Average Generation Time Research Articles

AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis.

Breast cancer is the most prevalent cancer among women worldwide. B-mode ultrasound (US) is essential for early detection, offering high sensitivity and specificity without radiation exposure. This study introduces a semi-automatic method to streamline breast US report generation, aiming to reduce the burden on radiologists. Our method synthesizes comprehensive breast US reports by combining the extracted information from radiologists' annotations during routine screenings with the analysis results from deep learning algorithms on multimodal US images. Key modules in our method include image classification using visual features (ICVF), type classification via deep learning (TCDL), and automatic report structuring and compilation (ARSC). Experiments showed that the proposed method reduced the average report generation time to 3.8 min compared to manual processes, even when using relatively low-spec hardware. Generated reports perfectly matched ground truth reports for suspicious masses without a single failure on our evaluation datasets. Additionally, the deep-learning-based algorithm, utilizing DenseNet-121 as its core model, achieved an overall accuracy of 0.865, precision of 0.868, recall of 0.847, F1-score of 0.856, and area under the receiver operating characteristics of 0.92 in classifying tissue stiffness in breast US shear-wave elastography (SWE-mode) images. These improvements not only streamline the report generation process but also allow radiologists to dedicate more time and focus on patient care, ultimately enhancing clinical outcomes and patient satisfaction.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278±0.122%, PSNR of 33.783±0.824dB, SSIM of 0.964±0.009, NCC of 0.968±0.011, HRS of 4.543, and SUV Error of 0.255±0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973±0.066%, PSNR of 36.172±0.801dB, SSIM of 0.984±0.004, NCC of 0.990±0.005, HRS of 4.428, and SUV Error of 0.151±0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

Validity of Markovian modeling for transient memory-dependent epidemic dynamics

The initial transient phase of an emerging epidemic is of critical importance for data-driven model building, model-based prediction of the epidemic trend, and articulation of control/prevention strategies. Quantitative models for real-world epidemics need to be memory-dependent or non-Markovian, but this presents difficulties for data collection, parameter estimation, computation, and analyses. In contrast, such difficulties do not arise in the traditional Markovian models. To uncover the conditions under which Markovian and non-Markovian models are equivalent, we develop a comprehensive computational and analytic framework. We show that the transient-state equivalence holds when the average generation time matches the average removal time, resulting in minimal Markovian estimation errors in the basic reproduction number, epidemic forecasting, and evaluation of control strategy. The errors depend primarily on the generation-to-removal time ratio, while rarely on the specific values and distributions of these times. Overall, our study provides a general criterion for modeling memory-dependent processes using Markovian frameworks.

Music-driven generative dance movement teaching game based on a multi-feature fusion strategy

The study aims to develop a music-driven dance teaching game that enhances players' dance learning. By utilizing a multi-feature fusion strategy, the game creates dance movements inspired by music. The model effectively combines music and dance features to generate various dance sequences. In performance tests, the research model successfully matches the tempo and intensity of the music with the generated dance. Overall, this approach improves players' dance movement learning ability. The teaching effectiveness of the research method is significantly superior, with an average value of 95.23 points in the teaching evaluation. In terms of running time, for simple dances, the average generation time is 10 s; for ordinary categories of dances, the average generation time is 15 s; for complex dances, the average generation time is 17 s. The fluency of the generated dances is 97.21 %. The study's findings confirm the effectiveness of the research method. The game's use of multi-feature fusion algorithms allows for adaptive adjustments in difficulty and complexity based on the player's learning situation and ability level. This provides a more diverse and immersive gaming experience, while also presenting innovative ideas for future game development and teaching design.

Investigation of stochastic models of packet generation in computer networks

Stochastic packet generation models are models that are used to generate traffic in computer networks with certain characteristics. These models can be used to simulate network activity and test network performance. Standard data transmission on the network is packet generation with delays, in which packets are sent at certain intervals. Various stochastic models can be used to generate delayed packets, including uniform distribution, exponential distribution, and Erlang distribution. In this work, an experimental setup was assembled and a client-server application was developed to conduct research and analyze the performance of the data transmission channel. An algorithm has been proposed that allows to restore the moment characteristics of a random value of the interval between packets for further use of queuing models. The analysis of the distribution laws on the performance of the experimental network sample was performed and estimates of the efficiency of channel use and the average packet generation time in network segments, as well as histograms of delays according to the distribution laws, were obtained. An experimental setup was created, and a client-server application was developed to analyze the performance of the data transmission channel. An algorithm for restoring the moment characteristics of the time intervals between packets is proposed. The analysis of the distribution laws on network performance was carried out, estimates of the efficiency of channel use and the average packet generation time in network segments were obtained, as well as histograms of delays according to the distribution laws. The generation of packets with delays according to stochastic distribution laws (uniform, exponential, Erlang) is of great importance in modeling and analyzing the operation of network systems. Also, the generation of packets with delays according to the above-mentioned distribution laws allows testing and debugging of network applications and devices in conditions close to real ones. This allows to identify possible problems and improve the operation of network systems. As a result of the experiment, an algorithm was proposed that allows to restore the moment characteristics of a random value of the interval between packets for further use of queuing models. Also, the analysis of the influence of distribution laws on the performance of the experimental network sample was performed and estimates of the efficiency of channel use and the average packet generation time in network segments, as well as histograms of delays according to distribution laws, were obtained.

Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study.

To evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications. Twenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models-Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics. ACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was<1mm for 10 of the 22 OARs contoured, 1-2mm for 6 OARs, and 2-3mm for 6 OARs. For ProtégéAI, the average mean distance was<1mm for 8 out of 17 OARs, 1-2mm for 6 OARs, and 2-3mm for 3 OARs. Both DLAS programs were proven to be valuable tools to significantly reduce the time required to generate large amounts of OAR contours in the head and neck region, even though manual editing of ACs is likely needed prior to implementation into treatment planning. The DSCs and MDs achieved were similar to those reported in other studies that evaluated various other DLAS solutions. Still, small volume structures with nonideal contrast in CT images, such as nerves, are very challenging and will require additional solutions to achieve sufficient results.

Research on the design of virtual experience of shadow play based on digital technology and immersive interactive design theory

Abstract With the industrialization and urbanization of modern society as well as the diversified development of entertainment forms, the inheritance of shadow puppetry as a folk art is facing great challenges. Based on this, this paper uses modern digital technology to help traditional shadow play quickly realize digital transformation and adapt to the current era. The YOLOv5 model is used to recognize the body movements of a person manipulating shadow puppets in virtual space, monitor the interaction status between the person and the virtual shadow puppet characters in virtual space, and at the same time, use the loss function to calculate and categorize the data loss in the recognition process. In order to avoid the data loss caused by the action moving too fast or the light, this paper introduces the Kalman filter algorithm to recover the lost and incoherent actions. It thus constructs the interaction recognition model based on the Kalman filter to improve YOLO. After conducting experiments in this paper, it was found that the model built in this paper has an accuracy of 94% in recognition of unobstructed actions, and it takes 1.31 seconds to recognize them. For occluded actions, the recognition accuracy rate is 86%. Overall, the recognition interaction model constructed in this paper maintains a recognition accuracy rate of more than 85% for actions. In the experiment on processing virtual shadow play characters, the average generation time of the virtual models of any shadow play character is no more than 0.5 seconds. The model constructed in this paper has a superior performance in action recognition and virtual shadow play character processing.

A novel generalized adversarial image method using descriptive features

Abstract— Currently, machine learning not only solves simple problems such as object classification but also machine learning is widely applied in the field of computer vision such as identification systems, object detection, and modules in the authentication system, intelligent processing algorithms such as automatic driving, chatbot, etc. Deep learning models on GAN networks can automatically generate image data such as objects, animals, human faces, or the like by learning the word features of images in datasets such as MS-COCO, ImageNET, CUB, etc. Using this technique, attackers can fake images in some cases with malicious intent. In this paper, the authors propose to build a Generative Adversarial Network to create images that fool the target model YOLOv7, INCEPTIONv3. Experimental results on the CUB dataset show our proposed model's ability to generate adversarial examples is highly effective with an average image generation time equal to 0.16 seconds/an image. The successful rate of fooling the model reached over 85%, average recognition rate reached over 45% for the YOLOv7 model. When experimenting on the INCEPTIONv3 model, the successful rate of fooling the model reached over 95%, average recognition rate reached over 50%. The image fidelity evaluated by the PSNR index reached an average of greater than 29.

