Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

Comparison Of Acetic Acid With As Formiic Acid Latex Pump Material

Research on the comparison of acetic acid and formic acid as a latex coagulant has been carried out. To the latex coconut shell charcoal (particle size 80 mesh) was added to the weight of 42 grams then the latex was coagulated with acetic acid and formic acid with a pH of 4.7. As a research, the quality of rubber was carried out by measuring the initial plasticity, plasticity retention index and viscosity of rubber mooney. Finally, latex coagulated with acetic acid produced a better rubber than latex coagulated with formic aci).

Positron Emission Tomography Imaging of Regional Versus Global Myocardial Sympathetic Activity to Improve Risk Stratification in Patients With Ischemic Cardiomyopathy

Background: Current risk assessment approaches fail to identify the majority of patients at risk of sudden cardiac arrest (SCA). Noninvasive imaging of the cardiac sympathetic nervous system using single-photon emission computed tomography and positron emission tomography offers the potential for refining SCA risk assessment. While various [ 11 C]meta-hydroxyephedrine quantification parameters have been proposed, it is currently unknown whether regional denervation or global innervation yields greater SCA risk discrimination. The aim of the study was to determine whether the global innervation parameters yield any independent and additive prognostic value over the regional denervation alone. Methods: In a post hoc competing-risks analysis of the PAREPET trial (Prediction of Arrhythmic Events With Positron Emission Tomography), we compared global innervation and regional denervation parameters using the norepinephrine analog [ 11 C]meta-hydroxyephedrine for SCA risk discrimination. Patients with ischemic cardiomyopathy (n=174) eligible for an implantable cardioverter-defibrillator for the primary prevention of SCA were recruited into the trial. [ 11 C]meta-hydroxyephedrine uptake and clearance rates were measured to assess global (left ventricle mean) retention index and volume of distribution. Regional defects were quantified as the percentage of the left ventricle having values <75% of the maximum. Results: During a median follow-up of 4.2 years, there were 56 cardiac-related deaths, of which 26 were SCAs. For any given regional denervation volume, there was substantial heterogeneity in global innervation scores. Global retention index and distribution volume did not decrease until regional defects exceeded 40% left ventricle. Global scale parameters, retention index, and distribution volume (area under the curve=0.61, P =0.034, P =0.046, respectively), yielded inferior SCA risk discrimination compared to regional heterogeneity (area under the curve=0.74). Conclusions: Regional denervation volume has superior cause-specific mortality prediction for SCA versus global parameters of sympathetic innervation. These results have widespread implications for future cardiac sympathetic imaging, which will greatly simplify innervation analysis. REGISTRATION: URL: ClinicalTrials.gov Identifier: NCT01400334 https://clinicaltrials.gov/ct2/show/NCT01400334