The metagenomic dataset presented in this paper encompasses gut microbial DNA from the lower order of subterranean termites. Amongst the various termite species, Coptotermes gestroi, along with the higher order groups, namely, Globitermes sulphureus and Macrotermes gilvus are found in the Malaysian region of Penang. Illumina MiSeq Next-Generation Sequencing was applied to sequence two replicates of each species, and QIIME2 was used for the subsequent analysis. C. gestroi's returned results comprised 210248 sequences; G. sulphureus's results included 224972 sequences; and M. gilvus's results amounted to 249549 sequences. The BioProject PRJNA896747 entry in the NCBI Sequence Read Archive (SRA) contained the sequence data. The analysis of community composition showed that _Bacteroidota_ was the most plentiful phylum in both _C. gestroi_ and _M. gilvus_, and _Spirochaetota_ was the most abundant in _G. sulphureus_.
Jamun seed (Syzygium cumini) biochar's application in batch adsorption experiments yields the dataset regarding ciprofloxacin and lamivudine from synthetic solutions. Using Response Surface Methodology (RSM), independent variables such as pollutant concentration (ranging from 10 to 500 ppm), contact time (from 30 to 300 minutes), adsorbent dosage (1 to 1000 mg), pH (1 to 14), and calcination temperature of the adsorbent (250-300, 600, and 750°C) were examined and optimized. Maximum removal efficiency projections for ciprofloxacin and lamivudine were generated using empirical models, which were then contrasted with experimental observations. Adsorbent dosage, pH, and contact time, following the impact of pollutant concentration, affected pollutant removal. This process resulted in a maximum removal of 90%.
Weaving is a popular technique in fabric manufacturing, a method frequently used. Warping, sizing, and weaving are fundamental stages within the weaving process. The weaving factory's processes are hereafter inextricably linked with a substantial amount of data. Unfortunately, the weaving industry remains devoid of machine learning or data science integration. Even though a range of methods are available for implementing statistical analysis, data science methodologies, and machine learning techniques. The dataset's creation relied upon the daily production reports accumulated over nine months. The resulting dataset encompasses 121,148 data entries, each featuring 18 parameters. While the unprocessed data boasts the identical count of entries, each possessing 22 columns. The raw data, incorporating the daily production report, necessitates extensive work to address missing data, rename columns, utilize feature engineering, and thereby derive the necessary EPI, PPI, warp, and weft count values, among others. The complete dataset resides at the following location: https//data.mendeley.com/datasets/nxb4shgs9h/1. Following further processing steps, the rejection dataset is saved and accessible at the given URL: https//data.mendeley.com/datasets/6mwgj7tms3/2. Future use of the dataset will be focused on predicting weaving waste, investigating the statistical interdependencies among the various parameters, and predicting production output.
A growing desire for biological economies has led to a mounting and accelerating need for wood and fiber from forestry operations. Ensuring a global timber supply will necessitate investments and advancements throughout the supply chain, but the forestry sector's capacity to raise productivity without jeopardizing sustainable plantation management is crucial. A trial program, active from 2015 to 2018, was developed in the New Zealand forestry sector with the objective of examining current and potential obstacles to timber production in plantations, after which, management strategies were altered to counter these limitations. Employing six sites in this Accelerator trial series, 12 distinct types of Pinus radiata D. Don stock, demonstrating varied traits concerning growth, health, and wood quality, were planted. Ten clones, a hybrid, and a seed lot of widely planted tree stock, used throughout New Zealand, formed a significant part of the planting stock. At each trial site, a diverse array of treatments, encompassing a control, was deployed. see more Considering the effect on wood quality and the necessity of environmental sustainability, treatments were designed for each location to address both present and prospective productivity constraints. Across the anticipated 30-year lifespan of each trial, site-specific treatments will be introduced and implemented. We present data for the pre-harvest and time zero states at each trial location. These data form a baseline that will underpin a thorough and comprehensive understanding of treatment responses as the ongoing trial series matures. This assessment of current tree productivity will determine if any enhancement has occurred, and if the improved site conditions will positively impact future harvests. A bold research initiative, the Accelerator trials, seek to dramatically improve the long-term productivity of planted forests, all while maintaining the sustainable management of future forest resources.
These data are directly linked to the article, 'Resolving the Deep Phylogeny Implications for Early Adaptive Radiation, Cryptic, and Present-day Ecological Diversity of Papuan Microhylid Frogs' [1]. A dataset of 233 tissue samples from the Asteroprhyinae subfamily, including representatives of every recognized genus, is further supported by the inclusion of three outgroup taxa. Over 2400 characters per sample are found in the sequence dataset for five genes, three of which are nuclear (Seventh in Absentia (SIA), Brain Derived Neurotrophic Factor (BDNF), and Sodium Calcium Exchange subunit-1 (NXC-1)), and two mitochondrial loci (Cytochrome oxidase b (CYTB), and NADH dehydrogenase subunit 4 (ND4)). This dataset is 99% complete. Each locus and accession number in the raw sequence data now has its own set of newly designed primers. Employing sequences and geological time calibrations, BEAST2 and IQ-TREE generate time-calibrated Bayesian inference (BI) and Maximum Likelihood (ML) phylogenetic reconstructions. Oral probiotic The ancestral character states for each lineage were established by gathering lifestyle data (arboreal, scansorial, terrestrial, fossorial, semi-aquatic) from both academic publications and field observations. To confirm the locations where multiple species, or potential species, shared a habitat, elevation and collection points were scrutinized. Medical tourism All sequence data, alignments, and pertinent metadata (voucher specimen number, species identification, type locality status, GPS coordinates, elevation, species list per site, and lifestyle) are provided, along with the code that generated the analyses and figures.
A UK domestic household in 2022 provided the data detailed in this data article. The data captures appliance-level power consumption and environmental conditions, presented as both time series and 2D images created using the Gramian Angular Fields (GAF) algorithm. The dataset's significance stems from (a) its provision of a comprehensive dataset combining appliance-level data with crucial environmental context; (b) its presentation of energy data as 2D images facilitating novel insights through data visualization and machine learning techniques. The methodology's core involves the installation of smart plugs into a multitude of household appliances, alongside environmental and occupancy sensors, all connected to a High-Performance Edge Computing (HPEC) system for the secure and private storage, pre-processing, and post-processing of the collected data. Several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (C), relative indoor humidity (RH%), and occupancy (binary), are part of the heterogeneous data. The dataset incorporates outdoor weather information, sourced from the Norwegian Meteorological Institute (MET Norway), detailing temperature in degrees Celsius, humidity in percentage, barometric pressure in hectopascals, wind direction in degrees, and wind speed in meters per second. This dataset is a valuable resource for computer vision and data-driven energy efficiency system development, validation, and deployment among energy efficiency researchers, electrical engineers, and computer scientists.
Phylogenetic trees depict the intricate evolutionary pathways taken by species and molecules. Although, the factorial of (2n – 5) influences, Phylogenetic trees, while constructible from datasets with n sequences, encounter a significant combinatorial explosion when attempting to determine the optimal tree via brute force, making this approach problematic. To achieve the construction of a phylogenetic tree, a method was developed which uses the Fujitsu Digital Annealer, a quantum-inspired computer that solves combinatorial optimization problems at high speed. The graph-cut principle is consistently applied to repeatedly divide a collection of sequences, ultimately leading to the generation of phylogenetic trees. A comparison of the proposed method's solution optimality, specifically the normalized cut value, was conducted against existing methodologies, using both simulated and real-world datasets. The dataset, generated through simulation and encompassing 32 to 3200 sequences, displayed a significant range of branch lengths, from 0.125 to 0.750, based on the normal distribution or Yule model, illustrating substantial sequence diversity. In a statistical sense, the dataset is characterized by two figures: transitivity and the average p-distance. With the anticipated refinement of methods for phylogenetic tree construction, this dataset promises to serve as a cornerstone for comparative analysis and the validation of results. A deeper examination of these analyses is detailed in W. Onodera, N. Hara, S. Aoki, T. Asahi, N. Sawamura's work, “Phylogenetic tree reconstruction via graph cut presented using a quantum-inspired computer,” Mol. Phylogenetic methods provide insights into the history of life. Evol.