Development of novel backscatter communication systems using a multi-hop framework and distributed beamforming

The goal of this thesis it to develop a wireless networking framework for battery-free devices based on passive, backscatter communication. In contrast to traditional, active communication systems, where the radio signal has to be generated using large amount of energy from batteries, the passive systems reflect the RF signal. The information is encoded by modulating the reflected signal, which consumes significantly less energy than active transmission. The existing passive, backscatter systems have limited communication capabilities. For example, the Radio Frequency Identification (RFID) systems support short-distance, direct communication between active reader and passive tags. The communication range is limited due to power and sensitivity limitations of transmitters and receivers respectively. Moreover, in contrast to a multi-hop ad hoc and sensor networks, the traditional backscatter systems limit themselves to a single-hop topology due to limited capabilities of passive tags and different challenges in passive communication. Existing literature lacks of understanding how such multi-hop, passive, and asymmetric networks can be realized and what are their theoretical limits. This thesis aims at understanding the communication and coverage challenge in backscatter systems and addressing them through: (a) a distributed beamforming that increases the transmission range to a specific tag/location (PAPER I), and (b) a multi-hop framework for the backscatter communication that increases effective communication range (PAPER II). The proposed beamforming methodology employs spatially distributed, passive scattering devices located between transmitter and receiver to increase the RF signal strength. The theoretical limits of such scheme are analyzed mathematically and in simulations with two beamforming approaches being proposed. Furthermore, a novel architecture is proposed for multi-hop backscatter-based networking for a passive RF communication that is not currently present. The paper presents the generic analysis of the system capabilities and demonstrates the feasibility of such multi-hop network. Furthermore, the connectivity models are studied in terms of k-connectivity of such a network of tags --Abstract, page iv

Comparative metagenomic analysis reveals mechanisms for stress response in hypoliths from extreme hyperarid deserts

Understanding microbial adaptation to environmental stressors is crucial for interpreting broader ecological patterns. In the most extreme hot and cold deserts, cryptic niche communities are thought to play key roles in ecosystem processes and represent excellent model systems for investigating microbial responses to environmental stressors. However, relatively little is known about the genetic diversity underlying such functional processes in climatically extreme desert systems. This study presents the first comparative metagenome analysis of cyanobacteria-dominated hypolithic communities in hot (Namib Desert, Namibia) and cold (Miers Valley, Antarctica) hyperarid deserts. The most abundant phyla in both hypolith metagenomes were Actinobacteria, Proteobacteria, Cyanobacteria and Bacteroidetes with Cyanobacteria dominating in Antarctic hypoliths. However, no significant differences between the twometagenomeswere identified. The Antarctic hypolithicmetagenome displayed a high number of sequences assigned to sigma factors, replication,recombination andrepair, translation, ribosomal structure,andbiogenesis. In contrast, theNamibDesert metagenome showed a high abundance of sequences assigned to carbohydrate transport and metabolism. Metagenome data analysis also revealed significantdivergence inthe geneticdeterminantsof aminoacidandnucleotidemetabolismbetween these two metagenomes and those of soil from other polar deserts, hot deserts, and non-desert soils. Our results suggest extensive niche differentiation in hypolithic microbial communities from these two extreme environments and a high genetic capacity for survival under environmental extremes.Fil: Le, Phuong Thi. University of Pretoria; Sudáfrica. Vlaams Instituut voor Biotechnologie; Bélgica. University of Ghent; BélgicaFil: Makhalanyane, Thulani P.. University of Pretoria; SudáfricaFil: Guerrero, Leandro Demián. University of Pretoria; Sudáfrica. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Vikram, Surendra. University of Pretoria; SudáfricaFil: Van De Peer, Yves. University of Pretoria; Sudáfrica. Vlaams Instituut voor Biotechnologie; Bélgica. University of Ghent; BélgicaFil: Cowan, Don A.. University of Pretoria; Sudáfric

Key microbial taxa in the rhizosphere of sorghum and sunflower grown in crop rotation

Microbes are key determinants of plant health and productivity. Previous studies have characterized the rhizosphere microbiomes of numerous plant species, but little information is available on how rhizosphere microbial communities change over time under crop rotation systems. Here, we document microbial communities in the rhizosphere of sorghum and sunflower (at seedling, flowering and senescence stages) grown in crop rotation in four different soils under field conditions. A comprehensive 16S rRNA-based amplicon sequencing survey revealed that the differences in alpha-diversity between rhizosphere and bulk soils changed over time. Sorghum rhizosphere soil microbial diversity at flowering and senescence were more diverse than bulk soils, whereas the microbial diversity of sunflower rhizosphere soils at flowering were less diverse with respect to bulk soils. Sampling time was also important in explaining the variation in microbial community composition in soils grown with both crops. Temporal changes observed in the rhizosphere microbiome were both plant-driven and due to seasonal changes in the bulk soil biota. Several individual taxa were relatively more abundant in the rhizosphere and/or found to be important in maintaining rhizosphere microbial networks. Interestingly, some of these taxa showed similar patterns at different sampling times, suggesting that the same organisms may play the same functional/structural role at different plant growth stages and in different crops. Overall, we have identified prominent microbial taxa that might be used to develop microbiome-based strategies for improving the yield and productivity of sorghum and sunflower.Supplementary data Fig. S1. a) Average Good's coverage estimates (%) and b) rarefaction curves. BS, bulk soil; RS, rhizosphere soil. S, seedling; F, flowering; H, harvest. LB, Limpopo black; LR, Limpopo red; FN, Free State new; FO, Free State old.Supplementary data Fig. S2. PCoA of soil parameters at pre-planting, using Euclidean distances with standardized data, and PERMANOVA tables. P-values were obtained after correction for multiple comparisons using Benjamini-Hochberg discovery rate. LB, Limpopo black; LR, Limpopo red; FN, Free State new; FO, Free State old.Supplementary data Fig. S3. Faith's phylogenetic diversity (PD) at pre-planting. Different letters indicate significant differences in PD (Wilcoxon-Mann-Whitney P < 0.05) between soils. LB, Limpopo black; LR, Limpopo red; FN, Free State new; FO, Free State old.Supplementary data Fig. S4. PCoA of soil bacterial communities at pre-planting, using weighted UniFrac distances, and PERMANOVA tables. P-values were obtained after correction for multiple comparisons using Benjamini-Hochberg discovery rate. LB, Limpopo black; LR, Limpopo red; FN, Free State new; FO, Free State old.Supplementary data Fig. S5. Faith's phylogenetic diversity (PD), richness and Chao1 estimator. Least-square mean for each crop (So, sorghum; Su, sunflower) is plotted with ± 1 s.e of the mean. Different letters indicate significant differences (ANOVA P < 0.05).Supplementary data Fig. S6. PCoA of post-planting soil bacterial communities, using weighted UniFrac distances, and PERMANOVA tables. LB, Limpopo black; LR, Limpopo red; FN, Free State new; FO, Free State old. So, sorghum; Su, sunflower.Supplementary data Fig. S7. PCoA of post-planting soil parameters, using Euclidean distances with standardized data, and PERMANOVA tables. LB, Limpopo black; LR, Limpopo red; FN, Free State new; FO, Free State old. So, sorghum; Su, sunflower.Supplementary data Fig. S8. Soil chemistry. All concentrations are expressed in mg/kg.Supplementary data Fig. S9. Relative abundance over time of the nine most abundant phyla. PP, pre-planting; S, seedling; F, flowering; H, harvest.Supplementary data Fig. S10. Abundance of the phyla enriched in each habitat type (bulk and rhizosphere soils). S, seedling; F, flowering; H, harvest.Supplementary data Fig. S11. Relative abundance over time of the ten most abundant families. PP, pre-planting; S, seedling; F, flowering; H, harvest.Supplementary data Fig. S12. Abundance of the families enriched in each habitat type (bulk and rhizosphere soils).Supplementary data Fig. S13. Rhizosphere networks a) sorghum at seedling, b) sorghum at flowering, c) sorghum at harvest, d) sunflower at seedling, e) sunflower at flowering, f) sunflower at harvest. C, connectors; MH, module hubs; NH, network hubs. Bacteria are depicted as ellipses and archaea as squares.Supplementary data Table S1. Good's coverage estimates indicating percentage of OTUs sampled. (https://ars.els-cdn.com/content/image/1-s2.0-S0048969717335969-mmc1.txt)Supplementary data Table S2. . Soil chemistry. All concentrations are expressed in mg/kg. (https://ars.els-cdn.com/content/image/1-s2.0-S0048969717335969-mmc2.txt)Supplementary data Table S3. OTUs (137 in total) showing relative abundances higher than 0.1%. These OTUs represented 43.3% of the total number of reads and belonged to the bacterial phyla Proteobacteria, Actinobacteria, Bacteroidetes and the archaeal phylum Crenarchaeota. (https://ars.els-cdn.com/content/image/1-s2.0-S0048969717335969-mmc3.txt)Supplementary data Table S4. Differentially abundant OTUs (Bulk soil vs Rhizosphere soil). (https://ars.els-cdn.com/content/image/1-s2.0-S0048969717335969-mmc4.txt)Supplementary data Table S5. Key rhizosphere OTUs. Defined as those found in greater abundance in the rhizosphere compared to the bulk soil in at least two growth stages and/or being module hubs, networks hubs and connectors in rhizosphere networks. (https://ars.els-cdn.com/content/image/1-s2.0-S0048969717335969-mmc5.txt)The International Centre for Genetic Engineering and Biotechnology (ICGEB Contract No. CRP/13/018 ) and the National Research Foundation (Grant No. CPRR14071676470 ).http://www.elsevier.com/locate/scitotenv2019-05-15hj2018Genetic

Diversity structure of the microbial communities in the guts of four neotropical termite species

The termite gut microbiome is dominated by lignocellulose degrading microorganisms. This study describes the intestinal microbiota of four Argentinian higher termite species with different feeding habits: Microcerotermes strunckii (hardwood), Nasutitermes corniger (softwood), Termes riograndensis (soil organic matter/grass) and Cornitermes cumulans (grass) by deep sequencing of amplified 16S rRNA and ITS genes. In addition, we have performed a taxonomic and gut community structure comparison incorporating into the analysis the previously reported microbiomes of additional termite species with varied diets. The bacterial phylum Spirochaetes was dominant in the guts of M. strunckii, N. corniger and C. cumulans, whereas Firmicutes predominated in the T. riograndensis gut microbiome. A single bacterial genus, Treponema (Spirochaetes), was dominant in all termite species, except for T. riograndensis. Both in our own sequenced samples and in the broader comparison, prokaryotic α-diversity was higher in the soil/grass feeders than in the wood feeders. Meanwhile, the β-diversity of prokaryotes and fungi was highly dissimilar among strict wood-feeders, whereas that of soil- and grass-feeders grouped more closely. Ascomycota and Basidiomycota were the only fungal phyla that could be identified in all gut samples, because of the lack of reference sequences in public databases. In summary, higher microbial diversity was recorded in termites with more versatile feeding sources, providing further evidence that diet, along with other factors (e.g., host taxonomy), influences the microbial community assembly in the termite gut.Instituto de BiotecnologíaFil: Vikram, Surendra. University of Pretoria. Centre for Microbial Ecology and Genomics. Department Biochemistry. Genetics and Microbiology; SudáfricaFil: Arneodo Larochette, Joel Demian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO); ArgentinaFil: Arneodo Larochette, Joel Demian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Calcagno, Javier. Universidad Maimonides. Centro de Ciencias Naturales, Ambientales y Antropológicas; ArgentinaFil: Ortiz, Maximiliano. University of Pretoria. Centre for Microbial Ecology and Genomics. Department Biochemistry. Genetics and Microbiology; SudáfricaFil: Mon, Maria Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO); ArgentinaFil: Mon, Maria Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Etcheverry, Clara. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura. Biología de los Invertebrados; ArgentinaFil: Cowan, Donald. University of Pretoria. Centre for Microbial Ecology and Genomics. Department Biochemistry. Genetics and Microbiology; SudáfricaFil: Talia, Paola Mónica. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Agrobiotecnología y Biología Molecular (IABIMO); ArgentinaFil: Talia, Paola Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Metagenomic approach towards bioprospection of novel biomolecule(s) and environmental bioremediation

