Different pioneer plant species select specific rhizosphere bacterial communities in a high mountain environment

The rhizobacterial communities of 29 pioneer plants belonging to 12 species were investigated in an alpine ecosystem to assess if plants from different species could select for specific rhizobacterial communities. Rhizospheres and unvegetated soils were collected from a floristic pioneer stage plot at 2,400 m a.s.l. in the forefield of Weisskugel Glacier (Matsch Valley, South Tyrol, Italy), after 160 years of glacier retreat. To allow for a culture-independent perspective, total environmental DNA was extracted from both rhizosphere and bare soil samples and analyzed by Automated Ribosomal Intergenic Spacer Analysis (ARISA) and Denaturing Gradient Gel Electrophoresis (DGGE). ARISA fingerprinting showed that rhizobacterial genetic structure was extremely different from bare soil bacterial communities while rhizobacterial communities clustered strictly together according to the plant species. Sequencing of DGGE bands showed that rhizobacterial communities were mainly composed of Acidobacteria and Proteobacteria whereas bare soil was colonized by Acidobacteria and Clostridia. UniFrac significance calculated on DGGE results confirmed the rhizosphere effect exerted by the 12 species and showed different bacterial communities (P < 0.05) associated with all the plant species. These results pointed out that specific rhizobacterial communities were selected by pioneer plants of different species in a high mountain ecosystem characterized by oligotrophic and harsh environmental conditions, during an early primary succession

Safe-site effects on rhizosphere bacterial communities in a high-altitude alpine environment

The rhizosphere effect on bacterial communities associated with three floristic communities (RW, FI, and M sites) which differed for the developmental stages was studied in a high-altitude alpine ecosystem. RW site was an early developmental stage, FI was an intermediate stage, M was a later more matured stage. The N and C contents in the soils confirmed a different developmental stage with a kind of gradient from the unvegetated bare soil (BS) site through RW, FI up to M site. The floristic communities were composed of 21 pioneer plants belonging to 14 species. Automated ribosomal intergenic spacer analysis showed different bacterial genetic structures per each floristic consortium which differed also from the BS site. When plants of the same species occurred within the same site, almost all their bacterial communities clustered together exhibiting a plant species effect. Unifrac significance value (P < 0.05) on 16S rRNA gene diversity revealed significant differences (P < 0.05) between BS site and the vegetated sites with a weak similarity to the RW site. The intermediate plant colonization stage FI did not differ significantly from the RW and the M vegetated sites. These results pointed out the effect of different floristic communities rhizospheres on their soil bacterial communities

Statistical parameter identification of analog integrated circuit reverse models

We solve the manufacturing problem of identifying the model statistical parameters ensuring a satisfactory quality of analog circuits produced in a photolithographic process. We formalize it in a statistical framework as the problem of inverting the mapping from the population of the circuit production variables to the performances\u2019 population. Both variables and performances are random. From a sample of the joint population we want to identify the statistical features of the former producing a performance distribution that satisfies the design constraints with a good preassigned probability. The key idea of the solution method we propose consists of describing the above mapping in terms of a mixture of granular functions, where each is responsible for a fuzzy set within the input-output space, hence for a cluster therein. The way of synthesizing the whole space as a mixture of these clusters is learnt directly from the examples. As a result we have an analytical form both of the mapping approximating complex Spice models in terms of polynomials in the production variables, and of the distribution law of the induced performances that allows a relatively quick and easy management of the production variables\u2019 statistical parameters as a function of the probability with which we plan to satisfy the design constraint. We apply the method to case studies and real production data where our method outperforms current methods\u2019 running times and accuracies

Statistical parameter identification of analog integrated circuit reverse models

We solve the manufacturing problem of identifying the model statistical parameters ensuring a satisfactory quality of analog circuits produced in a photolithographic process. We formalize it in a statistical framework as the problem of inverting the mapping from the population of the circuit model parameters to the population of the performances. Both parameters and performances are random. From a sample of the latter population we want to identify the statistical features of the former that produce a performance distribution complying with production samples. The key artifact of the solution method we propose consists of describing the above mapping in terms of a mixture of granular functions, where each is responsible for a fuzzy set within the input-output space, hence for a cluster therein. The way of synthesizing the whole space as a mixture of these clusters is learnt directly from the examples. As a result, we have an analytical form of the mapping that approximates complex Spice models in terms of polynomials in the model parameters, and an implicit expression of the distribution law of the induced performances that allows a relatively quick and easy management of the model distribution statistical parameters. This flows into a semiautomatic procedure managing an adaptive composition of different granular modules to cope with the circuit peculiarities. We check the method both on real world manufacturing problems and on ad hoc benchmarks

A Three-Scale Analysis of Bacterial Communities Involved in Rocks Colonization and Soil Formation in High Mountain Environments

Alpha and beta diversities of the bacterial communities growing on rock surfaces, proto-soils, riparian sediments, lichen thalli, and water springs biofilms in a glacier foreland were studied. We used three molecular based techniques to allow a deeper investigation at different taxonomic resolutions: denaturing gradient gel electrophoresis, length heterogeneity-PCR, and automated ribosomal intergenic spacer analysis. Bacterial communities were mainly composed of Acidobacteria, Proteobacteria, and Cyanobacteria with distinct variations among sites. Proteobacteria were more represented in sediments, biofilms, and lichens; Acidobacteria were mostly found in proto-soils; and Cyanobacteria on rocks. Firmicutes and Bacteroidetes were mainly found in biofilms. UniFrac P values confirmed a significant difference among different matrices. Significant differences (P < 0.001) in beta diversity were observed among the different matrices at the genus-species level, except for lichens and rocks which shared a more similar community structure, while at deep taxonomic resolution two distinct bacterial communities between lichens and rocks were found

Smart System Case Studies

This chapter presents two case studies showing how the proposed approach applies to smart system design and optimization. The former is the virtual prototyping platform built for a laser pico-projector actuator, where MEMS, analog and digital components are simulated with the aim of optimizing the resulting image quality by means of firmware tuning. The latter, in the context of wearable equipment for inertial body motion reconstruction, deals with the modeling of an inertial sensor node, supporting system accuracy evaluation and sensor fusion enhancement. Finally, the Open-Source Test Case (OSTC) is described, showing a complete modeling and simulation flow on a publicly available design