Preoperative transthoracic echocardiography does not lead to decreased postoperative mortality but with increased time to surgery and length of stay in Chinese geriatric hip fracture patients.

PURPOSE: The present study aimed to investigate the influence of preoperative TTE on postoperative short-term mortality, surgery delay, as well as other economic and clinical outcomes in Chinese geriatric hip fracture patients. METHODS: This retrospective, matched-cohort study enrolled geriatric hip fracture patients (≥ 60 years) who underwent surgical interventions at our center between 2015 and 2020. The primary exposure was inpatient preoperative TTE. Demographic and clinical data that were reported as risk factors for postoperative mortality were retrieved from the medical data center as the covariates. The primary clinical outcomes were all-cause mortality at 30 days, 90 days, 180 days, and 1 year. Time from hospital presentation to surgery, length of stay (LOS), inpatient cost, frequency of cardiology consultation and coronary angiography (CAG) were also assessed. The propensity score matching (PSM) was performed in a ratio of 1:1. RESULTS: 447 patients were identified and 216 of them received a preoperative TTE (48.3%). After successfully matching 390 patients (87.2%), patients receiving TTE showed significantly higher 30-day mortality (6.6% vs 2.0%, P = 0.044). But no significant difference was found in 90-day, 180-day, and 365-day mortality as well as the 1-year accumulated survival rate. Receipt of TTE was also associated with significant increases in LOS (13.6 days vs 11.4 days, P = 0.017), waiting time for surgery (5.9 days vs 4.3 days, P < 0.001), and lower proportion of receiving surgery within 48 h (7.2% vs. 26.2%, P < 0.001). According to the multivariable logistic analysis, only ejection fraction (30 days, 90 days), aorta diameter (30 days, 90 days, 180 days, 365 days), left ventricular posterior wall diameter (90 days, 180 days, 365 days), aortic valve velocity (90 days) and mitral valve A-peak (90 days, 180 days) were association with postoperative mortality among the 17 parameters in the TTE reports. Besides, TTE has no influence on the frequency of preoperative cardiology consultation. CONCLUSION: Preoperative TTE does not lead to decreased postoperative mortality but with increased time to surgery and length of stay in Chinese geriatric hip fracture patients. The predictive ability of TTE parameters is limited for postoperative mortality.

Real-time control of urban drainage systems using neuro-evolution.

With climate change and urbanization, existing urban drainage systems are being stressed beyond their design capacity in many parts of the world. Real-time control (RTC) can improve the performance of these systems and reduce the need for system upgrades. However, developing optimal control policies for RTC is a challenging research area due to computational demands, high uncertainties and system dynamics. This study presents a new RTC method using neuro-evolution for controlling combined sewer overflow (CSO) in urban drainage systems. Neuro-evolution is an approach to neural network research by evolutionary algorithms. Neuro-evolution realizes RTC by training the control policy in advance, thus avoiding the online optimization process in the application period. The simulation results of the benchmark Astlingen network indicate that the trained control policy outperforms the equal filling degree strategy in terms of CSO volume reduction and robustness in the face of tank level uncertainty. The performance analysis of the typical CSO events shows that the control policy mainly makes positive contributions during 'small' CSO events rather than 'large' ones. In particular, the effectiveness of the control policy in 'small' CSO events is more prominent in the initial phase of the events compared with the final phase. This work stands to support a foundation for future studies in the control of urban water systems based on neuro-evolution.

Intraoperative intravenous versus periarticular injection of glucocorticoids in improving clinical outcomes after total knee arthroplasty: A prospective, randomized and controlled study.

BACKGROUND: Glucocorticoids have been widely used in perioperative period for postoperative pain relief after total knee arthroplasty (TKA). However, the optimal administration protocols of glucocorticoids remain controversial. This study aims to compare the efficacy of glucocorticoids between intravenous and periarticular injection on clinical outcomes. METHODS: A total of 114 patients were randomly assigned to intravenous (IV) group (n = 57) and periarticular injection (PI) group (n = 57). The IV group received 10 mg dexamethasone intravenously and the PI group received periarticular injection of 10 mg dexamethasone during the procedure. The clinical outcomes were assessed using visual analogue scale (VAS), knee society score (KSS), range of motion (ROM), knee swelling, inflammation markers and complications after TKA. RESULTS: The VAS score during walking at 2nd day postoperatively was lower in the PI group compared with the IV group (2.08 ± 1.45 vs 2.73 ± 1.69, p = .039), and there was no significant difference at the other time points of VAS score in two groups. The inflammation markers, knee swelling, knee ROM and KSS score were not statistically different. Vomiting and other complications occurrence were not significantly different between the two groups. CONCLUSIONS: Intraoperative periarticular injection of glucocorticoids has similar analgesic effect compared to intravenous in the postoperative period following TKA and may be even more effective on the second postoperative day. In addition, periarticular injection of glucocorticoids does not impose an excess risk or complication on patients.

