Monitoring ER Ca2+ by Luminescence with Low Affinity GFP-Aequorin Protein (GAP).

The endoplasmic reticulum (ER) is the main cellular reservoir of Ca2+, able to accumulate high amounts of calcium close to the millimolar range and to release it upon cell activation. Monitoring of Ca2+ dynamics within the ER lumen is best achieved using genetically encoded and targeted reporters. Luminescent probes based on the photoprotein aequorin have provided significant insight to measure subcellular Ca2+. Here we describe a robust and quantitative method based on the Ca2+ indicator of the GFP-Aequorin Protein (GAP) family, targeted to the ER lumen. A low Ca2+ affinity version of GAP, GAP1, carrying mutations in two EF-hands of aequorin, reconstituted with coelenterazine n has a reduced affinity for Ca2+ such that it conforms with the [Ca2+] values found in the ER and it slows the consumption of the probe by Ca2+. This feature is advantageous because it avoids fast aequorin consumption allowing long-term (longer than 1 h) ER Ca2+ measurements. GAP1 targeted to the ER allows monitoring of resting [Ca2+]ER and Ca2+ dynamics in intact cells stimulated with IP3-produced agonists. In addition, GAP1 can record Ca2+ mobilization in permeabilized cells challenged with IP3. We also provide a detailed calibration procedure which allows to accurately convert the luminescence signal into [Ca2+]ER.

3-axis computer numerical control machine positioning error dataset for thermal error compensation.

This article reports on a comprehensive dataset detailing positioning errors in a 3-axis milling center machine (MCM) with computer numerical control (CNC) specifically curated for thermal error compensation. The data, which includes separate datasets for the X, Y, and Z axes, was collected through systematic measurements using an interferometric laser (IL) system under monitored thermal conditions. Each axis's acquisition was recorded with a resolution to capture dynamic variations influenced by thermal fluctuations. Temperature measurements were obtained using resistance temperature detectors (RTD) installed in the bearing housings of each axis for monitoring of thermal conditions throughout the data collection process in each axis. The dataset comprises raw positional and error data for each axis alongside metadata describing parameters such as bearing temperature, heating cycle, and machine operating conditions. This dataset can potentially be a valuable resource for researchers, enabling them to develop and validate real-time thermal error compensation algorithms, thereby enhancing CNC machining precision for each axis independently and collectively. Furthermore, the dataset's structured format facilitates comparative studies across different machine configurations and operational contexts, contributing to advancements in manufacturing technology and improvements in process parameter design and optimization.

Fully Integrated Biosensing System for Dynamic Monitoring of Sweat Glucose and Real-Time pH Adjustment Based on 3D Graphene MXene Aerogel.

The development of noninvasive glucose sensors capable of continuous monitoring without restricting user mobility is crucial, particularly for managing diabetes, which demands consistent and long-term observation. Traditional sensors often face challenges with accuracy and stability that curtail their practical applications. To address these issues, we have innovatively applied a three-dimensional porous aerogel composed of Ti3C2Tx MXene and reduced graphene oxide (MX-rGO) in electrochemical sensing. It significantly reduces the electron-transfer distance between the enzyme's redox center and the electrode surface while firmly anchoring the enzyme layer to effectively prevent any leakage. Another pivotal advancement in our study is the integration of the sensor with a real-time adaptive calibration mechanism tailored specifically for analyzing sweat glucose. This sensor not only measures glucose levels but also dynamically monitors and adjusts to pH fluctuations in sweat. Such capabilities ensure the precise delivery of physiological data during physical activities, providing strong support for personalized health management.

Measurement and Analysis of Surface Temperature Gradients on Magnetron Sputtered Thin Film Growth Studied Using NiCr/NiSi Thin Film Thermocouples.

In the general analysis of thin-film growth processes, it is often assumed that the temperature of the film growth surface is the same as the temperature of the film growth substrate. However, a temperature gradient exists between the film growth surface and film growth substrate. Using the growth surface of TiO2 thin films as an example, the temperature gradient of the film growth surface is tested and analyzed. A NiCr/NiSi thin-film thermocouple is fabricated using the direct-current pulse magnetron sputtering method. A three-layer NiCr/NiSi thin-film thermocouple temperature measurement system is established to measure the temperature gradient of the film growth surface. The growth surface temperature and substrate temperature of the TiO2 thin films are measured. For a sputtering power density of 0.83 W cm- 2, the temperature difference between the first and second layers is 104.79 °C, while the temperature difference between the second and third layers is 39.92 °C. A standard K-type thermocouple is used to measure the substrate temperature, which is recorded to be 132.05 °C, consistent with common measurements of substrate temperature. The heat conduction on the film growth surface in the vacuum chamber is examined and a model for the temperature measurement device during film growth is constructed.

