Highest Relative Importance Research Articles

The potential of decision trees as a tool to simplify broiler chicken welfare assessments

To simplify fast-growth broiler welfare assessments and use them as a benchmarking tool, decision trees were used to identify iceberg indicators discriminating flocks passing/failing welfare assessments as with the complete AWIN protocol. A dataset was constructed with data from 57 flocks and 3 previous projects. A final flock assessment score, previously not included in the dataset, was calculated and used as the benchmarking assessment classifier (pass/fail). A decision tree to classify flocks was built using the Chi-square Automatic Interaction Detection (CHAID) criterion. Cost-complexity pruning, and tenfold cross-validation were used. The final decision tree included cumulative mortality (%), immobile, lame birds (%), and birds with back wounds (%). Values were (mean ± se) 2.77 ± 0.14%, 0.16 ± 0.02%, 0.25 ± 0.02%, and 0.003 ± 0.001% for flocks passing the assessment; and 4.39 ± 0.49%, 0.24 ± 0.05%, 0.49 ± 0.09%, and 0.015 ± 0.006% for flocks failing. Cumulative mortality had the highest relative importance. The validated model correctly predicted 80.70% of benchmarking assessment outcomes. Model specificity was 0.8696; sensitivity was 0.5455. Decision trees can be useful to simplify welfare assessments. Model improvements will be possible as more information becomes available, and predictions are based on more samples.

Trade-off between herbal and allopathic treatments: An ethnopharmacological case study in Rema-kalenga wildlife sanctuary, Bangladesh

The Rema-Kalenga Wildlife Sanctuary (RKWS) is a protected forest in Bangladesh that houses a variety of rare flora and fauna and supports the sustenance of 13 ethnic communities. This forest's indigenous and other inhabitants traditionally have a strong cultural connection to the plants, particularly medicinal plants. Due to modern allopathic medicine's rapid growth and commercial tree species prioritization, many medicinal plants are now on the verge of endangerment. Under such circumstances, it is crucial to explore how the local community perceives the importance of herbal treatments in contrast to allopathy, the underlying reasons for their perceptions, and the specific ailments for which they use the plants. The main objectives are: 1) to evaluate the perceptions of the local community towards allopathy and herbal medicine using Nussbaum's central capabilities approach, 2) to identify medicinal plant diversity, therapeutic usages, and quantitative indices, 3) to determine the factors that influence the use of medicinal plants. Repeated interviews and field surveys were conducted at the RKWS, interviewing 145 people, including the indigenous community (72.42 %) and traditional healers (8.27 %) from the surrounding seven villages. The study identified 51 medicinal plant species belonging to 39 families for their potent medicinal properties in treating various ailments. The predominant parts of the plants used in the treatments were leaves and roots. The uses were classified into 12 categories according to Nussbaum's central capabilities. The findings identified Aloe vera, Phyllanthus emblica, and Azadirachta indica as highly culturally important species. In contrast, Terminalia arjuna, Swertia chirata, and Azadirachta indica were found to have the highest relative importance. The underlying determinants influencing the preferences of individual users towards herbal medicine were income from agroforestry, beliefs, knowledge, and ethnicity, as revealed by the analysis of the ordinal logit model. The respondents viewed their strong inclination toward herbal medicine with many positive attitudes. Herbal medicine users held a negative perception of allopathy except for affiliation and practical reasons being viewed as the positive outcomes. Conversely, allopathic medicine users expressed mixed perceptions towards the treatment, with both positive and negative aspects being identified. Promoting the sustainable use of medicinal plants and their conservation efforts is imperative for the benefit of present and future generations in this region.

