Assembly Process Research Articles

Metagenomic assemblies tend to break around antibiotic resistance genes

BackgroundAssembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs) and inform interventions to prevent further spread of resistant bacteria. However, similar to other conserved regions, such as ribosomal RNA genes and mobile genetic elements, almost identical ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections. To systematically investigate the impact of this issue on detection, quantification and contextualization of ARGs, we evaluated the performance of different assembly approaches, including genomic-, metagenomic- and transcriptomic-specialized assemblers. We quantified recovery and accuracy rates of each tool for ARGs both from in silico spiked metagenomic samples as well as real samples sequenced using both long- and short-read sequencing technologies.ResultsThe results revealed that none of the investigated tools can accurately capture genomic contexts present in samples of high complexity. The transcriptomic assembler Trinity showed a better performance in terms of reconstructing longer and fewer contigs matching unique genomic contexts, which can be beneficial for deciphering the taxonomic origin of ARGs. The currently commonly used metagenomic assembly tools metaSPAdes and MEGAHIT were able to identify the ARG repertoire but failed to fully recover the diversity of genomic contexts present in a sample. On top of that, in a complex scenario MEGAHIT produced very short contigs, which can lead to considerable underestimation of the resistome in a given sample.ConclusionsOur study shows that metaSPAdes and Trinity would be the preferable tools in terms of accuracy to recover correct genomic contexts around ARGs in metagenomic samples characterized by uneven coverages. Overall, the inability of assemblers to reconstruct long ARG-containing contigs has impacts on ARG quantification, suggesting that directly mapping reads to an ARG database should be performed as a complementary strategy to get accurate ARG abundance and diversity measures.

Enhancing vertical assembly of Dot-LEDs for display application using metal chelate coordination chemical linkers

The chemical linker assembly technique for dot-LEDs described in this study offers practical solutions to critical challenges in micro-LED display production, such as high costs, low yield in mass transfer processes, and high cost of micro-LED chips. Dot-LEDs feature a cylindrical structure with a diameter ranging from 0.75 to 1.35 μm and an aspect ratio of 1.1∼1.2 for height to diameter. We introduce a viable approach using N-(carboxymethyl)-N-(11-mercaptoundecyl)glycine (gly-thiol) chemical linkers for the face-selective vertical assembly of dot-LEDs. The size scale of dot-LED enables solution assembly processes when manufacturing pixels. By bonding the Au surface of the p-GaN face to the bottom Au electrodes using the chelate bond of Au-gly-thiol-Zn2+-gly-thiol-Au, we intend to enhance the face-selective vertical assembly. The process involves forming two chelate coordination bonds of Zn2+ ion surrounded by a gly-thiol-treated dot and bottom electrodes, achieving over 60 % efficiency in face-selective vertical assembly. We achieved bright and uniform dot-LED electroluminescence devices, with a peak external quantum efficiency (EQE) of 8.1 % at 3.4 V and a luminance of 22,387 cd/m2 at 9.0 V. Our findings suggest substantial improvements in the assembly and efficiency of micro-nano-scale dot-LED displays, providing a display platform technology for their application in future display systems.

Applications of Newer Technologies in Enhancement of Pharmaceutical Industry

Computerized disruption in the drug industry refers to a revolutionary shift in customary processes in drug discovery, assembly, and patient consideration, spurred by emerging technologies. The unification of artificial intelligence (AI) and machine learning (ML) accelerates the development of new drugs by analysing large datasets to identify potential rivals for existing medications and predict patient responses. Digital therapies (DTx) are evidence-based programming interventions that improve medication adherence and chronic illness management while providing accessible and personalized medical service arrangements. Cloud computing and enormous information examination smooth out information reconciliation and investigation, empowering more productive innovative work processes. These innovations support the improvement of customized medication by utilizing genomic and clinical information, cultivating the making of designated treatments. The Internet of Things (IoT) upgrades the drug production network by checking ecological circumstances and guaranteeing the respectability of medication conveyance, while blockchain innovation guarantees information trustworthiness and straightforwardness, especially in clinical preliminaries and production network the executives. Regulatory Technology (RegTech) computerizes consistence processes, decreasing expenses and guaranteeing adherence to administrative norms. Furthermore, additive manufacturing (3D printing) speeds up drug prototyping and the creation of customized measurements structures, while supportability drives driven by advanced development lessen the natural effect of drug tasks. As computerized advances keep on developing, they offer uncommon chances to further develop effectiveness, lessen expenses, and improve patient results in the drug area. The fruitful coordination of these advancements presents difficulties however vows to alter the business by making medical services more customized, productive, and available.

