MDSC-DeepLabv3+ outperformed the compared models with mPA and mIoU of 97.55% and 94.84%, respectively. Compared to the baseline DeepLabv3+, MDSC-DeepLabv3+ demonstrated significant improvements in mPA and mIoU and reduced Params, FLOPs, and inference time, making it suitable for deployment on edge devices and real-time inference. The average relative errors of breakage and impurity ratios between estimated and measured values were 11.3% and 6.5%, respectively. Overall, this novel approach enables high-precision, efficient, and intelligent detection of breakage and impurity ratios for raw sugarcane.Broken cane and impurities such as top, leaf in harvested raw sugarcane significantly influence the yield of the sugar manufacturing process. It is crucial to determine the breakage and impurity ratios for assessing the quality and price of raw sugarcane in sugar refineries. However, the traditional manual sampling approach for detecting breakage and impurity ratios suffers from subjectivity, low efficiency, and result discrepancies. To address this problem, a novel approach combining an estimation model and semantic segmentation method for breakage and impurity ratios detection was developed. A machine vision-based image acquisition platform was designed, and custom image and mass datasets of cane, broken cane, top, and leaf were created. For cane, broken cane, top, and leaf, normal fitting of mean surface densities based on pixel information and measured mass was conducted. An estimation model for the mass of each class and the breakage and impurity ratios was established using the mean surface density and pixels. Furthermore, the MDSC-DeepLabv3+ model was developed to accurately and efficiently segment pixels of the four classes of objects. This model integrates improved MobileNetv2, atrous spatial pyramid pooling with deepwise separable convolution and strip pooling module, and coordinate attention mechanism to achieve high segmentation accuracy, deployability, and efficiency simultaneously. Experimental results based on the custom image and mass datasets showed that the estimation model achieved high accuracy for breakage and impurity ratios between estimated and measured value with  In China, sugarcane-based sugar production reached 4.6 million tons in 2022, which is 4.3 times that of beet sugar . In receTo address the aforementioned problem, an estimation model was established, and machine vision technology was employed to provide a more objective, efficient, accurate, and intelligent approach for quantifying the cane, broken cane, and impurities, as well as the ratios of breakage and impurity. This enables seamless integration with the sugarcane harvesting and sugar processing stages. Both cane and broken cane can be used as raw materials, but broken cane is considered in mass deduction by sugar refineries because it results in the loss of sugar content and impacts the quality of the final sugar product. The sugarcane top, leaf, root, sand, gravel, and soil and so forth are collectively referred to as impurities . AdjustiEstimation models and machine vision technology have been widely used for the detection and monitoring of impurities in grain crops such as rice, wheat, and corn. For example, Detection of impurities in cash crops such as soybean, cotton, and walnut during harvesting or processing has also been extensively studied in recent years. k nearest neighbors (KNN), to classify cane and its impurities. In recent years, researchers have also achieved notable progress in the field of impurity detection in sugarcane. From the perspective of recognition tasks, the aforementioned studies can be categorized into three types: image classification, object detection, and semantic segmentation. Image classification-based approaches  cannot cstem. Popular and widely applied deep learning (DL)-based semantic segmentation approaches have achieved excellent results in image processing in agriculture . Among tThis study aims to address the detection of breakage and impurity ratios in raw sugarcane. The specific research content of this study includes: (1) Designing a machine vision-based acquisition platform for online image collection of raw sugarcane  and impurities . Custom datasets of masses and corresponding images were constructed. (2) Establishing a normal fitting model to determine the mean surface density of each class based on measured masses and extracted pixels. Additionally, an estimation model was developed to assess the ratios of breakage and impurity using the estimated mass of each class, along with their pixels and fitted mean surface density. (3) Developing a MDSC-DeepLabv3+ model for accurate segmentation of raw sugarcane and impurity pixels based on DeepLabv3+. The model was further improved by incorporating improved MobileNetv2, atrous spatial pyramid pooling (ASPP) with deepwise separable convolution (DSC) and strip pooling (SP) named ASPP_DS, and coordinate attention (CA) mechanism to enhance segmentation accuracy, reduce parameters, and optimize inference time. (4) Conducting experiments to verify the accuracy of the proposed estimation model in assessing breakage and impurity ratios, and evaluate the capability of MDSC-DeepLabv3+ in rapidly and accurately identifying the pixels of cane, broken cane, top, and leaf. Comprehensive experimental results show that the average relative errors of breakage and impurity ratio between predicted values and measured values are low. These findings have significant implications for the development of intelligent detection and cleaning system for sugarcane impurity.22.12.1.1In order to provide a stable environment and meet the continuous image acquisition requirements that align with the raw sugarcane convey process in the sugar refinery, a dedicated platform for image acquisition of raw sugarcane and impurities was designed, as shown in The portable energy storage is used to supply power to the platform, especially in situations where electricity supply is limited. The interior of the image acquisition room, as depicted in FOVH) and vertical (FOVV) dimensions are set to the belt width of 450mm and indoor length of 600mm, respectively. The selected industrial camera has a horizontal (CMOSH) and vertical (CMOSV) size of the image sensor as 7.6×5.7mm, and the working distance (DW) is set to 490mm considering the inner height of the acquisition room. The imaging principle of this acquisition platform is illustrated in CMOSH, CMOSV and DW, the field of view can be determined using Eq.(1).f = DW × (CMOSV/FOVV) = 490 × (7.6/3450) = 8.27mm, and the MVL-MF0828M-8MP industry lens is selected.As a result, the focal length is determined by 2.1.2The image and mass acquisition of raw sugarcane and impurities took place in the sugarcane unloading workshop of Junshi sugar refinery in Jijia Town, Leizhou City, Guangdong Province. The data collection period started from the middle of February to the end of the month in 2023, coinciding with the local sugarcane harvesting season. For this study, large-scale cultivated sugarcane variety “Yuetang 159” was selected. The raw sugarcane samples were randomly collected from different machine-harvested vehicles at various time intervals throughout the day using a loader. These samples were then manually placed on the conveyor belt of the acquisition platform for image collection. In total, 910 RGB 8-bit photos with jpg format and a resolution of 1624×1240 were captured. Each image contains four categories: cane, broken cane, top, and leaf, as shown in 2.1.3The original dataset consists of 910 images containing cane, broken cane, top, leaf, and the background. These images were manually labeled and colored using the image annotation tool Labelme. The labeled regions of the five classes of objects were used to evaluate the training loss of intersection over union (IoU) between predicted bounding boxes and ground truth. The RGB values for cane, broken cane, top, and leaf were set to , , , and , respectively, while the background was set to . To ensure model performance validation and testing, the dataset was randomly divided into training (546 images), validation (182 images), and test sets (182 images) with a ratio of 6:2:2.In order to improve the generalization of the model, data augmentation techniques were applied to the training, validation, and test sets separately. Techniques such as random rotation, affine transformation, fogging, Gaussian noise, median filtering, and cutout were used to enhance the original images. After augmentation, the images were checked and corrected using Labelme to ensure accurate labeling of each class in every image. The annotated images were stored in the PASCAL VOC format and named Raw Sugarcane and Impurity (RSI). The label counting algorithm was used to calculate the number of labels in the RSI images, and the corresponding statistics are shown in 2.22.2.1In general, previous estimation models that are based on image pixels for assessing the mass of crops  often assume that the surface density (mass/pixel) of each crop category remains stable across different images . HoweverCP, BP, TP andLP represent the number of pixels of cane, broken cane, top, and leaf in each image sample, respectively, and their corresponding masses are denoted as CM, BM, TM and LM, respectively. The spatial distribution of the surface density for raw sugarcane, including cane and broken cane, as well as the top and leaf, is presented in The analysis of surface density distribution was conducted using 300 samples of mass data and the corresponding images for each category. The OpenCV threshold function was utilized to count the number of pixels in each category. Let μ for each category was then obtained through the fitting process, and the results are demonstrated in R2 are greater than 0.95, indicating high fitting accuracy.To address the scattered surface density of broken cane, top, and leaf, a Gaussian distribution probability density function was used to fit the frequency histograms of surface density for each category. The mean surface density cμ = 1.52E-3, Bμ 7.4E-4, Tμ = 8.8E-4 and Lμ = 3E-5 with unit g/pix, respectively. Moreover, it is evident that the mean value of cane cμ is approximately twice the mean value of broken cane Bμ and top surface density Tμ, and cμ is more than fifty times Lof μ. The mass error of leaf has little effect on the overall mass error. Therefore, when establishing the estimation model, the accuracy of the estimated mass of cane should be ensured first, followed by broken cane, top, and finally leaf. This approach is consistent with the low deduction percentage setting (as low as 0.2%) employed by sugar refineries for leaf impurities.The fitting results showed that the mean surface density of cane, broken cane, top, and leaf are 2.2.2CM’, broken cane BM’, top TM’, and leaf LM’ based on their pixels can be expressed as follows:On the basis of the mean values of surface density given in 2R value of 0.983. This indicates that the linear regression model is capable of explaining the numerical relationship between the measured mass and the estimated mass of the cane. The 2R value for broken cane and top are 0.894 and 0.88, respectively, demonstrating the regression model’s good fitting capability. The 2R value for the leaf is 0.764 suggesting that the model can still adequately fit the relationship between the measured mass and the estimated mass. In addition, the results of ANOVA in F<0.01 between estimated cane, broken cane, top, and leaf and their measured values proves a high correlation.Furthermore, a linear regression of the estimated and measured mass was conducted to validate the accuracy of the mass estimation model defined by Eq.(1)-(4). Based on the distribution characteristics shown in BR) and impurity (IR) is defined as:Based on the mass of each category, the ratios of breakage -(6) with estimated mass M'C, M'B, M'T and M'L. Thereby Eq.