The effects of cold stress on plants are mainly reflected in enzyme activity, membrane system, cell water loss, etc., leading to cell metabolism disorder, and even death. The SLAC1 gene family encodes S-type anionic channel proteins, which play an essential role in plant response to environmental stimuli by regulating stomatal opening and closure of guard cells. Therefore, the tomato SLAC1 gene family was used as the object of this study to explore its function and mechanism in tomato cold resistance. Firstly, bioinformatics analysis showed that most of the promoter regions of the SLAC1 gene family members contained stress-related cis-acting elements. Tran-scriptomic data and qRT-PCR experiments result show that SlSLAC1-6 might be a key gene in the resistance of the family. After cold treatment, the increase of antioxidant enzymes in SLAC1-6 silenced plants resulted in the inability of plants to maintain normal osmotic pressure of cells and significantly reduced the cold tolerance of tomato plants compared with the control (pTRV2:00) tomato plants. In addition, the stomatal aperture of the SLAC1-6 silenced plant was significantly more extensive than that of the pTRV2:00 tomato plants. The results showed that SlSLAC1-6 was a positive regulator of tomato cold resistance. In conclusion, our study provides a more extensive analysis of the tomato SLAC1 gene family and will provide a reference for future research on the mechanism of the SlSLAC1-6 gene in tomato cold resistance.

NAC proteins in plants respond to stress and play an important role in plant growth regulation. This regulation occurs through a variety of downstream effects mediated by plant hormones in response to stress. In this study, we performed a systematic genome-wide analysis of the NAC gene family in tomato based on genome version SL4.0. We identified 99 SlNAC genes by abiotic stress analysis of conserved motifs and gene structure, phylogenetic analysis, cis-element analysis, chromosome localization analysis, synteny relationship and expression pattern analysis. These 99 SlNAC genes were distributed on all 12 chromosomes. Except for genes without introns, there was no significant correlation between gene structures and phylogenetic relationships. Most of the cis-elements identified were associated with plant hormones and environmental responses. There were 77 SlNAC genes that showed no homogeneity between tomato and wild rice, suggesting that these homologous genes arose after the differentiation of dicotyledonous and monocotyledonous plants. The comprehensive analysis of the SlNAC gene family provides a valuable resource for studying this gene family in tomato and a foundation for further study of the functional characteristics of these genes.

The energy of wireless sensor networks is limited and the energy consumption of wireless communication takes most of the total energy consumption of sensor nodes. This paper proposes an energy-efficient data aggregation transfer protocol based on clustering and data prediction called DACP. In the initialization phase sensor network nodes send messages to sink node, then sink node divides entire networks into several clusters and elects cluster head nodes for each cluster. In the prediction phase sensor member nodes receive predicted data and compare it with sensed data to decide whether send it or not. In the data aggregation phase cluster head nodes aggregate sensed data received from cluster member nodes and decide to send it to sink node or not according to receiving predicted data. It is effective to reduce data transmission and improve the efficiency of data aggregation by data prediction. Simulation results demonstrate that the proposed protocol can significantly provide longer lifetime and prolong the life of every node to survive.

[Objective] To explore clinical features and risk factors of diabetes mellitus(DM) complicated pulmonary tuberculosis(TB),and provide some evidence for determining prognosis,formulating corresponding program and taking some measures to prevent the disease.[Methods] The clinical data of 87 cases of DM with TB were analyzed and summarized.[Results] DM with TB patients had more obvious clinical symptoms,extensive pathological changes,and were prone to develop cavities diagnosed late.If their blood glucose was well controlled,the negative rate of smear of sputum and pathological absorption rate was higher.The cases were featured by poor blood glucose,lower social economy status,long psychological pressure,bad habits and so on.[Conclusion] the rate of DM with TB is high,and this disease should to be cured at the same time.It is much better to control blood glucose well.The colony with higher risk needs more social care.

Trichomes are specialized epidermal cells that are commonly present on leaves, stems, and sepals of plants. They are suggested to provide a first line of defense against invading pests and pathogens. By differential display reverse transcription (DDRT)-polymerase chain reaction (PCR) and reverse Northern analysis, an Arabidopsis thaliana gene, AtTSG1, has been identified from epidermal cells of leaves, and found to be specifically expressed in the epidermis. Following BLAST analysis, it is found that this gene encodes a putative translational activator protein, At1G64790, with similarity to HsGCN1. A 1.3-kb promoter sequence of the AtTSG1 gene has been cloned. This promoter is shown to direct the specific expression of the reporter uidA gene, β-glucuronidase (GUS), in trichomes of leaves and stems of Arabidopsis plants. Promoter deletion analysis has revealed that the region from ×300 bp to ×1 bp is sufficient to direct trichome-specific expression, and that a novel cis-acting element to direct trichome-specific expression is involved in the region from ×250 bp to ×200 bp. The AtTSG1 promoter sequence from ×100 to ×1 bp likely contains an initiator (Inr) sequence as a core promoter element to mediate the same function as that of a TATA element. As this AtTSG1 promoter is associated with trichomes, it may provide an efficient bioengineering element for enhancing pest and pathogen resistance in transgenic plants.

In this paper we present an approach for bench-marking and profiling novel classification algorithms. We apply it to AIRS, an Artificial Immune System algorithm inspired by how the natural immune system recognizes and remembers intruders. We provide basic benchmarking results for AIRS, to our knowledge the first such test under standardised conditions. We also investigate how data set properties (data set size) relate to AIRS performance, and what other algorithms produce similar patterns over over- and underperformance on specific data sets. We present three methods for computing algorithm similarity that may be useful for profiling novel algorithms in general.

Several acoustic ranging signals' TOA (time of arrival) detection methods are researched and a new method based on the digital signal rectifying process is introduced It is used in the wireless sensor network's node self localization, includes the,following steps: increase signal noise ratio, remove DC (direct current) value, full-wave rectifying process, low-pass filter process and TOA estimate. It has been implemented with a dsPIC6014A microcontroller. Test results show that the estimate error of TOA is less than 3.5% with 30 meters distance when the time of computing was about 1.5 seconds (10MHz clock) and the sample length was 4096 (12bits of each sample data)points.

Artificial Immune Systems are a new class of algorithms inspired by how the immune system recognizes, attacks and remembers intruders. This is a fascinating idea, but to be accepted for mainstream data mining applications, extensive benchmarking is needed to demonstrate the reliability and accuracy of these algorithms. In our research we focus on the AIRS classification algorithm. It has been claimed previously that AIRS consistently outperforms other algorithms. However, in these papers AIRS was compared to benchmark results from literature. To ensure consistent conditions we carried out benchmark tests on all algorithms using exactly the same set up. Our findings show that AIRS is a stable and robust classifier that produces around average results. This contrasts with earlier claims but shows AIRS is mature enough to be used for mainstream data mining.

In this paper we present the results of an extensive benchmark on AIRS, an artificial immune systems algorithm for classification. We show that previous claims about AIRS were too strong, however the experiments indicate AIRS is mature and stable enough to be used for real world data mining applications.

This study is aimed at evaluating the inhibitory effects of the association of hematoporphyrin and ultrasound at variable intensities with a fixed frequency of 1.1MHz in tumor nodules. Specifically, the effects were studied both in solid and ascitic S180 tumors transplanted in mice by clinical, cytochemical and ultrastructural evaluation. The results indicated that the use of hematoporphyrin alone had no significant effect on destroying tumor cells. The ultrasound alone had little effect. Interestingly, the inhibition was much more effective when hematoporphyrin was combined with ultrasound. The inhibition was 3 times better than ultrasound alone and 8 times better than hematoporphyrin used alone. Our results also indicated that the changes on cell structure and cytochrome oxidation activity are important factors that could inhibit tumor cell growth and induce cell death. Apoptosis of tumor cells could be induced by hematoporphyrin. Our study investigated the killing mechanism on S180 tumor cells by using hematoporphyrin and low frequency ultrasound at cell, tissue and individual level.