Global warming is causing a negative impact on plant growth and adversely impacts on crop yield. MicroRNAs (miRNAs) are critical in regulating the expression of genes involved in plant development as well as defense responses. The effects of miRNAs on heat-stressed Arabidopsis warrants further investigation. Heat stress increased the expression of miR160 and its precursors but considerably reduced that of its targets, ARF10, ARF16, and ARF17. To study the roles of miR160 during heat stress, transgenic Arabidopsis plants overexpressing miR160 precursor a (160OE) and artificial miR160 (MIM160), which mimics an inhibitor of miR160, were created. T-DNA insertion mutants of miR160 targets were also used to examine their tolerances to heat stress. Results presented that overexpressing miR160 improved seed germination and seedling survival under heat stress. The lengths of hypocotyl elongation and rachis were also longer in 160OE than the wild-type (WT) plants under heat stress. Interestingly, MIM160 plants showed worse adaption to heat. In addition, arf10, arf16, and arf17 mutants presented similar phenotypes to 160OE under heat stress to advance abilities of thermotolerance. Moreover, transcriptome and qRT-PCR analyses revealed that HSP17.6A, HSP17.6II, HSP21, and HSP70B expression levels were regulated by heat in 160OE, MIM160, arf10, arf16, and arf17 plants. Hence, miR160 altered the expression of the heat shock proteins and plant development to allow plants to survive heat stress.

To inspect band mura of LCD panels, it is realized to replace human eyes by CCD measurement system with an advanced algorithm. The analyzed results not only qualitatively describe band mura but also are used to define quantification factors which have good agreement with judged values of several inspectors.

The size of flat-panel liquid-crystal displays is getting larger; as a result, it is becoming harder to inspect for defects and may require a human visual inspector to judge the severity of the defects on the final product. Recently, mura phenomenon, which is defined as a visual blemish with non-uniform shapes and boundaries, is becoming a serious unpleasant effect which needs to be detected and inspected in order to standardize the LCD's quality. Hence, an automation process based on machine vision has proven to be a good choice to facilitate and stabilize the process. An effective general algorithm for detecting different types of mura defects with various contrast, shape, and direction, based on the fusion of the normalized magnitude of first-and second-order derivative responses in four directions, is proposed. The experiments applied on various types of pseudo-mura with different shapes show an efficient detection rate of more than 90%.