The contribution of seasonal variations and Zostera marina presence to the bacterial community assembly of seagrass bed sediments

BackgroundMicroorganisms play pivotal roles in seagrass ecosystems by facilitating material and elemental cycling as well as energy flux. However, our understanding of how seasonal factors and seagrass presence influence the assembly of bacterial communities in seagrass bed sediments is limited. Employing high-throughput sequencing techniques, this study investigates and characterizes bacterial communities in the rhizosphere of eelgrass (Zostera marina) and the bulk sediments across different seasons. The research elucidates information on the significance of seasonal variations and seagrass presence in impacting the microbial communities associated with Zostera marina.ResultsThe results indicate that seasonal variations have a more significant impact on the bacterial community in seagrass bed sediments than the presence of seagrass. We observed that the assembly of bacterial communities in bulk sediments primarily occurs through stochastic processes. However, the presence of seagrass leading to a transition from stochastic to deterministic processes in bacterial community assembly. This shift further impacts the complexity and stability of the bacterial co-occurrence network. Through LEfSe analysis, different candidate biomarkers were identified in the bacterial communities of rhizosphere sediments in different seasons, indicating that seagrass may possess adaptive capabilities to the environment during different stages of growth and development.ConclusionsSeasonal variations play a significant role in shaping these communities, while seagrass presence influences the assembly processes and stability of the bacterial community. These insights will provide valuable information for the ecological conservation of seagrass beds.

Intimate microbe-water-mineral interactions mediate alkalization in the pyroxene-rich iron ore mines in Panxi area, Southwest China

In contrast to acid mine drainage, the microbial assembly and (bio)geochemical processes in alkaline mine conditions remain under-investigated. Here, microbe-water-mineral interactions were systematically investigated in two representative iron mines with alkaline conditions in the Panxi mining area, Southwest China. Compared to reference riverine samples less interfered by mining activities, the iron ore samples, composed of vanadium-titanium magnetite and pyroxene-rich bedrocks, exhibited elevated levels of Fe, HCl-extractable Fe(II), total sulfur, nitrate and sulfate, but lower total carbon (TC). Meanwhile, the mine drainage showed significantly higher sulfate, but lower TC concentrations than the riverine samples. Intriguingly, the Serpentinimonas spp., typically reported in serpentinites, prevailed in the microbial communities from the mine samples exhibiting higher pH. This suggests that the alkaline environments in Panxi mines result from serpentinization-like reactions. Enrichment of Thiobacillus spp. was observed in the mine-dwelling microbial communities, positively correlated with total sulfur, sulfate, nitrate, and Fe(II). Genome-resolved metagenomics suggested a chemoautotrophic lifestyle for the Thiobacillus species (e.g., carbon fixation, sulfur oxidation, and oxygen respiration), which may generate H+ and mitigate alkalization. This study provides valuable insights into progressive development of alkaline mine ecosystems and offers guidance for developing appropriate engineering strategies to restore the abandoned alkaline mines.

Deciphering Pathways and Thermodynamics of Protein Assembly Using Native Mass Spectrometry.

Protein oligomerization regulates many critical physiological processes, and its dysregulation can contribute to dysfunction and diseases. Elucidating the assembly pathways and quantifying their underlying thermodynamic and kinetic parameters are crucial for a comprehensive understanding of biological processes and for advancing therapeutics targeting abnormal protein oligomerization. Established binding assays, with limited mass precision, often rely on simplified models for data interpretation. In contrast, high-resolution native mass spectrometry (nMS) can directly determine the stoichiometry of biomolecular complexes in vitro. However, quantification is hindered by the fact that the relative abundances of gas-phase ions generally do not reflect solution concentrations due to nonuniform response factors. Recently, slow mixing mode (SLOMO)-nMS, which can quantify the relative response factors of interacting species, has been demonstrated to reliably measure the affinity (Kd) of binary biomolecular complexes. Here, we introduce an extended form of SLOMO-nMS that enables simultaneous quantification of the thermodynamics in multistep association reactions. Application of this method to homo-oligomerization of concanavalin A and insulin confirmed the reliability of the assay and uncovered details about the assembly processes that had previously resisted elucidation. Results acquired using SLOMO-nMS implemented with charge detection shed new light on the binding of recombinant human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein. Importantly, new assembly pathways were uncovered, and the affinities of these interactions, which regulate host cell infection, were quantified. Together, these findings highlight the tremendous potential of SLOMO-nMS to accelerate the characterization of protein assembly pathways and thermodynamics and, in so doing, enhance fundamental biological understanding and facilitate therapeutic development. https://orcid.org/0000-0002-3389-7112.

A systematic quality-integrated diagnostic method for complex product assembly using multi-task spatial–temporal transfer learning

The assembly process is generally considered one of the primary factors influencing the quality of complex products. Currently, most existing quality-integrated diagnostic methods for products tend to deteriorate over different processes and degrade over time. To address this issue, this paper introduces a systematic quality-integrated diagnostic method for complex product assembly processes. First, the influence factors and error sources in the complex assembly process are analyzed using the 5M1E and FAHP methodologies. Next, similarity-based multi-task clustering and dismantling using RGMM is applied to divide the assembly tasks. Finally, MMD-MSE is employed to develop a quality prediction model for the complex assembly process using a spatial–temporal transfer learning approach. Experiments were conducted on an array antenna assembly task, comparing the proposed method with conventional methods. The results show that the accuracy and PrUP of the proposed model are 97.6% and 95.2%, respectively, for the quality-integrated diagnostic of complex assembly processes, with a fluctuation in accuracy of less than 6%. The diagnostic results effectively meet expert evaluations and provide a stable, reliable, and practical solution for addressing quality fluctuations in complex production assembly processes.

Simulation Model of One-Parameter Selective Assembly of Two Elements With Multivariate Set-Making

Analytical and simulation methods are used to model assembly processes and technological systems in order to determine and optimize their performance and functioning parameters. The first one, describes an object or process using mathematical relationships indirectly. Simulation modelling is one of the modern methods of production system experimental research, allowing to take the presence of various components in the system, nonlinear characteristics, non-determinism and dynamism of ongoing processes into account. This work is devoted to the simulation model development of the technological process of multivariate one-parameter selective assembly of two elements. Multivariate set-making is defined by generalized rules with a set of weights which elements are set the distribution of elements between sets of different types. Selectively assembled elements have parameters determined by the manufacturing stage, which are independent random variables with known probabilistic characteristics. It is assumed that the input-output relationship is linear with given coefficients, the set-making equations linking the numbers of selective groups and assembly sets are known. A simulation model of the assembly process was built in the GPSS World, which, in contrast to the known one, operates with multivariate set-making rules for the case of assembly of two elements. The model allows to determine the quantitative indicators of the assembly process, in particular, the probabilities of assembly set formation, work-in-progress and preliminary rejects. As an example, the process of multivariate set-making of two parts forming a precision engineering product is considered. The data of parts distributions of both types by selective groups before assembly are given, the technological process indicators are calculated. Based on the modelling results the advantages and disadvantages of the multivariate set-making process are outlined.

