A Recurrent Neural Network Enhanced Unscented Kalman Filter for Human Motion Prediction

This paper presents a deep learning enhanced adaptive unscented Kalman filter (UKF) for predicting human arm motion in the context of manufacturing. Unlike previous network-based methods that solely rely on captured human motion data, which is represented as bone vectors in this paper, we incorporate a human arm dynamic model into the motion prediction algorithm and use the UKF to iteratively forecast human arm motions. Specifically, a Lagrangian-mechanics-based physical model is employed to correlate arm motions with associated muscle forces. Then a Recurrent Neural Network (RNN) is integrated into the framework to predict future muscle forces, which are transferred back to future arm motions based on the dynamic model. Given the absence of measurement data for future human motions that can be input into the UKF to update the state, we integrate another RNN to directly predict human future motions and treat the prediction as surrogate measurement data fed into the UKF. A noteworthy aspect of this study involves the quantification of uncertainties associated with both the data-driven and physical models in one unified framework. These quantified uncertainties are used to dynamically adapt the measurement and process noises of the UKF over time. This adaption, driven by the uncertainties of the RNN models, addresses inaccuracies stemming from the data-driven model and mitigates discrepancies between the assumed and true physical models, ultimately enhancing the accuracy and robustness of our predictions. Compared to the traditional RNN-based prediction, our method demonstrates improved accuracy and robustness in extensive experimental validations of various types of human motions

DE-TGN: Uncertainty-Aware Human Motion Forecasting using Deep Ensembles

Ensuring the safety of human workers in a collaborative environment with robots is of utmost importance. Although accurate pose prediction models can help prevent collisions between human workers and robots, they are still susceptible to critical errors. In this study, we propose a novel approach called deep ensembles of temporal graph neural networks (DE-TGN) that not only accurately forecast human motion but also provide a measure of prediction uncertainty. By leveraging deep ensembles and employing stochastic Monte-Carlo dropout sampling, we construct a volumetric field representing a range of potential future human poses based on covariance ellipsoids. To validate our framework, we conducted experiments using three motion capture datasets including Human3.6M, and two human-robot interaction scenarios, achieving state-of-the-art prediction error. Moreover, we discovered that deep ensembles not only enable us to quantify uncertainty but also improve the accuracy of our predictions

Feasibility Study of Hybrid Inverse Planning with Transmission Beams and Single-energy Spread-out Bragg Peaks for Proton Flash Radiotherapy

Ultra-high dose rate (FLASH) proton planning with only transmission beams (TBs) has limitations in normal tissue sparing. The single-energy spread-out Bragg peaks (SESOBPs) of FLASH dose rate have been demonstrated feasible for proton FLASH planning. A hybrid inverse optimization method was developed to combine the TBs and SESOBPs (TB-SESOBP) for FLASH planning. The SESOBPs were generated from spreading out the BPs by pre-designed general bar ridge filters and placed at the central target by range shifters to obtain a uniform dose within the target. The SESOBPs and TBs were fully sampled field-by-field allowing automatic spot selection and weighting in the optimization process. The TB-SESOBP plans were validated in comparison with the TB only (TB-only) plans and the plans with the combination of TBs and BPs (TB-BP) regarding 3D dose and dose rate distributions for five lung cases. Comparing to the TB-only plans, the mean spinal cord D1.2cc drastically reduced 41%, the mean lung V7Gy and V7.4Gy moderately reduced by up to 17% and the target dose homogeneity slightly increased in the TB-SESOBP plans. Comparable dose homogeneity was achieved in both TB-SESOBP and TB-BP plans. Besides, prominent improvements were achieved in lung sparing for the cases of relatively large targets by the TB-SESOBP plans comparing to the TB-BP plans. The targets were fully covered with the FLASH dose rate in all the three plans. For the OARs, V40Gy/s = 100% was achieved by the TB-only plans while V40Gy/s > 85% was obtained by the other two plans. We have demonstrated that the hybrid TB-SESOBP planning was feasible to achieve FLASH dose rate for proton therapy. The hybrid TB-SESOBP planning has great potential in improving OAR sparing while maintaining high target dose homogeneity, and can be potentially implemented for adaptive radiotherapy

