Analyzing relationships between latent topics in autonomous vehicle crash narratives and crash severity using natural language processing techniques and explainable XGBoost.

Safety is one of the most essential considerations when evaluating the performance of autonomous vehicles (AVs). Real-world AV data, including trajectory, detection, and crash data, are becoming increasingly popular as they provide possibilities for a realistic evaluation of AVs' performance. While substantial research was conducted to estimate general crash patterns utilizing structured AV crash data, a comprehensive exploration of AV crash narratives remains limited. These narratives contain latent information about AV crashes that can further the understanding of AV safety. Therefore, this study utilizes the Structural Topic Model (STM), a natural language processing technique, to extract latent topics from unstructured AV crash narratives while incorporating crash metadata (i.e., the severity and year of crashes). In total, 15 topics are identified and are further divided into behavior-related, party-related, location-related, and general topics. Using these topics, AV crashes can be systematically described and clustered. Results from the STM suggest that AVs' abilities to interact with vulnerable road users (VRUs) and react to lane-change behavior need to be further improved. Moreover, an XGBoost model is developed to investigate the relationships between the topics and crash severity. The model significantly outperforms existing studies in terms of accuracy, suggesting that the extracted topics are closely related to crash severity. Results from interpreting the model indicate that topics containing information about crash severity and VRUs have significant impacts on the model's output, which are suggested to be included in future AV crash reporting.

Postmenopausal endometrial non-benign lesion risk classification through a clinical parameter-based machine learning model.

OBJECTIVE: This study aimed to develop and evaluate a machine learning model utilizing non-invasive clinical parameters for the classification of endometrial non-benign lesions, specifically atypical hyperplasia (AH) and endometrioid carcinoma (EC), in postmenopausal women. METHODS: Our study collected clinical parameters from a cohort of 999 patients with postmenopausal endometrial lesions and conducted preprocessing to identify 57 relevant characteristics from these irregular clinical data. To predict the presence of postmenopausal endometrial non-benign lesions, including atypical hyperplasia and endometrial cancer, we employed various models such as eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), as well as two ensemble models. Additionally, a test set was performed on an independent dataset consisting of 152 patients. The performance evaluation of all models was based on metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F1 score. RESULTS: The RF model demonstrated superior recognition capabilities for patients with non-benign lesions compared to other models. In the test set, it attained a sensitivity of 88.1% and an AUC of 0.93, surpassing all alternative models evaluated in this study. Furthermore, we have integrated this model into our hospital's Clinical Decision Support System (CDSS) and implemented it within the outpatient electronic medical record system to continuously validate and optimize its performance. CONCLUSIONS: We have trained a model and deployed a system with high discriminatory power that may provide a novel approach to identify patients at higher risk of postmenopausal endometrial non-benign lesions who may benefit from more tailored screening and clinical intervention.

Deep optimization of water quality index and positive matrix factorization models for water quality evaluation and pollution source apportionment using a random forest model.

Effective evaluation of water quality and accurate quantification of pollution sources are essential for the sustainable use of water resources. Although water quality index (WQI) and positive matrix factorization (PMF) models have been proven to be applicable for surface water quality assessments and pollution source apportionments, these models still have potential for further development in today's data-driven, rapidly evolving technological era. This study coupled a machine learning technique, the random forest model, with WQI and PMF models to enhance their ability to analyze water pollution issues. Monitoring data of 12 water quality indicators from six sites along the Minjiang River from 2015 to 2020 were used to build a WQI model for determining the spatiotemporal water quality characteristics. Then, coupled with the random forest model, the importance of 12 indicators relative to the WQI was assessed. The total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (CODCr), dissolved oxygen (DO), and five-day biochemical oxygen demand (BOD5) were identified as the top five significant parameters influencing water quality in the region. The improved WQI model constructed based on key parameters enabled high-precision (R2 = 0.9696) water quality prediction. Furthermore, the feature importance of the indicators was used as weights to adjust the results of the PMF model, allowing for a more reasonable pollutant source apportionment and revealing potential driving factors of variations in water quality. The final contributions of pollution sources in descending order were agricultural activities (30.26%), domestic sewage (29.07%), industrial wastewater (26.25%), seasonal factors (6.45%), soil erosion (6.19%), and unidentified sources (1.78%). This study provides a new perspective for a comprehensive understanding of the water pollution characteristics of rivers, and offers valuable references for the development of targeted strategies for water quality improvement.

