The non-technical skills needed by graduates of technical colleges in metalwork technology

This study centered on the views of the professionals about the non-technical skills considered crucial as recruitment prerequisite among graduates of metalwork technology from technical and vocational institutions in Nigeria. It will also examine the possibility of integrating these skills into the curriculum. The study made use of the survey research design. One research question guided the study. The population for the study comprised metalwork technology professionals in Ogun State, Southwestern Nigeria. A 36-item questionnaire was the instrument used for data collection. The instrument was validated by experts from the University of Lagos (UNILAG). The reliability of the instrument was determined using Cronbach Alpha and the reliability index which stood at 0.79. The data collected were analysed using mean statistic and standard deviation. The study revealed that professionals placed great importance on communication skills, problem-solving skills, teamwork skills and self-management qualities among others, as important recruitment prerequisite among graduates of metalwork technology from technical and vocational institutions in Nigeria. The study concludes with the suggestions that non-technical skills should be integrated into the curriculum particularly in the field of metalwork technology in technical colleges. Students should be familiar with the employers’ recruitment criteria while they are still in schools in order to equip them with the necessary skills needed for employment

The Association of AMPK with ULK1 Regulates Autophagy

Autophagy is a highly orchestrated intracellular bulk degradation process that is activated by various environmental stresses. The serine/threonine kinase ULK1, like its yeast homologue Atg1, is a key initiator of autophagy that is negatively regulated by the mTOR kinase. However, the molecular mechanism that controls the inhibitory effect of mTOR on ULK1-mediated autophagy is not fully understood. Here we identified AMPK, a central energy sensor, as a new ULK1-binding partner. We found that AMPK binds to the PS domain of ULK1 and this interaction is required for ULK1-mediated autophagy. Interestingly, activation of AMPK by AICAR induces 14-3-3 binding to the AMPK-ULK1-mTORC1 complex, which coincides with raptor Ser792 phosphorylation and mTOR inactivation. Consistently, AICAR induces autophagy in TSC2-deficient cells expressing wild-type raptor but not the mutant raptor that lacks the AMPK phosphorylation sites (Ser722 and Ser792). Taken together, these results suggest that AMPK association with ULK1 plays an important role in autophagy induction, at least in part, by phosphorylation of raptor to lift the inhibitory effect of mTOR on the ULK1 autophagic complex

Cells and gene expression programs in the adult human heart

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and strategies to improve therapeutic opportunities require deeper understanding of the molecular processes of the normal heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavor. Here, using large-scale single cell and nuclei transcriptomic profiling together with state-of-the-art analytical techniques, we characterise the adult human heart cellular landscape covering six anatomical cardiac regions (left and right atria and ventricles, apex and interventricular septum). Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, revealing distinct subsets in the atria and ventricles indicative of diverse developmental origins and specialized properties. Further we define the complexity of the cardiac vascular network which includes clusters of arterial, capillary, venous, lymphatic endothelial cells and an atrial-enriched population. By comparing cardiac cells to skeletal muscle and kidney, we identify cardiac tissue resident macrophage subsets with transcriptional signatures indicative of both inflammatory and reparative phenotypes. Further, inference of cell-cell interactions highlight a macrophage-fibroblast-cardiomyocyte network that differs between atria and ventricles, and compared to skeletal muscle. We expect this reference human cardiac cell atlas to advance mechanistic studies of heart homeostasis and disease

Cells of the adult human heart

Abstract: Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and therapeutic strategies require a deeper understanding of the molecular processes involved in the healthy heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavour. Here, using state-of-the-art analyses of large-scale single-cell and single-nucleus transcriptomes, we characterize six anatomical adult heart regions. Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, and reveal distinct atrial and ventricular subsets of cells with diverse developmental origins and specialized properties. We define the complexity of the cardiac vasculature and its changes along the arterio-venous axis. In the immune compartment, we identify cardiac-resident macrophages with inflammatory and protective transcriptional signatures. Furthermore, analyses of cell-to-cell interactions highlight different networks of macrophages, fibroblasts and cardiomyocytes between atria and ventricles that are distinct from those of skeletal muscle. Our human cardiac cell atlas improves our understanding of the human heart and provides a valuable reference for future studies

Cells of the adult human heart

Abstract: Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and therapeutic strategies require a deeper understanding of the molecular processes involved in the healthy heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavour. Here, using state-of-the-art analyses of large-scale single-cell and single-nucleus transcriptomes, we characterize six anatomical adult heart regions. Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, and reveal distinct atrial and ventricular subsets of cells with diverse developmental origins and specialized properties. We define the complexity of the cardiac vasculature and its changes along the arterio-venous axis. In the immune compartment, we identify cardiac-resident macrophages with inflammatory and protective transcriptional signatures. Furthermore, analyses of cell-to-cell interactions highlight different networks of macrophages, fibroblasts and cardiomyocytes between atria and ventricles that are distinct from those of skeletal muscle. Our human cardiac cell atlas improves our understanding of the human heart and provides a valuable reference for future studies

Application of machine learning in colloids transport in porous media studies: Lattice Boltzmann simulation results as training data

Colloid transport through a porous medium changes geometrical and hydraulic properties of the pore space. The impact of this effect depends on the colloid types and pore space surface properties which determine the likelihood of pore clogging. Colloid attachment and subsequent detachment are key factors in pore clogging. In this study, the impact of four major fluid and colloids properties on the pore surface and hydraulic conductivity alteration during colloids transport were evaluated using machine learning. These four parameters include solution ionic strength, zeta potential, colloid size and fluid flow velocity. A combined lattice Boltzmann-smoothed profile method was used to simulate accurately coupled mechanisms governing colloid transport to evaluate the impact of the four parameters on the resulting pore space properties after colloid transport. The result of several simulations revealed significant changes of pore surface coverage by the attached colloids, and conductivity, void fraction and coordination number of colloid agglomerates created during transport of individual colloids. Since the simulation of the impact of combination of all possible sets of four parameters is very time consuming, an Artificial Neural Network (ANN) was used as a prognostic method to use the results of several simulations to predict the behavior for a wide range of pore, colloidal and fluid properties. Reported results from a set of 162 simulation case studies for different possible combination of solution ionic strength, zeta potentials, colloid size and flow velocity were selected as input parameters for the machine learning. Four output parameters, namely, pore surface coverage, conductivity, void fraction and coordination number of the colloidal particles were selected. To lower the prediction error value, which is targeted to be lower than 10%, networks were trained 50 times using a MATLAB code, and in each training, after at most 10 epochs, networks were trained. A maximum relative error value of 8.95% was obtained, which is very well within the range of training quality criteria. The results show that the ANN can profoundly predict the simulation outcomes for a wide range of ionic strength (IS) and can be directly used to obtain the value of dependent variables through simple calculations using network weights and transfer functions