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The pressing need for sustainable energy solutions has driven significant research in optimizing solar photovoltaic (PV) systems which is crucial for maximizing energy conversion efficiency. Here, a novel hybrid gazelle‐Nelder–Mead (GOANM) algorithm is proposed and evaluated. The GOANM algorithm synergistically integrates the gazelle optimization algorithm (GOA) with the Nelder–Mead (NM) algorithm, offering an efficient and powerful approach for parameter extraction in solar PV models. This investigation involves a thorough assessment of the algorithm's performance across diverse benchmark functions, including unimodal, multimodal, fixed‐dimensional multimodal, and CEC2020 benchmark functions. Notably, the GOANM consistently outperforms other optimization approaches, demonstrating enhanced convergence speed, accuracy, and reliability. Furthermore, the application of the GOANM is extended to the parameter extraction of the single diode and double diode models of RTC France solar cell and PV model of Photowatt‐PWP201 PV module. The experimental results consistently demonstrate that the GOANM outperforms other optimization approaches in terms of accurate parameter estimation, low root mean square values, fast convergence, and alignment with experimental data. These results emphasize its role in achieving superior performance and efficiency in renewable energy systems. [ABSTRACT FROM AUTHOR]

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy. [ABSTRACT FROM AUTHOR]

Food production is a complex matter, affecting people's lives, organizations' profits, and the well-being of the whole planet, and has multifaceted ethical considerations surrounding its production, distribution, and consumption. This paper addresses the pressing need to confront ethical challenges within the food system, encompassing issues such as environmental sustainability, food security, and individual food choices for better oral and systemic health of all individuals around the globe. From agricultural practices to global trade and food waste, ethical implications are addressed across various domains, highlighting the interconnectedness of ethical decision-making in the food industry. Central themes explored include the ethical dimensions of food production methods, the impact of global trade on food ethics, and the role of individuals in making ethically informed food choices. Additionally, this paper considers the spiritual and physical significance of food, particularly through the lens of oral health as a gateway to holistic well-being. Recognizing the complexity of the food and mouth ecosystem, this paper calls for serious interventions in legislation and economics to promote ethical protocols and techniques for sustainability reasons. It emphasizes the importance of ethical considerations in food safety management systems, regulatory frameworks, and quality standards. Moreover, this paper underlines the need for a comprehensive approach to address ethical dilemmas and moral values inherent in the food industry and oral health policies, adopting the precautionary principle and ethical decision-making frameworks. This article finally aims to serve as a call to action for stakeholders across the food industry and the healthcare sector, to prioritize ethical practices, promote transparency, rearrange economic parameters, and work towards a more sustainable and equitable food system for inner and outer oral and systemic health and human sustainability for all. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is associated with a high prevalence of cancer-related deaths. The survival rates of patients are significantly lower in late-stage ccRCC than in early-stage ccRCC, due to the spread and metastasis of late-stage ccRCC, surgery has not reached the goal of radical cure, and the effect of traditional radiotherapy and chemotherapy is poor. Thus, it is crucial to accurately assess the prognosis and provide personalized treatment at an early stage in ccRCC. This study aims to develop an efficient nomogram model for stratifying and predicting the survival of ccRCC patients based on tumor stage. Methods: We first analyzed the microarray expression data of ccRCC patients from the Gene Expression Omnibus (GEO) database and categorized them into two groups based on the disease stage (early and late stage). Subsequently, the GEO2R tool was applied to screen out the genes that were highly expressed in all GEO datasets. Finally, the clinicopathological data of the two patient groups were obtained from The Cancer Genome Atlas (TCGA) database, and the differences were compared between groups. Survival analysis was performed to evaluate the prognostic value of candidate genes (PSAT1, PRAME, and KDELR3) in ccRCC patients. Based on the screened gene PSAT1 and clinical parameters that were significantly associated with patient prognosis, we established a new nomogram model, which was further optimized to a single clinical variable-based model. The expression level of PSAT1 in ccRCC tissues was further verified by qRT-PCR, Western blotting, and immunohistochemical analysis. Results: The datasets GSE73731, GSE89563, and GSE150404 identified a total of 22, 89, and 120 over-expressed differentially expressed genes (DEGs), respectively. Among these profiles, there were three genes that appeared in all three datasets based on different stage groups. The overall survival (OS) of late-stage patients was significantly shorter than that of early-stage patients. Among the three candidate genes (PSAT1, PRAME, and KDELR3), PSAT1 was shown to be associated with the OS of patients with late-stage ccRCC. Multivariate Cox regression analysis showed that age, tumor grade, neoadjuvant therapy, and PSAT1 level were significantly associated with patient prognosis. The concordance indices were 0.758 and 0.725 for the 3-year and 5-year OS, respectively. The new model demonstrated superior discrimination and calibration compared with the single clinical variable model. The enhancer PSAT1 used in the new model was shown to be significantly overexpressed in tissues from patients with late-stage ccRCC, as demonstrated by the mRNA level, protein level, and pathological evaluation. Conclusion: The new prognostic prediction nomogram model of PSAT1 and clinicopathological variables combined was thus established, which may provide a new direction for individualized treatment for different-stage ccRCC patients. [ABSTRACT FROM AUTHOR]

Periodontal disease is very common in pregnant women. Paraprobiotics are a subset of probiotics. They can be defined as inactivated microbial cells providing health benefits to the host and are considered particularly safe. The aim of this study was to compare the periodontal health of pregnant women and puerperae after 6 months of home use of paraprobiotics. A total of 30 pregnant women were enrolled and divided into two groups: the test group, who had to use a paraprobiotic-based toothpaste (Biorepair Peribioma Pro, Coswell S.p.A., Funo di Argelato, BO, Italy) and mousse (Mousse Mouthwash Biorepair Peribioma, Coswell S.p.A.) twice a day, and the control group, who had to use only the paraprobiotic-based toothpaste. The time frames of the study were: 1 month (T1), 3 months (T2) and 6 months (T3), and data were collected during pregnancy and in the period immediately following delivery. The following indices were evaluated at T0, T1, T2 and T3: clinical attachment loss (CAL), probing pocket depth (PPD), bleeding on probing (BOP), plaque control record (PCR), modified marginal gingival index (mMGI), papillary marginal gingival index (PMGI) and recessions (R). All data were subjected to statistical analysis. PCR decreased significantly from T0 to T1 in the control group and from T0 to T2 and from T0 to T3 in the test group. BOP tended to decrease in both groups, but a significant reduction was observed only in the test group. CAL, PPD, PMGI and mMGI tended to decrease gradually in both groups without significant differences between or within groups. The combination of the paraprobiotic-based toothpaste and the paraprobiotic-based mousse significantly reduced BoP and plaque control over time, although there were no significant differences with the use of the paraprobiotic-based toothpaste alone. In addition, the combination of the two products promoted a trend towards the better stabilization of recessions. [ABSTRACT FROM AUTHOR]

Several studies have shown fluctuations in the maternal microbiota at various body sites (gut, oral cavity, and vagina). The skin microbiota plays an important role in our health, but studies on the changes during pregnancy are limited. Quantitative and qualitative variations in the skin microbiota in pregnant woman could indeed play important roles in modifying the immune and inflammatory responses of the host. These alterations could induce inflammatory disorders affecting the individual's dermal properties, and could potentially predict infant skin disorder in the unborn. The present study aimed to characterize skin microbiota modifications during pregnancy. For this purpose, skin samples were collected from 52 pregnant women in the first, second, and third trimester of non-complicated pregnancies and from 17 age- and sex-matched healthy controls. The skin microbiota composition was assessed by next generation sequencing (NGS) of the V3–V4 region of the bacterial rRNA 16S. Our results indicate that from the first to the third trimester of pregnancy, changes occur in the composition of the skin microbiota, microbial interactions, and various metabolic pathways. These changes could play a role in creating more advantageous conditions for fetal growth. [ABSTRACT FROM AUTHOR]

This paper investigates the casting defects of a stainless steel pump impeller manufactured through the sand casting process. The material characterization of austenitic steel AISI 316L was initially carried out, which examined the chemical composition of the casting and its microstructure. The next step was to determine the cause of the casting defects using numerical simulations. The numerical simulations were performed using ProCAST software (Version 18.0). Initial results of the filling and solidification simulations were conducted using the parameters employed in the actual casting process, revealing casting defects in corresponding locations. The casting process was subsequently modified to achieve improved results. This involved reconstructing the gating system, redesigning the riser, and incorporating a cylindrical chiller. The results show that the modified casting process significantly reduces the occurrence of defects in the final product. The study provides useful insights into the analysis and modification of the casting process for stainless steel pump impellers produced through sand casting. The results can help improve the quality of such products and reduce production costs associated with casting defects. [ABSTRACT FROM AUTHOR]

In this work, we investigated aroma volatiles emanated by dry roots, stems, leaves, flowers, and fruits of Cardaria draba (L.) Desv. growing wild in Tunisia and its aerial part essential oils (EOs) composition. A total of 37 volatile organic compounds (96.7%-98.9%) were identified; 4 esters, 4 alcohols, 7 hydrocarbons, 12 aldehydes, 5 ketones, 1 lactone, 1 organosulfur compound, 2 organonitrogen compounds, and 1 acid. The hydrocarbons form the main group, representing 49.5%-84.6% of the total detected volatiles. The main constituent was 2,2,4,6,6-pentamethylheptane (44.5%-76.2%) reaching the highest relative percentages. Forty-two compounds were determined in the two fractions of EOs, representing 98.8% and 97.2% of the total oil composition, respectively. The principal components were hexadecanoic acid (34.6%), 6-methyl-5-hepten-2-one (18.3%), decanal (15.0%), 6,10,14-trimethyl2-pentadecanone (13.2%), and n-pentacosane (13%). Micromorphological details of the leaf and stem epidermis using light microscopy revealed polygonal cells with sinuous walls in the adaxial and abaxial leaf surfaces and nearly rectangular and long ones with linear and thick walls for the stem epidermis. The stomata complexes were anisocytic in the leaf epidermis and mainly anisocytic and rarely paracytic in the stem epidermis. Non-glandular trichomes were unbranched and long with an acute apex or short with a convex apex. The glandular ones were identified for the first time in this species. They were short-stalked with a large secretory head. The highest stomatal index (17.02%) was recorded in the abaxial leaf surface. The identification of headspace volatiles and essential oil compounds can be used to characterize this species, and the various epidermis micromorphological features are very useful for biosystematics taxonomic studies within Brassicaceae. [ABSTRACT FROM AUTHOR]

