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Abstract Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external beam radiation therapy (EBRT) and brachytherapy (BT). The purpose of this study was to evaluate the accuracy of EBRT planning structures derived from DL based auto-segmentation compared with standard manual delineation. Auto-segmentation model based on convolutional neural networks (CNN) was developed to delineate clinical target volumes (CTVs) and organs at risk (OARs) in cervical cancer radiotherapy. A total of 300 retrospective patients from multiple cancer centers were used to train and validate the model, and 75 independent cases were selected as testing data. The accuracy of auto-segmented contours were evaluated using geometric and dosimetric metrics including dice similarity coefficient (DSC), 95% hausdorff distance (95%HD), jaccard coefficient (JC) and dose-volume index (DVI). The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. The right and left kidney, bladder, right and left femoral head showed superior geometric accuracy (DSC: 0.88–0.93; 95%HD: 1.03 mm–2.96 mm; JC: 0.78–0.88), and the Bland–Altman test obtained dose agreement for these contours (P > 0.05) between manual and DL based methods. Wilcoxon’s signed-rank test indicated significant dosimetric differences in CTV, spinal cord and pelvic bone (P

This paper focuses on the moving target localization and velocity measurement in incoherent centralized multiple-input and multiple-output (MIMO) radar systems with widely separated antennas in 3D space. In this paper, we assume that parameters such as time-of-arrival (TOA), frequency-of-arrival (FOA), azimuth angles and elevation angles have already been measured. With these measurements, a final closed-form solution of the target position and velocity can be obtained via the weighted least squares (WLS) method. When the target is located in the far field, due to poor system observability, a first-order Taylor expansion based on the WLS solution is necessary to obtain a more accurate and unbiased solution. Unlike the preceding papers which were based on the two-stage weighted least squares (2SWLS) method [1]-[3], in this paper, the angle information is introduced into the time delay equations and the Doppler frequency equations, so that the intermediate variables in the estimator can be eliminated [4], [5]. Meanwhile, the time delay equations and the Doppler equations are transformed into linear equations only related to the position and the speed of the target. This method, unlike 2SWLS-based methods [1]-[3], does not introduce auxiliary variables, so it does not require the decorrelation procedure. Simulation results show that the root mean-square error (RMSE) of position and velocity can reach Cramer-Rao lower bound (CRLB) when the noise is at a moderate level before the thresholding effect occurs.

Abstract Background and purpose How to protect the ovarian function during radiotherapy is uncertain. The purpose of this study was to explore the association between the location of the transposed ovary and the ovarian dose in patients with cervical cancer received radical hysterectomy, ovarian transposition, and postoperative pelvic radiotherapy. Methods A retrospective analysis was conducted of 150 young patients with cervical cancer who received radical hysterectomy, intraoperative ovarian transposition, and postoperative adjuvant radiotherapy in Zhejiang Cancer Hospital. Association between location of the transposed ovaries and ovarian dose was evaluated. The transposed position of ovaries with a satisfactory dose was explored using a receiver operator characteristic curve (ROC) analysis. Patients’ ovarian function was followed up 3 months and 1 year after radiotherapy. Results A total of 32/214 (15%) transposed ovaries were higher than the upper boundary of the planning target volume (PTV). The optimum cutoff value of > 1.12 cm above the iliac crest plane was significantly associated with ovaries above the upper PTV boundary. When the ovaries were below the upper boundary of PTV, the optimum cutoff value of transverse distance > 3.265 cm between the ovary and PTV was significantly associated with ovarian max dose (Dmax) ≤ 4Gy, and the optimum cutoff value of transverse distance > 2.391 cm was significantly associated with ovarian Dmax≤5Gy. A total of 77 patients had received complete follow-up, and 56 patients (72.7%) showed preserved ovarian function 1 year after radiotherapy, which was significantly increased compared with 3 months (44.2%) after radiotherapy. Conclusions The location of transposed ovaries in patients with cervical cancer is significantly correlated with ovarian dose in adjuvant radiotherapy. We recommend transposition of ovaries > 1.12 cm higher than the iliac crest plane to obtain ovarian location above PTV. When the transposed ovary is below the upper boundary of PTV, ovarian Dmax ≤4Gy may be obtained when the transverse distance between the ovary and PTV was > 3.265 cm, and the ovarian Dmax≤5Gy may be obtained when the transverse distance was > 2.391 cm.

