DataSheet1_Relative specificity as an important consideration in the big data era.xls

Technological breakthroughs such as high-throughput methods, genomics, single-cell studies, and machine learning have fundamentally transformed research and ushered in the big data era of biology. Nevertheless, current data collections, analyses, and modeling frequently overlook relative specificity, a crucial property of molecular interactions in biochemical systems. Relative specificity describes how, for example, an enzyme reacts with its many substrates at different rates, and how this discriminatory action alone is sufficient to modulate the substrates and downstream events. As a corollary, it is not only important to comprehensively identify an enzyme’s substrates, but also critical to quantitatively determine how the enzyme interacts with the substrates and to evaluate how it shapes subsequent biological outcomes. Genomics and high-throughput techniques have greatly facilitated the studies of relative specificity in the 21st century, and its functional significance has been demonstrated in complex biochemical systems including transcription, translation, protein kinases, RNA-binding proteins, and animal microRNAs (miRNAs), although it remains ignored in most work. Here we analyze recent findings in big data and relative specificity studies and explain how the incorporation of relative specificity concept might enhance our mechanistic understanding of gene functions, biological phenomena, and human diseases.</p

Solubility Measurement and Modeling for the NaCl–NH₄Cl–Monoethylene Glycol–H₂O System from (278 to 353) K

The solubilities of NH₄Cl and NaCl in the mixtures of monoethylene glycol (MEG) and water were determined, respectively, in the temperature range of (278 to 353) K by a dynamic method. The NaCl–NH₄Cl–MEG–H₂O system with MEG mole fraction of 0.30 on a salt-free basis was also investigated from (278 to 353) K to determine its phase equilibrium as a function of temperature and the concentration of electrolytes. The solubilities of both NH₄Cl and NaCl in the MEG–H₂O mixtures were found to decrease with the addition of MEG and the increasing concentration of the secondary electrolyte. The results show that the increment of temperature causes a marked increase in the solubility of NH₄Cl but only has a slight impact on the solubility of NaCl. The mixed-solvent electrolyte (MSE) model was applied to model solid–liquid equilibrium for the system containing NaCl, NH₄Cl, MEG, and H₂O. Binary interaction parameters for MEG–NH₄⁺, MEG–Na⁺, and Na⁺–NH₄⁺ were newly determined by regressing the experimental data. The MSE model with new parameters presented very high accuracy to calculate solubilities for the NaCl–NH₄Cl–MEG–H₂O system. The average absolute relative deviations (AARD) between the prediction and the experimental solubility are 0.75 and 0.88% for NH₄Cl and NaCl, respectively

Phase Equilibria for the Glycine–Methanol–NH₄Cl–H₂O System

An investigation of the phase equilibria of the glycine–methanol–NH₄Cl–H₂O system was carried out with the objective of optimizing the monochloroacetic acid (MCA) process for the production of glycine. Phase equilibrium of the glycine–NH₄Cl–H₂O system at temperatures over the range of 283.2–353.2 K was determined for concentrations ranging up to the multiple saturation points. The solubilities of both glycine and NH₄Cl were found to increase with increasing temperature, as well as with increasing concentration of other solutes. The Bromley–Zemaitis model for ions and the Pitzer formulation for glycine neutral species implemented in the OLI platform were used in the regression of the experimental solubilities. The average absolute deviations between the regressed solubility values and the experimental data were found to be 1.4% for glycine and 0.93% for NH₄Cl. Three binary interaction parameters of the Pitzer formulation were newly obtained and coupled with the Bromley–Zemaitis parameters documented in OLI’s databank to predict the multiple saturation points of the system. Additionally, the solubility of glycine in methanol–H₂O mixtures was also measured from 283.2 to 323.2 K, and a sharp decline was observed as a function of the content of methanol. Such thermodynamic information is definitely useful for improving the existing industrial process, as well as providing fundamentals for the development of new glycine production processes

Measurement and Chemical Modeling of the Solubility of Na₂SiO₃·9H₂O and Na₂SiO₃ in Concentrated NaOH Solution from 288 to 353 K

