Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

PlaneRecTR: Unified Query Learning for 3D Plane Recovery from a Single View

3D plane recovery from a single image can usually be divided into several subtasks of plane detection, segmentation, parameter estimation and possibly depth estimation. Previous works tend to solve this task by either extending the RCNN-based segmentation network or the dense pixel embedding-based clustering framework. However, none of them tried to integrate above related subtasks into a unified framework but treat them separately and sequentially, which we suspect is potentially a main source of performance limitation for existing approaches. Motivated by this finding and the success of query-based learning in enriching reasoning among semantic entities, in this paper, we propose PlaneRecTR, a Transformer-based architecture, which for the first time unifies all subtasks related to single-view plane recovery with a single compact model. Extensive quantitative and qualitative experiments demonstrate that our proposed unified learning achieves mutual benefits across subtasks, obtaining a new state-of-the-art performance on public ScanNet and NYUv2-Plane datasets. Codes are available at https://github.com/SJingjia/PlaneRecTR.Comment: To be published in Proceedings of IEEE International Conference on Computer Vision (ICCV 2023). Camera Ready Version. Codes: https://github.com/SJingjia/PlaneRecTR , Video: https://youtu.be/YBB7totHGJ

-Norm Regularization in Volumetric Imaging of Cardiac Current Sources

Advances in computer vision have substantially improved our ability to analyze the structure and mechanics of the heart. In comparison, our ability to observe and analyze cardiac electrical activities is much limited. The progress to computationally reconstruct cardiac current sources from noninvasive voltage data sensed on the body surface has been hindered by the ill-posedness and the lack of a unique solution of the reconstruction problem. Common L2- and L1-norm regularizations tend to produce a solution that is either too diffused or too scattered to reflect the complex spatial structure of current source distribution in the heart. In this work, we propose a general regularization with Lp-norm () constraint to bridge the gap and balance between an overly smeared and overly focal solution in cardiac source reconstruction. In a set of phantom experiments, we demonstrate the superiority of the proposed Lp-norm method over its L1 and L2 counterparts in imaging cardiac current sources with increasing extents. Through computer-simulated and real-data experiments, we further demonstrate the feasibility of the proposed method in imaging the complex structure of excitation wavefront, as well as current sources distributed along the postinfarction scar border. This ability to preserve the spatial structure of source distribution is important for revealing the potential disruption to the normal heart excitation

Active Surveillance of Carbapenemase-Producing Organisms (CPO) Colonization With Xpert Carba-R Assay Plus Positive Patient Isolation Proves to Be Effective in CPO Containment

Background: Rapid screening of patients for colonization with carbapenemase-producing organisms (CPO), coupled with implementation of infection prevention strategies, has the potential to contain the spread of CPO.Methods: We first evaluated the performance of Xpert Carba-R assay (in comparison with other phenotypic methods) for carbapenemase detection using clinical isolates, and then used it to determine the intestinal CPO colonization in hospitalized patients. We then assessed the effectiveness of patient isolation in controlling the spread of CPO in a medical intensive care unit.Results: The Xpert Carba-R assay required the least processing time to reveal results and showed a 94.5% sensitivity and specificity in carbapenemase detection, except for IMP-8 (n = 4). During a 6-month study period, 134 patients in one ward were studied for CPO colonization and infection. Fifteen patients (11.2%) were colonized by CPO as detected by Xpert Carba-R assay, including three NDM, three IMP, and nine KPC possessing strains. The overall colonization and CPO infection rates were both 11.2% each. Isolation of patients with CPO led to a reduction in both colonization (from 28.6 to 5.6%) and infection rates (from 35.7 to 2.8%) during the study period (p < 0.05).Conclusion: Active surveillance of CPO utilizing the Xpert Carba-R assay supplemented with immediate patient isolation, proved to be an effective strategy to limit the spread of CPO in a health care setting

Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.

The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis

The neutrophil percentage-to-albumin ratio is an independent risk factor for poor prognosis in peritoneal dialysis patients

AbstractAim This study aimed to investigate the predictive ability of the neutrophil percentage-to-albumin Ratio (NPAR) concerning all-cause mortality and cardio-cerebrovascular mortality in patients undergoing peritoneal dialysis (PD).Methods We included a total of 807 PD patients from the Peritoneal Dialysis Center of the Second Affiliated Hospital of Soochow University between January 2009 and December 2019 in this study. Patients were categorized into three groups based on their baseline NPAR. The Kaplan-Meier method, multivariate Cox proportional hazard model, and Fine-Gray competing risk model were employed to examine the relationship between NPAR level and all-cause mortality and cardio-cerebrovascular mortality among PD patients. Furthermore, the ROC curve and calibration plots were utilized to compare the performance between NPAR and other conventional indicators.Results The mean follow-up period was 38.2 months. A total of 243 (30.1%) patients passed away, with 128 (52.7%) succumbing to cardio-cerebrovascular diseases. The mortality rates of the Middle and High NPAR groups were significantly greater than that of the Low NPAR group (p < 0.001), and NPAR was independently associated with all-cause mortality and cardio-cerebrovascular mortality. Receiver Operating Characteristic (ROC) analysis indicated that the Area Under the Curve (AUC) of NPAR (0.714) was significantly superior to those of C-reactive protein (CRP) (0.597), neutrophil to lymphocyte ratio (NLR) (0.589), C-reactive protein to albumin ratio (CAR) (0.698) and platelet to lymphocyte ratio (PLR) (0.533).Conclusion NPAR served as an independent predictive marker for all-cause mortality and cardio-cerebrovascular mortality in PD patients. Moreover, NPAR demonstrated superior predictive potential compared to CRP, CAR, NLR, and PLR

Going Open Access: The Attitudes and Actions of Scientific Journal Editors in China

This study aims to investigate the attitudes and actions of scientific journal editors in China towards open access. Semi-structured interviews were conducted with 17 Chinese editors from various scientific journals during September and October of 2022. The results indicate that the editors generally possess knowledge of open access and have implemented an appropriate open access model for their respective journals. However, the Chinese-language journal editors expressed a lack of motivation to adopt open access, unless there is a reform in the mechanism of academic publishing or a policy is imposed. On the other hand, the English-language journal editors acknowledged that they have no other choice but to adopt open access. This study helps us learn about Chinese editors’ understanding and attitudes towards open access, the current status of open access in China’s scientific journals, and the mechanisms of academic publishing in China

Dissatisfaction of people with type 2 diabetes with the care received at a diabetes clinic in Ningbo, China: a cross-sectional study

Aim: The study aimed to assess the dissatisfaction of people with type 2 diabetes mellitus (T2DM) with the care that they received at a diabetes outpatient clinic in Ningbo, China and to determine the associated factors. Methods: A cross-sectional study was conducted among 406 adults with T2DM in 2020–21. Those who were treated at the diabetes outpatient clinic for at least six consecutive months prior to the survey date were eligible. The Short Assessment of Patient Satisfaction (SAPS) was used to assess participants’ dissatisfaction with the care that they received.Results: 25.1% of participants were not satisfied with the care that they received at the diabetes outpatient clinic in Ningbo. The odds of dissatisfaction were higher in physically active people compared to those who were not (OR 3.41, 95% CI 1.56 to 7.45) and those with ＞1-5 years of T2DM compared to ≤1 year (2.18, 1.05 to 4.53). Conclusion: A quarter of people with T2DM were dissatisfied with the care that they received at the diabetes outpatient clinic in Ningbo, China, and the factors associated with dissatisfaction were identified

Association of air pollutants and osteoporosis risk: The modifying effect of genetic predisposition

Background: Limited studies have examined the association between air pollutants and osteoporosis incidence; however, the results are conflicting. We aimed to quantify the effects of selected air pollutants on osteoporosis risk and explore the modifying effect of genetic predisposition. Methods: A total of 422,955 subjects who did not have osteoporosis at baseline in the UK Biobank were included from 2006 to 2010. We conducted a Cox proportional hazards model with adjustment for covariates to examine the association between air pollutant scores and individual air pollutants and incident osteoporosis. Furthermore, a polygenic risk score (PRS) of osteoporosis was built and examined to determine whether genetic susceptibility modified the effect of air pollutants on osteoporosis. The relationship between air pollutants and osteoporosis was examined by using a restricted cubic spline (RCS) method. Results: After confounder adjustment, the results showed a remarkable increase in the risk of osteoporosis with each 10 unit increase in exposure to air pollution (hazard ratio: 1.06, 95 % confidence interval: 1.03–1.08), PM2.5 (1.94, 1.52–2.48), NO2 (1.06, 1.02–1.10), and NOX (1.03, 1.01–1.04). However, no significant association was observed between PM10 or PM2.5-10 exposure and osteoporosis. Subjects with high air pollutant exposure levels and a high PRS had a noteworthy increase in osteoporosis risk compared to those with low air pollutant exposure levels and a low PRS. Air pollutants and genetic variants exerted additive effects on the risk of osteoporosis. Positive correlations were observed between osteoporosis and PM2.5 (P < 0.001), NO2 (P = 0.001), and NOx (P = 0.002) exposure. Conclusions: Exposure to PM2.5, NO2 and NOx was associated with an increase in osteoporosis risk, and this effect was more pronounced in populations with high genetic risk. The association between PM2.5, NO2 and NOx exposure and osteoporosis is modified by genetic variations