An exploration of the impact of phylogenetic tree structure on NTI and βNTI estimates of community assembly

The nearest taxon index (NTI) and beta nearest taxon index (βNTI) are popular metrics used to infer community assembly in a variety of ecosystems. However, the dependence these metrics have on the underlying phylogenetic tree is often overlooked and can potentially lead to erroneous conclusions. In this paper, we investigated how misclassified sequences in the phylogenetic tree and reduction of the metacommunity richness affect estimates of the assembly processes. We estimated assembly processes in two datasets investigating fish-microbiota interactions. We found that non-bacterial misclassified sequences can drastically change the interpretation of assembly processes. NTI consistently increased after misclassified sequences were removed, which changed the ecological interpretation for 82.8% of the communities in one of the datasets. Similarly, βNTI was strongly affected by misclassified sequences in the phylogenetic tree, changing the ecological interpretation for 44.7% (Dataset 1) and 6.3% (Dataset 2) of the community comparisons. Lower metacommunity richness generally resulted in decreased NTI and increased βNTI, but the effect sizes were small and generally did not result in a change in ecological interpretation. Based on these findings, we recommend: (1) constructing and visually inspecting a phylogenetic tree for 16 S rRNA gene amplicon datasets to identify and remove misclassified sequences, and (2) evaluating the robustness of community assembly conclusions by performing analyses with varying metacommunity richness and clearly specifying the metacommunity used for the null model approach.

Congruence between co-occurrence and trait-based networks is scale-dependent: a case study with flea parasites of small mammalian hosts.

We applied a novel framework based on network theory and a concept of modularity that estimates congruence between trait-based ( = functional) co-occurrence networks, thus allowing the inference of co-occurrence patterns and the determination of the predominant mechanism of community assembly. The aim was to investigate the relationships between species co-occurrence and trait similarity in flea communities at various scales (compound communities: across regions within a biogeographic realm or across sampling sites within a geographic region; component communities: across sampling sites within a geographic region; and infracommunities: within a sampling site). We found that compound communities within biogeographic realms were assembled via environmental or host-associated filtering. In contrast, functional and spatial/host-associated co-occurrence networks, at the scale of regional compound communities, mostly indicated either stochastic processes or the lack of dominance of any deterministic process. Analyses of congruence between functional and either spatial (for component communities) or host-associated (for infracommunities) co-occurrence networks demonstrated that assembly rules in these communities varied among host species. In component communities, stochastic processes prevailed, whereas environmental filtering was indicated in 4 and limiting similarity/competition in 9 of 31 communities. Limiting similarity/competition processes dominated in infracommunities, followed by stochastic mechanisms. We conclude that assembly processes in parasite communities are scale-dependent, with different mechanisms acting at different scales.

Two-stage assembly of patchy ellipses: From bent-core particles to liquid crystal analogs.

We investigate the two-dimensional behavior of colloidal patchy ellipsoids specifically designed to follow a two-step assembly process from the monomer state to mesoscopic liquid-crystal phases via the formation of the so-called bent-core units at the intermediate stage. Our model comprises a binary mixture of ellipses interacting via the Gay-Berne potential and decorated by surface patches, with the binary components being mirror-image variants of each other-referred to as left-handed and right-handed ellipses according to the position of their patches. The surface patches are designed so as in the first stage of the assembly the monomers form bent-cores units, i.e., V-shaped dimers with a specific bent angle. The Gay-Berne interactions, which act between the ellipses, drive the dimers to subsequently form the characteristic phase observed in bent-core liquid crystals. We numerically investigate-by means of both Molecular Dynamics and Monte Carlo simulations-the described two-step process: we first optimize a target bent-core unit and then fully characterize its state diagram in temperature and density, defining the regions where the different liquid crystalline phases dominate.

High Temperature, Isothermal Growth Promotes Close Packing and Thermal Stability in DNA-Engineered Colloidal Crystals.