Large stone porous asphalt mixes (LSPMs) have the characteristics of large gaps, large content of coarse aggregate, and large particle size of coarse aggregate. Compared with the traditional asphalt-treated base (ATB), LSPM forms a more obvious skeleton structure in the process of bearing load. In this study, the discrete element method (DEM) model of LSPM is established, and an evaluation method for studying the bearing capacity and high-temperature performance of LSPM through an internal skeleton network is proposed. Firstly, the DEM model of asphalt mixture with different gradations is established, and the virtual uniaxial penetration test is carried out. Then, the performance of the skeleton network inside the asphalt mixture is analyzed, and the contribution of each group of aggregates to the skeleton network is further studied. The experimental results show that the bearing capacity of an asphalt mixture is closely related to the performance of internal skeleton network. The skeleton network inside the asphalt mixture is extracted by the value of the contact force, the angle between the contacts, and the contact continuity. When the skeleton network has better bearing capacity and load-transfer capacity, the asphalt mixture can bear more external load in the virtual penetration test. The contribution of different coarse aggregates to the skeleton network is analyzed from four perspectives: total contact force, vertical work, contact number, and effective coordination number. The aggregates of 19–26.5 and 4.75–9.5 mm constitute the main body of the load-transfer path and play a major role in bearing and transmitting the load in the skeleton network. [ABSTRACT FROM AUTHOR]

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system. [ABSTRACT FROM AUTHOR]

Kidney disease is a devastating condition affecting millions of people worldwide, where over 100,000 patients in the United States alone remain waiting for a lifesaving organ transplant. Concomitant with a surge in personalized medicine, single-gene mutations, and polygenic risk alleles have been brought to the forefront as core causes of a spectrum of renal disorders. With the increasing prevalence of kidney disease, it is imperative to make substantial strides in the field of kidney genetics. Nephrons, the core functional units of the kidney, are epithelial tubules that act as gatekeepers of body homeostasis by absorbing and secreting ions, water, and small molecules to filter the blood. Each nephron contains a series of proximal and distal segments with explicit metabolic functions. The embryonic zebrafish provides an ideal platform to systematically dissect the genetic cues governing kidney development. Here, we review the use of zebrafish to discover nephrogenesis genes. [ABSTRACT FROM AUTHOR]

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems. [ABSTRACT FROM AUTHOR]

Background: Ecosystems are brimming with myriad compounds, including some at very low concentrations that are indispensable for insect survival and reproduction. Screening strategies for identifying active compounds are typically based on bioassay-guided approaches. Results: Here, we selected two candidate odorant receptors from a major pest of cruciferous plants—the diamondback moth Plutella xylostella—as targets to screen for active semiochemicals. One of these ORs, PxylOR16, exhibited a specific, sensitive response to heptanal, with both larvae and adult P. xylostella displaying heptanal avoidance behavior. Gene knockout studies based on CRISPR/Cas9 experimentally confirmed that PxylOR16 mediates this avoidance. Intriguingly, rather than being involved in P. xylostella–host plant interaction, we discovered that P. xylostella recognizes heptanal from the cuticular volatiles of the parasitoid wasp Cotesia vestalis, possibly to avoid parasitization. Conclusions: Our study thus showcases how the deorphanization of odorant receptors can drive discoveries about their complex functions in mediating insect survival. We also demonstrate that the use of odorant receptors as a screening platform could be efficient in identifying new behavioral regulators for application in pest management. [ABSTRACT FROM AUTHOR]

For a long time, urine has been considered sterile in physiological conditions, thanks to the particular structure of the urinary tract and the production of uromodulin or Tamm–Horsfall protein (THP) by it. More recently, thanks to the development and use of new technologies, i.e., next-generation sequencing and expanded urine culture, the identification of a microbial community in the urine, the so-called urobiota, became possible. Major phyla detected in the urine are represented by Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. Particularly, the female urobiota is largely represented by Lactobacillus spp., which are very active against urinary pathogenic Escherichia (E.) coli (UPEC) strains via the generation of lactic acid and hydrogen peroxide. Gut dysbiosis accounts for recurrent urinary tract infections (UTIs), so-called gut–bladder axis syndrome with the formation of intracellular bacterial communities in the course of acute cystitis. However, other chronic urinary tract infections are caused by bacterial strains of intestinal derivation. Monomicrobial and polymicrobial infections account for the outcome of acute and chronic UTIs, even including prostatitis and chronic pelvic pain. E. coli isolates have been shown to be more invasive and resistant to antibiotics. Probiotics, fecal microbial transplantation, phage therapy, antimicrobial peptides, and immune-mediated therapies, even including vaccines for the treatment of UTIs, will be described. [ABSTRACT FROM AUTHOR]

Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R 2 , prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment., (© 2022. The Author(s).)

Since the 2015 Paris Agreement, efforts for environmental protection have gained prominence worldwide. Accordingly, electric vehicles have become increasingly relevant. Thus, improving the performance of the water pump, a key component of cooling systems in electric vehicles, is crucial. Electric vehicles operate on batteries and motors, making their cooling systems remarkably complex. Efficient operation of the water pump is directly related to the stable performance of electric vehicles and is therefore critical. This study conducted numerical analyses using Ansys Fluent to evaluate water pump performance by varying key parameters, namely, number of blades and outer diameter of the impeller. When the number of blades was changed to 7, 9, 11, and 13, the efficiency, head, and thrust tended to increase. In particular, for blade counts greater than 11, the fluid flow was found to stabilize with negligible effect on pump performance. When the outer diameter of the impeller was 70, 69, 68, and 67 mm, although efficiency decreased, the head and thrust tended to increase. Based on these comprehensive results, a structure was proposed for the shape of the optimized water pump. The development of efficient and stable water pumps is expected to contribute to the performance improvement of electric vehicles. [ABSTRACT FROM AUTHOR]

In this study, we explored the extracellular electron transfer (EET) capabilities of two bacterial strains, OTU0001 and OTU0002, which are demonstrated in biofilm formation in mouse gut and the induction of autoimmune diseases like multiple sclerosis. OTU0002 displayed significant electrogenic behaviour, producing microbial current on an indium tin-doped oxide electrode surface, particularly in the presence of glucose, with a current density of 60 nA/cm2. The presence of cell-surface redox substrate potentially mediating EET was revealed by the redox-based staining method and electrochemical voltammetry assay. However, medium swapping analyses and the addition of flavins, a model redox mediator, suggest that the current production is dominated by soluble endogenous redox substrates in OTU0002. Given redox substrates were detected at the cell surface, the secreted redox molecule may interact with the cellular surface of OTU0002. In contrast to OTU0002, OTU0001 did not exhibit notable electrochemical activity, lacking cell-surface redox molecules. Further, the mixture of the two strains did not increase the current production from OTU0001, suggesting that OTU0001 does not support the EET mechanism of OTU0002. The present work revealed the coexistence of EET and non-EET capable pathogens in multi-species biofilm. [ABSTRACT FROM AUTHOR]

To address the significant energy waste generated by axial flow pumps, this paper proposes an intelligent optimization method based on physics-considering machine learning. First, a highly parameterized geometric design theory is constructed using six featured variables to achieve a complete three-dimensional modeling of the blade geometry. Four hundred preliminary cases are studied using the computational fluid dynamics method with various combinations of these featured variables to obtain a preliminary solution. The best preliminary design has an efficiency of 83.33%, and a head of 5.495 m. To further improve this performance, this paper also presents a high-precision prediction model for the energy performance of axial flow pump based on back-propagation neural network and the encoding layers of random sampling and local feature aggregator network created. Afterwards, a multi-population genetic algorithm is used to quickly find the optimal solution within the prediction mode range. The algorithm achieved a highest efficiency of 86.373% and was validated by numerical simulation with a value of 86.057% and a prediction error of 0.316%. Compared with the preliminary solution, the efficiency of the optimized axial flow pump is increased by 1.615%, with a wider high-efficiency range and an optimal operating point closer to the design conditions. Overall, this intelligent optimization method has the potential to significantly reduce the design time of axial pumps and increase their performance. [ABSTRACT FROM AUTHOR]

Due to the complex structure of a double-suction pump's suction chamber, the flow in the pump's cavity is often ignored in numerical simulations because of difficulties in structured hexahedral meshing. However, the wear ring clearance interlinking the pump chamber leads the fluid at the impeller inlet directly to the impeller area. This significantly impacts the pump's internal flow field, so the influence of the clearance on the internal flow of a double-suction pump cannot be ignored. This paper develops four three-dimensional double-suction pump models with different wear ring clearances to investigate their influence on pump performance, and structured hexahedral meshes were used for all the computational domains. The clearances varied from 0.2 to 0.5 mm in 0.1 mm increments. The influences of the clearance on the energy loss, external characteristics, and internal flow field distribution of the pump were simulated via a verified computational fluid dynamics method. The results show that the wear ring leakage decreases with the flow rate and increases with wear ring clearance. The increase in backflow leads to an internal flow disorder inside the impeller, resulting in a decreased head and efficiency. Energy loss is mainly caused by increasing the turbulence entropy production with an increasing wear ring clearance. Also, the low-pressure region in the pump cavity expands to the volute with increasing clearance, and the impeller outlet pressure decreases. This study's research on wear ring clearance provides a reference for the design and application of double-suction centrifugal pumps. [ABSTRACT FROM AUTHOR]

Macroinvertebrates play a vital role in coastal ecosystems and are an important indicator of ecosystem quality. Both anthropogenic activity and environmental changes may lead to significant changes in the marine macroinvertebrate community. However, the assembly process of benthic biodiversity and its mechanism driven by environmental factors at large scales remains unclear. Here, using the benthic field survey data of 15 years at large spatial and temporal scales from the Yellow Sea Large Marine Ecosystem, we investigated the relative importance of environmental selection, dispersal processes, random‐deterministic processes of macroinvertebrates community diversity assembly, and the responses of this relative importance driven by temperature and nutrients. Results showed that the macroinvertebrates community diversity is mainly affected by dispersal. Nitrogen and phosphorus are the most important negative factors among environmental variables, while geographical distance is the main limiting factor of β diversity. Within the range of 0.35–0.70 mg/L of nutrients, increasing nutrient concentration can significantly facilitate the contribution of the decay effect to β diversity. Within the temperature range studied (15.0–18.0°C), both warming and cooling can lead to a greater tendency for species diversity assembly processes to be dominated by deterministic processes. The analysis contributes to a better understanding of the assembly process of the diversity of coastal marine macroinvertebrates communities and how they adapt to global biogeochemical processes. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) with a large chemical diversity are emitted by plant flowers. These compounds play an important role in the ecology of plants. This review presents the different ecological roles of VOCs present in the odor plumes of plant flowers, such as pollination, defense, adaptation to their environment, and communication with other organisms. The production and accumulation sites of VOCs in plants with their spatial and temporal variations, including environmental issues, are also summarized. To evaluate the qualitative and quantitative chemical composition of VOCs, several methods of extraction and analysis were used. Headspace (HS) sampling coupled with solid phase microextraction (SPME) is now well-developed for the extraction process. Parameters are known, and several fibers are now available to optimize this extraction. Most of the time, SPME is coupled with gas chromatography-mass spectrometry (GC-MS) to determine the structural identification of the VOCs, paying attention to the use of several complementary methods for identification like the use of databases, retention indices, and, when available, comparison with authentic standards analyses. The development of the knowledge on VOCs emitted by flowers is of great importance for plant ecology in the context of environmental and climate changes. [ABSTRACT FROM AUTHOR]

Background: Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly 13 C -enriched glucose ( 13 C 6 -Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups. Results: We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux. Conclusions: Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak caused by SARS-CoV-2 in late 2019 has spread rapidly across the world to form a global epidemic of respiratory infectious diseases. Increased investigations on diagnostic tools are currently implemented to assist rapid identification of the virus because mass and rapid diagnosis might be the best way to prevent the outbreak of the virus. This critical review discusses the detection principles, fabrication techniques, and applications on the rapid detection of SARS-CoV-2 with three categories: rapid nuclear acid augmentation test, rapid immunoassay test and biosensors. Special efforts were put on enhancement of nanomaterials on biosensors for rapid, sensitive, and low-cost diagnostics of SARS-CoV-2 virus. Future developments are suggested regarding potential candidates in hospitals, clinics and laboratories for control and prevention of large-scale epidemic. [ABSTRACT FROM AUTHOR]

In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods. [ABSTRACT FROM AUTHOR]

A dead-end trap plant is a plant species that is highly attractive for oviposition and other activities of target pests, but on which they cannot complete their development, reproduction or survival. Due to its unique insecticidal mechanism and environment-friendly characteristics, it has received increasing attention in recent years. There are many species that can be used as trap plant, but few of them can be used as dead-end trap plants. These plants are commonly utilized for lepidopteran pest management in graminaceous crops, cruciferous vegetables and other cropping systems. At present, vetiver grass, Chrysopogon zizanioides, is widely used in the integrated pest management (IPM) of rice borers in southern China as an alternative to chemical pesticides. This article lists plant species that can be used as dead-end trap plants, together with the target pests and relevant cropping systems. In addition, the trapping principle and insecticidal mechanism of dead-end traps is reviewed, and the application of vetiver grass as a dead-end trap in rice borer IPM introduced. The future research directions of dead-end trap plants towards the protection of crops are also discussed. [ABSTRACT FROM AUTHOR]

NKG2D is an activating receptor expressed by all human NK cells and CD8 T cells. Harnessing the NKG2D/NKG2D ligand axis has emerged as a viable avenue for cancer immunotherapy. However, there is a long-standing controversy over whether soluble NKG2D ligands are immunosuppressive or immunostimulatory, originating from conflicting data generated from different scopes of pre-clinical investigations. Using multiple pre-clinical tumor models, we demonstrated that the impact of the most characterized human solid tumor-associated soluble NKG2D ligand, the soluble MHC I chain-related molecule (sMIC), on tumorigenesis depended on the tumor model being studied and whether the tumor cells possessed stemness-like properties. We demonstrated that the potential of tumor formation or establishment depended upon tumor cell stem-like properties irrespective of tumor cells secreting the soluble NKG2D ligand sMIC. Specifically, tumor formation was delayed or failed if sMIC-expressing tumor cells expressed low stem-cell markers; tumor formation was rapid if sMIC-expressing tumor cells expressed high stem-like cell markers. However, once tumors were formed, overexpression of sMIC unequivocally suppressed tumoral NK and CD8 T cell immunity and facilitated tumor growth. Our study distinguished the differential impacts of soluble NKG2D ligands in tumor formation and tumor progression, cleared the outstanding controversy over soluble NKG2D ligands in modulating tumor immunity, and re-enforced the viability of targeting soluble NKG2D ligands for cancer immunotherapy for established tumors. [ABSTRACT FROM AUTHOR]

In order to optimize the performance of a Solar Photovoltaic (PV) system, it is necessary to develop an appropriate PV cell model and accurately determine the unknown parameters associated with the model. The process of extracting parameters for PV models is a complex optimization issue that involves nonlinearity and multiple models. Accurate estimation of the characteristics of PV units is crucial since these factors significantly affect the performance of PV systems in terms of power and current generation. Consequently, this research presents an advanced methodology, known as the Pelican Optimization Algorithm (POA), aimed to find the optimal values for the unspecified parameters of PV units. In this study, the Single Diode Model (SDM) is employed to analyze four datasets like RTC France, Photowatt-PWP201, STP-120/36, as well as STM6-40/36 PV panels. The POA algorithm is utilized to determine the unknown parameters of solar PV modules. Furthermore, to enhance the precision of the obtained solutions, this study incorporates the Newton–Raphson (NR) method into the POA algorithm. The POA achieves the optimum Root Mean Square Error (RMSE) values for the four PV models (RTC France, Photowatt-PWP201, STM6-40/36 and STP6-120/36) and the values are found to be 7.7298E-04, 2.0528E-03, 1.7220E-03 and 1.4458E-02 respectively. From the results, it is observed that, POA exhibit superior performance compared to the other MH optimization algorithms. Furthermore, the statistical findings show that the POA algorithm has a higher average robustness and accuracy than the other algorithms. [ABSTRACT FROM AUTHOR]

