Hardware/Software Co-design Applied to Reed-Solomon Decoding for the DMB Standard

This paper addresses the implementation of Reed- Solomon decoding for battery-powered wireless devices. The scope of this paper is constrained by the Digital Media Broadcasting (DMB). The most critical element of the Reed-Solomon algorithm is implemented on two different reconfigurable hardware architectures: an FPGA and a coarse-grained architecture: the Montium, The remaining parts are executed on an ARM processor. The results of this research show that a co-design of the ARM together with an FPGA or a Montium leads to a substantial decrease in energy consumption. The energy consumption of syndrome calculation of the Reed- Solomon decoding algorithm is estimated for an FPGA and a Montium by means of simulations. The Montium proves to be more efficient

Salvaging Affymetrix probes after probe-level re-annotation

Background: Affymetrix GeneChips can be re-annotated at the probe-level by breaking up the original probe-sets and recomposing new probe-sets based on up-to-date genomic knowledge, such as available in Entrez Gene. This results in custom Chip Description Files (CDF). Using these custom CDFs improves the quality of the data and thus the results of related gene expression studies. However, 44-71% of the probes on a GeneChip are lost in this re-annotation process. Although generally aimed at less known genes, losing these probes obviously means a substantial loss of expensive experiment data. Biologists are therefore very reluctant to adopt this approach. Findings: We aimed to re-introduce the non-affected Affymetrix probe-sets after these re-annotation procedures. For this, we developed an algorithm (CDF-Merger) and applied it to standard Affymetrix CDFs and custom Brainarray CDFs to obtain Hybrid CDFs. Thus, salvaging lost Affymetrix probes with our CDF-Merger restored probe content up to 94%. Because the salvaged probes (up to 54% of the probe content on the arrays) represent less-reliable probe-sets, we made the origin of all probe-set definitions traceable, so biologists can choose at any time in their analyses, which subset of probe-sets they want to use. Conclusion: The availability of up-to-date Hybrid CDFs plus R environment allows for easy implementation of our approach

Novel applications of therapeutic hypothermia: report of three cases

Therapeutic hypothermia can provide neuroprotection in various situations where global or focal neurological injury has occurred. Hypothermia has been shown to be effective in a large number of animal experiments. In clinical trials, hypothermia has been used in patients with postanoxic injury following cardiopulmonary resuscitation, in traumatic brain injury with high intracranial pressure, in the perioperative setting during various surgical procedures and for various other indications. There is thus evidence that hypothermia can be effective in various situations of neurological injury, although a number of questions remain unanswered. We describe three patients with unusual causes of neurological injury, whose clinical situation was in fundamental aspects analogous to conditions where hypothermia has been shown to be effective

Applying a User-centred Approach to Interactive Visualization Design

Analysing users in their context of work and finding out how and why they use different information resources is essential to provide interactive visualisation systems that match their goals and needs. Designers should actively involve the intended users throughout the whole process. This chapter presents a user-centered approach for the design of interactive visualisation systems. We describe three phases of the iterative visualisation design process: the early envisioning phase, the global specification hase, and the detailed specification phase. The whole design cycle is repeated until some criterion of success is reached. We discuss different techniques for the analysis of users, their tasks and domain. Subsequently, the design of prototypes and evaluation methods in visualisation practice are presented. Finally, we discuss the practical challenges in design and evaluation of collaborative visualisation environments. Our own case studies and those of others are used throughout the whole chapter to illustrate various approaches

Robot-assisted laparoscopic surgery of the infrarenal aorta: The early learning curve

Background Recently introduced robot-assisted laparoscopic surgery (RALS) facilitates endoscopic surgical manipulation and thereby reduces the learning curve for (advanced) laparoscopic surgery. We present our learning curve with RALS for aortobifemoral bypass grafting as a treatment for aortoiliac occlusive disease. Methods Between February 2002 and May 2005, 17 patients were treated in our institution with robot-assisted laparoscopic aorto-bifemoral bypasses. Dissection was performed laparoscopically and the robot was used to make the aortic anastomosis. Operative time, clamping time, and anastomosis time, as well as blood loss and hospital stay, were used as parameters to evaluate the results and to compare the first eight (group 1) and the last nine patients (group2). Results Total median operative, clamping, and anastomosis times were 365 min (range: 225–589 min), 86 min (range: 25–205 min), and 41 min (range: 22–110 min), respectively. Total median blood loss was 1,000 ml (range: 100–5,800 ml). Median hospital stay was 4 days (range: 3–57 days). In this series 16/18 anastomoses were completed with the use of the robotic system. Three patients were converted (two in group 1, one in group 2), and one patient died postoperatively (group 1). Median clamping and anastomosis times were significantly different between groups 1 and 2 (111 min [range: 85–205 min] versus 57.5 min [range: 25–130 min], p < 0.01 and 74 min [range: 40–110 min] versus 36 min [range: 22–69 min], p < 0.01, respectively) Total operative time, blood loss, and hospital stay showed no significant difference between groups 1 and 2. Conclusions Robot-assisted aortic anastomosis was shown to have a steep learning curve with considerable reduction of clamping and anastomosis times. However, due to a longer learning curve for laparoscopic dissection of the abdominal aorta, operation times were not significantly shortened. Even with robotic assistance, laparoscopic aortoiliac surgery remains a complex procedure