Bond strength prediction of externally bonded reinforcement on groove method (EBROG) using MARS-POA

The Externally Bonded Reinforcement on Grooves (EBROG) method represents an advancement in externally bonded reinforcement (EBR) techniques, specifically addressing the challenge of premature debonding often encountered in conventional Fiber Reinforced Polymer (FRP) applications directly bonded to concrete. This article introduces a novel and straightforward mathematical equation for predicting bond strength in the EBROG method using soft computing techniques for the first time. The study delves into the combined potential of the Multivariate Adaptive Regression Spline (MARS) model and the Pelican Optimization Algorithm (POA) for bond strength prediction in this method. The input parameters include FRP width, FRP thickness, elasticity modulus of FRP, concrete strength, groove length, groove width, and groove depth, while the output is EBROG bond strength. The study demonstrates exceptional accuracy with R2 values of 0.9629 for training and 0.9623 for testing, highlighting the model’s precision. The proposed bond strength prediction equation for the EBROG method undergoes validation against existing models, encompassing thirteen equations for EBR and a recent one specific to EBROG. Statistical metrics confirm the accuracy and reliability of the proposed equation. Notably, FRP stiffness emerges as the parameter with the highest relative importance, while groove width exhibits the lowest impact on bond strength.

Application of telemetry-based fish habitat models to predict spatial habitat availability and inform ecological restoration.

Conservation decisions surrounding which fish habitats managers choose to protect and restore are informed by fish habitat models. As acoustic telemetry has allowed for improvements in our ability to directly measure fish positions year-round, so too have there been opportunities to refine and apply fish habitat models. In an area with considerable anthropogenic disturbance, Hamilton Harbour in the Laurentian Great Lakes, we used telemetry-based fish habitat models to identify key habitat variables, compare habitat associations among seasons, and spatially identify the presence distribution of six fish species. Using environmental data and telemetry-based presence-absence from 2016 to 2022, random forest models were developed for each species across seasons. Habitat variables with the highest relative importance across species included fetch, water depth, and percentage cover of submerged aquatic vegetation. The presence probability of each species was spatially predicted for each season within Hamilton Harbour. Generally, species showed a spatial range expansion with greater presence probability in the fall and winter to include parts of the harbor further offshore, and a range contraction in the spring and summer toward the nearshore, sheltered areas, with summer having the most limited habitat availability. Greater habitat suitability was predicted in western Hamilton Harbour for the majority of species, whereas the east end was less suitable and may benefit from habitat restoration. These types of fish habitat models are highly flexible and can be used with a variety of data, not just telemetry, and should be considered as an additional tool for fish habitat and fisheries managers alike.

Relative contribution of comorbid diseases to health-related quality of life in patients with Parkinson’s disease

BackgroundMultimorbidity is common in elderly people, and one of the major consequences of multimorbidity is low health-related quality of life (HRQoL). The aim of this study was to investigate the frequency of comorbid diseases in patients with Parkinson’s disease (PD) and to analyze their relative importance in HRQoL. The aim was also to examine agreement between the generic 15D questionnaire and the PD-specific Parkinson’s Disease Questionnaire (PDQ-8) to further validate 15D in the evaluation of HRQoL in patients with PD.MethodsPatients with PD (N = 551) filled a questionnaire on comorbid diseases, and the 15D questionnaire yielding a 15-dimensional health profile and a score representing the overall HRQoL. Self-organizing map was used for an unsupervised pattern recognition of the health profiles. Relative importance analysis was used to evaluate the contribution of 16 comorbid diseases to the 15D score. The agreement between 15D and PDQ-8 questionnaires was studied in a subset of 81 patients that were examined clinically.Results533 patients (96.7%) reported comorbid diseases. The most affected dimensions in the 15D questionnaire were secretion, usual activities, discomfort and symptoms, and sexual activity. Self-organizing map identified three patterns of health profiles that included patients with high, low or transition HRQoL. The transition subgroup was similar to low HRQoL subgroup in non-motor dimensions. Sixteen comorbid diseases explained 33.7% of the variance in the 15D score. Memory deficit, depression, heart failure, and atrial fibrillation had the highest relative importance. The intraclass correlation coefficient between the generic 15D and the PD-specific PDQ-8 was 0.642 suggesting moderate reliability.ConclusionsThe most marked differences in HRQoL were in the dimensions of secretion, usual activities, and sexual activity. Pattern detection of 15D health dimensions enabled the detection of a subgroup with disproportionately poor HRQoL in non-motor dimensions. The comorbid diseases affecting most to HRQoL were memory deficit and depression. The generic 15D questionnaire can be used in the evaluation of HRQoL in PD patients.

Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature.

To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [18F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients. We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion's volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression. The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS. Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.

Information needs preferences of Chinese colorectal cancer patients receiving chemotherapy: A discrete choice experiment

ObjectiveThe study aims to investigate the information needs and preferences of colorectal cancer (CRC) patients undergoing chemotherapy using a discrete choice experiment (DCE) to optimize and improve the information support strategy for these patients. MethodsBetween May and July 2023, 165 patients with CRC who were receiving chemotherapy at a single hospital in China completed the questionnaire. The survey instruments included a general information questionnaire, a DCE questionnaire, and the Brief Health Literacy Screening Scale. A conditional logit model was used with Stata 16.0 software to analyze patients' preferences. ResultsA total of 159 valid questionnaires were collected, and the questionnaire response rate was 96.4%. All 7 included attributes had an impact on patients' information needs preference (P < 0.05). Among them, information providers, knowledge content, and social support had high relative importance, which were 12.16%, 7.57% and 2.25%, respectively. Patients showed a preference for attending doctors (β = 1.9439, P < 0.05) and primary nurses (β = 1.7985, P < 0.05). Providing knowledge related to disease basis, treatment, and health promotion also had a significant impact (β = 1.6224, P < 0.05). ConclusionsHealthcare professionals should be the primary information source for patients and improve the accessibility of information by establishing professional information platforms or identifying reliable channels. It is recommended to provide continuous information on treatment and health promotion to CRC patients at various stages of chemotherapy. Attention should be paid to identifying and providing measures to alleviate the economic and psychological burden and to meet the social support needs of patients.

Risk factors and predictors of recurrence of febrile seizures in children in Nantong, China: a retrospective cohort study

BackgroundAlthough most children with febrile seizures (FS) have a favorable prognosis, some experience recurrence within 1–3 years. Age, peak temperature, and family history are now recognized as important risk factors for FS recurrence, yet studies in this area are lacking in China. This study aimed to investigate the risk factors for FS recurrence in children in Nantong, China, and to develop a prediction model.MethodsThis retrospective cohort study analyzed 463 children diagnosed with febrile seizures (FS) who presented to the Affiliated Hospital of Nantong University between January 2015 and June 2020. Basic information, disease characteristics, and laboratory and imaging data were collected. A follow-up survey was conducted one year post-discharge to assess the recurrence status of FS in children. Univariate logistic regression and random forest models were used to identify and rank the predictive ability of risk factors for recurrence.ResultsOf the 463 children with FS, 70 experienced recurrences within 1 year of discharge, resulting in a one-year recurrence rate of 15%. Age (OR = 0.61, 95% CI: 0.46, 0.80, P < 0.001), duration of the first episode (OR = 1.03, 95% CI: 1.00, 1.06, P = 0.040), and peak temperature (OR = 0.68, 95% CI: 0.47, 0.98, P = 0.036) were identified as independent risk factors for FS recurrence. Age had the highest relative importance in predicting FS recurrence, followed by the duration of the first episode, with an area under the ROC curve of 0.717.ConclusionYoung age and duration of the first seizure are important independent risk factors for FS recurrence and are key considerations for predicting recurrence. Further research is needed to confirm the potential use of Neutrophil–lymphocyte ratio (NLR) as a predictor of FS recurrence.