A Study on Automatic Composition for Chinese Wind Piano in the Framework of Autoregressive Language Modeling

Abstract Based on the framework of autoregressive language modeling, this paper analyzes the word frequency characteristics and introduces a quartile inverse probability weighted sampling algorithm in probability distribution prediction to regulate the quality and diversity of the generated music. Through the effective division of the subset of high-frequency words by this algorithm, a polyphonic piano transcription model is established, which enhances the rationality of the predicted probability distribution of piano composition. Meanwhile, objective evaluation metrics are designed for the pentatonic tonal form of Gong tuning to quantitatively assess the results of automatic composition for Chinese-style piano. It is proved that the proposed model performs well in music generation, with an average generation time of only 6.9s and a model parameter count of 2.7M, which can provide strong support and validation for the automatic composition of the Chinese wind piano.

572. Growth rate comparisons for clinical Klebsiella pneumoniae (KP) isolates using nutrient-rich and -depleted broth stratified by inner colony production during fosfomycin (FOF) disk diffusion (DD) testing

Abstract Background Recent studies have indicated the presence of non-susceptible IC appearing within the zone of inhibition during FOF DD testing. Limited studies focusing on Escherichia coli have demonstrated that observed increases in resistance come with decreased isolate fitness. We sought to compare growth rates of susceptible KP non-IC-producers, IC-producers, and their non-susceptible IC (as determined by broth microdilution) in both nutrient-rich and -depleted broth to understand in vitro fitness differences. Methods Two isolates which had never produced IC (254, 275), two susceptible IC-producers (131, 209), and two corresponding resistant IC (131D, 209D) were included. Each isolate was grown in both full and ¼ strength Mueller-Hinton broth (MHB) for 24 hours with samples taken at standard time intervals in two technical replicates, with duplicates performed on separate days. Using the generation time equation (Figure 1) for the logarithmic phase of growth, rates were calculated and compared across three groups: (1) individual parent and IC pairs, (2) non-IC producers and IC-producers, and (3) IC-producers and IC as a whole. Comparisons across the three groups in both nutrient-rich and -depleted conditions were completed using Student’s t-tests. Results Average generation time for each isolate in full and ¼ strength MHB are shown in Table 1. Difference in generation time in full-strength MHB for parent and IC pairs were [131/131D] 0.04 hours and [209/209D] 0.05 hours (Table 2). In ¼ strength MHB, difference in generation time for parent and IC pairs were [131/131D] 0.03 hours and [209/209D] 0.01 hours. Average growth rate difference between non-IC-producers and IC-producers was 0.05 hours and 0.01 in full- and ¼ strength, respectively. The average generation time difference between IC-producers and IC was &amp;lt; 0.01 hours and 0.01 in full- and ¼ strength, respectively. Conclusion Our data reveal no significant difference in growth rates between these KP isolate types which may signify no in vitro fitness differences for IC or IC-producers in both nutrient-rich and nutrient-depleted conditions. While the findings of this study are limited due to small sample size, they justify further investigation into the fitness of these isolate types through an in vivo mouse model and genetic testing. Disclosures Elizabeth B. Hirsch, PharmD, FCCP, FIDSA, Melinta: Honoraria|Merck: Grant/Research Support

A Study on an Intelligent Algorithm for Automatic Test Paper Generation and Scoring in University English Exams

