Research on jet electrochemical machining with coaxial megasonic assistance.

In order to address the problem of poor localization in electrochemical machining (ECM), a coaxial megasonic assisted jet ECM method was proposed. Based on theoretical analysis, experiments were conducted to compare the effects of various electrolyte flow rates, electrolytic voltage and megasonic power levels on pit ECM. The results indicate that, in the range of experimental parameters, the increase of electrolyte flow rate and megasonic power can increase the machining depth, so as to improve the depth-diameter ratio of ECM pits. The use of coaxial megasonic-assisted jet ECM can enhance the depth-diameter ratio of etched pits compared to the without megasonic one. When applying a megasonic power of 22 W, the dimensions of the ECM pit were measured as 0.81 mm in depth and 5.73 mm in diameter, resulting in an depth-diameter ratio of 0.140. Under the same conditions, without megasonic assistance, the pit diameter is reduced to 0.65 mm while the pit depth increases to 6.36 mm, resulting in a depth-diameter ratio of 0.102. Additionally, The results also demonstrate that, the increase of electrolytic voltage makes the depth to diameter ratio of pit further increase on the original basis. With an electrolyte flow rate of 0.9 L/min and a megasonic power of 22 W, the use of electrolysis voltage of 50 V increased the depth-diameter ratio of etched pits to 0.173. Using the above preferred parameters, electrolytic milling of the wide groove is carried out. The depth-diameter ratio of the wide groove is increased from 0.039 to 0.059 by appending coaxial megasonic. This further verified the effectiveness of the coaxial megasonic-assisted jet ECM method.

Supervisor's neuroticism and problematic Internet use among graduate students: the mediating role of supervisor-student relationship quality and the moderating role of fear of the supervisor's negative evaluation.

BACKGROUND: Graduate students exhibit vulnerability to problematic Internet use, which can result in adverse physical, psychological, and social consequences. However, limited studies have addressed this issue among graduate students, and even fewer have explored the unique factors contributing to their problematic Internet use. Therefore, to address this gap, the current study aims to probe the relationship between supervisor's neuroticism and problematic Internet use among graduate students, the mediating effect of the supervisor-student relationship quality, as well as the moderating effect of fear of the supervisor's negative evaluation. METHODS: A cross-sectional study was conducted in 2018 at three universities in Beijing, China. Anonymous data from 448 graduate students were collected regarding problematic Internet use, supervisor's neuroticism, supervisor-student relationship quality, and the fear of the supervisor's negative evaluation. A moderated mediation analysis was performed using Hayes' PROCESS macro (Model 14). RESULTS: Supervisor's neuroticism was positively linked to graduate students' problematic Internet use, supervisor-student relationship quality mediated the linkage, and fear of the supervisor's negative evaluation played a moderating role in the second stage. Specifically, for students lower in fear of the supervisor's negative evaluation, supervisor-student relationship quality negatively predicted students' problematic Internet use. While for the graduate students higher in fear of the supervisor's negative evaluation, supervisor-student relationship quality could not significantly predict students' problematic Internet use. The mediating effect was only significant for graduate students lower in fear of the supervisor's negative evaluation. CONCLUSIONS: This study established a theoretical model linking supervisor's neuroticism to graduate students' problematic Internet use, highlighting the potential roles of supervisor-student relationship quality and fear of the supervisor's negative evaluation. Reducing the neuroticism level of the supervisor, enhancing the quality of the supervisor-student relationship, and mitigating students' fear of the supervisor's negative evaluation will contribute to the reduction of problematic Internet use among graduate students.

Study of liquid-phase mass transfer and residual stress in jet electrodeposition process with coaxial megasonic agitation.

The electrodeposition process confronts significant challenges arising from mass transfer limitation and residual stress. To address these issues, an innovative method, combining megasonic agitation with coaxial jet electrodeposition, is introduced. This approach aims to enhance mass transfer and mitigate residual stress. First, an electrodeposition nozzle device was designed, and the liquid-phase mass transfer during electrodeposition was analyzed through finite element simulation. Simulation results indicate that the mass transfer coefficient increases with rising megasonic power density. Notably, when the megasonic power density reached 20 W/cm2, the mass transfer coefficient increased from 0.45 × 10-7 m/s to 18.63 × 10-7 m/s, compared to electrodeposition without megasonic agitation. Secondly, electrodeposition experiments were conducted both with and without megasonic assistance. X-ray diffraction (XRD) was employed to measure the residual stress values of the electrodeposited layers. The results reveal that samples processed with megasonic assistance exhibit lower residual stress values compared to those without. Specifically, at a megasonic power density of 10 W/cm2, the residual stress was 94.3 MPa, representing a 37.7 % reduction compared to the residual stress of 151.5 MPa observed in samples without megasonic agitation. Overall, the findings demonstrate that coaxial megasonic agitation can effectively enhance the liquid-phase mass transfer capability during electrodeposition and reduce the residual stress of the electroplated layer. This innovative method presents a promising avenue for improving electrodeposition processes and achieving superior material properties.

Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization.

INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. OBJECTIVES: To compare models developed using ML with those developed using CSM to predict 30-day readmission for cardiovascular and noncardiovascular causes in HF patients. METHODS: We retrospectively enrolled 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests for competing risks (RSF-CR). Models were evaluated in the validation set using both discrimination and calibration metrics. RESULTS: In the validation sample of 3602 patients, RSF-CR (c-statistic=0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic=0.621) for 30-day cardiovascular readmission. In contrast, for 30-day noncardiovascular readmission, the Fine-Gray model (c-statistic=0.641) slightly outperformed the RSF-CR model (c-statistic=0.632). For both outcomes, The Fine-Gray model displayed better calibration than RSF-CR using calibration plots of observed vs predicted risks across the deciles of predicted risk. CONCLUSIONS: Fine-Gray models had similar discrimination but superior calibration to the RSF-CR model, highlighting the importance of reporting calibration metrics for ML-based prediction models. The discrimination was modest in all readmission prediction models regardless of the methods used.

AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.

Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.

Rice-crayfish farming system promote subsoil microbial residual carbon accumulation and stabilization by mediating microbial metabolism process.

Rice-crayfish farming systems (RCs) can help mitigate climate change by enhancing soil organic carbon (SOC) sequestration. However, the mechanisms that govern the responses of microbial residues carbon (MRC), a key component of SOC, in RCs are not fully understood. We conducted a 6-year field experiment comparing RCs and rice monoculture systems (RMs). Specifically, we explored how MRC formation and stabilization differ between the two systems and how those differences are linked to changes in the metabolic processes of microbes. Results showed that MRC levels in RCs were 5.2 % and 40.0 % higher in the topsoil and subsoil, respectively, compared to RMs, indicating depth-dependent effects. Notably, MRC accumulation and stabilization in RCs were promoted through a cascade of processes of dissolved organic carbon (DOC) accessibility-microbial metabolism-mineral protection. In addition, the mechanism of MRC accumulation in subsoil differed between the two systems. Specifically, RMs improved accessibility of DOC by reducing humification and aromaticity of subsoil DOC, which helped microbes access to resources at lower cost. This decreased the respiration rate of microbes, thereby increasing microbial carbon pump (MCP) efficiency and thus promoting MRC accumulation. By contrast, the crayfish in RCs facilitated carbon exchange between topsoil and subsoil through their burrowing behaviors. This increased carbon allocation for microbial metabolism in the subsoil, supporting a larger microbial population and thus enhancing the MCP capacity, while reducing MRC re-decomposition via enhanced mineral protection, further increasing subsoil MRC accumulation. That is, MRC accumulation in the subsoil of RCs was predominantly driven by microbial population numbers (MCP capacity) whereas that of RMs was mostly driven by microbial anabolic efficacy (MCP efficiency). Our findings reveal a key mechanism by which RCs promoted soil MRC accumulation and stabilization, highlighting the potential role of DOC accessibility-microbial metabolism-mineral protection pathway in regulating MRC accumulation and stabilization.

Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics.

Rationale: Multimodal imaging provides important pharmacokinetic and dosimetry information during nanomedicine development and optimization. However, accurate quantitation is time-consuming, resource intensive, and requires anatomical expertise. Methods: We present NanoMASK: a 3D U-Net adapted deep learning tool capable of rapid, automatic organ segmentation of multimodal imaging data that can output key clinical dosimetry metrics without manual intervention. This model was trained on 355 manually-contoured PET/CT data volumes of mice injected with a variety of nanomaterials and imaged over 48 hours. Results: NanoMASK produced 3-dimensional contours of the heart, lungs, liver, spleen, kidneys, and tumor with high volumetric accuracy (pan-organ average %DSC of 92.5). Pharmacokinetic metrics including %ID/cc, %ID, and SUVmax achieved correlation coefficients exceeding R = 0.987 and relative mean errors below 0.2%. NanoMASK was applied to novel datasets of lipid nanoparticles and antibody-drug conjugates with a minimal drop in accuracy, illustrating its generalizability to different classes of nanomedicines. Furthermore, 20 additional auto-segmentation models were developed using training data subsets based on image modality, experimental imaging timepoint, and tumor status. These were used to explore the fundamental biases and dependencies of auto-segmentation models built on a 3D U-Net architecture, revealing significant differential impacts on organ segmentation accuracy. Conclusions: NanoMASK is an easy-to-use, adaptable tool for improving accuracy and throughput in imaging-based pharmacokinetic studies of nanomedicine. It has been made publicly available to all readers for automatic segmentation and pharmacokinetic analysis across a diverse array of nanoparticles, expediting agent development.

The impact of internet adaptability on internet addiction: the serial mediation effect of meaning in life and anxiety.

Introduction: Previous research has demonstrated the significant role of individual characteristics in adolescent Internet addiction. In line with this, our previous research has introduced the concept of "Internet adaptability" as a potential factor that enables individuals to effectively cope with the negative consequences of Internet use. However, further investigation is required to understand the impact of Internet adaptability on problematic Internet use, including Internet addiction, as well as its associated internal psychological factors. To address this research gap, the present study aims to examine the impact of Internet adaptability on internet addiction and explore the mediating roles of meaning in life and anxiety within this relationship. Methods: A questionnaire was used to survey 2,144 adolescents from high schools in central China to investigate internet adaptability, meaning in life, anxiety, and internet addiction. Results: The results revealed a significant negative correlation between Internet adaptability and adolescent internet addiction (r = -0.199, p < 0.01). Furthermore, our results indicated that Internet adaptability negatively predicts internet addiction (ß = -0.086, p < 0.001). Additionally, mediation analyses revealed that both meaning in life (ß = -0.060, p < 0.001) and anxiety (ß = -0.032, p < 0.01) mediate the relationship between Internet adaptability and internet addiction. Moreover, a serial mediation effect involving meaning in life and anxiety was observed between Internet adaptability and internet addiction (ß = -0.027, p < 0.001). Conclusion: These findings suggest that Internet adaptability plays an important role in alleviating individual internet addiction. Our results indicate that increasing individuals' sense of meaning in life can help reduce anxiety, thereby potentially reducing internet addiction.

Development of the Colorimetric and/or Fluorescent Probes for Detecting Fluoride ions in Aqueous Solution.

Fluoride ion is a strong Lewis base and one of the essential trace elements in human body. It plays a very important role in human health and ecological balance. The deficiency or excessive intake of fluoride ions will cause serious health problems, so the development of a sensitive and accurate detection method for fluoride ions is very important. The colorimetric and/or fluorescence sensing method has been a long standing attractive technique with high sensitivity and fast response. To date, most reported probes for fluoride ion are applicable only in organic solvents or organic-containing aqueous solutions. However, the probes for fluoride ion used in aqueous solution are more practically needed in view of environment protection and human health. In this paper, the materials and designing ideas of the colorimetric and/or fluorescent probes for fluoride ion based on different detection mechanisms in recent years were reviewed. Two main categories including formation of hydrogen bonds and formation of coordination covalent bonds were discussed. The latter one is further subdivided into three types, formation of B-F bond, formation of Si-F bond and formation of Mn+-F bond.

Applying computerized adaptive testing to the Desires for Speed Questionnaire in the Chinese population: A simulation study.

The current methods for measuring patient-reported outcomes for amphetamine (speed) craving have limitation ability to adapt to the needs of individual patients while maintaining consistency in their scores. This study aimed to investigate whether the 40-item Desires for Speed Questionnaire (DSQ) could be improved for assessing clinical subjects using computerized adaptive testing (CAT). A sample of 677 participants from four drug addiction treatment centers in China was utilized in the study. Two types of analysis were conducted using the response data. First, the psychometric properties of all items were evaluated to meet the requirements of CAT. Second, multiple CAT simulations were carried out using real response data. The results indicated that the CAT method, which only required a small number of items (50%-75%), produced results that were only slightly different from the full DSQ assessment in terms of measuring amphetamine craving and criterion validity. In conclusion, this study suggests that developing a DSQ CAT for clinical subjects is useful as it leads to more efficient measurement without compromising the reliability of the test outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Research on the evolution law of hot spots in the field of coal seam hydraulic fracturing based on bibliometric analysis: review from a new scientific perspective.

As an unconventional natural gas, the calorific value of coal seam gas (CSG) is equivalent to that of natural gas. It is a high-quality, clean, and efficient green low-carbon energy source. Coal seam hydraulic fracturing is an important permeability enhancement measure in the process of CSG drainage. In order to further understand the overall research progress in the field of coal seam hydraulic fracturing, the Web of Science (WOS) database is used as a sample source, and the bibliometric analysis of the literature is carried out by CiteSpace software. The visual knowledge maps of the number of publications, the research countries, institutions, and keyword clustering are drawn. The research shows that it has gone through two stages of slow development and rapid growth in terms of time distribution. In terms of cooperation networks, the main active countries include China, the USA, Australia, Russia, and Canada, composed of China University of Mining and Technology, Chongqing University, Henan Polytechnic University, and China University of Petroleum as the core research institutions. Taking keywords as the theme, the coal seam hydraulic fracturing research field mainly involves high-frequency keywords such as hydraulic fracturing, permeability, model, and numerical simulation. The hotspot evolution law and frontier development trend of keywords with time are analyzed and obtained. On this basis, from a new perspective, the "scientific research landscape map" in the field of coal seam hydraulic fracturing is outlined, in order to provide a scientific reference for the research in this field.

Effects of optimized nitrogen fertilizer management on the yield, nitrogen uptake, and ammonia volatilization of direct-seeded rice.

BACKGROUND: Direct-seeded rice has been developed rapidly because of labor savings. Changes in rice cultivation methods put forward new requirements for nitrogen (N) fertilizer management practices. Field experiments with five different fertilizer ratios of basal, tillering and panicle fertilizer, namely N1 (10:0:0), N2 (6:2:2), N3 (4:3:3), N4 (2:4:4) and N5 (0:5:5), were conducted to investigate the effects of different N fertilizer management practices on yield formation, N uptakes, and ammonia (NH3 ) volatilization from paddy fields in direct-seeded rice. RESULTS: The results showed that the N4 treatment improved grain yield by 5.1% while decreasing NH3 volatilization by 20.4% compared with that of conventional fertilizer treatment (N2). The panicle number per unit area was the key factor to determine the yield of direct-seeded rice (72%). Excessive N application of basal fertilizer (N1) reduced seedling emergence, N use efficiency, and yield by 45.3%, 160.6%, and 6.9% respectively and increased NH3 volatilization by 28.1% compared with that of the N4 treatment. Removal of basal N fertilizer (N5) N reduced spike number and yield by 13.0% and 6.9% respectively, minimizing NH3 volatilization while affecting the construction of high-yielding populations compared with that of the N4 treatment. CONCLUSION: Optimized N fertilizer management achieved delayed senescence (maintenance of higher leaf Soil Plant Analysis Development meter values in late reproduction), higher canopy photoassimilation (suitable leaf area), higher N fertilizer use efficiency, and less N loss (lower cumulative NH3 volatilization). © 2023 Society of Chemical Industry.

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure.

BACKGROUND: Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints. METHODS: Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration. RESULTS: Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI, 0.769, 1.139), and at 30 days (-0.026; 95% CI, -0.374, 0.322, and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches. CONCLUSIONS: Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.

Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning.

