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Objective: Osteoarthritis (OA) is characterized by articular cartilage loss, associated with synovial inflammation. We recently reported increased pro-inflammatory macrophages in murine post-traumatic OA (PTOA) joints, and blockade of the ubiquitin-proteasome system alleviates PTOA progression. However, the mechanisms whereby protein ubiquitination influences PTOA pathology are not well studied. We hypothesized that loss of the negative regulator of inflammation, E3 ligase Itch, in macrophages contributes to joint OA tissue damage by promoting pro-inflammatory polarization of macrophages.Methods: Mice deficient Itch in macrophages (MΔItch) were generated by crossing Itchfl/fl mice with LysM-Cre mice. PTOA surgery was performed on global Itch knockout, Itch-/-, mice and MΔItch mice. Joint tissue damage and synovial macrophages were examined. Itch-/- cells were treated with IL-1 and pro-inflammatory polarization was determined. Expression of Itch protein and mRNA in PTOA synovium were assessed at different time points post PTOA.Results: Similar to Itch-/- mice, MΔItch mice developed more severe joint damage than control mice following PTOA surgery (mean difference of OARSI score: 1.17 (95% CI 0.31-2.03) between MΔItch and Itchfl/fl mice), accompanied by increased the inflammatory macrophage infiltration in the synovium (mean difference of % F4/80 + CD86 + CD36-inflammatory macrophages: 14.81 (95% CI 8.90-20.73) between MΔItch and Itchfl/fl mice). Itch-/- macrophages exerted pro-inflammatory phenotype in response to IL-1β treatment. Itch protein, but not mRNA levels decreased during PTOA progression.Conclusion: The negative regulator of inflammation, Itch, limits PTOA progression by inhibiting macrophage pro-inflammatory polarization. Itch protein degradation may contribute to PTOA pathology. [ABSTRACT FROM AUTHOR]

L'intelligence artificielle permet d'analyser de grandes quantités de données très rapidement. Elle peut être utilisée pour interpréter des images ou des données textuelles afin de déterminer l'épidémiologie de certaines maladies, telles que le cancer. Dans cet article, nous présentons et discutons de ses applications dans ce domaine. Artificial Intelligence can be leveraged to analyze great amounts of data. It can be used on images or textual data to define the epidemiology of diseases, such as cancer. In this review, we will present and discuss the applications of AI in this setting. [ABSTRACT FROM AUTHOR]

Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n = 5), head and neck (n = 5), esophageal (n = 3), rectal (n = 3), pancreatic (n = 2) cancer and brain metastases (n = 2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models. [ABSTRACT FROM AUTHOR]

