Outcomes after multi-modality treatment of musculoskeletal tumours

Outcomes of treatment of musculoskeletal tumours are evaluated for effectiveness of chemotherapy protocols, function obtained after surgery and survival after treatment. Quality of life achieved after multi-modality treatment is dependent on a combination of all of these factors. Quality of life varies significantly along the treatment pathway, and continuously through the life of a patient. The patient's perception of outcome is based on the total effect of the disease and its treatment, rather than necessarily focussing on separate items of treatment. We have found that visual analogue scales can be used effectively to gauge the patient's perception of their quality of life. Such a method has shown that, overall, perceptions of quality of life seem to be better for those patients who have undergone successful limb salvage surgery when compared with those who have undergone amputation, but the differences are not as great as might be assumed.

An evaluation of the pedestrian classification in a multi-domain multi-modality setup

The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains. The experiments show that features families, intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP) and histogram of oriented gradients (HOG), computed from FIR and visible domains are highly complementary, but their relative performance varies across different modalities. In our experiments, the FIR domain has proven superior to the visible one for the task of pedestrian classification, but the overall best results are obtained by a multi-domain multi-modality multi-feature fusion.

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Hybrid warfare – just a twist of compound warfare? : views on warfare from the United States Armed Forces perspective

The wars the Western armies are involved with today are different from those that were fought in the end of 20th century. To explain this change, the Western military thinkers have come up with various different types of definitions of warfare over the last 30 years, each describing the tendencies involved in the conflicts of the time. The changing nature of conflicts surfaced a new term – hybrid warfare. The term was to describe and explain the multi-modality and complexity of modern day conflict. This thesis seeks the answer for the question: what is the development of thought behind hybrid warfare? In this thesis the Vietnam War (1965-1975) is used as an example of compound warfare focusing on the American involvement in the war. The Second Lebanon War (2006) serves as an example of hybrid warfare. Both case studies include an irregular opposing force, namely National Liberation Front in Vietnam War and Hezbollah in the Second Lebanon War. These two case studies are compared with the term full spectrum operations introduced in the current U.S. Department of Army Field Manual No. 3-0 Operations to see the differences and similarities of each term. The perspective of this thesis is the American point of view. This thesis concludes that hybrid warfare, compound warfare and full spectrum operations are very similar. The first two terms are included in the last one. Although hybrid warfare is not officially defined, it will most likely remain to be used in the discussion in the future, since hybrid wars and hybrid threats are officially accepted terms.

Chemotherapy versus support cancer treatment in advanced gastric cancer: a meta-analysis

The aim of the present study was to compare the efficacy of chemotherapy and support treatment in patients with advanced non-resectable gastric cancer in a systematic review and meta-analysis of randomized clinical trials that included a comparison of chemotherapy and support care treatment in patients diagnosed with gastric adenocarcinoma, regardless of their age, gender or place of treatment. The search strategy was based on the criteria of the Cochrane Base, using the following key words: 1) randomized clinical trials and antineoplastic combined therapy or gastrointestinal neoplasm, 2) stomach neoplasm and drug therapy, 3) clinical trial and multi-modality therapy, 4) stomach neoplasm and drug therapy or quality of life, 5) double-blind method or clinical trial. The search was carried out using the Cochrane, Medline and Lilacs databases. Five studies fulfilled the inclusion criteria, for a total of 390 participants, 208 (53%) receiving chemotherapy, 182 (47%) receiving support care treatment and 6 losses (1.6%). The 1-year survival rate was 8% for support care and 20% for chemotherapy (RR = 2.14, 95% CI = 1.00-4.57, P = 0.05); 30% of the patients in the chemotherapy group and 12% in the support care group attained a 6-month symptom-free period (RR = 2.33, 95% CI = 1.41-3.87, P < 0.01). Quality of life evaluated after 4 months was significantly better for the chemotherapy patients (34%; RR = 2.07, 95% CI = 1.31-3.28, P < 0.01) with tumor mass reduction (RR = 3.32, 95% CI = 0.77-14.24, P = 0.1). Chemotherapy increased the 1-year survival rate of the patients and provided a longer symptom-free period of 6 months and an improvement in quality of life.

A Diagnostic Assessment Of Evolutionary Multiobjective Optimization For Water Resources Systems

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

Precise Near-to-Head Acoustics with Binaural Synthesis

For enhanced immersion into a virtual scene more than just the visual sense should be addressed by a Virtual Reality system. Additional auditory stimulation appears to have much potential, as it realizes a multisensory system. This is especially useful when the user does not have to wear any additional hardware, e.g., headphones. Creating a virtual sound scene with spatially distributed sources requires a technique for adding spatial cues to audio signals and an appropriate reproduction. In this paper we present a real-time audio rendering system that combines dynamic crosstalk cancellation and multi-track binaural synthesis for virtual acoustical imaging. This provides the possibility of simulating spatially distributed sources and, in addition to that, near-to-head sources for a freely moving listener in room-mounted virtual environments without using any headphones. A special focus will be put on near-to-head acoustics, and requirements in respect of the head-related transfer function databases are discussed.