(5)-(6) can be taken as the estimation model for breakage and impurity ratios.Where 2.32.3.1C, M'B, M'TM', LM', BR' and IR' calculation, a segmentation model, MDSC-DeepLabv3+, was developed for the intelligent extraction of pixels of cane CP, broken cane BP, top TP, and leaf LP in each image sample. MDSC-DeepLabv3+ is an improvement upon the DeepLabv3+. The DeepLabv3+ comprises two modules: an encoder and a decoder , which mainly consists of dimensionality expansion, feature extraction and dimensionality compress three main steps. The MobileNetv2 employs 3×3 depthwise convolution (Dwise) and 1×1 convolution to construct two IRBs with s= 1, s=2 . In caseTo reduce computing costs and memory usage, this study utilizes the first 8 layers of the MobileNetv2 model. This choice is made because starting from the 9th layer, the number of output channel increases to 1280, leading to higher computing resource consumption. To minimize the loss of down-sampling information while increasing receptive field, the stride of the 7th layer is modified to 1 .t is the expansion factor, c is the output channel, n is the number of repetitions of bottleneck, s is the first module strid, and r is dilation rate. When dilation rate of 1 results in atrous convolution being equivalent to a regular convolution. This design achieves a balance between computational resource consumption and network performance requirements.Furthermore, dilated convolutions with a factor not exceeding 1 are utilized to replace conventional convolutions. According to research by 2.3.3CR×H×W is the input tensor, C denotes the number of channels, H denotes the height, and W denotes the width. First, the input X is pooled horizontally and vertically to obtain hy ϵ CR×H×1 and vy ϵ CR×1×W, respectively. Then, the feature maps are expanded to the same resolution C×H×W as the input X using a 1D convolution with a kernel size of 3×3 to obtain the expanded hy, vy. Next, the expanded feature maps are fused to obtain a final representation.To better handle the segmentation of broken cane and top with irregular and complex shapes, a lightweight strip pooling layer was added in parallel to DSC in the ASPP. This allows for more efficient acquisition of information from a large receptive field, facilitating the collection of remote contextual information from different spatial dimensions by ASPP. Strip pooling utilizes a pooling kernel (rectangular area) that performs pooling operations along the horizontal and vertical dimensions. The structure of strip pooling  is shownFinally, after a 1 × 1 standard convolution and a sigmoid layer, the final output Z of strip pooling is obtained by multiplying the corresponding elements with the original input.σ is the sigmoid function, and f is the 1×1 convolution, y is feature fusion results.where Scale  is the element-level multiplication, i,j),1≤i≤H,1≤j≤W corresponds to the result of strip pooling of the horizontal and the vertical pooling window in the input tensor. By repeatedly applying the aggregation process using long and narrow pooling kernels, the ASPP_DS module can efficiently capture information from a wide receptive field throughout the entire scene. Due to the design of the elongated and narrow shape of the pooling kernel, it not only establishes remote dependency relationships between regions distributed discretely but also focuses on capturing local detailed features.The element of specified location in the output tensor , hybrid attention (e.g. CBAM), temporal attention (e.g. GLTR), branch attention (e.g. SKNet), and position attention mechanisms (e.g. CA). These attention mechanisms have been widely applied in fields such as object detection  and imagThe CA not only models channel relationships but also utilizes positional information to capture long-range dependencies . TherefoIntroduction of CA before low feature processing and after the features fusion of ASPP_DS is beneficial in fully utilizing positional information. This allows the model to accurately capture the spatial relationships and contextual information of the target, thus improving the accuracy of sugarcane and impurity phenotype segmentation in denser images.33.1CM, BM, TM and LM, along with the number of pixels for each category manually labeled in the selected 285 images  were obtained. Then, estimated masses of CM', BM', TM' and LM' for the four categories were determined based on the mean surface density Cμ, Bμ, Tμ and Lμ according to Eq.(1)-(4). Next, the measured and estimated ratios of breakage and impurity were obtained according to Eq.(5)-(6) based on the measured and estimated masses. Finally, the measured breakage and impurity ratios were linearly fitted with the estimated breakage and impurity ratios, and the fitting results are shown in The effectiveness of estimation model for breakage and impurity ratios defined in Section 2.2.2 was validated by fitting estimated and measured value. First, the measured mass of cane, broken cane, top, and cane leaf 2R values are as high as 0.976 and 0.968, respectively. In addition, the results of the ANOVA presented in F<0.01. Therefore, it is feasible to utilize the fitted surface density to estimate mass for each category and furthermore predict the breakage and impurity ratios for raw sugarcane.It can be observed that the fitting 3.23.2.1The semantic segmentation categories considered in this study are background, cane, broken cane, top, and leaf. In the process of sugarcane harvesting, raw sugarcane is primarily composed of cane, with cane tops and leaves present as impurities to a lesser extent. Broken cane represents the category with the lowest representation, leading to an extreme class imbalance. Consequently, this often leads to imbalanced positive and negative samples, along with varying sample difficulties. Therefore, this study utilizes the Focal Loss function as the primary loss function to address the imbalance between easy and difficult samples, facilitating better parameter optimization during the backpropagation process . In additp is the confidence value of the sample category prediction. γ is an adjustable parameter, and the default is 2.Where The Dice loss for multi-objective segmentation is defined as.N is the number of samples, c is the target class, and i,jp is the softmax output of class j target class; gt is the ground-truth label of class j target, and jW is the weight of the objective of class j, jW = 1/j.Where, The experiments were conducted on a server in the lab with the configuration shown in In order to comprehensively evaluate the performance of the proposed and comparative semantic segmentation models, three aspects of each model, namely accuracy, deployability, and efficiency, are comprehensively evaluated. The commonly used mIoU and mPA were utilized as accuracy evaluation metrics. And the model deployability was evaluated using model parameter quantity (Param) and model computation volume floating point operations (FLOPs). Efficiency was evaluated using inference time for each image. The metrics of IoU, mIoU and mPA which is represented by the following Eq. (7)-(9), respectively.c denotes the number of categories, so c=4 , ijP or jiP denotes the number of category prediction that is incorrect, while iiP denotes the number of correct predictions made by categories.Where 3.2.2The size of the input image is a crucial factor affecting the model’s performance. Increasing the image size enhances accuracy by preserving semantic information for small targets and preventing information loss caused by low-resolution feature maps. However, excessively large image sizes can lead to reduced detection accuracy due to the limited receptive field imposed by the fixed network structure. This, in turn, diminishes the network’s ability to accurately predict targets of various scales . In pracThe segmentation results of models using different loss functions are displayed in 3.2.3To verify the effectiveness of the three improvements, including improved MobileNetv2, ASPP_DS and CA presented in Section 2.3, the following 7 models were constructed according to the control variable method, with a downsampling factor of 8.DeepLabv3+_base: MobileNetv2 replaced the backbone Xception in DeepLabv3+.M-DeepLabv3+: MobileNetv2 in DeepLabv3+_base was enhanced with atrous convolution operation.MDS-DeepLabv3+: ASPP_DS replaced ASPP module in M-DeepLabv3+.MC1-DeepLabv3+: CA was applied independently before 1×1 Conv of low-level features by the decoder in M-DeepLabv3+.MC2-DeepLabv3+: CA was applied independently after the fusion of ASPP in M-DeepLabv3+.MC-DeepLabv3+: CA was added separately before 1×1 Conv the low-level features and after the fusion of ASPP features in M-DeepLabv3+.MDSC-DeepLabv3+: CA was added separately before processing the low-level features and after the fusion of ASPP_DS features in MDS-DeepLabv3+.In order to visually demonstrate the improvement of the models, Grad-CAM  was usedIn 3.2.4To further validate the superiority of the proposed model MDSC-DeepLabv3+, comparative experiments were conducted using the RSI dataset under the same experimental conditions. The compared models include UNet, PSPNet, SegFormer-B0, and the baseline DeepLabv3+. Previous research results have shown that UNet  and PSPNIt can be seen that the accuracy of MDSC-DeepLabv3+ surpasses that of the aforementioned four models with significant improvements. Specifically, the mIoU of MDSC-DeepLabv3+ is higher by 0.81, 5.22, 12.47, and 0.28 compared to UNet, PSPNet, SegFormer-B0, and DeepLabv3+, respectively. Moreover, the mPA of MDSC-DeepLabv3+ reaches an impressive 97.55%, which outperforms UNet, PSPNet, SegFormer-B0, and DeepLabv3+ by 0.69, 2.7, 7.76, and 0.34, respectively. These remarkable improvements can be attributed to the adoption of the advanced DeepLabv3+ as the basic model, coupled with the enhancements introduced through strip pooling and CA. Strip pooling plays a crucial role in collecting remote contextual information from different spatial dimensions and addressing the issue of information loss resulting from the atrous convolution operation in DeepLabv3. On the other hand, CA efficiently utilizes positional information, enabling accurate capturing of the spatial relationships and contextual information of the detected cane, broken cane, top, and leaf.In terms of deployability, MDSC-DeepLabv3+ demonstrates remarkable reductions in Params and FLOPs when compared to UNet, PSPNet, and DeepLabv3+. Specifically, it reduces Params by 83.65%, 91.29%, and 90.35%, and FLOPs by 89.49%, 59.9%, and 66.37% compared to UNet, PSPNet, and DeepLabv3+ respectively. This significant reduction in model size and computational complexity makes MDSC-DeepLabv3+ highly efficient and resource-friendly. Moreover, MDSC-DeepLabv3+ achieves impressive segmentation efficiency, with a recognition speed of only 13.85ms per image. This inference time per image is far less than the above three models, with reductions of 48.97%, 10.18%, and 43.31%, respectively. This indicates that MDSC-DeepLabv3+ is able to perform fast and accurate segmentation, making it highly suitable for real-time applications. Although SegFormer-B0 may have some advantages in terms of deployability, its accuracy is much lower compared to MDSC-DeepLabv3+ (89.79% vs. 97.55%). The reason for this superior performance is the utilization of the improved lightweight MobileNetv2, which replaces Xception in DeepLabv3+, leading to an efficient and accurate model overall. In summary, the proposed MDSC-DeepLabv3+ outperforms the compared four models in the task of segmenting sugarcane and impurities, offering a winning combination of high segmentation accuracy, deployability, and recognition speed.Instances of the results obtained using the aforementioned segmentation models are illustrated in 3.3The breakage and impurity ratios of raw sugarcane were estimated using the estimation model presented in Section 2.2 and the MDSC-DeepLabv3+ segmentation model presented in Section 2.3. These estimated values were then compared with the measured breakage and impurity ratios obtained through manual weighing to assess the effectiveness of the proposed method.First, a subset of 25% (70) of the images was randomly selected from the mass dataset with 300 samples. The MDSC-DeepLabv3+ model was applied to semantically segment the selected 70 images and determine the number of cane, broken cane, top, and leaf pixels for each image. Then, corresponding masses were estimated using Eq.(1)-(4), based on the mean values of the surface density for each category obtained through normal fitting. The ratios of breakage and impurity were calculated according to the estimation model defined in Eq.