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General General

Learning and inferring the diurnal variability of cyanobacterial blooms from high-frequency time-series satellite-based observations.

In Harmful algae

Observational evidences have suggested that the surface scums of cyanobacterial harmful blooms (CyanoHABs) are highly patchy, and their spatial patterns can vary significantly within hours. This stresses the need for the capacity to monitor and predict their occurrence with better spatiotemporal continuity, in order to understand and mitigate their causes and impacts. Although polar-orbiting satellites have long been used to monitor CyanoHABs, these sensors cannot be used to capture the diurnal variability of the bloom patchiness due to their long revisit periods. In this study, we use the Himawari-8 geostationary satellite to generate high-frequency time-series observations of CyanoHABs on a sub-daily basis not possible from previous satellites. On top of that, we introduce a spatiotemporal deep learning method (ConvLSTM) to predict the dynamics of bloom patchiness at a lead time of 10 min. Our results show that the bloom scums were highly patchy and dynamic, and the diurnal variability was assumed to be largely associated with the migratory behavior of cyanobacteria. We also show that, ConvLSTM displayed fairly satisfactory performance with promising predictive capability, with Root Mean Square Error (RMSE) and determination coefficient (R2) varying between 0.66∼1.84 μg/L and 0.71∼0.94, respectively. This suggests that, by adequately capturing spatiotemporal features, the diurnal variability of CyanoHABs can be well learned and inferred by ConvLSTM. These results may have important practical implications, because they suggest that spatiotemporal deep learning integrated with high-frequency satellite observations could provide a new methodological paradigm in nowcasting of CyanoHABs.

Li Hu, Qin Chengxin, He Weiqi, Sun Fu, Du Pengfei

2023-Mar

CyanoHABs, Diurnal variability, High-frequency remote sensing, Learning and inferring, Spatiotemporal deep learning

General General

Estimation of reduced greenhouse gas emission from municipal solid waste incineration with electricity recovery in prefecture- and county-level cities of China.

In The Science of the total environment

Municipal solid waste (MSW) without proper managements could be a significant source of greenhouse gas (GHG) emissions. MSW incineration with electricity recovery (MSW-IER) is recognized as a sustainable way to utilize waste, but its effectiveness on reducing GHG emissions at the city scale in China remain unclear due to limited data of MSW compositions. The aim of the study is to investigate reduction potential of GHG from MSW-IER in China. Based on the MSW compositions covering 106 Chinese prefecture-level cities during the period of 1985 to 2016, random forest models were built to predict MSW compositions in Chinese cities. MSW compositions in 297 cities of China from 2002 to 2017 were predicted using the model trained by a combination of socio-economic, climate and spatiotemporal factors. Spatiotemporal and climatic factors (such as economic development level, precipitation) accounted for 6.5 %-20.7 % and 20.1 %-37.6 % to total contributions on MSW composition, respectively. The GHG emissions from MSW-IER in each Chinese city were further calculated based on the predicted MSW compositions. The plastic is the main GHG emission source, accounting for over 91 % of the total emission during 2002-2017. Compared to baseline (landfill) emission, the GHG emission reduction from MSW-IER was 12.5 × 107 kg CO2-eq in 2002 and 415 × 107 kg CO2-eq in 2017, with an average annual growth rate of 26.3 %. The results provide basic data for estimating GHG emission in MSW management in China.

Zhao Qing, Tang Weihao, Han Mengjie, Cui Wenjing, Zhu Lei, Xie Huaijun, Li Wei, Wu Fengchang

2023-Mar-07

Green energy, Greenhouse gas reduction emission, Incineration, Machine-learning, Municipal solid waste

General General

Magnitude and efficiency of straw return in building up soil organic carbon: A global synthesis integrating the impacts of agricultural managements and environmental conditions.

In The Science of the total environment

Enhancing soil organic carbon (SOC) through straw return (SR) has been widely recommended as a promising practice of climate-smart agriculture. Many studies have investigated the relative effect of straw return on SOC content, while the magnitude and efficiency of straw return in building up SOC stock remain uncertain. Here, we present an integrative synthesis of the magnitude and efficiency of SR-induced SOC changes, using a database comprising 327 observations at 115 sites globally. Straw return increased SOC by 3.68 ± 0.69 (95 % Confidence Interval, CI) Mg C ha-1, with a corresponding C efficiency of 20.51 ± 9.58 % (95 % CI), of which <30 % was contributed directly by straw-C input. The magnitude of SR-induced SOC changes increased (P < 0.05) with increasing straw-C input and experiment duration. However, the C efficiency decreased significantly (P < 0.01) with these two explanatory factors. No-tillage and crop rotation were found to enhance the SR-induced SOC increase, in both magnitude and efficiency. Straw return sequestrated larger amount of C in acidic and organic-rich soils than in alkaline and organic-poor soils. A machine learning random forest (RF) algorithm showed that the amount of straw-C input was the most important single factor governing the magnitude and efficiency of straw return. However, local agricultural managements and environmental conditions were together the dominant explanatory factors determining the spatial differences in SR-induced SOC stock changes. This entails that by optimizing agricultural managements in regions with favorable environmental conditions the farmer can accumulate more C with minor negative impacts. By clarifying the significance and relative importance of multiple local factors, our findings may aid the development of tailored region-specific straw return policies integrating the SOC increment and its environmental side costs.

Li Binzhe, Liang Fei, Wang Yajing, Cao Wenchao, Song He, Chen Jingsheng, Guo Jingheng

2023-Mar-07

Agricultural management, Efficiency, Environmental condition, Magnitude, Soil organic carbon, Straw return

General General

Identification of soil parent materials in naturally high background areas based on machine learning.

In The Science of the total environment

Recently, farmlands with high geological background of Cd derived from carbonate rock (CA) and black shale areas (BA) have received wide attention. However, although both CA and BA belong to high geological background areas, the mobility of soil Cd differs significantly between them. In addition to the difficulty in reaching the parent material in deep soil, it is challenging to perform land use planning in high geological background areas. This study attempts to determine the key soil geochemical parameters related to the spatial patterns of lithology and the main factors influencing the geochemical behavior of soil Cd, and ultimately uses them and machine-learning methods to identify CA and BA. In total, 10,814 and 4323 surface soil samples were collected from CA and BA, respectively. Hot spot analysis revealed that soil properties and soil Cd were significantly correlated with the underlying bedrock, except for TOC and S. Further research confirmed that the concentration and mobility of Cd in high geological background areas were mainly affected by pH and Mn. The soil parent materials were then predicted using artificial neural network (ANN), random forest (RF) and support vector machine (SVM) models. The ANN and RF models showed higher Kappa coefficients and overall accuracies than those of the SVM model, suggesting that ANNs and RF have the potential to predict soil parent materials from soil data, which might help in ensuring safe land use and coordinating activities in high geological background areas.

Li Cheng, Zhang Chaosheng, Yu Tao, Ma Xudong, Yang Yeyu, Liu Xu, Hou Qingye, Li Bo, Lin Kun, Yang Zhongfang, Wang Lei

2023-Mar-07

High geological background, Hot spot analysis, Machine learning, Parent material, Soil cadmium

Surgery Surgery

Artificial Intelligence Autonomously Measures Cup Orientation, Corrects for Pelvis Orientation, and Identifies Retroversion from Antero-Posterior Pelvis Radiographs.

In The Journal of arthroplasty ; h5-index 65.0

BACKGROUND : Measuring cup orientation is time consuming and inaccurate, but orientation influences the risk of impingement and dislocation following total hip arthroplasty (THA). This study designed an artificial intelligence (AI) program to autonomously determine cup orientation, correct for pelvis orientation, and identify cup retroversion from an antero-posterior pelvic radiographs.

METHODS : There were 2,945 patients between 2012 and 2019 identified to have 504 Computed Tomographic (CT) scans of their THA. A 3-dimensional (3D) reconstruction was performed on all CTs, where cup orientation was measured relative to the anterior pelvic plane. Patients were randomly allocated to training (4,000 x-rays), validation (511 x-rays), and testing (690 (x-rays) groups. Data augmentation was applied to the training set (n=4,000,000) to increase model robustness. Statistical analyses were performed only on the test group in their accuracy with CT measurements.

