Accurate Weight Research Articles

Systematic Characterization of the Chemical Components of Canna edulis Ker-Gawl Based on UHPLC Q-Exactive Orbitrap MS Technology

Canna edulis Ker-Gawl is a versatile crop that integrates food, energy, and feed in China. The rhizome is a traditional Chinese medicine with a long history. However, there are few studies on chemical components. Crude plant extracts are a complex mixture of several biologically active secondary metabolites. Therefore, rapid and accurate identification and quantification are critical in phytochemical analysis. An efficient approach based on ultrahigh-performance liquid chromatography coupled with Q-Exactive Orbitrap tandem mass spectrometry (UHPLC Q-Exactive Orbitrap MS) was used to analyze the chemical components systematically. We used retention time, accurate molecular weight, parent peaks, fragment peaks, fragmentation characteristics, comparative analysis with the literature, reference standards, and standard databases, including ChemSpider, mzVault, MassBank, and mzCloud. A total of 54 chemical constituents were identified from the methanol extract of the rhizome, including 36 organic acids (with six phenolic acids), three phenols, one sugar (and its derivatives), one coumarin, one triterpenoid, ten N-containing organic compounds (including seven amino acids), and two other compounds. Moreover, three compounds were quantitatively analyzed and a methodological study of the three components was carried out. The established method provided satisfactory precision and accuracy, acceptable recovery, good linearity, and a reasonable detection limit. The UHPLC Q-Exactive Orbitrap MS method was used for the first time to accurately, quickly, and systematically characterize the compounds of the rhizome, revealing the pharmacodynamic substances and providing a theoretical basis for its quality control, in-depth research, and development.

Comprehensive characterization of narirutin metabolites in vitro and in vivo based on Analogous-Core recursion analysis strategy using UHPLC-Q-Exactive Orbitrap MS/MS

Narirutin, extracted from the dried ripe pericarp of Rutaceae orange and its cultivated varieties, is a dihydroflavone compound with various biological activities and pharmacological effects. So far as we know, the metabolism profiling of narirutin has been insufficient until now. In the present study, an efficient method was employed to perform rapid analysis and identification of narirutin metabolites by using ultra-high performance liquid chromatography quadrupole exactive orbitrap MS/MS (UHPLC-Q-Exactive Orbitrap MS/MS) combined with an analogous core recursion (ACR) analysis strategy. Firstly, according to the basic core structure of the dihydroflavonoid, the cleavage mode and fragmentation ions of narirutin were summarized. Secondly, based on the difference of the substituent groups, the fragment ions of narirutin were preliminarily inferred and verified by the secondary mass spectra of its standard compound. Thirdly, the fragment ions of narirutin in positive and negative ion modes were summarized to provide a basis for the identification of metabolites. Fourthly, candidate metabolites were accurately and tentatively identified according to the cleavage law of mass spectrometry, literature reports, comparison of reference substances, and especially the diagnostic fragment ion clusters (DFICs) and characteristic suggestive ions (CSIs) deduced preliminarily. Finally, a total of 46 metabolites (30 in vivo and 19 in vitro), including prototype drugs were identified based on the ACR analysis strategy, chromatographic retention behavior of metabolites, corresponding ClogP value and accurate molecular weight. Among them, 22 metabolites were discovered in rats plasma, 12 in urine, 4 in liver tissue, 19 in liver microsomes, respectively. Additionally, metabolites relative contents were also studied by extracted ion chromatography (EIC) method. Our result also illustrated that narirutin primarily underwent glucuronidation, sulfation, glutathione binding, deglycosylation, oxidation, reduction, methylation, hydroxylation, ring-opening and their composite reactions. A novel strategy was constructed to comprehensively elucidate the biotransformation pathways of narirutin in vitro and in vivo, and a systematic metabolic profile of narirutin was generated. This study provided a great reference for further understanding of the biotransformation pathways and guiding for clinical application of narirutin.