(5)-(6) based on the estimated masses. Finally, the measured breakage and impurity ratios were determined using the measured mass and the relative errors between the estimated and measured results were calculated. Additionally, the visualization of measured and estimated ratios of the 70 samples is depicted in 4In practice, objective, efficient, accurate, and intelligent detection of breakage and impurity ratios is an urgent requirement in the sugar refinery. Therefore, this study developed a novel approach combining the estimation model and MDSC-DeepLabv3+ segmentation network to tackle this problem. First, a machine vision-based acquisition platform was designed, and custom image and mass datasets of raw sugarcane and impurities were constructed. Then, estimation model was built to assess the ratios of breakage and impurity, considering the variation of surface density for the four categories of objects. Finally, the MDSC-DeepLabv3+ segmentation network dedicated to the detection of cane, broken cane, top, and leaf was developed. It effectively incorporated improved MobileNetv2, ASPP_DS, and CA based on DeepLabv3+ to enhance segmentation accuracy, reduce parameters and inference time. The analysis of the experimental results leads to the following conclusions:2R of 0.976 and 0.968, respectively.The breakage and impurity ratios obtained through estimation model based on normal fitted surface density exhibit high accuracy, with corresponding The proposed MDSC-DeepLabv3+ achieved superiority considering segmentation accuracy, deployability, and efficiency simultaneously. The mPA and mIoU achieved by MDSC-DeepLabv3+ were as high as 97.55% and 94.84%, respectively, surpassing the baseline DeepLabv3+ by 0.34 and 0.28. This improvement in accuracy was accomplished with 38.12M, 93.73G, and 10.58ms reduction in Params, FLOPs, and inference time, respectively, making it advantageous for deployment on edge devices and real-time inference.cane is due to their burr and ambiguous boundaries, resulting in a higher average relative error of the breakage ratio.The estimated data obtained according to the approach developed in this study fit the manually obtained breakage and impurity ratios with average relative errors of 11.3% and 6.5%, respectively. The lower segmentation accuracy of broken The raw sugarcane not only includes top and leaf impurities but also contains other impurities like dispersed root whiskers. The upcoming research will emphasize mechanical cleaning of sand, gravel, soil, and similar substances. Additionally, a pivotal aspect of the forthcoming study will involve counting sugarcane roots and estimating their quality through object detection.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.XL: Methodology, Writing – original draft, Writing – review & editing. ZZ: Funding acquisition, Methodology, Writing – review & editing. SL: Methodology, Writing – review & editing. TL: Data curation, Investigation, Visualization, Writing – original draft. JZ: Data curation, Visualization, Writing – original draft. TN: Visualization, Writing – original draft. CJ: Investigation, Writing – original draft.


Background: Both lower urinary tract symptoms (LUTS) and excessive daytime sleepiness (EDS) could negatively impair the patients’ quality of life, increasing the sensitivity to psychological distress that results in mental health disorders. The relationships of both urinary and respiratory domains with psychological distress in obstructive sleep apnea patients is still underestimated. Methods: This study was a post hoc analysis of a web-based Italian survey, which included 1998 participants. Three hierarchical multiple linear regression analyses with psychological distress as dependent variable were performed on the study of 1988 participants enrolled in the final analysis. Cohen’s f2 was used for the assessment of the effect size. Results: From the hierarchical multiple linear regression analyses, it emerged that the final statistical model  for all dimensions accounted for 16.7% of the variance in psychological distress, with a medium effect size (f2 = 0.15). Conclusions: People reported psychological distress was impaired by the presence of LUTS and EDS. Specifically, our study showed that higher levels of distress were scored especially in young women exhibiting urinary symptoms and with high values of daytime sleepiness. Excessive daytime sleepiness (EDS) is defined as “the inability to stay awake and alert during the major waking episodes of the day, resulting in unintended lapses into drowsiness and sleep” ,2. ConceDistress is defined as a negative psycho-physical response to critical situations that breaks into the life of the individual, who experiences an imbalance between the magnitude of the event and the perceived resources available, leading to mental health endangering ,11,12.To cope with the stressors, individuals must bring into play capacities and resources that go beyond the normal repertoire, but this process may fail and give rise to complex symptoms such as mood disorders and anxiety ,14,15. BSpecifically, sleep disturbance (including EDS and/or sleep duration) plays a significant role in the assessment, pathogenesis, and treatment of mood disorders and related-stress . People This study was a post hoc analysis of a web-based Italian survey, which included 1998 participants. Firstly, the survey was administered via Google Forms between 17 July and 31 October 2022 . The parParticipants could be enrolled in the survey if they satisfied the following inclusion criteria: (1) being at least 18 years old ; (2) having experienced snoring or apnea during the sleeping, with a suspicious diagnosis of OSAS; (3) having experienced excessive daytime sleepiness, with a suspicious diagnosis of OSAS; and (4) not having experienced snoring or apnea during the sleeping but having been screened for OSAS. A total of 1998 participants took part in the survey. Among these, 10 did not satisfy at least one of the inclusion criteria. Thus, the final sample was composed of 1988 Italian participants.The following sociodemographic and clinic variables were collected: age, gender (men vs. women), weight, height, body mass index (BMI), comorbidities , and smoking history (yes for “past and current smokers” vs. no for “never smoker”).Psychological distress was evaluated with the Kessler Psychological Distress Scale (K-10) , a 10-itThe subjective perceptions of EDS were measured through the Epworth Sleepiness Scale (ESS) . The ESSThe urinary functioning was measured through the validated International Prostate Symptom Score (IPSS) questionnaire. The IPSS is a 7-item questionnaire, plus 1 question relating to the overall QoL, assessing the severity of LUTS ,30. It cWe first analyzed participants’ characteristics through descriptive statistics and bivariate correlations between study variables with Pearson’s coefficient. Then, we performed three hierarchical multiple linear regression analyses with psychological distress as dependent variable. In the tested models we entered age, gender, and BMI as covariates in step 1, smoking and comorbidities in step 2, and daytime sleepiness and urinary symptoms in step 3. Cohen’s f2 was used for the assessment of the effect size, with f2 ≥ 0.02, f2 ≥ 0.15, and f2 ≥ 0.35 representing respectively small, moderate, and large effect sizes . FurtherFull socio-demographic and clinical characteristics of 1988 participants are reported in Pearson correlations . Specifically, the overall participants were young (with a mean age of 34.11 ± 10.45), overweight (mean BMI of 26.20 ± 21.17), and smokers (nearly one-third). According to their medical history, participants are generally healthy with less than half affected by the most prevalent comorbidities. Specifically, 2.2%, 4.6%, 5%, 10.4% of participants were affected by diabetes, respiratory disease, arterial hypertension, and dysthyroidism, respectively. As to the significance of covariate variables, we found that being young and woman was associated with higher scores on psychological distress. We underlined that the survey took place in the post COVID-19 era, reflecting its consequences. Indeed, during the COVID-19 pandemic the feeling of anxiety and depression increased exponentially, specifically in the sub-population of young adults, such as college students ,36. Consp < 0.001), and the psychological distress and EDS , emerged. Furthermore, the respiratory impairment and urinary alterations were also significantly correlated with each other . These findings reflected that the presence of both urinary impairment and EDS influences the stress of participants, regardless of the severity of symptoms experienced. Moreover, we identified that the magnitude of psychological distress may be driven by the women, but the addition of comorbidities, and urinary and respiratory symptoms to the hierarchical regression model seemed to flatten this phenomenon, confirming the results achieved. However, a possible explanation for the strong correlation between urinary impairment and EDS is related to sleeping disruption. Indeed, among LUTS, nocturia, defined as “waking at night to void”, has a prevalence rate of 54.5% and 48.6% for females and males, respectively, in 18 years or older people [Secondly, ESS and IPSS questionnaires resulted in noteworthy results, especially proportionate to the participants’ number. Indeed, 15% of participants and almost a half of the total sample harbored excessive daytime sleepiness and moderate-to-severe urinary symptoms, respectively. Furthermore, the K-10 results were alarming with one-half of participants harboring highest scores , underpinning a likelihood to have a moderate-to-severe mental disorder. A significant correlation between the psychological distress and urinary alterations . Second, there is a clear gender disparity in the sample, as 1750 participants (88%) were female, and only 238 (12%) were male. Moreover, only Italian participants were recruited. Future research should consider whether including a more gender- and ethnical-balanced sample might offer the opportunity to improve these results. Third, the ESS questionnaire represents a subjective measure of a patient’s sleepiness that could be influenced by several additional factors. Therefore, future studies could include a more objective measure to evaluate DS, improving the accuracy levels of the measurement. Fourth, this study was conducted online, thus only participants who have internet access were recruited. As a consequence, elderly people or potential participants with disadvantaged socio-economic conditions were ruled out. Thus, future studies could include a wider sample, guarantying a wider access to the study participation.Despite limitations, the presence of such a wide participation endorsed that the current findings may improve the management of patients experiencing LUTS and EDS. Indeed, it is important to stress that clinicians (psychologists and/or psychiatrists) must be aware that urinary and respiratory impairments could be a triggering factor for mental distress and could increase the psychological distress leading to mental disorders such as depression and anxiety. Conversely, for the urologists and ENT specialists, an appropriate patients’ counseling (using K-10 tool as screening questionnaire for distress) and a clinical joint assessment are required to better improve the awareness of patients on the symptoms they experience. Finally, LUTS and EDS could also worsen these sudden conditions when present. This is the reason it is fundamental that clinicians would offer an accurate diagnostic therapeutic management for the underlying conditions and psychological support for relieving the stress of urinary and respiratory dysfunctions.People reported that their psychological distress was impaired by the presence of LUTS and EDS. Specifically, our study showed that higher levels of distress were scored, especially in young women exhibiting urinary symptoms and with a high value of daytime sleepiness. These aspects must be considered in the clinical management of these subsets of patients to improve their QoL and to reduce the mental health burden.


