Total Variation Research Articles

Multi-trait selection in yellow kernel maize (Zea mays L.) genotypes using multi-trait genotype-ideotype distance index

The production of food crops is greatly influenced by maize, which is essential to global food security. Genetic variation and selection are key components in maize breeding that maximize genetic gain and productivity. The present study, 238 maize genotypes were investigated for fourteen quantitative traits to identify diverse and desirable genotypes for future breeding and varietal development programs. Significant genotypic effects were observed for grain yield and its attributes and other agronomic traits, indicating potential for genotype selection. Multivariate PCA analysis revealed that the first four PCs (70.1 % of total variation) effectively captured the considerable diversity within traits. Key traits such as flowering time, plant height, ear height, ear characteristics, and grain yield were essential in distinguishing between the genotypes analyzed. A recently introduced multi-trait-ideotype distance index (MGIDI) was used to predict the selection gain and identify the effectively performed genotypes by considering multiple traits. The MGIDI predicts significant desired genetic gains across all characteristics. Strengths and weaknesses of selected genotypes based on MGIDI provided insights into their overall suitability and factor contributions. The genotypes G32, G76, G163, G212, and G169 were identified as performing better using the MGIDI method, considering their strengths and weaknesses for the traits analyzed. MGIDI is a powerful tool that can help breeders effectively select the most desirable genotypes in maize.

Production Location Affects Tuber Shape Phenotype of Late-Season Russet Selections and Cultivars from the Northwest Potato Variety Development Program (NWPVDP)

Abstract Potato tuber length-to-width ratio (L/W) is an important trait for consumer visual acceptance, pack-out efficiency, French fry production, and waste recovery. In the U.S. Northwest Potato Variety Development Program (NWPVDP), L/W accounts for 13% of the overall postharvest score. Selections with L/W ≥ 1.8 are preferred for maximum raw-product recovery (≥ 7.6-cm-long fries). However, tuber shape often depends on production location. Our objectives were to (1) model the effects of production location on L/W using 26 years of historical data from the NWPVDP, and (2) quantify the variability in tuber shape phenotype of selected NWPVDP varieties across production locations (WA, ID, OR). A mixed linear model demonstrated that tuber L/W was strongly affected by location, year, and location-by-year interactions (40% of total variation), while variety and variety-by-location accounted for 43% of total variation. L/W of the 227-284-g tubers of twenty-two released varieties from the NWPVDP averaged 1.65, 1.70, and 1.92 for WA, OR, and ID, respectively, and ranged from a low of 1.51 and 1.53 (cvs. Payette Russet & Palisade Russet) to a high of 2.06 and 2.12 (cvs. Ranger Russet & Echo Russet) across locations. The round phenotype of Palisade was stable across locations, leading to consistently low fry recovery (84% FW). By contrast, Payette tubers were invariably round in WA and OR, but elongated when grown in ID. The elongated phenotypes of Echo and Ranger from all locations ensured maximum fry recovery (94 to 95% FW), despite the relatively high variation in L/W (9–13%) attributable to location. Collectively, our results suggest an opportunity for genetic selection of L/W that are stable across locations for market specific reductions in shape waste. Future work should focus on identifying key mechanisms which are impacted by production environment for improved stability of L/W. Furthermore, cultural management strategies should be optimized for L/W consistency and usability by all sectors of the potato industry.

Physically enhanced neural network based on projection map synthesis for chemiluminescence tomography reconstruction with limited views

In many combustion systems, data collection through optical windows is often hindered by fixed mechanical components that limit the number of available observation angles. This restriction poses a significant challenge to the accurate reconstruction of chemiluminescence tomography images with limited views. To address this limitation, we propose a novel projection interpolation approach for physically enhanced neural networks (PIPEN) to address this limitation. The PIPEN utilizes projection maps from two intersecting viewpoints and employs an interpolation network to estimate the projection maps for occluded views between these two angles. The interpolated and original projections are input into a physically enhanced neural network (PENN) to perform a volumetric tomography reconstruction. The PENN was designed to accommodate practical scenarios in which ground-truth data are unavailable. Furthermore, the loss function in PENN is enhanced with a total variation (TV) regularization term that mitigates noise and artifacts and improves the quality of the visual reconstruction. Experimental evaluations indicate that the PIPEN achieves a reconstruction performance comparable to that using a complete set of seven directions despite only utilizing projection maps from two orthogonal views. These results suggest that the PIPEN has significant potential for practical 3D flame reconstruction under constrained observation conditions.

Dietary and environmental factors affecting the dynamics of the gut bacteria in Tibetan Awang sheep (Ovis aries) across divergent breeding models

IntroductionTibetan Awang sheep (Ovis aries), indigenous to the Qinghai-Tibet Plateau, are highly adapted to high-altitude environment. However, knowledge regarding their gut bacterial composition remains limited.MethodsA comprehensive 16S rRNA highthroughput sequencing was performed on fecal samples from 15 Awang sheep under pure grazing, semi-captivity, and full captivity breeding models.ResultsOur results revealed that Firmicutes and Bacteroidetes were the most abundant bacterial phyla, while Christensenellaceae_R-7_group, Romboutsia, Rikenellaceae_RC9_gut_group, Ruminococcus, and Bacteroides were prevalent genera in the gut microbiota of Awang sheep. Meanwhile, the predominant presence of Bacteroides with increasing altitude of breeding locations indirectly demonstrates its crucial role in mediating energy acquisition among Awang sheep at high altitudes. Furthermore, PCoA and ANOSIM analysis exhibited significant differences in bacterial composition across all breeding models (r > 0.6, p < 0.001). Christensenellaceae_R-7_group, Romboutsia, and Ruminococcus were significantly abundant in the pure grazing breeding model, while Rikenellaceae_RC9_gut_group and Bacteroides were more abundant in the semi-captivity breeding model. An abnormally high abundance of Acinetobacter indicated a potential risk of Acinetobacter infection in the fully captive group. The environmental association analysis exhibited that meadows diet (R2 = 0.938, Pr[>r] = 0.001) and altitude (R2 = 0.892, Pr[>r] = 0.001) had significant effects on the dominant genera, explaining a substantial proportion of the total variation in community composition.DiscussionOur study indicated that breeding conditions significantly impact the gut microbiota of Awang sheep. The environmental association analysis underscores the importance of diet and altitude in shaping the gut microbiota of Awang sheep. The present findings provide insights into the microbiota dynamics of Awang sheep and offer guidance for their scientific husbandry management.

Validation and assessment of the arabic psychological first aid scale among physicians, nurses, and counselors in Jordan

BackgroundPsychological first aid (PFA) involves strategies to tackle problems that occur as a result of disasters. This study aimed to validate the Arabic version of PFA scale through validity (face, content, convergent, discriminant validity, and confirmatory factor analysis) and reliability (Cronbach’s alpha). Furthermore, it aimed to assess the perceived levels of knowledge, skills, and attitudes of PFA among healthcare providers (HCPs). Moreover, it determined the differences between HCPs based on their knowledge, skills, and attitudes of PFA.MethodsA sample size of 389 HCPs (physicians = 68, nurses = 173, and counselors = 148) was conveniently selected to participate during the period of 10 December 2023 to 10 February 2024 after completing the translated PFA scale into the Arabic language. Descriptive, translation, validation process, and reliability were conducted.ResultsThe translation process was satisfied. The content validity index was 0.91, yielding proper clarity of items. Three constructs were loaded with a total variation of 63.43%, indicating a proper model fit. The goodness-of-fit indices for the PFA model revealed that relative chi-square was 1.47, comparative fit index was 0.91, Tucker-Lewis index was 0.89, and the root mean square of error approximation was 0.046. Cronbach alpha values for knowledge, skills, and attitudes of PFA were 0.90, 0.89, 0.87, respectively. We found that counselors have more knowledge (64.4%), skills (73.4%), and attitudes (73.4%) compared to physicians and nurses. There are significant differences (p < 0.001) between the selected HCP groups, particularly for the counselors’ group in terms of knowledge, skills, and attitudes of PFA.ConclusionsThe Arabic PFA scale is valid and reliable among HCPs. The results highlight the emergent need to provide knowledge, skills, and attitudes of PFA interventions among medical professionals, particularly for physicians and nurses. The Arabic version of PFA helps all HCPs in Arab countries to assess, apply, and implement PFA interventions.

