Optimal Experimental Design Research Articles

Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling.

Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 μg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.

A Python Clustering Analysis Protocol of Genes Expression Data Sets.

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

Online Aging Diagnostics Using Optimally Designed Experiments

Degradation of lithium-ion batteries is the result of many complex phenomena occurring simultaneously at varying time and length scales. The underlying electrochemical and mechanical phenomena have received much attention from researchers [1]. Physics-based models of these effects support the mechanistic understanding of degradation modes and can thereby help reduce their severity. Few studies target changing electrochemical parameters such as diffusion coefficients or reaction rate constants that have a direct impact on model accuracy and manifest themselves in observable aging. Lyu et al. [2] used a simplified electrochemical model and monitored battery degradation by following changes in diffusion time constants, electrode balancing, reaction rate coefficients, and ohmic resistance. However, several of the parameters they attempted to track could only be identified with low accuracy as they used the same data-set to identify all parameters. In this study, we investigate parameters of a full order Newman-type model [3] over the course of a batteries lifetime under real-world load-cycles. To ensure parameter identifiability, optimally designed experiments are used for parameter estimation.In a previous study [4] the feasibility of optimal experiment design for parametrization of electrochemical battery models was demonstrated. We now extend this work and re-evaluate key parameters over the course of an aging study on commercial, nickel-rich 18650 lithium-ion batteries. We highlight how quantifying changes in physical battery parameters can extend standard performance metrics for a batteries state-of-health by, e.g., including degradation in rate-capability. Additionally, the importance of battery usage conditions such as C-rate or state-of-charge window on model parameter trajectories is investigated and their relationship with conventional performance metrics such as the bulk cell resistance or rate-capability determined.Quantifying how specific mechanisms contribute to apparent capacity or power fade is a major step towards battery lifetime optimization. This could enable designs more tailored for specific applications and significantly extend batteries useful lifetime. Furthermore, updating parameters is essential for electrochemical control strategies relying on accurate model predictions of battery states as illustrated in Figure 1. This re-calibration would make a battery management system aging-sensitive and enable more efficient utilization and a physics-informed state-of-health.Figure 1: The central plot shows how parameters change during aging. If this change is not considered, model performance deteriorates between beginning-of-life (BOL) and end-of-life (EOL) (in blue, right-hand side). This is normally handled by using conservative battery management systems and over-sizing systems. The proposed strategy (orange) achieves higher model accuracy during the entire useful life and the parameter estimates can be used to formulate an extended state-of-health.

High-throughput screening of optimal process conditions using model predictive control.

Modern biotechnological laboratories are equipped with advanced parallel mini-bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed-batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well-defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini-bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.

Maximizing Archimedes spiral packing density area.

In this paper, we experimentally demonstrate a broadband Archimedes spiral delay line with high packing density on a silicon photonic platform. This high density is achieved by optimizing the gap between the adjacent waveguides (down to sub-micron scale) in the spiral configuration. However, care must be taken to avoid evanescent coupling, the presence of which will cause the spiral to behave as a novel type of distributed spiral resonator. To this end, an analytical model of the resonance phenomenon was developed for a simple spiral. Moreover, it is demonstrated that this distributed spiral resonator effect can be minimized by ensuring that adjacent waveguides in the spiral configuration have different propagation constants (β). Experimental validations were accomplished by fabricating and testing multiple spiral waveguides with varying lengths (i.e., 0.4, 0.8, and 1.4 mm) and separation gaps (i.e., 300 and 150 nm). Finally, a Linear Density Figure of Merit (LDFM) is introduced to evaluate the packing efficiency of various spiral designs in the literature. In this work, the optimum experimental design with mitigated resonance had a length of 1.4mm and occupied an area of 60 × 60µm, corresponding to an LDFM of 388km-1.

Comparing Instrumentation Selection Techniques for Vibration Testing

Abstract Vibration testing of complex aerospace structures requires substantial pretest planning. Ground and flight testing of structures can be costly to execute in terms of time and money, so it is pertinent that tests are properly set up to capture mode shapes or dynamics of interest. One of the most important planning tasks is the placement of sensors to acquire measurements for control and characterization of the results. In this paper, we will examine two techniques that can leverage available output from finite element modeling to intelligently place accelerometers for a vibration test to capture the structural dynamics throughout a specified frequency range with a data acquisition channel budget. These two techniques are effective independence (EI) and optimal experimental design (OED). Both methods will be applied to an aerospace structure. Effects of the chosen sets on system equivalent reduction and expansion process (SEREP) are detailed alongside simpler comparison metrics, like the auto-modal assurance criterion (auto-MAC). In addition to comparing the resulting instrumentation sets, the application of the two approaches will be compared in terms of the inputs required, the information obtained from their application, and the computation time requirements. Both OED and EI offer an effective method for selecting an instrumentation set for a given vibration test. EI is a straightforward, computationally inexpensive approach that provides effective instrumentation sets. OED provides an effective alternative that is less sensitive to the impact of local modes and leads to a natural ranking of importance for each chosen degree-of-freedom (DOF).

