Single-cell transcriptome sequencing reveals tumor heterogeneity in family neuroblastoma

Neuroblastoma(NB) is the most common extracranial solid tumor in childhood, and it is now believed that some patients with NB have an underlying genetic susceptibility, which may be one of the reasons for the multiplicity of NB patients within a family line. Even within the same family, the samples show great variation and can present as ganglioneuroblastoma or even benign ganglioneuroma. The genomics of NB is still unclear and more in-depth studies are needed to reveal its key components. We first performed single-cell RNA sequencing(sc-RNAseq) analysis on clinical specimens of two family neuroblastoma(FNB) and four sporadic NB cases. A complete transcriptional profile of FNB was constructed from 18,394 cells from FNB, and we found that SDHD may be genetically associated with FNB and identified a prognostic related CAF subtype in FNB: Fib-4. Single-cell flux estimation analysis (scFEA) results showed that malignant cells were associated with arginine spermine, oxaloacetate and hypoxanthine, and that malignant cells metabolize lactate at lower levels than T cells. Our study provides new resources and ideas for the development of the genomics of family NB, and the mechanisms of cell-to-cell interactions and communication and the metabolic landscape will provide new therapeutic targets

An Expanded Three Band Model to Monitor Inland Optically Complex Water Using Geostationary Ocean Color Imager (GOCI)

Due to strict spectral band requirements, the three-band (TB) chlorophyll-a concentration (Cchla) estimation algorithm cannot be applied to GOCI image, which has great potential in frequently monitoring inland complex waters. In this study, the TB algorithm was expanded and applied to GOCI data. The GOCI TB algorithm was subsequently calibrated using an in-situ dataset which contains 281 samples collected from 17 inland lakes in China between 2013 and 2020. MERIS TB and GOCI band ratio (BR) models were selected as comparisons to assess the proposed model. The results showed that the proposed GOCI TB model has similar accuracy with MERIS TB model and overperformed GOCI BR model. The root mean square error (RMSE) of the GOCI TB, MERIS TB, and GOCI BR algorithms are 14.212 μg/L, 12.096 μg/L, and 20.504 μg/L, respectively. The mean absolute percentage error (MAPE) (when Cchla is larger than 10 μg/L) of the three models were 0.377, 0.250, and 0.453, respectively. Similar conclusion could be drawn from a match-up dataset containing 40 samples. Finally, a simulation experiment was carried out to analyze the robustness of the models under various total suspended matter concentration (CTSM) conditions. Both the in-situ validation and simulation experiment indicated that the GOCI TB factor could effectively eliminate the optical influence of CTSM. Furthermore, the broader spectral range requirement of GOCI TB model made it proper for many other multispectral sensors such as Sentinel two Multispectral Instrument (S2 MSI), Moderate Resolution Imaging Spectroradiometer (MODIS) (onboard the Terra/Aqua satellite), and Visible Infrared Imaging Radiometer Suite (VIIRS) (onboard the National Polar-orbiting Partnership satellite). Compared with the GOCI BR algorithm, the GOCI TB algorithm has stronger stability, better accuracy, and greater potential in practice

Environment Sustainability Is a Corporate Social Responsibility: Measuring the Nexus between Sustainable Supply Chain Management, Big Data Analytics Capabilities, and Organizational Performance

Sustainability has profound implications for environmental competitiveness, yet little has been done to study the feasibility of sustainable supply chain management (SSCM) practices as a predictor of organizational performance (operational and environmental performance). By integrating stakeholder theory and dynamic capability theory, this study aims to determine the impact of corporate social responsibility (CSR) on SSCM practices and assess its impact on organizational performance. This research also investigates the role of big data analytical capabilities (BDAC) in mediating the relationship between SSCM practices and organizational performance. The authors collected data online, examined 320 valid responses, and tested research hypotheses. The findings suggest that CSR (both internal and external CSR) positively promotes SSCM practices and contributes to expanding dynamic capacity theory in the context of BDA capabilities. BDAC is also a key mediator between SSCM practices and organizational performance. These results contribute to and improve the research on stakeholder theory and SSCM practice and provide a new perspective for scholars to further study this issue

Environment Sustainability Is a Corporate Social Responsibility: Measuring the Nexus between Sustainable Supply Chain Management, Big Data Analytics Capabilities, and Organizational Performance

Sustainability has profound implications for environmental competitiveness, yet little has been done to study the feasibility of sustainable supply chain management (SSCM) practices as a predictor of organizational performance (operational and environmental performance). By integrating stakeholder theory and dynamic capability theory, this study aims to determine the impact of corporate social responsibility (CSR) on SSCM practices and assess its impact on organizational performance. This research also investigates the role of big data analytical capabilities (BDAC) in mediating the relationship between SSCM practices and organizational performance. The authors collected data online, examined 320 valid responses, and tested research hypotheses. The findings suggest that CSR (both internal and external CSR) positively promotes SSCM practices and contributes to expanding dynamic capacity theory in the context of BDA capabilities. BDAC is also a key mediator between SSCM practices and organizational performance. These results contribute to and improve the research on stakeholder theory and SSCM practice and provide a new perspective for scholars to further study this issue

Managing Price and Fleet Size for Courier Service with Shared Drones

10.1016/j.omega.2021.102482Omega102482-10248

A Sliding-Mode-Based Duty Ratio Controller for Multiple Parallelly-Connected DC–DC Converters with Constant Power Loads on MVDC Shipboard Power Systems

The development of powered electronic technology has made many aware of the design and control of ship power systems (SPSs), and has made medium voltage DC (MVDC) architecture the main research direction in the future. The negative impedance characteristic of constant power load (CPL) generated by the coupling of powered electronic converters will seriously affect the stability of the systems if these converters are not properly controlled. The conventional linear control method can only guarantee the small-signal stability of the system near its equilibrium point. When the operating point changes in a large range, linear control methods will be ineffective. More importantly, research for the large-signal stability of the multi-converter system with CPLs is still rarely involved. In this paper, a sliding-mode-based duty ratio controller (SMDC) is proposed for voltage regulation and current sharing of the multiple parallelly-connected DC–DC converters system loaded by CPLs. By controlling the output voltage of each converter with SMDC, large-signal stability of the coupled bus voltage is ensured. Meanwhile, proportional current sharing between the parallel converters is achieved by droop control integrated in the reference value of converter voltage. Simulation studies were conducted in MATLAB/Simulink, where two typical operating conditions, including the variation of load power and bus voltage, were designed to verify the effectiveness of the proposed method. Moreover, a traditional PID controller was used as a comparison to reflect the superiority of the former. Simulation results showed that the proposed method is able to guarantee large-signal stability of the system in the presence of large-scale variations in load power and bus voltage. The output current of the parallel converters can also be distributed in desired proportions according to the droop coefficient