This paper utilised a solid‐state transformer (SST) with an active clamped L‐L type current fed front‐end converter for maximum power extraction (MPE) and voltage boost operation. The proposed system employed perturb and observe (P&O) based MPE and upheld the ZVS operation of all primary side switches and ZCS operation of all secondary side rectifier diodes for various insolation conditions. The proposed system developed a new fundamental extractor titled multi‐level cascaded dual double fundamental signal extractor (MCDDFSE) and is utilized in the control algorithm for synchronization with the utility grid. The main objective of the proposed system was to deliver a compact size, reduced weight and cheaper magnetic components‐based front‐end converter of SST on the photovoltaic (PV) array side and a fast, secure and trustworthy mitigation technique for the grid current harmonics on the utility grid side. The proposed system was trialled on the platform of OPAL‐RT (RT‐LABv2021.3.2.307) for various grid abnormalities and its behaviour was found well within the IEEE 519 standards. [ABSTRACT FROM AUTHOR]

This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO's primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO's success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively. [ABSTRACT FROM AUTHOR]

Tumor-initiating cells (TICs) reprogram their metabolic features to meet their bioenergetic, biosynthetic, and redox demands. Our previous study established a role for wild-type isocitrate dehydrogenase 1 (IDH1WT) as a potential diagnostic and prognostic biomarker for non–small cell lung cancer (NSCLC), but how IDH1WT modulates NSCLC progression remains elusive. Here, we report that IDH1WT activates serine biosynthesis by enhancing the expression of phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase 1 (PSAT1), the first and second enzymes of de novo serine synthetic pathway. Augmented serine synthesis leads to GSH/ROS imbalance and supports pyrimidine biosynthesis, maintaining tumor initiation capacity and enhancing gemcitabine chemoresistance. Mechanistically, we identify that IDH1WT interacts with and stabilizes PHGDH and fragile X–related protein-1 (FXR1) by impeding their association with the E3 ubiquitin ligase parkin by coimmunoprecipitation assay and proximity ligation assay. Subsequently, stabilized FXR1 supports PSAT1 mRNA stability and translation, as determined by actinomycin D chase experiment and in vitro translation assay. Disrupting IDH1WT-PHGDH and IDH1WT-FXR1 interactions synergistically reduces NSCLC stemness and sensitizes NSCLC cells to gemcitabine and serine/glycine–depleted diet therapy in lung cancer xenograft models. Collectively, our findings offer insights into the role of IDH1WT in serine metabolism, highlighting IDH1WT as a potential therapeutic target for eradicating TICs and overcoming gemcitabine chemoresistance in NSCLC. Editor's summary: Tumor-initiating cells (TICs) have high metabolic plasticity and have been suggested as the cause for tumorigenesis and relapse in many cancers including non–small cell lung cancer (NSCLC). Here, Zhang et al. have examined the role of serine synthesis in TICs IDH1 wild-type NSCLC and identified that inhibiting this pathway sensitizes cells and xenografts to standard of care. Their study suggests a potential metabolic target for treated NSCLC that requires further study. —Dorothy Hallberg [ABSTRACT FROM AUTHOR]

Based on the data of listed companies in China's iron and steel industry from 2007 to 2020, the article investigates the impact mechanism and the path of action of China's carbon emissions trading pilot on the green total factor productivity of iron and steel enterprises by constructing a multi-period difference-in-difference model difference-in-differences. The study finds that: 1) China's iron and steel enterprises significantly improve their green total factor productivity driven by the carbon trading pilot, and the findings pass the corresponding robustness tests. 2) the mechanism analysis indicates that the carbon trading pilot promotes the green total factor productivity of iron and steel enterprises by forcing the technological progress of enterprises. 3) The heterogeneity analysis shows that the positive effect is more significant for large iron and steel enterprises with high social responsibility rating and high local government competition intensity, but not for small enterprises with low social responsibility rating and low local government competition intensity. 4) the dynamic effect shows that there is a certain lag in the promotion effect of the carbon emission trading pilot on the green total factor productivity of iron and steel enterprises, but its long-term effect is more obvious. This paper puts forward corresponding suggestions for accelerating the construction of a national unified green and low-carbon market system and actively promoting the deepening of the "dual-carbon" goal. [ABSTRACT FROM AUTHOR]

A subset of microorganisms that perform respiration can endogenously utilize insoluble electron donors, such as Fe(II) or a cathode, in a process called extracellular electron transfer (EET). However, it is unknown whether similar endogenous EET can be performed by primarily fermentative species like lactic acid bacteria. We report for the first time electron uptake from a cathode by Lactiplantibacillus plantarum, a primarily fermentative bacteria found in the gut of mammals and in fermented foods. L. plantarum consumed electrons from a cathode and coupled this oxidation to the reduction of both an endogenous organic (pyruvate) and an exogenous inorganic electron acceptor (nitrate). This electron uptake from a cathode reroutes glucose fermentation toward lactate degradation and provides cells with a higher viability upon sugar exhaustion. Moreover, the associated genes and cofactors indicate that this activity is mechanistically different from that one employed by lactic acid bacteria to reduce an anode and to perform respiration. Our results expand our knowledge of the diversity of electroactive specie [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications. [ABSTRACT FROM AUTHOR]

The impeller, regarded as the central component of a centrifugal pump, plays a pivotal role in dictating overall performance. Overcoming challenges arising from the complexity of design parameters and the time-intensive nature of the design process has been a persistent obstacle to widespread adoption. In this study, we integrated ANSYS-CFX 2023 software with innovative inverse design techniques to optimize the impeller design within a centrifugal pump system. Our investigation reveals groundbreaking insights, highlighting the significant influence of both blade load and shaft surface geometry on impeller performance. Notably, through load optimization, substantial enhancements in centrifugal pump efficiency were achieved, demonstrating improvements of 1.8% and 1.7% under flow conditions of 1.0 Q and 0.8 Q, respectively. Further, the efficiency gains of 0.44% and 0.36% were achieved in their corresponding flow conditions. The optimization of blade load and shaft surface configuration notably facilitated a more homogenized internal flow pattern within the impeller. These novel findings contribute substantively to the theoretical foundations underpinning centrifugal pump impeller design, offering engineers a valuable reference to elevate their performance. Our utilization of ANSYS-CFX software in conjunction with inverse design methodologies showcases a promising avenue for advancing impeller design, ultimately culminating in superior efficiency and performance for centrifugal pumps. [ABSTRACT FROM AUTHOR]

Production of microalgae is a potential technology for capturing and recycling carbon dioxide from cement kiln emissions. In this study, a process of selecting a suitable strain that would effectively utilize carbon dioxide and generate biomass was investigated. A down-selection screening method was applied to 28 strains isolated from the area surrounding a commercial cement plant. In laboratory-scale (1 L) continuous-mode chemostats, observed productivity was > 0.9 g L−1 d−1 for most strains studied. Chlorella sorokiniana (strain SMC-14M) appeared to be the most tolerant to cement kiln gas emissions in situ, delivered under control of a pH-stat system, and was down-selected to further investigate growth and biomass production at large-scale (1000 L) cultivation. Results demonstrated little variability in lipid, crude protein, and carbohydrate composition throughout growth between kiln-gas grown algal biomass and biomass produced with laboratory grade CO2. The growth rate at which the maximum quantity of CO2 from the emissions is recycled also produced the maximum amount of the targeted biomass components to increase commercial value of the biomass. An accumulation of some heavy metals throughout its growth demonstrates the necessity to monitor the biomass cultivated with industrial flue gases and to carefully consider the potential applications for this biomass; despite its other attractive nutritional properties. Key points: • Studied high biomass producing algal strains grown on CO2from cement flue gas. • Chlorella sorokiniana SMC-14M grew well at large scale, in situ on cement flue gas. • Demonstrated the resulting commercial potential of the cultured algal biomass. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is the most frequent inflammatory and demyelinating disease of the CNS. The disease course in MS is highly variable and driven by a combination of relapse-driven disease activity and relapse-independent disease progression. The formation of new focal demyelinating lesions is associated with clinical relapses; however, the pathological mechanisms driving disease progression are less well understood. Current concepts suggest that ongoing focal and diffuse inflammation within the CNS in combination with an age-associated failure of compensatory and repair mechanisms contribute to disease progression. The aim of our study was to characterize the diffuse microglia activation in periplaque white matter (PPWM) of MS patients, to identify factors modulating its extent and to determine its potential correlation with loss or preservation of oligodendrocytes. We analyzed microglial and oligodendroglial numbers in PPWM in a cohort of 96 tissue blocks from 32 MS patients containing 100 lesions as well as a control cohort (n = 37). Microglia activation in PPWM was dependent on patient age, proximity to lesion, lesion type, and to a lesser degree on sex. Oligodendrocyte numbers were decreased in PPWM; however, increased microglia densities did not correlate with lower oligodendroglial cell counts, indicating that diffuse microglia activation is not sufficient to drive oligodendroglial loss in PPWM. In summary, our findings support the notion of the close relationship between focal and diffuse inflammation in MS and that age is an important modulator of MS pathology. [ABSTRACT FROM AUTHOR]

Background: Pregnant women are highly susceptible to periodontal disease due to changes in hormonal and immune levels, which places a huge burden on the healthcare system and requires multidisciplinary interventions. This study aimed to assess the scientific profile and research trends related to periodontal disease in pregnancy through a bibliometric approach.Methods: Publications about periodontal disease in pregnancy from 2000 to 2022 were extracted from Science Citation Index Expanded. The knowledge networks of countries, institutions, authors, journals, references, and keywords in this field were constructed using the Citespace, VOSviewer, Bibliometrix, and BIBLIOMETRIC.COM platforms. Furthermore, correlations between the characteristics of countries and the number or impact of publications were analyzed.Results: 1162 original studies and reviews were included. There was a trend toward increased publications and citations in this field. The United States had the highest academic productivity and impact by a significant margin, while correlation analyses indicated that economic power may correlate with national scientific activity. The University of North Carolina and Offenbacher S were the most influential institution and author, respectively, taking center stage in the collaborative networks. However, only several loose connections between countries or institutions were identified in the global collaborative network analysis. Six of the top ten most productive journals were in Q1 in the Journal Citation Report, and there was intensive interaction between different research subfields, such as immunology, molecular biology, and microbiology. Frontier topics were primarily clustered in two areas: (1) oral microbiology, such as microbiome, oral bacteria, and Fusobacterium nucleatum; and (2) public health, such as quality of life, pregnancy outcomes, oral health, obesity, and classification.Conclusion: Since 2000, periodontal disease in pregnancy is receiving increasingly widespread attention and is rapidly evolving at a multidisciplinary level. Oral microbiological pathogenesis and public health impact-related research deserve more exploration and may be the future direction of research. Enhanced Collaboration and interdisciplinary communication may further facilitate progress in this discipline. [ABSTRACT FROM AUTHOR]

Since double-suction centrifugal pumps consume quantities of energy, revealing critical factors for energy dissipation is helpful for energy-saving design. This study aims to investigate the relationship between energy dissipation and vortex based on Rortex method in a double-suction centrifugal pump. Detached eddy simulation was applied to obtain the flow field. Enstrophy was used to present the strength of the local rigid vortex and shear. The results indicate that the local shear dominates energy dissipation in the pump. Owing to jet flows, the energy loss on blade leading edges (LE) and trailing edges (TE) were 10²-10³ times that of the middle region at 0.4Qd and ten times at 1.4Qd. The energy dissipation on pressure sides (PS) was ten times greater than that on suction sides (SS) at the TE, while flow separation at the middle of SS caused by wake flow increased energy dissipation to nearly ten times that of PS. Jet-wake flow near volute inlet was the dominant factor for energy dissipation at part-load, while separation flows in volute discharge was more significant at overload. The induced high local shear strength was responsible for energy dissipation. Therefore, reducing local shear is a potential energy-saving approach in pumps. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are widely used in various fields and have enormous energy-saving potential. In order to predict efficiency quickly and accurately, firstly, by establishing a hydraulic loss model for centrifugal pump impellers, the functional relationship between impeller hydraulic loss and hydraulic efficiency is constructed to establish the objective function. Secondly, calculate the main dimensions of the impeller using the velocity coefficient method and establish the objective function variables of the number of blades Z and outlet placement angle β2. And the velocity coefficient k0. Then, the method of mathematical statistics, namely response surface analysis, is used to solve the relationship between hydraulic efficiency and dependent variables within the range of variables, which plays the role of predicting hydraulic efficiency. Finally, the accuracy of the predictions is verified by numerical simulation. The results show that the hydraulic efficiency of a high specific speed centrifugal pump reaches its maximum at 1000m³/h operating conditions with a blade number of 3, a speed coefficient of 3.9 and an outlet angle of 30°. The study provides a new direction for the hydraulic design of high specific speed centrifugal pumps to achieve more accurate predictions of high specific speed centrifugal pump efficiency. [ABSTRACT FROM AUTHOR]

Aim: Periodontitis is a potential risk factor for preterm birth (PTB) in women; however, the causal relationship or the exact mechanism remain unknown. This study aimed to compare the oral microbiome features of mothers with full-term birth (FTB) with those who had preterm delivery., Methods: This study prospectively enrolled 60 women (30 mothers with PTB and 30 mothers with FTB), and subgingival plaque samples were collected and analysed by metagenomic 16S rDNA sequencing. Clinical measurements, including periodontal probing depth, clinical attachment level, modified gingival index (mGI) and plaque index, were performed to determine the periodontal state of the participants. Medical and obstetric data were collected as well., Results: Among the periodontal measurements, mGI score, reflecting the level of gingival inflammation, exhibited a statistically significant association with PTB (adjusted odds ratio 2.705, 95% confidence interval 1.074-6.811, p = .035). When subgroup analysis was conducted based on mean mGI scores (mGI ≥ 2, high inflammation [HI] versus mGI < 2, low inflammation [LI]), microbiome analysis revealed clear distinctions in microbial compositions between PTB and FTB mothers in both the HI and LI groups. Especially in the HI group, alpha diversity exhibited a decreasing trend in PTB mothers compared to FTB mothers. Beta diversity also revealed significant differences between the two groups. In Linear Discriminant Analysis Effect Size analysis, certain anaerobic taxa, including the genera Spirochaetes, Treponema and Porphyromonas, were relatively abundant in the FTB/HI group, whereas the PTB/HI group showed a high abundance of the order Actinomycetales. Network analysis showed that the FTB/HI had relatively stronger connectivity in microbial composition than the PTB/HI group. Dysbiosis ratio of plaque microbiome, in terms of periodontitis, was significantly lower in PTB/HI group compared to FTB/HI group., Conclusion: The compositions of maternal subgingival microbiomes differed between PTB and FTB mothers in both the high and low levels of gingival inflammation groups. In the presence of high level of gingival inflammation, dysbiosis in plaque microbiome, in terms of periodontitis, was decreased in PTB mothers compared to FTB mothers., (© 2024 The Author(s). Journal of Periodontal Research published by John Wiley & Sons Ltd.)

Timely and accurate detection of the causal agent of a disease is crucial to restrict suffering and save lives. Mere symptoms are often not enough to detect the root cause of the disease. Better diagnostics applied for screening at a population level and sensitive detection assays remain the crucial component of disease surveillance which may include clinical, plant, and environmental samples, including wastewater. The recent advances in genome sequencing, nucleic acid amplification, and detection methods have revolutionized nucleic acid-based testing (NABing) and screening assays. A typical NABing assay consists of three modules: isolation of the nucleic acid from the collected sample, identification of the target sequence, and final reading the target with the help of a signal, which may be in the form of color, fluorescence, etc. Here, we review current NABing assays covering the different aspects of all three modules. We also describe the frequently used target amplification or signal amplification procedures along with the variety of applications of this fast-evolving technology and challenges in implementation of NABing in the context of disease management especially in low-resource settings., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)