Highlights • An analytical springback model considering the heterogeneous material properties and geometric characteristics of welded tube was established for bending. • In the model, the variations of the neutral layer, thickness, Young's modulus and anisotropic parameter with deformation were considered simultaneously. • The springback angles of a QSTE340 welded tube in numerical control bending were accurately predicted. • The influences of mechanical properties matching between weld and parent metal on springback were obtained. Abstract Predicting springback is important for improving the precision of tube bending forming. Compared with a homogeneous tube, the springback of a welded tube is more complex owing to the uneven material properties and geometric characteristics. An analytical springback model for welded tube bending was established according to the static equilibrium condition, with the aim of improving the prediction accuracy for the welded tube springback. In this model, the continuous variations of the weld material properties and the anisotropic parameter (contractile strain ratio), as well as the effect of the weld position were considered. Using the model, the springback angles of a QSTE340 welded tube in numerical control bending were predicted. Comparisons with experimental results suggest that this springback model can accurately predict the springback angle and present higher prediction accuracy than the existing models. In addition, the influence of description exactitude about the weld material property on the prediction accuracy was analyzed. The results suggest that the continuous weld material model adopted can significantly improve the prediction accuracy, and the improvement degree varies with the position of weld during bending. Finally, the effects of the mechanical properties matching between the weld and parent metal and the geometric parameters of the welded tube on the springback were determined using the established springback model. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Aperture shape optimization is one of the most time-consuming parts in VMAT planning. Current state-of-the-art algorithms rely on gradient information to reduce the search space. Here we propose to incorporate past plan knowledge into this key part of optimization. Specifically, we train a deep learning model based on the self-attention mechanism from the transformer architecture, to predict the aperture shapes directly from the patient contour volume. We use a network structure similar to the compact convolutional transformer. It applies convolutions to convert 2D or 3D images to a sequence of embeddings which the transformer can work on. The input to the transformer is the 3D volume of the patient structure set where each voxel number stands for the OAR or PTV type. For each gantry angle from 0, 2,... to 358, we calculated the ray casting 3D volume at the beams eye view (BEV) coordinates with a size of 32 × 32 × 80 and a resolution of 5 mm. The 180 BEV 3D volumes are tokenized by twelve 3D convolutional layers and flattened to 128 dimensional vectors which are later summed with positional embeddings to preserve the positional information between gantry angles. This is then fed to a transformer encoder which consists of a multi head attention layer with 8 heads and a feed forward layer with a latent dimension of 1024. During the training phase, the 180 aperture shapes, prefixed by a fully closed aperture and represented by 2D matrix with a size of 32 × 32 and resolution of 5 mm, are tokenized in similar ways to the BEV volumes except the convolutional layers are 2D. This is then processed by a decoder with a similar structure to the encoder except with a causal attention mask which forces the prediction to only attend to tokens at earlier locations. This and the encoder output are then sent through another decoder which generates the aperture shapes and weights. At inference phase, a fully closed aperture and BEV volumes are sent into the transformer to predict the next aperture, which are then used as the input to the following apertures. We collected 11 lung SBRT plans as our training data. The transformer is trained using root mean squared propagation with a loss function of binary cross entropy. The DVHs are calculated from the predicted aperture and weighting. A broadly applicable transformer network for VMAT optimization has been established. The transformer efficiently learns from the training data and predicts apertures close to the past plan. The calculated DVH matches well with the plan data. The transformer predicted apertures can work as a warm start point for any VMAT optimization algorithms to speed up the clinical workflow. To enhance the robustness and generality of the approach, further study with larger training sets and better model parameters optimization will be useful. [ABSTRACT FROM AUTHOR]

Liver cancer is the second most frequent cancer to cause death in men and sixth for women. Detection of liver cancer early using computed tomography (CT) could assist doctors in making accurate hepatocellular carcinoma evaluation and treatment planning, which prevent millions of patients' death every year. Traditionally, radiologists delineate the liver lesion by manually reading hundreds of CT scans slice by slice, which is an enormous burden and suffers from inter- or intra-operator variations. Therefore, automatic liver tumor segmentation methods are highly demanded in clinical practice. Although many deep convolutional neural network-based techniques for automatic segmentation have been proposed, the small tumor remains challenging to segment due to convolutional and pooling operations that result in the loss of small object information. This study aims to propose a deep learning method for accurate segmentation for small liver tumors. This study included 131 patients with liver cancer. The in-plane resolution of the patients' CTs is from 0.55 mm to 1.0 mm and slice spacing from 0.45 mm to 6.0 mm. We randomly selected 100 CT scans as the training set and others as the testing set. Each CT slice of the testing set was separated into groups according to tumor size as follows: 0.1–2.0, 2.1–5.0, 5.1–10.0, and 10.1–20.0 cm. The CT slice without tumor or tumor size > 20 cm were excluded. This work presents a novel attention-based deep learning method for small liver tumor segmentation, which exploits the relation between small and large tumors by computing the original image of small tumors and the feature maps of large tumors. The relation of small-large tumors can compensate for the information loss of small tumors during the convolutional and pooling operations and improve the performance of small tumor segmentation. Among 20,693 CT slices of the 31 testing patients, 3.0% CT slices with tumors ≤2 cm, 6.7% ≤5 cm, 10.6% ≤10 cm, and 13.4%≤20 cm. We compared our method with six widely used segmentation models. The results show our model outperforms other methods on all sizes of liver tumors, especially for small size tumors: For the 0.1-2.0 cm liver tumor, it achieved 8.4%, 10.0%, 11.3%, 9.1%, 10.9%, and 9.6% improvement compared to Unet, PAN, DeepLabV3, FPN, LinkNet, and PSPNet, respectively. The tumors of different sizes share similar imaging characteristics. The information of large tumors can compensate for the information loss of small tumors from the feature propagation processes of deep learning methods. The small-large tumors relation can significantly improve small liver tumor segmentation, which is beneficial for disease diagnosis and treatment planning and has the potential for other types of tumor detection and segmentation. [ABSTRACT FROM AUTHOR]