With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA.

Accurate reliability and residual life analysis is paramount during the designing of reliability requirements and rotation of power measuring equipment (PME). However, the sample dataset of failure is usually sparse and contains inevitable pollution data, which has an adverse effect on the reliability analysis. To tackle this issue, this paper first applies nonlinear regression to fuse the failure rate and environmental features of PME collected from various locations. Then, a novel binary hierarchical Bayesian probability method is proposed to model the failure trend and identify outliers, in which the outlier identification structure is embedded into hierarchical Bayesian. Integrating binary hierarchical Bayesian and the bagging method, a binary hierarchical Bayesian with bagging (BHBB) framework is further introduced to improve predictive performance in a small sample dataset by resampling. Last, the influence of typical environmental features, failure rate, and reliability are obtained by the BHBB under the real sample dataset from multiple typical locations. Experiments show that our framework has superior performance and interpretability comparing with other typical data-based approaches.

Background/Aims: Naked mole rats (NMRs) survive and thrive in dark, dank environments with low levels of oxygen and poor quality nutrition. Their long lifespan is attributed to sustained good health and pronounced resistance to cancer. Physiological and biochemical processes, such as autophagy, may contribute to the successful aging of this exceptionally long-lived species. We demonstrated that NMRs have higher levels of autophagy than short-lived C57BL/6 mice, and this may play an important role in the maintenance of cellular protein quality and the defense of cells against intracellular and extracellular aggressors in NMRs. The present study assesses autophagy as a means for cells to flexibly respond to environmental changes (H2O2 treatment and a shortage of nutrients). Methods: Primary NMR HSCs were isolated from liver and treated with serum-free medium. Cells in the experimental group were incubated with different concentrations of hydrogen peroxide (H2O2) in the presence and / or absence of 3-MA (5 mM).The LC3-II/LC3-I ratio was determined by western blot analysis. Western blotting was performed to analyze the expression level of Beclin 1 protein. Apoptosis and cell-cycle progression were analyzed by flow cytometry. Results: Our data reveal that both poor quality nutrition and H2O2 treatment induces apoptosis and autophagy in NMR hepatic stellate cells(HSCs). Conclusion: NMR cells have the capacity to induce cell death through apoptosis and downregulate the energy consuming processes through inhibition of proliferation when they become superfluous or irreversibly damaged.

Background/Aims: The maximum lifespan of the naked mole rat is over 28.3 years, which exceeds that of any other rodent species, suggesting that age-related changes in its body composition and functionality are either attenuated or delayed in this extraordinarily long-lived species. However, the mechanisms underlying the aging process in this species are poorly understood. In this study, we investigated whether long-lived naked mole rats display more autophagic activity than short-lived mice. Methods: Hepatic stellate cells isolated from naked mole rats were treated with 50 nM rapamycin or 20 mM 3-methyladenine (3-MA) for 12 or 24 h. Expression of the autophagy marker proteins LC3-II and beclin 1 was measured with western blotting and immunohistochemistry. The induction of apoptosis was analyzed by flow cytometry. Results: Our results demonstrate that one-day-old naked mole rats have higher levels of autophagy than one-day-old short-lived C57BL/6 mice, and that both adult naked mole rats (eight months old) and adult C57BL/6 mice (eight weeks old) have high basal levels of autophagy, which may be an important mechanism inhibiting aging and reducing the risk of age-related diseases. Conclusion: Here, we report that autophagy facilitated the survival of hepatic stellate cells from the naked mole rat, and that treatment with 3-MA or rapamycin increased the ratio of apoptotic cells to normal hepatic stellate cells.

