Recent Advances In Visualization Research Articles

The Knosp Criteria Revisited: 3-Dimensional Volumetric Analysis as a Predictive Tool for Extent of Resection in Complex Endoscopic Pituitary Surgery.

The Knosp criteria have been the historical standard for predicting cavernous sinus invasion, and therefore extent of surgical resection, of pituitary macroadenomas. Few studies have sought to reappraise the utility of this tool after recent advances in visualization and modeling of tumors in complex endoscopic surgery. To evaluate our proposed alternative method, using 3-dimensional (3D) volumetric imaging, and whether it can better predict extent of resection in nonfunctional pituitary adenomas. Patients who underwent endoscopic transsphenoidal resection of pituitary macroadenomas at our institution were reviewed. Information was collected on neurological, endocrine, and visual function. Volumetric segmentation was performed using 3D Slicer software. Relationship of tumor volume, clinical features, and Knosp grade on extent of resection was examined. One hundred forty patients were identified who had transsphenoidal resection of nonfunctional pituitary adenomas. Macroadenomas had a median volume of 6 cm 3 (IQR 3.4-8.7), and 17% had a unilateral Knosp grade of at least 3B. On multiple logistic regression, only smaller log-transformed preoperative tumor volume was independently associated with increased odds of gross total resection (GTR; odds ratio: 0.27, 95% CI: 0.07-0.89, P < .05) when controlling for tumor proliferative status, age, and sex (area under the curve 0.67). The Knosp criteria did not independently predict GTR in this cohort ( P > .05, area under the curve 0.46). Increasing use of volumetric 3D imaging may better anticipate extent of resection compared with the Knosp grade metric and may have a greater positive predictive value for GTR. More research is needed to validate these findings and implement them using automated methods.

Hitting the Wall-Sensing and Signaling Pathways Involved in Plant Cell Wall Remodeling in Response to Abiotic Stress.

Plant cells are surrounded by highly dynamic cell walls that play important roles regulating aspects of plant development. Recent advances in visualization and measurement of cell wall properties have enabled accumulation of new data about wall architecture and biomechanics. This has resulted in greater understanding of the dynamics of cell wall deposition and remodeling. The cell wall is the first line of defense against different adverse abiotic and biotic environmental influences. Different abiotic stress conditions such as salinity, drought, and frost trigger production of Reactive Oxygen Species (ROS) which act as important signaling molecules in stress activated cellular responses. Detection of ROS by still-elusive receptors triggers numerous signaling events that result in production of different protective compounds or even cell death, but most notably in stress-induced cell wall remodeling. This is mediated by different plant hormones, of which the most studied are jasmonic acid and brassinosteroids. In this review we highlight key factors involved in sensing, signal transduction, and response(s) to abiotic stress and how these mechanisms are related to cell wall-associated stress acclimatization. ROS, plant hormones, cell wall remodeling enzymes and different wall mechanosensors act coordinately during abiotic stress, resulting in abiotic stress wall acclimatization, enabling plants to survive adverse environmental conditions.

Recent advances in visualization, imaging, and navigation in hepatobiliary and pancreatic sciences

Recent introduction of multi-detector CT (MDCT) and high-speed magnetic resonance (MR) imaging have dramatically advanced visualization and imaging technology in diagnostic and therapeutic strategy in hepatobiliary pancreatic disease. However, image diagnostics have progressed with a background of the essence of anatomy, pathology, and physiology. It is important to object the reflection of the patient's condition and pathology of each disease and remove pattern recognition in what they were depicted as an image. Visualization plays another important role in various medical diagnostics. Trends in scientific visualization will depend on advancements in molecular technology and computer hardware as well as trends in engineering disciplines. In this special issue, the recent advances in visualization and imaging in the field of hepatobiliary and pancreatic sciences are featured including application of advanced visualization techniques, data management, data compression, feature extraction. We discuss the potential benefits of new technologies and procedures in hepatobiliary and pancreatic areas, that are circulating tumor cells, MR imaging for hepatocellular carcinoma, indocyanine green using fluorescence under infrared light observation, carbon dioxide enhanced MDCT virtual cholangiopancreatography, endoscopic ultrasonography-guided biliary drainage, natural orifice translumenal endoscopic surgery, MR-laparoscopy, and image overlay navigation surgery by OsiriX. Some of the recent trends are discussed in terms of visualization and imaging in hepatobiliary and pancreatic sciences. The goal in using visualization is to assist existing scientific procedures by providing new insight through visual representation.

