A marine isotope stage 13 Acheulian sequence from the Amanzi Springs Area 2 Deep Sounding excavation, Eastern Cape, South Africa.

Renewed research at Amanzi Springs has increased resolution on the timing and technology of the Acheulian industry in South Africa. The archeology from the Area 1 spring eye has recently been dated to MIS 11 (∼404-390 ka), and analyses revealed significant technological variability when compared to other southern African Acheulian assemblages. We expand on these results in presenting new luminescence dating and technological analyses of Acheulian stone tools from three artifact-bearing surfaces exposed within the White Sands unit of the Deep Sounding excavation in the Area 2 spring eye. The two lowest surfaces (Surfaces 3 and 2) are sealed within the White Sands and dated between ∼534 to 496 ka and ∼496 to 481 ka (MIS 13), respectively. Surface 1 represents materials deflated onto an erosional surface that cut the upper part of the White Sands (∼481 ka; late MIS 13), which occurred before the deposition of younger Cutting 5 sediments (<408-<290 ka; MIS 11-8). Archaeological comparisons reveal that the older Surface 3 and 2 assemblages are predominated by unifacial and bifacial core reduction and relatively thick, cobble-reduced large cutting tools. In contrast, the younger Surface 1 assemblage is characterized by discoidal core reduction and thinner large cutting tools, mostly made from flake blanks. Typological similarities between the older Area 2 White Sands and younger Area 1 (404-390 ka; MIS 11) assemblages further suggest long-term continuity in site function. We hypothesize Amanzi Springs represent a workshop locality that Acheulian hominins repeatedly visited to access unique floral, faunal, and raw material resources from at least ∼534 to 390 ka.

Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry.

Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algorithm and two more recent ones (BHI-PRO 1 and 2). Given the high technological variability, the quantification failed to restitute the known quantities of five out of nine proteins present in a controlled solution. There was a linear relationship between protein quantities and peak intensities for four out of nine peaks with all algorithms. The biological component of the variance was higher with BHI-PRO than with the classical algorithm (80-95% with BHI-PRO 1, 79-95% with BHI-PRO 2 vs. 56-90%); thus, BHI-PRO were more efficient in protein quantification. The technological component of the variance was higher with the classical algorithm than with BHI-PRO (6-25% vs. 2.5-9.6% with BHI-PRO 1 and 3.5-11.9% with BHI-PRO 2). The chemical component was also higher with the classical algorithm (3.6-18.7% vs. < 3.5%). Thus, BHI-PRO were better in removing noise from signal when the expected peaks are detected. Overall, either BHI-PRO algorithm may reduce the technological variance from 25 to 10% and thus improve protein quantification and biomarker validation.