Making Waves: Towards data-centric water engineering.

Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water challenges in a changing world. Building on the historical evolution of water engineering from empirical and theoretical paradigms to the current computational paradigm, we argue that a fourth paradigm, i.e., data-centric water engineering, is emerging driven by recent AI advances. Here we define a new framework for data-centric water engineering in which data are transformed into knowledge and insight through a data pipeline powered by AI technologies. It is proposed that data-centric water engineering embraces three principles - data-first, integration and decision making. We envision that the development of data-centric water engineering needs an interdisciplinary research community, a shift in mindset and culture in the academia and water industry, and an ethical and risk framework to guide the development and application of AI. We hope this paper could inspire research and development that will accelerate the paradigm shift towards data-centric water engineering in the water sector and fundamentally transform the planning and management of water infrastructure.

Enhanced Stability of Dimethyl Ether Carbonylation through Pyrazole Tartrate on Tartaric Acid-Complexed Cobalt-Iron-Modified Hydrogen-Type Mordenite.

In this study, pyrazole tartrate (Pya·DL) and tartaric acid (DL) complexed with cobalt-iron bimetallic modified hydrogen-type mordenite (HMOR) were prepared using the ion exchange method. The results demonstrate that the stability of the dimethyl ether (DME) carbonylation reaction to methyl acetate (MA) was significantly improved after the introduction of Pya·DL to HMOR. The Co∙Fe∙DL-Pya·DL-HMOR (0.8) sample exhibited sustainable stability within 400 h DME carbonylation, exhibiting a DME conversion rate of about 70% and MA selectivity of above 99%. Through modification with the DL-complexed cobalt-iron bimetal, the dispersion of cobalt-iron was greatly enhanced, leading to the formation of new metal Lewis acidic sites (LAS) and thus a significant improvement in catalysis activity. Pya·DL effectively eliminated non-framework aluminum in HMOR, enlarged its pore size, and created channels for carbon deposition diffusion, thereby preventing carbon accumulation and pore blockage. Additionally, Pya·DL shielded the Bronsted acid sites (BAS) in the 12 MR channel, effectively suppressing the side reactions of carbon deposition and reducing the formation of hard carbon deposits. These improvements collectively contribute to the enhanced stability of the DME carbonylation reaction.

The effect of green infrastructure on resilience performance in combined sewer systems under climate change.

Climate change is currently reshaping precipitation patterns, intensifying extremes, and altering runoff dynamics. Particularly susceptible to these impacts are combined sewer systems (CSS), which convey both stormwater and wastewater and can lead to combined sewer overflow (CSO) discharges during heavy rainfall. Green infrastructure (GI) can help mitigate these discharges and enhance system resilience under historical conditions; however, the quantification of its effect on resilience in a future climate remains unknown in the literature. This study employs a modified Global Resilience Analysis (GRA) framework for continuous simulation to quantify the impact of climate change on CSS resilience, particularly CSOs. The study assesses the efficacy of GI interventions (green roofs, permeable pavements, and bioretention cells) under diverse future rainfall scenarios based on EURO-CORDEX regional climate models (2085-2099) and three Representative Concentration Pathways (2.6, 4.5, 8.5 W/m2). The findings underscore a general decline in resilience indices across the future rainfall scenarios considered. Notably, the total yearly CSO discharge volume increases by a range of 145 % to 256 % in response to different rainfall scenarios. While GI proves effective in increasing resilience, it falls short of offsetting the impacts of climate change. Among the GI options assessed, green roofs routed to pervious areas exhibit the highest adaptive capacity, ranging from 9 % to 22 % at a system level, followed by permeable pavements with an adaptation capacity between 7 and 13 %. By linking the effects of future rainfall scenarios on CSO performance, this study contributes to understanding GI's potential as a strategic tool for enhancing urban resilience.

Improving the interpretability of deep reinforcement learning in urban drainage system operation.

Deep reinforcement learning (DRL) has been increasingly used as an adaptive and efficient solution for real-time control (RTC) of the urban drainage system (UDS). Despite the promising potential of DRL, it is a black-box model whose control logic and control consequences are difficult to be understood and evaluated. This leads to issues of interpretability and poses risks in practical applications. This study develops an evaluation framework to analyze and improve the interpretability of DRL-based UDS operation. The framework includes three analysis methods: Sobol sensitivity analysis, tree-based surrogate modelling, and conditional probability analysis. It is validated using two different DRL approaches, i.e., deep Q-learning network (DQN) and proximal policy optimization (PPO), which are trained to reduce combined sewer overflow (CSO) discharges and flooding in a real-world UDS. According to the results, the two DRLs have been shown to perform better than a rule-based control system that is currently being used. Sobol sensitivity analysis indicates that DQN is particularly sensitive to the flow of links and rainfall, while PPO is sensitive to all the states. Tree-based surrogate models effectively reveal the control logic behind the DRLs and indicate that PPO is more comprehensible but DQN is more forward-looking. Conditional probability analysis demonstrates the potential control consequences of the DRLs and identifies three situations where the DRLs are ineffective: a) the storage of UDS is fully utilized; b) peak flows have already passed through actuators; c) a substantial amount of water enters one location simultaneously. The proposed evaluation framework enhances the interpretability of DRL in UDS operations, fostering trust and confidence from operators, stakeholders, and regulators.

Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data.

Ensuring the safety and reliability of drinking water supply requires accurate prediction of water quality in water distribution networks (WDNs). However, existing hydraulic model-based approaches for system state prediction face challenges in model calibration with limited sensor data and intensive computing requirements, while current machine learning models are lack of capacity to predict the system states at sites that are not monitored or included in model training. To address these gaps, this study proposes a novel gated graph neural network (GGNN) model for real-time water quality prediction in WDNs. The GGNN model integrates hydraulic flow directions and water quality data to represent the topology and system dynamics, and employs a masking operation for training to enhance prediction accuracy. Evaluation results from a real-world WDN demonstrate that the GGNN model is capable to achieve accurate water quality prediction across the entire WDN. Despite being trained with water quality data from a limited number of sensor sites, the model can achieve high predictive accuracies (Mean Absolute Error = 0.07 mg L-1 and Mean Absolute Percentage Error = 10.0 %) across the entire network including those unmonitored sites. Furthermore, water quality-based sensor placement significantly improves predictive accuracy, emphasizing the importance of careful sensor location selection. This research advances water quality prediction in WDNs by offering a practical and effective machine learning solution to address challenges related to limited sensor data and network complexity. This study provides a first step towards developing machine learning models to replace hydraulic models in WDN modelling.

Gated graph neural networks for identifying contamination sources in water distribution systems.

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications.

Nano wear particles and the periprosthetic microenvironment in aseptic loosening induced osteolysis following joint arthroplasty.

Joint arthroplasty is an option for end-stage septic arthritis due to joint infection after effective control of infection. However, complications such as osteolysis and aseptic loosening can arise afterwards due to wear and tear caused by high joint activity after surgery, necessitating joint revision. Some studies on tissue pathology after prosthesis implantation have identified various cell populations involved in the process. However, these studies have often overlooked the complexity of the altered periprosthetic microenvironment, especially the role of nano wear particles in the etiology of osteolysis and aseptic loosening. To address this gap, we propose the concept of the "prosthetic microenvironment". In this perspective, we first summarize the histological changes in the periprosthetic tissue from prosthetic implantation to aseptic loosening, then analyze the cellular components in the periprosthetic microenvironment post prosthetic implantation. We further elucidate the interactions among cells within periprosthetic tissues, and display the impact of wear particles on the disturbed periprosthetic microenvironments. Moreover, we explore the origins of disease states arising from imbalances in the homeostasis of the periprosthetic microenvironment. The aim of this review is to summarize the role of relevant factors in the microenvironment of the periprosthetic tissues, in an attempt to contribute to the development of innovative treatments to manage this common complication of joint replacement surgery.

