Hanging Noncalculous Gallbladder

The removal of acalculous and not acutely inflamed gall-bladder in patients with typical biliary pain remains a questionable procedure. This study was conducted to present our experience. In the period 1982- 90, 1089 cases of calculous and acalculous gallbladder disease were treated in our clinic. In this period, 27 patients were subjected to cholecystectomy because of an acalculous, non inflamed gallbladder which was elongated lying in an abnormal position with a long cystic duct. The mean duration ofsymptoms supportive of cholelithiasis, was 5 years. Oral cholecystogram and ultrasonography led to the diagnosis and other causes ofchronic abdominal pain were excluded. There were 13 lumbar, 9 pelvic and 5 iliac gallbladders, with poor function in 20 of them. During cholecystectomy, the organ was invested by peritoneum and suspended in 7 cases from a mesentery. On pathological examination mild chronic inflammation was reported in 19 cases and minimal changes in 8. The minimum follow up was one year and the maximum 9 years. Complete relief of symptoms was achieved in all the cases. In conclusion, cholecystectomy should be offered in these symptomatic "hanging" gallbladders

External Biliary Fistula

We report 210 cases of external biliary fistula treated in our clinics between 1970–1992. In 7 cases, fistulas were formed after iatrogenic bile duct injury, in 4 cases after exploration of common bile duct, in 4 cases due to disruption of biliary-intestinal anastomosis, and in 2 cases due to liver trauma. In 85 cases bile leak was observed after cholecystomy, in 103 cases after hydatid disease surgery, and in 4 cases after the passage of P.T.C. catheter. In one patient the appearance of the fistula was due to spontaneous discharge of a gallbladder empyema. 173 cases were managed conservatively, and 37 cases surgically

Targeted alpha-radionuclide therapy of functionally critically located gliomas with 213Bi-DOTA-[Thi8,Met(O2)11]-substance P: a pilot trial

Purpose: Functionally critically located gliomas represent a challenging subgroup of intrinsic brain neoplasms. Standard therapeutic recommendations often cannot be applied, because radical treatment and preservation of neurological function are contrary goals. The successful targeting of gliomas with locally injected beta radiation-emitting 90Y-DOTAGA-substance P has been shown previously. However, in critically located tumours, the mean tissue range of 5mm of 90Y may seriously damage adjacent brain areas. In contrast, the alpha radiation-emitting radionuclide 213Bi with a mean tissue range of 81µm may have a more favourable toxicity profile. Therefore, we evaluated locally injected 213Bi-DOTA-substance P in patients with critically located gliomas as the primary therapeutic modality. Methods: In a pilot study, we included five patients with critically located gliomas (WHO grades II-IV). After diagnosis by biopsy, 213Bi-DOTA-substance P was locally injected, followed by serial SPECT/CT and MR imaging and blood sampling. Besides feasibility and toxicity, the functional outcome was evaluated. Results: Targeted radiopeptide therapy using 213Bi-DOTA-substance P was feasible and tolerated without additional neurological deficit. No local or systemic toxicity was observed. 213Bi-DOTA-substance P showed high retention at the target site. MR imaging was suggestive of radiation-induced necrosis and demarcation of the tumours, which was validated by subsequent resection. Conclusion: This study provides proof of concept that targeted local radiotherapy using 213Bi-DOTA-substance P is feasible and may represent an innovative and effective treatment for critically located gliomas. Primarily non-operable gliomas may become resectable with this treatment, thereby possibly improving the prognosi

Unsupervised Video Summarization via Attention-Driven Adversarial Learning

This paper presents a new video summarization approach that integrates an attention mechanism to identify the signi cant parts of the video, and is trained unsupervisingly via generative adversarial learning. Starting from the SUM-GAN model, we rst develop an improved version of it (called SUM-GAN-sl) that has a signi cantly reduced number of learned parameters, performs incremental training of the model's components, and applies a stepwise label-based strategy for updating the adversarial part. Subsequently, we introduce an attention mechanism to SUM-GAN-sl in two ways: i) by integrating an attention layer within the variational auto-encoder (VAE) of the architecture (SUM-GAN-VAAE), and ii) by replacing the VAE with a deterministic attention auto-encoder (SUM-GAN-AAE). Experimental evaluation on two datasets (SumMe and TVSum) documents the contribution of the attention auto-encoder to faster and more stable training of the model, resulting in a signi cant performance improvement with respect to the original model and demonstrating the competitiveness of the proposed SUM-GAN-AAE against the state of the art

Data Mining in MRO

Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementation

Data Mining in MRO

Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementation