The Controlled Oxidation of Leonardite

The controlled oxidation or dissolved leonarclite was carried out in a heated autoclave under elevated oxygen pressure. Oxiaation was carried out in oraer to increase the aciaity oi: leonaraite and obtain higher humic acid yields. The solvent was an aqueous solution of sodium hydroxide in the range of concentrations from l\u3eo to Si-.-.. The reaction temperature was varied from 25°C to I25°C; the pressure was kept at 500 psig and reaction time was constant at one hour. The product was analyzed both for carooxyiie acid and for hydroxyl groups. The Increase in carboxyl and hydroxyl groups was slight under the optimum reaction conditions studied. The optimum conditons were: for temperature, between 50°C ana 75°C, and for concentration, between 2% and 6%. However at the 4% caustic concentration level, there was no increase in carboxyl and hydroxyl groups between 50°C and 75°C. Higher temperatures and higher concentrations of caustic resulted in lower yields of humic acids either because decarboxylation occurred or because the acids became water soluble

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Approximation Refinement for Interpolation−Based Model Checking

Model checking using Craig interpolants provides an effective method for computing an over-approximation of the set of reachable states using a SAT solver. This method requires proofs of unsatisfiability from the SAT solver to progress. If an over-approximation leads to a satisfiable formula, the computation restarts using more constraints and the previously computed approximation is not reused. Though the new formula eliminates spurious counterexamples of a certain length, there is no guarantee that the subsequent approximation is better than the one previously computed. We take an abstract, approximation-oriented view of interpolation based model checking. We study counterexample-free approximations, which are neither over- nor under-approximations of the set of reachable states but still contain enough information to conclude if counterexamples exist. Using such approximations, we devise a model checking algorithm for approximation refinement and discuss a preliminary implementation of this technique on some hardware benchmarks

Restructuring Resolution Refutations for Interpolation

Interpolants are the cornerstone of several approximate verification techniques. Current interpolation techniques restrict the search heuristics of the underlying decision procedure to compute interpolants, incurring a negative impact on performance, and apply primarily to the lazy proof explication framework. We bridge the gap between fast decision procedures that aggressively use propositional reasoning and slower interpolating decision procedures by extending the scope of the latter to non-lazy approaches and relaxing the restrictions on search heuristics. Both are achieved by combining a simple set of transformations on resolution refutations. Our experiments show that this method leads to speedups when computing interpolants and to reductions in proof size

Restructuring Resolution Refutations for Interpolation

Interpolants are the cornerstone of several approximate verification techniques. Current interpolation techniques restrict the search heuristics of the underlying decision procedure to compute interpolants, incurring a negative impact on performance, and apply primarily to the lazy proof explication framework. We bridge the gap between fast decision procedures that aggressively use propositional reasoning and slower interpolating decision procedures by extending the scope of the latter to non-lazy approaches and relaxing the restrictions on search heuristics. Both are achieved by combining a simple set of transformations on resolution refutations. Our experiments show that this method leads to speedups when computing interpolants and to reductions in proof size

Interpolant Strength

Interpolant-based model checking is a SAT-based, approximate method for computing inductive invariants of transition systems. The performance of the model checker is contingent on the approximation computed, which in turn depends on the logical strength of the interpolants. A good approximation is coarse enough to enable rapid convergence but strong enough to be contained within the weakest inductive invariant. We present a system for constructing propositional interpolants of different strength from a resolution refutation. This system subsumes existing methods and allows interpolation systems to be ordered by the logical strength of the obtained interpolants. Interpolants of different strength can also be obtained by transforming a resolution proof. We analyse an existing proof transformation, generalise it, and characterise the interpolants obtained

Approximation Refinement for Interpolation−Based Model Checking

Model checking using Craig interpolants provides an effective method for computing an over-approximation of the set of reachable states using a SAT solver. This method requires proofs of unsatisfiability from the SAT solver to progress. If an over-approximation leads to a satisfiable formula, the computation restarts using more constraints and the previously computed approximation is not reused. Though the new formula eliminates spurious counterexamples of a certain length, there is no guarantee that the subsequent approximation is better than the one previously computed. We take an abstract, approximation-oriented view of interpolation based model checking. We study counterexample-free approximations, which are neither over- nor under-approximations of the set of reachable states but still contain enough information to conclude if counterexamples exist. Using such approximations, we devise a model checking algorithm for approximation refinement and discuss a preliminary implementation of this technique on some hardware benchmarks