Tetraphenylethene-based mononuclear aggregation-induced emission (AIE)-active mechanofluorochromism gold(I) complexes with different auxiliary ligands.

In this study, a series of tetraphenylethene-containing gold(I) complexes with different auxiliary ligands have been synthesized. These complexes were characterized using a variety of techniques including nuclear magnetic resonance spectroscopy, mass spectrometry, and single crystal X-ray diffraction. Their aggregation-induced emission (AIE) behaviors were investigated through ultraviolet/visible and photoluminescence spectrum analyses, and dynamic light scattering measurements. Meanwhile, their mechanofluorochromic properties were also studied via solid-state photoluminescence spectroscopy. Intriguingly, all these mononuclear gold(I) molecules functionalized by tetraphenylethene group demonstrated AIE phenomena. Furthermore, five gold(I) complexes possessing diverse auxiliary ligands exhibited distinct fluorescence changes in response to mechanical grinding. For luminogens 2-5, their solids showed reversible mechanofluorochromic behaviors triggered by the mutual transformation of crystalline and amorphous states, while for luminogen 1, blue-green-cyan three-color solid fluorescence conversion was realized by sequential mechanical grinding and solvent fumigation. Based on this stimuli-responsive tricolored fluorescence feature of 1, an information encryption system was successfully constructed.

[Auxiliary diagnostic value of T cells spot test of Mycobacterium tuberculosis infection for pulmonary and extra-pulmonary tuberculosis among the elderly].

OBJECTIVE: To evaluate the auxiliary diagnostic value of T cells spot test of Mycobacterium tuberculosis infection (T-SPOT.TB) for pulmonary and extra-pulmonary tuberculosis among the elderly. METHODS: A total of 173 elderly patients at ages of 60 years and older and with suspected tuberculosis that were admitted to People's Hospital of Xinjiang Uygur Autonomous Region during the period from October 2022 through February 2024 were enrolled, and all patients underwent T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests. The etiological tests of MTB served as a gold standard, and the diagnostic values of T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests for pulmonary and extra-pulmonary tuberculosis were compared among the elderly patients. RESULTS: Of the 173 elderly patients suspected of tuberculosis, there were 44 patients definitely diagnosed with pulmonary tuberculosis, 30 cases with extra-pulmonary tuberculosis, and 99 cases without tuberculosis. The sensitivities of T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests were 86.5%, 27.0% and 54.1% for diagnosis of tuberculosis. The sensitivities of T-SPOT.TB were 86.4% and 86.7% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with an 80.8% specificity for diagnosis of tuberculosis. The sensitivities of GeneXpert MTB/RIF were 56.8% and 50.0% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with a 100.0% specificity each, and the sensitivities of acid fast staining were 31.8% and 20.0% for diagnosis of pulmonary tuberculosis and extra-pulmonary tuberculosis, with a 100.0% specificity each. In addition, the areas under the receiver operating characteristic curve were 0.836, 0.635 and 0.770 for diagnosis of tuberculosis with T-SPOT.TB, acid fast staining and GeneXpert MTB/RIF tests among the elderly patients, respectively. CONCLUSIONS: T-SPOT.TB has a high auxiliary diagnostic value for both pulmonary and extra-pulmonary tuberculosis among elderly patients.

Precision enhancement in variance estimation for complex environmental populations using adaptive cluster sampling.

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

Expansion of Auxiliary Activity Family 5 sequence space via biochemical characterization of six new copper radical oxidases.

