Percutaneous Catheter Dilatation of Benign Ureteroenteric Anastomotic Strictures Followed or not by Retrograde Transconduit Placement of a Catheter: Long Term Results

BACKGROUND: We report our experience on multiple balloon dilatations for benign ureteroenteric anastomotic strictures after total cystectomy and urinal deviation by ileal conduit, followed or not by retrograde placement of a permanent catheter through the stoma of the ileal conduit. PATIENTS AND METHODS: Patients were classified in two groups: Group A included patients treated only by multiple balloon dilatations and Group B patients in whom multiple dilatations were followed by retrograde insertion of a permanent catheter through the stoma of the ileal conduit, which then had to be replaced regularly. Records of survival and patency rates were recorded. RESULTS: Twenty patients with 24 benign ureteroenteric anastomotic strictures referred to radiology department. Long-term results were available in only 15 patients, who finally included in the study. In Group A long term follow-up was achieved in five patients. Mean primary patency time of stenoses (interval between initial dilatation and recurrence) was 33.2 months. This time-period proved to be the same as the survival time of Group A patients, since all five patients eventually succumbed to the underlying disease or other reasons. In Group B, 6 patients are still alive and 4 patients eventually succumbed to the underlying disease or other reasons. Mean primary patency time of stenoses was 38.1 months. CONCLUSIONS: Balloon dilatations of benign ureteroenteric anastomotic strictures, due to radical cystectomy and urinal deviation by ileal conduit, were technically successful in all cases. Patency rate was comparable in the two study groups. However, regular catheter replacement through the ileal conduit is well tolerated and gives a sense of security to both patient and physician

Finding Semantically Related Videos in Closed Collections

Modern newsroom tools offer advanced functionality for automatic and semi-automatic content collection from the web and social media sources to accompany news stories. However, the content collected in this way often tends to be unstructured and may include irrelevant items. An important step in the verification process is to organize this content, both with respect to what it shows, and with respect to its origin. This chapter presents our efforts in this direction, which resulted in two components. One aims to detect semantic concepts in video shots, to help annotation and organization of content collections. We implement a system based on deep learning, featuring a number of advances and adaptations of existing algorithms to increase performance for the task. The other component aims to detect logos in videos in order to identify their provenance. We present our progress from a keypoint-based detection system to a system based on deep learning

Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future

Molecular Mechanisms Associated with Nicotine Pharmacology and Dependence.

Tobacco dependence is a leading cause of preventable disease and death worldwide. Nicotine, the main psychoactive component in tobacco cigarettes, has also been garnering increased popularity in its vaporized form, as derived from e-cigarette devices. Thus, an understanding of the molecular mechanisms underlying nicotine pharmacology and dependence is required to ascertain novel approaches to treat drug dependence. In this chapter, we review the field's current understanding of nicotine's actions in the brain, the neurocircuitry underlying drug dependence, factors that modulate the function of nicotinic acetylcholine receptors, and the role of specific genes in mitigating the vulnerability to develop nicotine dependence. In addition to nicotine's direct actions in the brain, other constituents in nicotine and tobacco products have also been found to alter drug use, and thus, evidence is provided to highlight this issue. Finally, currently available pharmacotherapeutic strategies are discussed, along with an outlook for future therapeutic directions to achieve to the goal of long-term nicotine cessation

Text classification with semantically enriched word embeddings

The recent breakthroughs in deep neural architectures across multiple machine learning fields have led to the widespread use of deep neural models. These learners are often applied as black-box models that ignore or insufficiently utilize a wealth of preexisting semantic information. In this study, we focus on the text classification task, investigating methods for augmenting the input to deep neural networks (DNNs) with semantic information. We extract semantics for the words in the preprocessed text from the WordNet semantic graph, in the form of weighted concept terms that form a semantic frequency vector. Concepts are selected via a variety of semantic disambiguation techniques, including a basic, a part-of-speech-based, and a semantic embedding projection method. Additionally, we consider a weight propagation mechanism that exploits semantic relationships in the concept graph and conveys a spreading activation component. We enrich word2vec embeddings with the resulting semantic vector through concatenation or replacement and apply the semantically augmented word embeddings on the classification task via a DNN. Experimental results over established datasets demonstrate that our approach of semantic augmentation in the input space boosts classification performance significantly, with concatenation offering the best performance. We also note additional interesting findings produced by our approach regarding the behavior of term frequency - inverse document frequency normalization on semantic vectors, along with the radical dimensionality reduction potential with negligible performance loss. © 2021 Cambridge University Press. All rights reserved

Crowdsourcing in Single-document Summary Evaluation: the Argo Way

In this chapter, we present the Argo crowdsourcing system and related functionalities for crowd-based summary evaluation. Then, we evaluate the pros and cons of three different approaches to single-document, multi-lingual summary evaluation: i) traditional, expertbased; ii) crowdsourcing, majority-based; and iii) crowdsourcing, Argobased. The evaluation is performed over two languages of the MultiLing-2015 Single Document Summarization (MSS) Dataset, examining result under different aspects

Activity of oxacillin versus that of vancomycin against oxacillin-susceptible mecA-positive Staphylococcus aureus clinical isolates evaluated by population analyses, time-kill assays, and a murine thigh infection model

We compared the activity of dicloxacillin with that of vancomycin against 15 oxacillin-susceptible, methicillin-resistant Staphylococcus aureus (OS-MRSA) clinical isolates. By population analyses, we found that 6 OS-MRSA isolates were able to grow in the presence of up to 8 μg/ml dicloxacillin and 9 isolates were able to grow in 12 to >32 μg/ml dicloxacillin; all isolates grew in up to 2 μg/ml vancomycin. Both drugs exhibited similar bactericidal activities. In experimental infections, the therapeutic efficacy of dicloxacillin was significant (P < 0.05 versus untreated controls) in 10 OS-MRSA isolates and vancomycin was effective (P < 0.05) against 12 isolates; dicloxacillin had an efficacy that was comparable to that of vancomycin (P > 0.05) in 8 isolates. The favorable response to dicloxacillin treatment might suggest that antistaphylococcal penicillins could be used against OS-MRSA infections. Copyright © 2012, American Society for Microbiology. All Rights Reserved

Activity of Oxacillin versus That of Vancomycin against Oxacillin-Susceptible mecA-Positive Staphylococcus aureus Clinical Isolates Evaluated by Population Analyses, Time-Kill Assays, and a Murine Thigh Infection Model

We compared the activity of dicloxacillin with that of vancomycin against 15 oxacillin-susceptible, methicillin-resistant Staphylococcus aureus (OS-MRSA) clinical isolates. By population analyses, we found that 6 OS-MRSA isolates were able to grow in the presence of up to 8 mu g/ml dicloxacillin and 9 isolates were able to grow in 12 to >32 mu g/ml dicloxacillin; all isolates grew in up to 2 mu g/ml vancomycin. Both drugs exhibited similar bactericidal activities. In experimental infections, the therapeutic efficacy of dicloxacillin was significant (P0.05) in 8 isolates. The favorable response to dicloxacillin treatment might suggest that antistaphylococcal penicillins could be used against OS-MRSA infections