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy

Proton FLASH therapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. To prepare for the delivery of high doses to targets, we aim to develop a deep learning-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization before FLSAH beam delivery. The proposed framework comprises four modules, including orthogonal kV x-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and water equivalent thickness evaluation. We investigated volumetric image reconstruction using four kV projection pairs with different source angles. Thirty lung patients were identified from the institutional database, and each patient contains a four-dimensional computed tomography dataset with ten respiratory phases. The retrospective patient study indicated that the proposed framework could reconstruct patient volumetric anatomy, including tumors and organs at risk from orthogonal x-ray projections. Considering all evaluation metrics, the kV projections with source angles of 135 and 225 degrees yielded the optimal volumetric images. The proposed framework has been demonstrated to reconstruct volumetric images with accurate lesion locations from two orthogonal x-ray projections. The embedded WET module can be used to detect potential proton beam-specific patient anatomy variations. The framework can deliver fast volumetric image generation and can potentially guide treatment delivery systems for proton FLASH therapy

The investigation of mechanical and thermal properties of super-hydrophobic nitinol surfaces fabricated by hybrid methods of laser irradiation and carbon ion implantation

Comparing with laser irradiation only, the laser ablation combined with chemical modification process is a widely used technique to obtain bio-inspired super-hydrophobic surface. However, the as-prepared surfaces may be polluted by toxic substance during chemical modification such as fluoroalkyl silane and stearic acid. The side effect of polluted functional surface on organisms and environment limited its application value. In this paper, a green and environmental-friendly super-hydrophobic surface was quickly fabricated on nitinol substrates through hybrid of nanosecond laser ablation and carbon ion implantation. The time that turning from super-hydrophilicity to super-hydrophobicity was only 16 hours exhibiting high efficiency compared with pure laser processing. Surface morphology and chemical component were systematically investigated to reveal the formation mechanism of super-hydrophobicity in such short time. The mechanical abrasion tests implied that the mechanical properties of surface microstructure could be heightened after carbon ion implantation, showing the superior structure stability. It is noted that chemical modified super-hydrophobicity could be hardly destroyed under high temperature, and the thermal stability of this ion implanted super-hydrophobic surface was on a par with it. This hybrid method of laser irradiation and carbon ion implantation paves a green way for rapid fabrication super-hydrophobic surface on nitinol, which would have great application value in biomedicine and industry

Definitive intensity modulated proton re-irradiation for lung cancer in the immunotherapy era

IntroductionAs immunotherapy has improved distant metastasis-free survival (DMFS) in Non-Small Cell Lung Cancer (NSCLC), isolated locoregional recurrences have increased. However, management of locoregional recurrences can be challenging. We report our institutional experience with definitive intent re-irradiation using Intensity Modulated Proton Therapy (IMPT).MethodRetrospective cohort study of recurrent or second primary NSCLC or LS-SCLC treated with IMPT. Kaplan-Meier method and log-rank test were used for time-to-event analyses.Results22 patients were treated from 2019 to 2021. After first course of radiation (median 60 Gy, range 45-70 Gy), 45% received adjuvant immunotherapy. IMPT re-irradiation began a median of 28.2 months (8.8-172.9 months) after initial radiotherapy. The median IMPT dose was 60 GyE (44-60 GyE). 36% received concurrent chemotherapy with IMPT and 18% received immunotherapy after IMPT. The median patient’s IMPT lung mean dose was 5.3 GyE (0.9-13.9 GyE) and 5 patients had cumulative esophagus max dose >100 GyE with 1-year overall survival (OS) 68%, 1-year local control 80%, 1-year progression free survival 45%, and 1-year DMFS 60%. Higher IMPT (HR 1.4; 95% CI 1.1-1.7, p=0.01) and initial radiotherapy mean lung doses (HR 1.3; 95% CI 1.0-1.6, p=0.04) were associated with worse OS. Two patients developed Grade 3 pneumonitis or dermatitis, one patient developed Grade 2 pneumonitis, and seven patients developed Grade 1 toxicity. There were no Grade 4 or 5 toxicities.DiscussionDefinitive IMPT re-irradiation for lung cancer can prolong disease control with limited toxicity, particularly in the immunotherapy era

Proton vs. Photon Radiation Therapy for Primary Gliomas: An Analysis of the National Cancer Data Base

