<div>AbstractPurpose:<p>Radiotherapy is a curative therapeutic modality used to treat cancers as a single agent or in combination with surgery and chemotherapy. Advanced radiotherapy technologies enable treatment with large fractions and highly conformal radiation doses to effect free-radical damage to cellular DNA leading to cell-cycle arrest, cell death, and innate immune response (IIR) stimulation.</p>Experimental Design:<p>To understand systemic clinical responses after radiation exposure, proteomic and metabolomic analyses were performed on plasma obtained from patients with cancer at intervals after prostate stereotactic body radiotherapy. Pathway and multivariate analyses were used to delineate molecular alterations following radiotherapy and its correlation with clinical outcomes.</p>Results:<p>DNA damage response increased within the first hour after treatment and returned to baseline by 1 month. IIR signaling also increased within 1 hour of treatment but persisted for up to 3 months thereafter. Furthermore, robust IIR and metabolite elevations, consistent with an early proinflammatory M1-mediated innate immune activation, were observed in patients in remission, whereas patients experiencing prostate serum antigen–determined disease progression demonstrated less robust immune responses and M2-mediated metabolite elevations.</p>Conclusions:<p>To our knowledge, these data are the first report of longitudinal proteomic and metabolomic molecular responses in patients after radiotherapy for cancers. The data supports innate immune activation as a critical clinical response of patients receiving radiotherapy for prostate cancer. Furthermore, we propose that the observed IIR may be generalized to the treatment of other cancer types, potentially informing multidisciplinary therapeutic strategies for cancer treatment.</p></div>
ABSTRACT Background Urinary extracellular vesicles (EVs) are a source of biomarkers with broad potential applications across clinical research, including monitoring radiation exposure. A key limitation to their implementation is minimal standardization in EV isolation and analytical analysis methods. Further, most urinary EV isolation protocols necessitate large volumes of sample. This study aimed to compare and optimize isolation and analytical methods for EVs from small volumes of urine. Methods 3 EV isolation methods were compared: ultracentrifugation, magnetic bead-based, and size-exclusion chromatography from 0.5 mL or 1 mL of rat and human urine. EV yield and mass spectrometry signals (Q-ToF and Triple Quad) were evaluated from each method. Metabolomic profiling was performed on EVs isolated from the urine of rats exposed to ionizing radiation 1-, 14-, 30- or 90-days post-exposure, and human urine from patients receiving thoracic radiotherapy for the treatment of lung cancer pre- and post-treatment. Results Size-exclusion chromatography is the preferred method for EV isolation from 0.5 mL of urine. Mass spectrometry-based metabolomic analyses of EV cargo identified biochemical changes induced by radiation, including altered nucleotide, folate, and lipid metabolism. We have provided standard operating procedures for implementation of these methods in other laboratories. Conclusions We demonstrate that EVs can be isolated from small volumes of urine and analytically investigated for their biochemical contents to detect radiation induced metabolomic changes. These findings lay a groundwork to develop future methods to monitor response to radiotherapy and can be extended to an array of molecular phenotyping studies aimed at characterizing EV cargo.
The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters ( R c 2 = 0.834, RMSEC = 0.262, RPD c = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) ( R c 2 = 0.816, RMSEC = 0.276, RPD c = 2.331) and raw spectra ( R c 2 = 0.822, RMSEC = 0.271, RPD c = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.
A user-friendly timber simulation program was developed by using object oriented programming technique. The component object model (COM) was used to design and implement the program. Three major modules were included in the harvesting simulation system: RUN, ANALYSIS, and REPORT. Input to the system includes species composition, stand density, spatial pattern, felling machine and cutting method for the felling simulation, and extraction machine and extraction pattern for the extraction simulation. Output is either a 2-dimensional or 3-dimensional stand map that lists species, DBH, spatial location, and total height for each individual tree and a harvested site together with all operation data saved to the relational database. The decision support system for timber harvesting operations could aid forest managers to examine the harvesting techniques and interactions among stand, harvest, and machines in terms of production, cost, and traffic intensity. It is a useful tool for decision-making in harvesting operation management.
The production rates and costs of two cut-to-length harvesting systems was simulated using a modular ground-based simulation model and stand yield data from fully stocked, second growth even aged central Appalachian hardwood forests. The two harvesters simulated were a modified John Deere 988 tracked excavator with a model RP 1600 single grip sawhead and an excavator based Timbco 425 with an ultimate 5600 single grip sawhead. The forwarder used in the simulations was a Valmet 524 with 8-foot log bunks. Production rates and costs were simulated for a range of stand conditions. The results should be valuable to managers, planners, and loggers considering the use of CTL and forwarding systems in the region.
O-linked β-N-acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions in a site-specific manner. However, the experimental identification of the O-GlcNAc sites remains challenging in many scenarios. Herein, by leveraging the recent progress in cataloguing experimentally identified O-GlcNAc sites and advanced deep learning approaches, we establish an ensemble model, O-GlcNAcPRED-DL, a deep learning-based tool, for the prediction of O-GlcNAc sites. In brief, to make a benchmark O-GlcNAc data set, we extracted the information on O-GlcNAc from the recently constructed database O-GlcNAcAtlas, which contains thousands of experimentally identified and curated O-GlcNAc sites on proteins from multiple species. To overcome the imbalance between positive and negative data sets, we selected five groups of negative data sets in humans and mice to construct an ensemble predictor based on connection of a convolutional neural network and bidirectional long short-term memory. By taking into account three types of sequence information, we constructed four network frameworks, with the systematically optimized parameters used for the models. The thorough comparison analysis on two independent data sets of humans and mice and six independent data sets from other species demonstrated remarkably increased sensitivity and accuracy of the O-GlcNAcPRED-DL models, outperforming other existing tools. Moreover, a user-friendly Web server for O-GlcNAcPRED-DL has been constructed, which is freely available at http://oglcnac.org/pred_dl.
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