Study on the influence of syphilis on the outcome of frozen-thawed embryo transfer in infertility patients.

Objective: In this study, the effect of in vitro Fertilization-Embryo Transfer (IVF-ET) on the clinical outcome of patients with syphilis infertility during resuscitation cycle. Methods: A retrospective single-center method was adopted. This study included 4430 pairs of infertile patients who underwent syphilis detection. The influence of the syphilis freeze-thaw embryos transplantation outcome was studied in the patients with infertility by comparing the general clinical characteristics of patients (age, years of infertility, body mass index (BMI), basal follicle stimulating hormone (FSH), serum basal estradiol (Estradiol, E2), transplanted intimal thickness, the number of embryos transferred) and the clinical pregnancy (biochemical pregnancy rate, clinical pregnancy rate, implantation rate, live birth rate and abortion rate). Results: Firstly, in the clinical outcome of one frozen-thawed embryos transfer, the live birth rate of the woman's syphilis-infected group was lower than that of the uninfected group (71.3 % vs. 50.0 %), while the abortion rate was higher than that of the uninfected group (7.8 % vs. 26.7 %), and there was a statistical difference (P < 0.05), and there was no statistical difference in other indicators between other groups (P > 0.05). Secondly, in the clinical outcome of two frozen-thawed embryos transfers, the biochemical pregnancy rate (61.3 % vs. 28.6 %) and clinical pregnancy rate (42.9 % vs. 14.3 %) of the group which was infected with syphilis alone were lower than those of the uninfected group (P < 0.05), and other indicators among the other groups showed no statistical difference (P > 0.05). Thirdly, in the clinical outcomes of frozen-thawed embryos transfer three times or more, there was no significant difference in the clinical indicators between the syphilis infertility patients and the non-infected infertility patients (P > 0.05). Conclusion: When the syphilis infertility patients and the non-infected infertile patients underwent IVF-ET treatment for the first time, the live birth rate and abortion rate of the syphilis group were significantly different (P < 0.05). In the outcome of two transplants, the biochemical pregnancy rate and clinical Pregnancy rates were significantly reduced so patients with syphilis infertility who undergo IVF-ET should be informed about the risk of adverse clinical outcomes.

Eclipse web service application programming interface.

Objective.Low-coupling seamless integration of multiple systems is the core foundation of smart radiotherapy. Following Service-Oriented Architecture style, a set of named operations (Eclipse Web Service API, EWSAPI) was developed for realizing network call of Eclipse.Approach.Under the guidance of Vertical Slice Architecture, EWSAPI was implemented in the C# language and based on ASP .Net Core 6.0. Each operation consists of three components: Request, Endpoint and Response. Depending on the function, the exchanged data for each operation, as input or output parameters, is the empty or a predefined JSON data. These operations were realized and enriched gradually, layer by layer, with reference to the clinical business classification. The business logic of each operation was developed and maintained independently. In situations where Eclipse Scripting API(ESAPI) was required, constraints of ESAPI were followed.Main results.Selected features of Eclipse TPS were encapsulated as standard web services, which can be invocated by other software through network. Several processes for data quality control and planning were encapsulated into interfaces, thereby extending the functionality of Eclipse. Currently, EWSAPI already covers testing of service interface, quality control of radiotherapy data, automation tasks for plan designing and DICOM RT files' transmission. All the interfaces support asynchronous invocation. A separate Eclipse context will be created for each invocation, and is released in the end.Significance.EWSAPI which is a set of standard web services for calling Eclipse features through network is flexible and extensible. It is an efficient way to integration of Eclipse and other systems and will be gradually enriched with the deepening of clinical applications.

The investigation of ion association characteristics in lanthanum sulfate solution by the density functional theory and molecular dynamics simulations.