A computer vision-assisted method for identifying wheel loads of moving vehicles from dynamic bridge responses

Bridge weigh-in-motion (BWIM) technology, which identifies traffic information such as the axle weights, wheelbases, and velocities of vehicles passing over a bridge from bridge responses, can be beneficial for bridge design, management, and maintenance. However, conventional BWIM methods simplified the coaxial wheel loads of a vehicle using one concentrated force, commonly known as the axle load. Such simplification would lead to two problems. Firstly, it fails to calculate the wheel weights of moving vehicles, which are essential for evaluating the service status of roads and is required to be measured by national weigh-in-motion specifications, such as ASTM E1318-09. Secondly, the transverse distance between the coaxial wheels (track width), and unequal load distributions between left and right coaxial wheels (unbalance) were ignored in the conventional BWIM methods. However, these two factors represented the actual characteristics of the vehicle load applied on the bridge. Hence, ignoring these two factors could lead to significant errors in estimating the vehicle weight when the track width or the unbalance ratio of the test vehicle was different from that of the calibration vehicle. To address the above two problems, this study proposed a new BWIM method, where two concentrated loads with fixed transverse distance were used to represent the coaxial wheel loads of the truck in analyzing the interactions between the truck and the bridge. By doing so, the wheel weights of moving vehicles can be calculated, and both the effects of the unbalance and the track width can be considered in estimating the axle weights of the vehicle. Both experiment results and simulation results demonstrated that the proposed method can effectively identify the wheel weights of moving vehicles, and provide more accurate axle weights than conventional BWIM methods under complex traffic scenarios.

Are self-reported anthropometric data reliable enough to meet antibiotic prophylaxis guidelines in orthopedic surgery?

BackgroundSurgical site infection is a serious complication in orthopedic surgery. The use of antibiotic prophylaxis (AP) combined with other prevention strategies has been shown to reduce this risk to 1% for hip arthroplasty and 2% for knee arthroplasty. The French Society of Anesthesia and Intensive Care Medicine (SFAR) recommends doubling the dose when the patient's weight is greater than or equal to 100 kg, and the body mass index (BMI) is greater than or equal to 35 kg/m2. Similarly, patients with a BMIgreater than40 kg/m2 orlesser than18 kg/m2 are ineligible for surgery in our hospital. Self-reported anthropometric measurements are commonly used in clinical practice to calculate BMI, but their validity has not been assessed in the orthopedic literature. Therefore, we conducted a study comparing self-reported with systematically measured values and observed the impact these differences may have on perioperative AP regimens and contra-indications to surgery. HypothesisThe hypothesis of our study was that self-reported anthropometric values differed from those measured during preoperative orthopedic consultations. Materials and methodsThis single-center retrospective study with prospective data collection was conducted between October and November 2018. The patient-reported anthropometric data were first collected and then directly measured by an orthopedic nurse. Weight was measured with a precision of 500 g and height was measured with a precision of 1 cm. ResultsA total of 370 patients (259 women and 111 men) with a median age of 67 years (17–90) were enrolled. The data analysis found significant differences between the self-reported and measured height [166 cm (147–191) vs. 164 cm (141–191) (p<0.0001)], weight [72.9 kg (38–149) vs. 73.1 kg (36–140) (p<0.0005)] and BMI [26.3 (16.2–46.4) vs. 27 (16–48.2) (p<0.0001)]. Of these patients, 119 (32%) reported an accurate height, 137 (37%) an accurate weight, and 54 (15%) an accurate BMI. None of the patients had two accurate measurements. The maximum underestimation was 18 kg for weight, 9 cm for height, and 6.15 kg/m2 for BMI. The maximum overestimation was 28 kg for weight, 10 cm for height, and 7.2 kg/m2 for BMI. The verification of the anthropometric measurements identified another 17 patients who had contra-indications to surgery (12 with a BMI>40 kg/m2 and 5 with a BMI<18 kg/m2) and who would not have been detected based on the self-reported values. ConclusionsAlthough patients underestimated their weight and overestimated their height in our study, these had no impact on the perioperative AP regimens. However, this misreporting failed to detect potential contraindications to surgery. Level of evidenceIV; retrospective study with prospective data collection and no control group.

