Initiating PeriCBD to probe perinatal influences on neurodevelopment during 3-10 years in China.

Adverse perinatal factors can interfere with the normal development of the brain, potentially resulting in long-term effects on the comprehensive development of children. Presently, the understanding of cognitive and neurodevelopmental processes under conditions of adverse perinatal factors is substantially limited. There is a critical need for an open resource that integrates various perinatal factors with the development of the brain and mental health to facilitate a deeper understanding of these developmental trajectories. In this Data Descriptor, we introduce a multicenter database containing information on perinatal factors that can potentially influence children's brain-mind development, namely, periCBD, that combines neuroimaging and behavioural phenotypes with perinatal factors at county/region/central district hospitals. PeriCBD was designed to establish a platform for the investigation of individual differences in brain-mind development associated with perinatal factors among children aged 3-10 years. Ultimately, our goal is to help understand how different adverse perinatal factors specifically impact cognitive development and neurodevelopment. Herein, we provide a systematic overview of the data acquisition/cleaning/quality control/sharing, processes of periCBD.

Establishment of a mortality risk nomogram for predicting in-hospital mortality of sepsis: cohort study from a Chinese single center.

Objective: To establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population. Methods: Data were obtained from the medical records of sepsis patients enrolled at the Affiliated Huadu Hospital, Southern Medical University, between 2019 and 2021. A total of 696 sepsis patients were initially included in our research, and 582 cases were finally enrolled after screening and divided into the survival group (n = 400) and the non-survival group (n = 182) according to the incidence of mortality during hospitalization. Twenty-eight potential sepsis-related risk factors for mortality were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. We used binary logistic regression to build a model for predicting mortality from the variables based on LASSO regression selection. Binary logistic regression was used to establish a nomogram based on independent mortality risk factors. To validate the prediction accuracy of the nomogram, receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and restricted cubic spline (RCS) analysis were employed. Eventually, the Hosmer-Lemeshow test and calibration curve were used for nomogram calibration. Results: LASSO regression identified a total of ten factors, namely, chronic heart disease (CHD), lymphocyte count (LYMP), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), C reactive protein (CRP), Procalcitonin (PCT), lactic acid, prothrombin time (PT), alanine aminotransferase (ALT), total bilirubin (Tbil), interleukin-6 (IL6), that were incorporated into the multivariable analysis. Finally, a nomogram including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6 was established by multivariable logistic regression. The ROC curves of the nomogram in the training and validation sets were 0.9836 and 0.9502, respectively. DCA showed that the nomogram could be applied clinically if the risk threshold was between 29.52 and 99.61% in the training set and between 31.32 and 98.49% in the testing set. RCS showed that when the value of independent risk factors from the predicted model exceeded the median, the mortality hazard ratio increased sharply. The results of the Hosmer-Lemeshow test (χ2 = 0.1901, df = 2, p = 0.9091) and the calibration curves of the training and validation sets showed good agreement with the actual results, which indicated good stability of the model. Conclusion: Our nomogram, including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6, exhibits good performance for predicting mortality risk in adult sepsis patients.

EvoVis: A Visual Analytics Method to Understand the Labeling Iterations in Data Programming.

Obtaining high-quality labeled training data poses a significant bottleneck in the domain of machine learning. Data programming has emerged as a new paradigm to address this issue by converting human knowledge into labeling functions(LFs) to quickly produce low-cost probabilistic labels. To ensure the quality of labeled data, data programmers commonly iterate LFs for many rounds until satisfactory performance is achieved. However, the challenge in understanding the labeling iterations stems from interpreting the intricate relationships between data programming elements, exacerbated by their many-to-many and directed characteristics, inconsistent formats, and the large scale of data typically involved in labeling tasks. These complexities may impede the evaluation of label quality, identification of areas for improvement, and the effective optimization of LFs for acquiring high-quality labeled data. In this paper, we introduce EvoVis, a visual analytics method for multi-class text labeling tasks. It seamlessly integrates relationship analysis and temporal overview to display contextual and historical information on a single screen, aiding in explaining the labeling iterations in data programming. We assessed its utility and effectiveness through case studies and user studies. The results indicate that EvoVis can effectively assist data programmers in understanding labeling iterations and improving the quality of labeled data, as evidenced by an increase of 0.16 in the average F1 score when compared to the default analysis tool.

A nomogram for predicting in-hospital overall survival of hypertriglyceridemia-induced severe acute pancreatitis: A single center, cross-sectional study.

Background: Hypertriglyceridemia-induced severe acute pancreatitis (HTG-SAP) is a type of pancreatitis characterized by an abnormal elevation of plasma triglyceride. HTG-SAP has been associated with various complications and a high mortality rate. In this study, we established a nomogram for predicting the overall survival (OS) of HTG-SAP patients during hospitalization. Methods: 128 HTG-SAP cases hospitalized at the Affiliated Huadu Hospital, Southern Medical University, from 2019 to 2022 were analyzed retrospectively. A nomogram including prognostic factors correlated with OS during hospitalization was established by multivariate Cox regression analysis. We internally validated the nomogram using time-dependent (at 1-, 2-, and 3- months) survival receiver operating characteristic (SROC) and calibration curve with 500 iterations of bootstrap resampling. Time-dependent decision curve analysis (DCA) was employed to validate the clinical value of the nomogram. Results: Multivariate Cox regression indicated that serum triglyceride, red blood cell distribution width (RDW), lactic acid, and interleukin-6 (IL6) were independent prognostic factors for OS of HTG-SAP patients during hospitalization and were used to construct a nomogram. The time-dependent area under the curve (AUC) values at 1-, 2-, and 3- months were 0.946, 0.913, and 0.929, respectively, and the Concordance index (C-index) of the nomogram was 0.916 (95%CI 0.871-0.961). The time-dependent calibration curves indicated good consistency between the observed and predicted outcomes. The time-dependent DCAs also revealed that the nomogram yielded a high clinical net benefit. After stratifying the included cases into two risk groups based on the risk score obtained from the nomogram, the high-risk group exhibited a significantly inferior overall survival (OS) compared to the low-risk group (p < 0.0001). Conclusions: Our nomogram exhibited good performance in predicting the overall survival of HTG-SAP patients during hospitalization.

Effect of NaClO and ClO2 on the bacterial properties in a reclaimed water distribution system: efficiency and mechanisms.

Extensive application of reclaimed water alleviated water scarcity obviously. Bacterial proliferation in reclaimed water distribution systems (RWDSs) poses a threat to water safety. Disinfection is the most common method to control microbial growth. The present study investigated the efficiency and mechanisms of two widely used disinfectants: sodium hypochlorite (NaClO) and chlorine dioxide (ClO2) on the bacterial community and cell integrity in effluents of RWDSs through high-throughput sequencing (Hiseq) and flow cytometry, respectively. Results showed that a low disinfectant dose (1 mg/L) did not change the bacterial community basically, while an intermediate disinfectant dose (2 mg/L) reduced the biodiversity significantly. However, some tolerant species survived and multiplied in high disinfectant environments (4 mg/L). Additionally, the effect of disinfection on bacterial properties varied between effluents and biofilm, with changes in the abundance, bacterial community, and biodiversity. Results of flow cytometry showed that NaClO disturbed live bacterial cells rapidly, while ClO2 caused greater damage, stripping the bacterial membrane and exposing the cytoplasm. This research will provide valuable information for assessing the disinfection efficiency, biological stability control, and microbial risk management of reclaimed water supply systems.

Improvement of heavy metal separation performance by positively charged small-sized graphene oxide membrane.

Multilayered graphene oxide (GO) membranes are promising to be widely applied to purify water effectively. However, the performance of most membranes prepared at present is not ideal, which may be related to the pore diameter of the substrate (determining the real loading amount of GO) and the size of the GO nanosheets (determining the number of channels on the unit area), which has not been fully studied. In this study, a rotating dip-coating reactor were firstly developed to ensure the uniform deposition of reactants on the surface of the substrate. Then, the preparation method for the membrane was improved. Microfiltration membranes were used as the supporting substrate, polydopamine was deposited as the adhesive layer, ethylenediamine was used to restrict the layer spacing to strengthen the size exclusion effect, and positively charged polyethyleneimine (PEI) was used to strengthen the Donnan effect. Finally, the effects of the pore size of the substrate and the size of the GO nanosheets on the membrane performance were investigated. Compared with the substrates with a pore size of 0.22 µm in most literatures, substrates of 0.1 µm can retain more small GO (SGO) nanosheets, thereby improving the performance. The performance of the SGO membrane was much better than that of the large-sized GO membrane. With a water permeability of no less than 7.9 L/(m2·h·bar), rejection rates for Pb2+ and Cd2+ of the SGO membrane could reach more than 97%. These findings are constructive to separate heavy metals from water effectively.

Dual Space Coupling Model Guided Overlap-Free Scatterplot.

The overdraw problem of scatterplots seriously interferes with the visual tasks. Existing methods, such as data sampling, node dispersion, subspace mapping, and visual abstraction, cannot guarantee the correspondence and consistency between the data points that reflect the intrinsic original data distribution and the corresponding visual units that reveal the presented data distribution, thus failing to obtain an overlap-free scatterplot with unbiased and lossless data distribution. A dual space coupling model is proposed in this paper to represent the complex bilateral relationship between data space and visual space theoretically and analytically. Under the guidance of the model, an overlap-free scatterplot method is developed through integration of the following: a geometry-based data transformation algorithm, namely DistributionTranscriptor; an efficient spatial mutual exclusion guided view transformation algorithm, namely PolarPacking; an overlap-free oriented visual encoding configuration model and a radius adjustment tool, namely frdraw. Our method can ensure complete and accurate information transfer between the two spaces, maintaining consistency between the newly created scatterplot and the original data distribution on global and local features. Quantitative evaluation proves our remarkable progress on computational efficiency compared with the state-of-the-art methods. Three applications involving pattern enhancement, interaction improvement, and overdraw mitigation of trajectory visualization demonstrate the broad prospects of our method.

Study on release and occurrence of typical metals in corrosion products of drinking water distribution systems under stagnation conditions.

Metal contaminants in corrosion products of drinking water distribution systems (DWDS) can be released into potable water under specific conditions, thereby polluting drinking water and posing a health risk. Under stagnation conditions, the release characteristics, occurring forms, and environmental risks of ten metals were determined in loose and tubercle scale solids of an unlined cast iron pipe with a long service history, before and after immersion. Most Al, As, Cr, Fe, and V in corrosion scales existed in the residual fraction, with the released concentration and pollution risk being low. Since more than 59% of Ca in pipe scales existed in the exchangeable fraction, Ca release was high. Although the Pb and Cd content of corrosion solids was low, a high proportion of Pb and Cd was present in non-residual fractions with high mobility. Sudden severe Pb or Cd pollution events in DWDS could result in high pollution and environmental risk levels. The total content and released amount of Mn and Zn in corrosion scales were both high. Therefore, while special attention should be paid to Mn and Zn, Pb and Cd also present a high risk in pipe scales, despite their low concentrations. During stagnation immersion, metal release from powdered pipe scales occurred via the processes of mass release, re-adsorption into scales, and slow release until equilibrium was reached. The levels of metal re-adsorption into scales were much higher than the concentrations dissolved into bulk water. However, the amount of metal re-adsorption into tubercle scale blocks was less. Importantly, these findings highlight that during DWDS operation, the sudden release of metal pollutants caused by pipe scale breakage should be avoided.

The effect of adsorption on the fate of colloidal polystyrene microplastics in drinking water distribution system pipe scales.