The Use of the QbD Approach to Optimize the Co-Loading of Simvastatin and Doxorubicin in Liposomes for a Synergistic Anticancer Effect.

The present study aimed to optimize a liposomal formulation co-encapsulating simvastatin (SIM) and doxorubicin (DOX) that has future perspectives in anticancer therapy. The optimization process was performed by implementing the Quality by Design concept and by considering the results of a previous screening study. Failure Mode and Effects Analysis was used for the identification of the potential critical factors, i.e., phospholipid, SIM and DOX concentration, which were assessed in an optimization experimental design with the purpose of designing an optimal formulation. The optimal formulation, meeting the established quality profile, was additionally characterized in terms of the release profile and antiproliferative effects. During dissolution studies, a novel chronoamperometric method was used for the simultaneous quantification of SIM and DOX. The obtained data confirmed the similarity of this method with a validated HPLC method. The anticancer potential of the optimal formulation was tested against two human cancerous cell lines, namely T47D-KBluc human mammary ductal carcinoma cell line and A549 human pulmonary cancer cell line. The results highlighted that the antiproliferative effect of the optimal formulation is concentration dependent and favors a synergistic effect of the two drugs.

Optimal experiment design for element specific atom counting using multiple annular dark field scanning transmission electron microscopy detectors

This paper investigates the possible benefits for counting atoms of different chemical nature when analysing multiple 2D scanning transmission electron microscopy (STEM) images resulting from independent annular dark field (ADF) detector regimes. To reach this goal, the principles of statistical detection theory are used to quantify the probability of error when determining the number of atoms in atomic columns consisting of multiple types of elements. In order to apply this theory, atom-counting is formulated as a statistical hypothesis test, where each hypothesis corresponds to a specific number of atoms of each atom type in an atomic column. The probability of error, which is limited by the unavoidable presence of electron counting noise, can then be computed from scattering-cross sections extracted from multiple ADF STEM images. Minimisation of the probability of error as a function of the inner and outer angles of a specified number of independent ADF collection regimes results in optimal experimental designs. Based on simulations of spherical Au@Ag and Au@Pt core–shell nanoparticles, we investigate how the combination of two non-overlapping detector regimes helps to improve the probability of error when unscrambling two types of atoms. In particular, the combination of a narrow low angle ADF detector with a detector formed by the remaining annular collection regime is found to be optimal. The benefit is more significant if the atomic number Z difference becomes larger. In addition, we show the benefit of subdividing the detector regime into three collection areas for heterogeneous nanostructures based on a structure consisting of three types of elements, e.g., a mixture of Au, Ag and Al atoms. Finally, these results are compared with the probability of error resulting when one would ultimately use a pixelated 4D STEM detector and how this could help to further reduce the incident electron dose.

Generating exact optimal designs via particle swarm optimization: Assessing efficacy and efficiency via case study

In this study we address existing deficiencies in the literature on applications of Particle Swarm Optimization (PSO) to generate optimal designs. We present the results of a large computer study in which we bench-mark both efficiency and efficacy of PSO to generate high quality candidate designs for small-exact response surface scenarios commonly encountered by industrial practitioners. A preferred version of PSO is demonstrated and recommended for generating optimal experimental designs. Further, in contrast to popular local optimizers such as the coordinate exchange, PSO is demonstrated to, even in a single run, generate highly efficient designs with large probability at small computing cost. Therefore, it appears beneficial for more practitioners to adopt and use PSO as tool for generating candidate experimental designs.

