Impacts of Fuel Stage Ratio on the Morphological and Nanostructural Characteristics of Soot Emissions from a Twin Annular Premixing Swirler Combustor.

Soot particles emitted from aircraft engines constitute a major anthropogenic source of pollution in the vicinity of airports and at cruising altitudes. This emission poses a significant threat to human health and may alter the global climate. Understanding the characteristics of soot particles, particularly those generated from Twin Annular Premixing Swirler (TAPS) combustors, a mainstream combustor in civil aviation engines, is crucial for aviation environmental protection. In this study, a comprehensive characterization of soot particles emitted from TAPS combustors was conducted using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and Raman spectroscopy. The morphology and nanostructure of soot particles were examined across three distinct fuel stage ratios (FSR), at 10%, 15%, and 20%. The SEM analysis of soot particle morphology revealed that coated particles constitute over 90% of the total particle sample, with coating content increasing proportionally to the fuel stage ratio. The results obtained from HRTEM indicated that average primary particle sizes increase with the fuel stage ratio. The results of HRTEM and Raman spectroscopy suggest that the nanostructure of soot particles becomes more ordered and graphitized with an increasing fuel stage ratio, resulting in lower oxidation activity. Specifically, soot fringe length increased with the fuel stage ratio, while soot fringe tortuosity and separation distance decreased. In addition, there is a prevalent occurrence of defects in the graphitic lattice structure of soot particles, suggesting a high degree of elemental carbon disorder.

Factors associated with glycemic control in patients with T2DM: evidence from a cross-sectional study in China.

OBJECTIVE: This study aimed to analyze the factors influencing glycemic control in patients with type 2 diabetes mellitus (T2DM). METHODS: Baseline data, encompassing basic information, lifestyle habits, and treatment of 305 T2DM patients from March 2021 to January 2023, were collected and analyzed using SPSS 26.0 software. RESULTS: Univariate and multivariate logistic regression analyses identified insulin therapy (OR = 2.233; 95%Cl = 1.013-4.520; P = 0.026) and regular clinic visits (OR = 0.567; 95%Cl = 0.330-0.973; P = 0.040) as independent factors influencing glycemic control. No observed interactions between the two variables were noted. CONCLUSION: History of insulin therapy and regular clinic visits were significantly and independently associated with glycated hemoglobin control in T2DM patients. Tailored interventions based on individual circumstances are recommended to optimize glycemic control.

The association between methylmalonic acid, a biomarker of mitochondrial dysfunction, and cause-specific mortality in Alzheimer's disease and Parkinson's disease.

Background: Alzheimer's disease (AD) and Parkinson's disease (PD) are the leading causes of death among the elderly. Recent research has demonstrated that mitochondrial dysfunction, which is hallmark of neurodegenerative diseases, is a contributor to the development of these diseases. Methods and materials: Methylmalonic acid (MMA), AD, PD, inflammatory markers and covariates were extracted from the National Health and Nutrition Examination Survey (NHANES). The classification of the inflammatory markers was done through quartile conversion. A restricted cubic spike function was performed to study their dose-response relationship. MMA subgroups from published studies were used to explore the correlation between different subgroups and cause-specific mortality. Multivariable weighted Cox regression was carried out to investigate MMA and cause-specific mortality in patients with AD and PD. Weighted survival analysis was used to study the survival differences among MMA subgroups. Results: A non-linear correlation was observed between MMA and AD-specific death and PD-specific mortality. The presence of MMA Q4 was linked to increased death rates among AD patients (HR = 6.39, 95%CI: 1.19-35.24, P = 0.03) after controlling for potential confounders in a multivariable weighted Cox regression model. In PD patients, the MMA Q4 (Q4: HR: 5.51, 95 % CI: 1.26-24, P = 0.02) was also related to increased mortality. The results of survival analysis indicated that the poorer prognoses were observed in AD and PD patients with MMA Q4. Conclusion: The higher level of mitochondria-derived circulating MMA was associated with a higher mortality rate in AD and PD patients. MMA has the potential to be a valuable indicator for evaluating AD and PD patients' prognosis in the clinic.

FMLens: Towards Better Scaffolding the Process of Fund Manager Selection in Fund Investments.

The fund investment industry heavily relies on the expertise of fund managers, who bear the responsibility of managing portfolios on behalf of clients. With their investment knowledge and professional skills, fund managers gain a competitive advantage over the average investor in the market. Consequently, investors prefer entrusting their investments to fund managers rather than directly investing in funds. For these investors, the primary concern is selecting a suitable fund manager. While previous studies have employed quantitative or qualitative methods to analyze various aspects of fund managers, such as performance metrics, personal characteristics, and performance persistence, they often face challenges when dealing with a large candidate space. Moreover, distinguishing whether a fund manager's performance stems from skill or luck poses a challenge, making it difficult to align with investors' preferences in the selection process. To address these challenges, this study characterizes the requirements of investors in selecting suitable fund managers and proposes an interactive visual analytics system called FMLens. This system streamlines the fund manager selection process, allowing investors to efficiently assess and deconstruct fund managers' investment styles and abilities across multiple dimensions. Additionally, the system empowers investors to scrutinize and compare fund managers' performances. The effectiveness of the approach is demonstrated through two case studies and a qualitative user study. Feedback from domain experts indicates that the system excels in analyzing fund managers from diverse perspectives, enhancing the efficiency of fund manager evaluation and selection.

Multidimensional features of sporadic Creutzfeldt-Jakob disease in the elderly: a case report and systematic review.

Background: As a rare neurodegenerative disease, sporadic Creutzfeldt-Jakob disease (sCJD) is poorly understood in the elderly populace. This study aims to enunciate the multidimensional features of sCJD in this group. Methods: A case of probable sCJD was reported in a 90-year-old Chinese man with initial dizziness. Then, available English literature of the elderly sCJD cases (aged 80 years and over) was reviewed and analyzed. Patients (15 cases) were subdivided and compared geographically. Results: In the elderly sCJD cohort, the onset age was 84.9 ± 4.5 years and the median disease duration was 6.8 months, with respiratory infection/failure as the commonest death cause. Various clinical symptoms were identified, with cognitive disorder (86.7%) as the commonest typical symptom and speech impairment (66.7%) as the most atypical one. Restricted hyperintensities were reported in 60.0% cases on DWI, periodic sharp wave complexes in 73.3% cases on electroencephalogram, and cerebral hypoperfusion/hypometabolism in 26.7% cases on molecular imaging. The sensitive cerebrospinal fluid biomarkers were total tau (83.3%), 14-3-3 protein (75.0%), and PrP RT-QuIC (75.0%). Neuropathological profiles in the cerebral cortex revealed vacuolar spongiosis, neuronal loss, gliosis, and aging-related markers, with synaptic deposit as the commonest PrP pattern (60.0%). The polymorphic PRNP analysis at codon 129 was M/M (90.9%), with MM1 and MM2C as the primary molecular phenotypes. Latency to first clinic visit, hyperintense signals on DWI, and disease duration were significantly different between the patient subgroups. Conclusion: The characteristics of sCJD are multidimensional in the elderly, deepening our understanding of the disease and facilitating an earlier recognition and better care for this group.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases.

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R2 > 0.95) and cruise phases (R2 > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SYNOPSIS: The utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.

Orf virus as an adjuvant enhances the immune response to a PCV2 subunit vaccine.

Orf virus (ORFV), a member of the genus Parapoxvirus, possesses an excellent immune activation capability, which makes it a promising immunomodulation agent. In this study, we evaluated ORFV as a novel adjuvant to enhance the immune response of mice to a subunit vaccine using porcine circovirus type 2 (PCV2) capsid (Cap) protein as a model. Our results showed that both inactivated and live attenuated ORFV activated mouse bone marrow-derived dendritic cells and increased expression of immune-related cytokines interleukin (IL)-1ß, IL-6, and TNF-α. Enhanced humoral and cellular immune responses were induced in mice immunized with PCV2 Cap protein combined with inactivated or live attenuated ORFV adjuvant compared with the aluminum adjuvant. Increased secretion of Th1 and Th2 cytokines by splenic lymphocytes in immunized mice further indicated that the ORFV adjuvant promoted a mixed Th1/Th2 immune response. Moreover, addition of the ORFV adjuvant to the PCV2 subunit vaccine significantly reduced the viral load in the spleen and lungs of PCV2-challenged mice and prevented pathological changes in lungs. This study demonstrates that ORFV enhances the immunogenicity of a PCV2 subunit vaccine by improving the adaptive immune response, suggesting the potential application of ORFV as a novel adjuvant.

Modulation of evapotranspiration and stream runoff by weathered bedrock in arid and semi-arid mountains.

Earth's Critical Zone exhibits remarkable heterogeneity and complexity. Hence, further investigation is required to examine the composition of Earth's Critical Zone as well as the diverse eco-hydrological patterns they exhibit under varying climatic and geological circumstances. This exploration should primarily be conducted through the investigation and experiments of the hillslope unit, where the topography and weathered bedrock are representative, with particular emphasis on semi-arid regions where water resources serve as the primary limiting factor. Here, we have determined that the structure of the weathering profile displays systematic variation across the topography and heterogeneous landscape on uninterrupted slopes. Differences in the structure of the subsurface critical zone led to differences in its water storage capacity at the same time. Runoff in alpine shrubs and forests was dominated by subsurface runoff, and grassland was dominated by surface runoff. In the alpine shrub immediately adjacent to the watershed, an estimated quantity of 129 mm of water is stored within the unsaturated zone of the soil, serving as exchange water to replenish moisture in the underlying bedrock. In contrast to alpine shrubs, an estimated quantity of 62.7 mm of water originates from the unsaturated zone of soil and weathered bedrock in the forest. However, approximately 21.1 mm of moisture is unavailable to plants. The soil water storage in grasslands exhibits a decline throughout the growing season, with a subsequent augmentation occurring solely after substantial precipitation events exceeding 20 mm. In wet years, dynamic storage predominantly manifests as groundwater saturation throughout the entire ground and high subsurface runoff. In dry years, the limited runoff response indicates that the catchment's dynamic water storage primarily comprises "indirect" water storage, which predominantly resides within the soil, saprolite, and weathered rock below the "field capacity", subsequently being released into the atmosphere through evapotranspiration.

Gradient Hydrogels Spatially Trapped Optical Cell Profiling for Quantitative Blood Cellular Osmotic Analysis.

The quantitative exploration of cellular osmotic responses and a thorough analysis of osmotic pressure-responsive cellular behaviors are poised to offer novel clinical insights into current research. This underscores a paradigm shift in the long-standing approach of colorimetric measurements triggered by red cell lysis. In this study, we engineered a purpose-driven optofluidic platform to facilitate the goal. Specifically, creating photocurable hydrogel traps surmounts a persistent challenge─optical signal interference from fluid disturbances. This achievement ensures a stable spatial phase of cells and the acquisition of optical signals for accurate osmotic response analysis at the single-cell level. Leveraging a multigradient microfluidic system, we constructed gradient osmotic hydrogel traps and developed an imaging recognition algorithm, empowering comprehensive analysis of cellular behaviors. Notably, this system has successfully and precisely analyzed individual and clustered cellular responses within the osmotic dimension. Prospective clinical testing has further substantiated its feasibility and performance in that it demonstrates an accuracy of 92% in discriminating complete hemolysis values (n = 25) and 100% in identifying initial hemolysis values (n = 25). Foreseeably, this strategy should promise to advance osmotic pressure-related cellular response analysis, benefiting further investigation and diagnosis of related blood diseases, blood quality, drug development, etc.

Homogeneous water vapor condensation with a deep neural network potential model.

Molecular-level nucleation has not been clearly understood due to the complexity of multi-body potentials and the stochastic, rare nature of the process. This work utilizes molecular dynamics (MD) simulations, incorporating a first-principles-based deep neural network (DNN) potential model, to investigate homogeneous water vapor condensation. The nucleation rates and critical nucleus sizes predicted by the DNN model are compared against commonly used semi-empirical models, namely extended simple point charge (SPC/E), TIP4P, and OPC, in addition to classical nucleation theory (CNT). The nucleation rates from the DNN model are comparable with those from the OPC model yet surpass the rates from the SPC/E and TIP4P models, a discrepancy that could mainly arise from the overestimated bulk free energy by SPC/E and TIP4P. The surface free energy predicted by CNT is lower than that in MD simulations, while its bulk free energy is higher than that in MD simulations, irrespective of the potential model used. Further analysis of cluster properties with the DNN model unveils pronounced variations of O-H bond length and H-O-H bond angle, along with averaged bond lengths and angles that are enlarged during embryonic cluster formation. Properties such as cluster surface free energy and liquid-to-vapor density transition profiles exhibit significant deviations from CNT assumptions.

FSLens: A Visual Analytics Approach to Evaluating and Optimizing the Spatial Layout of Fire Stations.