With microplastics (MPs) being continuously found in various environments, the pollution of water supply systems by MPs is receiving increasing attention. As the sediment in drinking water distribution systems (DWDSs), pipe scales act as the interface for complex reactions between bulk water and pipe surfaces. Consequently, the fate of MPs in pipe scales requires exploration, especially colloidal MPs. In this study, MPs were detected in different pipe scale layers, with concentrations of 0.32-3.10 items g-1. Subsequently, the adsorption interaction mechanisms between pipe scales and colloidal polystyrene microplastics (PSMPs) were investigated through batch adsorption experiments. The findings indicated that pipe scales showed a potential adsorption capacity for PSMPs. The adsorption kinetics and isotherms results demonstrated that the PSMP adsorption process was physically dominant and complicated. van der Waals and electrostatic interactions, hydrogen bonding, and pore filling were the main adsorption mechanisms. These results verify that colloidal MPs can be adsorbed by pipe scales, demonstrating that pipe scales play an essential role in the fate of colloidal MPs in DWDSs and the quality and security of drinking water. The secondary release of MPs from pipe scales is also worthy of attention due to the environmental and health risks.

Characteristics of vanadium release from layered steel pipe scales to bulk, steady, and occluded water in drinking water distribution systems.

The release of vanadium (V) from drinking water distribution systems (DWDS) can endanger water quality and human health. Therefore, in this study, the physicochemical characteristics of old steel pipe scales were analyzed, and dynamic pipeline devices were constructed. Subsequently, static release experiments were conducted to find an optimum scale-water ratio and investigate the release behaviors of V in lumpy pipe scales. Besides, the release behaviors of V from layered pipe scales to bulk, steady, and occluded water under the combined effect of multiple water quality conditions were studied for the first time. Computational fluid dynamics (CFD) was adopted to explain the release behaviors of V in the dynamic pipeline. Results revealed that the adsorption performance of the layered scales decreased in the order of surface layer > porous core layer > hard shell-like layer. The release behaviors of V in the lumpy pipe scales were mainly divided into rapid desorption and colloidal agglomeration stages. The Double constant and Weber-Morris models can suitably describe release stage I (R2 > 0.919) and release stage II (R2 > 0.948), respectively. Notably, the release of V was aggravated by low pH, high temperature, and high SO42- concentration, and the release amount of V in the pipeline was more significant than the layered pipe scales. Steady water in the gaps of scales contained more V than bulk water, and the malignant occluded water encased in scales contained relatively low V concentrations. In short, the main mechanism of V release was competitive adsorption in the early stage, and pH was the main influencing factor in the later stage. The above results are of great significance for revealing the release behaviors of V and reducing its release in DWDS.

Towards Visual Explainable Active Learning for Zero-Shot Classification.

Zero-shot classification is a promising paradigm to solve an applicable problem when the training classes and test classes are disjoint. Achieving this usually needs experts to externalize their domain knowledge by manually specifying a class-attribute matrix to define which classes have which attributes. Designing a suitable class-attribute matrix is the key to the subsequent procedure, but this design process is tedious and trial-and-error with no guidance. This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems. This approach promotes human-AI teaming with four actions (ask, explain, recommend, respond) in each interaction loop. The machine asks contrastive questions to guide humans in the thinking process of attributes. A novel visualization called semantic map explains the current status of the machine. Therefore analysts can better understand why the machine misclassifies objects. Moreover, the machine recommends the labels of classes for each attribute to ease the labeling burden. Finally, humans can steer the model by modifying the labels interactively, and the machine adjusts its recommendations. The visual explainable active learning approach improves humans' efficiency of building zero-shot classification models interactively, compared with the method without guidance. We justify our results with user studies using the standard benchmarks for zero-shot classification.

Field study on the characteristics of scales in damaged multi-material water supply pipelines: Insights into heavy metal and biological stability.

Microbial corrosion and heavy metal accumulation in metal water supply pipelines aggravate scale formation and may result in pipeline leakage or bursting events. To better understand the corrosion and corrosion products in the damaged pipes, deposits excavated from three damaged pipes after 22-26 year service periods were analyzed. Using a combination of advanced micro-mineral techniques and 16S rRNA high-throughput sequencing, the micromorphology, chemical composition, and bacterial community were investigated systematically. Unlined pipe wall scales ruptured while lined pipes leaked due to joint scales. Dendrogram correlation results demonstrated that V/As, Al/Pb, and Cr/Mn clusters exhibited co-adsorption and co-precipitation characteristics. FTIR and XRD analysis detected the presence of γ-FeOOH, α-FeOOH in loose scales, and Fe3O4 in rigid scales. Scales were colonized by various corrosion bacteria, with sulfate reducing bacteria and ammonia producing bacteria being dominant in the scales of anticorrosive and non-corrosive pipe, respectively. Tl, Ca, Al, and Pb exhibited an extremely positive correlation with Rhodocyclaceae, Ferritrophicum, Thermodesulfovibrionia, and Clostridiaceae. Al and V presented a potential Hazard Quotient risk to consumers, while Cd was potentially bioavailable in all inner scales. Overall, this study provides valuable information for the effective management and avoidance of corrosion-induced pipeline damage and heavy metal release.

Co-release potential and human health risk of heavy metals from galvanized steel pipe scales under stagnation conditions of drinking water.

The re-release of heavy metals accumulated in the drinking water distribution systems (DWDSs) may pose a significant threat to water quality and human health. In this work, the pipe scales in the actual DWDS were collected, and their physicochemical characteristics were investigated by SEM, XRF, XRD, XPS, and sequential extraction procedure. The co-release potential of heavy metals under different scale dosages, temperatures, and stagnation times was explored by stagnation release tests. Pearson correlation analysis on metal release and human health risk assessment was used to reveal the inter-metal correlation and potential risk of metal release. The results showed that the metal release potential under stagnation water conditions arose primarily from the acid-soluble fraction. The chronic non-carcinogenic risk of soluble metals followed the order: Mn > Fe > Zn > Pb. The risk caused by the soluble metal release could be ignored (HI < 1, HI: hazard index) under normal stagnation times (within 8 h). The major finding of this work was that Ca and Mn were more labile to release and had a significant linear co-release correlation (scale powder: R2 = 0.906, p < 0.01; pipe section: R2 = 0.982, p < 0.01), which indicated their co-existence and linear co-release. Ca was recognized as the "major metal" that affected the release of trace metals. The health risk probably increased with the release of Ca, which could also be used as an "indicator" of Mn release.

Galex: Exploring the Evolution and Intersection of Disciplines.

Revealing the evolution of science and the intersections among its sub-fields is extremely important to understand the characteristics of disciplines, discover new topics, and predict the future. The current work focuses on either building the skeleton of science, lacking interaction, detailed exploration and interpretation or on the lower topic level, missing high-level macro-perspective. To fill this gap, we design and implement Galaxy Evolution Explorer (Galex), a hierarchical visual analysis system, in combination with advanced text mining technologies, that could help analysts to comprehend the evolution and intersection of one discipline rapidly. We divide Galex into three progressively fine-grained levels: discipline, area, and institution levels. The combination of interactions enables analysts to explore an arbitrary piece of history and an arbitrary part of the knowledge space of one discipline. Using a flexible spotlight component, analysts could freely select and quickly understand an exploration region. A tree metaphor allows analysts to perceive the expansion, decline, and intersection of topics intuitively. A synchronous spotlight interaction aids in comparing research contents among institutions easily. Three cases demonstrate the effectiveness of our system.

Far-field radiative thermal rectifier based on nanostructures with vanadium dioxide.

Radiative thermal rectifiers capable of realizing asymmetric heat flux transfer have attracted a lot of research interests recently, mainly focusing on the engineering of the emissivity spectra. In this Letter, we propose a far-field radiative thermal rectifier utilizing the phase change material vanadium dioxide (VO2). The thermal rectifier consists of a metamaterial infrared absorber and a two-layer thin-film structure acting as the active and the passive components, respectively. Numerical optimization has been carried out to control the emissivity spectra of both parts and maximize the overall rectification effect. A large thermal rectification factor of 3.5 is predicted at a temperature bias of ΔT=100 K.