• A new micromechanics-based damage constitutive model considering the phases, grains and voids information is proposed for aluminum alloys. • Voids are divided into three types with different mechanisms and separately modeled relative to their local micro-strain/stress and microstructures. • The micro-strain/stress of the phases is determined via a self-consistent method considering the voids softening and microstructure effects on the constitutive behavior of each phase. • The predicted evolution of each type of void, flow stress and fracture behavior are validated by XCT measurements and tension testing of specimens with various microstructures. • The mutual effects between microstructure, voids and deformation behavior of aluminum alloys are captured and revealed. Aluminum alloys undergoing deformation exhibit complex multi-type-void damage evolution driven by microscale heterogeneous deformation; in return, the accumulated damage evolution would affect the microscale heterogeneous deformation. Meanwhile, the above interaction depends significantly on the microstructure characteristics and determines the macroscale flow and ductile fracture behaviors. These make it difficult to model the deformation response and ductile fracture of aluminum alloys with various microstructures. Aiming at this issue, a micromechanics-based damage constitutive model considering the mutual effects between microstructure, micro/macro-scale deformation, and damage and fracture was developed in this study for deformation of aluminum alloys under tension-dominated loading conditions. During the modeling, the damage was characterized in terms of the evolutions of coexisting multi-type voids. The multiple-type voids were modeled separately as functions of the local micro-strain/stress of their respective phases and microstructure characteristics according to the micromechanical mechanisms. Furthermore, a self-consistent method was employed to calculate the heterogeneous micro-strain/stress of phases and the overall macroscale flow behavior of the multiphase aggregate. In particular, the constitutive behavior of each phase in the self-consistent method was modeled by coupling the softening effect of void evolution and microstructure characteristics, thus the interaction between microscale heterogeneous deformation and damage evolution was captured. The model parameters were determined by matching the calculated and experimental evolutions of the multi-type voids measured via in-situ synchrotron radiation X-ray computed tomography and load-displacement curves during deformation. Applied to the 2219 aluminum alloy, this model accurately predicts the flow stress, damage evolution and fracture behavior under various microstructures and those of tailor-welded blanks with a gradient microstructure. Furthermore, the model clarifies the mutual effects between the microstructure, damage evolution and deformation behavior of aluminum alloys. The developed model can also be generalized to other multiphase metals containing a distribution of hard-brittle particles. And it is thus believed to be with a well general prediction for the deformation response and ductile fracture of this kind of multiphase metals with various microstructures or gradient microstructures, and further facilitates the understanding of their complex damage evolution and flow behavior. [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of this study was to review recent records on the removal of projectiles in the maxillofacial area from patients with firearm wounds, and to summarize our experience in the use of computed tomography (CT) navigation combined with an endoscope in the treatment of these patients. Twenty-four patients injured by firearms and with projectile retention were identified. For surgical planning and intraoperative navigation, an iCT system integrating navigation with intraoperative CT scanning (iPlan) and an electronic endoscope were used. The records of the 24 patients were reviewed retrospectively. All projectiles in these cases were accessed and removed with CT navigation combined with endoscope guidance; no major complications occurred and the surgery time was reduced compared to conventional procedures. Additional incisions were used in 10 patients; removal of the retained projectile through the original wound is not appropriate in certain cases. There was no excessive intraoperative bleeding except in one case, and no patient experienced postoperative bleeding. In 29.2% of cases, the foreign projectiles involved occupied more than one space. CT navigation combined with endoscope guidance is a useful method for removing most projectiles retained in the maxillofacial and neck area. [ABSTRACT FROM AUTHOR]

We have developed a new method for minimally-invasive treatment of uncomplicated oral ranulas using a mucosal tunnel, and we report the clinical outcome. We constructed a mucosal tunnel for each of 35 patients who presented with an oral ranula, by making 2 parallel incisions across the top of the protruding ranula 2–3 mm apart, and dissected the soft tissue along the incisions to its wall. The fluid was removed and the cavity irrigated with normal saline. The wall of the ranula was not treated. The first mucosal tunnel was made by suturing the base of the mucosal strip to the deepest part of the wall of the ranula. The mucosal base of the tunnel and the deepest part of the base of the ranula were fixed with absorbable sutures. The two external edges of the incisions were sutured together to form the second mucosal tunnel, and apposing sutures were inserted between the two parallel incisions to form two natural mucosal tunnels. The duration of follow-up ranged from 1 to 5 years. One patient was lost to follow-up and 34 patients were cured. Outcomes were satisfactory without relapse during the follow-up period and the patients were satisfied with the outcome. The mucosal tunnel is a safe, effective, simple, and minimally-invasive treatment for oral ranula. [ABSTRACT FROM AUTHOR]