Bacterial and fungal copper radical oxidases (CROs) from Auxiliary Activity Family 5 (AA5) are implicated in morphogenesis and pathogenesis. The unique catalytic properties of CROs also make these enzymes attractive biocatalysts for the transformation of small molecules and biopolymers. Despite a recent increase in the number of characterized AA5 members, especially from subfamily 2 (AA5_2), the catalytic diversity of the family as a whole remains underexplored. In the present study, phylogenetic analysis guided the selection of six AA5_2 members from diverse fungi for recombinant expression in Komagataella pfaffii (syn. Pichia pastoris) and biochemical characterization in vitro. Five of the targets displayed predominant galactose 6-oxidase activity (EC 1.1.3.9), and one was a broad-specificity aryl alcohol oxidase (EC 1.1.3.7) with maximum activity on the platform chemical 5-hydroxymethyl furfural (EC 1.1.3.47). Sequence alignment comparing previously characterized AA5_2 members to those from this study indicated various amino acid substitutions at active site positions implicated in the modulation of specificity.IMPORTANCEEnzyme discovery and characterization underpin advances in microbial biology and the application of biocatalysts in industrial processes. On one hand, oxidative processes are central to fungal saprotrophy and pathogenesis. On the other hand, controlled oxidation of small molecules and (bio)polymers valorizes these compounds and introduces versatile functional groups for further modification. The biochemical characterization of six new copper radical oxidases further illuminates the catalytic diversity of these enzymes, which will inform future biological studies and biotechnological applications.

Research on Monitoring Assistive Devices for Rehabilitation of Movement Disorders through Multi-Sensor Analysis Combined with Deep Learning.

This study aims to integrate a convolutional neural network (CNN) and the Random Forest Model into a rehabilitation assessment device to provide a comprehensive gait analysis in the evaluation of movement disorders to help physicians evaluate rehabilitation progress by distinguishing gait characteristics under different walking modes. Equipped with accelerometers and six-axis force sensors, the device monitors body symmetry and upper limb strength during rehabilitation. Data were collected from normal and abnormal walking groups. A knee joint limiter was applied to subjects to simulate different levels of movement disorders. Features were extracted from the collected data and analyzed using a CNN. The overall performance was scored with Random Forest Model weights. Significant differences in average acceleration values between the moderately abnormal (MA) and severely abnormal (SA) groups (without vehicle assistance) were observed (p < 0.05), whereas no significant differences were found between the MA with vehicle assistance (MA-V) and SA with vehicle assistance (SA-V) groups (p > 0.05). Force sensor data showed good concentration in the normal walking group and more scatter in the SA-V group. The CNN and Random Forest Model accurately recognized gait conditions, achieving average accuracies of 88.4% and 92.3%, respectively, proving that the method mentioned above provides more accurate gait evaluations for patients with movement disorders.

Improved modified estimator for estimation of median using auxiliary information under simple random sampling.

This article aims to suggest an improved estimator for estimation of population median using auxiliary information under simple random sampling. The expression for the bias and mean square error are obtained up to first order approximation. We determine the MLE of the optimal values of the describing scalars. The proficiency of the suggested estimator is evaluated in comparison to the preliminary estimators using the MSE threshold. The suggested estimators are compared numerically to the ones that are currently studied in this study. The performance and novelty of the estimators was evaluated using real data sets and a simulation study. To check the efficiency of estimators empirical and theoretical study has been studied. Based on numerical result it is to be noted that our suggested estimator is more efficient as compared to existing estimators which is considered in this article in terms of least MSE and greater PRE.

Efficient enhancement of low-rank tensor completion via thin QR decomposition.

Low-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues. The core tensor nuclear norm minimization (CTNM) method based on Tucker decomposition is one of common LRTC methods. However, the CTNM methods based on Tucker decomposition often have a large computing cost due to the fact that the general factor matrix solving technique involves multiple singular value decompositions (SVDs) in each loop. To address this problem, this article enhances the method and proposes an effective CTNM method based on thin QR decomposition (CTNM-QR) with lower computing complexity. The proposed method extends the CTNM by introducing tensor versions of the auxiliary variables instead of matrices, while using the thin QR decomposition to solve the factor matrix rather than the SVD, which can save the computational complexity and improve the tensor completion accuracy. In addition, the CTNM-QR method's convergence and complexity are analyzed further. Numerous experiments in synthetic data, real color images, and brain MRI data at different missing rates demonstrate that the proposed method not only outperforms in terms of completion accuracy and visualization, but also conducts more efficiently than most state-of-the-art LRTC methods.

Perioperative management in pediatric domino liver transplantation for metabolic disorders: A narrative review.

Domino liver transplantation and domino-auxiliary partial orthotopic liver transplantation are emerging techniques that can expand the liver donor pool and provide hope for children with liver disease. The innovative technique of domino liver transplantation has emerged as a pioneering strategy, capitalizing on structurally preserved livers from donors exhibiting single enzymatic defects within a morphologically normal context, effectively broadening the donor pool. Concurrently, the increasingly prevalent domino-auxiliary partial orthotopic liver transplantation method assumes a critical role in bolstering available donor resources. These advanced transplantation methods present a unique opportunity for pediatric patients who, despite having structurally and functionally intact livers and lacking early signs of portal hypertension or extrahepatic involvement, do not attain priority on conventional transplant lists. Utilizing optimal clinical conditions enhances posttransplant outcomes, benefiting patients who would otherwise endure extended waiting periods for traditional transplantation. The perioperative management of children undergoing these procedures is complex and requires careful consideration of some factors, including clinical and metabolic conditions of the specific metabolic disorder, and the need for tailored perioperative management planning. Furthermore, the prudent consideration of de novo disease development in the recipient assumes paramount significance when selecting suitable donors for domino liver transplantation, as it profoundly influences prognosis, mortality, and morbidity. This narrative review of domino liver transplantation will discuss the pathophysiology, clinical evaluation, perioperative management, and prognostic expectations, focusing on perioperative anesthetic considerations for children undergoing domino liver transplantation.

Thallium spill shifts the structural and functional characteristics of viral communities with different lifestyles in river sediments.

Thallium (Tl), a highly toxic heavy metal, can affect microbial community, while little is known about its effect on viral community. The present study investigated the variation of viral communities, as well as their interactions with microbial hosts under Tl stress. Tl in sediments significantly altered the composition and diversity of the viral communities, but showed no significant links with the prokaryotic communities, which may reveal a potential discrepancy in the sensitivity of the viral and prokaryotic communities to heavy metal stress. Auxiliary metabolic genes (AMGs) involved in denitrification, methane oxidation and organic sulfur transformation were enriched at T1-contaminated sites, while the abundance of AMGs related to methanogenesis and sulfate reduction were higher at pristine sites. Specially, the enrichment of AMGs involved in assimilatory sulfate reduction in Tl-contaminated sites could possibly reduce Tl bioavailability by enhancing the microbially-driven sulfur cycling to generate sulfides that could be complexed with Tl. Moreover, there was a significantly positive correlation between virus-carrying metal resistant genes and the sedimentary Tl concentration, implying that Tl contamination might enhance the metal resistant potential of the viruses. Serving as the functional gene reservoir, the response of viral AMGs to Tl stress could represent a potential pathway for microorganisms to be adapted to the metal-polluted environments. Our study provided novel insights into the impact of Tl spill on viral communities, shedding light on functional characteristics and the links of virus-host interaction with Tl level.

Viral and Bacterial Community Dynamics in Food Waste and Digestate from Full-Scale Biogas Plants.

Anaerobic digestion (AD) is commonly used in food waste treatment. Prokaryotic microbial communities in AD of food waste have been comprehensively studied. The role of viruses, known to affect microbial dynamics and metabolism, remains largely unexplored. This study employed metagenomic analysis and recovered 967 high-quality viral bins within food waste and digestate derived from 8 full-scale biogas plants. The diversity of viral communities was higher in digestate. In silico predictions linked 20.8% of viruses to microbial host populations, highlighting possible virus predators of key functional microbes. Lineage-specific virus-host ratio varied, indicating that viral infection dynamics might differentially affect microbial responses to the varying process parameters. Evidence for virus-mediated gene transfer was identified, emphasizing the potential role of viruses in controlling the microbiome. AD altered the specific process parameters, potentially promoting a shift in viral lifestyle from lysogenic to lytic. Viruses encoding auxiliary metabolic genes (AMGs) were involved in microbial carbon and nutrient cycling, and most AMGs were transcriptionally expressed in digestate, meaning that viruses with active functional states were likely actively involved in AD. These findings provided a comprehensive profile of viral and bacterial communities and expanded knowledge of the interactions between viruses and hosts in food waste and digestate.

Viral communities in a pH>10 serpentinite-like environment: insight into diversity and potential roles in modulating the microbiomes by bioactive vitamin B9 synthesis.

Viral communities exist in a variety of ecosystems and play significant roles in mediating biogeochemical processes, whereas viruses inhabiting strongly alkaline geochemical systems remain underexplored. In this study, the viral diversity, potential functionalities, and virus-host interactions in a strongly alkaline environment (pH = 10.4-12.4) exposed to the leachates derived from the serpentinization-like reactions of smelting slags were investigated. The viral populations (e.g., Herelleviridae, Queuovirinae, and Inoviridae) were closely associated with the dominating prokaryotic hosts (e.g., Meiothermus, Trueperaceae, and Serpentinomonas) in this ultrabasic environment. Auxiliary metabolic genes (AMGs) suggested that viruses may enhance hosts' fitness by facilitating cofactor biosynthesis, hydrogen metabolism, and carbon cycling. To evaluate the activity of synthesis of essential cofactor vitamin B9 by the viruses, a viral folA (vfolA) gene encoding dihydrofolate reductase (DHFR) was introduced into a thymidine-auxotrophic strain Escherichia coli MG1655 ΔfolA mutant, which restored the growth of the latter in the absence of thymidine. Notably, the homologs of the validated vDHFR were globally distributed in the viromes across various ecosystems. The present study sheds new light on the unique viral communities in hyperalkaline ecosystems and their potential beneficial impacts on the coexisting microbial consortia by supplying essential cofactors. IMPORTANCE: This study presents a comprehensive investigation into the diversity, potential functionalities, and virus-microbe interactions in an artificially induced strongly alkaline environment. Functional validation of the detected viral folA genes encoding dihydrofolate reductase substantiated the synthesis of essential cofactors by viruses, which may be ubiquitous, considering the broad distribution of the viral genes associated with folate cycling.

Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes.

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

Effect of endoscopic full-thickness resection assisted by distal serosal turnover with floss traction for gastric submucosal masses.

BACKGROUND: Complex and high-risk surgical complications pose pressing challenges in the clinical implementation and advancement of endoscopic full-thickness resection (EFTR). Successful perforation repair under endoscopy, thereby avoiding surgical intervention and postoperative complications such as peritonitis, are pivotal for effective EFTR. AIM: To investigate the effectiveness and safety of EFTR assisted by distal serosal inversion under floss traction in gastric submucosal tumors. METHODS: A retrospective analysis of patients with gastric and duodenal submucosal tumors treated with EFTR assisted by the distal serosa inversion under dental floss traction from January 2023 to January 2024 was conducted. The total operation time, tumor dissection time, wound closure time, intraoperative bleeding volume, length of hospital stay and incidence of complications were analyzed. RESULTS: There were 93 patients, aged 55.1 ± 12.1 years. Complete tumor resection was achieved in all cases, resulting in a 100% success rate. The average total operation time was 67.4 ± 27.0 min, with tumor dissection taking 43.6 ± 20.4 min. Wound closure times varied, with gastric body closure time of 24.5 ± 14.1 min and gastric fundus closure time of 16.6 ± 8.7 min, showing a significant difference (P < 0.05). Intraoperative blood loss was 2.3 ± 4.0 mL, and average length of hospital stay was 5.7 ± 1.9 d. There was no secondary perforation after suturing in all cases. The incidence of delayed bleeding was 2.2%, and the incidence of abdominal infection was 3.2%. No patient required other surgical intervention during and after the operation. CONCLUSION: Distal serosal inversion under dental-floss-assisted EFTR significantly reduced wound closure time and intraoperative blood loss, making it a viable approach for gastric submucosal tumors.

An effective and economic estimation of population mean in stratified random sampling using a linear cost function.

Improvement in the estimation of population mean has been an area of interest in sampling theory. So many estimators have been suggested for elevated estimation of the population mean in stratified random sampling, but there is still a gap for more closely estimating the population mean. In this paper, the authors propose a ratio-product-cum-exponential-cum-logarithmic type estimator for the enhanced estimation of population mean by implying one auxiliary variable in stratified random sampling using conventional ratio, exponential ratio, and logarithmic ratio type estimators. The suggested estimator is a generalization of ratio, exponential ratio, and logarithmic ratio type estimators, and therefore these are special cases of the proposed estimator. The proposed estimator's bias and MSE are determined and compared with those of influential estimators, with the linear cost function being used to investigate and compare alternatives. Use Cramer's rule to determine the optimal value of the proposed estimator. The proposed estimator is more effective than other existing estimators, according to theoretical observations. For various applications, we suggest using a proposed estimator with the minimal MSE, which is verified by a numerical example, to have practical applicability of theoretical conclusions in real life.

Using theoretical constraints and the TASI taxonomy to delineate predictably replicable findings.

The focus of the present article is not on failures to replicate but on the more optimistically framed and more fruitful question: What stable findings can be reproduced reliably and can be trusted by decision makers, managers, health agents, or politicians? We propagate the working hypothesis that a twofold key to stable and replicable findings lies in the existence of theoretical constraints and, no less important, in researchers' sensitivity to metatheoretical, auxiliary assumptions. We introduce a hierarchy of four levels of theoretical constraints-a priori principles, psychophysical, empirical, and modelling constraints-combined with the TASI taxonomy of theoretical, auxiliary, statistical, and inferential assumptions Trafimow, Journal for the Theory of Social Behaviour, 52, 37-48, (2022). Although theoretical constraints clearly facilitate stable and replicable research findings, TASI reminds us of various reasons why even perfectly valid hypotheses need not always be borne out. The presented framework should help researchers to operationalize conditions under which theoretical constraints render empirical findings most predictable.

A modeling framework for detecting and leveraging node-level information in Bayesian network inference.

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modeling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximization algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.

Auxiliary field Quantum Monte Carlo for dilute neutrons on the lattice.

We employ constrained path Auxiliary Field Quantum Monte Carlo (AFQMC) in the pursuit of studying physical nuclear systems using a lattice formalism. Since AFQMC has been widely used in the study of condensed-matter systems such as the Hubbard model, we benchmark our method against published results for both one- and two-dimensional Hubbard model calculations. We then turn our attention to cold atomic and nuclear systems. We use an onsite contact interaction that can be tuned in order to reproduce the known scattering length and effective range of a given interaction. Developing this machinery allows us to extend our calculations to study nuclear systems within a lattice formalism. We perform initial calculations for a range of nuclear systems from two- to few-body neutron systems. This article is part of the theme issue 'The liminal position of Nuclear Physics: from hadrons to neutron stars'.

Comprehensive Analyses of the Effects of the Small-Molecule Inhibitor of the UHM Domain in the Splicing Factor U2AF1 in Leukemia Cells.

