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Yi Gao,1,&ast; Yuqing Li,1,&ast; Xiaolin Chen,1,&ast; Chen Wu,2 Ziqiang Guo,1 Geng Bai,1 Tong Liu,1 Guangping Li1 1Tianjin Key Laboratory of Logic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, 300211, People’s Republic of China; 2Department of Emergency Medicine, The Second Hospital of Tianjin Medical University, Tianjin, 300211, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guangping Li; Tong Liu, Tel +86-022-88328648, Fax +86-022-28261158, Email tic_tjcardiol@126.com; liutong@tmu.edu.cnBackground: Systemic inflammation index (SII: neutrophil count &ast; platelet count/lymphocyte count) is a new inflammatory marker that can reflect the degree of systemic inflammatory response after coronary artery disease (CAD). However, the predictive value of the SII for clinical prognosis in patients with initially diagnosed acute coronary syndrome (ACS) has yet to be thoroughly studied.Patients and Methods: Patients with initially diagnosed ACS who underwent primary coronary angiography in our hospital from January 2019 to April 2021 were included in this study. 757 patients with ACS who underwent primary coronary angiography were enrolled. According to the baseline SII level, the patients were divided into a high SII group and a low SII group. The primary endpoint was major cardiovascular events (MACEs), defined as cardiac death, non-fatal myocardial infarction (MI), and non-fatal stroke.Results: At a median follow-up of 33.9 months, 140 (18.5%) MACEs were recorded. Receiver operating characteristic (ROC) curve analysis showed that SII’s best cut-off value for predicting MACEs was 713.9&ast;109/L. Kaplan-Meier survival curve analysis showed that the survival rate of the low SII group was higher than the high SII group (P< 0.001). Compared with the low SII group, the risk of MACEs w

Yuqing Li,&ast; Geng Bai,&ast; Yi Gao,&ast; Ziqiang Guo, Xiaolin Chen, Tong Liu, Guangping Li Tianjin Key Laboratory of Logic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin, 300211, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guangping Li, Tel + 86-22-88328648, Fax + 86-22-28261158, Email tic_tjcardiol@126.comBackground: Recently, the systemic immune inflammatory response index (SIIRI), a novel and expanded inflammatory response marker, has been an independent predictor of lesion severity in patients with acute coronary syndrome (ACS). However, its predictive role in patients with initially diagnosed coronary artery disease (CAD) remains to be explored.Patients and Methods: We evaluated 959 patients with CAD undergoing an initial coronary intervention. Each patient had laboratory measurements, including blood cell counts, taken after admission and before interventional treatment. The primary endpoint was major cardiovascular events (MACEs), defined as cardiovascular death, nonfatal myocardial infarction(MI), and nonfatal stroke. The secondary endpoints included MACEs and readmission for congestive heart failure(HF).Results: During a mean follow-up period of 33.3± 9.9 months, 229 (23.9%) MACEs were recorded. ROC curve analysis displayed that the best cut-off value of SIIRI for predicting MACEs was 247.17&ast;1018/L2. Kaplan-Meier survival curve analysis showed that the survival rate of the low SIIRI group was higher than that of the high SIIRI group (P< 0.001). Compared with the low SIIRI group, the high SIIRI group had a significantly higher risk of MACEs (187 cases (39.53%) vs.42 patients (8.64%), P< 0.001). Univariate and multivariate Cox regression analyses displayed that high SIIRI levels were independently associated with the occurrence of MACEs in patients with initial

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.