RESULTS : AI predictions averaged 0.22+0.03 seconds to run on a given radiograph. Pearson correlation coefficient was 0.976 and 0.984 for AI measurements with CT, while hand measurements were 0.650 and 0.687 for anteversion and inclination, respectively. The AI measurements more closely represented CT scans when compared to hand measurements (P<.001). Measurements averaged 0.04+2.21°, 0.14+1.66°, -0.31+8.35°, and 6.48°+7.43° from CT measurements for AI anteversion, AI inclination, hand anteversion, and hand inclination, respectively. AI predictions identified 17 radiographs as retroverted with 100.0% accuracy (total retroverted, n=45).

CONCLUSION : The AI algorithms may correct for pelvis orientation when measuring cup orientation on radiographs, out-perform hand measurements, and may be implemented in a timely fashion. This is the first method to identify a retroverted cup from a single AP radiograph.

Murphy Michael P, Killen Cameron J, Winfrey Sara R, Schmitt Daniel R, Hopkinson William J, Wu Karen, Brown Nicholas M

2023-Mar-07

Abduction, Acetabular component orientation, Anteversion, Inclination, Total hip arthroplasty

Cardiology Cardiology

Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.

In American heart journal ; h5-index 58.0

BACKGROUND : Lifelong oral anticoagulation is recommended in patients with atrial fibrillation (AF) to prevent stroke. Over the last decade, multiple new oral anticoagulants (OACs) have expanded the number of treatment options for these patients. While population-level effectiveness of OACs has been compared, it is unclear if there is variability in benefit and risk across patient subgroups.

METHODS : We analyzed claims and medical data for 34,569 patients who initiated a non-vitamin K antagonist oral anticoagulant (NOAC; apixaban, dabigatran, and rivaroxaban) or warfarin for nonvalvular AF between 08/01/2010 and 11/29/2017 from the OptumLabs® Data Warehouse. A machine learning (ML) method was applied to match different OAC groups on several baseline variables including, age, sex, race, renal function, and CHA2DS2 -VASC score. A causal ML method was then used to discover patient subgroups characterizing the head-to-head treatment effects of the OACs on a primary composite outcome of ischemic stroke, intracranial hemorrhage, and all-cause mortality.

RESULTS : The mean age, number of females and white race in the entire cohort of 34,569 patients were 71.2 (SD, 10.7) years, 14916 (43.1%), and 25051 (72.5%) respectively. During a mean follow up of 8.3 (SD, 9.0) months, 2110 (6.1%) of patients experienced the composite outcome, of whom 1675 (4.8%) died. The causal ML method identified 5 subgroups with variables favoring apixaban over dabigatran; 2 subgroups favoring apixaban over rivaroxaban; 1 subgroup favoring dabigatran over rivaroxaban; and 1 subgroup favoring rivaroxaban over dabigatran in terms of risk reduction of the primary endpoint. No subgroup favored warfarin and most dabigatran vs warfarin users favored neither drug. The variables that most influenced favoring one subgroup over another included Age, history of ischemic stroke, thromboembolism, estimated glomerular filtration rate, Race, and myocardial infarction.

CONCLUSIONS : Among patients with AF treated with a NOAC or warfarin, a causal ML method identified patient subgroups with differences in outcomes associated with OAC use. The findings suggest that the effects of OACs are heterogeneous across subgroups of AF patients, which could help personalize the choice of OAC. Future prospective studies are needed to better understand the clinical impact of the subgroups with respect to OAC selection.

Ngufor Che, Yao Xiaoxi, Inselman Jonathan W, Ross Joseph S, Dhruva Sanket S, Graham David J, Lee Joo-Yeon, Siontis Konstantinos C, Desai Nihar R, Polley Eric, Shah Nilay D, Noseworthy Peter A

2023-Mar-07