Feature pre-inpainting enhanced transformer for video inpainting

Transformer-based video inpainting methods aggregate coherent contents into missing regions by learning dependencies spatial–temporally. However, existing methods suffer from the inaccurate self-attention calculation and excessive quadratic computational complexity, due to uninformative representations of missing regions and inefficient global self-attention mechanisms, respectively. To mitigate these problems, we propose a Feature pre-Inpainting enhanced Transformer (FITer) video inpainting method, in which the feature pre-inpainting network (FPNet) and local–global interleaving Transformer are designed. The FPNet pre-inpaints missing features before the Transformer by exploiting spatial context, and the representations of missing regions are thus enhanced with more informative content. Therefore, the interleaving Transformer can calculate more accurate self-attention weights and learns more effective dependencies between missing and valid regions. Since the interleaving Transformer involves both global and window-based local self-attention mechanisms, the proposed FITer method can effectively aggregate spatial–temporal features into missing regions while improving efficiency. Experiments on YouTube-VOS and DAVIS datasets demonstrate that the FITer method outperforms previous methods qualitatively and quantitatively.

An artificial synapse based on La:BiFeO3 ferroelectric memristor for pain perceptual nociceptor emulation

Recently, memristors with brain-like synaptic behavior have attracted widespread attention, offering unprecedented opportunities for constructing future artificial intelligent bio-inspired electronic systems. Herein, we report a ferroelectric memristor with a Pd/La0.1Bi0.9FeO3(LBFO)/La0.67Sr0.33MnO3/SrTiO3/P+-Si structure, which exhibits the multilevel storage capacity of 12 resistive states, each with a retention time of 2 × 104 s and 200 ns ultra-fast pulse conductance modulation. Moreover, the synaptic properties are successfully mimicked, including paired-pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity. In addition, pattern training with high tolerance to fault disturbance and variations is realized in the LBFO memristors array, endowing with highly accurate synaptic weights update capability. More importantly, the device can emulate essential characteristics of pain perception nociceptors with fast operating pulse and low threshold level, and a piezoresistive sensor is further introduced to integrate with LBFO memristor, realistically simulating the artificial pain perception function. Overall, the proposed device provides a promising avenue for the development of electronic skins, neurorobotics, and human–computer interaction technologies.

Factors Affecting Weigh Tape Reading in the Measurement of Equine Body Weight.

Accurate measurement of equine body weight is important for evaluating medication dosages and feed quantities. Different methods exist for measuring body weight, including weigh tapes (WT), though accuracy varies. Measurements could be affected by external variables, such as time of day, human error, or uneven surfaces, and also horse-based variables, such as height and body condition score (BCS). The aim of this study was to investigate how different horse-based variables affect WT reading. A retrospective analysis was performed using anonymised data from feed company nutrition consultations (Baileys Horse Feeds). Data included a range of horse-based variables, a WT reading, and true body weight measured on a weighbridge. All horses were over two years of age. Likelihood ratio tests were used to assess whether adding different horse-based variables significantly improved the fit of the quadratic regression model. The variables included were height, BCS, breed, muscle top-line score, and bone type. Exploratory analysis showed that the WT generally underestimated body weight, particularly for horses with higher body weight. Adding height and muscle top-line scores did not significantly improve the fit of the model, suggesting no influence on WT reading over and above actual body weight. Adding breed groupings, BCS, and bone density did improve the fit. Each 0.5 unit increase in BCS increased the WT estimate by 1.24 kg (p < 0.001). These results confirm that a WT does not provide accurate body weight measurements, and generally underestimates body weight, though more so for heavier horses, being more accurate in pony breeds.

Development of an automatic measurement system using atmospheric pressure photoionization ultrahigh-resolution mass spectrometry and application for on-line analysis of particulate matter

On-line chemical characterization of atmospheric particulate matter (PM) with soft ionization technique and ultrahigh-resolution Mass Spectrometry (UHRMS) provides molecular information of organic constituents in real time. Here we describe the development and application of an automatic measurement system that incorporates PM2.5 sampling, thermal desorption, atmospheric pressure photoionization, and UHRMS analysis. Molecular formulas of detected organic compounds were deducted from the accurate (±10 ppm) molecular weights obtained at a mass resolution of 100,000, allowing the identification of small organic compounds in PM2.5. Detection efficiencies of 28 standard compounds were determined and we found a high sensitivity and selectivity towards organic amines with limits of detection below 10 pg. As a proof of principle, PM2.5 samples collected off-line in winter in the urban area of Beijing were analyzed using the Ionization Module and HRMS of the system. The automatic system was then applied to conduct on-line measurements during the summer time at a time resolution of 2 hr. The detected organic compounds comprised mainly CHON and CHN compounds below 350 m/z. Pronounced seasonal variations in elemental composition were observed with shorter carbon backbones and higher O/C ratios in summer than that in winter. This result is consistent with stronger photochemical reactions and thus a higher oxidation state of organics in summer. Diurnal variation in signal intensity of each formula provides crucial information to reveal its source and formation pathway. In summary, the automatic measurement system serves as an important tool for the on-line characterization and identification of organic species in PM2.5.

Validation of three age based weight formulae for estimating weight among Indian children

Background: Weight estimation in paediatric emergencies is often required to calculate drug dosages, fluid therapy and defibrillation. The ‘gold standard’ of actually weighing the patient is not practically possible in emergency conditions. Many formulae have been developed in an attempt to accurately estimate a child’s weight using age like advanced paediatric life support (APLS), Luscombe, and Argall’s modification. However, these formulae might not be applicable in the Indian context. This study aims is to validate standard age-based formulae (APLS, Luscombe, and Argall’s) and their accuracy in estimating the weight of Indian children. Methods: The study used secondary data from the fourth round of the National Family Health Survey (2015-16). The estimated weights as computed by the APLS guidelines, the Luscombe and the Argall formulae were compared to their respective measured weights during the survey. Results: All three formulae (APLS, Luscombe, and Argall) overestimated the weights. The APLS formula exhibited the least weight difference among the different age groups (-3.51±2.08 kg). The average percentage difference between the estimated weights using the APLS, Luscombe, and Argall formula and the actual weights was 23%, 38%, and 28%, respectively. Conclusions: All the three age-based weight formulae, namely APLS, Argyll, and Luscombe, overestimated the weight among the Indian children, clearly showing the need to adjust the formulae for accurate weight estimation of Indian children.

Nonuniform magnetic domain-wall synapses enabled by population coding

Traditional artificial intelligence implemented in software is usually executed on accurate digital computers. Nevertheless, the nanoscale devices for the implementation of neuromorphic computing may not be ideally identical, and the performance is reduced by nonuniform devices. In biological brains, information is usually encoded by a cluster of neurons such that the variability of nerve cells does not influence the accuracy of human cognition and movement. Here, we introduce the population encoding strategy in neuromorphic computing and demonstrate that this strategy can overcome the problems caused by nonuniform devices. Using magnetic memristor device based on current-induced domain-wall motion as an example, we show that imperfect storage devices can be applied in a hardware network to perform principal component analysis (PCA), and the accuracy of unsupervised classification is comparable to that of conventional PCA using ideally accurate synaptic weights. Our results pave the way for hardware implementation of neuromorphic computing and lower the criteria for the uniformity of nanoscale devices.

Body condition and hair coat length impact weight estimation in dogs and cats presented to an emergency department.

To examine weight estimations by veterinarians, veterinary nurses, and veterinary students to determine the accuracy of weight estimation in a veterinary emergency department. Secondary objectives included an initial evaluation of how body condition score (BCS), hair coat length, and hospital position of the estimator impacted accuracy of weight estimation. 101 dogs and 28 cats that presented to the emergency department from October 17, 2017, to September 3, 2019. A written form was prospectively distributed to veterinarians, veterinary nurses, and veterinary students asking them to estimate a patient weight. Additional information about the patient, including estimated hair length and BCS, was recorded. As dogs' weights increased, all estimators underestimated the patients' weight. As BCS increased, dogs' weights were underestimated by veterinary students. Longer hair coat statistically significantly impacted accurate weight estimation in dogs. In cats, as BCS increased, veterinarians and veterinary nurses overestimated patient weight. Only 32% (32/101) and 20% (6/28) of weight estimations in dogs and cats, respectively, were within 10% of the patient's actual weight. Veterinarians, veterinary nurses, and veterinary students may not be reliable estimators of patient weight in the emergency department, especially with overweight animals. Prompt measurement of weight, if possible, is important for appropriate patient care.

