3D reservoir characterization of the Mangahewa Formation,Mangahewa Field,Taranaki Basin,New Zealand

The Mangahewa Formation is the primary reservoir target in the Mangahewa Field in the Taranaki Basin,New Zealand.This formation is distinguished by its marginal marine substantial tight-sand reservoir,having thickness exceeding 800 m.The aim of this study is to assess the reservoir properties of the Mangahewa Formation through 3D reservoir modeling,employing 3D seismic data,core data,and well data from the Mangahewa Field.Utilizing variance attributes,the faults and horizons have been identified successfully within the field.The majority of the interpreted faults exhibit dip angles exceeding 60°,with a maximum displacement of 118 m.To detect direct hydrocarbon indicators,root-mean-square amplitude seismic attribute,envelope,and generalized spectral decomposition techniques have been employed.Subsequently,four lithofacies,comprising 78.3%sandstone,9.2%siltstone,9.5%claystone,and 3.0%coal have been established by utilizing the Sequential Indicator Simulation(SIS)algorithm to create a lithofacies model.A property model has been generated using the Sequential Gaussian Simulation(SGS)algorithm.Petrophysical evaluation indicates that the Mangahewa Formation exhibits reservoir qualities ranging from fair to good,with porosity levels between 8%and 11%,permeability averaging up to 10 mD,variable shale volumes,and hydrocarbon saturation in the range of 40%-50%.This study's methodologies and findings can serve as a valuable foundation for similar investigations in other tightsand gas fields located in different regions.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Exploring the modulatory role of bovine lactoferrin on the microbiome and the immune response in healthy and Shiga toxin‑producing E.coli challenged weaned piglets

Background Post-weaned piglets suffer from F18+Escherichia coli(E.coli)infections resulting in post-weaning diar-rhoea or oedema disease.Frequently used management strategies,including colistin and zinc oxide,have contrib-uted to the emergence and spread of antimicrobial resistance.Novel antimicrobials capable of directly interacting with pathogens and modulating the host immune responses are being investigated.Lactoferrin has shown promising results against porcine enterotoxigenic E.coli strains,both in vitro and in vivo.Results We investigated the influence of bovine lactoferrin(bLF)on the microbiome of healthy and infected weaned piglets.Additionally,we assessed whether bLF influenced the immune responses upon Shiga toxin-producing E.coli(STEC)infection.Therefore,2 in vivo trials were conducted:a microbiome trial and a challenge infection trial,using an F18+STEC strain.BLF did not affect theα-andβ-diversity.However,bLF groups showed a higher relative abundance(RA)for the Actinobacteria phylum and the Bifidobacterium genus in the ileal mucosa.When analysing the immune response upon infection,the STEC group exhibited a significant increase in F18-specific IgG serum levels,whereas this response was absent in the bLF group.Conclusion Taken together,the oral administration of bLF did not have a notable impact on theα-andβ-diversity of the gut microbiome in weaned piglets.Nevertheless,it did increase the RA of the Actinobacteria phylum and Bifi-dobacterium genus,which have previously been shown to play an important role in maintaining gut homeostasis.Furthermore,bLF administration during STEC infection resulted in the absence of F18-specific serum IgG responses.

Recent advances in graphene-based phase change composites for thermal energy storage and management

Energy storage and conservation are receiving increased attention due to rising global energy demands.Therefore,the development of energy storage materials is crucial.Thermal energy storage(TES)systems based on phase change materials(PCMs)have increased in prominence over the past two decades,not only because of their outstanding heat storage capacities but also their superior thermal energy regulation capability.However,issues such as leakage and low thermal conductivity limit their applicability in a variety of settings.Carbon-based materials such as graphene and its derivatives can be utilized to surmount these obstacles.This study examines the recent advancements in graphene-based phase change composites(PCCs),where graphene-based nanostructures such as graphene,graphene oxide(GO),functionalized graphene/GO,and graphene aerogel(GA)are incorporated into PCMs to substantially enhance their shape stability and thermal conductivity that could be translated to better storage capacity,durability,and temperature response,thus boosting their attractiveness for TES systems.In addition,the applications of these graphene-based PCCs in various TES disciplines,such as energy conservation in buildings,solar utilization,and battery thermal management,are discussed and summarized.

An Analysis of Supply Chain Environment in Great Wall Motor Based on Life-Cycle Assessment

Great Wall Motor(GWM),a leading automotive manufacturer,places a strong emphasis on environmental sustainability and social responsibility.The company focuses on comprehensively evaluating and enhancing its supply chain to align with these objectives.This evaluation spans the entire product life cycle,encompassing design,manufacturing,packaging,distribution,usage,and recycling and disposal processes.Key areas of focus include optimizing raw material selection,improving product recyclability,reducing energy consumption and waste emissions,and minimizing carbon emissions during transportation.Through these endeavors,GWM not only enhances its environmental performance by reducing carbon emissions and resource consumption but also bolsters its brand image and competitiveness in the market.GWM’s dedication to environmental innovation and technological leadership serves as a driving force behind sustainable development and social responsibility within the industry.

Comparative evaluation of artificial intelligence systems'accuracy in providing medical drug dosages:A methodological study

BACKGROUND Medication errors,especially in dosage calculation,pose risks in healthcare.Artificial intelligence(AI)systems like ChatGPT and Google Bard may help reduce errors,but their accuracy in providing medication information remains to be evaluated.AIM To evaluate the accuracy of AI systems(ChatGPT 3.5,ChatGPT 4,Google Bard)in providing drug dosage information per Harrison's Principles of Internal Medicine.METHODS A set of natural language queries mimicking real-world medical dosage inquiries was presented to the AI systems.Responses were analyzed using a 3-point Likert scale.The analysis,conducted with Python and its libraries,focused on basic statistics,overall system accuracy,and disease-specific and organ system accuracies.RESULTS ChatGPT 4 outperformed the other systems,showing the highest rate of correct responses(83.77%)and the best overall weighted accuracy(0.6775).Disease-specific accuracy varied notably across systems,with some diseases being accurately recognized,while others demonstrated significant discrepancies.Organ system accuracy also showed variable results,underscoring system-specific strengths and weaknesses.CONCLUSION ChatGPT 4 demonstrates superior reliability in medical dosage information,yet variations across diseases emphasize the need for ongoing improvements.These results highlight AI's potential in aiding healthcare professionals,urging continuous development for dependable accuracy in critical medical situations.

Pinning energies of organic semiconductors in high-efficiency organic solar cells

With the emergence of new materials for high-efficiency organic solar cells(OSCs),understanding and finetuning the interface energetics become increasingly important.Precise determination of the so-called pinning energies,one of the critical characteristics of the material to predict the energy level alignment(ELA)at either electrode/organic or organic/organic interfaces,are urgently needed for the new materials.Here,pinning energies of a wide variety of newly developed donors and nonfullerene acceptors(NFAs)are measured through ultraviolet photoelectron spectroscopy.The positive pinning energies of the studied donors and the negative pinning energies of NFAs are in the same energy range of 4.3−4.6 eV,which follows the design rules developed for fullerene-based OSCs.The ELA for metal/organic and inorganic/organic interfaces follows the predicted behavior for all of the materials studied.For organic-organic heterojunctions where both the donor and the NFA feature strong intramolecular charge transfer,the pinning energies often underestimate the experimentally obtained interface vacuum level shift,which has consequences for OSC device performance.

Reservoir quality evaluation using sedimentological and petrophysical characterization of deep-water turbidites:A case study of Tariki Sandstone Member,Taranaki Basin,New Zealand

The current study aims to ascertain the reservoir characteristics of the Tariki Sandstone Member of the Otaraoa Formation,Taranaki Basin,New Zealand.This study was carried out by integrating the comprehensive petrophysical evaluation,sedimentological and petrographic studies,as well as well log analysis by using data from six wells.The porosity-permeability relationship is used to divide the samples of the Tariki Sandstone Member into reservoir and non-reservoir facies.A thorough petrophysical analysis shows that the maximum porosity values fluctuate between 16.6%and 22.1%,while permeability ranges from 102 mD to 574 mD,which indicates fair to good reservoir quality.Moreover,the Tariki sandstone represents six hydraulic flow units with a high reservoir quality index and flow zone indicator representing good reservoir characteristics.The pore size varies between nano and megapores with dominant macropores.Based on the sedimentological and petrographic analysis,the Tariki Sandstone Member is classified as a combination of subarkose,arkose,and lithic arkose with fine to medium and moderately to moderately well-sorted grains.The main diagenetic factor affecting the reservoir quality is cementation,which occupied all the pores with calcite.On the bright side,the secondary pores are developed due to the dissolution of calcite cement and few grains.The well log analysis demonstrates the presence of low clay volume ranging from 0.3%to 3.1%,fair to good effective porosity values between 13.6%and 15.9%,net pay thickness from 18.29 m to 91.44 m,and hydrocarbon saturation from 56%to 77.9%.The findings from this study revealed that the Tariki Sandstone Member possesses fair to good reservoir quality and hydrocarbon potential,which indicate submarine fans as appealing hydrocarbon reservoirs.This study can be used in similar depositional environments elsewhere in the world.

Identification of KASP markers and putative genes for pre-harvest sprouting resistance in common wheat(Triticum aestivum L.)

Common wheat(Triticum aestivum L.)is the most important crop in the world and a typical allopolyploid with a large and complex genome.Pre-harvest sprouting(PHS)leads to a significant reduction in grain quality worldwide.PHS is a complex trait with related QTL located on different chromosomes.However,the study of markers and genes related to PHS resistance is limited especially for whitegrained wheat.Four pairs of near isogenic lines(NILs)from a white-grained wheat cross of Chara
DM5637B*8 targeting a major QTL for PHS resistance(Qphs.ccsu-3A.1)on wheat chromosme 3AL were genotyped using the 90K SNP Illumina iSelect array.Ten SNPs were identified,with a 75%-100%consistency between genotype and phenotype in the resistant or susceptible isolines.The 10 SNPs were converted to cost-effective kompetitive allele-specific PCR(KASP)markers.Screening of 48 wheat cultivars with different phenotypes of PHS identified four KASP markers with 81.3%-85.4%conformity between genotype and phenotype.Further investigation revealed that the four SNPs(BS00022245_51,Kukri_c49927_151,BS00022884_51 and BS00110550_51)corresponding to the four validated KASP markers are residing in three independent genes(TraesCS3A03G1072800,TraesCS3A03G1072400,TraesCS3A03G1071800)close to each other with a distance of 4.28-4.48 Mb to the targeted QTL.These three annotated genes have potential functions related to PHS resistance.Our study revealed that combined use of NILs and the 90K SNP chip is a powerful approach for developing KASP markers and mining functional genes in wheat.The KASP markers for PHS resistance on chromosome 3AL are useful for high-throughput evaluation and marker-assisted selection,and the three identified genes could lead to a better understanding of the genetic pathways controlling PHS.

Chained Dual-Generative Adversarial Network:A Generalized Defense Against Adversarial Attacks

Neural networks play a significant role in the field of image classification.When an input image is modified by adversarial attacks,the changes are imperceptible to the human eye,but it still leads to misclassification of the images.Researchers have demonstrated these attacks to make production self-driving cars misclassify StopRoad signs as 45 Miles Per Hour(MPH)road signs and a turtle being misclassified as AK47.Three primary types of defense approaches exist which can safeguard against such attacks i.e.,Gradient Masking,Robust Optimization,and Adversarial Example Detection.Very few approaches use Generative Adversarial Networks(GAN)for Defense against Adversarial Attacks.In this paper,we create a new approach to defend against adversarial attacks,dubbed Chained Dual-Generative Adversarial Network(CD-GAN)that tackles the defense against adversarial attacks by minimizing the perturbations of the adversarial image using iterative oversampling and undersampling using GANs.CD-GAN is created using two GANs,i.e.,CDGAN’s Sub-ResolutionGANandCDGAN’s Super-ResolutionGAN.The first is CDGAN’s Sub-Resolution GAN which takes the original resolution input image and oversamples it to generate a lower resolution neutralized image.The second is CDGAN’s Super-Resolution GAN which takes the output of the CDGAN’s Sub-Resolution and undersamples,it to generate the higher resolution image which removes any remaining perturbations.Chained Dual GAN is formed by chaining these two GANs together.Both of these GANs are trained independently.CDGAN’s Sub-Resolution GAN is trained using higher resolution adversarial images as inputs and lower resolution neutralized images as output image examples.Hence,this GAN downscales the image while removing adversarial attack noise.CDGAN’s Super-Resolution GAN is trained using lower resolution adversarial images as inputs and higher resolution neutralized images as output images.Because of this,it acts as an Upscaling GAN while removing the adversarial attak noise.Furthermore,CD-GAN has a modular design such that it can be prefixed to any existing classifier without any retraining or extra effort,and 2542 CMC,2023,vol.74,no.2 can defend any classifier model against adversarial attack.In this way,it is a Generalized Defense against adversarial attacks,capable of defending any classifier model against any attacks.This enables the user to directly integrate CD-GANwith an existing production deployed classifier smoothly.CD-GAN iteratively removes the adversarial noise using a multi-step approach in a modular approach.It performs comparably to the state of the arts with mean accuracy of 33.67 while using minimal compute resources in training.

