Identification of Conserved miRNAs in Solanum Lycopersicum Response to Long-term RPM-treatment

Abstract-T o identify the miRNAs associated with the simulated microgravity response in plants and to ascertain the regulation network mediated by miRNAs under simulated microgravity conditions, we constructed a miRNA library by direct cloning method and analyzed the library. Six conserved Solanum lycopersicum miRNAs were identified for the first time in Solanum lycopersicum under simulated microgravity condition. Gene ontology analysis showed that most of the predicted targeted genes were involved in organelle part, transcription factor, signal transduction and metabolic process, implying a complicated relationship among the external signal, internal transduction and final phenotype. Cis-elements located in the upstream sequences of each miRNA were identified and their roles in gene regulation were investigated. In the study, miRNAs were identified in S. lycopersicum for the first time under long-term simulated microgravity condition, which will help reveal the regulation mechanism mediated by miRNAs under simulated microgravity condition and adaptation to Earth's gravity

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model’s specificity, including adding Grouping Cross Merge (GCM) modules at the model’s jump joints to strengthen the model’s feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids

Resistivity correction for drilling fluid invasion using LWD and wire-line logging data: A case from high-porosity and low-permeability carbonate reservoirs, DLL Oilfield, Oman

The effect of drilling fluid invasion on the resistivity of oil-bearing zones during the period from penetrating the zone to completion well logging was studied using intergraded logging while drilling (LWD) and wire-line log data. The results indicate that resistivity change during invasion responds to some important factors such as porosity, oil saturation, pressure differential between drilling mud column and formation, mud filtrate salinity and invasion time. It increases as an exponential function of porosity, a logarithmic function of pressure differential, and a power function of invasion time and oil saturation. Based on the LWD and MDT data, the corrected resistivity equation subject to the drilling fluid invasion was acquired. With the equation, the oil saturation (So) increases by 6.3%–20.0%, averaging at 10.2%. 摘 要: 基于阿曼DLL油田高孔低渗碳酸盐岩油藏的随钻测井（LWD）和电缆测井资料，综合研究了从钻开储集层到完井测井时间内钻井液侵入对高孔低渗碳酸盐岩储集层电阻率的影响。结果表明，钻井液侵入对储集层电阻率的影响程度与储集层的孔隙度、钻井液柱与地层压力差、含水饱和度、钻井液矿化度以及侵入时间相关，其与孔隙度增加呈指数增大关系，与钻井液柱和地层压力差呈对数增大关系，与含水饱和度以及侵入时间呈幂指数增大关系。根据DLL油田LWD测井资料和MDT压力资料，得出储集层电阻率受钻井液侵入影响的校正方程。由校正后电阻率计算的含油饱和度比电阻率校正前计算的含油饱和度增加了6.3%～20.0%，平均增加10.2%。图9表4参16 Key words: drilling fluid invasion, resistivity decrease degree, horizontal well, LWD, wire-line logging, high-porosity and low-permeability reservoir, carbonate roc

A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO 2 /O 2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models

International audienceModeling and the prediction of material flows (plant production, CO2/O2 concentrations, H2O) is an important but challenging task in the design and control of closed ecological life support systems (CELSS). The aim of this study was to develop a novel knowledge-and-data-driven modeling (KDDM) approach for simultaneously simulating plant production and CO2/O2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models. The KDDM approach consists of a ‘knowledge-driven (KD)’ sub-model and a ‘data-driven (DD)’ sub-model. The KD sub-model describes hourly and up to daily plant photosynthesis, respiration and assimilation partitioning using the components of GreenLab and TomSim models. The DD sub-model describes the dynamics of CO2 production and O2 consumption by the crew member using a piecewise linear model. The two sub-models were integrated with a mass balance model for CO2/O2 concentrations in a closed system. The KDDM was applied with a two-person, 30-day integrated CELSS test. This model provides accurate computation of both the dry weights of different plant compartments and CO2/O2 concentrations. The model also quantifies the underlying material flows among the crew members, plants and environment. This approach provides a computational basis for lifetime optimization of cabin design and experimental setup of CELSS (e.g., environmental control, planting schedule). With extension, this methodology can be applied to a half-closed system such as a glasshouse