The ion association behavior in aqueous lanthanum sulfate solutions was investigated using density functional theory (DFT). The structures and properties of [La(SO4)m·(H2O)n](3-2m) clusters, where m = 1 to 3 and n = 1 to 9, were examined at the PBE0/6-311+G(d, p) level. The results show that Lanthanum sulfate hydrated clusters exist in the aqueous solution's microscopic state of contact ion pairs (CIP). [La(SO4)(H2O)n]+ and [La(SO4)2·(H2O)n]-, and [La(SO4)3·(H2O)n]3- clusters approximately reach the saturation of the first water shell at n = 7 and 6 and 3. [La(SO4)2·(H2O)6]- and [La(SO4)3·(H2O)3]3- clusters have lower binding energy than [LaSO4·(H2O)n]+. This indicates that lanthanum sulfate tends to aggregate in an aqueous solution. Compared to the gas-phase cluster structures, the distance of R(La-O)H2O expands in the PCM solvent model, while R(La-O)SO4 contracts. The hydration energy of LaSO4·(H2O)7, La(SO4)2·(H2O)6, and La(SO4)3·(H2O)3 were -76.5, -54.1 and -332.0 kcal/mol, respectively. The molecular dynamics simulation results show that La is more inclined to coordinate with sulfate's oxygen than water's oxygen, and the coordination number of water around La3+ is 6.075. These results are consistent with the calculated results by DFT.

Revealing the diagnostic value and immune infiltration of senescence-related genes in endometriosis: a combined single-cell and machine learning analysis.

Introduction: Endometriosis is a prevalent and recurrent medical condition associated with symptoms such as pelvic discomfort, dysmenorrhea, and reproductive challenges. Furthermore, it has the potential to progress into a malignant state, significantly impacting the quality of life for affected individuals. Despite its significance, there is currently a lack of precise and non-invasive diagnostic techniques for this condition. Methods: In this study, we leveraged microarray datasets and employed a multifaceted approach. We conducted differential gene analysis, implemented weighted gene co-expression network analysis (WGCNA), and utilized machine learning algorithms, including random forest, support vector machine, and LASSO analysis, to comprehensively explore senescence-related genes (SRGs) associated with endometriosis. Discussion: Our comprehensive analysis, which also encompassed profiling of immune cell infiltration and single-cell analysis, highlights the therapeutic potential of this gene assemblage as promising targets for alleviating endometriosis. Furthermore, the integration of these biomarkers into diagnostic protocols promises to enhance diagnostic precision, offering a more effective diagnostic journey for future endometriosis patients in clinical settings. Results: Our meticulous investigation led to the identification of a cluster of genes, namely BAK1, LMNA, and FLT1, which emerged as potential discerning biomarkers for endometriosis. These biomarkers were subsequently utilized to construct an artificial neural network classifier model and were graphically represented in the form of a Nomogram.

The Effect of Low-Molecular-Weight Heparin on Immune Balance of Patients with Repeated Implantation Failure During the Implantation Window.

This study investigates the effect of low-molecular-weight heparin (LMWH) on cytokines TNF-α, IFN-γ, IL-2, IL-4, IL-6, and IL-10 in peripheral blood of patients with repeated implantation failure during the implantation window. From May 2019 to March 2021, we enrolled 32 patients with recurrent implantation failure (RIF group) and 30 patients with successful pregnancy after the first frozen embryo transfer (control group) in the Reproductive Medicine Centre of Wuxi Maternity and Child Health Care Hospital. During the implantation window, the following features were compared between two groups and between different time points using ELISA: the status of immune cytokines in peripheral blood; Th1 cytokines (TNF-α, IFN-γ, and IL-2) and Th2 cytokines (IL-4, IL-6, and IL-10) in peripheral blood. The levels of Th1 cytokines in the RIF group before treatment were higher in comparison with the control group. In the RIF group, the LMWH treatment can inhibit the expression of Th1 cytokines and enhance the expression of Th2 cytokines. Using LMWH during the implantation window can improve the immune imbalance of patients with repeated implantation failure, which makes it a potential treatment strategy for patients with abnormal cellular immunity.

High carrier frequency of pathogenic PATL2 gene mutations predicted in population: a bioinformatics-based approach.