Challenging factors across industry players in implementing the green building concept in Malaysia

Abstract This study examines the obstacles to implementing the green building concept in Greater Kuala Lumpur, a relatively novel concept in Malaysia. The country currently lacks awareness regarding existing green building technologies. In 2007, a Zero Energy Building in Kuala Lumpur, constructed based on the Green Building Concept, faced challenges, ultimately unable to effectively reduce energy consumption after three years of efforts. The research focuses on evaluating internal factors and external factors that pose challenges to the Green Building Concept among industry players in Greater Kuala Lumpur. The study involved 100 respondents in the property and construction field, utilizing a cluster sampling technique. Survey results revealed that both external and internal factors act as barriers to the implementation of the green building industry in Malaysia. The Relative Important Index identified high cost and pricing factors as significant external challenges, with relative importance indices of 0.946 and 0.936, respectively. Internally, the lack of consumer demand emerged as the most formidable obstacle, boasting a high relative importance index of 0.936. Additionally, the study highlighted the lack of incentive support as a notable internal factor, with a relative importance index of 0.934. To address these challenges, the study recommends collective responsibility among all stakeholders in the property and construction industry, including architects, property agents, the government, universities, and clients. Establishing a collaborative ecosystem can foster the growth of the green building industry in Malaysia, promoting sustainability and contributing to economic development.

Multi-stage incentive contract design of aviation complex product multi-task delivery strategy

The increasing demand for aviation complex products makes complex product manufacturers pay more attention to delivery efficiency, and the efficiency and effect of delivery problems are the key factors that restrict delivery efficiency. A series of delivery strategy combinations of delivery subjects will affect delivery output and delivery efficiency. This study aims to design a long-term effective incentive contract, which stimulates the subjective initiative of the subject and then investigates the effect of the incentive contract on delivery efficiency under different combinations of delivery strategies. We consider the behavioral characteristics of each technical business department to expand the benchmark incentive model, such as the degree of perceived revenue fairness, the variation of the three relationships (neutrality, competition, and cooperation), and the level of technological transformation capability. The multi-stage incentive mechanism which combines explicit incentives and implicit incentives is developed to explore the optimal delivery strategies based on a benchmark model. The results show that incentive contracts can improve delivery efficiency and benefit delivery centers and technical business departments. The delivery decisions of the department are influenced by the closeness of department relationships and the fairness of assignment, and the departments tend to pay more effort in tasks with high relative importance and low effort cost. When the relative importance of tasks is equal to the ratio of marginal cost, a weak incentive zone in the delivery incentive contract exists. The incentive effect of reputation effect is obvious except for the last stage. Based on the designed incentive contracts, the subjective initiative of the participants can be effectively stimulated to empower complex product delivery.

Ethnomedicinal Plants and Herbal Preparations Used by Rural Communities in Tehsil Hajira (Poonch District of Azad Kashmir, Pakistan).

The present study emphasizes the importance of documenting ethnomedicinal plants and herbal practices of the local rural communities of Tehsil Hajira (Pakistan). The aim was to document, explore and quantify the traditional ethnomedicinal knowledge. Ethnobotanical data were collected using semi-structured questionnaires and analyzed using various quantitative indices. The results showed that 144 medicinal plant species from 70 families and 128 genera play an important role in herbal preparations. The most common type of preparation was powder (19.0%), followed by paste (16.7%), aqueous extract (15.7%), decoction (14.7%) and juice (11.0%). Fragaria nubicola (0.94) and Viola canescens (0.93) had the highest relative frequency of mention (RFC), while Berberis lycium (1.22) and Fragaria nubicola (1.18) had the highest use value (UV). Geranium wallichianum (85.5), Ligustrum lucidum (83) and Indigofera heterantha (71.5) were the most important species in the study area with the highest relative importance (RI) value. The diseases treated were categorized into 17 classes, with diseases of the digestive system and liver having the highest Informant Consensus Factor (ICF) value, followed by diseases of the oropharynx and musculoskeletal system. Important plants mentioned for the treatment of various diseases of the gastrointestinal tract are Zanthoxylum alatum, Berberis lycium, Mentha longifolia, Punica granatum, Rubus ellipticus and Viola canescens. New applications of rarely documented plants from this area are: Oxalis corniculata paste of the whole plant to treat vitiligo, Carthamus tinctorius flowers to treat chicken pox, Dioscorea deltoidea tuber powder to treat productive cough, Inula cappa root decoction to treat miscarriage, Habenaria digitata tuber juice for the treatment of fever, Viola canescens leaves and flowers for the treatment of sore throat and Achillea millefolium root and leaf juice for the treatment of pneumonia. These plants may contain interesting biochemical compounds and should be subjected to further pharmacological studies to develop new drugs. Traditional medicinal knowledge in the area under study is mainly limited to the elderly, traditional healers and midwives. Therefore, resource conservation strategies and future pharmacological studies are strongly recommended.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

Image denoising and model-independent parameterization for IVIM MRI

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versus b-value curve. Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial ‘pseudokernels’. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis. Main results. Image denoising improved blind image quality metrics, smoothed the signal versus b-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising. AUC parameters were more correlated with dose and had higher relative importance than exponential model parameters. Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versus b-value curve with model-independent parameters like the AUC and preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions

Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.

Investigating Canadian Engineering Students’ Perceptions of Graduate Attributes: Frequency, Criticality, and Relative Importance

This study investigates the perceptions of Canadian engineering students regarding the frequency and criticality of the 12 graduate attributes (knowledge, skills, values, and behaviors that engineering students are expected to demonstrate upon graduation) outlined by the Canadian Engineering Accreditation Board (CEAB). This study aims to assist engineering educators in gaining a better understanding of students’ expectations regarding how engineering competencies will be demonstrated in practice. This information can guide the improvement of engineering curricula and help engineering programs meet accreditation requirements for continuous enhancement. Descriptive and test statistics were used to analyze a quantitative survey administered to 340 undergraduate engineering students at a large Canadian university. Findings suggest that the students perceived the frequency and criticality of most graduate attributes differently. Individual and teamwork, communication, professionalism, lifelong learning and engineering tools, were viewed as more frequent than critical, while ethics and equity, impact of engineering, investigation, and design were perceived as more critical than frequent. The study also found that communication, individual and teamwork, and problem analysis were perceived as the graduate attributes with the highest relative importance (frequency multiplied by criticality), which is consistent with the literature.

Evaluating the role of caretaker‐rated personality traits for reproductive outcomes in a highly endangered Hawaiian corvid

AbstractPersonality studies are increasingly relevant and applicable to a broad range of conservation issues, including a role for selecting compatible mates in conservation breeding programs (CBPs). Choosing compatible breeding pairs is essential for maximizing reproductive success in CBPs, which provide assurance populations for species facing extinction and candidates for translocation to support wild populations. We investigated the value of caretaker‐assessed personality ratings of the critically endangered ‘Alalā (Hawaiian Crow, Corvus hawaiiensis) in predicting reproductive outcomes: nest quality, fertile egg production, and hatchability in the ‘Alalā CBP. Caretakers used well‐defined operational definitions to rate 22 personality traits for 69 male and 59 female ‘Alalā, achieving high inter‐rater agreement (ICC ≥ 0.85) for 8 traits – aggressive, bold/fearless, fearful, imprinted/abnormal, inept, submissive, territorial, and tendency to feather pluck (a stereotypic behavior) – used in a principal components analysis to distill intercorrelated variables into three principal components. While we did not find that personality influences nest quality or hatchability (based on limiting inferences to fixed effects with high relative importance scores; ≥0.8), we found some evidence that pairs with males that were more territorial/aggressive and fearful than their mates had a higher probability of egg fertilization, but there was substantial variability in this relationship, with confidence intervals overlapping zero. These personality traits may influence pair dynamics, enhancing compatibility, may predict better pair coordination on the nest, or both. Our results highlight the ability of caretakers to successfully identify ‘Alalā personality traits and provide some support for integrating personality into the pairing process; however, further research is needed to understand the value of ‘Alalā personality as a mate selection tool. The application of similar approaches to enhance CBP performance in other taxa is promising, but will require overcoming methodological challenges surrounding the complexity of data collection and interpretation of consistency among raters.

