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Abstract Background The very low density lipoprotein receptor gene (VLDLR), a member of the low density lipoprotein receptor (LDLR) gene family, plays a crucial role in the synthesis of yolk protein precursors in oviparous species. Differential splicing of this gene has been reported in human, rabbit and rat. In chicken, studies showed that the VLDLR protein on the oocyte surface mediates the uptake of yolk protein precursors into growing oocytes. However, information on the VLDLR gene in duck is still scarce. Methods Full-length duck VLDLR cDNA was obtained by comparative cloning and rapid amplification of cDNA ends (RACE). Tissue expression patterns were analysed by semi-quantitative reverse-transcription polymerase chain reaction (RT-PCR). Association between the different genotypes and egg performance traits was investigated with the general linear model (GLM) procedure of the SAS® software package. Results In duck, two VLDLR transcripts were identified, one transcript (variant-a) containing an O-linked sugar domain and the other (variant-b) not containing this sugar domain. These transcripts share ~70 to 90% identity with their counterparts in other species. A phylogenetic tree based on amino acid sequences showed that duck VLDLR proteins were closely related with those of chicken and zebra finch. The two duck VLDLR transcripts are differentially expressed i.e. VLDLR-a is mainly expressed in muscle tissue and VLDLR-b in reproductive organs. We have localized the duck VLDLR gene on chromosome Z. An association analysis using two completely linked SNP sites (T/C at position 2025 bp of the ORF and G/A in intron 13) and records from two generations demonstrated that the duck VLDLR gene was significantly associated with egg production (P < 0.01), age of first egg (P < 0.01) and body weight of first egg (P < 0.05). Conclusions Duck and chicken VLDLR genes probably perform similar function in the development of growing oocytes and deposition of yolk lipoprotein. Therefore, VLDLR could be a candidate gene for duck egg performance and be used as a genetic marker to improve egg performance in ducks.

Application of big data analytics in reservoir engineering has gained wide attention in recent years. However, designing practical data-driven models for correlating petrophysical measurements and Steam-Assisted Gravity Drainage (SAGD) production profiles using actual field data remains difficult. Parameterization of the complex reservoir heterogeneities in these reservoirs is not trivial. In this study, a set of attributes pertinent to characterizing stochastic distributions of shales and lean zones is formulated and used for correlating against a number of production performance measures. A comprehensive investigation of the heterogeneous distribution (continuity, size, proportions, permeability, location, orientation and saturation) of shale barriers and lean zones is presented. First, a series of two-dimensional SAGD models based on typical Athabasca oil reservoir properties and operating conditions are constructed. Geostatistical techniques are applied to stochastically model shale barriers, which are imbedded in a region of degraded rock properties referred to as Low-Quality Sand or LQS, among a background of clean sand. Parameters including correlation lengths, orientation, proportions and permeability anisotropy of the different rock facies are varied. Within each facies, spatial variations in water saturation are modeled probabilistically. In contrast to many previous simulation studies, representative multiphase flow functions and capillarity models are assigned in accordance to individual facies. A set of input attributes based on facies proportions and dimensionless correlation lengths are formulated. Next, to facilitate the assessment of different scenarios, production performance is quantified by numerous dimensionless output attributes defined from recovery factor and steam-to-oil ratio profiles. An additional dimensionless indicator is implemented to capture the production time during which the instantaneous steam-to-oil ratio has exceeded a particular economic threshold. Finally, results of the sensitivity analysis are employed as training and testing datasets in a series of neural network models to correlate the pertinent system attributes and the production performance measures. These models are also used to assess the consequences of ignoring lateral variation of heterogeneities when extracting petrophysical (log) data from vertical delineation wells alone. An important contribution of this work is that it proposes a set of input attributes for correlating reservoir heterogeneity introduced by shale barriers and lean zones to SAGD production performance. It demonstrates that these input attributes, which can be extracted from petrophysical logs, are highly correlated with the ensuing recovery response and heat loss. This work also exemplifies the feasibility and utility of data-driven models in correlating SAGD performance. Furthermore, the proposed set of system variables and modeling approach can be applied directly in field-data analysis and scale-up study of experimental models to assist field-operation design and evaluation.

The operating room is an important place for the treatment of patients undergoing surgery, the quality level of clinical nursing has a direct influence in the therapeutic effect of patients. In the present study, four components instructional design mode (4C training mode) was applied in the training of professional nurses in the operating room, it involved the following aspects: regular skill knowledge analysis, goals setting, as well as the construction and implementation of the project. Targeted training methods and training content were formulated in accordance with the learning ability and knowledge reserve of different professional nurses, and different skills required to master, to fully mobilize the enthusiasm and initiative of nurses, thereby to improve the operation and comprehensive level of nurses in the operation room to achieve good results.