Experimental designs for accelerated degradation tests based on linear mixed effects models

Accelerated degradation testing has considerable significance in reliability inference due to its ability to provide accurate estimation of lifetime characteristics of highly reliable systems within a relatively short testing time period. The measured data from particular experiments at high stress conditions are extrapolated, through a technically reasonable statistical model, to obtain estimates of certain reliability properties under normal use levels. In this work, we consider repeated measures accelerated degradation tests with multiple stress variables, where the degradation path is assumed to follow a linear mixed effects model which is quite common in settings when repeated measures are made. As opposed to the vast majority of numerical derivations in the literature, we contribute in this work with analytical results in regards to optimal experimental designs for minimizing the asymptotic variance for estimating the median failure time under normal use conditions when the time points for measurements are fixed in advance.

Optimal experimental designs for estimating genetic and non-genetic effects underlying infectious disease transmission

BackgroundThe spread of infectious diseases in populations is controlled by the susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection), and recoverability (propensity to recover/die) of individuals. Estimating genetic risk factors for these three underlying host epidemiological traits can help reduce disease spread through genetic control strategies. Previous studies have identified important ‘disease resistance single nucleotide polymorphisms (SNPs)’, but how these affect the underlying traits is an unresolved question. Recent advances in computational statistics make it now possible to estimate the effects of SNPs on host traits from epidemic data (e.g. infection and/or recovery times of individuals or diagnostic test results). However, little is known about how to effectively design disease transmission experiments or field studies to maximise the precision with which these effects can be estimated.ResultsIn this paper, we develop and validate analytical expressions for the precision of the estimates of SNP effects on the three above host traits for a disease transmission experiment with one or more non-interacting contact groups. Maximising these expressions leads to three distinct ‘experimental’ designs, each specifying a different set of ideal SNP genotype compositions across groups: (a) appropriate for a single contact-group, (b) a multi-group design termed “pure”, and (c) a multi-group design termed “mixed”, where ‘pure’ and ‘mixed’ refer to groupings that consist of individuals with uniformly the same or different SNP genotypes, respectively. Precision estimates for susceptibility and recoverability were found to be less sensitive to the experimental design than estimates for infectivity. Whereas the analytical expressions suggest that the multi-group pure and mixed designs estimate SNP effects with similar precision, the mixed design is preferred because it uses information from naturally-occurring rather than artificial infections. The same design principles apply to estimates of the epidemiological impact of other categorical fixed effects, such as breed, line, family, sex, or vaccination status. Estimation of SNP effect precisions from a given experimental setup is implemented in an online software tool SIRE-PC.ConclusionsMethodology was developed to aid the design of disease transmission experiments for estimating the effect of individual SNPs and other categorical variables that underlie host susceptibility, infectivity and recoverability. Designs that maximize the precision of estimates were derived.

Application of optimal experiment design method to detect the ideal sensor positions: A case study of Milan metro line 5

This paper proposes a concept of optimal experimental design (OED) to enable an efficient parameter identification through an inverse analysis and to design an optimized monitoring set up. This method intends to provide a systematic and reproducible approach for parameter determination in mechanized tunnel modelling from field measurements during excavation. In this concept, identification of most relevant parameters is followed by a secondary spatial and temporal global sensitivity analyses in various time scales to find the most relevant positions in the existing monitoring set-up for parameter identification. To afford the computational costs, metamodeling approach was employed to substitute the FE model with a response surface function. To validate the numerical model and to justify the applicability of this concept for a real case, the measured tunnel-induced settlements in a shallow founded nine-storey building, undercrossed by the new double tube Milan metro line 5, are adopted. Results indicated that the OED-assisted design of sensor arrangements enables adequate identification of the model parameters with least uncertainty and less monitoring effort (e.g. fewer sensors), while no expert decision is required in the process. Finally, comparison of results from standard and OED-designed monitoring setups indicates a significant enhancement in the reliability of numerical results considering the measurement tolerances.

Optimal Design of Complementary Experiments for Parameter Estimation at Elevated Temperature of Food Processing.

Simultaneous estimation of thermal properties can be challenging, especially when the parameters are temperature-dependent. Previous research has shown that by using a complementary experiment, temperature-dependent thermal conductivity can be estimated using a single experiment. The objective of this study was to optimize the complementary experiments that can facilitate the simultaneous estimation of temperature-dependent thermal conductivity and volumetric heat capacity. A theoretical study was conducted with two experiments in a single trial with the sample being kept in a cylindrical sample holder, which had a thin film heater in the center. The first part of the experiment was conducted by keeping the external surface temperature at 50 °C for 300 s and allowing the center temperature to equilibrate with the boundary temperature. Then, the second part of the experiment followed, where the thin film heater was supplied with electrical power to increase the center temperate to 140 °C. Several heating profiles were studied to maximize the information obtained from the complementary experiments, and the best one was the power profile with a sinusoidal function. All four parameters of sweet potato puree temperature-dependent thermal conductivity (0.509 to 0.629 W/mK at 25 °C and 140 °C, respectively) and volumetric heat capacity (3.617 × 106 to 4.180 × 106 J/m3K at 25 °C and 140 °C, respectively) were estimated with low standard errors.

Hierarchical optimal designs and modeling for engineering: A case‐study in the rail sector

AbstractComplex engineering and technological processes typically generate data with a non‐trivial hierarchical structure. To this end, in this article we propose a full procedure for optimizing such processes through optimal experimental designs and modeling. In order to study a hierarchical structure, several types of experimental factors may arise, making the building of the experimental design challenging. Starting from the analysis of a preliminary dataset and a pilot design including nested, branching, and shared experimental factors, as well as a new type of experimental factor called composite‐form‐factor, we build a hierarchical D‐optimal experimental design using genetic algorithms. We apply our proposal to a real case‐study in the rail sector aimed at optimizing the payload distribution of freight trains. In this case‐study we also achieve the best train configuration by minimizing the in‐train forces. The results are very satisfactory and confirm that our full procedure represents a valid method to be successfully applied for solving similar technological problems.

Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends

Different meta-heuristic optimization algorithms have been used in a variety of fields due to their intelligent behavior and fast convergence. However, use of these algorithms in the engine behavior optimization is very-limited. The development of so-called hybrid optimization technique when these algorithms are combined with experimental design technique is an upcoming method in the field of renewable energy. Hence in this research, meta-heuristic optimization algorithms and experimental design methods were combined to optimize the engine behavior. Additionally, artificial neural networks (ANN) were employed to forecast the performance and emission behaviors of a CI engine running on a novel hybrid biodiesel blend of Cucurbita pepo. L (pumpkin) and Prosopis juliflora, mixed with a novel Elaeocarpus ganitrus (Rudraksha) additive. To assess the success of the ANN, four statistical benchmarks (R2, and MSE) were used. Experiments were designed according to Design of Experiments (DOE) rules with performance and emission parameters as outputs. Response surface methodology (RSM) was employed to find the effect of interaction factors. Single objective and multi-objective optimization using highly efficient hybrid RSM-particle swarm optimization (RPSO) and dragon fly algorithm (RMODA) were employed to optimize the response of the obtained RSM equations. The outcomes demonstrated that RSM and ANN were excellent modelling techniques for these kinds of situations, with good accuracy. In addition, ANN's prediction performance (R2 = 0.978 for BTE) was somewhat better than RSM's (R2 = 0.960 for BTE). On the other hand, the PJB20 blend with 5 mL additive increased BTE by 52.8% and reduced BSFC by 34.9% at maximum load. The smoke opacity was lowered by 7.1% when compared to pure diesel, without any engine modifications. CO2 emission was seen to be shortened by 19.14%. Finally, it can be concluded that this novel biodiesel can be possibly utilized in CI engines with no modification and the engine characteristics can be controlled by optimization and prediction models.

Stream-based active learning with linear models

The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.

Optimization experimental design of stable production life and estimated ultimate recovery in gaseous compound of hydrogen and carbon from shale reservoir

In this study, a mixed numerical model is established to simulate the production performance of gaseous compound of hydrogen and carbon in shale reservoir. Estimated ultimate recovery (EUR) and stable production life (SPL) of gaseous compound of hydrogen and carbon are affected by reservoir parameters and production control parameters, such as Langmuir pressure, Langmuir volume, critical desorption pressure, fracture half length, permeability in stimulated reservoir volume (SRV) area, bottom hole flow pressure and production of gaseous compound of hydrogen and carbon. Therefore, in the later research, in order to reduce the number of numerical experiments, the orthogonal experimental method is used to optimize the influence of hydrocarbon ERU and SPL, and analyze the main control factors affecting these two indices at the same time. The purpose of this paper is to optimize the production of hydrocarbons by orthogonal experimental method and find the main controlling factors affecting the production of compound of hydrogen and carbon. Two groups of orthogonal experiments were designed to find the best scheme in the process of compound of hydrogen and carbon development. The calculative results show that the output of hydrogen and carbon compounds has a highly significant impact on SPL, Langmuir volume and capability in SRV have a significant impact, and Langmuir pressure Critical destruction pressure and hydraulic fracture half-length had no significant effects.