The exogenous addition of these six autotoxins significantly inhibited the germination of faba bean seeds in a concentration-dependent manner. Furthermore, field experiments were conducted to investigate the effects of various levels of nitrogen fertilizer on the autotoxin content and the aboveground dry weight of the faba bean in a faba bean–wheat intercropping system. The application of various levels of nitrogen fertilizer in the faba bean–wheat intercropping system could significantly reduce the content of autotoxins and increase the aboveground dry weight in faba bean, particularly at the N2 level (90 kg/hm2). The above results showed that the water extracts of faba bean roots, stems, leaves, and rhizosphere soil inhibited faba bean seed germination. The autotoxicity in faba bean under continuous cropping could be caused by p-hydroxybenzoic acid, vanillic acid, salicylic acid, ferulic acid, benzoic acid, and cinnamic acid. The autotoxic effects in the faba bean were effectively mitigated by the application of nitrogen fertilizer in a faba bean–wheat intercropping system.Continuous cultivation of the faba bean will lead to its autotoxicity. Faba bean–wheat intercropping can effectively alleviate the autotoxicity of the faba bean. In order to investigate the autotoxicity of water extracts of various parts of the faba bean, we prepared water extracts of various parts of the faba bean, such as the roots, stems, leaves, and rhizosphere soil. The results showed various parts of the faba bean significantly inhibited the germination of faba bean seeds. The main autotoxins in these parts were analyzed using HPLC. Six autotoxins, namely,  Vicia faba L.) is the main leguminous crop in China. With the increase in the area of specialized, large-scale, and intensive planting, continuous faba bean planting has caused a high incidence of Fusarium wilt, growth inhibition, and a sharp decline in yield . There were 8 treatments in total, and each treatment was repeated 3 times. There were 24 plots in total, and the area of each experimental cell was 5.4 × 6 = 32.4 m2, in a completely random block arrangement.The field experiment involved a two-factor design : A was the planting mode: faba bean monocropping and faba bean–wheat intercropping; B refers to four levels of nitrogen application [no nitrogen , and potassium sulfate (K2O 50%), respectively. The nitrogen application rate to faba bean was 0, 45, 90, and 135 kg/hm2 and that to wheat was 135 kg/hm2. The nitrogen fertilizer applied to wheat was divided into base fertilizer and topdressing (1/2 for each) twice. The base fertilizer was applied at the time of sowing, and topdressing was performed at the jointing stage of wheat. The amount of phosphate and potassium fertilizers applied to faba bean and wheat was 90 kg/hm2, and they were applied once at the time of sowing. No organic fertilizer was applied during crop growth. No pesticides or agronomic measures were used during the growth of the faba bean.The N, P, and K fertilizers used during the test were urea (N 46%), ordinary superphosphate  and Excel 2010. All data were analyzed using SPSS v26.0 . The single factor test was performed using the single factor analysis of variance (ANOVA) to evaluate the difference between the treatments of the same substance, and further, LSD test was performed at the 5% probability level. All data were expressed as mean ± standard error. The two factor experiment involved a two factor ANOVA to evaluate the significant differences between the treatments, and further, Tukey’s test was conducted at the 5% probability level. All data were expressed as mean ± standard error.p-hydroxybenzoic acid, vanillic acid, salicylic acid, ferulic acid, benzoic acid, and cinnamic acid may have been the autotoxic substances underlying the negative effects of the continuous cropping of the faba bean. Reasonable application of nitrogen fertilizer in a faba bean–wheat intercropping system can effectively alleviate the autotoxicity of the faba bean.The water extract of faba bean roots, stems, leaves, and rhizosphere soil exhibited autotoxic effects on the seed germination, and the relationship of its autotoxicity was leaf > stem > root > rhizosphere soil. Furthermore,


Objectives Surgical endodontics (hemisection) commonly involves the alveolar bone socket and the periradicular tissue. In today's era, optimizing the bone healing process is updated by using bone graft induction. This study explores the mechanisms of bone healing of the alveolar bone socket post-dental extraction of Wistar rats after administration of a bovine tooth graft (hydroxyapatite bovine tooth graft [HAp-BTG]).Materials and Methods Fifty Wistar rats were randomly selected into two groups, control and treatment, and into five subgroups on days 3, 7, 14, 21, and 28. The postextraction socket was filled with polyethylene glycol (PEG) as the control and PEG + HAp-BTG as the treatment group. On days 3, 7, 14, 21, and 28, Wistar rats were sacrificed, mandibles were taken, paraffin blocks were made, cut 4 µm thick, and made into glass preparations for microscopic examination. The variable analysis was performed by staining hematoxylin-eosin for osteoblasts (OBs) and osteoclasts (OCs) and immunohistochemistry for runt-related transcription factor 2 (RUNX2), osterix (OSX), osteocalcin (OCN), bone morphogenic protein (BMP) 2. We analyzed the expressed cell count per microscope field.Results In general, the number of cell expressions in the treatment group was significantly higher and faster, except for significantly lower OC. The high variables peak occurred on day 14 for RUNX2 and OCN, on day 7 for OSX, while OB significantly increased on day 21 and remained until day 28. The decrease of OC cells occurred on day 7 and remained low until 28 days. BMP2 was first dominantly induced byHAp-BTG, then the others.Conclusion HAp-BTG can induce higher and faster bone healing biomarkers. BMP2 is the dominant first impacted. On the 28th day, it did not significantly express the suppression of OC by OB, which entered the bone formation and remodeling step. Endodontic surgery is a part of the field of endodontics through hemisection procedures. This procedure involves the alveolar bone socket, which will impact the defect in the alveolar bone and the periradicular tissue of the surrounding teeth. Trauma that occurs due to tooth extraction will experience a natural healing process by going through three stages of wound healing: inflammation, proliferation, and remodeling. In the bone healing processes, there is the involvement of osteoblasts (OBs) and osteoclasts (OCs).HAp), is commonly used in dentistry. This graft has osteoconductive and osteoinductive properties. It allows new bone tissue to grow in the spaces between its mineral particles.The reconstruction of bone defects is still a challenge for endodontists in the field of endodontic surgery. It is because the healing process is often interrupted or even fails. Ideally, the success of treatment depends on new bone regeneration. In the clinical practice for improving the bone healing process, commonly a substitute material, namely bone graft, is widely used in regenerative bone procedures.HApas a bone graft material.HApbovine tooth graft (HAp-BTG), a graft material derived from bovine teeth.Xenografts are commonly used as an alternative to fillers scaffolds. They are relatively easy to use in the maintenance of dental alveolar bone sockets and facilitate bone formation and promote wound healing. Many current studies have developedBovine teeth have inorganic and organic components that resemble human teeth components.BMP has an essential role in embryonic development, including brain and bone formation. BMP-2 increases OCN expression, and its short-term expression is required to sufficiently induce bone formation. BMP-2 also has a unique role in postnatal bone formation.HAp. Limited information is available on the study ofHAp-BTG material, especially for the osteogenesis process of alveolar bone sockets. The bovine bone xenograft study showed the significant expression of RUNX2, type I collagen, alkaline phosphatase (ALP), and OCN within 14 and 28 days.Many studies have succeeded in using bone graft material from bovine bone containing microsizedHAp-BTG graft material in dental socket postdental extraction in Wistar rat. The core factors were used as indicators, namely BMP-2, RUNX2, OSX, OCN, OBs, and OCs. This study hypothesized that most biomarkers would increase faster healing in the treatment group rather than control.This study will explore the cellular and subcellular mechanisms of bone healing by applyingHAp-BTG also needs to be explored.How about the process and mechanisms of bone healing based on the interaction of biomarkers after induction with HAp-BTG? The maximum expression of factors for bone regeneration after applyingThis research is an experimental study using laboratory animal using a posttest-only control group design. This study got ethical approval from the Ethics Committee, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia (348/HRECC.FODM/VII/2020). We conducted the study at the Biochemistry Laboratory, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia.Rattus norvegicus strain Wistaraged 12 to 14 weeks, male with a body weight of 250 to 300 g, healthy rats, no tooth decay, or defects in the whole body.HAp-BTG powder type (particle size is ∼3.5 µm) which was sterilized by gamma rays at BATAN , Jakarta, Indonesia. The preparation ofHAp-BTG was conducted by mixing with polyethylene glycol (PEG) as a carrier. PEG was provided by making a mixture of PEG-400 liquid and PEG-4000 crystal, with an 80% (20 g):20% (5 g) ratio.The experimental animals wereThe bone graft technology commonly inserts the graft powder or solution in fluid or water. One of the drawbacks is after inserting the graft, this material would get diluted or adrift and go out from the dental socket. PEG is a high molecular weight material or gel that can settle in liquid material. PEG is also known