Modeling and chewing parameters calibration of Euryale ferox based on discrete element method.

Euryale ferox was chosen for this study to examine its mechanical properties during chewing. Experiments and the discrete element method were used to conduct the study. Initially, the intrinsic and contact parameters of E. ferox were established through physical tests. The maximum compressive force of breakage and chewing mechanical properties (hardness, springiness, and chewiness) were measured using a texture profile analyzer (TPA). A Hertz-Mindlin with bonding model of the E. ferox was constructed. Optimal values for significant factors, including the normal stiffness per unit area, shear stiffness per unit area, and bonding radius, were obtained through single-factor, Plackett-Burman, steepest ascent, and Box-Behnken response surface tests, with the maximum compressive force as the index. Under optimal parameter combination conditions, the relative error between the simulated and experimental values of the maximum compressive force was 0.79%, and the relative errors between the simulated and TPA test values of all indicators were less than 5.65%. This study provides a valuable reference for simulating the textural characteristics of agricultural products.

Visible diffuse reflectance smartphone spectrometer with high spectral accuracy.

A smartphone-based spectrometer employing principle of diffuse reflection is reported for the surface analysis of solid samples. The instrument utilizes a thin-film grating to diffract incoming light, while a diffuse reflecting surface projects the image of this diffracted light onto the detector plane. The CMOS camera of smartphone camera directly captures the diffusely reflected photons within its limited field-of-view thus eliminating the need for collection, conditioning and converging optics. The optical setup of the instrument provides facility to calibrate the spectral response considering the nonlinear distribution of the wavelength across the diffraction direction. Additional correction in the detector response at different light intensity results a reduced spectral error with a maximum wavelength resolution of δλ=0.08 nm/pixel in the camera within the spectral range Δλ = (400 - 700) nm. As a proof of the concept, the instrument demonstrates successful detection of color pigments in food samples by absorption measurement of the samples at an average spectral error < 6 %. The distinct absorption peak associated with standard food colors are compared against the absorption profile of unknown food colors used in pastry cake. This field-functional smart analysis with internet connectivity opens opportunity of identifying food adulteration by using toxic chemical colors at the point-of-test and immediate reporting to others. The overall instrument is fabricated by utilizing low-cost and light weight plastic wood to make compact (110 mm × 105 mm × 125 mm), robust, inexpensive (∼$ 50) and suitable for field-portable (∼145 gm) hand-held operation.

Classifying physical activity levels using Mean Amplitude Deviation in adults using a chest worn accelerometer: validation of the Vivalink ECG Patch.

BACKGROUND: The development of readily available wearable accelerometers has enabled clinicians to objectively monitor physical activity (PA) remotely in the community, a superior alternative to patient self-reporting measures. Critical to the value of these monitors is the ability to reliably detect when patients are undergoing ambulatory activity. Previous studies have highlighted the strength of using mean amplitude deviation (MAD) as a universal measure for analysing raw accelerometery data and defining cut-points between sedentary and ambulatory activities. Currently however there is little evidence surrounding the use of chest-worn accelerometers which can provide simultaneous monitoring of other physiological parameters such as heart rate (HR), RR intervals, and Respiratory Rate alongside accelerometery data. We aimed to calibrate the accelerometery function within the VivaLink ECG patch to determine the cut-point MAD value for differentiating sedentary and ambulatory activities. METHODS: We recruited healthy volunteers to undergo a randomised series of 9 activities that simulate typical free-living behaviours, while wearing a VivaLink ECG Patch (Campbell, California). MAD values were applied to a Generalised Linear Mixed Model to determine cut-points between sedentary and ambulatory activities. We constructed a Receiver Operating Characteristic (ROC) curve to analyse the sensitivity and specificity of the cut-off MAD value. RESULTS: Eighteen healthy adults volunteered to the study and mean MAD values were collected for each activity. The optimal MAD cut-point between sedentary and ambulatory activities was 47.73mG. ROC curve analysis revealed an area under the curve of 0.99 (p < 0.001) for this value with a sensitivity and specificity of 98% and 100% respectively. CONCLUSION: In conclusion, the MAD cut-point determined in our study is very effective at categorising sedentary and ambulatory activities among healthy adults and may be of use in monitoring PA in the community with minimal burden. It will also be useful for future studies aiming to simultaneously monitor PA with other physiological parameters via chest worn accelerometers.

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 1. Calibration workflow.

In this work, a high-fidelity digital twin was developed to support the design and testing of control strategies for drug product manufacturing via direct compression. The high-fidelity digital twin platform was based on typical pharmaceutical equipment, materials, and direct compression continuous processes. The paper describes in detail the material characterization, the Discrete Element Method (DEM) model and the DEM model parameter calibration approach and provides a comparison of the system's response to the experimental results for stepwise changes in the API concentration at the mixer inlet. A calibration method for a cohesive DEM contact model parameter estimation was introduced. To assure a correct prediction for a wide range of processes, the calibration approach contained four characterization experiments using different stress states and different measurement principles, namely the bulk density test, compression with elastic recovery, the shear cell, and the rotating drum. To demonstrate the sensitivity of the DEM contact parameters to the process response, two powder characterization data sets with different powder flowability were applied. The results showed that the calibration method could differentiate between the different material batches of the same blend and that small-scale material characterization tests could be used to predict the residence time distribution in a continuous manufacturing process.

Development of a High-Fidelity digital twin using the discrete element method for a continuous direct compression process. Part 2. Validation of calibration workflow.

This paper is the second in a series of two that describes the application of discrete element method (DEM) and reduced order modeling to predict the effect of disturbances in the concentration of drug substance at the inlet of a continuous powder mixer on the concentration of the drug substance at the outlet of the mixer. In the companion publication, small-scale material characterization tests, a careful DEM parameter calibration and DEM simulations of the manufacturing process were used to develop a reliable RTD models. In the current work, the same calibration workflow was employed to evaluate the predictive ability of the resulting reduced-order model for an extended design space. DEM simulations were extrapolated using a Relay Race method and the cumulative RTD was accurately parameterized using the n-CSTR model. By performing experiments and simulations, a calibrated DEM model predicted the response of a continuous powder mixer to step changes in the inlet concentration of an API. Thus, carefully calibrated DEM models was used to guide and reduce experimental work and to establish an adequate control strategy. In addition, a further reduction in the computational effort was obtained by using the Relay Race method to extrapolate results. The predicted RTD curves were then parameterized to develop reduced order models and used to simulate the process in a matter of seconds. Overall, a control strategy evaluation tool based on high-fidelity DEM simulations was developed using material-sparing small-scale characterization tests.

2D/3D registration based on biplanar X-ray and CT images for surgical navigation.

BACKGROUND AND OBJECTIVES: Image-based 2D/3D registration is a crucial technology for fluoroscopy-guided surgical interventions. However, traditional registration methods relying on a single X-ray image into surgical navigation systems. This study proposes a novel 2D/3D registration approach utilizing biplanar X-ray images combined with computed tomography (CT) to significantly reduce registration and navigation errors. The method is successfully implemented in a surgical navigation system, enhancing its precision and reliability. METHODS: First, we simultaneously register the frontal and lateral X-ray images with the CT image, enabling mutual complementation and more precise localization. Additionally, we introduce a novel similarity measure for image comparison, providing a more robust cost function for the optimization algorithm. Furthermore, a multi-resolution strategy is employed to enhance registration efficiency. Lastly, we propose a more accurate coordinate transformation method, based on projection and 3D reconstruction, to improve the precision of surgical navigation systems. RESULTS: We conducted registration and navigation experiments using pelvic, spinal, and femur phantoms. The navigation results demonstrated that the feature registration errors (FREs) in the three experiments were 0.505±0.063 mm, 0.515±0.055 mm, and 0.577±0.056 mm, respectively. Compared to the point-to-point (PTP) registration method based on anatomical landmarks, our method reduced registration errors by 31.3%, 23.9%, and 26.3%, respectively. CONCLUSION: The results demonstrate that our method significantly reduces registration and navigation errors, highlighting its potential for application across various anatomical sites. Our code is available at: https://github.com/SJTUdemon/2D-3D-Registration.

Early diagnosis of Alzheimer's disease using a group self-calibrated coordinate attention network based on multimodal MRI.

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer's disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.

A binocular camera calibration method based on circle detection.

Camera calibration is a crucial step in binocular measurement, and the accuracy of camera calibration largely determines the measurement accuracy of binocular vision. However, the calibration accuracy of existing calibration methods is difficult to satisfy the requirements of variable calibration environments and engineering applications. Therefore, based on the principle of Zhang's calibration method, a calibration method is proposed by combining bundle adjustment and diagonal constraints of the calibration target. Firstly, the improved Canny edge extraction algorithm is used to obtain the sub-pixel center of mass of ellipse (CME). Then, the Zhang's calibration method is used to obtain the initial values of the calibration parameters. The camera calibration parameters are optimized by using the bundle adjustment. Finally, diagonal constraints are used to further optimize the extrinsic camera position parameters. The experimental results show that compared to the other excellent methods, the proposed method can significantly improve calibration accuracy and has certain engineering application value.