This paper mainly studied the automatic test paper generation and scoring problems in university English exams. Firstly, an automatic test paper generation model was established. Then, an improved genetic algorithm (IGA) was designed for intelligent test paper generation, and it was also used to automatically score answers to Chinese-to-English translation questions in terms of syntax and semantics. It was found that compared with the traditional GA and particle swarm optimization algorithm, the IGA method was faster in generating test papers, with an average generation time of 25 s, and had a higher success rate (94%), suggesting higher validity, and the difficulty and differentiation degrees of the test papers were closer to the preset values. The results of automatic scoring also had a correlation of more than 0.8 with the manual scoring results. The results prove the effectiveness of the automatic test paper generation and scoring method. It can be promoted and applied in practice to enhance the security and fairness of large-scale English exams, as well as achieve objectivity and consistency in scoring.

Risk of resistance and the metabolic resistance mechanism of Laodelphax striatellus (Fallén) to cyantraniliprole

Cyantraniliprole is a highly effective diamide insecticide used to control of Laodelphax striatellus (Fallén). This study aimed to assess the insecticide resistance risk of L. striatellus and its metabolic resistance mechanisms. After 25 continuous generations of selection, the resistance of L. striatellus to cyantraniliprole increased by 17.14-fold. The realistic heritability of resistance was 0.0751. After successive rearing for five generations without exposure to insecticides, the resistance ratio for the resistant strain of L. striatellus decreased by 3.47-fold, and the average resistance decline rate per generation was 0.0266. Cyantraniliprole-resistant strains did not exhibit cross-resistance to triflumezopyrim, pymetrozine, flonicamid, sulfoxaflor, dinotefuran, clothianidin, thiamethoxam, nitenpyram, or imidacloprid. Compared to those of the sensitive strain, the 2nd, 3rd, and 4th instars, nymphal stage durations, total preoviposition period, and average generation time of the resistant strain were markedly reduced. Furthermore, the activity of cytochrome P450 monooxygenase (P450) and carboxylesterase (CarE) were markedly increased. The upregulation of CYP419A1v2 expression was most evident among the P450 genes, with a 6.10-fold increase relative to that in the sensitive strain. The CarE gene LsCarE5 was significantly upregulated by 1.94-fold compared with that in the sensitive strain. With the continuous use of cyantraniliprole, L. striatellus may develop resistance to this insecticide. This resistance may be related to the increase in metabolic enzyme activities regulated by the overexpression of P450 and CarE genes.

Land use alters bacterial growth dynamics in soil.

Microbial growth and mortality are major determinants of soil carbon cycling. We measured in situ growth dynamics of individual bacterial taxa in cropped and successional soils in response to a resource pulse. We hypothesized that land use imposes selection pressures on growth characteristics. We estimated growth and death for 453 and 73 taxa, respectively. The average generation time was 5.04 ± 6.28 (SD; range 0.7-63.5) days. Lag times were shorter in cultivated than successional soils and resource amendment decreased lag times. Taxa exhibiting the greatest growth response also exhibited the greatest mortality, indicative of boom-and-bust dynamics. We observed a bimodal growth rate distribution, representing fast- and slow-growing clusters. Both clusters grew more rapidly in successional soils, which had more organic matter, than cultivated soils. Resource amendment increased the growth rate of the slower growing but not the faster-growing cluster via a mixture of increased growth rates and species turnover, indicating that competitive dynamics constrain growth rates in situ. These two clusters show that copiotrophic bacteria in soils may be subdivided into different life history groups and that these subgroups respond independently to land use and resource availability.

Environmental Stability and Its Importance for the Emergence of Darwinian Evolution.

The emergence of Darwinian evolution represents a central point in the history of life as we know it. However, it is generally assumed that the environments in which life appeared were hydrothermal environments, with highly variable conditions in terms of pH, temperature or redox levels. Are evolutionary processes favored to appear in such settings, where the target of biological adaptation changes over time? How would the first evolving populations compete with non-evolving populations? Using a numerical model, we explore the effect of environmental variation on the outcome of the competition between evolving and non-evolving populations of protocells. Our study found that, while evolving protocells consistently outcompete non-evolving populations in stable environments, they are outcompeted in variable environments when environmental variations occur on a timescale similar to the average duration of a generation. This is due to the energetic burden represented by adaptation to the wrong environmental conditions. Since the timescale of temperature variation in natural hydrothermal settings overlaps with the average prokaryote generation time, the current work indicates that a solution must have been found by early life to overcome this threshold.

Fast textile pattern generation combining MRF-based and Gram-based methods

Textile pattern design is a tedious and challenging task for designers. This paper proposes a fast textile pattern generation algorithm that combines MRF-based and Gram-based methods. First, the reconstruction method based on image optimisation is determined after analysing the specific requirements of textile pattern design. The pre-trained VGG19 is selected as the style feature extraction neural network. Then, we compare the generation results of various combinations of style loss functions and propose a multi-resolution image optimisation method. Finally, the smoothing loss and colour histogram matching are added to improve the generation quality further, thus constructing an image generation algorithm for textile pattern design. Experimental results demonstrate that our algorithm can effectively generate complex textile patterns with global style and local detail features. The average image generation time is 575s, over 84.3% faster than traditional algorithms. At the same time, this algorithm is convenient for switching styles and requires lower computational capability. It can improve pattern design efficiency and promote the application of image generation technology in textile design.

Multiple Sources of Uncertainty Confound Inference of Historical Human Generation Times.

Wang et al. (2023) recently proposed an approach to infer the history of human generation intervals from changes in mutation profiles over time. As the relative proportions of different mutation types depend on the ages of parents, binning variants by the time they arose allows for the inference of changes in average paternal and maternal generation intervals. Applying this approach to published allele age estimates, Wang et al. (2023) inferred long-lasting sex differences in average generation times and surprisingly found that ancestral generation times of West African populations remained substantially higher than those of Eurasian populations extending tens of thousands of generations into the past. Here, we argue that the results and interpretations in Wang et al. (2023) are primarily driven by noise and biases in input data and a lack of validation using independent approaches for estimating allele ages. With the recent development of methods to reconstruct genome-wide gene genealogies, coalescence times, and allele ages, we caution that downstream analyses may be strongly influenced by uncharacterized biases in their output.

Pooling is not Favorable: Decentralize Mining Power of PoW Blockchain Using Age-of-Work

As the underlying consensus protocol of Bitcoin and Ethereum blockchains, Proof-of-Work (PoW) features a cryptographic mathematical puzzle whose solution is easy to verify but extremely hard to solve. Under PoW, miners maintain the security of blockchain by devoting computing powers to solve the puzzle; the miner who has solved the puzzle successfully generates a block, along with a reward (e.g., a set of cryptocurrency). The average waiting time to generate a block is inversely proportional to the computing power of the miner. To reduce the average block generation time, a group of individual miners can form a centralized mining pool to aggregate their computing power to solve the puzzle together and share the reward contained in the block. However, if the aggregated computing power of the pool forms a substantial portion of the total computing power in the network, the pooled mining undermines the core spirit of blockchain, i.e., the decentralization, and harms its security. To discourage the pooled mining, we develop a new consensus protocol called Proof-of-Age (PoA) that builds upon the native PoW protocol. The core idea of PoA lies in using Age-of-Work (AoW) to measure the effective mining periods that the miners have devoted to maintaining the security of blockchain. Unlike in the native PoW protocol, in our PoA protocol, miners benefit from its effective mining periods even if they have not successfully mined a block. We first employ a continuous time Markov chain (CTMC) to model the block generation process of the PoA based blockchain. Based on this CTMC model, we then analyze the block generation rates of the mining pool and solo miners respectively. Our analytical results verify that under PoA, the block generation rates of miners in the mining pool are reduced compared to that of solo miners, thereby disincentivizing the pooled mining. Finally, we simulate the mining process in the PoA blockchain to demonstrate the consistency of the analytical results.