Abstract: A newly-proposed control system design for the GTHTR300C, a nuclear power and heat cogeneration system employing a Very High Temperature Reactor (VHTR), offering load-following capability is presented. The system is designed to follow significant seasonal variations of electric load demand. It combines several fundamental control methods including inventory control used to adjust primary coolant pressure, flow bypass control used to regulate the coolant flow rate and turbine inlet temperature, and additional flow control to vary the IHX heat rate. The goal of the control system design is such that the wide range of load-following capability can be performed at constant reactor power and thermal efficiency, which maximizes plant economics, while minimizing transient thermal stresses in the reactor equipment. The control simulation of a representative load-following operation is carried out using a system analysis code and discussed in the paper. The results of the simulation show that the design goal can be effectively met by monitoring and controlling constant a few selected operating parameters of reactor and gas turbine including reactor outlet temperature, turbine inlet temperature and speed. The original control system design shows that load-following operation at constant reactor power and thermal efficiency by a VHTR power and heat cogeneration system is feasible. [Copyright &y& Elsevier]

Abstract: Conventional steelmaking processes involve intensive fossil fuel consumption and CO2 emission. The system resulting from this study ties a steelmaking plant to a nuclear plant. The latter supplies the former all energy and feedstock with the exception of iron ore. The actual design takes on a multi-disciplinary approach: The nuclear plant employs a proven next-generation technology of fission reactor with 950 °C outlet temperature to produce electricity and heat. The plant construction saving and high efficiency keep the cogeneration cost down. The steelmaking plant employs conventional furnaces but substitutes hydrogen and oxygen for hydrocarbons as reactant and fuel. Water decomposition through an experimentally-demonstrated thermochemical process manufactures the feedstock gases required. Through essential safety features, particular a fully-passive nuclear safety, the design achieves physical proximity and yet operational independence of the two plants to facilitate inter-plant energy transmission. Calculated energy and material balance of the integrated system yields slightly over 1000 t steel per 1 MWt yr nuclear thermal energy. The steel cost is estimated competitive. The CO2 emission amounts to 1% of conventional processes. The sustainable performance, economical potential, robust safety, and use of verified technological bases attract near-term deployment of this nuclear steelmaking system. [Copyright &y& Elsevier]

Excessively applied manure contains a considerable amount of nutrient content such as nitrogen and phosphorus that could potentially pollute groundwater and soil. The present paper evaluated the use of nonlinear regression methods, such as artificial neural networks (ANN), for developing near infrared reflectance spectroscopy calibration models to predict nutrient content in poultry manure. Four representative nutrient ingredients (ammonia nitrogen, AN; total potassium, TK; total nitrogen, TN; total phosphorus, TP) in poultry manure were selected for evaluating ANN feasibility using 91 diverse samples in which three-fourths of the samples were used as a training set and one-fourth as a validation set. The performance of the ANN models was compared with the partial least squares (PLS) models. We found that the ANN models for all 4 nutrient contents consistently gave better predictions than PLS models. The ratios of prediction to deviation of 2.62 (AN), 1.51 (TK), 2.75 (TN), and 2.01 (TP) with the PLS models were improved to 3.02 (AN), 1.74 (TK), 3.41 (TN), and 2.71 (TP) with the corresponding ANN models. These findings demonstrated that the near infrared reflectance spectroscopy model based on the ANN method may be an appropriate tool to predict nutrient content in poultry manure. [ABSTRACT FROM AUTHOR]

Abstract: Dielectric spectroscopy was performed on single crystals of pure, Ce-doped or Ce,Zr-codoped Lu3Al5O12, before and after UV- or X-irradaiation, at various frequencies within the range 100Hz–1MHz as the temperature was scanned from 110 to 353K. All samples previously subjected to ionising radiations gave spectra showing loss peaks with Arrhenius characteristics of permanent dipoles relaxation. We attribute the dipoles to defect-stabilised pairs of anion–anion vacancies (oxygen ions and oxygen vacancies) that have captured holes and photo-electrons separately, thus forming O−- and F+-like centers. The dielectric relaxation peaks disappeared in undoped or doped samples annealed at 573K, suggesting that charge carrier traps are relatively deep. UV–visible absorption spectra have also been measured, which tend to support our proposed interpretation. Further evidence for deep traps has come from thermally stimulated luminescence experiments. [Copyright &y& Elsevier]

