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Characterization of microbiomes has been enabled by high-throughput metagenomic sequencing. However, existing methods are not designed to combine reads from short- and long-read technologies. We present a hybrid metagenomic assembler named OPERA-MS that integrates assembly-based metagenome clustering with repeat-aware, exact scaffolding to accurately assemble complex communities. Evaluation using defined in vitro and virtual gut microbiomes revealed that OPERA-MS assembles metagenomes with greater base pair accuracy than long-read (>5×; Canu), higher contiguity than short-read (~10× NGA50; MEGAHIT, IDBA-UD, metaSPAdes) and fewer assembly errors than non-metagenomic hybrid assemblers (2×; hybridSPAdes). OPERA-MS provides strain-resolved assembly in the presence of multiple genomes of the same species, high-quality reference genomes for rare species (<1%) with ~9× long-read coverage and near-complete genomes with higher coverage. We used OPERA-MS to assemble 28 gut metagenomes of antibiotic-treated patients, and showed that the inclusion of long nanopore reads produces more contiguous assemblies (200× improvement over short-read assemblies), including more than 80 closed plasmid or phage sequences and a new 263 kbp jumbo phage. High-quality hybrid assemblies enable an exquisitely detailed view of the gut resistome in human patients.

Background Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size. Results We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it. Conclusions Compositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.

Brassinosteroids (BRs) are a group of naturally occurring plant steroidal compounds having varied biological activities. This group was recently added in the category of classical hormones and considered as sixth novel plant growth hormone. BRs regulate numerous important pomological attributes such as initiation and cessation of flowering, plant canopy architecture, micropropagation, cell division and elongation, vegetative growth, flowering, fruit set, fruit ripening, quality and yield. Besides, BRs can improve resistance/tolerance to biotic and abiotic stresses. These are also known to enhance postharvest fruit quality. Owing to multiple and diverse physiological roles in plant growth and development, BRs have been collectively referred to as ‘pleotropic phytohormones’. In the present review, we have attempted to highlight the conceptual research and development of BRs with their wide range of physiological functions and economic significance in relation to modern fruit production.

ZnO–Fe x ( x  = 0, 0.05, 0.075, and 0.1 M) nanoparticles based photocatalysts are successfully synthesized by co-precipitation method. The synthesized nanoparticles are characterized using X-ray diffraction, scanning electron microscopy with energy-dispersive X-ray spectroscopy, and UV–visible double beam spectroscopy techniques. The prepared catalysts and its photocatalytic activity were evaluated by methylene blue and methyl orange dye under UV light irradiation. The effect of various photocatalyst parameters such as pH, catalyst dosage, and initial dye concentration on the photodegradation was examined in detail.

Electromagnetic interference (EMI) cause the equipments used in electronics and avionics industries to either deteriorate or malfunction. Therefore, EMI shielding is essential to ensure electronics equipment safety. The present investigation explores the EMI shielding effectiveness of micro-size cenosphere (2 wt.%, 4 wt.%, and 6 wt.%) filled aluminum matrix syntactic foams. The aluminum matrix syntactic foams were fabricated using stir-casting. The prepared samples were studied for their EMI shielding, microstructural characterization, compressive deformation behavior, and hardness. The cenosphere-filled aluminum syntactic foam exhibited better EMI shielding effectiveness, varied from −41.38 dB to −56.68 dB measured in the frequency range 8 to 12 GHz. This is due to microwave absorption and the dielectric nature of porous cenosphere fillers in the syntactic foam. The highest plateau strength 316.48 MPa and compressive yield strength of 289.53 MPa was achieved in 6 wt% cenosphere-filled syntactic foam. The microstructural study showed the presence of cenospheres in spherical form with homogeneous distribution. The observation shows good bonding between the matrix and cenosphere filler element. The micro-hardness measurement varied from 114 HV to 125.6 HV for the cenosphere filled syntactic foam (up to 4 wt %); further increasing the cenosphere wt% reduced the hardness. The XRD analysis showed the presence of reaction compounds AlSiO2, and Al2SiO5 which enhanced the brittleness in the syntactic foams.

Natural fiber composite laminates are nowadays used in structural application such as aerospace, automobile and in sports goods because of their high strength to weight ratio and renewability. Hence the study of mechanical behaviors of natural fiber composites is very important in using these composite laminates for such specific applications. This project aims at identifying the mechanical properties of hybrid natural Jute/Kenaf fiber. The major drawbacks in natural fiber are its Resin incompatibility. Surface treatment of fiber is made to improve the interfacial bonding between the fiber and resin and to reduce the moisture absorption. Laminates are fabricated using Hand lay-up technique. Mechanical properties such as tensile, flexural, and Impact test for jute/kenaf hybrid laminates were obtained. Specimen preparation and Mechanical property testing were carried out as per ASTM standards. Micro structures of the different layer of hybrid specimens are scanned by the Scanning Electron Microscope.

To investigate magnetic anisotropy in Si/Ni multilayers, ferromagnetic resonance (FMR) and magnetization studies are carried out by preparing a series of samples of the form [Si(10 Å)/Ni( t Ni Å)] 20 , where t Ni and 20 denote the Ni layer thickness and number of bilayers, respectively. The samples are prepared using a DC magnetron sputtering system by fixing the Si layer thickness at 10 Å and by varying the Ni layerthickness from 10 to 100 Å. The surface morphology study confirms that the multilayers were deposited smoothly over the substrates. The variation of the surface roughness with t Ni attains a maximum for t Ni  = 50 Å. The structural study shows that the average crystallite size increases with as t Ni increases. The magnetic anisotropy exhibited by the samples has been systematically studied through static and dynamic processes. The effective magnetization obtained from the MH loops and FMR spectra increases with increasing t Ni and approaches towards the bulk value of Ni. Static and dynamic magnetization processes yeilds the values of surface anisotropy constant ( K s ) and volume anisotropy constant ( K v ) to be positive and negative, respectively. Through FMR studies, it is found that the rougher surfaces/interfaces lead to the multiple resonances peaks, higher asymmetry ratio, minimum value of spectroscopic splitting factor g and higher surface anisotropy.

In the current scenario, natural fiber-based biodegradable composites have increased because natural composite fibers are very cheap, biodegradable, lightweight, fireproof, and nontoxic. The present research work was carried out to optimize the mechanical properties of hybrid composites reinforced by Calotropis gigantea and hemp. To achieve these objectives, the following process parameters were determined, and RSM carried out optimization with the Box-Behnken experimental setup at three different levels: compression molding temperature (°C), pressure (bar), and time (min). The fibers were pretreated for 4 hours with a 5% NaOH solution to prevent moisture absorption. Regression equations were constructed to evaluate the mechanical properties, and the best process parameters were established. The results reveal that a pressure of 35 bar, a time duration of 7 minutes, and a temperature of 176°C are the best conditions for compression molding. The second aim was to compare CGF and hemp fiber-derived activated carbon adsorbents by determining physical adsorption properties, chemical compositions, and scanning electron microscope. Natural fibers were shown to be ideal candidates for manufacturing mesoporous activated carbon adsorbents with high surface area (1389–1433 m2/g), high mesopore percentage (63–68%), and high carbon content (80–87%). Even though hemp activated carbon had a greater mesoporous structure (69%) than CGF-derived activated carbons, the CGF-derived activated carbons had larger surface areas and higher C content.

Herein, rGO–MnO 2 nanocomposite was synthesized via a hydrothermal method. The prepared rGO–MnO 2 powders underwent various characterizations to examine their structural, morphological, and electrical properties. XRD was used for preliminary identification of the synthesized powders, while Raman spectroscopy confirmed bond types and functional groups. UV–visible spectroscopy provided key insights into the electronic and optical properties through linear optical studies. SEM and TEM were employed to evaluate the catalyst’s morphological structure. Electrochemical sensing properties were analyzed using cyclic voltammetry, revealing that rGO–MnO 2 exhibits good sensitivity toward histamine, making it a promising candidate for real-time histamine sensing in the human body. Additionally, the rGO–MnO 2 nanocomposite demonstrated remarkable photocatalytic activity, achieving a 91.7% degradation efficiency of Eriochrome Black T, significantly higher than pure MnO 2 and rGO catalysts. This enhanced performance is primarily due to effective charge transfer from MnO 2 nanowires’ conduction band to reduced graphene oxide. Furthermore, the stability of the nanocomposite was confirmed through repeated experiments, and the key active radicals were identified using trapping experiments.

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.