Prior Information Research Articles

ELISA-R: an R-based method for robust ELISA data analysis.

Enzyme-linked immunosorbent assay (ELISA) is a technique to detect the presence of an antigen or antibody in a sample. ELISA is a simple and cost-effective method that has been used for evaluating vaccine efficacy by detecting the presence of antibodies against viral/bacterial antigens and diagnosis of disease stages. Traditional ELISA data analysis utilizes a standard curve of known analyte, and the concentration of the unknown sample is determined by comparing its observed optical density against the standard curve. However, in the case of vaccine research for complicated bacteria such as Mycobacterium tuberculosis (Mtb), there is no prior information regarding the antigen against which high-affinity antibodies are generated and therefore plotting a standard curve is not feasible. Consequently, the analysis of ELISA data in this instance is based on a comparison between vaccinated and unvaccinated groups. However, to the best of our knowledge, no robust data analysis method exists for "non-standard curve" ELISA. In this paper, we provide a straightforward R-based ELISA data analysis method with open access that incorporates end-point titer determination and curve-fitting models. Our modified method allows for direct measurement data input from the instrument, cleaning and arranging the dataset in the required format, and preparing the final report with calculations while leaving the raw data file unchanged. As an illustration of our method, we provide an example from our published data in which we successfully used our method to compare anti-Mtb antibodies in vaccinated vs non-vaccinated mice.

Kernels of Motor Memory Formation: Temporal Generalization in Bimanual Adaptation.

In daily life, we coordinate both simultaneous and sequential bimanual movements to manipulate objects. Our ability to rapidly account for different object dynamics suggests there are neural mechanisms to quickly deal with them. Here we investigate how actions of one arm can serve as a contextual cue for the other arm, and facilitate adaptation. Specifically, we examine the temporal characteristics that underlie motor memory formation and recall, by testing the contextual effects of prior, simultaneous, and post contralateral arm movements in both male and female human participants. To do so, we measure their temporal generalization in three bimanual interference tasks. Importantly, the timing context of the learned action plays a pivotal role in the temporal generalization. While motor memories trained with post adaptation contextual movements generalize broadly, motor memories trained with prior contextual movements exhibit limited generalization, and motor memories trained with simultaneous contextual movements do not generalize to prior or post contextual timings. This highlights temporal tuning in sensorimotor plasticity: different training conditions yield substantially different temporal generalization characteristics. Since these generalizations extend far beyond any variability in training times, we suggest that the observed differences may stem from inherent differences in the use of prior, current and post-adaptation contextual information in the generation of natural behavior. This would imply differences in the underlying neural circuitry involved in learning and executing the corresponding coordinated bimanual movements.Significance Statement This study addresses a fundamental question in the field of sensorimotor neuroscience of how multiple movements are temporally linked within a single motor memory. We examine the temporal generalization of motor memory formation by varying the timing of contextual movements associated with a learned motor memory across a range of prior, current, and post adaptation movement times using a bimanual motor learning task. We observed distinct patterns of temporal generalization based on whether the contextual movements occurred prior to, simultaneously with, or after the adaptation movement. For the first time, our findings reveal that the timing of contextual movements is crucial in the formation and generalization of motor memories.

Adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination

Abstract The attitude of a vehicle can be largely determined by integrating the global navigation satellite system (GNSS) and inertial measurement unit (IMU) in land-based applications. However, traditional dual-antenna GNSS/IMU integrated systems are susceptible to signal reflection, diffraction, and even interruption, leading to reduced accuracy and reliability in challenging environments. To address this issue, this paper proposes an adaptive IMU error correction algorithm for dual-antenna GNSS/IMU integrated vehicle attitude determination. First, we propose an IMU-aided baseline length constraint model to support the ambiguity resolution (AR) process. This model incorporates accurate prior information from the IMU, improving the success rate of AR within the dual-antenna GNSS/IMU integrated system. Second, we present an adaptive IMU error correction mechanism based on long short-term memory (LSTM) and particle swarm optimization (PSO) to assist in vehicle attitude determination and mitigate attitude error drift of the lMU during GNSS outages. Field test results verified that, compared to two other candidate algorithms, the proposed algorithm improves accuracy in roll, pitch, and yaw by 19.23%, 30.56%, and 67.12%, respectively, and by 12.50%, 10.71%, and 38.39% respectively. Moreover, in two distinct scenarios where GNSS is blocked for 120 seconds, the proposed algorithm still provides accurate and stable attitude information for vehicle navigation, with the accuracy of roll, pitch, and yaw kept within 0.08 degrees.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

Evoked Component Analysis (ECA): Decomposing the Functional Ultrasound Signal With GLM-Regularization.

Analysis of functional neuroimaging data aims to unveil spatial and temporal patterns of interest. Existing analysis methods fall into two categories: fully data-driven approaches and those reliant on prior information, e.g. the stimulus time course. While using the stimulus signal directly can help identify the activated brain areas, it is known that the relationship between stimuli and the brain's response exhibits nonlinear and time-varying characteristics. As such, relying completely on the stimulus signal to describe the brain's temporal response leads to a restricted interpretation of the brain function. In this paper, we present a new technique called Evoked Component Analysis (ECA), which leverages prior information up to a defined extent. This is achieved by including the general linear model (GLM) design matrix as a regulatory term and estimating the factor matrices in both space and time through an alternating minimization approach. We apply ECA to 2D and swept-3D functional ultrasound (fUS) experiments conducted with mice. When decomposing 2D fUS data, we employ GLM regularization at various intensities to emphasize the role of prior information. Furthermore, we show that incorporating multiple hemodynamic response functions within the design matrix can provide valuable insights into region-specific characteristics of evoked activity. Finally, we use ECA to analyze swept-3D fUS data recorded from five mice engaged in two distinct visual tasks. Swept-3D fUS images the 3D brain sequentially using a moving probe, resulting in different slice acquisition time instants. We show that ECA can estimate factor matrices with a fine resolution at each slice acquisition time instant and yield higher t-statistics compared to GLM and correlation analysis for all subjects.

Can the young water fraction reduce predictive uncertainty in water transit time estimations?

Transit time distributions (TTDs) of streamflow are informative descriptors of catchment hydrological functioning and solute transport mechanisms. Conventional methods for estimating TTDs generally require model calibration against extensive tracer data time series, which are often limited to well-studied experimental catchments. We challenge this limitation and propose an alternative approach that uses the young water fraction (Fywobs), an increasingly used water age metric which represents the proportion of streamflow with a transit time younger than 2-3 months, and that can be robustly estimated with sparsely measured tracer data. To this end, we conducted a proof of concept study by modelling TTDs using StorAge Selection (SAS) functions with oxygen isotopes (δ18O) measurements for 23 diverse catchments in Germany. In a Monte-Carlo approach, we computed the (averaged) marginal TTDs of a prior parameter distribution and derived a model-based Fyw (Fywsim). We compared Fywsim with Fywobs, obtained from δ18O measurements, and constrained the prior SAS parameters distribution. Subsequently, we derived a posterior distribution of parameters and resulting model simulations. Our findings showed that using Fywobs to constrain the model effectively reduced parameter equifinality and simulation uncertainty. However, the value of Fywobs on reducing model uncertainty varied across sites, with larger values (Fywobs≥0.10) leading to simulations with a narrower uncertainty band and higher model efficiency, whilst smaller values (Fywobs≤0.05) had limited influence on reducing model output uncertainty. We discussed the potential and limitations of combining SAS functions with Fywobs, and considered broader implications of this approach for enhancing our understanding of catchment functioning and water quality status.

A Robust SVD-UKF Algorithm and Its Application in Integrated Navigation Systems

To improve the positioning accuracy in Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation and address the issue of measurement information anomalies, an integrated navigation algorithm based on an improved Huber-M estimation with Singular Value Decomposition Unscented Kalman Filter (SVD-UKF) is proposed. During the iterative update process of the prior information matrix in the UKF filtering, the Singular Value Decomposition (SVD) is introduced. This enhancement optimizes the prior information matrix, thereby enhancing numerical stability. The tail function of the Huber-M estimation is replaced with an exponential square loss function to improve the computational accuracy of the Huber-M estimation. The improved Huber-M estimation is then incorporated into the SVD-UKF framework. This approach reconstructs the UKF prior information and measurement information. The experimental results indicate that, compared to the traditional UKF and the Sage-Husa adaptive SVD-UKF algorithm, the improved algorithm enhances velocity accuracy by 68.7% and 31.2%, respectively, and improves position accuracy by 73.5% and 38.6%, respectively. The positioning accuracy and robustness of the INS/GNSS integrated navigation system were significantly improved, particularly under conditions with abnormal measurement information. The system demonstrated enhanced stability and resistance to interference in these scenarios.

An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations

AbstractObservations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.

Disposal practices, knowledge and attitude of adult patients visiting outpatient pharmacy services towards unused medicines in Debre Tabor, Northwest Ethiopia: a descriptive cross-sectional study

ObjectiveAccumulation at home and improper disposal of unused medicines pose a major public health problem. Knowledge and attitude of patients may contribute to accumulation and disposal practice of unused medicines....