The objectives of this study were to determine the effect of inoculant, enzymes and inoculant-enzymes mixture on fermentation quality, nutritive value, and microbial changes of sorghum straw silage. Sorghum straws were collected and treated with distilled water (control), inoculant, enzymes and inoculant+enzymes prior to ensiling. Three bag silos for each silage (denoted C, I, E and I+E, respectively) were opened after 3, 7, 11, 15, 30 and 60 days for chemical and microbial analyses. For all the silages, there was a rapid decline in pH during the first 3 days of ensiling. Relative to silage C, all the treatment (I, E and I+E) had higher (P <0.05) lactic acid concentration at all ensiling periods. Population of LAB during all ensiling time was numerically greater for treated than control silages. Separate addition of two additives, especially for enzymes, can effectively (P <0.05) decrease aNDF and ADF concentration. Treatments with enzymes (E, I+E) can also improve significantly silage IVDMD and IVNDFD concentration. These results indicated that the addition of additives can improve the sorghum straw silage fermentation quality at different extent. [Copyright &y& Elsevier]

Diverse samples (n = 91) were used to investigate the feasibility of near-infrared reflectance spectroscopy (NIRS) technology for rapid analysis of the nutrient composition of layer manure. Near-infrared reflectance spectroscopy calibration models for moisture, organic matter, total N, ammonium N, total P, total K, Cu, Fe, Mg, and Na were developed by using the modified partial least squares method. Results showed that the NIRS method could provide accurate predictions of moisture, organic matter, total N, and ammonium N concentrations, with a correlation coefficient of validation (r²v) and a ratio of SD over the root mean square error of prediction (RPD) of 0.86 (2.68), 0.89 (2.91), 0.88 (2.75), and 0.88 (2.62), respectively. Total P (r²v = 0.80, RPD = 2.01) could be approximately determined by NIRS. It was difficult to determine total K (r²v = 0.58, RPD = 1.51), Cu (r²v = 0.48, RPD = 1.38), Fe (r²v = 0.55, RPD = 1.47), Mg (r²v = 0.60, RPD = 1.51), and Na (r²v = 0.62, RPD = 1.87) by NIRS. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work was to apply the method of constrained least-squares to inverse treatment planning and to explore its potential for providing a fast interactive planning environment for intensity-modulated radiation therapy (IMRT).Methods and Materials: The description of the dose inside a patient is a linear matrix transformation of beamlet weights. The constrained least-squares method adds additional matrix operators and produces beamlet weights by a direct linear transformation. These matrix operators contain a priori knowledge about the radiation distribution. The constrained least-squares technique was applied to obtain IMRT plans for prostate and paraspinal cancer patients and compared with the corresponding plans optimized using the CORVUS inverse planning system.Results: It was demonstrated that a constrained least-squares technique is suitable for IMRT plan optimization with significantly increased computing speed. For the two cases we have tested, the constrained least-squares method was an order of magnitude faster than conventional iterative techniques because of the avoidance of the iterative calculations. We also found that the constrained least-squares method is capable of generating clinically acceptable treatment plans with less trial-and-error adjustments of system variables, and with improved target volume coverage as well as sensitive structure sparing in comparison with that obtained using CORVUS.Conclusions: The constrained least-squares method has the advantage that it does not require iterative calculation and thus significantly speeds up the therapeutic plan optimization process. Besides shedding important insight into the inverse planning problem, the technique has strong potential to provide a fast and interactive environment for IMRT treatment planning. [Copyright &y& Elsevier]

To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases. 117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models. Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820–0.901, 95%CI) and 0.843 (0.783–0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751–0.923, 95%CI) and 0.850 (0.763–0.937, 95%CI). The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms. • MRI radiomics predicts EGFR-TKI efficacy for NSCLC brain metastases. • Combined nomograms outperform single approaches in predicting EGFR-TKI efficacy. • Radiomics enhances non-invasive assessment and guides treatment decisions. [ABSTRACT FROM AUTHOR]