Enhancing phosphorus source apportionment in watersheds through species-specific analysis.

Phosphorus (P) is a pivotal element responsible for triggering watershed eutrophication, and accurate source apportionment is a prerequisite for achieving the targeted prevention and control of P pollution. Current research predominantly emphasizes the allocation of total phosphorus (TP) loads from watershed pollution sources, with limited integration of source apportionment considering P species and their specific implications for eutrophication. This article conducts a retrospective analysis of the current state of research on watershed P source apportionment models, providing a comprehensive evaluation of three source apportionment methods, inventory analysis, diffusion models, and receptor models. Furthermore, a quantitative analysis of the impact of P species on watersheds is carried out, followed by the relationship between P species and the P source apportionment being critically clarified within watersheds. The study reveals that the impact of P on watershed eutrophication is highly dependent on P species, rather than absolute concentration of TP. Current research overlooking P species composition of pollution sources may render the acquired results of source apportionment incapable of assessing the impact of P sources on eutrophication accurately. In order to enhance the accuracy of watershed P pollution source apportionment, the following prospectives are recommended: (1) quantifying the P species composition of typical pollution sources; (2) revealing the mechanisms governing the migration and transformation of P species in watersheds; (3) expanding the application of traditional models and introducing novel methods to achieve quantitative source apportionment specifically for P species. Conducting source apportionment of specific species within a watershed contributes to a deeper understanding of P migration and transformation, enhancing the precise of management of P pollution sources and facilitating the targeted recovery of P resources.

Macrophage KLF15 prevents foam cell formation and atherosclerosis via transcriptional suppression of OLR-1.

BACKGROUND: Macrophage-derived foam cells are a hallmark of atherosclerosis. Scavenger receptors, including lectin-like oxidized low-density lipoprotein (LDL) receptor-1 (OLR-1), are the principal receptors responsible for the uptake and modification of LDL, facilitating macrophage lipid load and the uptake of oxidized LDL by arterial wall cells. Krüppel-like factor 15 (KLF15) is a transcription factor that regulates the expression of genes by binding to the promoter during transcription. Therefore, this study aimed to investigate the precise role of macrophage KLF15 in atherogenesis. METHODS: We used two murine models of atherosclerosis: mice injected with an adeno-associated virus (AAV) encoding the Asp374-to-Tyr mutant version of human PCSK9, followed by 12 weeks on a high-fat diet (HFD), and ApoE-/-- mice on a HFD. We subsequently injected mice with AAV-KLF15 and AAV-LacZ to assess the role of KLF15 in the development of atherosclerosis in vivo. Oil Red O, H&E, and Masson's trichome staining were used to evaluate atherosclerotic lesions. Western blots and RT-qPCR were used to assess protein and mRNA levels, respectively. RESULTS: We determined that KLF15 expression was downregulated during atherosclerosis formation, and KLF15 overexpression prevented atherosclerosis progression. KLF15 expression levels did not affect body weight or serum lipid levels in mice. However, KLF15 overexpression in macrophages prevented foam cell formation by reducing OLR-1-meditated lipid uptake. KLF15 directly targeted and transcriptionally downregulated OLR-1 levels. Restoration of OLR-1 reversed the beneficial effects of KLF15 in atherosclerosis. CONCLUSION: Macrophage KLF15 transcriptionally downregulated OLR-1 expression to reduce lipid uptake, thereby preventing foam cell formation and atherosclerosis. Thus, our results suggest that KLF15 is a potential therapeutic target for atherosclerosis.

Effect of microbial communities on nitrogen and phosphorus metabolism in rivers with different heavy metal pollution.

