December 2015, Volume 29, Issue 8, pp 2021–2035Evaluation of statistical gap fillings for continuous energy flux (evapotranspiration) measurements for two different land cover typesOriginal PaperFirst Online: 16 June 2015DOI: 10.1007/s00477-015-1101-xCite this article as: Park, J., Byun, K., Choi, M. et al. Stoch Environ Res Risk Assess (2015) 29: 2021. AbstractOver the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data. These methods were Food and Agriculture Organization Penman–Monteith (FAO_PM) equation, mean diurnal variation (MDV), Kalman filter, and dynamic linear regression (DLR).

We used these methods to evaluate two flux towers at different land cover types located at Seolmacheon (SMC) and Cheongmicheon (CMC) in Korea. The LE estimated by four different approaches was a fairly close match to the observed LE, with the root mean square error ranging from 4.81 to 61.88 W m−2 at SMC and from 0.89 to 60.27 W m−2 at CMC. At both sites, the LE estimated by DLR showed the best result with the value of the coefficient of correlation (R), equal to 0.99. Cost-effectiveness analysis for evaluating four different gap-filling methods also confirmed that DLR showed the best cost effectiveness ratio (C/R). The Kalman filter showed the second highest C/R rank except in the winter season at SMC followed by MDV and FAO_PM. Energy closures with estimated LE led to further improved compare to the energy closure of the observed LE. The results showed that the estimated LE at CMC was a better fit with the observed LE than the estimated LE at SMC due to the more complicated topography and land cover at the SMC site.

This caused more complex interactions between the surface and the atmosphere. The estimated LE with all approaches used in this study showed improvement in energy closure at both sites. The results of this study suggest that each method can be used as a gap-filling model for LE. However, it is important to consider the strengths and weaknesses of each method, the purpose of research, characteristics of the study site, study period and data availability.Volume 88, Issue 5, 2 December 2015, Pages 1040–1053ritello air purifier price •The transcription factors Etv1 and Ctip2 distinguish entorhinal layers 5a and 5b•Layer 5a has extensive intratelencephalic projections, but layer 5b does not•Terminals of layer 2 stellate, but not pyramidal cells, are enriched in deep layers•Hippocampal and stellate cell inputs preferentially target layer 5b neuronsDeep layers of the medial entorhinal cortex are considered to relay signals from the hippocampus to other brain structuresholmes air purifier model hap706

, but pathways for routing of signals to and from the deep layers are not well established. Delineating these pathways is important for a circuit level understanding of spatial cognition and memory. We find that neurons in layers 5a and 5b have distinct molecular identities, defined by the transcription factors Etv1 and Ctip2, and divergent targets, with extensive intratelencephalic projections originating in layer 5a, but not 5b. This segregation of outputs is mirrored by the organization of glutamatergic input from stellate cells in layer 2 and from the hippocampus, with both preferentially targeting layer 5b over 5a. surround xj3000d air purifierOur results suggest a molecular and anatomical organization of input-output computations in deep layers of the MEC, reveal precise translaminar microcircuitry, and identify molecularly defined pathways for spatial signals to influence computation in deep layers.

Spatial cognition and episodic memory rely on signal processing by the medial entorhinal cortex (MEC), which is believed to function both as a relay of hippocampal outputs to other brain structures and as a major source of cortical input to the hippocampus (Burwell, 2000 and van Strien et al., 2009). These separate functions are attributed, respectively, to the deep and superficial layers of the MEC (Burwell and Amaral, 1998 and van Strien et al., 2009). Nevertheless, the organization of inputs to and outputs from neurons in the deep layers is not well understood. The identity of neurons receiving hippocampal input is not clear, and the possibility that spatially rich signals from superficial layers of the MEC can influence deeper layers directly, rather than via the hippocampus, has not been investigated experimentally.The view that deep layers of the MEC relay hippocampal signals to the neocortex is based on two sets of observations. First, axons from hippocampal neurons, and short latency electrical responses following stimulation of the hippocampus, are found in deep layers of the MEC (Kloosterman et al., 2003a and Kloosterman et al., 2003b).

Second, neurons in deep layers are labeled by retrograde tracers injected into telencephalic areas, including the neocortex, striatum, and amygdala (Agster and Burwell, 2009, Insausti et al., 1997, Meredith et al., 1990 and Swanson and Köhler, 1986). These findings are consistent with a view of deep layer circuits in which hippocampal inputs synapse with neurons that relay their signals directly to telencephalic targets. However, whether the organization of circuitry in deep layers of the MEC is sufficient to support processing beyond that required simply to relay signals is not clear.The direction of flow of information between superficial and deep layers is of potential importance to theories of spatial cognition and memory, as the high density of grid and border cells in superficial layers of the MEC is believed to be critical for path integration-based estimation of location (Hafting et al., 2005, McNaughton et al., 2006, Moser et al., 2008, Sargolini et al., 2006 and Tang et al., 2014).

In contrast, deep layers of the MEC contain a high density of cells with activity modulated by head direction and a much lower density of cells with grid-like spatial firing fields (Hafting et al., 2005 and Sargolini et al., 2006). Therefore, if deep layers do not receive input from layer 2 (L2), then the grid firing patterns of neurons in deep layers must be generated independently from grid firing in L2, either within deep layers or by inheritance from the pre- or parasubiculum (Boccara et al., 2010 and Canto et al., 2012). Conversely, direct connections from L2 could provide a substrate for grid and other spatial representations in deep layers to be inherited from or controlled by L2. Such projections could also provide a path for L2 to influence other brain regions via projection neurons found in the deep layers (van Strien et al., 2009). Distinguishing these possibilities requires experiments that establish whether L2 cells synapse with neurons in deep layers and that determine the identity of any neurons that receive such inputs.

In model systems, principles for connectivity have been established based on the location and molecular identity of presynaptic and postsynaptic neurons (Arber, 2012, Kolodkin and Tessier-Lavigne, 2011 and Sürmeli et al., 2011). In L2 of the MEC, there are two major principal cell populations. L2 stellate cells (L2SCs) express the protein reelin and project to the dentate gyrus and CA3 regions of the hippocampus, whereas L2 pyramidal cells (L2PCs) express calbindin and project to CA1 (Kitamura et al., 2014 and Varga et al., 2010). In deep layers of the MEC, cells have diverse morphological and electrophysiological characteristics, but sub-laminar organizing principles are unclear (Canto et al., 2008, Gloveli et al., 2001 and Hamam et al., 2000). Recently, we found that gene expression patterns delineate layers 5a, 5b, and 6 (Ramsden et al., 2015). However, in contrast to L2, it is not clear if this molecular organization reflects more general principles for organization of connectivity.

For example, if projections from L2 to deep layers exist, then it would be important to know whether the specificity of synaptic connections follows a logic that reflects the molecular identity, location, or projections of neurons in the deep layers.In this study, we use genetic, anatomical, and electrophysiological approaches to establish principles for organization and connectivity of deep layers of the MEC. We show that L5a is a major extra-hippocampal output center of the MEC and is distinguished by differential expression of the transcription factor Etv1 (ETS variant 1). In contrast, we are unable to identify intratelencephalic (IT) projections of L5b, which we find is identified by expression of the transcription factor COUP-TF interacting protein 2 (Ctip2) and may in part project to the thalamus. Utilizing two transgenic mouse lines that give genetic access to L2SCs and L2PCs, we find that the striking differences in efferent targets of L5 neurons are paralleled by specificity of projections from neurons in L2.