Dual water-electricity cooperation improves economic benefits and water equality in the Lancang-Mekong River Basin.

Transboundary river cooperation provides an effective pathway to maintain regional security and sustainable development; however, its implementation is a pressing and prominent concern due to lack of appropriate compensation measures and effective incentive strategies. Here we develop a dual water-electricity cooperation (DWEC) framework that combines water and electricity trading to meet the often-conflicting demands of participating countries. The results from the Lancang-Mekong River Basin reveal that substantial benefits in both economic and social aspects can be achieved through coupling regional water and electricity trades. Economic benefits can be obtained by expanding cooperation space and thereby greatly improving the willingness of countries to participate in basin-wide cooperation. Electricity trading plays a key role in loss compensation for water exporters, ensuring no loss for any party and maximizing basin-wide benefits. Furthermore, the DWEC improves regional water use equality, especially in water shortage periods when there is severe competition among water users. The proposed cooperation framework provides a viable way to implement cooperation in transboundary river basins.

Bone turnover biomarkers predict one-year all-cause mortality and walking ability in geriatric hip fracture patients.

PURPOSE: To investigate the utility of serum C-terminal cross-linking telopeptides (ß-CTX) and procollagen type I N propeptide (PINP) for predicting one-year mortality and walking ability in Chinese geriatric hip fracture patients who underwent surgical interventions. METHOD: Elderly patients (≥ 60 years) who underwent surgical interventions for unilateral low-energy hip fracture from 2015 to 2020 in our center were included. Demographic data was retrospectively retrieved from the electronic medical database. The PINP and ß-CTX concentrations were measured before the surgery. The patients were divided into two groups according to the outcome of mortality and walking ability after hip surgery, respectively. ß-CTX and PINP were divided into four grades based on quartiles [Quartile(Q)1-4] for further analysis. All the variables with p < 0.1 in univariable analysis were included in a multivariable model. RESULTS: In univariable analysis, the levels of serum ß-CTX (p = 0.007) and PINP (p = 0.025) was associated with one-year mortality, while the association between levels of serum ß-CTX (p = 0.072) or PINP (p = 0.055) with one-year disability was marginally significant. After adjustment for confounders, the relative risk [OR (95 % CI), Q4 v sQ1, p-value] of one-year mortality and one-year disability were 7.28 (2.08-29.78, p = 0.003) and 3.97 (1.44-11.69, p = 0.009) for ß-CTX and 5.87 (1.70-23.80, p = 0.008) and 3.48 (1.30-9.93, p = 0.016) for PINP, respectively. The coefficient of determination, AUC and bias-corrected C-index of predictive models based on previously reported predictors were significantly improved after integrating ß-CTX or PINP. CONCLUSION: Higher serum ß-CTX and PINP are independently associated with an increased risk of one-year mortality and disability in patients with hip fractures. The application of BTMs improves the performance of currently available predictive models.

Global resilience analysis of combined sewer systems under continuous hydrologic simulation.

Managing and reducing combined sewer overflow (CSO) discharges is crucial for enhancing the resilience of combined sewer systems (CSS). However, the absence of a standardised resilience analysis approach poses challenges in developing effective discharge reduction strategies. To address this, our study presents a top-down method that expands the existing Global Resilience Analysis to quantify resilience performance in CSS. This approach establishes a link between threats (e.g., rainfall) and impacts (e.g., CSOs) through continuous and long-term simulation, accommodating various rainfall patterns, including extreme events. We assess CSO discharge impacts from a resilience perspective by introducing eight new metrics. We conducted a case study in Fehraltorf, Switzerland, analysing the performance of three green infrastructure (GI) types (bioretention cells, green roofs, and permeable pavements) over 38 years. The results demonstrated that GI enhanced all resilience indices, with variations observed in individual CSO performance metrics and their system locations. Notably, in Fehraltorf, green roofs emerged as the most effective GI type for improving resilience, while the downstream outfall displayed the highest resilience enhancement. Overall, our proposed method enables a shift from event-based to continuous simulation analysis, providing a standardised approach for resilience assessment. This approach informs the development of strategies for CSO discharge reduction and the enhancement of CSS resilience.

Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale.

The assessment of flood risk and resilience has become increasingly important in recent years for effective urban flood management. While flood resilience and risk are two distinct concepts with unique assessment metrics, there is lack of quantitative analysis and understanding of the relationship between them. This study aims to investigate this relationship at the grid cell level in urban areas. To assess flood resilience for high-resolution grid cells, this study proposes a performance-based flood resilience metric, which is calculated using the system performance curve based on flood duration and magnitude. Flood risk is calculated as the product of maximum flood depth and probability, considering multiple storm events. The case study of Waterloo in London, UK is analyzed using a two-dimensional cellular automata-based model CADDIES, which consists of 2.7 million grid cells (5 m × 5 m). The results indicate that over 2 % of grid cells have risk values exceeding 1. Furthermore, there is a 5 % difference in resilience values below 0.8 between the 200-year and 2000-year design rainfall events, specifically 4 % for the former and 9 % for the latter. Additionally, the results reveal a complex relationship between flood risk and resilience, though decreasing flood resilience generally leads to increasing flood risk. However, this relationship varies depending on the land cover type, with building, green land, and water body cells showing higher resilience for the same level of flood risk compared to other land uses such as roads and railways. Classifying urban areas into four categories, including high risk vs. low resilience, high risk vs. high resilience, low risk vs. low resilience, and low risk vs. high resilience, is crucial in identifying flood hotspots for intervention development. In conclusion, this study provides an in-depth understanding of the relationship between risk and resilience in urban flooding, which could help improve urban flood management. The proposed performance-based flood resilience metric and the findings from the case study of Waterloo in London could be valuable for decision-makers in developing effective flood management strategies in urban areas.

Distribution of bone voids in the thoracolumbar spine in Chinese adults with and without osteoporosis: A cross-sectional multi-center study based on 464 vertebrae.

Bone void is a novel intuitive morphological indicator to assess bone quality but its use in vertebrae has not been described. This cross-sectional and multi-center study aimed to investigate the distribution of bone voids in the thoracolumbar spine in Chinese adults based on quantitative computed tomography (QCT). A bone void was defined as a trabecular net region with extremely low bone mineral density (BMD) (<40 mg/cm3), detected by an algorithm based on phantom-less technology. A total of 464 vertebrae from 152 patients (51.8 ± 13.4 years old) were included. The vertebral trabecular bone was divided into eight sections based on the middle sagittal, coronal, and horizontal planes. Bone void of the whole vertebra and each section were compared between healthy, osteopenia, and osteoporosis groups and between spine levels. Receiver operator characteristic (ROC) curves were plotted and optimum cutoff points of void volume between the groups were obtained. The total void volumes of the whole vertebra were 124.3 ± 221.5 mm3, 1256.7 ± 928.7 mm3, and 5624.6 ± 3217.7 mm3 in healthy, osteopenia, and osteoporosis groups, respectively. The detection rate of vertebrae with bone voids was higher and the normalized void volume was larger in the lumbar than in thoracic vertebrae. L3 presented the largest void (2165.0 ± 3396.0 mm3), while T12 had the smallest void (448.9 ± 699.4 mm3). The bone void was mainly located in the superior-posterior-right section (40.8 %). Additionally, bone void correlated positively with age and increased rapidly after 55 years. The most significant void volume increase was found in the inferior-anterior-right section whereas the least increase was found in the inferior-posterior-left section with aging. The cutoff points were 345.1 mm3 between healthy and osteopenia groups (sensitivity = 0.923, specificity = 0.932) and 1693.4 mm3 between osteopenia and osteoporosis groups (sensitivity = 1.000, specificity = 0.897). In conclusion, this study demonstrated the bone void distribution in vertebrae using clinical QCT data. The findings provide a new perspective for the description of bone quality and showed that bone void could guide clinical practice such as osteoporosis screening.

Preoperative Vitamin D Deficiency is Associated with Increased One-Year Mortality in Chinese Geriatric Hip Fracture Patients - A Propensity Score Matching Study.

