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•We compare the differences between sMRI and dMRI in predicting infant brain age.•Reveals that T2w and FA images focus on different brain regions.•Identify differences in macro- and micro-development of the cerebral cortex.•FA brain age differences are significant across gender and brain lateralization. Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

Fruit and vegetable picking robots are affected by the complex orchard environment, resulting in poor recognition and segmentation of target fruits by the vision system. The orchard environment is complex and changeable. For example, the change of light intensity will lead to the unclear surface characteristics of the target fruit; the target fruits are easy to overlap with each other and blocked by branches and leaves, which makes the shape of the fruits incomplete and difficult to accurately identify and segment one by one. Aiming at various difficulties in complex orchard environment, a two-stage instance segmentation method based on the optimized mask region convolutional neural network (mask RCNN) was proposed. The new model proposed to apply the lightweight backbone network MobileNetv3, which not only speeds up the model but also greatly improves the accuracy of the model and meets the storage resource requirements of the mobile robot. To further improve the segmentation quality of the model, the boundary patch refinement (BPR) post-processing module is added to the new model to optimize the rough mask boundaries of the model output to reduce the error pixels. The new model has a high-precision recognition rate and an efficient segmentation strategy, which improves the robustness and stability of the model. This study validates the effect of the new model using the persimmon dataset. The optimized mask RCNN achieved mean average precision (mAP) and mean average recall (mAR) of 76.3 and 81.1%, respectively, which are 3.1 and 3.7% improvement over the baseline mask RCNN, respectively. The new model is experimentally proven to bring higher accuracy and segmentation quality and can be widely deployed in smart agriculture.

•Successfully clustered the white matter functional networks of 10 neonates.•Identified significant differences in connectivity between preterm and full-term neonates.•Analyzed white matter connectivity and the effects of gender, age, and hemispheric differences.•Evaluated spontaneous activity of white matter networks with gender, age, and hemispheric effects. In recent years, the development of neonatal brain networks has become a research focus, with traditional studies primarily emphasizing gray matter (GM) functional networks. This study systematically explores the developmental characteristics of white matter (WM) functional networks in neonates. Utilizing data from the third release of the Developing Human Connectome Project (dHCP), we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 730 full-term and 157 preterm neonates. We successfully identified ten large-scale WM functional networks and validated their correspondence with established WM fiber tracts using diffusion tensor imaging (DTI). We examined WM functional networks from two dimensions: network functional connectivity and spontaneous activity, incorporating four factors: preterm birth status, age, sex, and hemispheric differences. The results indicate that WM network functional connectivity significantly increases with age, with preterm infants exhibiting lower connectivity than full-term infants, whereas no significant differences were observed between sexes or hemispheres. Regarding spontaneous activity, preterm infants showed lower amplitude in the low-frequency range, whereas in the high-frequency range, their amplitude distribution was more unstable and dispersed. Additionally, certain differences in spontaneous activity were observed between hemispheres and sexes. These findings provide novel insights into the early development of neonatal brain networks and hold significant implications for clinical interventions and treatment strategies for preterm infants.

Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network’s ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables.

Accurate detection and segmentation of the object fruit is the key part of orchard production measurement and automated picking. Affected by light, weather, and operating angle, it brings new challenges to the efficient and accurate detection and segmentation of the green object fruit under complex orchard backgrounds. For the green fruit segmentation, an efficient YOLOF-snake segmentation model is proposed. First, the ResNet101 structure is adopted as the backbone network to achieve feature extraction of the green object fruit. Then, the C5 feature maps are expanded with receptive fields and the decoder is used for classification and regression. Besides, the center point in the regression box is employed to get a diamond-shaped structure and fed into an additional Deep-snake network, which is adjusted to the contours of the target fruit to achieve fast and accurate segmentation of green fruit. The experimental results show that YOLOF-snake is sensitive to the green fruit, and the segmentation accuracy and efficiency are significantly improved. The proposed model can effectively extend the application of agricultural equipment and provide theoretical references for other fruits and vegetable segmentation.

Background Endometriosis is thought to affect the effectiveness of ART by an increased risk of miscarriage. We aimed to investigate the impact of endometriosis in women achieving singleton pregnancies through IVF fresh cycles and risk of miscarriage. Methods This retrospective cohort study included all women undergoing a first IVF cycle and achieving singleton pregnancies after fresh embryo transfer in a tertiary university hospital reproductive medical center between January 2008 and June 2016. Women with endometriosis were compared with women with no endometriosis. Women in the endometriosis group were all with a history of laparoscopy or laparotomy for endometriosis and/or with ovarian endometrioma. The control group was matched 1:2 according to age and study period. Results Among the cohort, we identified 1006 women with endometriosis as study group and 2012 unaffected women matched in a 1:2 ratios as control group. The miscarriage rate between women with and without endometriosis was similar (22.4 and 20.1%, P  = 0.085). The odds ratio after adjusting for the risk factors for miscarriage was 1.14 (95% confidence interval 0.95–1.37). In the study group, the women with and without endometrioma did not show a significant risk of miscarriage, (19.8 and 23.8%, P  = 0.152, OR 0.79, 95% CI 0.58–1.09). The miscarriage rate in women with endometrioma ≥30 mm (37.3 ± 7.1 mm) and < 30 mm (19.3 ± 5.5 mm) was not significantly different, (24.7 and 18.5%, P  = 0.229, OR 1.44, 95% CI 0.79–2.63). After adjustment for risk factors for miscarriage, the presence of endometrioma and the size of endometrioma, regression model confirmed no significant increase for the risk of miscarriage in the subgroup analyses. Conclusions The risk of miscarriage did not statistically increase in women with endometriosis who achieved pregnancy through IVF fresh cycles.