Small urban and rural rivers usually face heavy metal pollution as a result of urbanization and industrial and agricultural activities. To elucidate the metabolic capacity of microbial communities on nitrogen and phosphorus cycle in river sediments under different heavy metal pollution backgrounds, this study collected samples in situ from two typical rivers, Tiquan River and Mianyuan River, with different heavy metal pollution levels. The microbial community structure and metabolic capacity of nitrogen and phosphorus cycles of sediment microorganisms were analyzed by high-throughput sequencing. The results showed that the major heavy metals in the sediments of the Tiquan River were Zn, Cu, Pb, and Cd with the contents of 103.80, 30.65, 25.95, and 0.44 mg/kg, respectively, while the major heavy metals in the sediments of the Mianyuan River were Cd and Cu with the contents of 0.60 and 27.81 mg/kg, respectively. The dominant bacteria Steroidobacter, Marmoricola, and Bacillus in the sediments of the Tiquan River had positive correlations with Cu, Zn, and Pb while are negatively correlated with Cd. Cd had a positive correlation with Rubrivivax, and Cu had a positive correlation with Gaiella in the sediments of the Mianyuan River. The dominant bacteria in the sediments of the Tiquan River showed strong phosphorus metabolic ability, and the dominant bacteria in the sediments of the Mianyuan River showed strong nitrogen metabolic ability, corresponding to the lower total phosphorus content in the Tiquan River and the higher total nitrogen content in the Mianyuan River. The results of this study showed that resistant bacteria became dominant bacteria due to the stress of heavy metals, and these bacteria showed strong nitrogen and phosphorus metabolic ability. It can provide theoretical support for the pollution prevention and control of small urban and rural rivers and have positive significance for maintaining the healthy development of rivers.

Macrophage polarization states in atherosclerosis.

Atherosclerosis, a chronic inflammatory condition primarily affecting large and medium arteries, is the main cause of cardiovascular diseases. Macrophages are key mediators of inflammatory responses. They are involved in all stages of atherosclerosis development and progression, from plaque formation to transition into vulnerable plaques, and are considered important therapeutic targets. Increasing evidence suggests that the modulation of macrophage polarization can effectively control the progression of atherosclerosis. Herein, we explore the role of macrophage polarization in the progression of atherosclerosis and summarize emerging therapies for the regulation of macrophage polarization. Thus, the aim is to inspire new avenues of research in disease mechanisms and clinical prevention and treatment of atherosclerosis.

New insights into pollution source analysis using receptor models in the upper Yangtze river basin: Effects of land use on source identification and apportionment.

To effectively control pollution and improve water quality, it is essential to accurately analyze the potential pollution sources in rivers. The study proposes a hypothesis that land use can influence the identification and apportionment of pollution sources and tested it in two areas with different types of water pollution and land use. The redundancy analysis (RDA) results showed that the response mechanisms of water quality to land use differed among regions. In both regions, the results indicated that the water quality response relationship to land use provided important objective evidence for pollution source identification, and the RDA tool optimized the procedure of source analysis for receptor models. Positive matrix decomposition (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models identified five and four pollution sources along with their corresponding characteristic parameters. PMF attributed agricultural nonpoint sources (23.8%) and domestic wastewater (32.7%) as the major sources in regions 1 and 2, respectively, while APCS-MLR identified mixed sources in both regions. In terms of model performance parameters, PMF demonstrated better-fit coefficients (R2) than APCS-MLR and had a lower error rate and proportion of unidentified sources. The results show that considering the effect of land use in the source analysis can overcome the subjectivity of the receptor model and improve the accuracy of pollution source identification and apportionment. The results of the study can help managers clarify the priorities of pollution prevention and control, and provide a new methodology for water environment management in similar watersheds.

Evaluation of mulberry leaves' hypoglycemic properties and hypoglycemic mechanisms.

The effectiveness of herbal medicine in treating diabetes has grown in recent years, but the precise mechanism by which it does so is still unclear to both medical professionals and diabetics. In traditional Chinese medicine, mulberry leaf is used to treat inflammation, colds, and antiviral illnesses. Mulberry leaves are one of the herbs with many medicinal applications, and as mulberry leaf study grows, there is mounting evidence that these leaves also have potent anti-diabetic properties. The direct role of mulberry leaf as a natural remedy in the treatment of diabetes has been proven in several studies and clinical trials. However, because mulberry leaf is a more potent remedy for diabetes, a deeper understanding of how it works is required. The bioactive compounds flavonoids, alkaloids, polysaccharides, polyphenols, volatile oils, sterols, amino acids, and a variety of inorganic trace elements and vitamins, among others, have been found to be abundant in mulberry leaves. Among these compounds, flavonoids, alkaloids, polysaccharides, and polyphenols have a stronger link to diabetes. Of course, trace minerals and vitamins also contribute to blood sugar regulation. Inhibiting alpha glucosidase activity in the intestine, regulating lipid metabolism in the body, protecting pancreatic -cells, lowering insulin resistance, accelerating glucose uptake by target tissues, and improving oxidative stress levels in the body are some of the main therapeutic properties mentioned above. These mechanisms can effectively regulate blood glucose levels. The therapeutic effects of the bioactive compounds found in mulberry leaves on diabetes mellitus and their associated molecular mechanisms are the main topics of this paper's overview of the state of the art in mulberry leaf research for the treatment of diabetes mellitus.

A systematic review and meta-analysis of the seroconversion rates and adverse effects of COVID-19 mRNA vaccine and COVID-19 viral vector vaccine in kidney transplant recipient patients.

Patients received kidney transplantation (KTR) have a low seroconversion rate after vaccination. Our objective was to compare the seroconversion rates and adverse effects of additional different vaccinations in KTR patients in existing studies. Databases such as PubMed, Cochrane Library, Web of Science, Embase, ClinicalTrials.gov and others. Three high-quality RCT were included and showed no statistical difference in seroconversion rates between the two vaccines (RR = 0.93[0.76,1.13]). There was no statistical difference in seroconversion rates between the sexes, for men (RR = 0.93[0.69,1.25]) and women (RR = 0.91[0.62,1.33]). Among the adverse effects there was no statistically significant difference in fever (RR = 1.06[0.44,2.57]), while for injection site pain there was a statistically significant difference (RR = 1.14[1.18,1.84]). There was no significant difference in seroconversion rates in patients with KTR who received the two additional vaccines. Patients injected with the viral vector vaccine were less painful than those injected with the mRNA vaccine.

A method for researching the eutrophication and N/P loads of plateau lakes: Lugu Lake as a case.

Lugu Lake is one of the best plateau lakes in China in terms of water quality, but in recent years the eutrophication of Lugu Lake has accelerated due to high nitrogen and phosphorus loads. This study aimed to determine the eutrophication state of Lugu Lake. Specifically, the spatio-temporal variations of nitrogen and phosphorus pollution during the wet and dry seasons were investigated in Lianghai and Caohai, and the primary environmental effect factors were defined. Adopting the endogenous static release experiments and the exogenous improved export coefficient model, a novel approach (a combination of internal and external sources) was developed for the estimation of nitrogen and phosphorus pollution loads in Lugu Lake. It was indicated that the order of nitrogen and phosphorus pollution in Lugu Lake was Caohai > Lianghai and dry season > wet season. Dissolved oxygen (DO) and chemical oxygen demand (CODMn) were the main environmental factors causing nitrogen and phosphorus pollution. Endogenous nitrogen and phosphorus release rates in Lugu Lake were 668.7 and 42.0 t/a, respectively, and exogenous nitrogen and phosphorus input rates were 372.7 and 30.8 t/a, respectively. The contributions of pollution sources, in descending order, were sediment > land-use categories > residents and livestock breeding > plant decay, of which sediment nitrogen and phosphorus loads accounted for 64.3 % and 57.4 %, respectively. Regulating the endogenous release of sediment and obstructing the exogenous input from shrubland and woodland are emphasized for the management of nitrogen and phosphorus contamination in Lugu Lake. Thus, this study can serve as a theoretical foundation and technical guide for eutrophication control in plateau lakes.

Sophisticated yet Convenient Information Encryption/Decryption Based on Synergistically Time-/Temperature-Resolved Photonic Inks.

Exploring high-safety but convenient encryption and decryption technologies to combat threats of information leakage is urgently needed but remains a great challenge. Here, a synergistically time- and temperature-resolved information coding/decoding solution based on functional photonic inks is demonstrated. Encrypted messages can be stored into multiple channels with dynamic-color patterns, and information decryption is only enabled at appointed temperature and time points. Notably, the ink can be easily processed into quick-response codes and multipixel plates. With high transparency and responsive color variations controlled by ink compositions and ambient temperatures, advanced 3D stacking multichannel coding and Morse coding techniques can be applied for multi-information storage, complex anticounterfeiting, and information interference. This study paves an avenue for the design and development of dynamic photonic inks and complex encryption technologies for high-end anticounterfeiting applications.

Supernatural: Artificial Nucleobases and Backbones to Program Hybridization-Based Assemblies and Circuits.

The specificity and predictability of hybridization make oligonucleotides a powerful platform to program assemblies and networks with logic-gated responses, an area of research which has grown into a field of its own. While the field has capitalized on the commercial availability of DNA oligomers with its four canonical nucleobases, there are opportunities to extend the capabilities of the hardware with unnatural nucleobases and other backbones. This Topical Review highlights nucleobases that favor hybridizations that are empowering for assemblies and networks as well as two chiral XNAs than enable orthogonal hybridization networks.

Application of a Tele-Ultrasound Robot During COVID-19 Pandemic: A Feasibility Study.

OBJECTIVE: To investigate the accuracy of ultrasonic diagnosis using the tele-ultrasound robot in Leishen Shan Hospital. METHOD: Twenty-two patients with novel coronavirus pneumonia from Leishen Shan Hospital voluntarily participated in this study. Their thyroids, neck vessels, hepatobiliaries and kidneys were scanned by both a tele-ultrasound robot manufactured by Imabot Co., Ltd, Wuhan and conventional method. The ultrasound diagnosis of each patient was compared, and the ultrasound images obtained by the two methods were mixed together and double-blindly diagnosed by an experienced ultrasound radiologist. RESULTS: There were 44 positive lesions in 110 sites of 22 patients. Of which the two methods, 40 positive lesions were detected by the robotic method with 4 lesions missed (2 small polyps of gallbladder, 1 small hemangioma of liver and 1 small cyst of kidney) and 1 lesion misdiagnosed (normal carotid artery was misdiagnosed as carotid atherosclerotic plaque); 44 positive lesions were detected by conventional method with 1 small cyst of the liver was missed. There was no statistically significant difference in the accuracy rate between the robotic method and the conventional method using the chi-square test of the four-grid data (P>.05). CONCLUSION: The application of tele-ultrasound robot meets the standard of patient care during the pandemic. The method is feasible to provide adequate ultrasound information to diagnose common abdominal, vascular, superficial organ pathologies in patients with COVID-19 with acceptable accuracy compared with a conventional ultrasound scan.

Thermoformable and transparent one-component nanocomposites based on surface grafted cellulose nanofiber.

One-component nanocomposites based on poly(methyl methacrylate)(PMMA) and polystyrene (PS) grafted cellulose nanofiber (CNF) with high polymer graft percentage were fabricated. At relative ambient conditions, less active vinyl monomer, MMA, and styrene were grafted from CNF via surface-initiated Cu(0)-mediated reversible deactivation radical polymerizations (RDRP), and PMMA/PS grafted CNFs could reach a graft percentage as high as 7550 % and 3530 %, respectively. The one-component composite films were manufactured by simple hot-pressing subsequentially. Optical transparency, thermal stability, and glass transition temperature of one-component nanocomposites were enhanced dramatically in contrast with the bicomponent nanocomposite. The uniform fracture surface confirmed the uniform dispersity by morphological observation. Mechanical tests indicated that break elongation and tensile strength ascended notably, and tensile modulus slightly descended as the graft percentage increased for PS and PMMA grafted CNF one-component composite. It was concluded that for glassy graft chains, obtaining one-component nanocomposites with high enough graft chain length was essential to achieve moderated mechanical performance without compromising optical properties and thermal manufacturing ability.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers.

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

The effect of risk perception and other psychological factors on mobile phone use while crossing the street among pedestrians.

