We also found that BB supplementation preserves levels of hippocampal CaMKII phosphorylation after TBI, and changes in CaMKII correlated negatively with latency to locate the escape hole in the Barnes maze memory test. CaMKII, the main protein of the postsynaptic density and key BDNF signaling element, upon autophosphorylation increases synaptic efficacy and long-term synaptic memory.In fact, CaMKII dysregulation has been associated with several neuropsychiatric diseases.It is possible that the effects of BB on the BDNF levels results in autophosphorylation of tyrosine residues that rise intracellular calcium levels leading to CaMKII activation.Elevated levels of free radical formation are a common sequel of TBI pathology that can result in lipid peroxidation.In particular, lipid peroxidation has negative consequences for the function of the plasma membrane. It has been reported that optimal maintenance of membrane function is essential to support neuronal signaling that underlie synaptic plasticity, and that membrane function loss following TBI may be associated with cognitive deficits.Phospholipids are components of the plasma membrane that are particularly important for regulating cellular signaling and neuronal excitability.The fatty acids residues in phospholipids are sensitive targets to oxidative free radical attack to induce lipid peroxidation. Lipid peroxidation products can impair the barrier function, ion-channel activity,black plastic plant pots bulk and neurotransmitter release associated synaptic activity.Higher 4-HNE formation causes ionic disruption and membrane disturbance which contributes to additional reactive oxygen species production.
We presently found that TBI caused a marked increase in 4-HNE levels indicative of decreased neuronal excitability. Elevated levels of 4-HNE can form adducts with proteins, promotes oxidative stress,and contributes to membrane damage following TBI.BB is considered to have strong antioxidant capacity, important in its ability to attenuate oxidative stress.The fact that BB reduced the levels of 4-HNE supports the notion that BB attenuated TBI-related oxidative damage with positive consequences for neuronal excitability and plasma membrane function. Further, our reported positive correlation of spatial memory performance with 4-HNE levels suggest that a reduction of 4-HNE is important for behavioral outcome, in agreement with behavioral impact of 4-HNE on spatial memory performance.Interestingly, BB powder supplementation showed a positive effect on various aspects of brain function and plasticity in spite of the fact that the powder contains several components with recognized unhealthy effects. For example, the BB powder has high contents of sugars, particularly fructose, which upon consumption reduced levels of the same plasticity markers being decreased by BB supplementation in our study.It is important to note that in the present study, we matched the two diets for sugars and vitamin C. Therefore, it is likely that the flavonoid components are largely responsible for the observed positive effects of the BB powder possessing antioxidant property.These results seem to indicate that the combination of fructose with flavonoids in natural foods has an overall healthy action, which further suggests the importance of consuming natural foods. Also, the protective effects of BB against TBI pathology may be attributed to the presence of other bio-active compounds present in the powder such as β-carotenes and anthocyanins.
Anthocyanins, the important class of flavonoids has reported to be effective in promoting cognitive performance in animals through changes in synaptic plasticity via protein kinase signaling components such as c-Jun N-terminal kinase /Akt and phosphatidylinositol-3 kinase /Akt.Similarly, human intervention studies with anthocyanins have shown to promote a range of cognitive domains that include attention, visuospatial memory and executive function.Additionally, previous reports with anthocyanins have also reported hippocampal localization of glycosylated derivatives.In turn, β-carotenes and vitamins have strong potential to promote neuronal plasticity as reviewed by ref.70 and to delay cognitive decline.In the natural environment, the sense of smell, or olfaction, serves to detect toxins and judge nutritional content by taking advantage of the associations between compounds as they are created in biochemical reactions. This suggests that the nervous system can classify odors based on statistics of their co-occurrence within natural mixtures rather than from the chemical structures of the ligands themselves. We show that this statistical perspective makes it possible to map odors to points in a hyperbolic space. For example, these coordinates approximate the distance between species computed along dendrograms and, more generally, between points within hierarchical tree–like networks. We find that both natural odors and human perceptual descriptions of smells can be described using a three-dimensional hyperbolic space. This match in geometries can avoid distortions that would otherwise arise when mapping odors to perception. The reason that the sense of smell can be used to avoid poisons or estimate a food’s nutrition content is because biochemical reactions create many by-products. Thus, the emission of certain sets of volatile compounds will accompany the production of a specific poison by a plant or bacteria.
An animal can therefore judge the presence of poisons in the food by how the food smells. Other specific examples include the use of smell by bees when judging whether a flower has more pollen or nectar. Fruit flies select places to lay eggs based on odors. These examples suggest that, from a practical perspective, it would be useful for the nervous system to classify odors based on statistics of their co-occurrence. For example, if odor components that are strongly correlated are represented nearby within the nervous system, then detection of one component could be quickly used as an indicator for the likely existence of another component that is strongly correlated with it. With this perspective in mind, we set out to study the structure of the olfactory space based on odor co-occurrence. Before we describe the results, we review the reasons for why one might expect to find hyperbolic coordinates to be relevant for olfaction and biological systems in general. Biological data are often represented using dendrograms or hierarchical tree structures . These data can be equivalently represented using Venn diagrams, where larger circles correspond to broader classifications. For example, before Darwin, these Venn diagrams were used to classify species based on their properties . Darwin used the mapping from Venn diagrams to trees to infer the likely tree for speciation based on available descriptions of species properties . There is a deep mathematical reason underlying the equivalence between these two representations, and it involves hyperbolic spaces. Specifically, starting with the Venn diagram , one can assign points to a three- dimensional space whose horizontal x and y coordinates equal to center coordinates of the Venn circles, whereas the vertical coordinate equals to the circle radius. In this manner, larger circles get assigned to higher heights, which would then correspond to positions closer to the tip of the tree . Sometimes, the presence of partially overlapping circles leads to a structure that is not precisely a tree because it contains loop. Nevertheless, the resulting 3D space has a hyperbolic metric and can be described by the Poincare half-space model for the hyperbolic space. The fact that the metric is non-Euclidean can be ob- served from the fact that the shortest distance between two points goes up in the z-direction before descending back to the tar- get node. In Fig. 1D, we show an example shortest path between two points in a 2D half-space model and its discrete approximation. To foreshadow the results on olfactory odor classification, we note that 3D hyperbolic space is the lowest dimensional space where the descriptor sets are not 1D, as in Fig. 1D, but are 2D circles as in Fig. 1 . At least two axes have been described for the human odorant perception.Together,procona system these mathematical and biological observations point to the relevance of 3D hyperbolic geometry for odor perception.To analyze which space best describes the statistics of co-occurrence within natural odor mixtures, we used a recently developed a statistical method that can identify the presence of a geometric structure in data based on observed correlations between data components. This method is unaffected by linear or nonlinear monotonic transformations of inputs and therefore can be used to determine the overall geometry of the data without worrying at first about the precise scaling of the axes. The analysis starts by taking a set of measurements of concentrations of individual monomolecular odors, as they occur in the natural environment.
Our analyses will be based on four data sets of odors measured from samples of strawberries, tomatoes, blueberries, and mouse urine. To give an overview of the data, 69 monomolecular odors were measured across 50 different mouse urine samples, 66 monomolecular odors across 79 tomato samples, 45 monomolecular odors across 101 blueberry samples, and 78 monomolecular odors across 54 samples of strawberries. For high thresholds, the number of cycles will be low because most units are not connected. Similarly, at low thresholds, the number of cycles is also low because units form fully connected networks. Plotting the number of cycles as a function of density of edges, or equivalently the number of connected nodes, yields the so-called Betti curves. It turns out that the shapes of these Betti curves are quite sensitive to the statistics of correlations. This sensitivity makes it possible to infer the geometry of the space that can produce these correlations if we sample points from this space and assume that stronger correlations imply closer distances. Applying this statistical approach to each of the four data sets separately, we found the data in each case to be consistent with being drawn from a neighborhood of a sphere positioned within a 3D hyperbolic space together with a small amount of multiplicative noise added to the distances . The fact that hyperbolic space approximates hierarchical tree–like networks motivated this choice of the model, with odors reflecting leaves of the network—the neighborhood of the surface . Quantitatively, one can compare Betti curves derived from a model geometrical space and from a data set by computing the integral of the curve, the quantity referred to as the integrated Betti value. To find the best-fitting geometry, we optimized parameters of the model such that the noise magnitude and the range of radii within the space from which the sample points were drawn provided the best match to the first integrated Betti value. Then, we examined how these optimized parameters could account for the second and third integrated Betti values. For all four data sets, we found the measurements to be consistent with sampling from a 3D hyperbolic space . The first three Betti curves were also sufficient to show that Euclidean spaces could not account for the data, even when dimensionality and other parameters were optimized . As a control, we verified that shuffling odor concentrations between samples, which destroys correlations between odors, produced Betti curves that canbe fully ex- plained by random matrices . These matrices would not be consistent with the hyperbolic space plus the small amount noise that fits the real data . As additional controls, we verified that evaluating differences between Betti curves using L1 distances instead of the integrated Betti values or applying logarithm to concentration values before computing their correlations led to the same conclusions . In particular, hyperbolic 3D is consistent with measurements for all three Betti curves, whereas the best-fitting Euclidean model can be ruled out according to these measures. The corresponding P values are provided in tables S1 to S4. Note that hyperbolic spaces of dimensions higher than three cannot be ruled out . However, the 3D hyperbolic space remains the best-fitting model across the four data sets. This is true whether one uses either the integrated Betti value or the L1 distances between model and experimental Betti curves . To visualize how the points consistent with odorant correlation statistics might be distributed within the hyperbolic space, we used non- metric multidimensional scaling. The non-metric MDS algorithm embeds a set of points into the N-dimensional space while attempting to preserve the rank ordering of distances as best as possible . Traditionally, MDS is applied to the Euclidean space, but we modified it to work with hyperbolic distances. After testing the algorithm on synthetic data , we applied the modified algorithm to the four data sets. In Fig. 3, we show results for the four data sets. Because the points are located near a surface of a sphere , we present the points on a sphere using the two angles of latitude and longitude. The results show approximately uniform sampling in all four data sets. Notably, the points do not cluster based on functional chemical properties of the individual components . One can understand the absence of clustering from the fact that monomolecular odors with different functional properties are produced together in biochemical pathways.