Multiple Time Research Articles

Are acute hospital trust mergers associated with improvements in the quality of care?

This study aims to assess the extent to which acute hospital trust mergers in England are associated with quality improvements. We apply an event study design using difference-in-difference (DID) and coarsened exact matching to compare the before-and-after performance of eight mergers from 2011 to 2015. We find little evidence that mergers contribute to quality improvements other than some limited increases in the proportion of patients waiting a maximum of 18weeks from referral to treatment. We postulate that financial incentives and political influence could have biased management effort towards waiting time measures. Inherent sample size constraints may limit generalisability. Merger costs and complexity mean they are unlikely to offer an efficient strategy for helping to clear elective care backlogs. We recommend further research into causal mechanisms to help health systems maximise benefits from both mergers and emerging models of hospital provider collaboration. This paper is the first to study the quality impact of a new wave of acute hospital mergers taking place in the English National Health Service from 2011 onwards, applying a group-time DID estimator to account for multiple treatment timings.

MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot.

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection. In this study, we propose a confirmed case-driven time-series prediction of crowd in urban hot spot named MobCovid. The model is a deviation of Informer, a popular time-serial prediction model proposed in 2021. The model takes both the number of nighttime staying people in downtown and confirmed cases of COVID-19 as input and predicts both the targets. In the current period of COVID, many areas and countries have relaxed the lockdown measures on public mobility. The outdoor travel of public is based on individual decision. Report of large amount of confirmed cases would restrict the public visitation of crowded downtown. But, still, government would publish some policies to try to intervene in the public mobility and control the spread of virus. For example, in Japan, there are no compulsory measures to force people to stay at home, but measures to persuade people to stay away from downtown area. Therefore, we also merge the encoding of policies on measures of mobility restriction made by government in the model to improve the precision. We use historical data of nighttime staying people in crowded downtown and confirmed cases of Tokyo and Osaka area as study case. Multiple times of comparison with other baselines including the original Informer model prove the effectiveness of our proposed method. We believe our work can make contribution to the current knowledge on forecasting the number of crowd in urban downtown during the Covid epidemic.

The stochastic heat equation with fractional time and fractional time–space white noise

We give an introduction to the time-fractional stochastic heat equation driven by 1+d-parameter fractional time–space white noise, in the following two cases:(i) With additive fractional time–space white noise(ii) With multiplicative time–space Brownian noiseThe fractional time derivative is interpreted as the Caputo derivative of order α∈(0,2) and we assume that the Hurst coefficient H=(H0,H1,H2,…,Hd) of the time–space fractional white noise is in (12,1)1+d.We find an explicit expression for the unique solution in the sense of distribution of the equation in the additive noise case (i).In the multiplicative case (ii) we show that there is a unique solution in the Hida space (S)∗ of stochastic distributions and we show that the solution coincides with the solution of an associated fractional stochastic Volterra equation. Then we give an explicit expression for the solution of this Volterra equation. See Appendix below or Di Nunno et al. (2008), Holden et al. (2010) and Øksendal (2023) for more information about Hida spaces and white noise theory.A solution Y(t,x) is called mild if E[Y2(t,x)]<∞ for all t,x. For both the additive noise case and the multiplicative noise case we show that if α≥1 then the solution is mild if d=1 or d=2, while if α<1 the solution is not mild for any d.This paper is partly a survey paper, explaining the concepts and methods behind the results. It is also partly a research paper, in the sense that some results are new, to the best of our knowledge.