Fast and Accurate Weight Updating Strategy for Resistive Random-Access Memory (RRAM)-Based Neural Networks

The mathematical relationships between the operating voltages and the conductance of resistance random-access memory (RRAM) are found based on the statistical measurements of the 1-transistor 1-RRAM (1T1R) array. According to the corresponding relationships, a one-shot operation scheme without verification after programming is proposed for weight updating of RRAM-based neural networks. The proposed operation scheme enables parallel column-by-column SET and row-by-row RESET, which achieves fast and accurate conductance modulation in the 1T1R array. By utilizing the one-shot scheme for weight updating of the deep-Q network, the training efficiency improves 40 times compared with the incremental step pulse programming (ISPP) scheme.

Multi-Mode Electric Wheelchair with Health Monitoring and Posture Detection Using Machine Learning Techniques

Patients with cognitive difficulties and impairments must be given innovative wheelchair systems to ease navigation and safety in today’s technologically evolving environment. This study presents a novel system developed to convert a manual wheelchair into an electric wheelchair. A portable kit has been designed so that it may install on any manual wheelchair with minor structural changes to convert it into an electric wheelchair. The multiple modes include the Joystick module, android app control, and voice control to provide multiple features to multiple disabled people. The proposed system includes a cloud-based data conversion model for health sensor data to display on an android application for easy access for the caretaker. A novel arrangement of sensors has been applied according to the accurate human body weight distribution in a sitting position that has greatly enhanced the accuracy of the applied model. Furthermore, seven different machine learning algorithms are applied to compare the accuracy, i.e., KNN, SVM, logistic regression, decision tree, random forest, XG Boost, and NN. The proposed system uses force-sensitive resistance (FSR) sensors with prescribed algorithms incorporated into wheelchair seats to detect users’ real-time sitting positions to avoid diseases, such as pressure ulcers and bed sores. Individuals who use wheelchairs are more likely to develop pressure ulcers if they remain in an inappropriate posture for an extended period because the blood supply to specific parts of their skin is cut off owing to increased pressure. Two FSR configurations are tested using seven algorithms of machine learning techniques to discover the optimal fit for a high-efficiency and high-accuracy posture detection system. Additionally, an obstacle detection facility enables one to drive safely in unknown and dynamic environments. An android application is also designed to provide users of wheelchairs with the ease of selecting the mode of operation of the wheelchair and displaying real-time posture and health status to the user or caretaker.

Impact of Leucine and Magnesium Stearate on the Physicochemical Properties and Aerosolization Behavior of Wet Milled Inhalable Ibuprofen Microparticles for Developing Dry Powder Inhaler Formulation.

This study investigated the development and characterization of leucine and magnesium stearate (MgSt) embedded wet milled inhalable ibuprofen (IBF) dry powder inhaler (DPI) formulations. IBF microparticles were prepared by a wet milling homogenization process and were characterized by SEM, FTIR, DSC, XRD and TGA. Using a Twin-Stage Impinger (TSI), the in vitro aerosolization of the formulations with and without carrier lactose was studied at a flow rate of 60± 5 L/min and the IBF was determined using a validated HPLC method. The flow properties were determined by the Carr's Index (CI), Hausner Ratio (HR) and Angle of Repose (AR) of the milled IBF with 4-6.25% leucine and leucine containing formulations showed higher flow property than those of formulations without leucine. The fine particle fraction (FPF) of IBF from the prepared formulations was significantly (p = 0.000278) higher (37.1 ± 3.8%) compared to the original drug (FPF 3.7 ± 0.9%) owing to the presence of leucine, which enhanced the aerosolization of the milled IBF particles. Using quantitative phase analysis, the XPRD data revealed the crystallinity and accurate weight percentages of the milled IBF in the formulations. FTIR revealed no changes of the structural integrity of the milled IBF in presence of leucine or MgSt. The presence of 2.5% MgSt in the selected formulations produced the highest solubility (252.8 ± 0.6 µg/mL) of IBF compared to that of unmilled IBF (147.4 ± 1.6 µg/mL). The drug dissolution from all formulations containing 4-6.25% leucine showed 12.2-18.6% drug release in 2.5 min; however, 100% IBF dissolution occurred in 2 h whereas around 50% original and dry milled IBF dissolved in 2 h. The results indicated the successful preparation of inhalable IBF microparticles by the wet milling method and the developed DPI formulations with enhanced aerosolization and solubility due to the presence of leucine may be considered as future IBF formulations for inhalation.