Topoisomerase II homologue 2 (PATL2) has been confirmed to be a key gene that contributes to oocyte maturation. However, the allele distribution and carrier frequency of these mutations remain uncharacterized. So a bioinformatics subcategory analysis of PATL2 mutations from outcome data and Single Nucleotide Polymorphism (SNP) databases was conducted. Altogether, the causative PATL2 mutation number detected in patients with oocyte maturation defects in the clinical studies and pathogenic PATL2 mutation sites predicted by software based on the database was approximately 53. The estimated carrier frequency of pathogenic mutation sites was at least 1.14‰ based on the gnomAD and ExAC database, which was approximately 1/877. The highest frequency of mutations detected in the independent patients was c.223-14_223-2del13. The carrier frequency of this mutation in the population was 0.25‰, which may be a potential threat to fertility. Estimated allele and carrier frequency are relatively higher than those predicted previously based on clinical ascertainment. A review of PATL2 mutation lineage identified in 34 patients showed that 53.81%, 9.22% and 14.72% of the oocytes with PATL2 mutations were arrested at the germinal vesicle (GV) stage, metaphase I (MI) stage and first polar body stage, respectively. Oocytes that could develop to the first polar body stage were extremely rare to fertilise, and their ultimate fate was early embryonic arrest. Phenotypic variability is related to the function of the regions and degree of loss of function of PATL2 protein. A 3D protein structure changes predicted by online tools, AlphaFold, showed aberrations at the mutation sites, which may explain partially the function loss. When the mutated and wild-type proteins are not in the same amino acid category, the protein structure will be considerably unstable. The integration of additional mutation sites with phenotypes is helpful in drawing a complete picture of the disease. Bioinformatics analysis of PATL2 mutations will help reveal molecular epidemiological characteristics and provide an important reference for new mutation assessment, genetic counselling and drug research.

[PyRERT: Python Research Environment for Radiation Therapy].

OBJECTIVE: The Python research environment for radiation therapy (PyRERT) is a set of business software for hospital physicists to conduct radiation therapy research. METHODS: Choose the open source Enthought Tool Suite（ETS） as the core external dependency library of PyRERT. PyRERT is divided into base layer, content layer and interaction layer, and each layer is composed of different functional modules. RESULTS: PyRERT V1.0 provide a good development environment for scientific research programming in DICOM RT file processing, batch processing of water tank scan data, digital phantom creation, 3D medical image volume visualization, virtual radiotherapy equipment driver, and film scan image analysis. CONCLUSIONS: PyRERT enables the results of the research group to be iteratively inherited in the form of software. It's reusable basic classes and functional modules greatly improve the efficiency of scientific research task programming.

Symmetrical awareness network for cross-site ultrasound thyroid nodule segmentation.

Recent years have seen remarkable progress of learning-based methods on Ultrasound Thyroid Nodules segmentation. However, with very limited annotations, the multi-site training data from different domains makes the task remain challenging. Due to domain shift, the existing methods cannot be well generalized to the out-of-set data, which limits the practical application of deep learning in the field of medical imaging. In this work, we propose an effective domain adaptation framework which consists of a bidirectional image translation module and two symmetrical image segmentation modules. The framework improves the generalization ability of deep neural networks in medical image segmentation. The image translation module conducts the mutual conversion between the source domain and the target domain, while the symmetrical image segmentation modules perform image segmentation tasks in both domains. Besides, we utilize adversarial constraint to further bridge the domain gap in feature space. Meanwhile, a consistency loss is also utilized to make the training process more stable and efficient. Experiments on a multi-site ultrasound thyroid nodule dataset achieve 96.22% for PA and 87.06% for DSC in average, demonstrating that our method performs competitively in cross-domain generalization ability with state-of-the-art segmentation methods.

[Study on the sensitivity of a volumetric modulated arc therapy plan verification equipment on multi-leaf collimator opening and closing errors and its gamma pass rate limit].

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.

Sensitivity of Three Patient-Specific Quality Assurance Systems to MLC Aperture Errors With Volumetric Modulated Arc Therapy.

Purpose: To compare the sensitivity of ArcCHECK (AC), portal dosimetry (PD), and an in-house logfile-based system (LF) to multileaf collimators (MLC) aperture errors and the ability to identify these errors. Methods and Materials: For 12 retrospective original head and neck volumetric modulated arc therapy (VMAT) plans, MLC aperture errors of ± 0.4mm, ± 1.2mm, ± 2mm, and ± 3mm were introduced for each plan, resulting in 96 plans with errors. AC, PD, and LF were used for the gamma evaluation at 3%/3mm, 3%/2mm, and 2%/2mm criteria. Gradient analysis was used to evaluate the sensitivity to MLC aperture errors. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve was used to evaluate the ability to identify MLC aperture errors and dose errors, and the optimal cut-off value to identify the error was obtained. Results: The gamma pass rate (%GP) of LF had the smallest descent gradient as the MLC error increases in any case. The descent gradient of PD was larger than AC, except for the case at the 2%/2mm criteria. For the 3%/3mm criteria, the MLC aperture errors that can be perfectly identified by AC, PD, and LF were ± 3mm, ± 2mm, and ± 1.2mm, respectively, and the average percent dose error (%DEs) of dose metrics in targets that can be perfectly identified were 4% to 5%, 3% to 4%, and 2% to 3%, respectively. For the 3%/2mm criteria, the errors that AC, PD, and LF can perfectly identify were the same as the 3%/3mm criteria. For the 2%/2mm criteria, AC can perfectly identify the MLC error of ± 2mm and the %DE of 3% to 4%. PD and LF can identify the MLC error of ± 1.2mm and the %DE of 2% to 3%. Conclusion: Different patient-specific quality assurance (PSQA) systems have different sensitivity and recognition abilities to MLC aperture errors. Institutions should formulate their own customized %GP limits based on their PSQA process through ROC or other methods.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform.