Background/Aims: It has been demonstrated that KRAS mutations represent about 90% of cancer-associated mutations, and that KRAS mutations play an essential role in neoplastic transformation. Cancer-associated RAS mutations occur frequently in acute myeloid leukemia (AML), suggesting a functional role for Ras in leukemogenesis. Methods: We successfully established a mouse model of human leukemia by transplanting bone marrow cells co-transfected with the K-ras (G12D) mutation and AML1/ETO fusion protein. Results: Mice transplanted with AML/ETO+KRAS co-transduced cells had the highest mortality rate than mice transplanted with AML/ETO- or KRAS-transduced cells (115d vs. 150d). Upon reaching a terminal disease stage, EGFP-positive cells dominated their spleen, lymph nodes, peripheral blood and central nervous system tissue. Immunophenotyping, cytologic analyses revealed that AML/ETO+KRAS leukemias predominantly contained immature myeloid precursors (EGFP+/c-Kit+/Mac-1-/Gr-1-). Histologic analyses revealed that massive leukemic infiltrations were closely packed in dense sheets that effaced the normal architecture of spleen and thymus in mice transplanted with AML1/ETO + KRAS co-transduced cells. K-ras mRNA and protein expression were upregulated in bone marrow cells of the K-ras group and AML1/ETO + Kras group. The phosphorylation of MEK/ERK was significantly enhanced in the AML1/ETO + Kras group. The similar results of the AML1/ETO + Nras group were consistent with those reported previously. Conclusion: Co-transduction of KrasG12D and AML1/ETO induces acute monoblastic leukemia. Since expression of mutant K-ras alone was insufficient to induce leukemia, this model may be useful for investigating the multi-step leukemogenesis model of human leukemia.

This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

In order to understand how tumor cells can escape immune surveillance mechanisms and thus develop antitumor therapies, it is critically important to investigate the mechanisms by which the immune system interacts with the tumor microenvironment. In our current study, IL-17 deficiency results in reduced melanoma tumor size, diminished numbers of proliferating cells and blood vessels, and decreased percentage of CD11b+Gr-1+ MDSCs in tumor tissues. IL-17 promotes IL-6 induction and Stat3 activation. Treatment of Stat3 inhibitor WP1066 in B16-F10 tumor cells inoculated wild-type mice inhibits tumor growth. Additional administration of recombinant IL-6 into B16-F10 tumor-bearing IL-17−/− mice results in markedly increased tumor size and p-Stat3 expression, whereas additional recombinant IL-17 administration into B16-F10 tumor-bearing wild-type mice treated with anti-IL-6 mAb does not significantly alter the tumor growth and p-Stat3 expression. In our further study, blockade of Hmgb1-RAGE pathway inhibits melanoma tumor growth and reduces production of IL-23 and IL-17. All these data suggest that Hmgb1-IL-23-IL-17-IL-6-Stat3 axis plays a pivotal role in tumor development in murine models of melanoma, and blocking any portion of this axis will attenuate melanoma tumor growth.

Objective: We aimed to explore the geographic differences in psychological symptoms, sleep quality, and quality of life (QoL) among adult patients with inflammatory bowel disease (IBD). Methods: A unified questionnaire was developed to collect data on psychological status and QoL of IBD patients from 42 hospitals across 22 provinces, municipalities, and autonomous regions in China's mainland from September 2021 to May 2022. Results: A total of 2478 patients with IBD were surveyed. The proportions of patients with anxiety (28.5% vs 23.1%), depression (32.3% vs 27.8%), and poor QoL (44.8% vs 32.2%) were significantly higher in patients from the northern region compared to the southern region (all P < 0.05). In the western region, the proportions of patients with anxiety (31.9% vs 23.0%), depression (37.7% vs 26.7%), sleep disturbances (64.5% vs 58.5%), and poor QoL (44.9% vs 34.8%) were significantly higher than in the eastern and central regions (all P < 0.01). Patients from inland regions had significantly higher rates of anxiety (27.1% vs 23.3%), depression (32.5% vs 26.0%), sleep disturbance (62.0% vs 57.7%), and poor QoL (43.5% vs 29.9%) compared to those from coastal regions (all P < 0.05). In economically underdeveloped areas, the proportions of patients with depression (33.1% vs 28.5%) and poor QoL (52.0% vs 32.4%) were significantly higher than in economically (relatively) developed areas (both P < 0.05). Conclusion: There are significant geographic differences in psychological symptoms, sleep quality, and QoL among Chinese patients with IBD, which might provide valuable insights for global IBD research and clinical practice. [ABSTRACT FROM AUTHOR]