Background: Vitamin D deficiency is a common comorbidity in geriatric hip fracture patients. However, there is still an ongoing debate regarding the influence of preoperative Vitamin D status on postoperative mortality in hip fracture patients. Methods: Elderly patients (≥60 years) who underwent surgical interventions for unilateral hip fracture from 2015 to 2020 in our center were included. We retrospectively retrieved the demographic data from the electronic medical database. Preoperative serum total 25-hydroxy-Vitamin D was set as the independent variable and patients were classified as the Vitamin D deficiency (<20ng/mL) and the control groups consequently. Clinical outcomes include all-cause mortality, walking ability, and major postoperative complications in the first postoperative year. Propensity score matching (PSM) was performed in a ratio of 1:1 in the two groups for further comparison. Results: A total of 210 patients were included and 121 patients (57.6%) were diagnosed with Vitamin D deficiency. Patients in the Vitamin D deficiency group were much older and therefore preferred peripheral nerve block, and had significantly higher proportions of females, preoperative dementia, higher ASA grade, and lower baseline serum albumin level. Overall, 79 patients were identified in the Vitamin D deficiency and control groups after PSM, respectively. Patients diagnosed with Vitamin D deficiency showed a significantly higher one-year mortality (21.5% vs 6.3%, P=0.011) and a much lower one-year independent walking rate (67.1% vs.84.8%, P=0.016) after the matching. Regarding the dataset before PSM and after PSM, the AUC for serum Vitamin D for predicting one-year mortality was 0.656 (P=0.006) and 0.695 (P=0.002), respectively. Conclusion: Our retrospective PSM-design study provides new evidence that Vitamin D deficiency was associated with a significantly higher mortality and poor walking ability in the first year after surgical intervention based on southern Chinese populations.

Water transfer infrastructure buffers water scarcity risks to supply chains.

Inter-basin water transfer (IBWT) infrastructure has been expanding to deliver water across China to meet water demands in populated and industrial areas. Water scarcity may threaten the ability to produce and distribute goods through supply chains. Yet, it is not clear if IBWTs transmit or buffer water scarcity throughout supply chains. Here we combine a national database of IBWT projects and multi-region input-output analysis to trace water transferred by IBWT and virtual scarce water (scarcity weighted water use) from IBWT sourcing basins to production sites then to end consumers. The results indicate that production and final consumption of sectoral products have been increasingly supported by IBWT infrastructure, with physically transferred water volumes doubling between 2007 and 2017. Virtual scarce water is about half of the virtual water supporting the supply chain of the nation. IBWT effectively reduced virtual scarce water supporting the supply chains of most provinces, with the exposure to water scarcity reduced by a maximum of 56.7% and 15.0% for production and final consumption, respectively. IBWT Infrastructure development can thus buffer water scarcity risk to the supply chain and should be considered in water management and sustainable development policy decisions.

Baseline Knee Pain Predicts Long-Term Response of Intra-Articular Steroid Injection in Symptomatic Knee Osteoarthritis: Data from OAI.

OBJECTIVE: The current study aims to investigate the factors that could predict response to intra-articular corticosteroid injection (IACI) in patients with knee osteoarthritis (KOA). METHODS: Data of participants were retrieved from the Osteoarthritis Initiative database. Participants with at least one IACI treatment on single or bilateral knees within the first 5 years of follow-up were retrospectively included. Demographic data, clinical and radiographic variables were collected at both baseline and the first follow-up after IACI treatment. Positive response to IACI treatment was defined as >20% reduction of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score from V0 to V1. All the variables with P < 0.2 after the comparison between the response and non-response groups were included in a multivariable logistic regression model to identify independent response predictive patient-specific valuables. Receiver operating characteristic curves were performed to establish the cutoff values of independent predictors. RESULTS: The current study included a total of 385 participants (473 knees), with 155 and 318 knees classified into the response group and non-response group, respectively. Those with satisfied responses to IACI treatment had significantly higher WOMAC pain score (P < 0.001), disability score (P = 0.002), and stiffness score (P = 0.015) at the baseline. Baseline WOMAC pain score showed significant association with positive response to IACI treatment in multivariate logistic analysis and the best cutoff value was 5 points. The rate of analgesics utilization was lower (P = 0.014) in the response group than the non-response group after the IACI treatment. CONCLUSION: KOA patients with a baseline WOMAC pain score ≥5 are more likely to benefit from IACI treatment.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring.

Contamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks-a generator and a discriminator-the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.