FTS Measurements of Submillimeter-Wave Atmospheric Opacity at Pampa la Bola III. Water Vapor, Liquid Water, and 183 GHz Water Vapor Line Opacities

Further analysis has been made on the millimeter and submillimeter-wave (100-1600 GHz or 3 mm - 188 micron) atmospheric opacity data taken with the Fourier Transform Spectrometer (FTS) at Pampa la Bola, 4800 m above sea level in northern Chile, which is the site of the Atacama Large Millimeter/submillimeter Array (ALMA). Time-sequence plots of millimeter and submillimeter-wave opacities show similar variations to each other, except for during the periods with liquid water (fog or clouds) in the atmosphere. Using millimeter and submillimeter-wave opacity correlations under two conditions, which are affected and not affected by liquid water, we succeeded to separate the measured opacity into water vapor and liquid water opacity components. The water vapor opacity shows good correlation with the 183 GHz water vapor line opacity, which is also covered in the measured spectra. On the other hand, the liquid water opacity and the 183 GHz line opacity show no correlation. Since only the water vapor component is expected to affect the phase of interferometers significantly, and the submillimeter-wave opacity is less affected by the liquid water component, it may be possible to use the submillimeter-wave opacity for a phase-correction of submillimeter interferometers.Comment: 10 pages, 5 figures, PASJ, vol.55, no.1 (2003), in pres

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes

Effect of Peierls transition in armchair carbon nanotube on dynamical behaviour of encapsulated fullerene

The changes of dynamical behaviour of a single fullerene molecule inside an armchair carbon nanotube caused by the structural Peierls transition in the nanotube are considered. The structures of the smallest C20 and Fe@C20 fullerenes are computed using the spin-polarized density functional theory. Significant changes of the barriers for motion along the nanotube axis and rotation of these fullerenes inside the (8,8) nanotube are found at the Peierls transition. It is shown that the coefficients of translational and rotational diffusions of these fullerenes inside the nanotube change by several orders of magnitude. The possibility of inverse orientational melting, i.e. with a decrease of temperature, for the systems under consideration is predicted.Comment: 9 pages, 6 figures, 1 tabl

Hindered proton collectivity in the proton-rich nucleus ^<28>S : Possible magic number Z = 16

NUCLEAR STRUCTURE AND DYNAMICS 20129–13 July 201

Hindered proton collectivity in 28S: Possible magic number at Z=16

The reduced transition probability B(E2;0 ->2+) for 28S was obtained experimentally using Coulomb excitation at 53 MeV/nucleon. The resultant B(E2) value 181(31) e2fm4 is smaller than the expectation based on empirical B(E2) systematics. The double ratio |M_n/M_p|/(N/Z) of the 0+ ->2+ transition in 28S was determined to be 1.9(2) by evaluating the M_n value from the known B(E2) value of the mirror nucleus 28Mg, showing the hindrance of proton collectivity relative to that of neutrons. These results indicate the emergence of the magic number Z=16 in the |T_z|=2 nucleus 28S.Comment: 10 pages, 3 figures. Published in Phys. Rev. Lett (http://link.aps.org/doi/10.1103/PhysRevLett.108.222501

The impact of different GFR estimating equations on the prevalence of CKD and risk groups in a Southeast Asian cohort using the new KDIGO guidelines

<p>Abstract</p> <p>Background</p> <p>Recently, the Kidney Disease: Improving Global Outcomes (KDIGO) group recommended that patients with CKD should be assigned to stages and composite relative risk groups according to GFR (G) and proteinuria (A) criteria. Asians have among the highest rates of ESRD in the world, but establishing the prevalence and prognosis CKD is a problem for Asian populations since there is no consensus on the best GFR estimating (eGFR) equation. We studied the effects of the choice of new Asian and Caucasian eGFR equations on CKD prevalence, stage distribution, and risk categorization using the new KDIGO classification.</p> <p>Methods</p> <p>The prevalence of CKD and composite relative risk groups defined by eGFR from with Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI); standard (S) or Chinese(C) MDRD; Japanese CKD-EPI (J-EPI), Thai GFR (T-GFR) equations were compared in a Thai cohort (n = 5526)</p> <p>Results</p> <p>There was a 7 fold difference in CKD_3-5prevalence between J-EPI and the other Asian eGFR formulae. CKD_3-5prevalence with S-MDRD and CKD-EPI were 2 - 3 folds higher than T-GFR or C-MDRD. The concordance with CKD-EPI to diagnose CKD_3-5was over 90% for T-GFR or C-MDRD, but they only assigned the same CKD stage in 50% of the time. The choice of equation also caused large variations in each composite risk groups especially those with mildly increased risks. Different equations can lead to a reversal of male: female ratios. The variability of different equations is most apparent in older subjects. Stage G3aA1 increased with age and accounted for a large proportion of the differences in CKD_3-5between CKD-EPI, S-MDRD and C-MDRD.</p> <p>Conclusions</p> <p>CKD prevalence, sex ratios, and KDIGO composite risk groupings varied widely depending on the equation used. More studies are needed to define the best equation for Asian populations.</p