Pedestrians may be the most vulnerable group among road users, and mobile phone use while crossing the street is ubiquitous worldwide in this information era. However, previous studies have found that such distracting behaviors may increase the risk of injury and death. The present study primarily aimed to explore the effect of reinforcement sensitivity theory components (i.e., Behavioral Inhibition System (BIS), Behavioral Approach System (BAS)), risk perception, attitudes towards red light running, and fear of missing out (FoMO) on mobile phone use while crossing the street among pedestrians. Risk perception was measured in three ways (i.e., assessing the probability of a negative outcome (RP-Pro), judging the severity of the consequence (RP-Se), and evaluating the general riskiness of the behavior (RP-Ri)). An online questionnaire survey was conducted, and only valid responses (N = 425) were used for subsequent data analyses. The results indicated significant differences in the responses across the risk perception questions with different focuses. Participants who reported engaging in more distracted street-crossing (i.e., high-risk takers) perceived a significantly lower risk, and this difference did not depend on the focus of risk perception. Three path analysis models with differential risk perception constructs (RP-Pro, RP-Se, and RP-Ri) were developed to examine the relationship between risk perception and distracted street-crossing. The results suggest that the relationship between these two variables does not depend on the focus of the risk perception questions. Moreover, FoMO was a predictor of mobile phone use while crossing the street, while attitudes had both direct and indirect effects on behavior. BIS and BAS had the lowest total effect on mobile phone use among pedestrians. In particular, a direct association between BAS and distracted street-crossing was found only in the model in which risk perception was measured by judging the severity of crashes caused by mobile phone use distraction. This study may be meaningful for understanding the associations between psychological factors and mobile phone use among pedestrians. The implications of the findings for the development of safety interventions are discussed in this study.

Preoperative albumin corrected anion gap is associated with in-hospital and long-term mortality in patients undergoing coronary artery bypass grafting in a retrospective cohort study.

Background: Coronary artery disease remains a global health concern and the leading cause of death. Till today, coronary artery bypass grafting (CABG) is one of the main treatment strategies for coronary artery disease, especially for Multivessel coronary disease or complex coronary lesions. The present study aimed to explore the relationship of preoperative albumin corrected anion gap (ACAG) with mortality in all those patients who undergoing CABG. Methods: All the patients undergoing CABG were included in the study. All clinical data were collected from CareVue and MetaVision system. The predictive value of ACAG for mortality was determined by receiver operating characteristic (ROC) curves survival curves were estimated using the Kaplan-Meier method. Multivariate regression models were constructed to determine the association of ACAG with mortality. Results: A total of 2,180 patients were identified and divided into a high ACAG group (ACAG ≥16.0 mmol/L) and low ACAG group (ACAG <16.0 mmol/L) according to the ROC analysis. Patients in the high ACAG group were older and presented with more comorbidities and concomitant valvular surgeries. Further more, in the high ACAG group, we observed a higher length of stay in the intensive care unit [3.88 (2.15, 7.09) vs. 2.29 (1.29, 3.94), P<0.001]. Both the in-hospital mortality [28 (4.5%) vs. 11 (0.7%), P<0.001], and the 4-year mortality [125 (27.1%) vs. 111 (12.7%), P<0.001] were also rised in those patients. And it was also showed in the survival curves, patients with ACAG ≥16.0 mmol/L had a significant lower 4-year survival (P<0.001). While in the multivariate regression model, we found ACAG was act as an independent risk factor for both the in-hospital mortality [odds ratio (OR): 1.248 (1.060, 1.470), P=0.008] and the 4-year mortality [hazard ratio (HR): 1.134 (1.063, 1.210), P<0.001]. An ACAG ≥16.0 mmol/L was significantly associated with a 2.7-fold risk of in-hospital mortality [OR: 2.732 (1.129, 6.610), P=0.026]. Conclusions: Preoperative ACAG is an independent risk factor for in-hospital and long-term mortality in CABG patients. A higher ACAG may relate to severe coronary artery stenosis and cardiac dysfunction, which is more likely to lead to a postoperative systemic inflammatory response, microcirculation disorder, and subsequent complications.

100 Gbit/s co-designed optical receiver with hybrid integration.

We demonstrate a co-designed optical receiver, which is hybrid-integrated with a silicon-photonic photodetector (PD) and silicon-germanium (SiGe) trans-impedance amplifier (TIA). Accurate equivalent circuit models of PD and electrical parasitic of chip-on-board (COB) assembly are built for co-simulation with TIA. Inductive peaking and equalizer (EQ) techniques are proposed in the design of TIA to extend the bandwidth of the optical receiver. The measured electrical 3-dB bandwidth of TIA and optical-to-electrical (O-E) 3-dB bandwidth of optical receiver are above 36.8 GHz and 36 GHz, respectively. For the optical receiver, clear eye diagrams up to a data rate of 80 Gbit/s are realized. The bit-error ratios (BER) for the NRZ signal with a different bit rate and received optical power are experimentally measured, and 100 Gbit/s NRZ operation is successfully achieved with a soft-decision forward error correction (SD-FEC) threshold.