A novel method based on atomic force microscopy (AFM) working in Ringing mode (RM) to distinguish between two similar human colon epithelial cancer cell lines that exhibit different degrees of neoplastic aggressiveness is reported on. The classification accuracy in identifying the cell line based on the images of a single cell can be as high as 94% (the area under the receiver operating characteristic [ROC] curve is 0.99). Comparing the accuracy using the RM and the regular imaging channels, it is seen that the RM channels are responsible for the high accuracy. The cells are also studied with a traditional AFM indentation method, which gives information about cell mechanics and the pericellular coat. Although a statistically significant difference between the two cell lines is also seen in the indentation method, it provides the accuracy of identifying the cell line at the single‐cell level less than 68% (the area under the ROC curve is 0.73). Thus, AFM cell imaging is substantially more accurate in identifying the cell phenotype than the traditional AFM indentation method. All the obtained cell data are collected on fixed cells and analyzed using machine learning methods. The biophysical reasons for the observed classification are discussed.
A Boolean maximum constraint satisfaction problem, Max-CSP(f ), is a maximization problem specified by a constraint function f : {−1, 1} k → {0, 1}. An instance of Max-CSP(f ) consists of n variables and m constraints, where each constraint is f applied on a tuple of "literals" of k distinct variables chosen from the n variables. f is said to be symmetric if f (x) depends only on k i=1 x i , where x = (x 1 , . . . , x k ). Chou, Golovnev, and Velusamy [CGV20] obtained explicit constants characterizing the streaming approximability of all symmetric Max-2CSPs. More recently, Chou, Golovnev, Sudan, and Velusamy [CGSV21a] proved a general dichotomy theorem showing tight approximability of Boolean Max-CSPs with respect to sketching algorithms. For every f , they showed that there exists an optimal approximation ratio α(f ) ∈ (0, 1] such that for every ǫ > 0, Max-CSP(f ) is (α(f ) − ǫ)-approximable by a linear sketching algorithm in O(log n) space, but any (α(f ) + ǫ)-approximation sketching algorithm for Max-CSP(f ) requires Ω( √ n)space. While they show that α(f ) is computable to arbitrary precision in PSPACE, they do not give a closed-form expression.In this work, we build on the [CGSV21a] dichotomy theorem and give closed-form expressions for the sketching approximation ratios of multiple families of symmetric Boolean functions. These include kAND and Th k−1
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.