Mutations in RNA splicing factor genes including SF3B1, U2AF1, SRSF2, and ZRSR2 have been reported to contribute to development of myeloid neoplasms including myelodysplastic syndrome (MDS) and secondary acute myeloid leukemia (sAML). Chemical tools targeting cells carrying these mutant genes remain limited and underdeveloped. Among the four proteins, mutant U2AF1 (U2AF1mut) acquires an altered 3' splice site selection preference and co-operates with the wild-type U2AF1 (U2AF1wt) to change various gene isoform patterns to support MDS cells survival and proliferation. U2AF1 mutations in MDS cells are always heterozygous and the cell viability is reduced when exposed to additional insult affecting U2AF1wt function. To investigate if the pharmacological inhibition of U2AF1wt function can provoke drug-induced vulnerability of cells harboring U2AF1 mut , we conducted a fragment-based library screening campaign to discover compounds targeting the U2AF homology domain (UHM) in U2AF1 that is required for the formation of the U2AF1/U2AF2 complex to define the 3' splice site. The most promising hit (SF1-8) selectively inhibited growth of leukemia cell lines overexpressingU2AF1 mut and human primary MDS cells carrying U2AF1 mut . RNA-seq analysis of K562-U2AF1mut following treatment with SF1-8 further revealed alteration of isoform patterns for a set of proteins that impair or rescue pathways associated with endocytosis, intracellular vesicle transport, and secretion. Our data suggested that further optimization of SF1-8 is warranted to obtain chemical probes that can be used to evaluate the therapeutic concept of inducing lethality to U2AF1 mut cells by inhibiting the U2AF1wt protein.

Enantioselective Synthesis of the Active Sex Pheromone Components of the Female Lichen Moth, Lyclene dharma dharma, and Their Enantiomers.

The Lichen moth, Lyclene dharma dharma (Arctiidae, Lithosiinae), plays a significant role in forest ecosystem dynamics. A concise and novel method to synthesize the active sex pheromone components, (S)-14-methyloctadecan-2-one ((S)-1), (S)-6-methyloctadecan-2-one ((S)-2), and their enantiomers has been developed. Key steps in the synthesis include the use of Evans' chiral auxiliaries, Grignard cross-coupling reactions, hydroboration-oxidation, and Wacker oxidation. The synthesized sex pheromone components hold potential value for studies on communication mechanisms, species identification, and ecological management.

A panoramic view of the virosphere in three wastewater treatment plants by integrating viral-like particle-concentrated and traditional non-concentrated metagenomic approaches.

Wastewater biotreatment systems harbor a rich diversity of microorganisms, and the effectiveness of biotreatment systems largely depends on the activity of these microorganisms. Specifically, viruses play a crucial role in altering microbial behavior and metabolic processes throughout their infection phases, an aspect that has recently attracted considerable interest. Two metagenomic approaches, viral-like particle-concentrated (VPC, representing free viral-like particles) and non-concentrated (NC, representing the cellular fraction), were employed to assess their efficacy in revealing virome characteristics, including taxonomy, diversity, host interactions, lifestyle, dynamics, and functional genes across processing units of three wastewater treatment plants (WWTPs). Our findings indicate that each approach offers unique insights into the viral community and functional composition. Their combined use proved effective in elucidating WWTP viromes. We identified nearly 50,000 viral contigs, with Cressdnaviricota and Uroviricota being the predominant phyla in the VPC and NC fractions, respectively. Notably, two pathogenic viral families, Asfarviridae and Adenoviridae, were commonly found in these WWTPs. We also observed significant differences in the viromes of WWTPs processing different types of wastewater. Additionally, various phage-derived auxiliary metabolic genes (AMGs) were active at the RNA level, contributing to the metabolism of the microbial community, particularly in carbon, sulfur, and phosphorus cycling. Moreover, we identified 29 virus-carried antibiotic resistance genes (ARGs) with potential for host transfer, highlighting the role of viruses in spreading ARGs in the environment. Overall, this study provides a detailed and integrated view of the virosphere in three WWTPs through the application of VPC and NC metagenomic approaches. Our findings enhance the understanding of viral communities, offering valuable insights for optimizing the operation and regulation of wastewater treatment systems.