Susceptibility of dairy cows to subacute ruminal acidosis is reflected in both prepartum and postpartum bacteria as well as odd-and branched-chain fatty acids in feces

Background:The transition period is a challenging period for high-producing dairy cattle.Cows in early lactation are considered as a group at risk of subacute ruminal acidosis(SARA).Variability in SARA susceptibility in early lactation is hypothesized to be reflected in fecal characteristics such as fecal pH,dry matter content,volatile and odd-and branched-chain fatty acids(VFA and OBCFA,respectively),as well as fecal microbiota.This was investigated with 38 periparturient dairy cows,which were classified into four groups differing in median and mean time of reticular pH below 6 as well as area under the curve of pH below 6.Furthermore,we investigated whether fecal differences were already obvious during a period prior to the SARA risk(prepartum).Results:Variation in reticular pH during a 3-week postpartum period was not associated with differences in fecal pH and VFA concentration.In the postpartum period,the copy number of fecal bacteria and methanogens of unsusceptible(UN)cows was higher than moderately susceptible(MS)or susceptible(SU)cows,while the genera Ruminococcus and Prevotellacea_UCG-001 were proportionally less abundant in UN compared with SU cows.Nevertheless,only a minor reduction was observed in iso-BCFA proportions in fecal fatty acids of SU cows,particularly iso-C15:0and iso-C16:0,compared with UN cows.Consistent with the bacterial changes postpartum,the lower abundance of Ruminococcus was already observed in the prepartum fecal bacterial communities of UN cows,whereas Lachnospiraceae_UCG-001 was increased.Nevertheless,no differences were observed in the prepartum fecal VFA or OBCFA profiles among the groups.Prepartum fecal bacterial communities of cows were clustered into two distinct clusters with 70%of the SU cows belonging to cluster 1,in which they represented 60%of the animals.Conclusions:Inter-animal variation in postpartum SARA susceptibility was reflected in post-and prepartum fecal bacterial communities.Differences in prepartum fecal bacterial communities could alert for susceptibility to develop SARA postpartum.Our results generated knowledge on the association between fecal bacteria and SARA development which could be further explored in a prevention strategy.

Volatility spillovers,structural breaks and uncertainty in technology sector markets

This study uses the dynamic conditional correlation to investigate how technology subsector stocks interact with financial assets in the face of economic and financial uncertainty.Our results suggest that structural breaks have diverse effects on financial asset connectedness and that the level of bond linkage increases when the trend breaks.We see a growing co-movement between the technology sector and major financial assets when uncertainty is considered.Overall,our findings indicate that the connectedness response varies depending on the type of uncertainty shock.

A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure.Sine Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&Cosine.However,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal regions.In this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive weight.The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities.To evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are used.Moreover,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA.The results show that RDSCA has competitive results with other metaheuristics algorithms.

On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dMRI).Many analysis frameworks using dMRI data have been proposed to describe exchange,often using a double diffusion encoding(DDE)stimulated echo sequence.Techniques such as diffusion exchange weighted imaging(DEWI)and the filter exchange and rapid exchange models,use a specific subset of the full space DDE signal.In this work,a general representation of the DDE signal was employed with different sampling schemes(namely constant b1,diagonal and anti-diagonal)from the data reduction models to estimate exchange.A near-uniform sampling scheme was proposed and compared with the other sampling schemes.The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes.These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner.In synthetic data,the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth.In experimental data,the shifted diagonal and near-uniform sampling schemes outperformed the others,yielding the most consistent estimates with the full space estimation.The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes.In future studies,algorithms could be further developed for the optimization of sampling schemes,as well as incorporating additional properties,such as geometry and diffusion anisotropy,into exchange frameworks.

Analysis of the SBP-SAT Stabilization for Finite Element Methods Part Ⅱ:Entropy Stability

In the hyperbolic research community,there exists the strong belief that a continuous Galerkin scheme is notoriously unstable and additional stabilization terms have to be added to guarantee stability.In the first part of the series[6],the application of simultaneous approximation terms for linear problems is investigated where the boundary conditions are imposed weakly.By applying this technique,the authors demonstrate that a pure continu-ous Galerkin scheme is indeed linearly stable if the boundary conditions are imposed in the correct way.In this work,we extend this investigation to the nonlinear case and focus on entropy conservation.By switching to entropy variables,we provide an estimation of the boundary operators also for nonlinear problems,that guarantee conservation.In numerical simulations,we verify our theoretical analysis.

Advances in the Application of Perovskite Materials

Nowadays, the soar of photovoltaic performance of perovskite solar cells has set off a fever in the study of metal halide perovskite materials. The excellent optoelectronic properties and defect tolerance feature allow metal halide perovskite to be employed in a wide variety of applications. This article provides a holistic review over the current progress and future prospects of metal halide perovskite materials in representative promising applications, including traditional optoelectronic devices(solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices(artificial synapses and memristors) and pressure-induced emission. This review highlights the fundamentals, the current progress and the remaining challenges for each application, aiming to provide a comprehensive overview of the development status and a navigation of future research for metal halide perovskite materials and devices.

