The aim was to measure changes in the oxygen tension within the human placenta associated with onset of the maternal arterial circulation at the end of the first trimester of pregnancy, and the impact on placental tissues. Using a multiparameter probe we established that the oxygen tension rises steeply from <20 mmHg at 8 weeks of gestation to >50 mmHg at 12 weeks. This rise coincides with morphological changes in the uterine arteries that allow free flow of maternal blood into the placenta, and is associated with increases in the mRNA concentrations and activities of the antioxidant enzymes catalase, glutathione peroxidase, and manganese and copper/zinc superoxide dismutase within placental tissues. Between 8 to 9 weeks there is a sharp peak of expression of the inducible form of heat shock protein 70, formation of nitrotyrosine residues, and derangement of the mitochondrial cristae within the syncytiotrophoblast. We conclude that a burst of oxidative stress occurs in the normal placenta as the maternal circulation is established. We speculate that this may serve a physiological role in stimulating normal placental differentiation, but may also be a factor in the pathogenesis of pre-eclampsia and early pregnancy failure if antioxidant defenses are depleted.

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

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Finding the optimal size of a hybrid renewable energy system is certainly important. The problem is often modelled as an multi-objective optimization problem (MOP) in which objectives such as annualized system cost, loss of power supply probability etc. are minimized. However, the MOP model rarely takes the load characteristics into account. We argue that ignoring load characteristics may be inappropriate when designing HRES for a place with intermittent high load demand. For example, in a training base the load demand is high when there are training tasks while the demand decreases to a low level when there is no training task. This results in an interesting issue, that is, when the loss of power supply probability is determined at a specific value, say 15%, then it is very likely that most of loss of power supply would occur right in the training period which is unexpected. Therefore, this study proposes a constraint multi-objective model to deal with this issue—in addition to the general multi-objective optimization model, the loss of power supply probability over a critical period is set as a constraint. Correspondingly, the non-dominated sorting genetic algorithm II with a relaxed $$\epsilon $$ ϵ constraint handling strategy is proposed to address the constraint MOP. Experimental results on a real world application demonstrate that the proposed model and algorithm are both effective and efficient.