Weight perception analysis using pseudo-haptic feedback based on physical work evaluation

Introduction: Since kinesthetic cues are not present in virtual environments, users have difficulty feeling the heaviness of virtual objects. To address this issue, pseudo-haptic approaches have been proposed to illusorily induce the weight of virtual objects through the user’s visual sensory system.Methods: In this paper, we used two methods to induce the impression of virtual objects’ heaviness. One relies on the direct modification of the control-display (C/D) ratio when lifting objects, and the other depends on controlling this ratio based on a velocity restriction. We innovatively measured each approach’s efficiency by analyzing physical work as an objective metric. In addition, we used Borg CR10 to measure users’ hand fatigue during the experimental phases.Results: Our findings are discussed in terms of individual lifting behavior in different pseudo-haptic methods. Furthermore, different virtual weight-lifting behaviors were compared to the same real-world weight-lifting behaviors.Discussion: According to our results, the direct control of the C/D ratio method provides virtual reality users with a more accurate weight perception than the velocity restriction one. Furthermore, with this first method, users’ lifting behavior was closer to the behavior when lifting real objects.

Analysis and evaluation on Paeoniae Radix Alba from different cultivars by UPLC-Q-TOF-MS and HPLC

In this study, an established ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS) method was combined with multivariate statistical analysis to investigate the commonality and difference of main chemical components in the medicinal parts of Paeonia lactiflora from different cultivars; in addition, a high performance liquid chromatography(HPLC) method was established to simultaneously determine the content of eight active components in Paeoniae Radix Alba. Non-targeted analysis was carried out by UPLC-Q-TOF-MS on a Waters ACQUITY UPLC BEH C_(18)(2.1 mm×100 mm, 1.7 μm) column with a gradient elution of 0.1% aqueous formic acid(A)-acetonitrile(B) as the mobile phase at a flow rate of 0.2 mL·min~(-1). The column temperature was 30 ℃, and an electrospray ionization source was used to acquire mass spectrometry data in positive and negative ion modes. According to the accurate molecular weight and fragment ion information provided by multi-stage mass spectrometry and by comparison with reference substances and literature reports, thirty-six identical components were identified in Paeoniae Radix Alba from different cultivars with positive and negative ion modes. In the negative ion mode, two groups of samples were well separated; specifically, seventeen components with significant differences in content were screened and identified, and one component unique in &quot;Bobaishao&quot; was obtained. Quantitative analysis was conducted by high-performance liquid chromatography(HPLC) on an Agilent HC-C_(18)(4.6 mm×250 mm, 5 μm) column with a gradient elution of 0.1% aqueous phosphoric acid(A)-acetonitrile(B) as the mobile phase at a flow rate of 1.0 mL·min~(-1). The column temperature was 30 ℃ and the detection wavelength was at 230 nm. An HPLC method was developed for the simultaneous determination of eight active components(gallic acid, oxypaeoniflorin, catechin, albiflorin, paeoniflorin, galloylpaeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, benzoyl-paeoniflorin) in Paeoniae Radix Albaa from different cultivars. Satisfactory linearity was achieved within the investigated linear ranges and with fine coefficients(r&gt;0.999 0), and the methodological investigation showed that the method had good precision, repeatability and stability. The mean recoveries were 90.61% to 101.7% with RSD of 0.12% to 3.6%(n=6). UPLC-Q-OF-MS provided a rapid and efficient qualitative analytical method for the identification of the chemical components in Paeoniae Radix Alba, and the developed HPLC method was simple, rapid and accurate, which could provide a scientific basis for the evaluation of the germplasm resources and herbal quality of Paeoniae Radix Alba from different cultivars.

Self-calibrating microring synapse with dual-wavelength synchronization

As a resonator-based optical hardware in analog optical computing, a microring synapse can be straightforwardly configured to simulate the connection weights between neurons, but it faces challenges in precision and stability due to cross talk and environmental perturbations. Here, we propose and demonstrate a self-calibration scheme with dual-wavelength synchronization to monitor and calibrate the synaptic weights without interrupting the computation tasks. We design and fabricate an integrated 4 × 4 microring synapse and deploy our self-calibration scheme to validate its effectiveness. The precision and robustness are evaluated in the experiments with favorable performance, achieving 2-bit precision improvement and excellent robustness to environmental temperature fluctuations (the weights can be corrected within 1 s after temperature changes 0.5°C). Moreover, we demonstrate matrix inversion tasks based on Newton iterations beyond 7-bit precision using this microring synapse. Our scheme provides an accurate and real-time weight calibration independently parallel from computations and opens up new perspectives for precision boost solutions to large-scale analog optical computing.