Analog In-memory Circuit Design of Polynomial Multiplication for Lattice Cipher Acceleration Application

As the core operation of lattice cipher, large-scale polynomial multiplication is the biggest computational bottleneck in its realization process. How to quickly calculate polynomial multiplication under resource constraints has become an urgent problem to be solved in the hardware implementation of lattice ciphers. Therefore, an analog in-memory circuit for fast polynomial multiplication calculation is proposed. First, an in-memory computing circuit for Discrete Fourier Transform and Inverse Discrete Fourier Transform based on memristor array is designed. On this basis, a fully analog circuit that can realize polynomial multiplication in one step is designed. Compared with traditional hardware implementation, the in-memory calculation method used in this article decreases the calculation time of polynomial multiplication to the microsecond level, which greatly improves the speed of lattice cipher encryption and decryption. For the specific examples in this article, PSPICE simulation shows that the average accuracy of the calculation result is above 99.90%.

A three-dimensional criticality safety analysis code for spent fuel solution system

The nuclear criticality safety analysis is crucial for the nuclear safety of spent fuel reprocessing plants. Thus, a set of numerical tools with high accuracy and efficiency to predict the criticality and simulate the hypothetical accidents in the reprocessing procedure is of great importance. Among them, this paper focus on the analysis of the storage tank which is used for dissolution and storage of the spent fuel. According to the characteristics of spent fuel solution system, a parallel 3D critical safety analysis tool for fissile solution system, Hydra-TD, is developed. Generally, it contains the cross-section generation model, the three-dimensional space-time neutron kinetics model, and the R-Z two-dimensional heat conduction and radiolysis gas simulation model. The verifications based on the experiments of the SILENE and TRACY facility are conducted. The results show good accuracy of the prediction of the first fission power peak, multiplication time and total fission energy, indicating the reliability of these numerical models.

Nitrogen fertilization timing and rate influence N recovery efficiency and rice yield

AbstractNitrogen (N) fertilizer management in rice (Oryza sativa L.) varies with production practices. In drill‐seeded, delayed‐flood production systems, the most common production practice in Louisiana N fertilizer is applied at two application timings. The first application timing is just before the permanent flood is established. The second application is at midseason. Nitrogen fertilization before flooding is critical for maximum N uptake, nitrogen recovery efficiency (NRE), and yield. Field experiments were conducted from 2017 to 2020 to evaluate N timing effects on N uptake, NRE, and rice yield. The rice cultivar CL153 was drill‐seeded into a stale seedbed at a seed rate of 85 kg ha−1. Fertilizer‐N was applied utilizing multiple application timings and rates adding up to a seasonal rate of 155 kg ha−1 across treatments. A single N application 1‐day before flooding significantly increased grain yield in all trials, ranging from 8523 kg ha−1 in 2019 to 11,322 kg ha−1 in 2018. Compared to post‐flood applications, preflood N increased plant height, N uptake, and NRE. Split N application rates and timings after flooding did not impact rice yield or its agronomics, such as height, aboveground biomass, and time of heading. NRE and yield were significantly correlated (r = 0.805; p &lt; 0.001). Our results indicated that a single N application before flooding has the potential to be an alternative option for N management in the drill‐seeded, delayed‐flood rice system.

Modeling platform to assess the effectiveness of single and integrated Ixodes scapularis tick control methods

BackgroundLyme disease continues to expand in Canada and the USA and no single intervention is likely to curb the epidemic.MethodsWe propose a platform to quantitatively assess the effectiveness of a subset of Ixodes scapularis tick management approaches. The platform allows us to assess the impact of different control treatments, conducted either individually (single interventions) or in combination (combined efforts), with varying timings and durations. Interventions include three low environmental toxicity measures in differing combinations, namely reductions in white-tailed deer (Odocoileus virginianus) populations, broadcast area-application of the entomopathogenic fungus Metarhizium anisopliae, and fipronil-based rodent-targeted bait boxes. To assess the impact of these control efforts, we calibrated a process-based mathematical model to data collected from residential properties in the town of Redding, southwestern Connecticut, where an integrated tick management program to reduce I.xodes scapularis nymphs was conducted from 2013 through 2016. We estimated parameters mechanistically for each of the three treatments, simulated multiple combinations and timings of interventions, and computed the resulting percent reduction of the nymphal peak and of the area under the phenology curve.ResultsSimulation outputs suggest that the three-treatment combination and the bait boxes–deer reduction combination had the overall highest impacts on suppressing I. scapularis nymphs. All (single or combined) interventions were more efficacious when implemented for a higher number of years. When implemented for at least 4 years, most interventions (except the single application of the entomopathogenic fungus) were predicted to strongly reduce the nymphal peak compared with the no intervention scenario. Finally, we determined the optimal period to apply the entomopathogenic fungus in residential yards, depending on the number of applications.ConclusionsComputer simulation is a powerful tool to identify the optimal deployment of individual and combined tick management approaches, which can synergistically contribute to short-to-long-term, costeffective, and sustainable control of tick-borne diseases in integrated tick management (ITM) interventions.Graphical

Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models’ precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models’ effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions.

Expression pattern, immune signature, and prognostic value of RBM10 in human cancers.

RNA-binding motif protein 10 (RBM10) regulates the expression of genes involved in immune responses and is associated with a wide spectrum of cancers. Meanwhile, immunotherapy is the most promising cancer treatment of our time; nevertheless, the pan-cancer role of RBM10 remains to be elucidated. Data from multiple online databases, including ONCOMINE, UALCAN, GEPIA2, Kaplan-Meier Plotter, cBioPortal, STRING, and TIMER were analyzed. The protein expression levels of RBM10 in various tumor types were verified by immunohistochemistry (IHC). RBM10 is upregulated in multiple tumors compared with the corresponding normal tissues. In addition, RBM10 is highly mutated in various cancers. We also compared the levels of phosphorylated RBM10 between normal and primary tumor tissues. We found that the expression of RBM10 was positively correlated with Programmed cell death 1 (PD-L1) and Cytotoxic lymphocyte antigen 4 (CTLA4) in most cancers, except Thyroid carcinoma (THCA). Moreover, the expression of RBM10 was significantly related to immune cell infiltration in many cancers, suggesting that it is a promising target for cancer immunotherapy. RBM10 expression is closely related to tumor prognosis and the immune microenvironment. Our findings provide new insights into the role of RBM10 in cancer diagnosis and treatment.

Effects of Brownian motion on solitary wave structures for 1D stochastic Poisson–Nernst–Planck system in electrobiochemical

This study deals with the time fractional 1D stochastic Poisson–Nernst–Planck (TFSPNP) system under the effect of multiplicative time noise. The M-truncated derivative (MTD) takes into consideration the fractional order time derivative. This is a steady-state Poisson–Nernst–Planck (PNP) equations that have applications in bioelectric dressings and bandages. To obtain the soliton solutions of TFSPNP, we use the generalized exponential rational functional method. These findings are presented in the form of trigonometric, exponential, and hyperbolic functions. Moreover, to show the effect of multiplicative time noise and MTD, we construct the plot of some solutions in the form of three-dimensional, two-dimensional, and their corresponding contours. These plots clearly show the effect of randomness in the wave structures for the exact solitary wave solutions that are attained. In general, the solutions become more stable when a noise term disrupts their symmetry.

Global diversity, recurrent evolution, and recent selection on amylase structural haplotypes in humans.

The adoption of agriculture, first documented ~12,000 years ago in the Fertile Crescent, triggered a rapid shift toward starch-rich diets in human populations. Amylase genes facilitate starch digestion and increased salivary amylase copy number has been observed in some modern human populations with high starch intake, though evidence of recent selection is lacking. Here, using 52 long-read diploid assemblies and short read data from ~5,600 contemporary and ancient humans, we resolve the diversity, evolutionary history, and selective impact of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in populations with agricultural subsistence compared to fishing, hunting, and pastoral groups. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each exhibit multiple duplications/deletions with mutation rates >10,000-fold the SNP mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome graph-based approach to infer structural haplotypes across thousands of humans, we identify extensively duplicated haplotypes present at higher frequencies in modern day populations with traditionally agricultural diets. Leveraging 533 ancient human genomes we find that duplication-containing haplotypes (i.e. haplotypes with more amylase gene copies than the ancestral haplotype) have increased in frequency more than seven-fold over the last 12,000 years providing evidence for recent selection in West Eurasians. Together, our study highlights the potential impacts of the agricultural revolution on human genomes and the importance of long-read sequencing in identifying signatures of selection at structurally complex loci.

