Bioassay-guided isolation and identification of antimicrobial compounds from thyme essential oil by means of overpressured layer chromatography, bioautography and GC-MS

A simple method is described for efficient isolation of compounds having an antibacterial effect. Two thyme (Thymus vulgaris) essential oils, obtained from the market, were chosen as prospective materials likely to feature several bioactive components when examined by thin layer chromatography coupled with direct bioautography as a screening method. The newly developed infusion overpressured layer chromatographic separation method coupled with direct bioautography assured that only the active components were isolated by means of overrun overpressured layer chromatography with online detection and fractionation. Each of the 5 collected fractions represented one of the five antimicrobial essential oil components designated at the screening. The purity and the activity of the fractions were confirmed with chromatography coupled various detection methods (UV, vanillin-sulphuric acid reagent, direct bioautography). The antibacterial components were identified with GC-MS as thymol, carvacrol, linalool, diethylphthalate, and alpha-terpineol. The oil component diethyl-phthalate is an artificial compound, used as plasticizer or detergent bases in the industry. Our results support that exploiting its flexibility and the possible hyphenations, overpressured layer chromatography is especially attractive for isolation of antimicrobial components from various matrixes

Portfolio selection problems in practice: a comparison between linear and quadratic optimization models

Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices

The sign problem across the QCD phase transition

The average phase factor of the QCD fermion determinant signals the strength of the QCD sign problem. We compute the average phase factor as a function of temperature and baryon chemical potential using a two-flavor NJL model. This allows us to study the strength of the sign problem at and above the chiral transition. It is discussed how the $U_A(1)$ anomaly affects the sign problem. Finally, we study the interplay between the sign problem and the endpoint of the chiral transition.Comment: 9 pages and 9 fig

Developments in lattice quantum chromodynamics for matter at high temperature and density

A brief overview of the QCD phase diagram at nonzero temperature and density is provided. It is explained why standard lattice QCD techniques are not immediately applicable for its determination, due to the sign problem. We then discuss a selection of recent lattice approaches that attempt to evade the sign problem and classify them according to the underlying principle: constrained simulations (density of states, histograms), holomorphicity (complex Langevin, Lefschetz thimbles), partial summations (clusters, subsets, bags) and change in integration order (strong coupling, dual formulations)

The Acute Phase Protein Ceruloplasmin as a Non-Invasive Marker of Pseudopregnancy, Pregnancy, and Pregnancy Loss in the Giant Panda

After ovulation, non-pregnant female giant pandas experience pseudopregnancy. During pseudopregnancy, non-pregnant females exhibit physiological and behavioral changes similar to pregnancy. Monitoring hormonal patterns that are usually different in pregnant mammals are not effective at determining pregnancy status in many animals that undergo pseudopregnancy, including the giant panda. Therefore, a physiological test to distinguish between pregnancy and pseudopregnancy in pandas has eluded scientists for decades. We examined other potential markers of pregnancy and found that activity of the acute phase protein ceruloplasmin increases in urine of giant pandas in response to pregnancy. Results indicate that in term pregnancies, levels of active urinary ceruloplasmin were elevated the first week of pregnancy and remain elevated until 20–24 days prior to parturition, while no increase was observed during the luteal phase in known pseudopregnancies. Active ceruloplasmin also increased during ultrasound-confirmed lost pregnancies; however, the pattern was different compared to term pregnancies, particularly during the late luteal phase. In four out of the five additional reproductive cycles included in the current study where females were bred but no birth occurred, active ceruloplasmin in urine increased during the luteal phase. Similar to the known lost pregnancies, the temporal pattern of change in urinary ceruloplasmin during the luteal phase deviated from the term pregnancies suggesting that these cycles may have also been lost pregnancies. Among giant pandas in captivity, it has been presumed that there is a high rate of pregnancy loss and our results are the first to provide evidence supporting this notion

Taming the pion condensation in QCD at finite baryon density: a numerical test in a random matrix model

In the Monte Carlo study of QCD at finite baryon density based upon the phase reweighting method, the pion condensation in the phase-quenched theory and associated zero-mode prevent us from going to the low-temperature high-density region. We propose a method to circumvent them by a simple modification of the density of state method. We first argue that the standard version of the density of state method, which is invented to solve the overlapping problem, is effective only for a certain ‘good’ class of observables. We then modify it so as to solve the overlap problem for ‘bad’ observables as well. While, in the standard version of the density of state method, we usually constrain an observable we are interested in, we fix a different observable in our new method which has a sharp peak at some particular value characterizing the correct vacuum of the target theory. In the finite-density QCD, such an observable is the pion condensate. The average phase becomes vanishingly small as the value of the pion condensate becomes large, hence it is enough to consider configurations with π+ ≃ 0, where the zero mode does not appear. We demonstrate an effectiveness of our method by using a toy model (the chiral random matrix theory) which captures the properties of finite-density QCD qualitatively. We also argue how to apply our method to other theories including finite-density QCD. Although the example we study numerically is based on the phase reweighting method, the same idea can be applied to more general reweighting methods and we show how this idea can be applied to find a possible QCD critical point