We report a strategy to accelerate the synthesis and increase the crystallinity of colloidal crystals (CCs) engineered with DNA. Specifically, by holding the DNA-modified Au particle building blocks above the Tm of the DNA bonding elements (i.e., free from the particles), but slightly below the Tm of the anticipated CC during the assembly process, crystallinity is increased, and enthalpically favored phases with high degrees of facet registration are observed. We studied the utility of this approach with systems for which the commonly adopted slow-cooling approach yielded primarily amorphous aggregates. In particular, we used it to synthesize high-volume fraction CCs from large (80 nm) anisotropic nanoparticles (cubes and rhombic dodecahedra) with short (<14 nm) DNA designed to restrict the degrees of freedom for the DNA bonds and maintain the anisotropy of the particle building block. Small-angle X-ray scattering and electron microscopy studies show that the crystalline phases synthesized via this method are more thermally stable than their corresponding aggregate phases, likely due to an increased number of DNA-DNA bonds between particles. Crystal size tunability (between 0.5 and 15 μm edge lengths) and epitaxial growth were demonstrated using this strategy by modulating the NaCl concentration in tandem with previously synthesized CC nuclei. Taken together, this isothermal strategy demonstrates how to deliberately crystallize a wide variety of anisotropic colloidal materials and expands the phase space accessible to nanoparticles modified with DNA.

Conversion of broadleaf forest to plantation changes the assembly process and composition of soil microbial communities in a subtropical forest

Conversion of broadleaf forest to plantation changes the assembly process and composition of soil microbial communities in a subtropical forest

Model of evaluation of interference between blades shrouds during assembly of turbine impellers

The workability of the product depends on the quality of the assembly process. Special attention should be paid to the interaction of parts during assembly. In joints where the position of the contacting parts relative to each other is decisive, for effective operation of the product, it is necessary to find the force factors that have the greatest impact on the position of neighboring parts. This is so because due to the influence of the force factor, the parts move and interference and gaps are formed in the joints. For this reason, it is necessary to take into account the deformation of the contacting parts caused by the influence of force factors. The study developed an algorithm for calculating assembly parameters taking into account the rigidity of the contacting parts. On the example of the turbine impeller, a model was obtained that allows predicting the position of the blades under the influence of the force factor, taking into account the rigidity of the system.

Prediction of Overall Equipment Effectiveness in Assembly Processes Using Machine Learning

Abstract Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

Advances in longitudinal equivalent bending stiffness analysis-novel nonlinear characteristics in the assembly process of prefabricated subway stations

The longitudinal bending stiffness of quasi-rectangular prefabricated subway stations plays a great important role in studying their mechanical behavior and conducting safety assessments. To address the issue of immature stress assumptions for existing prefabricated subway stations, three stress assumptions and five bending modes are proposed while considering the coupling effect between axial force and bending moment. The study investigates novel nonlinear characteristics in the longitudinal equivalent bending stiffness and the influencing factors during the assembly of an existing subway station. An effective method for identifying the longitudinal bending modes suitable for the prefabricated subway station is established, the proposed solution has a good agreement with measured longitudinal bending stiffness efficiency. The study demonstrates that the longitudinal bending mode of the prefabricated subway station shifts from "upward convex" to "downward concave" due to the stepwise assembly method. The longitudinal bending stiffness efficiency displays a significant hysteresis curve, eventually stabilizing at approximately 1 %. Upon completion of the monitoring ring assembly, the concrete bears both longitudinal tensile and compressive stress. When assembling 10 rings from the monitoring ring, the longitudinal reinforcing bars bear tensile stress. The longitudinal bending stiffness of the prefabricated subway station exhibits significant time-variable characteristics throughout the assembly process. The research findings can provide effective references for the analysis of longitudinal mechanics and bending performance during the assembly process.

