The uniqueness of human mobility can be expressed by the use of three-dimensional dimensions: medium, high and medium - grain dimensions. Statistically, traces are more unique if they are medium in size and granular in these dimensions, but not in the high dimension. Next, we show that it is possible to estimate the uniqueness of a given track in terms of the middle dimension of the track, in this case the height and width. We show that the high and medium grain sizes appreciate the uniqueness of a railway in terms of the height and width of its tracks. TrBV genes are divided into two groups: the enriched fraction and the non-enriched fraction. The analysis of differential gene use shows that several TRBv genes were overrepresented in the V-gene distribution, with the V genes being significantly distorted in the direction of the high and medium grain sizes. PCA of the J gene distributions show significantly less pettiness, suggesting that selective pressure was preferred on V genes in this distribution. The analysis shows a very different TRBv gene distribution, which implies a CDR3 diversity in the respective extended rearrangements. Together, these results indicate that a compatible SAg selection process was used to select V genes in both the V and J gene distribution. We turn to structurally resolved TCRs that contain genes that encode Vb chains. In severe hyperinflammatory COVID in 19 patients, TRV24 was enriched in a non-enriched portion of the non-enriched fraction . We tested whether TCRs bind to the Vb chain of the CDR3 gene similar to T CRCs and bind them similarly to their respective V b chains. This superantigen - like the motive - is present in other SARS families and coronaviruses, which may explain the cytokine storms observed in adults in COVID-19. Using a structure-based computational model, we were able to show that T-CRCs have a high affinity to this motif in order to bind to the TCRs, and that ternary complexes can form in MHCII. The newly identified binding condition of the CDR3 gene to T-CRC is associated with COIDV19, and the presence of a T-CRC binds to a Vb chain of CDr3. The mutated spine is thus a unique feature that would otherwise have been equipped with the same properties as the normal SAg - such as the region of the RBD, but 682 and RAR-685 may have eluded our study. It could be constructive to develop antibodies targeting the S-Ag region to modulate the Sag-induced inflammatory cytokines, or to block the viral entry enabled by the T-CRC and the Vb chain of CDr3 in COIDV19. Alternatively, combination therapy against both SAG-like regions of R BD may prove beneficial. Antibodies or drugs against both S regions may be useful either by modulating the inflammatory cytokine - induced SAG - or by blocking the activating virus entries in the treatment of SARS. This will reduce the resolution of the record and aggregation will inevitably decrease . With medium-grained data sets, it is easier to attack data sets that are coarse in one dimension than in another dimension and in some dimensions, such as the number of cells in a sample or the size of a single cell , than with a large data set . R2 - 6426, the number of antennas in each region correlates with the population, and the regions covered by antennas range from 0 - 15 km2 in urban areas to 15 - 20 km 2 in rural areas. This shows that mobility traces are highly unique and can be identified with little information from outside. This plot underscores the importance of mobile data, especially that collected by smartphone apps. The dependence between b and p implies that a few additional points may be necessary to identify individuals in a low-resolution dataset. At four points and ten points we can roughly estimate what is required to identify a person. This means that privacy is likely to be gained by reducing the resolution of the record, but the dependence of b on p implies that there may be fewer additional points for identifying individuals. The mobility data set can be re-examined on the basis of information from only a few external locations, but only with low resolution . We are # I made it easy to switch these limitations in a script file, with the help of some simple commands and a bit of code in Python. A unique constraint ensures that the data contained in a column or group of columns is unique for each row in the table. It specifies that any combination of values in a specified column must be unique, so that a column normally does not need to be unique or ambiguous. This is not bound to a particular column, but appears as a comma - separated column.