Background: Salt intake in China was high and a series of salt reduction measures were accordingly carried out recently. Our study aimed to assess the long-term effect of a scale-up community randomized controlled trial (RCT); Methods: Individuals between the ages of 18 and 75, from six provinces in China, were recruited and randomized into control (n = 1347) and intervention (n = 1346) groups. A one-year salt reduction intervention was first implemented in the intervention group, followed by a two-year scale-up intervention in both groups. The 24 h urine sample, anthropometric measurement, and knowledge, attitude, and practice (KAP) of salt reduction, as well as lifestyle information, were collected at baseline, after one-year RCT (mid-term evaluation, n = 2456), and two-year scale-up intervention (terminal evaluation, n = 2267); Results: Both control (351.82 mg/24 h, p < 0.001) and intervention (192.84 mg/24 h, p = 0.006) groups showed a decrease in 24 h urinary sodium excretion from baseline to terminal evaluation. Except for an increase in 24 h urinary potassium excretion (85.03 mg/24 h, p = 0.004) and a decrease in systolic blood pressure (SBP) (2.95 mm Hg, p < 0.001) in the intervention group at the mid-term assessment, no statistically significant differences in other indicators were found between two groups. The KAP of salt reduction in two groups was gradually improved; Conclusions: After one-year RCT and two-year scale-up, all participants showed a decreasing trend in 24 h urinary sodium excretion and an increase in salt reduction KAP. The community salt reduction intervention package has the potential for broader application across other regions in China.

The detailed morphometry alterations of the human hippocampal formation (HF) for blind individuals are still understudied. 50 subjects were recruited from Yantai Affiliated Hospital of Binzhou Medical University, including 16 congenital blindness, 14 late blindness, and 20 sighted controls. Volume and shape analysis were conducted between the blind (congenital or late) and sighted groups to observe the (sub)regional alterations of the HF. No significant difference of the hippocampal volume was observed between the blind and sighted subjects. Rightward asymmetry of the hippocampal volume was found for both congenital and late blind individuals, while no significant hemispheric difference was observed for the sighted controls. Shape analysis showed that the superior and inferior parts of both the hippocampal head and tail expanded, while the medial and lateral parts constrained for the blind individuals as compared to the sighted controls. The morphometry alterations for the congenital blind and late blind individuals are nearly the same. Significant expansion of the superior part of the hippocampal tail for both congenital and late blind groups were observed for the left hippocampi after FDR correction. Current results suggest that the cross-model plastic may occur in both hemispheres of the HF to improve the navigation ability without the stimuli of visual cues, and the alteration is more prominent for the left hemisphere.

PurposeTo evaluate the efficacy of levonorgestrel-releasing intrauterine device (LNG-IUD) during controlled ovarian stimulation (COS) in patients with early-stage endometrioid endometrial cancer (EEC).MethodsA retrospective study was conducted on patients with stage IA1 EEC who achieved complete response after fertility-sparing treatment from December 2018 to December 2021, with all the women who underwent COS having LNG-IUDs inserted in their uterine cavity.Results16 patients were enrolled who underwent 26 COS cycles and average age was 33.19 ± 4.04 years. 12 patients had 19 subsequent frozen-thawed embryo transfer (FET) cycles. Among the other four patients, no embryos were obtained in 1 patient, 1 patient got pregnancy spontaneously with term delivery after COS, 1 patient relapsed before FET, and 1 patient did not receive embryo transfer for personal reason. Among 19 FET cycles, the clinical pregnancy and live birth rates in each ET cycle were 36.84% (7/19) and 26.32% (5/19), respectively. 7 clinical pregnancies resulted in 2 miscarriages (28.6%), and 5 live births (71.4%). Totally 6 patients achieved 7 live births, and the cumulative live birth rate was 37.5% (6/16). Three (18.75%) out of 16 patients relapsed after COS during the follow-up period (31.31 ± 15.89 months) and two of them were initially diagnosed with moderately differentiated EEC. Time interval from COS to relapse was 6.63,11.67 and 16.23 months, respectively.ConclusionThe combination of LNG-IUD treatment and segmented IVF may be a viable treatment strategy to improve oncological and reproductive outcomes for patients with early-stage EEC.

In recent years, environmental problems have occurred frequently in China, and the relationship between environmental performance (EP) and financial performance (FP) plays an indispensable role in exploring the internalization of corporate environmental responsibility. Using a sample of Chinese listed firms in heavily polluting industries and collected data of the Unit Sewage Fee, we empirically examine the impact of EP on FP. Our empirical results show a concave-down quadratic relationship between EP and FP. After heavily polluting industries became subject to stricter environmental disclosure regulations, however, we found that the quadratic relationship between EP and FP became weaker. Moreover, we revealed that for state-owned firms, the quadratic relationship between EP and FP is not as obvious as it is for non-state-owned firms. Our paper contributes to the growing literature on EP management. It also offers evidence of experience in perfecting the design of enterprise environmental information disclosure systems.