The medicinal chemistry of Urtica dioica L.:from preliminary evidence to clinical studies supporting its neuroprotective activity

Urtica dioica is a perennial herb from the family of Urticaceae that is commonly known as stinging nettle.This plant is widespread in Europe,Africa,America,and a part of Asia,as it adapts to different environments and climatic condi-tions.The leaves,stalk,and bark of U.dioica found applications in the field of nutrition,cosmetics,textile,pest control and pharmacology.In this connection,bioactive chemical constituents such as flavonoids,phenolic acids,amino acids,carotenoids,and fatty acids have been isolated from the plant.With this review,we aim at providing an updated and comprehensive overview of the contributions in literature reporting computational,in vitro,pre-clinical and clini-cal data supporting the therapeutic applications of U.dioica.Experimental evidence shows that U.dioica constituents and extracts can provide neuroprotective effects by acting through a combination of different molecular mecha-nisms,that are discussed in the review.These findings could lay the basis for the identification and design of more effective tools against neurodegenerative diseases.

Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

Structural basis for dsRNA recognition by NS1 protein of influenza A virus

Influenza A viruses are important human pathogens causing periodic pandemic threats. Nonstructural protein 1 （NS1） protein of influenza A virus （NS1A） shields the virus against host defense. Here, we report the crystal structure of NS1A RNA-binding domain （RBD） bound to a double-stranded RNA （dsRNA） at 1.7A. NS1A RBD forms a homodimer to recognize the major groove of A-form dsRNA in a length-independent mode by its conserved concave surface formed by dimeric anti-parallel a-helices, dsRNA is anchored by a pair of invariable arginines （Arg38） from both monomers by extensive hydrogen bonds. In accordance with the structural observation, isothermal titration calorimetry assay shows that the unique Arg38-Arg38 pair and two Arg35-Arg46 pairs are crucial for dsRNA binding, and that Ser42 and Thr49 are also important for dsRNA binding. Agrobacterium co-infiltration assay further supports that the unique Arg38 pair plays important roles in dsRNA binding in vivo.

Nitrite,a novel method to decrease ischemia/reperfusion injury in the rat liver

AIM:To investigate whether nitrite administered prior to ischemia/reperfusion(I/R)reduces liver injury.METHODS:Thirty-six male Sprague-Dawley rats were randomized to 3 groups,including sham operated(n=8),45-min segmental ischemia of the left liver lobe(IR,n=14)and ischemia/reperfusion(I/R)preceded by the administration of 480 nmol of nitrite(n=14).Serum transaminases were measured after 4 h of reperfusion.Liver microdialysate(MD)was sampled in 30-min intervals and analyzed for glucose,lactate,pyruvate and glycerol as well as the total nitrite and nitrate(NOx).The NOx was measured in serum.RESULTS:Aspartate aminotransferase(AST)at the end of reperfusion was higher in the IR group than in the nitrite group(40±6.8μkat/L vs 22±2.6μkat/L,P=0.022).Similarly,alanine aminotransferase(ALT)was also higher in the I/R group than in the nitrite group(34±6μkat vs 14±1.5μkat,P=0.0045).The NOx in MD was significantly higher in the nitrite group than in the I/R group(10.1±2.9μmol/L vs 3.2±0.9μmol/L,P=0.031)after the administration of nitrite.During ischemia,the levels decreased in both groups and then increased again during reperfusion.At the end of reperfusion,there was a tendency towards a higher NOx in the I/R group than in the nitrite group(11.6±0.7μmol/L vs 9.2±1.1μmol/L,P=0.067).Lactate in MD was significantly higher in the IR group than in the nitrite group(3.37±0.18 mmol/L vs 2.8±0.12 mmol/L,P=0.01)during ischemia and the first 30 min of reperfusion.During the same period,glycerol was also higher in the IRI group than in the nitrite group(464±38μmol/L vs 367±31μmol/L,P=0.049).With respect to histology,there were more signs of tissue damage in the I/R group than in the nitrite group,and29%of the animals in the I/R group exhibited necrosis compared with none in the nitrite group.Inducible nitric oxide synthase transcription increased between early ischemia(t=15)and the end of reperfusion in both groups.CONCLUSION:Nitrite administered before liver ischemia in the rat liver reduces anaerobic metabolism and cell necrosis,which could be important in the clinical setting.