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).

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.

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.

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.

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.

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.

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.

[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.