Volume-Associated Clinical and Histopathological Effects of Intranasal Instillation in Syrian Hamsters: Considerations for Infection and Therapeutic Studies.

Syrian hamsters are a key animal model of SARS-CoV-2 and other respiratory viruses and are useful for the evaluation of associated medical countermeasures. Delivery of an infectious agent or intervention to the respiratory tract mirrors natural routes of exposure and allows for the evaluation of clinically relevant therapeutic administration. The data to support instillation or inoculation volumes are important both for optimal experimental design and to minimize or avoid effects of diluent alone, which may compromise both data interpretation and animal welfare. Here we investigate four intranasal (IN) instillation volumes in hamsters (50, 100, 200, or 400 µL). The animals were monitored daily, and a subset were serially euthanized at one of four pre-determined time-points (1, 3, 7, and 14 days post-instillation). Weight, temperature, oxygen saturation, CBC, radiographs, and respiratory tissue histopathology were assessed to determine changes associated with instillation volume alone. With all the delivery volumes, we found no notable differences between instilled and non-instilled controls in all of the parameters assessed, except for histopathology. In the animals instilled with 200 or 400 µL, inflammation associated with foreign material was detected in the lower respiratory tract indicating that higher volumes may result in aspiration of nasal and/or oropharyngeal material in a subset of animals, resulting in IN instillation-associated histopathology.

Vis LED Photo-Fenton Degradation of 124-Trichlorobenzene at a Neutral pH Using Ferrioxalate as Catalyst.

Chlorinated organic compounds (COCs) are among the more toxic organic compounds frequently found in soil and groundwater. Among these, toxic and low-degradable chlorobenzenes are commonly found in the environment. In this work, an innovative process using hydrogen peroxide as the oxidant, ferrioxalate as the catalyst and a visible light-emitting diode lamp (Vis LED) were applied to successfully oxidize 124-trichlorobenzene (124-TCB) in a saturated aqueous solution of 124-TCB (28 mg L−1) at a neutral pH. The influence of a hydrogen peroxide (HP) concentration (61.5–612 mg L−1), Fe3+ (Fe) dosage (3–10 mg L−1), and irradiation level (Rad) (I = 0.12 W cm−2 and I = 0.18 W cm−2) on 124-TCB conversion and dechlorination was studied. A D–Optimal experimental design combined with response surface methodology (RSM) was implemented to maximize the quality of the information obtained. The ANOVA test was used to assess the significance of the model and its coefficients. The maximum pollutant conversion at 180 min () was obtained with Fe = 7 mg L−1, HP = 305 mg L−1, and I = 0.12 W cm−2. The effect of two inorganic anions usually presents in real groundwater (bicarbonate and chloride, 600 mg L−1 each) was investigated under those optimized operating conditions. A slight reduction in the 124-TCB conversion after 180 min of reaction was noticed in the presence of bicarbonate (8.31%) and chloride (7.85%). Toxicity was studied with Microtox® (Azur Environmental, Carlsbad, CA, USA) bioassay, and a remarkable toxicity decrease was found in the treated samples, with the inhibition proportional to the remaining 124-TCB concentration. That means that nontoxic byproducts are produced in agreement with the high dechlorination degrees noticed.

Recommendations for Preclinical Testing of Treatments Against Alzheimer's Disease-Related Epileptiform Spikes in Transgenic Rodent Models.

Recent evidence suggests that about 30%of patients with mild to moderate Alzheimer's disease (AD) without a known diagnosis of epilepsy may display epileptiform spikes during electroencephalographic (EEG) recordings. These abnormal discharges occur predominantly during sleep and may be associated with accelerated disease progression. Subclinical spikes may represent a relevant target for clinical drug interventions, and there is a clear unmet need for preclinical testing of novel disease modifying agents in suitable animal models. Transgenic rodent models of AD pathology exhibit various forms of epileptiform EEG activity related to the abnormal levels of amyloid species in the brain. Among them, large-amplitude cortical and hippocampal EEG spikes in mouse and rat AD models may be reminiscent of the subclinical epileptiform EEG spikes recorded in some AD patients. This article reports the recommendations of a multidisciplinary panel of experts on optimal EEG markers and experimental designs to measure and report epileptiform activities and their response to symptomatic and disease-modifying drugs in transgenic AD model rodents. These recommendations may harmonize future preclinical EEG studies in the drug discovery research and may increase the comparability of experimental outcomes and their translational clinical value.