Mitochondrial Physiology and Gene Expression Analyses Reveal Metabolic and Translational Dysregulation in Oocyte-Induced Somatic Nuclear Reprogramming

While reprogramming a foreign nucleus after somatic cell nuclear transfer (SCNT), the enucleated oocyte (ooplasm) must signal that biomass and cellular requirements changed compared to the nucleus donor cell. Using cells expressing nuclear-encoded but mitochondria-targeted EGFP, a strategy was developed to directly distinguish maternal and embryonic products, testing ooplasm demands on transcriptional and post-transcriptional activity during reprogramming. Specifically, we compared transcript and protein levels for EGFP and other products in pre-implantation SCNT embryos, side-by-side to fertilized controls (embryos produced from the same oocyte pool, by intracytoplasmic injection of sperm containing the EGFP transgene). We observed that while EGFP transcript abundance is not different, protein levels are significantly lower in SCNT compared to fertilized blastocysts. This was not observed for Gapdh and Actb, whose protein reflected mRNA. This transcript-protein relationship indicates that the somatic nucleus can keep up with ooplasm transcript demands, whilst transcription and translation mismatch occurs after SCNT for certain mRNAs. We further detected metabolic disturbances after SCNT, suggesting a place among forces regulating post-transcriptional changes during reprogramming. Our observations ascribe oocyte-induced reprogramming with previously unsuspected regulatory dimensions, in that presence of functional proteins may no longer be inferred from mRNA, but rather depend on post-transcriptional regulation possibly modulated through metabolism

Nuclear Reprogramming: Kinetics of Cell Cycle and Metabolic Progression as Determinants of Success

Establishment of totipotency after somatic cell nuclear transfer (NT) requires not only reprogramming of gene expression, but also conversion of the cell cycle from quiescence to the precisely timed sequence of embryonic cleavage. Inadequate adaptation of the somatic nucleus to the embryonic cell cycle regime may lay the foundation for NT embryo failure and their reported lower cell counts. We combined bright field and fluorescence imaging of histone H2b-GFP expressing mouse embryos, to record cell divisions up to the blastocyst stage. This allowed us to quantitatively analyze cleavage kinetics of cloned embryos and revealed an extended and inconstant duration of the second and third cell cycles compared to fertilized controls generated by intracytoplasmic sperm injection (ICSI). Compared to fertilized embryos, slow and fast cleaving NT embryos presented similar rates of errors in M phase, but were considerably less tolerant to mitotic errors and underwent cleavage arrest. Although NT embryos vary substantially in their speed of cell cycle progression, transcriptome analysis did not detect systematic differences between fast and slow NT embryos. Profiling of amino acid turnover during pre-implantation development revealed that NT embryos consume lower amounts of amino acids, in particular arginine, than fertilized embryos until morula stage. An increased arginine supplementation enhanced development to blastocyst and increased embryo cell numbers. We conclude that a cell cycle delay, which is independent of pluripotency marker reactivation, and metabolic restraints reduce cell counts of NT embryos and impede their development

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components

SURFACE ROUGHNESS PREDICTION OF ELECTRO-DISCHARGE MACHINED COMPONENTS USING ARTIFICIAL NEURAL NETWORKS

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism, which involves the formation of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena negatively affecting the surface integrity of EDMed workpieces need to be taken into account and studied in order to achieve the optimization of the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique capable to provide reliable results and readily integrated into a lot of technological areas. In this paper, ANN models for the prediction of the mean surface roughness of electro-discharge machined surfaces are presented. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for the reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components

Digital Technology to Enhance Project Leadership Practice: The Case of Civil Construction

Digital transformation is fundamentally influencing all aspects of business and society. It can enable individuals and organizations to transcend from one way of working to another. In construction, emphasis is shifting from project management to project leadership, as professionals need assisting tools to not only aid in managing tasks and activities, but aid in leading people. As such, the adoption of digital technologies may hold the key in taking project leadership practice into the future. To date, there have been few attempts to explore the potential of digital technology as an aid for project leadership development and practice. This research therefore aims to investigate this potential in the context of the Australian civil construction industry. We review the existing body of knowledge on both industry-specific leadership demands and relevant digital technology capabilities to identify areas of improvement and to guide interviews with construction project managers. So far, literature has focused on endorsing particular leadership behaviors and/or styles, while ignoring the difficulties faced by professionals in practicing these behaviors. We find that project managers of civil contractors within Sydney understand the significance of leadership but are often overwhelmed by its complexity

Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization

Atmospheric mass balance analyses suggest that terrestrial carbon (C) storage is increasing, partially abating the atmospheric [CO2] growth rate1, although the continued strength of this important ecosystem service remains uncertain2, 3, 4, 5, 6. Some evidence suggests that these increases will persist owing to positive responses of vegetation growth (net primary productivity; NPP) to rising atmospheric [CO2] (that is, ‘CO2 fertilization’)5, 6, 7, 8. Here, we present a new satellite-derived global terrestrial NPP data set9, 10, 11, which shows a significant increase in NPP from 1982 to 2011. However, comparison against Earth system model (ESM) NPP estimates reveals a significant divergence, with satellite-derived increases (2.8 ± 1.50%) less than half of ESM-derived increases (7.6  ±  1.67%) over the 30-year period. By isolating the CO2 fertilization effect in each NPP time series and comparing it against a synthesis of available free-air CO2 enrichment data12, 13, 14, 15, we provide evidence that much of the discrepancy may be due to an over-sensitivity of ESMs to atmospheric [CO2], potentially reflecting an under-representation of climatic feedbacks16, 17, 18, 19, 20 and/or a lack of representation of nutrient constraints21, 22, 23, 24, 25. Our understanding of CO2 fertilization effects on NPP needs rapid improvement to enable more accurate projections of future C cycle–climate feedbacks; we contend that better integration of modelling, satellite and experimental approaches offers a promising way forward