BACKGROUND: SARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection. METHODS: We linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada. Using ensemble-based algorithms, we examined 484 factors, including individual-level demographics, healthcare use, comorbidities, functional status, and laboratory results; and community-level characteristics to identify factors predictive of infection. Analyses were performed separately for January to April (early wave 1) and May to August (late wave 1). FINDINGS: Among 80,784 LTC residents, 64,757 (80.2%) were tested for SARS-Cov-2 (median age 86 (78-91) years, 30.6% male), of whom 10.2% of 33,519 and 5.2% of 31,238 tested positive in early and late wave 1, respectively. In the late phase (when restriction of visitors, closure of communal spaces, and universal masking in LTC were routine), regional-level characteristics comprised 33 of the top 50 factors associated with testing positive, while laboratory values and comorbidities were also predictive. The c-index of the final model was 0.934, and sensitivity was 0.887. In the highest versus lowest risk quartiles, the odds ratio for infection was 114.3 (95% CI 38.6-557.3). LTC-related geographic variations existed in the distribution of observed infection rates and the proportion of residents at highest risk. INTERPRETATION: Machine learning informed evaluation of predicted and observed risks of SARS-CoV-2 infection at the resident and LTC levels, and may inform initiatives to improve care quality in this setting. FUNDING: Funded by a Canadian Institutes of Health Research, COVID-19 Rapid Research Funding Opportunity grant (# VR4 172736) and a Peter Munk Cardiac Centre Innovation Grant. Dr. D. Lee is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto. Dr. Austin is supported by a Mid-Career investigator award from the Heart and Stroke Foundation. Dr. McAlister is supported by an Alberta Health Services Chair in Cardiovascular Outcomes Research. Dr. Kaul is the CIHR Sex and Gender Science Chair and the Heart & Stroke Chair in Cardiovascular Research. Dr. Rochon holds the RTO/ERO Chair in Geriatric Medicine from the University of Toronto. Dr. B. Wang holds a CIFAR AI chair at the Vector Institute.

Predictors of mortality among long-term care residents with SARS-CoV-2 infection.

BACKGROUND: While individuals living in long-term care (LTC) homes have experienced adverse outcomes of SARS-CoV-2 infection, few studies have examined a broad range of predictors of 30-day mortality in this population. METHODS: We studied residents living in LTC homes in Ontario, Canada, who underwent PCR testing for SARS-CoV-2 infection from January 1 to August 31, 2020, and examined predictors of all-cause death within 30 days after a positive test for SARS-CoV-2. We examined a broad range of risk factor categories including demographics, comorbidities, functional status, laboratory tests, and characteristics of the LTC facility and surrounding community were examined. In total, 304 potential predictors were evaluated for their association with mortality using machine learning (Random Forest). RESULTS: A total of 64,733 residents of LTC, median age 86 (78, 91) years (31.8% men), underwent SARS-CoV-2 testing, of whom 5029 (7.8%) tested positive. Thirty-day mortality rates were 28.7% (1442 deaths) after a positive test. Of 59,702 residents who tested negative, 2652 (4.4%) died within 30 days of testing. Predictors of mortality after SARS-CoV-2 infection included age, functional status (e.g., activity of daily living score and pressure ulcer risk), male sex, undernutrition, dehydration risk, prior hospital contacts for respiratory illness, and duration of comorbidities (e.g., heart failure, COPD). Lower GFR, hemoglobin concentration, lymphocyte count, and serum albumin were associated with higher mortality. After combining all covariates to generate a risk index, mortality rate in the highest risk quartile was 48.3% compared with 7% in the first quartile (odds ratio 12.42, 95%CI: 6.67, 22.80, p < 0.001). Deaths continued to increase rapidly for 15 days after the positive test. CONCLUSIONS: LTC residents, particularly those with reduced functional status, comorbidities, and abnormalities on routine laboratory tests, are at high risk for mortality after SARS-CoV-2 infection. Recognizing high-risk residents in LTC may enhance institution of appropriate preventative measures.

Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.

BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models. METHODS: In this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48·6 years [SD 17·3]; 17 196 [40·8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52·5 years [11·1]; 1079 [33·0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC). FINDINGS: In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p<0·0001). The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0·804 (99% CI 0·795-0·854) for 1-year predictions and 0·733 (0·729-0·769) for 5-year predictions. In the UHN dataset, the AUROC for the top-performing deep learning model was 0·807 (0·795-0·842) for 1-year predictions and 0·722 (0·705-0·764) for 5-year predictions. AUROCs ranged from 0·695 (0·680-0·713) for prediction of death from infection within 5 years to 0·859 (0·847-0·871) for prediction of death by graft failure within 1 year. INTERPRETATION: Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features. FUNDING: Canadian Donation and Transplant Research Program, CIFAR AI Chairs Program.

Hooped Amino-Group Chains in Porous Organic Polymers for Enhancing Heavy Metal Ion Removal.

By adjusting the stretch state of a triethylenetetramine (TETA) chain in an amine-functionalized porous organic polymer (POP), two adsorbents were designed to study the rational microenvironment for heavy metal ion removal. The quantum calculation elucidated that the hooped amino chains in FC-POP-CH2TETA-H exhibited stronger interactions with Pb(II) than the extended one in FC-POP-CH2TETA-E, not only through metal-ligand chelation but also metal coordination. The high binding energy of -2624 kJ mol-1 as well as the constructed microenvironment by the hooped amino chains ensured an extremely high Pb(II) capacity of 1134 mg g-1 on FC-POP-CH2TETA-H. Meanwhile, no more than 5 min to approach adsorption equilibrium revealed its ultrafast adsorption rate. It also showed excellent broad removal capability for multiple metal ions and nonsensitivity to pH. Therefore, by controlling the microenvironmental structures with suitable porosity, functional group stretching states, and coordination modes, the removal efficiency of heavy metal ions would be significantly enhanced, which further provided a promising strategy for designing a rational microenvironment to improve the task-specific separation properties.

Catalyst, Emulsion Stabilizer, and Adsorbent: Three Roles In One for Synergistically Enhancing Interfacial Catalytic Oxidative Desulfurization.

A Pickering emulsion catalytic system was proposed to reduce the transfer limitation between two immiscible reactant phases for enhancing the kinetics of heterogenetic oxidative desulfurization (ODS). By loading phosphotungstic acid (HPW) nanoparticles on a novel pyridine-based porous organic polymer of P[tVPB-VP x], the amphiphilic catalysts were produced and used as the stabilizer for Pickering emulsions. Specifically, an ultrafast ODS rate was realized in the HPW/P[tVPB-VP1]-stabilized Pickering emulsion catalytic system, and just within 15 min, 100 ppm dibenzothiophene (DBT) was completely oxidized by H2O2. Because the obtained hierarchical porous HPW/P[tVPB-VP x] catalysts showed both high adsorption capacity of DBT and excellent catalytic ODS performance, the catalysts assembling at the interface of emulsions provided this fastest reaction dynamics. Playing three roles of catalyst, emulsion stabilizer, and adsorbent, the synergistic functional catalytic emulsions can be a promising approach to significantly boost the heterogeneous catalytic ODS performance.

[Research on the health risk assessment of chemical pollutants in reclaimed water used for streets watering].

It is the first time for this research to utilize the method of health risk assessment. Combining with Beijing reclaimed water project, the exposure dose level and health risk to contact people are investigated and studied. The risk assessment model is built, and the exposure parameters of occupational workers and common people are provided, when reclaimed water was used for streets watering. The health risks of 19 main chemical pollutants in reclaimed water are evaluated quantificationally. The results show that the total risk caused by carcinogens is 8.47 x 10(-)6 for occupational workers and 3.78 x 10(-6) for common people.

[Exposure assessment of various reclaimed water uses].

The exposure assessment method and model of various reclaimed water uses are built combining with Beijing reclaimed water project. Firstly the daily ingesting dose and lifetime average daily dose (LADD) of exposure people are provided via field work and monitoring analysis, which could be used in health risk assessment as quantitative reference. Take park irrigation as a example, for occupational workers, the total daily ingesting dose is 0.07 L/d, LADD of disinfection by-products(DBPs) via the respiratory route is 2.8 x 10(-7) - 1.2 x 10(-5) mg x (kg x d)(-1), LADD of DBPs via the dermal route is 5.8 x 10(-8) - 2.4 x 10(-6) mg x (kg x d)(-1). For common people, the total daily ingesting dose is 0.04-0.05 L/d, LADD of DBPs via the respiratory route is 1.1 x 10(-7) - 6.8 x 10(-6) mg x (kg x d)(-1).