Plasma exosomal microRNAs are non-invasive biomarkers of moyamoya disease: A pilot study

Background: As a progressive cerebrovascular disease, Moyamoya Disease (MMD) is a common cause of stroke in children and adults. However, the early biomarkers and pathogenesis of MMD remain poorly understood. Methods and material: This study was conducted using plasma exosome samples from MMD patients. Next-generation high-throughput sequencing, real-time quantitative PCR, gene ontology analysis, and Kyoto Encyclopaedia of Genes and Genomes pathway analysis of ideal exosomal miRNAs that could be used as potential biomarkers of MMD were performed. The area under the Receiver Operating Characteristic (ROC) curve was used to evaluate the sensitivity and specificity of biomarkers for predicting events. Results: Exosomes were successfully isolated and miRNA-sequence analysis yielded 1,002 differentially expressed miRNAs. Functional analysis revealed that they were mainly enriched in axon guidance, regulation of the actin cytoskeleton and the MAPK signaling pathway. Furthermore, 10 miRNAs (miR-1306-5p, miR-196b-5p, miR-19a-3p, miR-22-3p, miR-320b, miR-34a-5p, miR-485-3p, miR-489-3p, miR-501-3p, and miR-487-3p) were found to be associated with the most sensitive and specific pathways for MMD prediction. Conclusions: Several plasma secretory miRNAs closely related to the development of MMD have been identified, which can be used as biomarkers of MMD and contribute to differentiating MMD from non-MMD patients before digital subtraction angiography

Geology-engineering integration to improve drilling speed and safety in ultra-deep clastic reservoirs of the Qiulitage structural belt

The Qiulitage structural belt in Tarim Basin has a large reservoir burial depth and complex geological conditions. Challenges such as ultra-depth, high temperature, high pressure and high stress lead to significant problems related to well control safety and project efficiency. To solve these key technical issues that set barriers to the process of exploration and development, a drilling technology process via the integration of geology and engineering was established with geomechanics as the bridge. An integrated key drilling engineering technology was formed for improving the drilling speed and safety of ultra-deep wells, including well location optimization, well trajectory optimization, formation pressure prediction before drilling, stratum drillability evaluation, and bit and speed-up tool design and optimization. Combined with the seismic data, logging data, structural characteristics, and lithology distribution characteristics, a rock mechanics data volume related to the three-dimensional drilling resistance characteristics of the block was established for the first time. The longitudinal and lateral heterogeneities were quantitatively characterized, providing a basis for bit design, improvement and optimization. During the drilling process, the geomechanical model was corrected in time according to the actual drilling information, and the drilling “three pressures” data were updated in real time to support the dynamic adjustment of drilling parameters. Through field practice, the average drilling complexity rate was reduced from 18% to 4.6%, and the drilling cycle at 8,500 m depth was reduced from 326 days to 257 days, which comprised significant improvements compared to the vertical wells deployed in the early stage without considering geology-engineering integration.Cited as: Chen, C., Ji, G., Wang, H., Huang, H., Baud, P., Wu, Q. Geology-engineering integration to improve drilling speed and safety in ultra-deep clastic reservoirs of the Qiulitage structural belt. Advances in Geo-Energy Research, 2022, 6(4): 347-356. https://doi.org/10.46690/ager.2022.04.0

A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity

IntroductionMummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult.MethodsUsing real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate.ResultsExperimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16.DiscussionThese features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification

Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions

This paper proposes an innovative strategy to integrate thermoelectric generator (TEG) and photovoltaic (PV) systems, aiming to enhance energy production efficiency by addressing the significant waste heat generated during traditional PV system operation. Additionally, photovoltaic-thermoelectric generator (PV-TEG) hybrid system encounters the dual challenge of partial shading conditions (PSC) and non-uniform temperature distribution (NTD). Thus, salp swarm optimization (SSA) is introduced to simultaneously tackle the negative impacts of PSC and NTD. In contrast to alternative meta-heuristic algorithms (MhAs) and conventional mathematical approaches, the streamlined and effective optimization mechanism inherent to SSA affords a shorter optimization time, while mitigating the risk of the PV-TEG hybrid system's optimization outcomes being confined to local maximum power points (LMPP). Furthermore, the optimization performance of SSA for PV-TEG hybrid systems is assessed via four case studies, including start-up test, stepwise variations in solar irradiation at constant temperature, stochastic change in solar irradiation, and field measured data for typical days in Hong Kong, in which simulation results show that SSA evinces unparalleled global exploration and local search capabilities, yielding heightened energy output (up to 43.75%) and effectively suppressing power fluctuations in the PV-TEG hybrid system (as evidenced by ΔVavg and ΔVmax)

Unraveling (electro)-chemical stability and interfacial reactions of Li 10 SnP 2 S 12 in all-solid-state Li batteries

Abstract(#br)Li 10 SnP 2 S 12 (LSPS) with high ionic conductivity and moderate price is a promising solid electrolyte for all-solid-state batteries. However, the instability of LSPS and LSPS/electrodes interfaces would cause poor cycle performance issues in the LSPS-based all-solid-state batteries, which have not been well understood. Herein, we address and unravel the decomposition products of LSPS and their Li + transfer characteristics, especially on the surface of LSPS/electrodes by using solid-state nuclear magnetic resonance (ss NMR) spectroscopy coupled with X-ray photoelectron spectroscopy (XPS). The results reveal that the high mechanical energy during ball-milling process leads to the decomposition of LSPS into Li 4 SnS 4 and Li 3 PS 4 . During charge/discharge cycling, specific capacity fading of batteries originates from the formation of new interfacial layer at LSPS/Acetylene black cathode and LSPS/Li metal anode interfaces. Furthermore, our results demonstrate that the rough and porous morphology of the interface formed after cycling, rather than the decomposition products, is the critical factor which results in the increases of the interfacial resistance at LSPS/Li interface and serious formation of Li dendrite. Our results highlight the significant roles of (electro)chemical and interfacial stability of sulfide solid electrolyte in the development of all-solid-state batteries

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme