Critical exponents and transport properties near the QCD critical endpoint from the statistical bootstrap model

We present an estimate of the behavior of the shear and bulk viscosity coefficients when the QCD critical point is approached from the hadronic side, describing hadronic matter within the statistical bootstrap model of strong interactions. The bootstrap model shows critical behavior near the quark-hadron transition temperature if the parameter characterizing the degeneracy of Hagedorn states is properly chosen. We calculate the critical exponents and amplitudes of relevant thermodynamic quantities near the QCD critical point and combine them with an Ansatz for the shear and bulk viscosity coefficients to derive the behavior of these coefficients near the critical point. The shear viscosity to entropy density ratio is found to decrease when the temperature is increased, and to approach the Kovtun–Son–Starinets bound $$1/(4\pi )$$ 1 / ( 4 π ) faster near the critical point, while the bulk viscosity coefficient is found to rise very rapidly.

The critical point of Bootstrap and Lattice QCD

It is shown that the hadronic matter formed at high temperatures, according to the prescription of the statistical bootstrap principle, develops a critical point at nonzero baryon chemical potential. The location of the critical point is evaluated with the use of lattice QCD pressure.

Proof of concept for identifying cystic fibrosis from perspiration samples

The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.