Public Heterogeneous Preferences for Low-Dose Computed Tomography Lung Cancer Screening Service Delivery in Western China: A Discrete Choice Experiment.

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) is an efficient method that can reduce lung cancer mortality in high-risk individuals. However, few studies have attempted to measure the preferences for LDCT LCS service delivery. This study aimed to generate quantitative information on the Chinese population's preferences for LDCT LCS service delivery. The general population aged 40 to 74 in the Sichuan province of China was invited to complete an online discrete choice experiment (DCE). The DCE required participants to answer 14 discrete choice questions comprising five attributes: facility levels, facility ownership, travel mode, travel time, and out-of-pocket cost. Choice data were analyzed using mixed logit and latent class logit (LCL) models. The study included 2529 respondents, with 746 (29.5%) identified as being at risk for lung cancer. Mixed logit model (MLM) analysis revealed that all five attributes significantly influenced respondents' choices. Facility levels had the highest relative importance (44.4%), followed by facility ownership (28.1%), while out-of-pocket cost had the lowest importance (6.4%). The at-risk group placed relatively more importance on price and facility ownership compared to the non-risk group. LCL model identified five distinct classes with varying preferences. This study revealed significant heterogeneity in preferences for LCS service attributes among the Chinese population, with facility level and facility ownership being the most important factors. The findings underscore the need for tailored strategies targeting different subgroup preferences to increase screening participation rates and improve early detection outcomes.

Survey on Aculops lycopersici and operational factors potentially affecting successful pest management among 50 tomato producers in Germany

Aculops lycopersici (Acari: Eriophyoidea) is a pest in tomato cultivation worldwide. In recent years, the number of reports of A. lycopersici infestations in tomato have increased in Germany. In the first half of 2019, a survey of 50 tomato producing farms was conducted to assess the occurrence of A. lycopersici and the impact this pest has on tomato cultivation in Germany. The participating farms represented ~ 3.5% of the 1448 farms in Germany with protected tomato production in 2019. Total tomato production area considered in the survey was 131.8 ha which corresponds to ~ 34% of the 385.63 ha of protected tomato production area in Germany in this year. A. lycopersici presence was reported by 33 of the 50 surveyed farms, within the last 5 years. Amongst these 50 participants it was the pest with the highest relative importance in terms of plant protection effort exerted. A. lycopersici occurrence was reported more frequently from production systems with a higher intensification. For instance, heating in cold months and a larger production area were considered intensification factors in this study. However, due to autocorrelation between intensification factors it was not possible to link increased occurrence to specific factors. As the intensification factors favouring A. lycopersici occurrence are more prevalent in integrated production, those farms faced A. lycopersici occurrence more often than the organic growers in this study. Plant protection strategies often combine broad treatments of sulphur with local abamectin treatments, removal of infested plant material and the introduction of natural enemies.

Modeling Current and Future Distribution of Cochlodina laminata in Eastern Europe under Climate Change

In this study, we focused on the eastern part of the Cochlodina laminata range. Although this door snail is widespread in Europe, even a widespread species may lose habitats if future climate change projections are realized. The range shift is one of the consequences of climate change. We applied SDM methods to model the current potential range of C. laminata and the range shift after 40 and after 80 years. We used climatic parameters as predictors. The annual mean temperature has the greatest impact on the modeling results (about 30–60% among models). The precipitation of the warmest quarter also had a high relative importance (about 15–40% among models). For future projections, we considered two shared socio-economic pathways (ssp245 and ssp585). We applied three algorithms: the generalized additive model (GAM), support vector machine (SVM) and multilayer perceptron (MLP) and ensemble prediction. Our projections showed a decrease in habitable area in the eastern part of the range of C. laminata in 40 and in 80 years. According to the forecast, the habitat suitable area will become more fragmented. The range shift with new suitable areas is expected toward the east direction.

Regression models for partially localized fMRI connectivity analyses.

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model-which uses only partial location information about pairs of ROI's-is comparably as high as the repeatability obtained using full location information. While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.