Establishment and Characterization of a New Cell Line from Enzootic Nasal Adenocarcinoma in Goats: ENA-1.

Enzootic nasal adenocarcinoma (ENA) is a contagious tumor disease of goats and sheep, which is caused by enzootic nasal tumor virus (ENTV). To better understand the pathogenesis of ENA, this study aimed to establish a goat ENA cell line (ENA-1). The cells have been characterized with regard to morphology, growth rate, ultrastructural features, chromosome number, expression of CK7 and CK18, tumorigenicity, species, and mycoplasma contamination. ENA-1 had an epithelioid cell morphology with an unstable chromosome number under a light microscope. Under an electron microscope, the cell nuclear heterogeneity was not obvious, and there were more intermediate filaments and a small number of immature retrovirus-like particles in the cytoplasm. ENA-1 had strong proliferative potential, and the cell multiplication time was about 36 h, which could make BALB/c nude mice develop tumors. CK7 and CK18 were expressed in the cytoplasm of primary goat tumors, in transplanted tumors from nude mice, and un ENA-1 cells with the same intensity. PCR revealed that ENA-1 continuously carried ENTV-2 up to the 17th generation with no germline contamination or mycoplasma contamination. In conclusion, using a serum-containing culture system, ENA-1 cells were successfully isolated, cultured, and purified from goat tumor tissues. The isolated ENA-1 cells retained robust proliferation potential and maintained their phenotype, indicating the potential application of the ENA-1 cell line as an in vitro model of ENA.

Establishment of Canine Oral Mucosal Melanoma Cell Lines and Their Xenogeneic Animal Models.

Canine oral melanoma is the most prevalent malignant tumor in dogs and has a poor prognosis due to its high aggressiveness and high metastasis and recurrence rates. More research is needed into its treatment and to understand its pathogenic factors. In this study, we isolated a canine oral mucosal melanoma (COMM) cell line designated as COMM6605, which has now been stably passaged for more than 100 generations, with a successful monoclonal assay and a cell multiplication time of 22.2 h. G-banded karyotype analysis of the COMM6605 cell line revealed an abnormal chromosome count ranging from 45 to 74, with the identification of a double-armed chromosome as the characteristic marker chromosome of this cell line. The oral intralingual and dorsal subcutaneous implantation models of BALB/c-nu mice were successfully established; Melan-A (MLANA), S100 beta protein (S100β), PNL2, tyrosinase-related protein 1 (TRP1), and tyrosinase-related protein 2 (TRP2) were stably expressed positively in the canine oral tumor sections, tumor cell lines, and tumor sections of tumor-bearing mice. Sublines COMM6605-Luc-EGFP and COMM6605-Cherry were established through lentiviral transfection, with COMM6605-Luc-EGFP co-expressing firefly luciferase (Luc) and enhanced green fluorescent protein (EGFP) and COMM6605-Cherry expressing the Cherry fluorescent protein gene. The COMM6605-Luc-EGFP fluorescent cell subline was injected via the tail vein and caused lung and lymph node metastasis, as detected by mouse live imaging, which can be used as an animal model to simulate the latter steps of hematogenous spread during tumor metastasis. The canine oral melanoma cell line COMM6605 and two sublines isolated and characterized in this study can offer a valuable model for studying mucosal melanoma.

COMPARATIVE OF RIVEST-SHAMIR-ADLEMAN CRYPTOSYSTEM AND ITS FOUR VARIANTS USING RUNNING TIME AND MEMORY CONSUMPTION ANALYSIS

The Rivest-Shamir-Adleman (RSA) algorithm, known for its slow single-precision multiplication (spm) and overall running time, is not commonly employed to encrypt user data directly. As a result, several researchers have developed various RSA-based cryptosystems to enhance the algorithm's performance while maintaining security. This paper presents a comparative analysis of different variants of the RSA cryptosystem, focusing on five specific cryptosystems: RSA, Somsuk-RSA, Modified-RSA (MRSA), Easy Simple Factoring-RSA (ESF-RSA), and Phony-RSA. The methodology involves evaluating the theoretical running time and memory usage through single-precision multiplication (spm) measurements, while the actual running time is estimated using Maple programming. The research has two primary objectives. Firstly, they examined each algorithm of the RSA variants and analysed them according to the proposed methodology. Secondly, to determine which cryptosystem consumes the most time and memory for key generation, encryption, and decryption. The results indicate that ESF-RSA and RSA are the fastest in terms of key generation, ESF-RSA is the quickest for encryption, and Phony-RSA excels in decryption speed. Additionally, ESF-RSA demonstrates the lowest memory usage, whereas MRSA requires the highest memory allocation for all processes.

A test of competing mediators linking trouble sleeping to cannabis use in adolescents and emerging adults.

This study examined day-level associations between trouble sleeping and three cannabis-use indices (likelihood/quantity of use and impaired control). We evaluated behavioral and cognitive mediators of the association between trouble sleeping and cannabis outcomes. Youth (N = 86, ages 15-24, 48.8% female, 58.8% White, 18.6% Latine) who regularly used cannabis were recruited for an intervention study. This preregistered secondary data analysis leveraged data from a 1-week ecological momentary assessment (EMA) study completed prior to intervention. Trouble sleeping, cannabis use, and impaired control over use were assessed each morning; negative affect, risk-taking propensity, and cannabis craving were assessed multiple times and aggregated to create a daily average. Multilevel structural equation modeling evaluated hypothesized temporally sequenced associations and putative mechanisms at the day (i.e., within) and person (i.e., between) level. In bivariate analyses at the person level, there were large-effect associations between trouble sleeping and craving and negative affect, and between craving and cannabis-use likelihood and quantity (rs from .34 to .48). In multilevel analyses at the day level, participants were less likely to use cannabis the next day after reporting more trouble sleeping (β = -.65, p < .001). Trouble sleeping was not directly associated with subsequent cannabis-use quantity or impaired control, or indirectly via negative affect, risk-taking propensity, or craving. Trouble sleeping had differential relations with cannabis-use indices at the day and person levels. To promote youth health and reduce cannabis use, future research may consider the unique, person- and situation-driven mechanistic processes at play. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Distributed State Estimation for Mixed Delays System Over Sensor Networks With Multichannel Random Attacks and Markov Switching Topology.

This article deals with the distributed state estimation for mixed delays system under unknown attacks. A new multichannel random attack model is established for the first time, where network attacks are considered to exist in three channels: the target-to-sensor channel, the senor-to-sensor channel, and the sensor-to-estimator channel. In the above model, transmitted packets are allowed to be attacked multiple times simultaneously, and when they are successfully attacked, the transmitted information is modified. Besides, the topology of the sensor network is considered to change dynamically according to the Markov chain. Based on the newly established distributed estimation model, the estimation error system is proven to be asymptotically mean-square stable under a given H∞ antidisturbance index by using a Lyapunov theory and a stochastic analysis technique; then, the estimator parameter matrices are solved utilizing a linearization method. Finally, several simulation examples are listed to testify the effectiveness of the designed algorithm.

Modified MF-DFA Model Based on LSSVM Fitting

This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents h(q) and scaling exponents τ(q) that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, h(q) and τ(q) are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

From Kyoto to Paris and beyond: A deep dive into the green shift

This study investigates the impact of the Kyoto Protocol and its associated regime, the Paris Agreement, on environmental outcomes and the green transition. We employ a difference-in-differences (DD) specification for country-level panel data from 1995 to 2018. The DD estimates suggest that the Kyoto Protocol of 2005 significantly reduced CO2 emissions and natural resources rents by around 20 percent and decreased the quantity of non-renewable electricity generated by about 36 percent, relative to the pre-Kyoto period. The protocol also led to increases in renewable energy consumption and generation of 18 percent and 60 percent, respectively. Robustness checks using system GMM estimates confirm that the effects of the Kyoto Protocol are likely not biased because of dynamic endogeneity from simultaneity problems or confounding trends. For the Paris Agreement, given documented issues in interpreting the average treatment effects using standard two-way fixed effects (TWFE), we leverage a DD specification with multiple treatment timings. We find no statistically significant effect of the Paris Agreement 2016 on the green transition except that it contributes to reducing CO2 emissions. The absence of a significant impact from the ad hoc Paris Agreement on most outcomes points to the need for greater effort in implementing environmental treaties.

PreVR: Variable-Distance Previews for Higher-Order Disocclusion in VR.

The paper introduces PreVR, a method for allowing the user of a VR application to preview a virtual environment (VE) around any number of corners. This way the user can gain line of sight to any part of the VE, no matter how distant or how heavily occluded it is. PreVR relies on a multiperspective visualization that implements a higher-order disocclusion effect with piecewise linear rays that bend multiple times as needed to reach the visualization target. PreVR was evaluated in a user study ($\mathrm{N}=88$) that investigates four points on the VR interface design continuum defined by the maximum disocclusion order $\delta$. In a first control condition (CC0), $\delta=0$, corresponds to conventional VR exploration with no preview capability. In a second control condition (CC1), $\delta=1$, corresponds to the prior art approach of giving the user a preview around the first corner. In a first experimental condition (EC3), $\delta=3$, so PreVR provided up to third-order disocclusion. In a second experimental condition (ECN), $\delta$ was not capped, so PreVR could provide a disocclusion effect of any order, as needed to reach any location in the VE. Participants searched for a stationary target, for a dynamic target moving on a random continuous trajectory, and for a transient dynamic target that appeared at random locations in the maze and disappeared 5s later. The study quantified VE exploration efficiency with four metrics: viewpoint translation, view direction rotation, number of teleportations, and task completion time. Results show that the previews afforded by PreVR bring a significant VE exploration efficiency advantage. ECN outperforms EC3, CC1, and CC0 for all metrics and all tasks, and EC3 frequently outperforms CC1 and CC0.

Splenic gene expression of cytokines at multiple time points following lipopolysaccharide challenge in layers.

To investigate inflammatory responses to lipopolysaccharide (LPS) injection in layers. 33 40-week-old laying hens were used. 30 laying hens were divided into 2 groups: the first group was injected with 8 mg/kg LPS, while the second group was injected with sterile saline. At the start of the study, 3 birds served as a baseline and were used as the time 0 controls for both the saline and LPS-treated groups. Blood and spleen tissues were collected at 0 (before) and 1, 2, 3, 4, and 6 hours after injection. LPS administration increased splenic mRNA levels of IL-1β, IL-2, IL-6, IL-8, IL-10, interferon-γ, and tumor necrosis factor-α (P < .001) and serum IL-6 levels (P < .01) compared to saline injection. The mRNA expression of most cytokine genes increased rapidly toward peak values within 2 hours after the LPS injection, and then the difference between the saline and LPS treatments got smaller as time went on; serum IL-6 reached its highest concentration 2 hours after LPS administration. The magnitude of LPS-induced upregulation of gene expression was the highest for IL-6, followed by IL-1β and IL-8, and tumor necrosis factor-α was the least affected. The temporal and quantitative profile of these inflammatory mediators generated from this study provides valuable information in identifying the optimal time window and appropriate biomarkers for LPS-induced inflammation, which has significant implications in evaluating the effects of interventions on the immune system of chickens.