as a nontoxic material.HAp-BTG was weighted at 0.5 g, mixed into 24.5 g of PEG, and was mixed homogenously. Every rat was injected 0.1 mL ofHAp-BTG-PEG (for the treatment group) or PEG (for the control group) into the dental socket.Fifty Wistar rats were randomly selected into two groups (control and treatment with 25 each). Every 25 rats in the control groups were randomly chosen to the subgroup 3, 7, 14, 21, and 28 days to get five rats in every subgroup. The same method was used for the treatment group and every subgroup was kept in separate cage.HAp-BTG in group II (treatment), as much as 0.1 mL using a syringe. Furthermore, the extraction wound is sutured with simple interrupted sutures using 3–0 nonabsorbable black silk sutures. The rats were put in supine position for 4 hours to maintainHAp-BTG stay in the postextraction slot. The rats were evaluated on 3, 7, 14, 21, and 28 days.Anesthesia was administered using a combination of xylazine and ketamine with a ratio of 1:1 intramuscular injection. Termination was conducted with an anesthetic injection in the right posterior femoral region. Lower left incisor teeth extraction was done using incisor extraction forceps. The apical site of the postextraction socket was filled with PEG in group I (control) and PEG + On the 3rd, 7th, 14th, 21st, and 28th day, each of the 5 Wistar rats was terminated from each group, retracted, necropsied, decapitated, took a left mandibular bone fragment, and then immersed in 10% formalin solution for tissue fixation. After fixation, excision, and decalcification, the left mandibular jaw was processed for immersion in paraffin. Sections were made in a semiserial longitudinal manner with a thickness of 4 μm from the hemimandibular containing the alveolar socket at 60 µm intervals and examined by hematoxylin-eosin (HE) staining and immunohistochemical (IHC) examination.HE staining was used to count the number of OBs and OCs.For the examination of BMP-2, OSX, OCN, and RUNX2 expressions, OB, and OC, we used a 1,000× magnification light microscope in 20 microscopic fields. The mean results per microscopic field were tabulated and analyzed with SPSS version 25.The experiment was conducted from January 18, 2020 to March 7, 2020. There were 50 Wistar rats in 2 groups, 25 control and 25 treatment rats, spread into 3, 7, 14, 21, and 28 days subgroup. All 50 rats were alive until the end of the experiment.Six preparations of evaluated variables for each rat's dental alveolar tissue were prepared and stained for each variable. The enumeration of cells per microscopic field, among 20 microscopic fields of 1,000× magnification, is shown inThe results of examining the number of cells expressing BMP-2 between the control and treatment group of observation days are given inThe results were similar for RUNX2 , OSX , and OCp < 0.05). Based on the evaluation days, there were no significant increase on day 3 compared with the control group, but it increased significantly on day 7. There was not a significant increase until days 14, 21 and 28. At the same time, in the control group, a significant rise in OSX expressing cells occurred on day 28 compared with day 3.The number of cells with OSX expression in the treatment group on days 3, 7, 14, 21, and 28 was higher and significantly different than the control group   . While < 0.05) . The reHAp-BTG, as the test material in this study, is a particle resulting from calcination, sintering, and milling process, with a size of approximately 3.5 µm. In the treatment group, the increase in all osteogenesis indicators generally showed a significantly higher expression than the control group. It indicates the role ofHAp-BTG in increasing the bone healing process in the area of the tooth alveolar socket.HAp-BTG graft enhances and accelerates the process of osteogenesis. According to Thahir et al, the process of bone resorption and formation in male marmots takes approximately 2 to 4 weeks.It shows the peak expression in the treatment group was reached within 14 days , and on day 7 (OSX). Meanwhile, the control group reached the peak on day 28 . The peak expression of the control group on day 28  was the same as that of the treatment group on day 7. It indicates that theIn the control group, OSX expression increased after day 3, but a significant increase was identified on day 28. In contrast, in the treatment group, a significant increase was identified since day 7  and conThere was no significant change in the number of OC in the control group between day 3 to 7, day 7 to 14, day 14 to 21, and day 21 to 28. While in the treatment group, it decreased significantly on day 7, and then there were no significant changes from day 7 to 14, day 21, and day 28. It showed that OC has a pivotal role in the finalization process of alveolar tissue growth.HApscaffolds areHApwith a porous matrix where the size of the pores inHApscaffolds can vary, depending on the volume of scaffold produced. It makesHApscaffolds easier to implant into bone tissue, does not inhibit the growth of natural bone tissue, and can prevent displacement and loss of implants induced into the body.HApscaffolds can serve as various materials, including polymers, ceramics, metals, and other composite materials.Bovine tooth graft has a role in osteogenesis in alveolar bone defects. After inserting the bone graft to the alveolar bone defect, there will be a blob of bone graft wrapped in blood in the early stages. Then on the 7th day, there will be an acute inflammatory response with an invasion of neutrophil cells, lymphocytes, and plasma cells. The inflammatory process that occurs causes the activation of premesenchymal cells, growth factors, and inflammatory mediators that can cause premesenchymal cells to differentiate into OBs so that bone formation or osteogenesis will occur.p < 0.05) between the treatment groups compared with the control group. The administration ofHAp-BTG in the treatment group showed an increase in BMP-2, significantly higher than the control group (p < 0.001). BMP signaling is one of the central signaling pathways that induce osteogenic differentiation and regulate bone formation. BMP induces osteogenesis through the role of autocrine, paracrine hormones, and the action of RUNX2.BMP-2 is well known to be a strong inducer of bone formation and to play important roles in the development and regeneration of bone and cartilage.p < 0.05) between the control and treatment groups. However, it significantly increased RUNX2 expression on days 3, 7, and 14. After that, it increased again on day 21. Then, there was no further increase until day 28 in the treatment group compared with the control group.The osteoblastic differentiation and maturation events in the defect were evaluated by immunohistochemistry analysis which exhibits a significant increase in expressions of RUNX2.p < 0.05).RUNX2 was first detected in pre-OBs and increased in the first week in immature OBs. At the fourth week, RUNX2 expression decreased during the maturation process of OBs, and RUNX2 expression was not significant in mature OBs.p > 0.05). In contrast, on days 7, 14, 21, and 28, there was a significant difference in OSX expression in the treatment group compared with the control group (p < 0.05) (There was a significant difference between the treatment group and the control group. OSX expression on day 3 showed no significant increase in the treatment group compared with control (< 0.05) . OSX isp < 0.05). However, based on time observations, there was a significant decrease in OBs on day 7. On days 14 to 21, the number of OBs increased and continued to increase significantly until day 28. OBs are responsible for collagen production (type I collagen) and NCPs. It includes OCN, BSP, OPN, and osteonectin. OBs also express some ALP which helps mineralization.OB expression in the mechanism of osteogenesis in this study showed the number of OBs in various groups . On dayp < 0.05)  . Howeve< 0.05) . OCs arHAp-BTG was observed on the 3rd day until day 28 through the BMP-2, OSX, OCN, and RUNX2, OBs and OCsin vivo. TheHAp-BTG scaffold material was proven to have the ability to induce osteogenesis in the alveolar bone socket of theRattus norvegicus strain Wistar ratsin vivo. ThisHAp-BTG scaffold has potential as a mineral because it secretes active metabolites such as cytokines and growth factors. These results show the pathway of the influence ofHAp-BTG and BMP-2 expression. The expression of BMP-2 affects the expression of RUNX2, which is the beginning of the process of osteogenesis. RUNX2 plays a role in the differentiation of mesenchymal stem cells into osteoprogenitors. The RUNX2 pathway affects the expression of OSX, which is the final stage of the osteogenic process. The role of OSX is to induce the differentiation of osteoprogenitors into pre-OBs. The OSX pathway affects OCN expression, which indicates that pre-OBs differentiate into OBs. OCN is the most abundant protein matrix found in bone. OBs express OCN in the bone matrix during alveolar bone remodeling.There is something new from this study: the mechanism of alveolar bone osteogenesis after administration ofHAp-BTG , there was no influence of OSX on OB; at 28 days, there was no effect on suppression of OC  It showLimitation of this study: (1) There were only six variables, BMP-2, RUNX2, OSX, OCN, OB, and OC. Thus, the mechanisms in more detail need to be further explored with more biomarkers. (2) This study was conducted in rats, to show the model of the mechanisms of bone healing. To explore the impact of bone synthesis completely and remodeling, further study in large animal is required.HAp-BTG administration in the dental socket can induce biomarker  expression higher and faster, improving the process of bone healing.(1) Postdental extraction,(2) BMP-2 is the dominant biomarker first induced during the bone healing process, then RUNX2, OSX, OB, and OCN/OC.(3) On the 28th day, it did not significantly express the suppression of OC by OB, which entered the bone formation and remodeling step.


The Tobit model was developed within a counterfactual causal inference framework, combined with a correlated random effects and control function approach (Tobit-CRE-CF), to explore the causal relationship between long-term exposure to air pollution and medical costs. We also explored whether different air pollutants exhibit comparable effects.2.5 and ground-level ozone signifying that a unit increase in PM2.5 and ground-level ozone results in increased total medical costs of 199.144 and 75.145 RMB for individuals who incurred fees in the previous year, respectively.This study encompassed 8928 participants and assessed various benchmark models, highlighting the potential biases from failing to account for air pollution endogeneity or overlooking respondents without medical costs. Using the Tobit-CRE-CF model, significant effects of air pollutants on increased individual medical costs were identified. Specifically, margin effects for PMThe results imply that long-term exposure to air pollutants contributes to increased medical costs for individuals, offering valuable insights for policymakers aiming to mitigate air pollution’s consequences.The online version contains supplementary material available at 10.1186/s12916-023-02839-1. Air pollution has long been recognized to have a wide range of effects on human health, and long-term exposure to air pollutants is associated with a range of adverse health outcomes, such as an increased risk of dementia , type 2 2.5 concentrations above 35 μg/m3 in 2018 [2.5 could result in $25.2 billion in health expenditure in 2030, approximately 2% of GDP, in the absence of a PM2.5 pollution control policy [China has adopted a number of measures to control air pollution in recent years and has made some progress; however, more improvements are expected in the future . Not onl in 2018 , 7. Morel policy .However, studies exploring the causal relationship between long-term air pollution and individual medical costs based on large cohorts are lacking, especially in developing countries such as China. Further, a significant problem faced by the current literature is that the effects of air pollution on health outcomes are often endogenous , 11, andTo this end, this study utilized balanced panel data from 2014, 2016, and 2018 waves of the Chinese Family Panel Study (CFPS), a large representative long-term nationwide cohort, to develop a Tobit regression model combined with correlated random effects and the control function method to explore the causal effect of long-term exposure to different types of air pollutants on individual medical costs. The findings of this study can inform policy recommendations for improved control of the medical costs associated with air pollution.N = 8928) aged 18 years or older, who had never moved out of their district/county of residence, and who were interviewed in 2014, 2016, and 2018 were selected for this study. The county/district coverage of the sample and the number of participants within each county/district were shown in Additional file The participants of this study were drawn from the CFPS, a nationwide, representative longitudinal cohort of Chinese adults. Participants for the CFPS were sampled from 25 provinces/autonomous regions/municipalities and were selected using a stratified multistage probability strategy. The survey has been conducted every 2 years since 2010. A 5-year balanced panel of adults  and wind speed were selected as instrumental variables in this study. PBLH reflects the depth of air at the Earth’s surface. Above the planetary boundary layer is the free atmosphere, and the transport of pollutants from the boundary layer to the free atmosphere is slow , 19. WinThe ERA5 database, a global atmospheric reanalysis product with high spatial and temporal resolution, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), was accessed to obtain monthly PBLH data in China at a 2.5 km × 2.5 km resolution . PBLH wa2.5, ground surface ozone, PBLH, and wind speed exposure for each respondent for the twelve months prior to his/her specific interview time (to the exact month) for each wave. This allowed for a more accurate assessment of each respondent's instrumental variable data and long-term air pollution exposure.In this study, participants were interviewed for three waves in 2014, 2016, and 2018, and we calculated the average PMThe CFPS recorded the total hospitalization costs  in the year prior to the interview; this included all medical-related and non-medical-related costs, such as medical examinations, medical treatments, accommodation, nursing care, and transportation fees due to hospitalization, regardless of whether the individual was reimbursed by health insurance. Non-hospitalization costs due to injury or illness, i.e., costs not related to the act of hospitalization in the year prior to the interview, were also recorded, including expenses such as buying medications. Thus, the total medical costs variable was recorded as the sum of the hospitalization-related and non-hospitalization costs, and this served as the dependent variable in this study.The CFPS employs rigorous instruments to ensure data quality, including the use of uniformly trained surveyors, field verification and audio confirmation. The reliability of the questionnaire has been previously confirmed.Considering that individual medical costs may be associated with a wide range of factors, a range of demographic, health-related, socioeconomic, and behavioral confounders were included as variables in this study. The demographic confounders included the age at interview and the gender of the participant. Health-related confounders included the number of chronic diseases suffered, depressive symptoms, self-rated health status, subjective memory impairment, and whether the participant was hospitalized in the last year. Socioeconomic confounders included the participant’s medical insurance, marriage status, type of household fuel, type of house, whether they obtained a subsidy, work status, and household income. In addition, the number of hospital beds per capita and income per capita in the participant’s city were recorded. Habits and behavioral confounders included drinking, smoking, exercising habits, and whether the participant surfed the internet. A set of year dummy variables was also included to control for annual unobservable fixed effects. Detailed information about the covariates is presented in Additional file The dependent variable was the total medical costs of the participants. This was a continuous variable with a value range greater than or equal to zero. If the distribution of the dependent variable is normal or approaches normal, the linear regression model  can be employed to explore the effects of long-term exposure to air pollution on individual medical costs. However, there are many people in China who do not spend any medical costs for a significant period of time. For example, in this study, more than 30% of participants did not spend any medical costs during the entire year prior to their interview. Thus, linear regression was not applicable in this study. As such, a two-step process was utilized to explore the effect of long-term exposure to air pollution on individual medical costs: (1) estimate whether the individual could spend medical costs first and (2) estimate the fees of the individuals who could spend medical costs. This type of response variable is also called a corner solution response or corner solution outcome .The Tobit regression model is appropriate when the observed range of the dependent variable is censored in some way, as in the case of the dependent variable in the current study , 25. Thei denotes the individual; t is the time (year); y* is the latent measure of the individual total medical costs; X is the group of independent variables, including the proxy of air pollutants (PM2.5 and ground surface ozone), then key independent variables, and a set of covariates ; In this study, because the dependent variable, the total medical costs of the respondent in the previous year, was greater than or equal to 0, it can be assumed to be censored on the left side, with a censored value of 0, and unlimited on the right side. The Tobit model in this study was designed as follows:y in Eq. (Maximum likelihood estimation (MLE) was used for the Tobit regression model. The estimation process can be summarized in three steps: 1) obtain the probability density function for  obtain tThe Tobit regression model is a useful benchmark but can be biased by unobserved time-invariant effects, individual-specific effects, and endogeneity . Based oAir pollutants denotes the PM2.5 concentration and ground surface ozone concentration; Z represents the instrumental variables; X is a set of covariates; V is the residuals of the endogenous variables fitted by formula (6); y* is the latent measure of the respondent’s total medical costs, as described in formula  can adequately explain variations in air pollution exposure (relevant prerequisite) and lack the ability to independently explain variations in medical costs  [F test and Sargan test are provided in Test S2 in the Supplementary File.CRE-CF estimations are robust to endogeneity and unbiased only if the instrumental variables (quisite) . The Craquisite) , 34 and quisite) , respectTo test whether there were unobserved time-invariant effects, individual-specific effects and potential endogeneity, the Hausman specification test was employed in this study with the null hypothesis that the differences in the estimates are not systematic . A detaiP < 0.05 was used to determine statistical significance. The standard errors of all estimations in the CRE-CF regression model were computed by the bootstrap method through the resampling of the dataset 100 times to obtain more robust estimates.All data management was performed using R 4.0.2. The statistical analyses were performed with Stata . Descriptive statistics for the categorical and continuous variables are reported in Table 2.5 concentration, ground surface ozone, PBLH, and wind speed were 48.49 µg/m3, 112.67 µg/m3, 488.16 m, and 1.03 m/s, respectively. There were slightly more females (51.83%) than males (48.17%). Nearly 80% of the participants across all waves of the study (78.73%) did not suffer from any chronic disease, and only approximately 9% suffered from two or more chronic diseases. The average CES-D score was 24.81, and approximately 55% of participants across all waves had no change in self-rated health condition compared to the previous year. Across all waves, nearly 87% of the participants were not hospitalized in the year prior to the interview.As shown in Table Across all waves, more than 90% were married. More than 60% (63.98%) of participants across all waves used clean cooking fuel in their daily life. Overall, 44.37% of participants lived in a one-story house during the study period, while 47.22% lived in a multi-story house. More than 50% did not receive any government subsidies. Across all waves, a total of 76.89% of participants were employed and nearly 70% did not report surfing the internet. Nearly 85% did not drink alcohol more than three times a week, and more than 80% did not smoke in daily life. Nearly 60% hardly or never take exercise in daily life.2.5 and ground surface ozone on individual total medical costs, respectively. Columns (1)–(5) of the tables report the regression results for the baseline linear (OLS), fixed-effects combined with two-stage least square (FE-2SLS), Pool-Tobit, Tobit-CRE, and Tobit-CRE-CF models, respectively. For each model, the variable marginal effects are reported. Moreover, the results of the Hausman test, Cragg-Donald Wald F statistic, and Sargan statistical tests are displayed in the last three rows of Tables Tables  Columns –(5) of tP values of the Hausman specification tests between the estimates of the Pool-Tobit and Tobit-CRE regression models were smaller than 0.05, suggesting the presence of unobserved time-invariant and individual-specific effects that could cause bias in the estimation of the Tobit regression model. In