Thermal calibration for triaxial gyroscope of MEMS-IMU based on segmented systematic method.

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method.

Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times.

PURPOSE: The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). METHODS: This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC). RESULTS: Scaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non-csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82 ± 1.20SI. After calibration, this bias was reduced to -0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28 ± 2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to -1.03SI, a 55% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%. CONCLUSIONS: The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.

Transitioning from specialisation to collaboration: interprofessional clinical simulation, metacognition and the phenomenon of convergence.

Research into metacognitive processes within interprofessional clinical simulation has been largely overlooked in the literature. This study explores how interprofessional simulation may influence cognitive and metacognitive processes across several professional programmes; medicine, nursing, pharmacy, and occupational therapy. This study focused on changes in performance pre- and post-simulation, with undergraduate students from each profession answering a set of questions related to the simulation case, requiring specialized knowledge from each profession. Question scores, item confidence judgment ratings, and calibration values were collected and analyzed. The data revealed a pattern of convergence in post-simulation assessments, where professions initially with lower performance in the pre-simulation phase improved, and those initially performing well demonstrating a decline in performance. Calibration values indicated that medical students developed metacognitive errors from their interactions in the simulation, which were not present pre-simulation, and that occupational therapy students suffered a loss of confidence and calibration in questions within their own field resulting from their experience (being more accurate pre-simulation). The authors anticipate that the phenomenon of convergence may have longer-term consequences, potentially fostering distrust among professions for those with declining performance. The authors propose that expanding awareness of convergence phenomena and conducting repeated simulations (thus facilitating further team development) could mitigate this issue.

Novel EBT3 film calibration using an integrated HSV color space analysis.

PURPOSE: Radiochromic EBT3 films are commonly used as dosimeter for clinical practice and research on radiotherapy. In principle, they are associated with a flatbed scanner to determine the optical density change, which can be correlated to the absorbed dose after calibration. Several approaches have been proposed to reduce the uncertainties during acquisition and to compensate the lighting inhomogeneities, thus improving the dose measurement. Those works have shown that good accuracy can be achieved for absorbed dose using EBT3 films, at the expense of complex data processing and time-consuming acquisition protocols. METHOD: We introduce the new method to determine the calibration curve based on the HSV color space analysis, which provides robustness and invariance to illumination changes. RESULTS: With this new approach, it allows to compute the calibration curve by performing only a single scan of film pieces regardless either the lateral positions or control points on the scanner bed. Using the hue channel in HSV color space, we prove that the dose can be accurately reconstructed with a much simpler protocol than when using RGB channels with blank scans rectification. Our HSV approach includes comparative gamma index for conventional film analysis. It achieves a gamma index (3%/3mm) over 99% when comparing measurement and AAA computation for a modulated beam. CONCLUSION: Compared to most existing approaches, our approach does not rely on complex mathematical reconstructions or additional scans. Instead, it uses another color model representation to rectify the scanner response, coping the dose measurement homogeneity problem over the scanner window. It facilitates the overall scan calibration to be much simpler, save time, and less manipulations, which also decreases the risk of human error.

Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment.

INTRODUCTION: The inability of large language models (LLMs) to communicate uncertainty is a significant barrier to their use in medicine. Before LLMs can be integrated into patient care, the field must assess methods to estimate uncertainty in ways that are useful to physician-users. OBJECTIVE: Evaluate the ability for uncertainty proxies to quantify LLM confidence when performing diagnosis and treatment selection tasks by assessing the properties of discrimination and calibration. METHODS: We examined confidence elicitation (CE), token-level probability (TLP), and sample consistency (SC) proxies across GPT3.5, GPT4, Llama2, and Llama3. Uncertainty proxies were evaluated against 3 datasets of open-ended patient scenarios. RESULTS: SC discrimination outperformed TLP and CE methods. SC by sentence embedding achieved the highest discriminative performance (ROC AUC 0.68-0.79), yet with poor calibration. SC by GPT annotation achieved the second-best discrimination (ROC AUC 0.66-0.74) with accurate calibration. Verbalized confidence (CE) was found to consistently overestimate model confidence. DISCUSSION AND CONCLUSIONS: SC is the most effective method for estimating LLM uncertainty of the proxies evaluated. SC by sentence embedding can effectively estimate uncertainty if the user has a set of reference cases with which to re-calibrate their results, while SC by GPT annotation is the more effective method if the user does not have reference cases and requires accurate raw calibration. Our results confirm LLMs are consistently over-confident when verbalizing their confidence (CE).

A Prospective Study on Risk Prediction of Preeclampsia Using Bi-Platform Calibration and Machine Learning.

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%.