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily relied on mathematical planning models to generate appropriate layouts by summarizing relevant evaluation criteria. However, this optimization process presents significant challenges due to the extensive decision space, inherent conflicts among criteria, and decision-makers' preferences. To address these challenges, we propose FSLens, an interactive visual analytics system that enables in-depth evaluation and rational optimization of fire station layout. Our approach integrates fire records and correlation features to reveal fire occurrence patterns and influencing factors using spatiotemporal sequence forecasting. We design an interactive visualization method to explore areas within the city that are potentially under-resourced for fire service based on the fire distribution and existing fire station layout. Moreover, we develop a collaborative human-computer multi-criteria decision model that generates multiple candidate solutions for optimizing firefighting resources within these areas. We simulate and compare the impact of different solutions on the original layout through well-designed visualizations, providing decision-makers with the most satisfactory solution. We demonstrate the effectiveness of our approach through one case study with real-world datasets. The feedback from domain experts indicates that our system helps them to better identify and improve potential gaps in the current fire station layout.

A trusted medical data sharing framework for edge computing leveraging blockchain and outsourced computation.

Traditional cloud-centric approaches to medical data sharing pose risks related to real-time performance, security, and stability. Medical and healthcare data encounter challenges like data silos, privacy breaches, and transmission latency. In response to these challenges, this paper introduces a blockchain-based framework for trustworthy medical data sharing in edge computing environments. Leveraging healthcare consortium edge blockchains, this framework enables fine-grained access control to medical data. Specifically, it addresses the real-time, multi-attribute authorization challenge in CP-ABE through a Distributed Attribute Authorization strategy (DAA) based on blockchain. Furthermore, it tackles the key security issues in CP-ABE through a Distributed Key Generation protocol (DKG) based on blockchain. To address computational resource constraints in CP-ABE, we enhance a Distributed Modular Exponentiation Outsourcing algorithm (DME) and elevate its verifiable probability to "1". Theoretical analysis establishes the IND-CPA security of this framework in the Random Oracle Model. Experimental results demonstrate the effectiveness of our solution for resource-constrained end-user devices in edge computing environments.

Higher oxidative balance score decreases risk of stroke in US adults: evidence from a cross-sectional study.

Background: The oxidative balance score (OBS) can be used to represent the overall burden of oxidative stress in an individual. This study aimed to explore the association between the risk of stroke and OBS. Methods and materials: The National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 was used to extract a series of variables for participants who took the stroke questionnaire. The construction of OBS relied on diet and lifestyle components, which included 16 nutrients and 4 lifestyle factors. Weighted multivariable-adjusted logistic regression was performed to investigate the association between stroke risk and OBS. A stratified analysis was also conducted. The dose-response relationship between stroke risk and OBS was elucidated by performing a restricted cubic spline function. Results: A total of 20,680 participants were included for analysis, 768 of whom suffered from stroke. Based on weighted multivariable logistic regression analysis, we discovered that the stroke prevalence decreased by 2% for each OBS unit added [OR: 0.98 (0.97-1.00), P < 0.01]. For the OBS subgroup, we also discovered that higher OBS was related to a reduction in the risk of stroke [Q4 vs. Q1: OR:0.65 (0.46-0.90), P < 0.01]. The prevalence of stroke declined by 3% with every OBS unit added to the diet component [OR: 0.97 (0.96-0.99), P < 0.01]. For the dietary OBS subgroup, higher OBS in diet components was associated with a decrease in the prevalence of stroke [Q4 vs. Q1: OR: 0.65, (0.47-0.91), P < 0.05]. Further stratified analysis showed that every OBS unit raised was associated with a decline in stroke prevalence, which was statistically significant in participants in subgroups of ≥60 years, female, no-diabetes mellitus and no-hypertension. OBS and stroke prevalence were correlated in a linear manner. Conclusion: The study found that a higher OBS was associated with a decrease in stroke prevalence, which could be a significant indicator for evaluating stroke risk.

A microfluidic hemostatic diagnostics platform: Harnessing coagulation-induced adaptive-bubble behavioral perception.

Clinical viscoelastic hemostatic assays, which have been used for decades, rely on measuring biomechanical responses to physical stimuli but face challenges related to high device and test cost, limited portability, and limited scalability.. Here, we report a differential pattern using self-induced adaptive-bubble behavioral perception to refresh it. The adaptive behaviors of bubble deformation during coagulation precisely describe the transformation of viscoelastic hemostatic properties, being free of the precise and complex physical devices. And the integrated bubble array chip allows microassays and enables multi-bubble tests with good reproducibility. Recognition of the developed bubble behaviors empowers automated and user-friendly diagnosis. In a prospective clinical study (clinical model development [n = 273]; clinical assay [n = 44]), we show that the diagnostic accuracies were 99.1% for key viscoelastic hemostatic assay indicators (reaction time [R], kinetics time [K], alpha angle [Angle], maximum amplitude [MA], lysis at 30 min [LY30]; n = 220) and 100% (n = 44) for hypercoagulation, healthy, and hypocoagulation diagnoses. This should provide fresh insight into existing paradigms and help more clinical needs.