Microorganisms have developed several physiological adaptations to survive within extreme ecological niches including environments contaminated with heavy metals, pesticides, polycyclic aromatic hydrocarbons, and nuclear wastes. Microorganisms in extreme habitat are potential source of “novel biomolecule(s)” such as whole microbial cells, extremozymes and extremolytes, significantly required for environmental, industrial, and red medical/pharmaceutical biotechnology. These novel biomolecule(s) are valuable resources and may help improve economic development. The scanty information about the factors governing the microbial growth within stressed environments is the major constraint in the recovery of novel biomolecule(s) from extreme habitats. Understanding the structure, metabolic capabilities, microbial physiology, and factors governing the composition and role of indigenous microorganism is the key to success of any study. In recent past the problems associated with classical cultivation techniques have been resolved by an emerging approach referred to as “metagenomics”. Metagenomic studies give an insight into details of the structure, metabolic and physiological capabilities of indigenous microbial communities. High-throughput sequencing technologies in conjunction with metagenomics has aided in the identification and characterization of novel culturable and uncultured microorganisms with unique capabilities. Metagenomic studies have been used for isolation and characterization of novel biomolecule(s) relevant for white, grey, and red biotechnologies. The major objective of this review is to discuss the applications of metagenomic approach for bioprospection of novel biomolecule(s) and environmental bioremediation.http://www.sciencedomain.org/journal/32am2018Genetic

Agulhas Current properties shape microbial community diversity and potential functionality

Understanding the impact of oceanographic features on marine microbial ecosystems remains a major ecological endeavour. Here we assess microbial diversity, community structure and functional capacity along the Agulhas Current system and the Subtropical Front in the South Indian Ocean (SIO). Samples collected from the epipelagic, oxygen minimum and bathypelagic zones were analysed by 16S rRNA gene amplicon and metagenomic sequencing. In contrast to previous studies, we found high taxonomic richness in surface and deep water samples, but generally low richness for OMZ communities. Beta-diversity analysis revealed significant dissimilarity between the three water depths. Most microbial communities were dominated by marine Gammaproteobacteria, with strikingly low levels of picocyanobacteria. Community composition was strongly influenced by specific environmental factors including depth, salinity, and the availability of both oxygen and light. Carbon, nitrogen and sulfur cycling capacity in the SIO was linked to several autotrophic and copiotrophic Alphaproteobacteria and Gammaproteobacteria. Taken together, our data suggest that the environmental conditions in the Agulhas Current system, particularly depth-related parameters, substantially influence microbial community structure. In addition, the capacity for biogeochemical cycling of nitrogen and sulfur is linked primarily to the dominant Gammaproteobacteria taxa, whereas ecologically rare taxa drive carbon cycling

The gut mycobiota of rural and urban individuals is shaped by geography

BACKGROUND : Understanding the structure and drivers of gut microbiota remains a major ecological endeavour. Recent studies have shown that several factors including diet, lifestyle and geography may substantially shape the human gut microbiota. However, most of these studies have focused on the more abundant bacterial component and comparatively less is known regarding fungi in the human gut. This knowledge deficit is especially true for rural and urban African populations. Therefore, we assessed the structure and drivers of rural and urban gut mycobiota. RESULTS : Our participants (n = 100) were balanced by geography and sex. The mycobiota of these geographically separated cohorts was characterized using amplicon analysis of the Internal Transcribed Spacer (ITS) gene. We further assessed biomarker species specific to rural and urban cohorts. In addition to phyla which have been shown to be ubiquitous constituents of gut microbiota, Pichia were key constituents of the mycobiota. We found that geographic location was a major driver of gut mycobiota. Other factors such as smoking where also determined gut mycobiota albeit to a lower extent, as explained by the small proportion of total variation. Linear discriminant and the linear discriminant analysis effect size analysis revealed several distinct urban and rural biomarkers. CONCLUSIONS : Together, our analysis reveals distinct community structure in urban and rural South African individuals. Geography was shown to be a key driver of rural and urban gut mycobiota.Additional file 1. Questionnaire Details of the questionnaire provided to participants prior to enrolment in the study. The questionnaire details essential required information, clinical information, voluntary dietary information and questions regarding data sharing.Additional file 2. Results from the partition of variance analysis in RDAAdditional file 3: Fig. S1. Rarefaction plot showing sequencing coverage. The estimated average sequence coverage of high-quality paired end reads after quality control assessed using Nonpareil (in alignment mode).Additional file 4: Fig. S2. Venn diagram showing the unique and shared phylotypes for samples collected from urban and rural participants.Additional file 5: Fig. S3. Taxa abundance data was normalised to obtain the proportion of most abundant taxa per sample. The diameter of the points at the bottom of the plot corresponds to the magnitude of the LCBD value for a particular sample. The bars correspond to taxa that are most abundant with the top taxa sharing a bigger portion of the bar for each sample.The South African Medical Research Foundation (TPM), the National Research Foundation, the Fulbright program, the Microbiomes in Transition (MinT) initiative at the Pacific Northwest National Laboratory in Richland, WA, USA.https://bmcmicrobiol.biomedcentral.comam2020BiochemistryGeneticsMicrobiology and Plant Patholog

Phylogenomic, pan-genomic, pathogenomic and evolutionary genomic insights into the agronomically relevant enterobacteria Pantoea ananatis and Pantoea stewartii

Pantoea ananatis is ubiquitously found in the environment and causes disease on a wide range of plant hosts. By contrast, its sister species, Pantoea stewartii subsp. stewartii is the host-specific causative agent of the devastating maize disease Stewart's wilt. This pathogen has a restricted lifecycle, overwintering in an insect vector before being introduced into susceptible maize cultivars, causing disease and returning to overwinter in its vector. The other subspecies of P. stewartii subsp. indologenes, has been isolated from different plant hosts and is predicted to proliferate in different environmental niches. Here we have, by the use of comparative genomics and a comprehensive suite of bioinformatic tools, analyzed the genomes of ten P. stewartii and nineteen P. ananatis strains. Our phylogenomic analyses have revealed that there are two distinct clades within P. ananatis while far less phylogenetic diversity was observed among the P. stewartii subspecies. Pan-genome analyses revealed a large core genome comprising of 3,571 protein coding sequences is shared among the twenty-nine compared strains. Furthermore, we showed that an extensive accessory genome made up largely by a mobilome of plasmids, integrated prophages, integrative and conjugative elements and insertion elements has resulted in extensive diversification of P. stewartii and P. ananatis. While these organisms share many pathogenicity determinants, our comparative genomic analyses show that they differ in terms of the secretion systems they encode. The genomic differences identified in this study have allowed us to postulate on the divergent evolutionary histories of the analyzed P. ananatis and P. stewartii strains and on the molecular basis underlying their ecological success and host range

The draft genome sequence of Hymenobacter sp. CRA2 isolated from Nama Karoo shrub land soils from South Africa

Here we report the draft genome sequence of Hymenobacter sp. CRA2 isolated from the Nama Karoo shrub land soils of the Northern Cape, South Africa. This genome is approximately 5.88 Mb long and the assembly comprised 45 contigs.http://www.elsevier.com/locate/gdataam2017GeneticsMicrobiology and Plant Patholog