Abstract WP22: Time Is Brain: Improving Door To Thrombolytic Times Through A Multidisciplinary Approach

Background: Research supports stroke as the 5th leading cause of death in the United States and the leading cause of long term disability. As a primary stroke center, it is necessary to establish and meet stroke standards of care. The American Heart Association Target: Stroke Phase III initiative focuses on timely intravenous thrombolytic administration. A community hospital primary stroke center encountered challenges with meeting established goal times. To improve rapid treatment times and patient outcomes, a multidisciplinary team collaborated to improve workflow. Purpose: To decrease door to thrombolytic therapy times in eligible ischemic stroke patients. Methods: As a result of retrospective chart audit findings from January through December 2020, a new workflow process was developed, which included: • Education with EMS regarding pre-notification • Relocating a weighted, zeroed patient bed near the ambulance entrance to obtain an accurate weight • Patient going directly to the computed tomography scanner accompanied by the entire stroke team • Earlier activation of the Tele stroke neurologist • Relocating the Tele stroke monitor to the computed tomography scanner area for immediate assessment • Pharmacist at bedside to assist with medication preparation • Real time feedback and data sharing Results: In 2021, the new work flow resulted in a 48% time reduction in door to IV thrombolytic administration times. Median door to IV thrombolytic times decreased from 58 minutes to 28 minutes with 75 % of the population receiving IV thrombolytic therapy in 30 minutes or less. Conclusions: A redesigned workflow process, along with a multidisciplinary team approach led to significantly decreased door to thrombolytic therapy times. Key success factors included a consistent team approach, feedback, education, and data sharing for continual improvement.

Live Pig-Weight Learning and Prediction Method Based on a Multilayer RBF Network

The live weight of pigs has always been an important reference index for growth monitoring and the health status of breeding pigs. An accurate weight acquisition of breeding pigs is the key to guide the scientific feeding of breeding pigs and improve economic benefits. Compared with the traditional contact measurement method, the non-contact weighing method of live pigs can greatly reduce human–pig contact and measurement errors. In this paper, a deep neural network is constructed which can automatically and accurately predict the weight of live pigs by measuring multiple body parameters. Because of the good generalization ability of the radial basis function (RBF) neural network and the better fitting ability of multilayer network than the traditional single-layer network, this paper introduces a full-connection model in the middle layer, connects multiple RBF layers, builds a multilayer RBF network, and invents the automatic learning method of pig weight based on the network. In this method, the body length, body height, body width, and five other body parameters are input, after normalization, into the multilayer RBF network model for training, and resultingly the network gives a predicted weight. Among our 4721 live pigs, there are 2452 sows and 2269 boars, among which 2000 samples of sows are randomly selected as training sets and 452 samples as test sets; 1930 samples of boars are taken as training sets and 339 samples as test sets. The test shows that the performance of the network structure is as follows: R2 is 0.63, MAE is 1.85, RMSE is 5.74, and MAPE is 1.68.

Prediction of body weight from chest radiographs using deep learning with a convolutional neural network.

Accurate body weights are not necessarily available in routine clinical practice. This study aimed to investigate whether body weight can be predicted from chest radiographs using deep learning. Deep-learning models with a convolutional neural network (CNN) were trained and tested using chest radiographs from 85,849 patients. The CNN models were evaluated by calculating the mean absolute error (MAE) and Spearman's rank correlation coefficient (ρ). The MAEs of the CNN models were 2.63kg and 3.35kg for female and male patients, respectively. The predicted body weight was significantly correlated with the actual body weight (ρ = 0.917, p < 0.001 for females; ρ = 0.915, p < 0.001 for males). The body weight was predicted using chest radiographs by applying deep learning. Our method is potentially useful for radiation dose management, determination of the contrast medium dose, and estimation of the specific absorption rate in patients with unknown body weights.