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Associations of Parity With Change in Global Cognition and Incident Cognitive Impairment in Older Women.

Background: The evidence of the association between parity and risk of mild cognitive impairment (MCI) or dementia is mixed, and the relationship between parity and longitudinal cognitive changes is less clear. We investigated these issues in a large population of older women who were carefully monitored for development of MCI and probable dementia. Methods: Using the Women's Health Initiative Memory Study, 7,100 postmenopausal women (mean age 70.1 ± 3.8 years) with information on baseline parity (defined as the number of term pregnancies), measures of global cognition (Modified Mini-Mental State Examination score) from 1996-2007, and cognitive impairment (centrally adjudicated diagnoses of MCI and dementia) from 1996-2016 were included. Multivariable linear mixed-effects models were used to analyze the rate of changes in global cognition. Cox regression models were used to evaluate the risk of MCI/dementia across parity groups. Results: Over an average of 10.5 years, 465 new cases of MCI/dementia were identified. Compared with nulliparous women, those with a parity of 1-3 and ≥4 had a lower MCI/dementia risk. The HRs were 0.75 (0.56-0.99) and 0.71 (0.53-0.96), respectively (P < 0.01). Similarly, a parity of 1-3 and ≥4 was related to slower cognitive decline (ß = 0.164, 0.292, respectively, P < 0.05). Conclusion: Higher parity attenuated the future risk for MCI/dementia and slowed the rates of cognitive decline in elderly women. Future studies are needed to determine how parity affects late-life cognitive function in women.

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.

FRDD-Net: Automated Carotid Plaque Ultrasound Images Segmentation Using Feature Remapping and Dense Decoding.

Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound images and heterogenous characteristics of carotid plaques. To address those problems, in this paper, we propose a novel deep convolutional neural network, FRDD-Net, with an encoder-decoder architecture to automatically segment carotid plaques. We propose the feature remapping modules (FRMs) and incorporate them into the encoding and decoding blocks to ameliorate the reliability of acquired features. We also propose a new dense decoding mechanism as part of the decoder, thus promoting the utilization efficiency of encoded features. Additionally, we construct a compound loss function to train our network to further enhance its robustness in the face of numerous cases. We train and test our network in multiple carotid plaque ultrasound datasets and our method yields the best performance compared to other state-of-the-art methods. Further ablation studies consistently show the advancement of our proposed architecture.

Initial Experience of a Tele-radiotherapy System for Training Radiation Oncologists in Rural Areas.

Telemedicine is considered to be an important approach for medical education in rural areas. Due to a significant shortage of radiation oncologists in rural areas of Sichuan Province in China, a tele-radiotherapy system has been designed and developed for training radiation oncologists in the Sichuan Cancer Hospital and Research Institute. The whole process of the radiotherapy teaching platform was designed and established in the tele-radiotherapy system. A detailed radiation therapy process could be obtained in rural areas through the tele-radiotherapy system. Through the tele-radiotherapy system, oncologists in rural hospitals are trained at any time and anywhere. And the experience of experts in the Sichuan Cancer Hospital and Research Institute is effectively and quickly conveyed to rural areas. A tele-radiotherapy system is considered to be an important means to promote the level of radiotherapy and to solve the shortage of radiation oncologists in rural areas.

Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation.

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder-decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.

Variability in Cardiometabolic and Inflammatory Parameters and Cognitive Decline.

INTRODUCTION: The relationship between variability in cardiometabolic and inflammatory parameters and cognitive changes is unknown. This study investigates the association of visit-to-visit variability in BMI, mean arterial pressure, total cholesterol, triglycerides, HbA1c, high-sensitivity C-reactive protein, ferritin, and fibrinogen with cognitive decline. METHODS: This population-based cohort study included 2,260 individuals (mean age=63.0 [SD=7.5] years) free of cognitive diseases who underwent ≥3 clinical measurements from 2004 to 2019. Variability was expressed as variability independent of the mean across visits. Participants were divided on the basis of quartiles of variability score, a scoring system generated to explore the composite effect of parameter variability (range=0-24), where 0 points were assigned for Quartile 1, 1 point was assigned for Quartile 2, 2 points were assigned for Quartile 3, and 3 points were assigned for Quartile 4, each for the variability of 8 parameters measured as variability independent of the mean. Linear mixed models evaluated the longitudinal associations with cognitive decline in memory and verbal fluency. All analyses were conducted in 2020-2021. RESULTS: Higher BMI, mean arterial pressure, total cholesterol, HbA1c, and ferritin variability were linearly associated with cognitive decline irrespective of their mean values. In addition, participants in the highest quartile of variability score had a significantly worse cognitive decline rate in memory (-0.0224 points/year, 95% CI= -0.0319, -0.0129) and verbal fluency (-0.0088 points/year, 95% CI= -0.0168, -0.0008) than those in the lowest quartile. CONCLUSIONS: A higher variability in cardiometabolic and inflammatory parameters was significantly associated with cognitive decline. Stabilizing these parameters may serve as a target to preserve cognitive functioning.

Depression as a Mediator of the Association Between Wealth Status and Risk of Cognitive Impairment and Dementia: A Longitudinal Population-Based Cohort Study.

BACKGROUND: Wealth and income are potential modifiable risk factors for dementia, but whether wealth status, which is composed of a combination of debt and poverty, and assessed by wealth and income, is associated with cognitive impairment among elderly adults remains unknown. OBJECTIVE: To examine the associations of different combinations of debt and poverty with the incidence of dementia and cognitive impairment without dementia (CIND) and to evaluate the mediating role of depression in these relationships. METHODS: We included 15,565 participants aged 51 years or older from the Health and Retirement Study (1992-2012) who were free of CIND and dementia at baseline. Dementia and CIND were assessed using either the modified Telephone Interview for Cognitive Status (mTICS) or a proxy assessment. Cox models with time-dependent covariates and mediation analysis were used. RESULTS: During a median of 14.4 years of follow-up, 4,484 participants experienced CIND and 1,774 were diagnosed with dementia. Both debt and poverty were independently associated with increased dementia and CIND risks, and the risks were augmented when both debt and poverty were present together (the hazard ratios [95% confidence intervals] were 1.35 [1.08-1.70] and 1.96 [1.48-2.60] for CIND and dementia, respectively). The associations between different wealth statuses and cognition were partially (mediation ratio range: 11.8-29.7%) mediated by depression. CONCLUSION: Debt and poverty were associated with an increased risk of dementia and CIND, and these associations were partially mediated by depression. Alleviating poverty and debt may be effective for improving mental health and therefore curbing the risk of cognitive impairment and dementia.

PSANet: Pyramid Splitting and Aggregation Network for 3D Object Detection in Point Cloud.

3D object detection in LiDAR point clouds has been extensively used in autonomous driving, intelligent robotics, and augmented reality. Although the one-stage 3D detector has satisfactory training and inference speed, there are still some performance problems due to insufficient utilization of bird's eye view (BEV) information. In this paper, a new backbone network is proposed to complete the cross-layer fusion of multi-scale BEV feature maps, which makes full use of various information for detection. Specifically, our proposed backbone network can be divided into a coarse branch and a fine branch. In the coarse branch, we use the pyramidal feature hierarchy (PFH) to generate multi-scale BEV feature maps, which retain the advantages of different levels and serves as the input of the fine branch. In the fine branch, our proposed pyramid splitting and aggregation (PSA) module deeply integrates different levels of multi-scale feature maps, thereby improving the expressive ability of the final features. Extensive experiments on the challenging KITTI-3D benchmark show that our method has better performance in both 3D and BEV object detection compared with some previous state-of-the-art methods. Experimental results with average precision (AP) prove the effectiveness of our network.

EPGNet: Enhanced Point Cloud Generation for 3D Object Detection.

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder-decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird's eye view) object detection compared with some previous state-of-the-art methods.