An efficient strategy based on proper orthogonal decomposition for rapid determination of the location and rate of water vapor humidification in aircraft cabins

Currently, the air in commercial aircraft cabins during flights routinely exhibits extremely low relative humidity. But none of the previously published studies have addressed optimization of the humidification parameters in order to, create the desired humidity distributions in aircraft cabins at minimal cost. This study proposed an efficient strategy to accomplish the task. Under this strategy, a database of various potential humidification locations for aircraft cabins is first constructed. One scenario in the database is then selected by computational fluid dynamics (CFD)-based enumeration. Next, with the humidification location as prior information, the humidification rate and the air-supply temperature from the main ventilation system can be inversely designed by proper orthogonal decomposition. The above strategy was applied to a three-dimensional aircraft cabin model for the validated CFD cases. The results showed that an appropriate humidification for the cabin might both improve the thermal comfort of passengers and increase the humidity levels in the cabin; a decrease of the air-supply temperature from the main ventilation system would be beneficial in reducing the water vapor humidification rate required for passengers and allowing a higher humidification rate to be tolerated for the cabin walls. The parameters that improved the cabin air environment without significant moisture accumulation on walls were also provided by the proposed strategy. Compared with the full CFD simulation method, the proposed strategy is capable of providing accurate ranges of parameters while reducing the computing time by more than 95%, which demonstrates its competitive prospects.

Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.

Prior-informed Active Galactic Nucleus Host Spectral Decomposition Using PyQSOFit

We introduce an improved method for decomposing the emission of active galactic nuclei (AGN) and their host galaxies using templates from principal component analysis (PCA). This approach integrates prior information from PCA with a penalized pixel fitting mechanism that improves the precision and effectiveness of the decomposition process. Specifically, we have reduced the degeneracy and overfitting in AGN host decomposition, particularly for those with low signal-to-noise ratios (SNRs), where traditional methods tend to fail. By applying our method to 76,565 Sloan Digital Sky Survey Data Release 16 quasars with z < 0.8, we achieve a success rate of ≈94%, thus establishing the largest host-decomposed spectral catalog of quasars to date. Our fitting results consider the impact of the host galaxy on the overestimation of the AGN luminosity and black hole mass (M BH). Furthermore, we obtained stellar velocity dispersion (σ ⋆) measurements for 4137 quasars. The slope of the M BH−σ ⋆ relation in this subsample is generally consistent with previous quasar studies beyond the local Universe. Our method provides a robust and efficient approach to disentangle the AGN and host galaxy components across a wide range of SNRs and redshifts.

Illuminating the Hierarchical Segmentation of Faults Through an Unsupervised Learning Approach Applied to Clouds of Earthquake Hypocenters

AbstractWe propose a workflow for the recognition of the hierarchical segmentation of faults through earthquake hypocenter clustering without prior information. Our approach combines density‐based clustering algorithms (DBSCAN and OPTICS), and principal component analysis (PCA). Given a spatial distribution of earthquake hypocenters, DBSCAN identifies first‐order clusters, representing regions with the highest density of connected seismic events. Within each first‐order cluster, OPTICS further identifies nested higher‐order clusters, providing information on their number and size. PCA analysis is applied to first‐ and higher‐order clusters to evaluate eigenvalues, allowing discrimination between seismicity associated with planar features and distributed seismicity that remains uncategorized. The identified planes are then geometrically characterized in terms of their location and orientation in the space, length, and height. This automated procedure operates within two spatial scales: the largest scale corresponds to the longest pattern of approximately equally dense earthquake clouds, while the smallest scale relates to earthquake location errors. By applying PCA analysis, a planar feature outputted from a first‐order cluster can be interpreted as a fault surface while planes outputted after OPTICS can be interpreted as fault segments comprised within the fault surface. The evenness between the orientation of illuminated fault surfaces and fault segments, and that of the nodal planes of earthquake focal mechanisms calculated along the same faults, corroborates this interpretation. Our workflow has been successfully applied to earthquake hypocenter distributions from various seismically active areas (Italy, Taiwan, and California) associated with faults exhibiting diverse kinematics.

China’s methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China’s CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a−1) in 2020 were closer to the official reported figure (53.57 Tg a−1) than to the prior (63.80 Tg a−1), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

AbstractA self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

SeaTrack: Rethinking Observation-Centric SORT for robust Nearshore Multiple Object Tracking

Nearshore Multiple Object Tracking (NMOT) aims to locate and associate nearshore objects. Current approaches utilize Automatic Identification Systems (AIS) and radar to accomplish this task. However, video signals can describe the visual appearance of nearshore objects without prior information such as identity, location, or motion. In addition, sea clutter will not affect the capture of living objects by visual sensors. Recognizing this, we analyzed three key long-term challenges of the vision-based NMOT and proposed a tracking pipeline that relies solely on motion information. Maritime objects are highly susceptible to being obscured or submerged by waves, resulting in fragmented tracklets. We first introduced guiding modulation to address the long-term occlusion and interaction of maritime objects. Subsequently, we modeled confidence, altitude, and angular momentum to mitigate the effects of motion blur, ringing, and overshoot artifacts to observations in unstable imaging environments. Additionally, we designed a motion fusion mechanism that combines long-term macro tracklets with short-term fine-grained tracklets. This correction mechanism helps reduce the estimation variance of the Kalman Filter (KF) to alleviate the substantial nonlinear motion of maritime objects. We call this pipeline SeaTrack, which remains simple, online, and real-time, demonstrating excellent performance and scalability in benchmark evaluations.

Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier

We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of mechanical and electrical systems. These systems are typically complex in structure and are monitored by multiple sensors simultaneously. Some sensor variables in the collected data may be incomplete due to sensor malfunctions or transmission errors. Additionally, labeling the data can be a time-consuming and labor-intensive task, resulting in many unlabeled samples. To address these challenges, the missing sensor variables and the unavailable labels are treated as hidden values and handled within the framework of the Expectation Maximization algorithm. We employ a semi-Bayesian technique with variational inference to estimate the model parameters. Specifically, we introduce prior distribution to the mean and covariance matrix to address the possible singularity of the empirical covariance matrix. The weighting coefficients are left without putting prior distribution so that their values can decay to zero, effectively removing redundant components of the mixture model. The factorized distribution is utilized to approximate the posterior distribution of the model parameters, as well as the missing labels and other latent variables. Numerical and real case studies are carried out to verify the effectiveness of the proposed technique.

MEFDPN: Mixture exponential family distribution posterior networks for evaluating data uncertainty

The computation of uncertainty are crucial for developing a reliable machine learning model. The natural posterior network (NatPN) provides uncertainty estimation for any single exponential family distribution, but real-world data is often complex. Therefore, we introduce a mixture exponential family posterior network (MEFDPN), which extends the prior distribution to a mixture of exponential family distributions, aiming to fit complex distributions that better represent real data. During network training, MEFDPN independently updates the posterior Bayesian estimates for each prior distribution, and the weights of these distributions are updated based on the forward propagation results. Furthermore, MEFDPN calculates two types of uncertainty (aleatoric and epistemic) and combines them using entropy weighting to obtain a comprehensive confidence measure for each data point. Theoretically, MEFDPN achieves higher prediction accuracy, and experimental results demonstrate its capability to compute high-quality data comprehensive confidence. Moreover, it shows encouraging accuracy in Out-of-Distribution(OOD) detection and validation experiments. Finally, we apply MEFDPN to a materials dataset, efficiently filtering out OOD data. This results in a significant enhancement of prediction accuracy for machine learning models. Specifically, removing only 5% of outlier data leads to a 2%–5% improvement in accuracy.

Study Protocol for LOMCAD Trial: Effect of Lomerizine Hydrochloride to Prevent Recurrence of Cerebral Ischemic Events in CADASIL Patients

ObjectivesCerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is one of the most common monogenic cerebral small vessel diseases. Our previous observational study suggested that lomerizine hydrochloride, a calcium channel blocker approved in Japan in 1999 for the prevention of migraine headaches, is also effective for preventing recurrent ischemic stroke in CADASIL patients. The aim of this study (LOMCAD trial) is to verify the efficacy of lomerizine hydrochloride. Materials and MethodsThis is a multicenter, prospective, single-arm trial, using a historical control for comparison. CADASIL patients with a history of two or more cerebral ischemic events within the last two years will be administered lomerizine hydrochloride (5-mg tablet twice daily) for 24 months. The primary endpoint is symptomatic cerebral ischemic events during the 24-month period. Using our historical data and Bayesian sample size calculation based on a prior predictive distribution, the planned sample size was determined as 20 subjects. ConclusionWe have planned a clinical trial to verify the effectiveness of lomerizine hydrochloride as prophylaxis to prevent recurrent cerebral ischemic events in CADASIL patients. RegistrationThe LOMCAD trial has been registered in the Japan Registry of Clinical Trials (jRCTs051220072, https://jrct.niph.go.jp/latest-detail/jRCTs051220072).

Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology.

The primary results of phase III oncology trials may be challenging to interpret, given that results are generally based on P value thresholds. The probability of whether a treatment is beneficial, although more intuitive, is not usually provided. Here, we developed and released a user-friendly tool that calculates the probability of treatment benefit using trial summary statistics. We curated 415 phase III randomized trials enrolling 338,600 patients published between 2004 and 2020. A phase III prior probability distribution for the treatment effect was developed on the basis of a three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of clinically meaningful benefit (hazard ratio [HR] <0.8). The distribution of signal-to-noise ratios and power of phase III oncology trials were compared with that of 23,551 randomized trials from the Cochrane Database. The signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane Database. Still, the median power of phase III oncology trials was only 49% (IQR, 14%-95%), and the power was <80% in 65% of trials. Using the phase III oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥90% probability of clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR <1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199). By enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials.