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Collagen changes in the extracellular matrix caused by neoadjuvant chemoradiotherapy are a potential mechanism of anastomotic leakage. We aimed to construct a fully quantitative collagen score to describe collagen structure changes in the extracellular matrix and then develop and validate a prediction model to identify patients who are at a high risk of postoperative anastomotic leakage. This is a retrospective study in which 372 patients were enrolled, and their baseline clinicopathological characteristics were collected. Anastomotic distal and proximal "doughnut" specimens underwent second harmonic generation imaging, and collagen features were extracted. A LASSO regression was used to select significant predictors, and the collagen score was constructed. A prediction model based on collagen score was developed and internally and externally validated. The primary cohort included 214 consecutive patients, and the anastomotic leakage rate was 8.9%. The validation cohort comprised 158 consecutive patients, and the anastomotic leakage rate was 10.1%. The collagen score was significantly related to anastomotic leakage in both cohorts (P <.001). Multivariate analysis revealed that tumor location, preoperative albumin, and collagen score were independent predictors of anastomotic leakage. These 3 predictors were incorporated into the prediction model, and a nomogram was established. The model showed good discrimination in the primary (area under the curve: 0.954) and validation (area under the curve: 0.928) cohorts. Decision curve analysis demonstrated that the nomogram was clinically useful. The collagen score is associated with anastomotic leakage, and the collagen nomogram based on the collagen score is useful for individualized prediction of anastomotic leakage in rectal cancer patients with neoadjuvant chemoradiotherapy after surgery. [ABSTRACT FROM AUTHOR]

• The first time to apply deep-learning method to deoverlapping operation. • Summarized the shortcomings of traditional algorithms on deoverlapping operation. • A better result than traditional deoverlapping algorithm is achieved. Flat-panel X-ray source is an experimental X-ray emitter with target application of static computer tomography (CT), which can save imaging space and time. However, the X-ray cone beams emitted by the densely arranged micro-ray sources are overlapped, causing serious structural overlapping and visual blur in the projection results. Traditional deoverlapping methods can hardly solve this problem well. We converted the overlapping cone beam projections to parallel beam projections through a U-like neural network and selected structural similarity (SSIM) loss as the loss function. In this study, we converted three kinds of overlapping cone beam projections of the Shepp–Logan, line-pairs, and abdominal data with two overlapping levels to corresponding parallel beam projections. Training completed, we tested the model using the test set data that was not used at the training phase, and evaluated the difference between the test set conversion results and their corresponding parallel beams through three indicators: mean squared error (MSE), peak signal-to-noise ratio (PSNR) and SSIM. In addition, projections from head phantoms were applied for generalization test. In the Shepp–Logan low-overlapping task, we obtained a MSE of 1.624× 10 - 5 , a PSNR of 47.892 dB, and a SSIM of 0.998 which are the best results of the six experiments. For the most challenging abdominal task, the MSE, PSNR, and SSIM are 1.563× 10 - 3 , 28.0586 dB, and 0.983, respectively. In more generalized data, the model also achieved good results. This study proves the feasibility of utilizing the end-to-end U-net for deblurring and deoverlapping in the flat-panel X-ray source domain. [ABSTRACT FROM AUTHOR]

Purpose Non-local means (NLM) based reconstruction method is a promising algorithm for few-view computed tomography (CT) reconstruction, but often suffers from over-smoothed image edges. To address this problem, an adaptive NLM reconstruction method based on rotational invariance (ART-RIANLM) is proposed. Methods The method consists of four steps: 1) Initializing parameters; 2) ART reconstruction using raw data; 3) Positivity constraint of the reconstructed image; 4) Image updating by RIANLM filtering. In RIANLM, two kinds of rotational invariance measures which are average gradient (AG) and region homogeneity (RH) are proposed to calculate the distance between two patches and a novel NLM filter is developed to avoid over-smoothed image. Moreover, the parameter h in RIANLM which controls the decay of the weights is adaptive to avoid over-smoothness, while it is constant in NLM during the whole reconstruction process. The proposed method is validated on two digital phantoms and real projection data. Results In our experiments, the searching neighborhood size is set as 15 × 15 and the similarity window is set as 3 × 3. For the simulated case of Shepp-Logan phantom, ART-RIANLM produces higher SNR (36.23 dB > 24.00 dB) and lower MAE (0.0006 < 0.0024) reconstructed images than ART-NLM. The visual inspection demonstrated that the proposed method could suppress artifacts or noises more effectively and recover image edges better. The result of real data case is also consistent with the simulation result. Conclusions A RIANLM based reconstruction method for few-view CT is presented. Compared to the traditional ART-NLM method, SNR and MAE from ART-RIANLM increases 51% and decreases 75%, respectively. [ABSTRACT FROM AUTHOR]

Purpose Limited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed. Methods Since the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV. Results Simulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm. Conclusion This paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods. [ABSTRACT FROM AUTHOR]

• Winter cover crops with chicken grazing increased nitrogen contents in soils and nitrogen transfer. • Winter cover crops with chicken grazing increased straw biomass and grain yield. • Winter cover crops with chicken grazing increased economic profits. Winter fallow is important for renewing and improving soil fertility under double-cropping rice systems, such as those in southern China. Using a regenerative farming technology of integrating grass-chicken farming in a winter fallow field, we investigated soil nitrogen conversion and assessed the agricultural economic benefits of the whole farmland ecosystem. To test the effects of chicken grazing on the fallow system, we established field treatments involving adding chickens to a field planted with the cover crops, including cover milk vetch (Astragalus sinicus) with chicken grazing treatment (MC) and cover ryegrass (Lolium spp.) with chicken grazing (RC); cover crops only, including cover milk vetch (Astragalus sinicus) treatment (M) and cover ryegrass (Lolium spp.) (R); and a bare fallow field treatment (CK). We found that both cover crops (M and R) and cover crops with chicken grazing (MC and RC) increased nitrate, ammonium, dissolved organic nitrogen, and total nitrogen contents, and the increase was higher in MC and RC treatments. We also observed increased straw biomass and grain yield in the all four treatments, with more increases with chicken treatments as compared with CK. On the economic profits, MC increased by 101.72% and RC increased by 104.12% as compared with CK, while R increased by 5.19% and M reduced by 1.86% as compared with CK. The nitrogen transfer rate (the output/input ratio) of MC, RC, M, and R increased by 66.71%, 71.50%, 65.97%, and 59.97%, respectively, while the nitrogen accumulation rate (input–output) of MC, RC, M, and R increased by 480.56%, 612.98%, 356.74%, and 267.65%, respectively. Our study demonstrates that retaining nitrogen and gaining economic profit by integrating cover crops with chicken grazing is potentially more sustainable than adding cover crops alone. We further suggest that using the integrated grass-livestock farming technology can reduce environmental damage caused by commercial fertilizers. [ABSTRACT FROM AUTHOR]

Highlights: [•] USV call number and duration differed in fmr1KO compared to FVB wild type pups. [•] Fmr1KO pups showed an increased number of USVs at P7. [•] Spectral analysis showed an increase specifically in frequency jump calls. [•] A developmental shift in the temporal distribution of calls showed distinct bout patterns at P10. [•] FMRP plays a minor role in the development of USVs compared to other autism-related genes. [ABSTRACT FROM AUTHOR]

• An effective and reasonable utilization of the 3D dose prediction on IMRT planning. • Voxel-based dose prediction exquisitely sculpts an individual dose distribution. • A dose sequence-based objective invites more flexibility into optimization. • Comprehensive evaluations on proposed feasibility and robustness. In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses. This objective was used to reformulate a traditional fluence map optimization model, which involves a tolerable spatial re-assignment of the predicted dose distribution to the OAR voxels based on their current doses' positions at a sorted dose sequencing. The feasibility of this method was evaluated with ten gynecology (GYN) cancer IMRT cases by comparing its generated plan quality with the original clinical plan. Results showed feasible plan by proposed method, with comparable planning target volume (PTV) dose coverage and greater dose sparing of the OARs. Among ten GYN cases, the average V 30 and V 45 of rectum were decreased by 4%±4% (p = 0.02) and 4%±3% (p <0.01), respectively. V 30 and V 45 of bladder were decreased by 8%±2% (p <0.01) and 3%±2% (p <0.01), respectively. Our predicted dose sequence-based planning optimization method for GYN IMRT offered a flexible use of predicted 3D doses while ensuring the output plan consistency. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.Materials and Methods: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models.Results: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37).Conclusion: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations. [ABSTRACT FROM AUTHOR]