Addressing sample selection bias for machine learning methods

SummaryWe study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular machine‐learning algorithms. Common machine learning practices such as weighting or including variables that influence selection into the training or prediction sample often worsen sample selection bias. We propose two control function approaches that remove the effects of selection bias before training and find that they reduce mean‐squared prediction error in simulations. We apply these approaches to predicting the vote share of the incumbent in gubernatorial elections using previously observed re‐election bids. We find that ignoring selection on unobservables leads to substantially higher predicted vote shares for the incumbent than when the control function approach is used.

Temporal classification of short time series data

MotivationWithin the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take.ResultsWe present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data.Availability and implementationAn open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass [58].

Health and Safety Executive research on wood dust exposure controls in British manufacturing.

To generate new intelligence on occupational exposure to wood dust in woodworking manufacturing activities in Britain, the Health and Safety Executive (HSE) performed 22 occupational hygiene site visits to assess exposure and exposure controls between 2014 and 2017. The work aimed to characterise good practice and therefore sites with a poor health and safety record, as identified from HSE inspection records, were not invited to participate. Sites selected covered furniture production, joinery, saw milling, and boat building and repair. Twenty-three follow-up telephone interviews were also carried out across 15 of the companies with supervisors and managers to explore how they tried to promote good practice among the workforce, and if there are any potential challenges encountered. The aim of the interviews was to gain a better understanding of how to enable organisations to improve the management of wood dust exposure. This study found that 6.0% of all wood dust exposure measurements (15 out of 252) were above 5 mg/m³, and 17.6% of exposures to hardwood dust or mixtures of hardwood and softwood dust (38 out of 216) were above 3 mg/m³ (the then current and future workplace exposure limits). Sanding, cleaning, and maintenance activities were of particular concern. Improvements to exposure controls are required, in particular, improvements to local exhaust ventilation controls for hand-held power tools and hand sanding. The management, selection, and use of respiratory protective equipment were poor. All the managers and supervisors recognised that exposure to wood dust can pose serious health risks, and that controls were crucial to protecting workers' health. The findings from the telephone interviews suggest that supervision and provision of information about the health effects of exposure to wood dust were common approaches that organisations used to raise awareness and promote good practice, in relation to managing wood dust exposure. Worker attitudes towards controls, such as perceptions that they hinder task completion and habitual ways of working, were identified as factors influencing the use of controls. Risk communication approaches that focus on increasing workers' awareness of their susceptibility to ill-health using credible sources, such as peers, can help enhance the uptake of messages on the use of controls. Financial constraints were identified as a challenge to improving the control of wood dust, particularly for small companies.

A Cross-cultural Comparison of ELT Curricula of Senior Secondary Schools in Mainland China and Hong Kong

English holds significant importance as a foreign language in Mainland China and Hong Kong, where ongoing curriculum revisions are shaped by economic and sociocultural advancements. Previous research on English Language Teaching (ELT) curriculum comparisons in Asia has frequently overlooked the exploration of underlying sociocultural and socio-political factors that contribute to the observed similarities and differences. Adopting a governmentality Perspective informed by Dean's analytics of government theory, this cross-cultural comparative study investigates the ELT curricula in senior secondary schools in Mainland China and Hong Kong. Through this analysis, the study explores how sociocultural and socio-political factors interact to influence the educational landscapes. By examining the dimensions of problematizations, rationalities, techne, and teleology within the curricula, the research uncovers common challenges, rationales, approaches, and objectives, as well as distinctions arising from the unique sociocultural, political, and economic contexts of Mainland China and Hong Kong. This examination underscores the profound impact of these contextual factors on ELT curriculum design, contributing valuable insights to the cross-cultural discourse. Furthermore, the findings lay the groundwork for future research and policy development in the realm of ELT curricula, emphasizing the need for additional studies employing diverse methods and focusing on the implemented and experienced curriculum in senior secondary schools to provide a comprehensive understanding of English language education in both Mainland China and Hong Kong.