Long-term safety and feasibility of three-vessel multimodality intravascular imaging in patients with ST-elevation myocardial infarction: the IBIS-4 (integrated biomarker and imaging study) substudy.

We assessed the feasibility and the procedural and long-term safety of intracoronary (i.c) imaging for documentary purposes with optical coherence tomography (OCT) and intravascular ultrasound (IVUS) in patients with acute ST-elevation myocardial infarction (STEMI) undergoing primary PCI in the setting of IBIS-4 study. IBIS4 (NCT00962416) is a prospective cohort study conducted at five European centers including 103 STEMI patients who underwent serial three-vessel coronary imaging during primary PCI and at 13 months. The feasibility parameter was successful imaging, defined as the number of pullbacks suitable for analysis. Safety parameters included the frequency of peri-procedural complications, and major adverse cardiac events (MACE), a composite of cardiac death, myocardial infarction (MI) and any clinically-indicated revascularization at 2 years. Clinical outcomes were compared with the results from a cohort of 485 STEMI patients undergoing primary PCI without additional imaging. Imaging of the infarct-related artery at baseline (and follow-up) was successful in 92.2 % (96.6 %) of patients using OCT and in 93.2 % (95.5 %) using IVUS. Imaging of the non-infarct-related vessels was successful in 88.7 % (95.6 %) using OCT and in 90.5 % (93.3 %) using IVUS. Periprocedural complications occurred <2.0 % of OCT and none during IVUS. There were no differences throughout 2 years between the imaging and control group in terms of MACE (16.7 vs. 13.3 %, adjusted HR1.40, 95 % CI 0.77-2.52, p = 0.27). Multi-modality three-vessel i.c. imaging in STEMI patients undergoing primary PCI is consistent a high degree of success and can be performed safely without impact on cardiovascular events at long-term follow-up.

Theoretical foundations of neural networks

Neural networks have often been motivated by superficial analogy with biological nervous systems. Recently, however, it has become widely recognised that the effective application of neural networks requires instead a deeper understanding of the theoretical foundations of these models. Insight into neural networks comes from a number of fields including statistical pattern recognition, computational learning theory, statistics, information geometry and statistical mechanics. As an illustration of the importance of understanding the theoretical basis for neural network models, we consider their application to the solution of multi-valued inverse problems. We show how a naive application of the standard least-squares approach can lead to very poor results, and how an appreciation of the underlying statistical goals of the modelling process allows the development of a more general and more powerful formalism which can tackle the problem of multi-modality.

Parametric study of femtosecond inscription of microstructures for OCT artefact fabrication

As optical coherence tomography (OCT) becomes widespread, validation and characterization of systems becomes important. Reference standards are required to qualitatively and quantitatively measure the performance between difference systems. This would allow the performance degradation of the system over time to be monitored. In this report, the properties of the femtosecond inscribed structures from three different systems for making suitable OCT characterization artefacts (phantoms) are analyzed. The parameter test samples are directly inscribed inside transparent materials. The structures are characterized using an optical microscope and a swept-source OCT. The high reproducibility of the inscribed structures shows high potential for producing multi-modality OCT calibration and characterization phantoms. Such that a single artefact can be used to characterize multiple performance parameters such the resolution, linearity, distortion, and imaging depths. © 2012 SPIE.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Robust indoor speaker recognition in a network of audio and video sensors

Situational awareness is achieved naturally by the human senses of sight and hearing in combination. Automatic scene understanding aims at replicating this human ability using microphones and cameras in cooperation. In this paper, audio and video signals are fused and integrated at different levels of semantic abstractions. We detect and track a speaker who is relatively unconstrained, i.e., free to move indoors within an area larger than the comparable reported work, which is usually limited to round table meetings. The system is relatively simple: consisting of just 4 microphone pairs and a single camera. Results show that the overall multimodal tracker is more reliable than single modality systems, tolerating large occlusions and cross-talk. System evaluation is performed on both single and multi-modality tracking. The performance improvement given by the audio–video integration and fusion is quantified in terms of tracking precision and accuracy as well as speaker diarisation error rate and precision–recall (recognition). Improvements vs. the closest works are evaluated: 56% sound source localisation computational cost over an audio only system, 8% speaker diarisation error rate over an audio only speaker recognition unit and 36% on the precision–recall metric over an audio–video dominant speaker recognition method.

First serial assessment at 6 months and 2 years of the second generation of absorb everolimus-eluting bioresorbable vascular scaffold: a multi-imaging modality study

Nonserial observations have shown this bioresorbable scaffold to have no signs of area reduction at 6 months and recovery of vasomotion at 1 year. Serial observations at 6 months and 2 years have to confirm the absence of late restenosis or unfavorable imaging outcomes.