In order to comprehensively use magnesium and silicon resources existing in serpentine ore, an efficient process for decomposing serpentine by the use of concentrated NaOH solution is proposed at elevated temperature. In this new process, magnesium is obtained as residues with the form of Mg­(OH)₂ after filtration, while silicon is dissolved in an alkaline solution and then separated by a crystallization operation with the common ion effect of NaOH at relatively low temperature. Solubilities of sodium metasilicate nonahydrate (Na₂SiO₃·9H₂O) and anhydrous sodium metasilicate (Na₂SiO₃) in NaOH solutions were measured. The experiments were carried out at the temperature range from 288.2 to 313.2 K and 343.2 to 353.2 K for these two solids, respectively. Over the investigated concentration range from 0.0 to 11.2 mol·kg^–1, the addition of NaOH caused the solubility of both Na₂SiO₃·9H₂O and Na₂SiO₃ to decrease due to common ion effect. It was also found that increasing the temperature favored the solubility of sodium metasilicate nonahydrate but depressed the solubility of anhydrous sodium metasilicate in NaOH solutions. A chemical model has been established with newly obtained interaction parameters of the Bromley–Zemaitis model by regressing the experimental solubility of Na₂SiO₃·9H₂O in NaOH solutions from 288.2 to 313.2 K along with the experimental solubility data of Na₂SiO₃ in NaOH solutions at 353.2 K

AMOVA for grouping of populations estimated using Φ-statistics based on control region sequence for chiru (<i>Pantholops hodgsonii</i>).

<p>AMOVA for grouping of populations estimated using Φ-statistics based on control region sequence for chiru (<i>Pantholops hodgsonii</i>).</p

Estimates of gene flow (Nem) and theta between geographic groups of Tibetan gazelle (<i>Procapra picticaudata</i>).

<p>Ne is the effective population size of females, µ is the mutation rate and m is the migration rate.</p

Map of the distribution and sampling locations.

<p>The sampling locations of the chiru (<i>Pantholops hodgsonii</i>) are indicated by rectangles (▪), and the capital letters indicate sampling locations (sample sizes in parentheses): A, Xinjiang (XJ, 19); B, Tibet (19); C, Qinghai (QH, 19); D, Zhuolaihu Lake (BH, 61). The sampling locations of the Tibetan gazelle (<i>Procapra picticaudata</i>) are indicated by black triangles (▴). The Arabic numerals indicate sampling locations (sample sizes in parentheses): 1, Geji (2); 2, Bange (2); 3, Mangkang (1); 4, Shengzha (1); 5, Qiangtang (4); 6, Arjin Shan (5); 7, Kekexili (8); 8, Tianjun (13); 9, Doulan (2); 10, Yushu (2); 11, Harshihar (1); and 12, Ruoergai (5).</p

Pairwise population differentiation values and Φ_ST values for chiru (<i>Pantholops hodgsonii</i>).

<p>Above diagonal: Pairwise Φ_ST values between populations. Diagonal elements: Average number of pairwise differences within population (PiX). Below diagonal: Corrected average pairwise difference (PiXY−(PiX+PiY)/2). Pairwise Φ_ST values and corrected average pairwise differences that are statistically different are indicated.</p

Estimates of gene flow (Nem) and theta between regional groups of chiru (<i>Pantholops hodgsonii)</i>.

<p>Ne is the effective population size of females, µ is the mutation rate and m is the migration rate.</p

<i>K</i>, delta <i>K</i> scores and Q-plot.

<p>A) & B) <i>K</i> and delta <i>K</i> scores of the chiru (<i>Pantholops hodgsonii</i>) and Tibetan gazelle (<i>Procapra picticaudata</i>). The scores are based on microsatellite data with all loci included and prior assumptions of 1–7 genotypic clusters (<i>K</i>); C) Q-plot of the Tibetan gazelle (<i>Procapra picticaudata</i>) at <i>K</i> = 3.</p