addition, since the estimation of the residuals of PM2.5 was statistically significant, it is likely that the endogeneity of PM2.5 would cause bias, and the resulting estimation results of the Tobit-CRE model would be inconsistent. The FE-2SLS model does not consider the distribution of the total medical costs, in which more than 30% of observations were equal to 0; thus, this would lead to biased estimates. Considering the inconsistent estimations provided by the OLS, FE-2SLS, Pool-Tobit, and Tobit-CRE regression models, the following discussions are mainly based on the results estimated by the Tobit-CRE-CF regression model.The 2.5 estimated by the Tobit-CRE-CF regression model was 526.396, as shown in column (5) of Table 2.5 exposure concentration was positively associated with total medical costs. This suggests that long-term exposure to PM2.5 increases CFPS participants’ total medical costs. However, the PM2.5 coefficients estimated by the OLS, FE-2SLS, Pool-Tobit, and Tobit-CRE models were − 0.008, 0.052, − 16.527, and − 47.180, as shown in columns (1)–(4) of Table The coefficient of PMThe coefficient of ground surface ozone estimated by the Tobit-CRE-CF regression model was 198.626, as shown in column (4) of Table 2.5 and ground surface ozone, the Cragg-Donald Wald F statistics were both greater than the Stock-Yogo weak ID test critical value of 10%, suggesting that the relevant prerequisite was valid. Namely, the instrumental variables are both strongly related to the endogenous variable. The P values of the Sargan statistics were both greater than 0.05, indicating that overidentification did not exist in this study. This indirectly suggests that the valid prerequisite of the instrumental variables was met.For PMy*); (2) the marginal effects for the latent measure of total medical costs in the condition where the observed total medical costs were greater than 0; namely, the marginal effects for the latent measure of total medical costs for the individuals who could spend medical costs last year (y*|y > 0); and (3) the marginal effects for the observed measure of total medical costs in the condition where the observed total medical costs were equal to 0; namely, the marginal effects for the observed measure of total medical costs for the individuals who could not spend medical costs last year (y|y > 0). Specifically, for PM2.5 concentration and ground surface ozone, the margin effects for the observed measure of the total medical costs for the individuals who spent any medical fees last year (y|y > 0) are 199.144 and 75.145, respectively, and both significant at 5% level, which means that when PM2.5 concentration and ground surface ozone increase one unit, the total medical costs for the individuals who spent medical fee last year increase by 199.144 and 75.145 RMB, respectively. The results are shown in Additional file In this study, the Tobit-CRE-CF regression model, a kind of limited dependent model, is used to estimate more accurate effects. However, as a compromise, it is hard to interpret the coefficients. To understand the results better, we calculate the marginal effect of the model: 1) the marginal effects for the latent measure of total medical costs  and (4) show the results of the modification analysis of age for PM2.5 and ground surface ozone. The estimated coefficient of the interaction term between female and PM2.5 is greater than 0 and significant at 5% level, which means the harmful effects of long-term PM2.5 exposure on individual medical costs are greater than male, while the modification effects of gender are not significant for ground surface ozone exposure. The estimated coefficient of the interaction term between age and ground surface ozone is greater than 0 and significant at 5% level, which means that the order the individual, the greater the harmful effects of long-term ground surface ozone exposure on the individual medical costs, while the modification effects of age are not significant for PM2.5 exposure.Column (1) and (2) of Table S4 show the results of the modification analysis of gender for PMTwo strategies were adopted to exclude the effects of extreme total medical cost values. First, the total medical cost data were trimmed so that maximum costs of 1% were deleted. Using the same regression model and methods, the robustness of our findings was tested. Second, the upper limit of the total medical costs was set to 100,000 RMB in the Tobit-CRE-CF. In this case, if the total medical costs were more than 100,000 RMB, they were recorded as 100,000 RMB.2.5 and ground surface ozone coefficients were 198.836 and 72.748, respectively, and both were statistically significant at the 5% level. Columns (3) and (4) of Table 2.5 and ground surface ozone coefficients were 484.622 and 175.289, respectively, and both were statistically significant at the 5% level. These results indicate that the PM2.5 concentration and ground surface ozone exposure concentration are positively associated with total medical costs, which is consistent with the main regression analyses. This suggests that the results are robust.Columns (1) and (2) of Table 2.5 and ground surface ozone coefficients were 238.334 and 92.122, respectively, and both were statistically significant at the 5% level. These results indicate that the PM2.5 concentration and ground surface ozone exposure are positively associated with non-hospitalization medical costs, suggesting that the causal effect of air pollution on medical costs persists after excluding hospitalization costs. Only the estimated coefficients of the independent variables of interest and the results of some tests are displayed in Table Non-hospitalization medical costs were also used as a dependent variable to test whether the causal effect of air pollution on medical costs persisted after excluding hospitalization costs. Columns (5) and (6) of Table This study successfully utilized a large representative national longitudinal cohort study to construct causal models using the Tobit-CRE-CF method and found that long-term exposure to air pollutants may lead to higher medical costs for individuals, regardless of whether the air pollution is severe enough to cause an individual to be hospitalized and whether the individual may take different measures to cope with the air pollutants.This study has the following strengths. First, to our knowledge, it is the first to assess the effects of different types of air pollutants on individual healthcare costs over time using a large, long-term, national cohort, and corresponding causal models. Currently, the literature mainly focuses on the health impact of air pollution and lacks evidence from developing countries , individImportantly, as mentioned above, this study provides a useful response to the problem of a significant proportion of respondents with zero medical costs incurred over the study period. China began its healthcare reform in 2009 in response to public dissatisfaction with the limited accessibility and high cost of healthcare, and while considerable results have been achieved, more progress is expected . Thus, aIn addition, this study developed, applied, and validated the Tobit-CRE-CF method, which is able to deal simultaneously with time-invariant effects, individual-specific effects, endogeneity of air pollutants, and the presence of some participants who had not to spend any medical costs during the study period , 24, 43.2.5 exposure may cause asthma [2.5 and ground surface ozone as two representatives. The consideration is that people may have different responses to different pollutants; for example, most people are more familiar with PM2.5 pollution, which is usually widely publicized and one of the pollutants that causes the greatest adverse health and economic burden, and are therefore more likely to take appropriate protective measures, such as reducing exposure by going out less or wearing a mask [Long-term exposure to air pollutants may have multiple adverse effects on human health. PMe asthma , atherose asthma , etc., we asthma , emphysee asthma , and dame asthma , etc., ag a mask , 51, wheg a mask , 14, 52.The medical costs in this study evaluated the sum of total hospitalization medical costs and total non-hospitalization medical costs of respondents due to illness or accidental injury. It is worth noting that the costs evaluated include not only direct medical costs, such as medicine, treatment, and ward fees but also indirect medical costs, such as lodging, meals, and caregiver fees. Considering the rigorous instruments adopted by the CFPS to ensure data quality, this gives us the opportunity to comprehensively evaluate the complete economic impact of air pollution exposure. In addition, this study further validates that there is a consistent effect of air pollution exposure on the costs associated with medical actions that are not severe enough to warrant hospitalization.Despite its strengths, there are several limitations of this study that should be noted. First, the pollutant exposure estimates in this study were made at the district/county level, which requires the assumption that respondents always stay within their district/county. However, given that only those who did not move out of the district/county during the study period were included, this is not likely to have had a substantial impact on the study conclusions. Second, despite the detailed justification of the research process in this paper, caution should be adopted when inferring the causal impact of long-term air pollution on individual medical costs, and future studies should combine multiple causal approaches to comprehensively assess the causal effects of air pollutants on medical costs while conducting a full range of heterogeneity and mechanism exploration. Third, it is possible that some minor diseases or injuries caused by long-term exposure to air pollutants would not prompt individuals to seek help and spend costs, regardless of whether they are hospitalization-related or non-hospitalization-related medical costs. In addition, data limitations make it difficult to assess the costs associated with death, which should have been included in medical cost considerations. Nonetheless, if this was the case, the current estimates would provide an underestimation of the effects of long-term exposure to air pollutants on individual medical costs. Finally, the rigorous methodology adapted in this paper to more accurately assess the causality of air pollution on health care costs also allows us to make some concessions in the ability to fully assess health care costs in China. Further research should be conducted in the future to assess the economic burden associated with air pollution.Using a large, long-term, representative nationwide cohort, this study developed and validated a Tobit-CRE-CF model to control for unobserved time-invariant effects, individual-specific effects, and endogeneity and to address the issue of the large number of participants who did not spend medical costs during the study period. The results revealed that long-term exposure to air pollutants contributes to higher individual medical costs, regardless of whether the air pollution causes individuals to get sick enough to require hospitalization or whether individuals may adopt various measures to minimize their pollutant exposure.Additional file 1:Text S1. Description of covariates. Text S2. Details of Tobit-CRE-CF. Figure S1. Coverage, number of participants, and pollutant concentrations in surveyed counties/districts across China in 2014. Figure S2. Coverage, number of participants, and pollutant concentrations in surveyed counties/districts across China in 2016. Figure S3. Coverage, number of participants, and pollutant concentrations in surveyed counties/districts across China in 2018. Table S1. Estimation results of the effects of PM2.5 on the total medical costs. Table S2. Estimation results of the effects of ground surface ozone on the total medical costs. Table S3. Various margin effects for the key independent variables in the model of Tobit-CRE-CF. Table S4. Heterogeneity analysis.


