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Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task.It automatically performs similarity searches of short sequences against the "nt" nucleotide database provided by NCBI and, out of every hit, extracts -if it is available -the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples.We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of

Background The aquaculture sector now accounts for almost 50% of all fish for human consumption and is anticipated to provide 62% by 2030. Innovative strategies are being sought to improve fish feeds and feed additives to enhance fish performance, welfare, and the environmental sustainability of the aquaculture industry. There is still a lack of knowledge surrounding the importance and functionality of the teleost gut microbiome in fish nutrition. In vitro gut model systems might prove a valuable tool to study the effect of feed, and additives, on the host’s microbial communities. Several in vitro gut models targeted at monogastric vertebrates are now in operation. Here, we report the development of an Atlantic salmon gut model, SalmoSim, to simulate three gut compartments (stomach, pyloric caecum, and midgut) and associated microbial communities. Results The gut model was established in a series of linked bioreactors seeded with biological material derived from farmed adult marine-phase salmon. We first aimed to achieve a stable microbiome composition representative of founding microbial communities derived from Atlantic salmon. Then, in biological triplicate, the response of the in vitro system to two distinct dietary formulations (fishmeal and fishmeal free) was compared to a parallel in vivo trial over 40 days. Metabarcoding based on 16S rDNA sequencing qPCR, ammoniacal nitrogen, and volatile fatty acid measurements were undertaken to survey the microbial community dynamics and function. SalmoSim microbiomes were indistinguishable (p = 0.230) from their founding inocula at 20 days and the most abundant genera (e.g., Psycrobacter, Staphylococcus, Pseudomonas) proliferated within SalmoSim (OTUs accounting for 98% of all reads shared with founding communities). Real salmon and SalmoSim responded similarly to the introduction of novel feed, with majority of the taxa (96% Salmon, 97% SalmoSim) unaffected, while a subset of taxa (e.g., a small fraction of Psychrobacter) was differentially affected across both systems. Consistent with a low impact of the novel feed on microbial fermentative activity, volatile fatty acid profiles were not significantly different in SalmoSim pre- and post-feed switch. Conclusion By establishing stable and representative salmon gut communities, this study represents an important step in the development of an in vitro gut system as a tool for the improvement of fish nutrition and welfare. The steps of the system development described in this paper can be used as guidelines to develop various other systems representing other fish species. These systems, including SalmoSim, aim to be utilised as a prescreening tool for new feed ingredients and additives, as well as being used to study antimicrobial resistance and transfer and fundamental ecological processes that underpin microbiome dynamics and assembly.

Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, , to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install , go to: https://github.com/xapple/seqenv.

A type of telecommunication technology called an ultra-wideband (UWB) is used to provide a typical solution for short-range wireless communication due to large bandwidth and low power consumption in transmission and reception. Printed monopole antennas are considered as a preferred platform for implementing this technology because of its alluring characteristics such as light weight, low cost, ease of fabrication, integration capability with other systems, etc. Therefore, a compact-sized ultra-wideband (UWB) printed monopole antenna with improved gain and efficiency is presented in this article. Computer simulation technology microwave studio (CSTMWS) software is used to build and analyze the proposed antenna design technique. This broadband printed monopole antenna contains a jug-shaped radiator fed by a coplanar waveguide (CPW) technique. The designed UWB antenna is fabricated on a low-cost FR-4 substrate with relative permittivity of 4.3, loss tangent of 0.025, and a standard height of 1.6 mm, sized at 25 mm × 22 mm × 1.6 mm, suitable for wireless communication system. The designed UWB antenna works with maximum gain (peak gain of 4.1 dB) across the whole UWB spectrum of 3–11 GHz. The results are simulated, measured, and debated in detail. Different parametric studies based on numerical simulations are involved to arrive at the optimal design through monitoring the effects of adding cuts on the performance of the proposed antennas. Therefore, these parametric studies are optimized to achieve maximum antenna bandwidth with relatively best gain. The proposed patch antenna shape is like a jug with a handle that offers greater bandwidth, good gain, higher efficiency, and compact size.