The contamination of soils with cadmium (Cd) has become a serious environmental issue that needs to be addressed. Elucidating the mechanisms underlying Cd accumulation may facilitate the development of plants that accumulate both high and low amounts of Cd. In this study, a combination of phenotypic, physiological, and comparative transcriptomic analyses was performed to investigate the effects of different Cd concentrations (0, 5, 10, 30, 50 mg/kg) on Brassica juncea L. Our results suggest that B. juncea L. seedlings had a degree of tolerance to the 5 mg/kg Cd treatment, whereas higher Cd stress (10–50 mg/kg) could suppress the growth of B. juncea L. seedlings. The contents of soluble protein, as well as MDA (malondialdehyde), were increased, but the activities of CAT (catalase) enzymes and the contents of soluble sugar and chlorophyll were decreased, when B. juncea L. was under 30 and 50 mg/kg Cd treatment. Comparative transcriptomic analysis indicated that XTH18 (xyloglucan endotransglucosylase/hydrolase enzymes), XTH22, and XTH23 were down-regulated, but PME17 (pectin methylesterases) and PME14 were up-regulated, which might contribute to cell wall integrity maintenance. Moreover, the down-regulation of HMA3 (heavy metal ATPase 3) and up-regulation of Nramp3 (natural resistance associated macrophage proteins 3), HMA2 (heavy metal ATPase 2), and Nramp1 (natural resistance associated macrophage proteins 1) might also play roles in reducing Cd toxicity in roots. Taken together, the results of our study may help to elucidate the mechanisms underlying the response of B. juncea L. to various concentrations of Cd.

Customer profiles that include gender and age information are important to businesses and can be used to promote sales and provide personalized services. This information is gathered in e-commerce by analyzing customer visit records in virtual web space. However, such practice is difficult in brick-and-mortar businesses because the data that can be utilized to infer customer profiles are limited in physical spaces. In this paper, we attempt to infer the gender and age of customers using indoor positioning data generated by the Wi-Fi engine. To achieve this, we first construct a synthesized features vector to distinguish different profiles. This vector contains both customer spatial–temporal mobility characteristics and interest preferences. A hidden Markov model group detection method is then applied to detect customers who shop together because they usually show the same shopping behavior and it is difficult to distinguish their profiles. Finally, a random forest inference model is proposed to infer profiles of customers who shop alone. The indoor positioning data collected in the Longhu Tianjie Plaza in Chongqing were used as a case study. The result shows that customer profiles are indeed inferable from indoor positioning data. The accuracy of the gender inference model reaches 73.9%, while that of the age inference model is 67.9%. This demonstrates the potential value of new “big data” for promoting precision marketing and customer management in brick-and-mortar businesses.

This study presented a quantitative comparison of cockpit and doline karst by examining the numbers and characteristics of typical types of landform entities that are developed in Guilin (Guangxi, China), La Alianza (PR, USA), Avalton (KY, USA), and Oolitic (IN, USA). Five types of landform entities were defined: isolated hill (IH), clustered hills (CHs), isolated sinkhole (IS), clustered sinkholes (CSs), and clustered hills with sinkholes (CHSs). An algorithm was developed to automatically identify these types of landform entities by examining the contour lines on topographic maps of two cockpit karst areas (Guilin and La Alianza) and two doline karst areas (Oolitic and Avalton). Within each specific study area, the CHSs is the least developed type yet with a larger size and higher relief. The IH and IS entities are smaller in size, lower in relief, and outnumber their clustered counterparts. The total numbers of these types of entities are quite different in cockpit and doline karst areas. Doline karst is characterized by more negative (IS and CSs) than positive (IH and IHs) landforms and vice versa for cockpit karst. For example, the Guilin study area has 1192 positive landform entities in total, which occupy 9.81% of the total study area. It has only 622 negative landform entities occupying only 3.91% of the total study area. By contrast, the doline karst in Oolitic has 130 negative while only 10 positive landform entities. The positive and negative landforms in Oolitic occupy 12.68% and 2.61% of the total study area, respectively. Furthermore, average relief and slope of the landform entities are much higher and steeper in the cockpit karst than the doline karst areas. For instance, the average slope of CHs in Alvaton is 3.90 degrees while it is 19.78 degrees in La Alianza. The average relief of CSs is 4.07 m and 34.29 m in Oolitic and Guilin respectively. Such a difference within a specific area or between the cockpit and doline karst may reveal different controls on the development of karst landscape.

No abstract