Abstract. In order to improve our understanding of air quality in Southeast Asia, the anthropogenic emissions inventory must be well represented. In this work, we apply different anthropogenic emission inventories in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) version 3.3 using Model for Ozone and Related Chemical Tracers (MOZART) gas-phase chemistry and Global Ozone Chemistry Aerosol Radiation and Transport (GO-CART) aerosols to examine the differences in predicted carbon monoxide (CO) and ozone (O 3 ) surface mixing ratios for Southeast Asia in March and December 2008. The anthropogenic emission inventories include the Reanalysis of the TROpospheric chemical composition (RETRO), the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B), the MACCity emissions (adapted from the Monitoring Atmospheric Composition and Climate and megacity Zoom for the Environment projects), the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS) emissions, and a combination of MACCity and SEAC4RS emissions. Biomass-burning emissions are from the Fire Inventory from the National Center for Atmospheric Research (NCAR) (FINNv1) model. WRF-Chem reasonably predicts the 2 m temperature, 10 m wind, and precipitation. In general, surface CO is underpredicted by WRF-Chem while surface O 3 is overpredicted. The NO 2 tropospheric column predicted by WRF-Chem has the same magnitude as observations, but tends to underpredict the NO 2 column over the equatorial ocean and near Indonesia. Simulations using different anthropogenic emissions produce only a slight variability of O 3 and CO mixing ratios, while biomass-burning emissions add more variability. The different anthropogenic emissions differ by up to 30 % in CO emissions, but O 3 and CO mixing ratios averaged over the land areas of the model domain differ by ∼ 4.5 % and ∼ 8 %, respectively, among the simulations. Biomass-burning emissions create a substantial increase for both O 3 and CO by ∼ 29 % and ∼ 16 %, respectively, when comparing the March biomass-burning period to the December period with low biomass-burning emissions. The simulations show that none of the anthropogenic emission inventories are better than the others for predicting O 3 surface mixing ratios. However, the simulations with different anthropogenic emission inventories do differ in their predictions of CO surface mixing ratios producing variations of ∼ 30 % for March and 10-20 % for December at Thai surface monitoring sites.

Sex determination is an important step in biological identification from skeletal remains, especially in forensic circumstances. Many authors suggested that the morphological study was more subjective than the metric. There are various craniometric studies in different populations. They revealed that there was population-specific for the sex discriminant equation derived from each population. Thus, the present study aimed to evaluate sexual dimorphism and develop the discriminant function from 200 Thai skulls. Twenty-five standard cranial measurements were examined. The results revealed that males' cranium were statistically significant larger than females' in all measurements (P<0.05), except for minimum breadth of nasal bone. Sexual dimorphism index also expressed relatively high male/female ratio indicating great sexual dimorphism. The best practical equation for sex determination with six measurements (maximum cranial length, bizygomatic breadth, biauricular breadth, nasal height, biorbital breadth and right mastoid length) was derived from a stepwise discriminant method. This equation with 90.6% accuracy (91.1% in male and 90.0% in female) can provide valuable application utilizing in sex determination from skull in a Thai population.

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BackgroundStarch serves as a temporal storage of carbohydrates in plant leaves during day/night cycles. To study transcriptional regulatory modules of this dynamic metabolic process, we conducted gene regulation network analysis based on small-sample inference of graphical Gaussian model (GGM).ResultsTime-series significant analysis was applied for Arabidopsis leaf transcriptome data to obtain a set of genes that are highly regulated under a diurnal cycle. A total of 1,480 diurnally regulated genes included 21 starch metabolic enzymes, 6 clock-associated genes, and 106 transcription factors (TF). A starch-clock-TF gene regulation network comprising 117 nodes and 266 edges was constructed by GGM from these 133 significant genes that are potentially related to the diurnal control of starch metabolism. From this network, we found that β-amylase 3 (b-amy3: At4g17090), which participates in starch degradation in chloroplast, is the most frequently connected gene (a hub gene). The robustness of gene-to-gene regulatory network was further analyzed by TF binding site prediction and by evaluating global co-expression of TFs and target starch metabolic enzymes. As a result, two TFs, indeterminate domain 5 (AtIDD5: At2g02070) and constans-like (COL: At2g21320), were identified as positive regulators of starch synthase 4 (SS4: At4g18240). The inference model of AtIDD5-dependent positive regulation of SS4 gene expression was experimentally supported by decreased SS4 mRNA accumulation in Atidd5 mutant plants during the light period of both short and long day conditions. COL was also shown to positively control SS4 mRNA accumulation. Furthermore, the knockout of AtIDD5 and COL led to deformation of chloroplast and its contained starch granules. This deformity also affected the number of starch granules per chloroplast, which increased significantly in both knockout mutant lines.ConclusionsIn this study, we utilized a systematic approach of microarray analysis to discover the transcriptional regulatory network of starch metabolism in Arabidopsis leaves. With this inference method, the starch regulatory network of Arabidopsis was found to be strongly associated with clock genes and TFs, of which AtIDD5 and COL were evidenced to control SS4 gene expression and starch granule formation in chloroplasts.

Age estimation from skeletal remains is an important step in forensic biological identification. The main objective of this study is to develop an age estimation equation for the Thai population from vertebral osteophytes. Each vertebra in the cervical, thoracic and lumbar segments was scored for degree of osteophyte formation. Classification was carried out in accordance with the criteria established by Snodgrass and Watanabe, and used a new modified score of the length of vertebral osteophyte for age estimation. The sample included 400 individuals (262 males, 138 females) ranging in age from 22 to 97 years. A sample of Thai vertebral columns was used, the columns being divided into the following groups of vertebrae: cervical (C2–C7), thoracic (T1–T12), and lumbar (L1–L5). Each vertebra was scored for the degree of osteophyte formation and the accumulated data was analyzed statistically. Correlation coefficients and R-squared from mean in lumbar vertebrae for females of criteria established by the method of Snodgrass and Watanabe, the new modified score by length of vertebral osteophytes were 0.801 and 0.642 ( P <0.01); 0.755 and 0.57 ( P <0.01); 0.786 and 0.618 ( P <0.01), respectively. This study presents all 23 subcategories (C2–L5) of the vertebrae to apply in real situations, showing all age estimation equations for males, females and combined sexes of unknown sex. One application of this study is age estimation when dealing with forensic cases in the Thai population.