Sentence examples for distinguished data from inspiring English sources

In the case of the intraparticle diffusion model based on the plot of qt vs. t1/2, the multi linearity with three adsorption stages can be distinguished (data not presented).

However, when the same experiment was performed with unpermeabilized cells, the histograms with the secondary antibody alone and the anti-VWF monoclonal antibody AXL205 could not be distinguished (Data not given), suggesting that we are detecting VWF that has entered the cell and not merely adsorbed on the cell surface.

Because the holes in the filters take up fluorescence, the sections that pass through the filter are easily identified, and the basal (next to the filter) and apical (farthest from the filter) surfaces can be distinguished (data not shown).

However, similar to the variability of hpp12_444/445 and hpp12_446 orthologs among ICE Hptfs4 islands, where two clades each can be distinguished (data not shown), no clear correlation of these different clades with individual geographic groups could be found.

Because the holes in the filters take up fluorescence, the sections that pass through the filter are easily identified, and the basal (next to the filter) and apical (furthest from the filter) surfaces can be distinguished (data not shown).

Due to distinguished data monotonous and outliers in first stage of DCSTREM which is dividing data window into some subsets, DCSTREAM achieves a higher quality and established performance.

Software with names like PacketHound, from Palisade Systems, or Packet Shaper, from Packeteer, enable network administrators to distinguish data that comes from the file-trading services and sequester it from the rest of the Internet traffic.

These requirements distinguish data warehouse projects from operational data stores and are often underestimated.

Distinguishing data and information flows inside and outside the DNF justifies the technical solution developed.

Then, only KM bits are sufficient to distinguish data rates for all subcarriers.

The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning).