Three cis-Regulatory Motifs, AuxRE, MYCRS1 and MYCRS2, are Required for Modulating the Auxin- and Mycorrhiza-Responsive Expression of a Tomato GH3 Gene.

Auxin is well known to be a key regulator that acts in almost all physiological processes during plant growth, and in interactions between plants and microbes. However, to date, the regulatory mechanisms underlying auxin-mediated plant-arbuscular mycorrhizal (AM) fungi symbiosis have not been well deciphered. Previously we identified a GH3 gene, SlGH3.4, strongly responsive to both auxin induction and mycorrhizal symbiosis. Here, we reported a refined dissection of the SlGH3.4 promoter activity using the ß-glucuronidase (GUS) reporter. The SlGH3.4 promoter could drive GUS expression strongly in mycorrhizal roots of soybean and rice plants, and in IAA-treated soybean roots, but not in IAA-treated rice roots. A promoter deletion assay revealed three cis-acting motifs, i.e. the auxin-responsive element, AuxRE, and two newly identified motifs named MYCRS1 and MYCRS2, involved in the activation of auxin- and AM-mediated expression of SlGH3.4. Deletion of the AuxRE from the SlGH3.4 promoter caused almost complete abolition of GUS staining in response to external IAA induction. Seven repeats of AuxRE fused to the Cauliflower mosaic virus (CaMV) 35S minimal promoter could direct GUS expression in both IAA-treated and AM fungal-colonized roots of tobacco plants. Four repeats of MYCRS1 or MYCRS2 fused to the CaMV35S minimal promoter was sufficient to drive GUS expression in arbuscule-containing cells, but not in IAA-treated tobacco roots. In summary, our results offer new insights into the molecular mechanisms underlying the potential cross-talk between the auxin and the AM regulatory pathways in modulating the expression of AM-responsive GH3 genes in diverse mycorrhizal plants.

Analysis of tomato plasma membrane H(+)-ATPase gene family suggests a mycorrhiza-mediated regulatory mechanism conserved in diverse plant species.

In plants, the plasma membrane H(+)-ATPase (HA) is considered to play a crucial role in regulating plant growth and respoding to environment stresses. Multiple paralogous genes encoding different isozymes of HA have been identified and characterized in several model plants, while limited information of the HA gene family is available to date for tomato. Here, we describe the molecular and expression features of eight HA-encoding genes (SlHA1-8) from tomato. All these genes are interrupted by multiple introns with conserved positions. SlHA1, 2, and 4 were widely expressed in all tissues, while SlHA5, 6, and 7 were almost only expressed in flowers. SlHA8, the transcripts of which were barely detectable under normal or nutrient-/salt-stress growth conditions, was strongly activated in arbuscular mycorrhizal (AM) fungal-colonized roots. Extreme lack of SlHA8 expression in M161, a mutant defective to AM fungal colonization, provided genetic evidence towards the dependence of its expression on AM symbiosis. A 1521-bp SlHA8 promoter could direct the GUS reporter expression specifically in colonized cells of transgenic tobacco, soybean, and rice mycorrhizal roots. Promoter deletion assay revealed a 223-bp promoter fragment of SlHA8 containing a variant of AM-specific cis-element MYCS (vMYCS) sufficient to confer the AM-induced activity. Targeted deletion of this motif in the corresponding promoter region causes complete abolishment of GUS staining in mycorrhizal roots. Together, these results lend cogent evidence towards the evolutionary conservation of a potential regulatory mechanism mediating the activation of AM-responsive HA genes in diverse mycorrhizal plant species.

A Detection-Service-Mobile Three-Terminal Software Platform for Point-of-Care Infectious Disease Detection System.

The traditional infectious disease detection process is cumbersome, and there is only a single application scenario. In recent years, with the development of the medical industry and the impact of the epidemic situation, the number of infectious disease detection instruments based on nursing point detection has been increasing. Due to this trend, many detection instruments and massive detection data urgently need to be managed. In addition, the experiment failed due to the abnormal fluorescence curve generated by a human operator or sample impurities. Finally, the geographic information system has also played an active role in spreading and preventing infectious diseases; this paper designs a "detection-service-mobile" three-terminal system to realize the control of diagnostic instruments and the comprehensive management of data. Machine learning is used to classify the enlarged curve and calculate the cycle threshold of the positive curve; combined with a geographic information system, the detection results are marked on the mobile terminal map to realize the visual display of the positive results of nucleic acid amplification detection and the early warning of infectious diseases. In the research, applying this system to portable field pathogen detection is feasible and practical.

A Data-Driven Volitional EMG Extraction Algorithm During Functional Electrical Stimulation With Time Variant Parameters.

OBJECTIVES: The goal of this study is to design a novel approach for extracting volitional electromyography (vEMG) contaminated by functional electrical stimulation (FES) with time variant amplitudes and frequencies. METHODS: A selective interpolation (SI) is adopted to eliminate the initial spike. Then the interpolated signal is decomposed into intrinsic mode functions by using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Each IMF is window-filtered based on a logistic regression (LR) classifier to identify the IMFs contaminated by FES. Semi-simulated signals were generated using EMG and stimulation response and three metrics were adopted to validate the performance of the proposed algorithm, including the a) signal-to-noise ratio (SNR), b) normalized root mean squared error (NRMSE) and c) cross-correlation coefficient between the clean EMG and the extracted EMG. Real FES-contaminated EMG was collected from six able bodied volunteers and one stroke patient. The correlation coefficients between the extracted EMG and the wrist torque were analyzed. RESULTS: The simulation results showed a higher SNR (2.12 to -2.13dB), higher correlation (0.88± 0.08) and lower NRMSE (0.78 to 1.29) than those of the comb filter and EMD-Notch algorithm. The EMG-Torque correlation coefficients were 0.75± 0.07 for monopolar pulses and 0.77± 0.12 for bipolar pulses. For the stroke patient, the algorithm also successfully extracted underlying vEMG from time variant FES noises. CONCLUSIONS: All results showed that SICEEMDAN-LR is capable of extracting EMG during FES with time-variant parameters.