Experimental and theoretical study on axial compression behaviour of the modular combined column

Between the modular units of the modular steel building, there exists a combination of the adjacent modular columns. Due to the gaps between the modular columns and the lack of horizontal connections, the integrity of the modular steel structure is weak, which limits the use of modular steel structures in high-rise buildings. This paper proposes a kind of modular combined column realized the connection of modular single columns through the connectors and concrete, which can improve the integrity of the modular steel building. The assembly process of this new modular combined column is described. For axial compressive performance tests, four modular combined column specimens were designed, each with different height or different number of connecting plates. The axial compressive bearing capacity, ductility, stiffness, and other mechanical parameters of the specimens were analyzed according to the phenomena and load-displacement curves of the test. Then, the axial compression failure mode of the modular combined column specimens is compared and analyzed in conjunction with the finite element model. The results indicate that the axial compressive performance and ductility of the combined column suggested in this research are exceptional. The connector can enhance the overall mechanical qualities of the combined column by augmenting the restraining impact of concrete. Finally, the theoretical equation of the axial compression bearing capacity of the modular combined column is obtained by employing the superposition principle, and the equation's reliability is confirmed, along with the test results and the results of the numerical analysis.

A novel experimental method for bolt stress distribution during the assembly process

Bolt fasteners play a crucial role in modern engineering equipment, and understanding the stress distribution in the bolt during the assembly process is essential for enhancing its reliability. However, traditional experimental methods based on the principle of photoelasticity may not accurately reflect the actual assembly process and engineering materials. To address this issue, a novel experimental approach is proposed in this paper, which utilizes optical fibers to measure stress in the bolt during the assembly process. By affixing an optical fiber to the inside of the bolt and employing the optical frequency domain reflectometry (OFDR) technique, the axial stress distribution in the bolt can be obtained. In addition, the concept of an axial force gain factor is introduced to establish the relationship between the maximum axial force and preload. It is found that the calculated maximum axial force of the bolt is approximately 1.4 to 1.5 times the preload. The experimental results show that the stress distribution obtained through the proposed method is consistent with conventional photoelasticity experiments. Furthermore, a comparison with previous studies validates the accuracy of the experimental findings. The experimental approach presented in this paper can realize the real-time measurement of bolt stress distribution, which is of great significance for engineering application.

A study of the ultrasonic and audible frequency response of commercial loudspeakers for use as parametric acoustic arrays

Parametric acoustic loudspeakers (PAL), also known as parametric acoustic arrays (PAA), are speakers that use ultrasonic waves to generate directive audible sound. They emit an ultrasonic carrier modulated in amplitude by the desired audible signal, and due to non-linear propagation in air, this signal is reproduced in the audible range. Typical applications include audio reproduction for entertainment, but also active noise control and acoustic measurement of material properties. PAA typically consist of an array of piezoelectric transducers set on a surface. However, there are other speakers in the market that, due to their characteristics, could also achieve the PAL effect. This work examines the performance of commercial speakers when reproducing. First, the ultrasonic frequency response of each speaker is examined in free-field conditions using exponential sine sweeps (ESS). This enables searching for potential carrier frequencies to modulate the ESS in the ultrasonic range. The ESS are next modulated in the desired ultrasonic region and emitted through each speaker, obtaining their audible and ultrasonic impulse responses. The results show that some commercial speakers can also operate as PALs, which is interesting from an industrial point of view as it lowers costs and simplifies the assembly process.

Electrochemical immunosensor based on gold nanoparticles for the detection of creatine kinase as a cardiac marker

This study presents the development of an electrochemical immunosensor utilizing gold nanoparticles for the detection of creatine kinase (CK), a crucial cardiac marker. Gold nanoparticles with an average diameter of 18.3 ± 2.1 nm were synthesized and characterized, exhibiting a characteristic surface plasmon resonance peak at 520 nm. The construction of the immunosensor was observed through cyclic voltammetry and electrochemical impedance spectroscopy, showing notable alterations in electron transfer rates during each step of the assembly process. Optimal performance was achieved at pH 7.4, with an antibody concentration of 100 μg/mL and an incubation time of 45 minutes. The sensor demonstrated a sensitivity of 152.6 Ω/(ng/mL) and maintained its response in the presence of common interfering substances, with maximum signal deviation of 7.2 %. Testing with spiked human serum samples yielded recovery rates between 96.5 % and 103.8 %. Comparison with ELISA using clinical samples further validated the sensor's accuracy. This AuNP-based immunosensor offers a promising platform for rapid and sensitive CK detection in cardiac health assessment.