Bernese mountain dogs are a large dog breed formed in the early 1900s in Switzerland. While originally farm dogs that were used for pulling carts, guarding, and driving cattle, today they are considered multi-purpose companion and family dogs. The breed is predisposed to several complex diseases, such as histiocytic sarcoma, degenerative myelopathy, or hip dysplasia. Using whole-genome sequencing (WGS) data, we assessed the genomic architecture of 33 unrelated dogs from four countries: France, Sweden, Switzerland, and the United States. Analysis of runs of homozygosity (ROH) identified 12,643 ROH with an average length of 2.29 Mb and an average inbreeding coefficient of 0.395. Multidimensional scaling analysis of the genetic relatedness revealed limited clustering of European versus USA dogs, suggesting exchanges of breeding stock between continents. Furthermore, only two mtDNA haplotypes were detected in the 33 studied dogs, both of which are widespread throughout multiple dog breeds. WGS-based ROH analyses revealed several fixed or nearly fixed regions harboring discreet morphological trait-associated as well as disease-associated genetic variants. Several genes involved in the regulation of immune cells were found in the ROH shared by all dogs, which is notable in the context of the breed’s strong predisposition to hematopoietic cancers. High levels of inbreeding and relatedness, strongly exaggerated in the last 30 years, have likely led to the high prevalence of specific genetic disorders in this breed. Originally, these dogs were used for guarding farms, pulling carts, and driving cattle in the mountain regions of Switzerland. Due to their versatility, they are now globally known and appreciated as companion and family dogs but are also used for various dog sports and activities such as obedience, tracking, cart pulling, water rescue, or canis therapy. [Bernese mountain dogs are a popular dog breed developed in the last hundred years using strong selection of long-haired tricolored all-purpose working farm dogs, as documented in the breed’s standard by the Fédération Cynologique Internationale . AccordiThe breed is known to have a high prevalence of several complex diseases, such as histiocytic sarcoma OMIA 000620-9615), degenerative myelopathy (OMIA 000263-9615), and hip dysplasia OMIA 000473-9615), all of which pose significant concern to the dog owners and contribute to decreased quality of life and shorter lifespan for affected dogs [73-9615, 20-9615, The population history of emerging dog breeds together with the current management strategies in dog breeding is increasingly recognized as the cause of reduced genetic diversity, impaired health, and animal welfare issues . In receIn this study, we aimed to analyze WGS data of 33 unrelated Bernese mountain dogs originating from France, Sweden, Switzerland, and the United States in order to assess the genetic diversity within the breed.n = 7), Sweden (n =7), Switzerland (n = 7), and the United States (n = 12). All dogs were unrelated for at least three generations according to the available pedigree information, and all were reportedly healthy at time of sampling. All samples were collected according to local regulations by veterinarians with the owner’s consent in the course of the dogs’ medical care and submitted to local biobanks: Cani-DNA BRC at the University of Rennes in France [EDTA blood samples from 33 Bernese mountain dogs were collected for genomic DNA isolation from France  was generated with PLINK v1.9 . The init v4.2.1 .PLINK v1.9  was usedBased on mean observed heterozygosity, number of samples, and number of markers, a minimum number of SNVs used to identify a ROH was calculated to be 72 in order to produce less than 5% of randomly generated ROH according to the previously described method . Parametwww.antagene.com, accessed on 20 January 2023) [A threshold of 90% was set for the detection of ROH islands, meaning genomic regions without a minimum size requirement that were called with consecutive homozygous SNVs in at least 90% of the dogs. Such areas may indicate regions under selection or resulting from population bottlenecks. However, the presence of ROH islands does not implicate an identical underlying haplotype . Gene cory 2023) .In total, 33 Bernese mountain dogs  from four different countries were studied. The year of birth ranged from 1991 to 2019 . The MDSROH detection was performed on the dataset of 6,344,366 quality pruned SNVs. A total of 12,643 ROH were detected across all dogs, with a mean length of 2.29 Mb (ranging from 0.30 to 51.39 Mb). On average, 383 ROH were seen per individual with a range from 320 to 451. The total length of ROH across the genome per individual was 878.1 Mb, on average, and ranged from 704.7 to 1163.6 Mb . The avep > 0.05). ROH were further divided into six groups based on their length: <1, 1–2, 2–4, 4–8, 8–10, and >10 Mb. The relative contribution of these six ROH size classes to the average F_ROH per country is indicated in The autosomal ROH-based average genomic inbreeding coefficient (F_ROH) was 0.395 with a minimum value of 0.317 and a maximum of 0.523 . F_ROH vOverall, most of the detected ROH (83.3%) were shorter than 4 Mb, and 4.2% were longer than 10 Mb a. The nuMSRB3, which has been previously associated with ear type in dogs (OMIA 000319-9615) [Of the 6,344,366 SNVs in the pruned dataset, 6,340,396 (99.9%) were located in a ROH in at least one dog. Of those, 48,841 (0.8%) SNVs on 11 chromosomes exceeded the 90% threshold among the 33 dogs assayed b. These 19-9615) . AnotherCCR6, GPR31, SMPDL3B, THEMIS2) or in DNA damage repair  [Five genome regions on chr1, chr2, chr6, and chr14 were identified for which 100% of the dogs shared overlapping ROH . These R, PP1R8) .GFAP variant associated with Alexander disease, which is characterized by myelin loss and abnormal protein deposits, was not detected at all, and all studied dogs had a homozygous reference genotype. In contrast, all three genotypes were found for the variants in SOD1 and its modifier SP110, which are associated with degenerative myelopathy. All four risk haplotypes that have been described for development of histiocytic sarcoma in Bernese mountain dogs [p < 0.01), while the haplotype frequency at the other three loci was not significantly different .The frequency of previously described variants that are associated with specific traits in Bernese mountain dogs was also investigated. The findings represent eight disease-related ,37,38,39ain dogs ). Only tIGF1 gene that was previously associated with large breeds (body mass > 25 kg) [MSRB3 reported previously in dogs with floppy ears [FGF5 gene commonly associated with long hair in various breeds [FGF5 long hair alleles described in Akita, Samoyed, Eurasier, and Afghan hound [ASIP gene [Variants associated with height, ear type, hair length, and coat color were also analyzed . All but> 25 kg) . Similarppy ears . All dogs breeds . No dog an hound . The disSIP gene . All 33 SIP gene .ND5 gene (CM022001.1: m.4378A>G) appeared in three Swedish and one USA dog, and a variant in the ATP6 gene (CM022001.1: m.7802A>G) was observed in a single French dog.Only two mitochondrial haplotypes were detected in the 33 sampled dogs, and both belong to the haplogroup A. Following the nomenclature standard based on the mtDNA D-loop region , seven dThis study of Bernese mountain dogs from Europe and the United States demonstrated that breed genetic diversity is limited, as would be expected in a typical modern dog breed . The obsIn total, 12,643 ROH were detected across all dogs, with an average length of 2.29 Mb. Generally, shorter ROH are considered to be indicators of more distant relatedness, while longer segments are a result of more recent inbreeding . Herein,MSRB3 region on chromosome 10, which has been associated with body mass and ear type, was found in both studies, highlighting the value of public repositories of data that can serve as references for other researchers and the value of validation studies. These regions have likely been under selection by breeders and should be investigated further in future studies.In a previous study, Gorssen et al.  reportedSOD1 alleles with incomplete penetrance have been reported in dogs affected by degenerative myelopathy, an adult-onset progressive neurological disease [Selected variants that may play a role in Bernese mountain dog health were genotyped in the 33 purebred dogs with no report of clinical signs of disease at the time of sampling. Two autosomal recessive  disease ,37. The  disease . Alexand disease . That va disease . The ris disease , which i disease .CCR6 encodes a chemokine receptor that has been involved in inflammatory and infectious diseases and that is associated with several cancers through immune cell control [SMPDL3B is involved in innate immunity and may be used as a prognostic biomarker in human cancers, such as myeloid leukemia and ductal carcinoma [THEMIS2 is expressed in a variety of cancer types and cell lines and was recently found to have tumor-suppressive role in breast cancer [In the context of strong breed predisposition to many hematopoietic cancers, especially histiocytic sarcomas, involving dendritic cells or macrophages, it could be suspected that Bernese mountain dogs present fixed homozygous regions related to immune dysregulation . Five RO control , while Sarcinoma ,55, and t cancer .ASIP promoter poses a technical problem due to many erroneous heterozygous calls in such regions. Furthermore, due to the extensive selection in the breed’s history, fixed haplotypes may have become too small to detect using the selected parameters. This is the most likely explanation for the FGF5 locus in Bernese mountain dogs, in which the size of the shared homozygous region was only ~2.4 kb. The fact that two variants with influence on body mass and ear type (IGF1 and MSRB3) were not fixed in all studied Bernese mountain dogs, even though the breed is homogeneous in these morphological characteristics, supports the notion that additional variants, perhaps in other genes, remain to be found [The fixed alleles associated with long hair and coat color typical for the breed were not detected as ROH islands. Complex structural variation such as in the be found .Two mtDNA haplotypes, A1b2a1a1 (A2) and A1b3b2 (A22), were detected in this study, which correspond to previous reports in Bernese mountain dogs ,26,58,59Whole-genome sequence data from 33 Bernese mountain dogs sampled from four countries demonstrated that within-breed diversity is low, likely due to artificial selection throughout the breed’s history, leading to high levels of inbreeding and relatedness in the current population. The detection of common ROH islands may facilitate further studies to clarify the functional impact of genes involved in these highly homozygous regions. Whole-genome analysis is efficient for the detection of breed-related variants and regions under selection and may be used for individual disease risk assessment and differential follow up of the animal’s health status. Such breed population genomic studies will enable breeders and kennel clubs to evaluate and set relevant rules for the monitoring and management of highly prevalent genetic disorders that severely affect the dog’s quality of life and diminish the breed’s average lifespan. Preserving the current genetic diversity level of Bernese mountain dog would be possible by increasing the overall number of sires in breeding programs while limiting the number of litters per sire/dam and avoiding repeated matings. In addition, genetic testing for known recessive diseases can be performed to help avoid carrier pairing. The use of HS risk test over almost 10 years showed that there is enough diversity for breeders to slowly select against cancer by favoring the dogs with index A. However, in light of the complexity of many disorders prevalent in this breed, outcrossing may be an effective breeding strategy for long-term genetic diversity improvement, which would contribute to population sustainability and health.