Background: To investigate the impact of proton radiotherapy (PBT) on overall survival (OS) and evaluate PBT usage trends for patients with gliomas in the National Cancer Data Base (NCDB).Methods: Patients with a diagnosis of World Health Organization (WHO) Grade I-IV glioma treated with definitive radiation therapy (RT) between the years of 2004–13 were identified. Patients were stratified based on WHO Grade and photon radiotherapy (XRT) vs. PBT. Univariate (UVA) and multivariable analysis (MVA) with OS were performed by Cox proportional hazards model and log-rank tests. Propensity score (PS) weighting was utilized to account for differences in patient characteristics and to minimize selection bias.Results: There were a total of 49,405 patients treated with XRT and 170 patients treated with PBT. Median follow-up time was 62.1 months. On MVA, the following factors were associated with receipt of PBT (all p < 0.05): WHO Grade I-II gliomas, treatment at an academic/research program, west geographic facility location, and surgical resection. After PS weighting, all patients treated with PBT were found to have superior median and 5 year survival than patients treated with XRT: 45.9 vs. 29.7 months (p = 0.009) and 46.1 vs. 35.5% (p = 0.0160), respectively.Conclusions: PBT is associated with improved OS compared to XRT for patients with gliomas. This finding warrants verification in the randomized trial setting in order to account for potential patient imbalances not adequately captured by the NCDB, such as tumor molecular characteristics and patient performance status.Importance of the Study: This is the first study that compares the outcomes of patients treated with photon based radiotherapy vs. proton based radiotherapy for patients with gliomas. In this retrospective analysis, the results demonstrate that proton therapy is associated with improved outcomes which support ongoing prospective, randomized clinical trials comparing the two modalities in patients with gliomas

Phylogeny Disambiguates the Evolution of Heat-Shock cis-Regulatory Elements in Drosophila

Heat-shock genes have a well-studied control mechanism for their expression that is mediated through cis-regulatory motifs known as heat-shock elements (HSEs). The evolution of important features of this control mechanism has not been investigated in detail, however. Here we exploit the genome sequencing of multiple Drosophila species, combined with a wealth of available information on the structure and function of HSEs in D. melanogaster, to undertake this investigation. We find that in single-copy heat shock genes, entire HSEs have evolved or disappeared 14 times, and the phylogenetic approach bounds the timing and direction of these evolutionary events in relation to speciation. In contrast, in the multi-copy gene Hsp70, the number of HSEs is nearly constant across species. HSEs evolve in size, position, and sequence within heat-shock promoters. In turn, functional significance of certain features is implicated by preservation despite this evolutionary change; these features include tail-to-tail arrangements of HSEs, gapped HSEs, and the presence or absence of entire HSEs. The variation among Drosophila species indicates that the cis-regulatory encoding of responsiveness to heat and other stresses is diverse. The broad dimensions of variation uncovered are particularly important as they suggest a substantial challenge for functional studies

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Effect of synergistic cold alkaline swelling and mechanical disintegration on the formation of biodegradable and translucent cellulose film

The objective of this work was to develop a path to create a plastic film alternative in packaging applications using cellulose materials. It was expected that a small amount of chemical and energy are needed during the process to align with the principles of eco-friendly development and sustainable development. It was also anticipated that the product of this work should have favorable optical properties and good mechanical performance comparable to ordinary plastic film products. In this study, without going through dissolution or nanofibrillation, Northern Bleached Softwood Kraft (NBSK) cellulose pulp is treated with a 10% NaOH solution at a subzero temperature (-10 °C) and mechanical disintegration using a domestic blender to create a substitute that resembles plastic films. The kraft pulp can be converted into a stable fibrous slurry that can then be processed into a translucent and hazy film using vacuum filtration. The prepared cellulose film demonstrated high transmittance (89% at 650 nm with integrated sphere), excellent biodegradability (completely degrade in 19 days when buried in soil), high mechanical strength (99.7 MPa tensile strength in the dry state and 17.2 MPa after being immersed in water for 30 days), and high thermal stability (Tmax of 350 °C). In sum, in this study a translucent, hazy, and strong cellulose film was developed through a simple chemical-saving and energy-saving fabrication method which involves treatment in 10% NaOH solution at -10 °C, blending and vacuum filtration, showing great promise as plastic alternative for packaging applications.Forestry, Faculty ofGraduat