Subspecies delineation of Ziziphora clinopodioides complex (Lamiaceae) based on morphological, sequence-related amplified polymorphism and essential oils analyses

Ziziphora clinopodioides (Lamiaceae) is recognized as a native Iranian herb used for the treatment of diarrhea, intestinal gas, nausea, and vomiting. There is lack of consensus among some authors on its taxonomic boundaries at the subspecific level. To resolve this issue, the present study aimed to employ a combination of morphological, molecular, and phytochemical analyses. Firstly, 90 vegetative and floral characteristics were measured from 90 specimens of this species followed by conducting multivariate analyses on the 46 significantly discriminating morphological traits. Additionally, molecular markers (SRAP) were utilized generating 601 total loci of which 593 (98.31%) were polymorphic. The SRAP markers proved to be efficient, as demonstrated by the principal component analysis plot, which grouped all individuals according to their morphotypes, regardless of geographical location. According to the analysis of molecular variance (AMOVA), 10.69% of the total variations were associated with within-populations, while 89.31% were observed among populations. Furthermore, chemical analysis of essential oils (EOs) revealed the presence of Pulegone, 1,8-Cineole, and P-Mentha-3-n-8-ol as the major (&gt;10%) EOs constituents in Z. clinopodioides. Notably, eight EOs compounds (Menthone, B-Pinene, Terpinen-4-ol, Minth-furanone, a-Thujene, Tricyclene, Caryophyllene-oxide, and Germacrene-d) identified in this study, show reliable yields with chemotaxonomic significance within the complex. A high correlation between the genetic markers and EOs content (Pearson’s correlation coefficient, r = 0.6) suggests that genetic markers may be linked to the EOs constituents. Overall, our results obtained from a combination of morphological, molecular, and chemical analyses indicate that nine subspecies of the Z. clinopodioides complex could be decreased to four subspecies.

Frequency-domain learning-driven lightweight phase recovery method for in-line holography

Retrieving phase information from a single-intensity image poses a highly ill-posed inverse problem in the field of optical imaging, particularly in applications such as in-line holography, where phase information is critical for accurate reconstruction. Traditional methods exhibit limited applicability in dynamic scenes and struggle to ensure consistent reconstruction quality. Recent advancements in deep learning and the emergence of physics-informed methods have introduced novel strategies to address this challenge. However, despite reduced reliance on extensive datasets, the complexity of network architectures remains a significant barrier. In this study, we propose a frequency-domain learning-driven lightweight phase recovery method (FNet) based on complex-valued networks. By analyzing the characteristics of the optical diffraction process in the frequency domain, we design models with fewer parameters through frequency-domain learning. This lightweight approach effectively minimizes computational resource demands, facilitating efficient phase recovery in resource-constrained environments. Simulation and experimental results on multiple in-line holography datasets demonstrate that FNet achieves performance comparable to conventional methods and real-valued networks while utilizing significantly fewer parameters and computational resources, thereby validating its efficacy. Furthermore, the incorporation of complex-valued total variation regularization markedly reduces artifacts and enhances reconstruction quality in complex datasets. We contend that this work highlights the necessity for alignment between neural networks and physical models, thereby improving operational efficiency and expanding the applicability of phase recovery technologies.

A sharp lower bound for a class of non-local approximations of the total variation

A sharp lower bound for a class of non-local approximations of the total variation

Shuffle Model of Differential Privacy: Numerical Composition for Federated Learning

In decentralized scenarios without fully trustable parties (e.g., in mobile edge computing or IoT environments), the shuffle model has recently emerged as a promising paradigm for differentially private federated learning. Despite many efforts of privacy accounting for federated learning with many sequential rounds in the shuffle model, they suffer from generality and tightness. For example, existing accounting methods are targeted to single-message shuffle protocols (which have intrinsic utility barriers compared to multi-message ones), and are untight for the commonly used vector randomized response randomizer. As countermeasures, we first present a tight total variation characterization of vector randomized response randomizers in the shuffle model, which demonstrates over 20% budget conservation. We then unify the representation of single-message and multi-message shuffle protocols and derive their privacy loss distribution (PLD). The PLDs are finally composed by Fourier analysis to obtain the overall privacy loss of many sequential rounds in the shuffle model. Through simulations in federated decision tree building and federated deep learning, we show that our approach saves up to 80% budget when compared to existing methods.

