Reliability and validity of the Tibetan version of the International Prostate Symptom Score.

OBJECTIVE: To validate the Tibetan version of the International Prostate Symptom Score (IPSS-Tib) in patients with and without urinary symptoms in a Tibetan population. METHODS: The validity and reliability of IPSS-Tib were studied in 85 patients with benign prostatic hyperplasia (BPH) and 62 controls without lower urinary tract symptoms (LUTS). Reliability was evaluated using the test-retest method and internal consistency using Cronbach's α, and the construct validity was assessed by the correlation between IPSS-Tib scores and quality of life questions (QoL-Tib). RESULTS: The Cronbach's α coefficient of the IPSS-Tib was 0.80 and of a single IPSS scoring item ranged from 0.77 to 0.86. The IPSS-Tib test-retest reliability was evaluated by the intraclass correlation coefficient, and its average value was 0.79 (P < 0.001). The mean (SEM, 95% CI) area under the ROC curve for the IPSS-Tib was 0.91 (0.87-0.96). The IPSS-Tib had a high correlation with the QoL-Tib (Spearman's rank correlation coefficient 0.84, P < 0.01). The mean IPSS score before transurethral resection of the prostate (TURP) was 21.9 (6.8), and dropped to 6.38 (1.54) after TURP (P < 0.001), and the average difference was 15.52 (6.23), related to the drop from 4.5 (0.9) to 1.46 (0.48) in the QoL (P < 0.001). CONCLUSION: The IPSS-Tib has good reliability and validity in the diagnosis and symptom severity assessment of patients with BPH in Tibetan areas. It is an ideal assessment tool that can be used in Tibetan-speaking areas for patients with BPH and as a method for evaluating postoperative curative effect assessment of patients with BPH.

Inference-Domain Network Evolution: A New Perspective for One-Shot Multi-Object Tracking.

The supervised one-shot multi-object tracking (MOT) algorithms have achieved satisfactory performance benefiting from a large amount of labeled data. However, in real applications, acquiring plenty of laborious manual annotations is not practical. It is necessary to adapt the one-shot MOT model trained on a labeled domain to an unlabeled domain, yet such domain adaptation is a challenging problem. The main reason is that it has to detect and associate multiple moving objects distributed in various spatial locations, but there are obvious discrepancies in style, object identity, quantity, and scale among different domains. Motivated by this, we propose a novel inference-domain network evolution to enhance the generalization ability of the one-shot MOT model. Specifically, we design a spatial topology-based one-shot network (STONet) to perform the one-shot MOT task, where a self-supervision mechanism is employed to stimulate the feature extractor to learn the spatial contexts without any annotated information. Furthermore, a temporal identity aggregation (TIA) module is proposed to assist STONet to weaken the adverse effects of noisy labels in the network evolution. This designed TIA aggregates historical embeddings with the same identity to learn cleaner and more reliable pseudo labels. In the inference domain, the proposed STONet with TIA performs pseudo label collection and parameter update progressively to realize the network evolution from the labeled source domain to an unlabeled inference domain. Extensive experiments and ablation studies conducted on MOT15, MOT17, and MOT20, demonstrate the effectiveness of our proposed model.

Application and Evaluation of a 64-Slice CT Three-Dimensional Fusion Technique in the Determination of the Effective Ablation Margin after Radiofrequency Ablation of Hepatocellular Carcinoma.

Due to the low accuracy of traditional three-dimensional fusion technology in radiofrequency ablation of hepatocellular carcinoma, this paper studies the advantages of three-dimensional CT fusion technology over traditional two-dimensional imaging technology in preoperative visualization and radiofrequency ablation path selection of hepatocellular carcinoma. To study the prognostic differences of hepatocellular carcinoma patients with different ablation margins (AM) in the three groups, so as to explore the best AM value, so as to minimize the liver injury caused by radiofrequency ablation. The selected patients underwent CT plain scan and three-phase enhancement at 1, 3, 6, and 12 months after operation and were rechecked every 6 months. For recurrent patients, CT was rechecked every three months. The images were obtained by GE 64-slice spiral CT. The thickness of the reconstruction layer is 1 mm, and the interval is 1 mm. The reconstructed image is imported into 3D fusion software. The three-dimensional images of tumor focus, hepatic artery, portal vein, and hepatic vein were reconstructed by two experienced doctors by superimposing the saved tumor images, merging the vascular images into the display, and measuring the ablation boundary (AM value). The results showed that the recurrence rate in group A was higher than that in group B (P = 0.041), and there was no significant difference between group B and group C (P = 1.000). Compared with traditional two-dimensional imaging, three-dimensional CT fusion technology can display the anatomical structure and three-dimensional spatial relationship of tumors and blood vessels and select the best radiofrequency ablation path, so as to achieve accurate radiofrequency ablation.

A Layer-Dependent Analytical Model for Printability Assessment of Additive Manufacturing Copper/Steel Multi-Material Components by Directed Energy Deposition.

Copper/steel bimetal, one of the most popular and typical multi-material components (MMC), processes excellent comprehensive properties with the high strength of steel and the high thermal conductivity of copper alloy. Additive manufacturing (AM) technology is characterized by layer-wise fabrication, and thus is especially suitable for fabricating MMC. However, considering both the great difference in thermophysical properties between copper and steel and the layer-based fabrication character of the AM process, the optimal processing parameters will vary throughout the deposition process. In this paper, we propose an analytical calculation model to predict the layer-dependent processing parameters when fabricating the 07Cr15Ni5 steel on the CuCr substrate at the fixed layer thickness (0.3 mm) and hatching space (0.3 mm). Specifically, the changes in effective thermal conductivity and specific heat capacity with the layer number, as well as the absorption rate and catchment efficiency with the processing parameters are considered. The parameter maps predicted by the model have good agreement with the experimental results. The proposed analytical model provides new guidance to determine the processing windows for novel multi-material components, especially for the multi-materials whose physical properties are significantly different.

Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Patients With Malignant Pheochromocytoma and Paraganglioma.

Background: Malignant pheochromocytoma and paraganglioma (PPGL) are rare tumors with few prognostic tools. This study aimed to construct nomograms for predicting 3- and 5-year survival for patients with malignant PPGL. Methods: The patient data was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. A total of 764 patients diagnosed with malignant PPGL from 1975 to 2016 were included in this study. The patients were randomly divided into two cohorts; the training cohort (n = 536) and the validation cohort (n = 228). Univariate analysis, Lasso regression, and multivariate Cox analysis were used to identify independent prognostic factors, which were then utilized to construct survival nomograms. The nomograms were used to predict 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) for patients with malignant PPGL. The prediction accuracy of the nomogram was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCAs) was used to evaluate the performance of survival models. Results: Age, gender, tumor type, tumor stage, or surgery were independent prognostic factors for OS in patients with malignant PPGL, while age, tumor stage, or surgery were independent prognostic factors for CSS (P <.05). Based on these factors, we successfully constructed the OS and CSS nomograms. The C-indexes were 0.747 and 0.742 for the OS and CSS nomograms, respectively. In addition, both the calibration curves and ROC curves for the model exhibited reliable performance. Conclusion: We successfully constructed nomograms for predicting the OS and CSS of patients with malignant PPGL. The nomograms could inform personalized clinical management of the patients.

Deep Spatial and Temporal Network for Robust Visual Object Tracking.

There are two key components that can be leveraged for visual tracking: (a) object appearances; and (b) object motions. Many existing techniques have recently employed deep learning to enhance visual tracking due to its superior representation power and strong learning ability, where most of them employed object appearances but few of them exploited object motions. In this work, a deep spatial and temporal network (DSTN) is developed for visual tracking by explicitly exploiting both the object representations from each frame and their dynamics along multiple frames in a video, such that it can seamlessly integrate the object appearances with their motions to produce compact object appearances and capture their temporal variations effectively. Our DSTN method, which is deployed into a tracking pipeline in a coarse-to-fine form, can perceive the subtle differences on spatial and temporal variations of the target (object being tracked), and thus it benefits from both off-line training and online fine-tuning. We have also conducted our experiments over four largest tracking benchmarks, including OTB-2013, OTB-2015, VOT2015, and VOT2017, and our experimental results have demonstrated that our DSTN method can achieve competitive performance as compared with the state-of-the-art techniques. The source code, trained models, and all the experimental results of this work will be made public available to facilitate further studies on this problem.

Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition.

In this paper, a layer-wise mixture model (LMM) is developed to support hierarchical visual recognition, where a Bayesian approach is used to automatically adapt the visual hierarchy to the progressive improvements of the deep network along the time. Our LMM algorithm can provide an end-to-end approach for jointly learning: (a) the deep network for achieving more discriminative deep representations for object classes and their inter-class visual similarities; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate assignment and organization of large numbers of object classes. By learning the tree classifier, the deep network and the visual hierarchy adaptation jointly in an end-to-end manner, our LMM algorithm can achieve higher accuracy rates on hierarchical visual recognition. Our experiments are carried on ImageNet1K and ImageNet10K image sets, which have demonstrated that our LMM algorithm can achieve very competitive results on the accuracy rates as compared with the baseline methods.

[Diagnosis and treatment of testicular and epididymal diseases in Tibetan area].

OBJECTIVE: To explore the clinical types, prevention and treatment of testicular and epididymal diseases in the Tibetan area. METHODS: We retrospectively analyzed the clinical data of 105 cases of testicular or epididymal diseases treated in our department from 2007 to 2009. All the patients were permanent inhabitants in Tibet. RESULTS: Among the 105 patients, the main types of testicular and epididymal diseases were tuberculosis (27 cases) and tumor (21 cases). And 99% of the patients were Tibetan farmers and herdsmen. CONCLUSION: Tibetan farmers and herdsmen have a poor knowledge about testicular and epididymal diseases and their prevention and treatment. Clinicians and related institutions need to strengthen health education on these diseases in Tibetan area to improve the early awareness, early diagnosis, early prevention and early treatment of testicular and epididymal diseases.