Self-Competitive Adsorption Behavior of Arsenic on the TiO₂ Surface

TiO2 is a commonly used material to remove arsenic from drinking water by adsorption as well as photocatalytic oxidation (PCO). In the present paper, arsenic adsorption and PCO at different pH environments are studied on the (1 1 0) facet of rutile TiO2 (r-TiO2). A self-competitive adsorption (SCA) behavior of arsenic is observed; i.e., arsenic species compete to adsorb on the surface. Related DFT calculations are carried out to simulate adsorption. SCA behavior is the key to connecting calculation results with experimental results. Furthermore, PCO of arsenite is performed at different pH values. Of note, PCO is related to adsorption; namely, the adsorption process determines the whole PCO reaction speed. Therefore, SCA is also helpful for the PCO reaction. The SCA behavior is useful not only for arsenic on r-TiO2 but also for arsenic on anatase TiO2 (a-TiO2). It may be helpful to further study arsenic adsorption and PCO on other materials such as Fe2O3 and MnO2. The SCA behavior extends our understanding of arsenic and provides new insights into arsenic removal and its cycle in nature

The Identification of Two Head Smut Resistance-Related QTL in Maize by the Joint Approach of Linkage Mapping and Association Analysis

<div><p>Head smut, caused by the fungus <i>Sphacelotheca reiliana</i> (Kühn) Clint, is a devastating threat to maize production. In this study, QTL mapping of head smut resistance was performed using a recombinant inbred line (RIL) population from a cross between a resistant line “QI319” and a susceptible line “Huangzaosi” (HZS) with a genetic map constructed from genotyping-by-sequencing (GBS) data and composed of 1638 bin markers. Two head smut resistance QTL were identified, located on Chromosome 2 (<i>q2</i>.<i>09HR)</i> and Chromosome 5 (<i>q5</i>.<i>03HR</i>), <i>q2</i>.<i>09HR</i> is co-localized with a previously reported QTL for head smut resistance, and the effect of <i>q5</i>.<i>03HR</i> has been validated in backcross populations. It was also observed that pyramiding the resistant alleles of both QTL enhanced the level of resistance to head smut. A genome-wide association study (GWAS) using 277 diverse inbred lines was processed to validate the mapped QTL and to identify additional head smut resistance associations. A total of 58 associated SNPs were detected, which were distributed in 31 independent regions. SNPs with significant association to head smut resistance were detected within the <i>q2</i>.<i>09HR</i> and <i>q5</i>.<i>03HR</i> regions, confirming the linkage mapping results. It was also observed that both additive and epistastic effects determine the genetic architecture of head smut resistance in maize. As shown in this study, the combined strategy of linkage mapping and association analysis is a powerful approach in QTL dissection for disease resistance in maize.</p></div

Genetic effects of head smut resistant QTL <i>q2</i>.<i>09HR</i> and <i>q5</i>.<i>03HR</i> in BC₄F₁ population.

<p>HZS stands for the allele contributed by susceptible parent HZS, QI319 stands for the allele contributed by the resistant parent QI319, the lighter gray part in the pie stands for the percentage of resistant plants within each group with the same genotype, the bracketed numbers stands for the total plants of each group. The probabilities of Analysis of Variance (ANOVA) among different allele combinations are noted in the brackets of pie right.</p

Distributions of head smut resistance QTL on chromosome 2 and 5 for the RIL population of QI319×HZS.

<p>Distributions of head smut resistance QTL on chromosome 2 and 5 for the RIL population of QI319×HZS.</p

Validation of head smut resistance QTL <i>q5</i>.<i>03HR</i> with a BC₄F₁ population.

<p>Validation of head smut resistance QTL <i>q5</i>.<i>03HR</i> with a BC₄F₁ population.</p

Single marker-trait testing for both the BC₅F₂ families and plants.

<p>The x-axes represents the genetic position of tested SNPs within the <i>q5</i>.<i>03HR</i> region, and the y-axes represents the negative log P value of t-test.</p

Detection of marker-resistance associations within the regions of <i>q2</i>.<i>09HR</i> (a) and <i>q5</i>.<i>03H</i> (b).

<p>The horizontal dashed line shows the significance with stringent threshold of–log₁₀ (0.00001).</p

LD heat-map for the region around the most significant SNP PZE-105072717 in <i>q5</i>.<i>03HR</i>.

<p>Numbers for each entry are the linkage disequilibrium (LD) level evaluated with D’. Colors varied from white to deep red mean that the LD level in pair-SNPs is increasing from 0% to 100%. The markers within triangle cycles are tightly linked together (with D’ more than 90%).</p

Histopathology versus lipophilicity.

<p>Calculated logD (clogD, A) or measured logD (mlogD, B) versus observed skin adverse effects. Data is jittered on the y-axis to improve visualization.</p

RNA biomarkers for sebaceous gland atrophy in skin. Listed are the 41 unique genes from the 42 probesets identified in the Training Set as shown in Figure 4 (Cxcl16 had 2 probesets).

<p>Fold change and ANOVA p values for compound treatments compared to their respective vehicle treatments, for both the Training and Test Sets, are included. The 26 probesets that are also significantly regulated in the Test Set are shown in bold. **  = ANOVA p<0.01; *  = ANOVA p<0.05; $ = ANOVA p<0.1.</p