Confinement-enhanced microalgal individuals biosensing for digital atrazine assay.

Microalgal sensors are widely recognized for their high sensitivity, accessibility, and low cost. However, the current dilemma of motion-induced spatial phase changes and concentration-related multiple scattering interferes with induced test instability and limited sensitivity, which has hindered their practical applications. Here, a differentiated strategy, named confinement-enhanced microalgal biosensing (C-EMB), is developed and proposed to pave the way. The in-situ printed microgel trap is designed to confine Chlamydomonas reinhardtii individuals, stabilizing their spatial phase. The microgel trap arrays are introduced to eliminate the multiple scattering of microalgae, breaking the existing effective concentration in traditional microalgal sensing and enabling sensitive assays. The integration with lab-on-a-chip technology and a developed digital imaging algorithm empower portable and automated detection. With this system, a microalgae analyzer is developed for atrazine detection, featuring a linear range of 0.04-100 µg/L. We assess the system's performance through practical atrazine assays on commercial food, using a double-blind test against a standard instrument. Our results demonstrate the good accuracy and test stability of this system with the mean bias atrazine detection in corn and sugarcane juice samples (SD) were 1.661 µg/L (3.122 µg/L) and 3.144 µg/L (4.125 µg/L), respectively. This method provides a new paradigm of microalgal sensors and should advance the further applications of microalgal sensors in commercial and practical settings.

Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis.

Introduction: Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain. Methods: In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data. Results: Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice. Conclusion: This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.

Valve-Adjustable Optofluidic Bio-Imaging Platform for Progressive Stenosis Investigation.

The clinical evidence has proven that valvular stenosis is closely related to many vascular diseases, which attracts great academic attention to the corresponding pathological mechanisms. The investigation is expected to benefit from the further development of an in vitro model that is tunable for bio-mimicking progressive valvular stenosis and enables accurate optical recognition in complex blood flow. Here, we develop a valve-adjustable optofluidic bio-imaging recognition platform to fulfill it. Specifically, the bionic valve was designed with in situ soft membrane, and the internal air-pressure chamber could be regulated from the inside out to bio-mimic progressive valvular stenosis. The developed imaging algorithm enhances the recognition of optical details in blood flow imaging and allows for quantitative analysis. In a prospective clinical study, we examined the effect of progressive valvular stenosis on hemodynamics within the typical physiological range of veins by this way, where the inhomogeneity and local enhancement effect in the altered blood flow field were precisely described and the optical differences were quantified. The effectiveness and consistency of the results were further validated through statistical analysis. In addition, we tested it on fluorescence and noticed its good performance in fluorescent tracing of the clotting process. In virtue of theses merits, this system should be able to contribute to mechanism investigation, pharmaceutical development, and therapeutics of valvular stenosis-related diseases.

DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins.

Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model's interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.

Towards Better Modeling With Missing Data: A Contrastive Learning-Based Visual Analytics Perspective.

Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML performance. These approaches rely on the observed data to estimate the missing values and therefore encounter three main shortcomings in imputation, including the need for different imputation methods for various missing data mechanisms, heavy dependence on the assumption of data distribution, and potential introduction of bias. This study proposes a Contrastive Learning (CL) framework to model observed data with missing values, where the ML model learns the similarity between an incomplete sample and its complete counterpart and the dissimilarity between other samples. Our proposed approach demonstrates the advantages of CL without requiring any imputation. To enhance interpretability, we introduce CIVis, a visual analytics system that incorporates interpretable techniques to visualize the learning process and diagnose the model status. Users can leverage their domain knowledge through interactive sampling to identify negative and positive pairs in CL. The output of CIVis is an optimized model that takes specified features and predicts downstream tasks. We provide two usage scenarios in regression and classification tasks and conduct quantitative experiments, expert interviews, and a qualitative user study to demonstrate the effectiveness of our approach. In short, this study offers a valuable contribution to addressing the challenges associated with ML modeling in the presence of missing data by providing a practical solution that achieves high predictive accuracy and model interpretability.