Cellulose synthesis: a complex complex

Cellulose is the world's most abundant biopolymer and a key structural component of the plant cell wall. Cellulose is comprised of hydrogen-bonded beta-1,4-linked glucan chains that are synthesized at the plasma membrane by large cellulose synthase (CESA) complexes. Recent advances in visualization of fluorescently labelled complexes have facilitated exploration of regulatory modes of cellulose production. For example, several herbicides, such as isoxaben and 2,6-dichlorobenzonitrile that inhibit cellulose production appear to affect different aspects of synthesis. Dual-labelling of cytoskeletal components and CESAs has revealed dynamic feedback regulation between cellulose synthesis and microtubule orientation and organization. In addition, fluorescently tagged CESA2 subunits may substitute for another subunit, CESA6, which suggests both plasticity and specificity for one of the components of the CESA complex.

Mineral exploration using modern data mining techniques

Colin T. Barnett and Peter M. Williams discuss how new data mining concepts such as visualization and probabilistic modelling can provide the key to improved exploration success in the mining industry. The article is a slightly abridged version of a contribution to the latest special publication from the Society of Economic Geologists, which this year celebrates its 100th anniversary. Following an analysis of recent performance of the gold industry, Schodde (2004) concludes that gold exploration is currently only a break-even proposition. In the last 20 years, the average cost of a new discovery has increased nearly fourfold, and the average size of a deposit has shrunk by 30%. The average rate of return for the industry has been 5–7 %, which is the same order as the cost of capital. Why should this be, and what can be done about it? Paterson (2003) observes that, historically, discoveries have taken place in waves, after the introduction of new methods or advances in the understanding of ore genesis. For instance, discovery rates jumped sharply between 1950 and 1975, following the development of new methods and instruments in exploration geophysics and geochemistry. In the last quarter century, there has been a comparable surge in digital electronics and computing that has resulted in a great increase in the quality and quantity of exploration data. Yet these developments, on their own, evidently were not sufficient to reverse a downward trend in the discovery rate during this period. So where should we look for new methods to drive the next wave of discoveries? It seems we are now collecting data faster than we can absorb it. But this is also true in bioinformatics with genome sequencing, or in making sense of other huge corpora of data available on the Internet. It is the thesis of this paper that the new methods are to be found in ways currently being developed for extracting meaningful information from data. Specifically, we should look to recent developments in visualization and data mining. Many data mining techniques are inspired by analogy with human intelligence and suggest a new idea of computing. Conventional computing is restricted to tasks for which a human can find an algorithm. Living creatures, however, are programmed by experience, not by spelling out every step of a process. Data mining is therefore about discovering how machines, like humans, might learn from data. Machine learning broadly distinguishes between supervised and unsupervised learning. Supervised learning, or learning from examples, requires sufficiently many labelled cases to be available. These form a set of known inputoutput pairs, usually called a training set, and the task is to learn the true input-output mapping from these examples. In the exploration case, the training set typically consists of a collection of known deposits and known barren regions. For unsupervised learning, we know only the inputs and not the corresponding outputs. The aim, then, is to search for ‘interesting’ features of the data, such as clusters or outliers, or for some latent structure which would account for how they were generated. In this paper only the case of supervised learning is considered, but see Williams (2002) for some further discussion of both approaches. The paper begins with a review of recent advances in visualization and supervised learning techniques, such as neural network models. The use of these ideas is then demonstrated in a study of gold exploration in the Walker Lane in the western United States. Finally it is shown how the results can be applied to a quantitative analysis of exploration risk, and how improved targeting accuracy can reduce exploration costs and increase the probability of success.