Different approaches have been proposed to determine the possible outliers existing in a dataset. The most widely used consists in the application of the data snooping test over the least squares adjustment results. This strategy is very likely to succeed for the case of zero or one outliers but, contrary to what is often assumed, the same is not valid for the multiple outlier case, even in its iterative application scheme. Robust estimation, computed by iteratively reweighted least squares or a global optimization method, is other alternative approach which often produces good results in the presence of outliers, as is the case of exhaustive search methods that explore elimination of every possible set of observations. General statements, having universal validity, about the best way to compute a geodetic network with multiple outliers are impossible to be given due to the many different factors involved (type of network, number and size of possible errors, available computational force, etc.). However, we see in this paper that some conclusions can be drawn for the case of a leveling network, which has a certain geometrical simplicity compared with planimetric or three-dimensional networks though a usually high number of unknowns and relatively low redundancy. Among other results, we experience the occasional failure in the iterative application of the data snooping test, the relatively successful results obtained by both methods computing the robust estimator, which perform equivalently in this case, and the successful application of the exhaustive search method, for different cases that become increasingly intractable as the number of outliers approaches half the number of degrees of freedom of the network.
100 rats were randomly divided into a sham-operated group and middle cerebral artery occlusion (MCAO) modeling groups. The sham group after surgery was observed for 14 days. After MCAO, some rats received isometric contraction training (ICT) which was as follows: an atraumatic tourniquet was placed around left or right hind limb to achieve hind limb ischemia for 5 min, followed by 5 min of reperfusion, 4 cycles for one time, once a day, and five days per week. The MCAO modeling groups included the following four groups: i) a group only received MCAO, and was observed for seven days (MCAO-7d), ii) a group only received MCAO, and was observed for 14 days (MCAO-14d), iii) a group, after MCAO, received ICT for seven days (ICT-7d), and iv) a group, after MCAO, received ICT for 14 days (ICT-14d). Brain infarct area, behavioral outcomes, the number of neurons, apoptosis, cerebral edema and cerebral water content were assessed, respectively. The mRNA expression of vascular endothelial growth factor (VEGF) was assayed with RT-PCR, and protein expression of VEGF was quantified with western blot. compared with MCAO controls, cerebral infarction, neurological deficits and neuronal apoptosis were reduced significantly in the ICT groups, while the number of neurons was increased. Moreover, the mRNA expression of VEGF and protein expression of VEGF were enhanced after 1 and 2 weeks of ICT. ICT may promote angiogenesis and neuroprotection after ischemic stroke and this new remodeling method provide a novel strategy for rehabilitation of stroke patients.