An efficient and high-quality scheme for cone-beam CT reconstruction from sparse-view dat

Computed tomography (CT) is capable of generating detailed cross-sectional images of the scanned objects non-destructively. So far, CT has become an increasingly vital tool for 3D modelling of cultural relics. Compressed sensing (CS)-based CT reconstruction algorithms, such as the algebraic reconstruction technique (ART) regularized by total variation (TV), enable accurate reconstructions from sparse-view data, which consequently reduces both scanning time and costs. However, the implementation of the ART-TV is considerably slow, particularly in cone-beam reconstruction. In this paper, we propose an efficient and high-quality scheme for cone-beam CT reconstruction based on the traditional ART-TV algorithm. Our scheme employs Joseph's projection method for the computation of the system matrix. By exploiting the geometric symmetry of the cone-beam rays, we are able to compute the weight coefficients of the system matrix for two symmetric rays simultaneously. We then employ multi-threading technology to speed up the reconstruction of ART, and utilize graphics processing units (GPUs) to accelerate the TV minimization. Experimental results demonstrate that, for a typical reconstruction of a 512 × 512 × 512 volume from 60 views of 512 × 512 projection images, our scheme achieves a speedup of 14 × compared to a single-threaded CPU implementation. Furthermore, high-quality reconstructions of ART-TV are obtained by using Joseph's projection compared with that using traditional Siddon's projection.

Tackling artefacts in the timing of relativistic pulsar binaries: Towards the SKA

Common signal-processing approximations produce artefacts when timing pulsars in relativistic binary systems, especially edge-on systems with tight orbits, such as the Double Pulsar. In this paper, we use extensive simulations to explore various patterns that arise from the inaccuracies of approximations made when correcting dispersion and Shapiro delay. In a relativistic binary, the velocity of the pulsar projected onto the line of sight varies significantly on short timescales, causing rapid changes in the apparent pulsar spin frequency, which is used to convert dispersive delays to pulsar rotational phase shifts. A well-known example of the consequences of this effect is the artificial variation of dispersion measure (DM) with binary phase, first observed in the Double Pulsar 20 years ago. We show that ignoring the Doppler shift of the spin frequency when computing the dispersive phase shift exactly reproduces the shape and magnitude of the reported DM variations. We also simulate and study two additional effects of much smaller magnitude, which are caused by the assumption that the spin frequency used to correct dispersion is constant over the duration of the sub-integration and over the observed bandwidth. We show that failure to account for these two effects leads to orbital phase-dependent dispersive smearing that leads to apparent orbital DM variations. The functional form of the variation depends on the orbital eccentricity. In addition, we find that a polynomial approximation of the timing model is unable to accurately describe the Shapiro delay of edge-on systems with orbits of less than four hours, which poses problems for the measurements of timing parameters, most notably the Shapiro delay. This will be a potential issue for sensitive facilities such as the Five-hundred-meter Aperture Spherical Telescope (FAST) and the forthcoming Square Kilometre Array (SKA); therefore, a more accurate phase predictor is indispensable.

Phenotyping and diversity assessment of Dendrobium species by using DUS guidelines

Dendrobiums are sympodial epiphytic orchids belonging to the family Orchidaceae. and have an extraordinary variation in bloom shape, size and colour. They are widely recognized for their enchanting beauty and longlasting quality. Morphological characterization of Dendrobium species was carried out using the DUS guidelines developed by the Protection of Plant Varieties and Farmers Rights Authority (PPV and FRA) in New Delhi. A total of 15 Dendrobium species were characterized for 52 vegetative and floral traits. Among the 52 traits, 8 were monomorphic, 12 were dimorphic and 32 were polymorphic, indicating their potential for characterization. Principal component analysis showed 86.14% variation in the first 4 principal components with an eigenvalue &gt;1.0. The first and second principal components explained 41.43% and 19.45% of the total variation respectively. The dendrogram produced by a Ward analysis validated the Principal Component Analysis (PCA) findings. The Dendrobium species were grouped into 2 major clusters with three sub-clusters based on the correlation between the morphological traits among the species. Characterization established distinctiveness amongst the Dendrobium species. Breeders/farmers can use this study to distinguish between different species and apply for protection through the PPV and FR Authority, New Delhi. The trait-specific data generated while characterizing can be used to develop new inter-specific hybrids, which are common in orchids.

Ensemble noise properties of the European Pulsar Timing Array

Abstract The null hypothesis in Pulsar Timing Array (PTA) analyses includes assumptions about ensemble properties of pulsar time-correlated noise. These properties are encoded in prior probabilities for the amplitude and the spectral index of the power-law power spectral density of temporal correlations of the noise. Because multiple realizations of time-correlated noise processes are found in pulsars, these ensemble noise properties could and should be modelled in the full-PTA observations by parameterising the respective prior distributions using the so-called hyperparameters. This approach is known as the hierarchical Bayesian inference. In this work, we introduce a new procedure for numerical marginalisation over hyperparameters. The procedure may be used in searches for nanohertz gravitational waves and other PTA analyses to resolve prior misspecification at negligible computational cost. Furthermore, we infer the distribution of amplitudes and spectral indices of the power spectral density of spin noise and dispersion measure variation noise based on the observation of 25 millisecond pulsars by the European Pulsar Timing Array (EPTA). Our results may be used for the simulation of realistic noise in PTAs.