Neutronic Parameter Analysis of Plate-Type Fueled TRIGA 2000 Reactor by MCNPX

A novel simulation to calculate the neutronic parameters of the TRIGA 2000 reactor using plate-type fuel has been performed. The plate fuel used was produced by the Indonesian Nuclear Industry (PT INUKI) with U3Si2-Al material. Neutronic parameters based on INUKI’s plate-type fuel dimension and the current TRIGA’s configuration were simulated using MCNPX. The simulation was performed by modeling the complete reactor’s configuration on a fresh fuel core state. We obtained the kinetic parameter values from the simulation, i.e., delayed neutron fraction of 8.11×10‑3, a prompt neutron lifetime of 2.0551×10‑4 s, and an average neutron generation time of 1.87×10‑4 s. The excess reactivity of the reactor was 9.02 %Δk/k, while reactivity in the one-stuck-rod state was below ‑0.5 $ with an average value of ‑3.40 %Δk/k (‑4.19 $). The average thermal neutron flux peak occurred at the central irradiation position with the value of 3.0×1013 to 3.1×1013 n/(cm2 s). The reactor has a power peaking factor of 1.379 in the control rod position of 0 % on D3 fuel. The reactor had a negative feedback reactivity coefficient, except for the moderator coefficient. These results suggest that the current configuration of plate-type fuel met the nuclear reactor neutronic safety standards.

Hydrogen stable isotope probing of lipids demonstrates slow rates of microbial growth in soil

The rate at which microorganisms grow and reproduce is fundamental to our understanding of microbial physiology and ecology. While soil microbiologists routinely quantify soil microbial biomass levels and the growth rates of individual taxa in culture, there is a limited understanding of how quickly microbes actually grow in soil. For this work, we posed the simple question: what are the growth rates of soil microorganisms? In this study, we measure these rates in three distinct soil environments using hydrogen-stable isotope probing of lipids with 2H-enriched water. This technique provides a taxa-agnostic quantification of in situ microbial growth from the degree of 2H enrichment of intact polar lipid compounds ascribed to bacteria and fungi. We find that growth rates in soil are quite slow and correspond to average generation times of 14 to 45 d but are also highly variable at the compound-specific level (4 to 402 d), suggesting differential growth rates among community subsets. We observe that low-biomass microbial communities exhibit more rapid growth rates than high-biomass communities, highlighting that biomass quantity alone does not predict microbial productivity in soil. Furthermore, within a given soil, the rates at which specific lipids are being synthesized do not relate to their quantity, suggesting a general decoupling of microbial abundance and growth in soil microbiomes. More generally, we demonstrate the utility of lipid-stable isotope probing for measuring microbial growth rates in soil and highlight the importance of measuring growth rates to complement more standard analyses of soil microbial communities.

Norepinephrine modified the interaction between Escherichia coli and levofloxacin, potentially affecting clinical outcomes and increasing the risk of bacterial colonization

Background: Norepinephrine is the most used vasopressor in intensive care units. It can modulate gene expressions and metabolism pathways in Escherichia coli. As fluoroquinolone’s bactericidal activity is affected by bacterial metabolism and growth rate, simultaneous exposure to these medications might alter the interaction between E. coli and levofloxacin. This study aims to investigate the effect of norepinephrine and levofloxacin given simultaneously on the in vitro growth of E. coli. Methods: Ten clinical isolates of E. coli were grown in minimal nutrition media with and without norepinephrine, levofloxacin, or both. Bacterial growth was observed for 20 h, and viable cell count was done every 2 h. Growth curves and generation times for each study group were calculated. Statistical analysis compared the viable cell counts on the 4, 14, and 20 h observation time points and the generation times. Results: Temporary inhibition of E. coli growth was observed until 4h of incubation when therapeutic concentrations of norepinephrine and levofloxacin were given simultaneously, followed by regrowth. The viable cell count of the norepinephrine–levofloxacin group was significantly lower than the control group by the 14 h and 20 h time points. Interestingly, the study group's average generation time of regrowth was 19.3 min, which was significantly faster than the control group (p &lt; 0.05). Conclusion: Norepinephrine caused alterations in the interaction between E. coli and levofloxacin, which may affect clinical outcomes and increase the risk of bacterial colonization in patients receiving simultaneous norepinephrine and levofloxacin therapy.