The quandary of sources and sinks of CO2 efflux in tree stems-new insights and future directions.

Stem respiration (RS) substantially contributes to the return of photo assimilated carbon to the atmosphere and, thus, to the tree and ecosystem carbon balance. Stem CO2 efflux (ECO2) is often used as a proxy for RS. However, this metric has often been challenged because of the uncertain origin of CO2 emitted from the stem due to post-respiratory processes. In this Insight, we (i) describe processes affecting the quantification of RS, (ii) review common methodological approaches to quantify and model RS and (iii) develop a research agenda to fill the most relevant knowledge gaps that we identified. Dissolution, transport and accumulation of respired CO2 away from its production site, reassimilation of respired CO2 via stem photosynthesis and the enzyme phosphoenolpyruvate carboxylase, axial CO2 diffusion in the gas phase, shifts in the respiratory substrate and non-respiratory oxygen (O2) consumption are the most relevant processes causing divergence between RS and measured stem gas exchange (ECO2 or O2 influx, IO2). Two common methodological approaches to estimate RS, namely the CO2 mass balance approach and the O2 consumption technique, circumvent some of these processes but have yielded inconsistent results regarding the fate of respired CO2. Stem respiration modelling has recently progressed at the organ and tree levels. However, its implementation in large-scale models, commonly operated from a source-driven perspective, is unlikely to reflect adequate mechanisms. Finally, we propose hypotheses and approaches to advance the knowledge of the stem carbon balance, the role of sap pH on RS, the reassimilation of respired CO2, RS upscaling procedures, large-scale RS modelling and shifts in respiratory metabolism during environmental stress.

Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests

Abstract. The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface model against surface energy flux, snow depth and soil temperature observations from four eddy-covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling.

The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects.

Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavor; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and δaδi). We demonstrate that, if not properly modeled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination before utilizing population genomic data to quantify the effects of genetic drift (i.e. as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modeled in downstream inference.

Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils.

Hydraulic conductivity (Kψ) is one of the most important soil properties that influences water and chemical movement within the soil and is a vital factor in various management practices, like drainage, irrigation, erosion control, and flood protection. Therefore, it is an essential component in soil monitoring and managerial practices. The importance of Kψ in soil-water relationship, difficulties for its measurement in the field, and its high variability led us to evaluate the potential of stepwise multiple linear regression (SMLR), and multilayer perceptron (MLPNNs) and radial-basis function (RBFNNs) neural networks approaches to predict Kψ at tensions of 15, 10, 5, and 0 cm (K15, K10, K5, and K0, respectively) using easily measurable attributes in calcareous soils. A total of 102 intact (by stainless steel rings) and composite (using spade from 0-20 cm depth) soil samples were collected from different land uses of Fars Province, Iran. The common physico-chemical attributes were determined by the common standard laboratory approaches. Additionally, the mentioned hydraulic attributes were measured using a tension-disc infiltrometer (with a 10 cm radius) in situ. Results revealed that the most of studied soil structure-related parameters (soil organic matter, soluble sodium, sodium adsorption ratio, mean weight diameter of aggregates, pH, and bulk density) are more correlated with K5 and K0 than particle-size distribution-related parameters (sand, silt, and standard deviation and geometric mean diameter of particles size). For K15 and K10, the opposite results were obtained. The applied approaches predicted K15, K10, K5, and K0 with determination coefficient of validation data (R2val) of 0.52 to 0.63 for SMLR; 0.71 to 0.82 for MLPNNs; and 0.58 to 0.78 for RBFNNs. In general, the capability of the applied methods for predicting Kψ at all the applied tensions was ranked as MLPNNs > RBFNNs > SMLR. Although the SMLR method provided easy to use pedotransfer functions for predicting Kψ in calcareous soils, the present study suggests using the MLPNNs approach due to its high capability for generating accurate predictions.