SUMMARYFour-dimensional variational data assimilation (4DVAR) is a powerful tool for data assimilation in meteorology and oceanography. However, a major hurdle in use of 4DVAR for realistic general circulation models is the dimension of the control space (generally equal to the size of the model state variable and typically of order 10 7 -10 8 ) and the high computational cost in computing the cost function and its gradient that require integration model and its adjoint model.In this paper, we propose a 4DVAR approach based on proper orthogonal decomposition (POD). POD is an efficient way to carry out reduced order modelling by identifying the few most energetic modes in a sequence of snapshots from a time-dependent system, and providing a means of obtaining a low-dimensional description of the system's dynamics. The POD-based 4DVAR not only reduces the dimension of control space, but also reduces the size of dynamical model, both in dramatic ways. The novelty of our approach also consists in the inclusion of adaptability, applied when in the process of iterative control the new control variables depart significantly from the ones on which the POD model was based upon. In addition, these approaches also allow to conveniently constructing the adjoint model.The proposed POD-based 4DVAR methods are tested and demonstrated using a reduced gravity wave ocean model in Pacific domain in the context of identical twin data assimilation experiments. A comparison with data assimilation experiments in the full model space shows that with an appropriate selection of the basis functions the optimization in the POD space is able to provide accurate results at a reduced computational cost. The POD-based 4DVAR methods have the potential to approximate the performance of full order 4DVAR with less than 1/100 computer time of the full order 4DVAR. The HFTN (Hessian-free truncated-Newton)algorithm benefits most from the order reduction (see (Int. J. Numer. Meth. Fluids, in press)) since computational savings are achieved both in the outer and inner iterations of this method.

A sensitive nucleic acid detection platform based on superhydrophilic microwells spotted on a superhydrophobic substrate is fabricated. Due to the wettability differences, ultratrace DNA molecules are enriched and the fluorescent signals are amplified to allow more sensitive detection. The biosensing interface based on superwettable materials provides a simple and cost-effective way for ultratrace DNA sensing.

We previously reported that IL-27, which belongs to the IL-12 family of cytokines, is elevated in the serum of patients infected with influenza A virus (IAV). Here, we show that the expression of IL-27 was significantly up-regulated in A549 human lung epithelial cells and human peripheral blood mononuclear cells infected with IAV. Additionally, IAV triggered IL-27 expression through protein kinase A and cAMP-response element-binding protein signaling, which was mediated by cyclooxygenase-2-derived prostaglandin E2. IL-27 inhibited IAV replication by STAT1/2/3 phosphorylation and activated antiviral factor protein kinase R phosphorylation. Clinical analysis showed that IL-27 levels were significantly elevated in a cohort of patients infected with IAV compared with healthy individuals and that circulating IL-27 levels were tightly and positively correlated with prostaglandin E2 levels. These results indicate that IL-27 expression is one host immune factor produced in response to IAV infection and that elevated IL-27 levels inhibit viral replication.

The proper orthogonal decomposition (POD) is shown to be an efficient model reduction technique for simulating physical processes governed by partial differential equations. In this paper, we make an initial effort to investigate problems related to POD reduced modeling of a large-scale upper ocean circulation in the tropic Pacific domain. We construct different POD models with different choices of snapshots and different number of POD basis functions. The results from these different POD models are compared with that of the original model. The main findings are: (1) the large-scale seasonal variability of the tropic Pacific obtained by the original model is well captured by a low dimensional system of order of 22, which is constructed using 20 snapshots and 7 leading POD basis functions. (2) the RMS errors for the upper ocean layer thickness of the POD model of order of 22 are less than 1m that is less than 1% of the average thickness and the correlations between the upper ocean layer thickness with that from the POD model is around 0.99. (3) Retaining modes that capture 99% energy is necessary in order to construct POD models yielding a high accuracy.