Drug toxicity prediction model based on enhanced graph neural network.

Prediction of drug toxicity remains a significant challenge and an essential process in drug discovery. Traditional machine learning algorithms struggle to capture the full scope of molecular structure features, limiting their effectiveness in toxicity prediction. Graph Neural Network offers a promising solution by effectively extracting drug features from their molecular graphs. However, existing graph learning algorithms fail to account for the interaction features between graph nodes and the indirect edges connecting them. This paper proposes an enhanced graph Neural Network algorithm that employs multi-view features for each node, capturing the feature interactions between each node and its neighbors. Additionally, the adjacency matrix is preprocessed to handle indirect edge interactions. A pooling technique is then applied to aggregate node features, followed by normalization and an activation layer. To further enhance the proposed algorithm, multi-scale attention is applied to learn graph features at different scales, utilizing weights to understand intricate relationships among node feature vectors. The proposed algorithm is evaluated using eight toxicity datasets, covering binary classification, multi-task multi-class, and regression tasks. For binary classification, the Tox21, AMES, Skin reaction, Carcinogens, and DILI datasets are tested. For multi-task multi-class, the ToxCast dataset is applied, and for regression, the LD50 and hREG datasets are tested. The proposed algorithm is compared with four well-known algorithms including Graph Convolution Network, Graph Attention Network, Graph Isomorphism Network, Enhanced Graph Isomorphism Network, and Graph Total Variation. For the classification task, the proposed algorithm achieves ROC-AUC scores of 0.752 for Tox21, 0.775 for AMES, 0.707 for Skin reaction, 0.845 for Carcinogens, 0.92 for DILI, and 0.691 for the ToxCast dataset. For the regression task, the algorithm attains mean square errors of 0.896 for the LD50 dataset and 0.766 for the hREG dataset. These results demonstrate an improvement over the compared algorithms across all evaluated datasets.

Machine learning for classifying chronic ankle instability based on ankle strength, range of motion, postural control and anatomical deformities in delivery service workers with a history of lateral ankle sprains.

Chronic ankle instability (CAI) frequently develops as a result of lateral ankle sprains (LAS) in delivery service workers (DSWs). Identifying risk factors for CAI is crucial for implementing targeted interventions. This study aimed to develop machine learning (ML) models for classifying CAI in DSWs with a history of LAS (DSWsLAS) and to identify key contributory factors. Exploratory, cross-sectional design. and participants: A total of 121 DSWsLAS were screened for eligibility among 289 DSWs. A total of 121 DSWsLAS were assessed for demographic characteristics, including ankle strength, range of motion, postural control, and anatomical deformities. Seven ML algorithms were trained and tested for classifying CAI. Principal component analysis (PCA) was used for feature extraction, and feature permutation importance (FPI) and Shapley additive explanations (SHAP) were employed to identify influential features. Model performances were assessed using area under the curve (AUC). To interpret the classifications, we used FPI and SHAP values. PCA derived 7 principal components (PCs) accounting for 83.5% of the total variation in the data. The support vector machine (SVM) algorithm achieved the highest classifying performance (AUC=0.817) among the ML models. FPI and SHAP revealed that PC1, PC2, PC5, and PC7 were the most influential features for classifying CAI in DSWsLAS. The SVM algorithm, utilizing PCA-derived factors related to body mass index and ankle muscle strength demonstrated high classifying performance for diagnosis of CAI in DSWsLAS, emphasizing the importance of considering multiple contributory factors in the prevention and management of this condition.

Hyperspectral image restoration via RPCA model based on spectral-spatial correlated total variation regularizer

Hyperspectral image restoration via RPCA model based on spectral-spatial correlated total variation regularizer

Pharmacological Treatment of Neuropsychiatric Symptoms in Huntington's Disease: A Systematic Review.

Studies focusing on the treatment of neuropsychiatric symptoms (NPS) in Huntington's disease (HD) are scarce and show a wide variation in design, outcome measures and methodological quality. The effectiveness of pharmacological treatment of NPS in HD has not been systematically reviewed so far. To provide an overview of the available literature on the effectiveness of pharmacological treatment of NPS in HD. PubMed and the Cochrane library were systematically searched for studies assessing the effects of pharmacotherapy of NPS, both as a primary and as secondary outcome. A risk of bias assessment was performed for each article. Fifteen articles qualified for critical evaluation: 10 randomized controlled trials (RCTs) (five placebo-controlled and five cross-over) and five open label studies. One RCT reported improvement of the overall NPS with nabilone treatment; another RCT reported that fluoxetine slightly improved irritability. Lower-level evidence from open studies suggests that the atypical antipsychotics cariprazine, olanzapine and risperidone may improve overall NPS, and that cariprazine, venlafaxine XR and olanzapine may improve depression. In addition, olanzapine may improve obsessive thoughts, aggression, anxiety and irritability. We conclude that although NPS in HD are common, hardly any clinical trials have addressed their treatment. As a result, convincing evidence that could guide clinical practice is lacking. More focused, and larger, multicenter trials focusing on NPS are urgently needed to generate the knowledge necessary to support the development of evidence-based clinical treatment guidelines.

Mesh Denoising and Inpainting Using the Total Variation of the Normal and a Shape Newton Approach

Mesh Denoising and Inpainting Using the Total Variation of the Normal and a Shape Newton Approach

Nondestructive testing of GFRP composites with compressive sensing-based terahertz single-pixel imaging

ABSTRACT Terahertz (THz) imaging technique has a wide range of applications, from industrial non-destructive testing (NDT) to various biomedical applications. Although the capabilities of terahertz radiation in defect detection are well-proven, the practical limitations have been associated with the long image acquisition time. Hence, to improve the image acquisition speed, the compressive sensing technique has been employed. However, the challenge is identifying the optimal modulation mask for compressive sensing as the image reconstruction metrics hugely depend on the mask. Hence, a systematic investigation of the different modulation masks has been done to reconstruct THz images of glass fibre-reinforced polymer (GFRP) composites using TVAL3 (Total Variation minimization by Augmented Lagrangian and ALternating direction Algorithm). The image reconstruction quality has been studied using mean square error, peak signal-to-noise ratio, and structural similarity index. From the results, it can be noticed with a 0.3 sampling ratio, reliable reconstruction of the THz image is possible, saving 70% of the image acquisition time. Further, the discrete cosine transform (DCT) mask is ideal for high frame rate NDT in low and medium noise scenarios. However, the Bernoulli basis offers high resistance to noise by resulting in the best image reconstruction results at high noise cases, outperforming the DCT.

Communicative health literacy with physicians in healthcare services– results of a Hungarian nationwide survey

BackgroundIn an efficient and effective healthcare delivery, good communication plays an essential role. The communicative health literacy (COMM-HL) of the patients is an important attribute, but the number of validated COMM-HL assessment tools is low, and they do not cover all aspects of COMM-HL. That’s why a new scale has been developed within an international collaboration. Our aims are to check the measurement properties of the Hungarian version of this COMM-HL questionnaire, to describe the COMM-HL of the Hungarian adult population and to investigate its determinants.MethodsA total of 1205 adults completed the telephone interview in 2020 as part of the European Health Literacy Population Survey 2019–2021. The questionnaire included items on sociodemographic data, self-perceived health, social support and COMM-HL. The questionnaire’s measurement properties were assessed using Cronbach’s alpha, Spearman-Brown, and item-total correlation coefficients, while the construct validity was investigated with principal component (PCA) and confirmatory factor analysis. The determinants of the COMM-HL were investigated by linear regression.ResultsBoth the value of Cronbach’s alpha and the Spearman-Brown correlation were 0.87. The items belonged to one factor and 62.2% of the total variation was explained by this factor based on the PCA. The fit indices indicated that the one-factor structure of the six-item questionnaire exhibited a good fit for the data. The mean score on the COMM-HL scale was 86.8. The easiest task was explaining health concerns while getting enough time in the consultation was rated as the most difficult one. COMM-HL was lower among females, while it was higher among people aged 66–75 years (compared with 18–25 years), those with greater social support and those without financial deprivation.ConclusionsThe questionnaire can be characterized with good internal consistency and the structure of the COMM-HL questionnaire was best explained as a one factor model. In consultations with patients, the effectiveness of communication should be improved, so that patients do not feel that there is not enough time for consultation. At-risk groups with low COMM-HL need special attention during the interactions and the role of social support has to be clarified, too.