How is vaccine R&D pipeline strategy going to evolve for pharm industry…?

The pharmaceutical industry has gone through huge changes since the early 2000s. Faced with patent expirations, increased cost of development, reduced R&D productivity and increased price pressure, the pharmaceutical industry had continuously to rethink its model to keep its success, and continues to do so. In the presentation, we’ll review how the model has evolved over time, and continues to evolve from Pharma 1.0 (traditional model), through Pharma 2.0 (2005-2010) to pharma 3.0 (\u3e 2020). National decision makers and Health care payers in the industrialized world are heavily focusing on the importance of cost effectiveness of public health interventions. We’ll discuss how, in this context, the importance of prevention and vaccines will grow as pressure on health care budgets increases, and the global population grows and ages. Breakthrough in better understanding of the immune pathways, availability of new vaccine technology platforms to successfully trigger these pathways, will be key contributors to vaccination become one of the key pillars of future healthcare, both in prevention, and, later on, in disease interception

Cord UIs: Controlling Devices with Augmented Cables

Cord UIs are sensorial augmented cords that allow for simple metaphor-rich interactions to interface with their connected devices. Cords offer a large underexplored space for interactions as well as unique properties and a diverse set of metaphors that make them potentially interesting tangible interfaces. We use cords as input devices and explore different interactions like tying knots, stretching, pinching and kinking to control the flow of data and/or power. We also look at ways to use objects in combination with augmented cords to manipulate data or properties of a device. For instance, placing a clamp on a cable can obstruct the audio signal to the headphones. Using special materials such as piezo copolymer cables and stretchable cords we built five working prototypes to showcase the interactions described in this paper

The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation

Purpose: The present study aimed to measure the effect of a morphometric atlas selection strategy on the accuracy of multi-atlas-based BP autosegmentation using the commercially available software package ADMIRE (R) and to determine the optimal number of selected atlases to use. Autosegmentation accuracy was measured by comparing all generated automatic BP segmentations with anatomically validated gold standard segmentations that were developed using cadavers. Materials and methods: Twelve cadaver computed tomography (CT) atlases were included in the study. One atlas was selected as a patient in ADMIRE (R), and multi-atlas-based BP autosegmentation was first performed with a group of morphometrically preselected atlases. In this group, the atlases were selected on the basis of similarity in the shoulder protraction position with the patient. The number of selected atlases used started at two and increased up to eight. Subsequently, a group of randomly chosen, non-selected atlases were taken. In this second group, every possible combination of 2 to 8 random atlases was used for multi-atlas-based BP autosegmentation. For both groups, the average Dice similarity coefficient (DSC), Jaccard index (JI) and Inclusion index (INI) were calculated, measuring the similarity of the generated automatic BP segmentations and the gold standard segmentation. Similarity indices of both groups were compared using an independent sample t-test, and the optimal number of selected atlases was investigated using an equivalence trial. Results: For each number of atlases, average similarity indices of the morphometrically selected atlas group were significantly higher than the random group (p<0,05). In this study, the highest similarity indices were achieved using multi-atlas autosegmentation with 6 selected atlases (average DSC = 0,598; average JI = 0,434; average INI = 0,733). Conclusions: Morphometric atlas selection on the basis of the protraction position of the patient significantly improves multi-atlas-based BP autosegmentation accuracy. In this study, the optimal number of selected atlases used was six, but for definitive conclusions about the optimal number of atlases and to improve the autosegmentation accuracy for clinical use, more atlases need to be included

Optimal number of atlases and label fusion for automatic multi-atlas-based brachial plexus contouring in radiotherapy treatment planning

Background: The present study aimed to define the optimal number of atlases for automatic multi-atlas-based brachial plexus (BP) segmentation and to compare Simultaneous Truth and Performance Level Estimation (STAPLE) label fusion with Patch label fusion using the ADMIRE (R) software. The accuracy of the autosegmentations was measured by comparing all of the generated autosegmentations with the anatomically validated gold standard segmentations that were developed using cadavers. Materials and methods: Twelve cadaver computed tomography (CT) atlases were used for automatic multiatlas-based segmentation. To determine the optimal number of atlases, one atlas was selected as a patient and the 11 remaining atlases were registered onto this patient using a deformable image registration algorithm. Next, label fusion was performed by using every possible combination of 2 to 11 atlases, once using STAPLE and once using Patch. This procedure was repeated for every atlas as a patient. The similarity of the generated automatic BP segmentations and the gold standard segmentation was measured by calculating the average Dice similarity (DSC), Jaccard (JI) and True positive rate (TPR) for each number of atlases. These similarity indices were compared for the different number of atlases using an equivalence trial and for the two label fusion groups using an independent sample-t test. Results: DSC's and JI's were highest when using nine atlases with both STAPLE (average DSC = 0,532; JI = 0,369) and Patch (average DSC = 0,530; JI = 0,370). When comparing both label fusion algorithms using 9 atlases for both, DSC and JI values were not significantly different. However, significantly higher TPR values were achieved in favour of STAPLE (p < 0,001). When fewer than four atlases were used, STAPLE produced significantly lower DSC, JI and TPR values than did Patch (p = 0,0048). Conclusions: Using 9 atlases with STAPLE label fusion resulted in the most accurate BP autosegmentations (average DSC = 0,532; JI = 0,369 and TPR = 0,760). Only when using fewer than four atlases did the Patch label fusion results in a significantly more accurate autosegmentation than STAPLE

An artificial iris ASIC with high voltage liquid crystal driver and 10nA light range detector and 40nA blink detector for LCD flicker removal

In a functional eye, the iris controls the pupil diameter to regulate the exposure of the retina. While iris deficiencies such as aniridia or leiomyoma can be mitigated with fixed or adaptive artificial irises [1] and adaptive transparency glasses exist to alleviate this situation, they do not mimic the normal functionality of the natural iris. To address this, a fully encapsulated, self-contained artificial iris embedded in a smart contact lens is proposed. A control ASIC is developed in 0.18 μm 16 V BCD TSMC with typ. 1.9 μw current consumption from 3 V supply voltage at office light condition

Glucose-induced posttranslational activation of protein phosphatases PP2A and PP1 in yeast

The protein phosphatases PP2A and PP1 are major regulators of a variety of cellular processes in yeast and other eukaryotes. Here, we reveal that both enzymes are direct targets of glucose sensing. Addition of glucose to glucose-deprived yeast cells triggered rapid posttranslational activation of both PP2A and PP1. Glucose activation of PP2A is controlled by regulatory subunits Rts1, Cdc55, Rrd1 and Rrd2. It is associated with rapid carboxymethylation of the catalytic subunits, which is necessary but not sufficient for activation. Glucose activation of PP1 was fully dependent on regulatory subunits Reg1 and Shp1. Absence of Gac1, Glc8, Reg2 or Red1 partially reduced activation while Pig1 and Pig2 inhibited activation. Full activation of PP2A and PP1 was also dependent on subunits classically considered to belong to the other phosphatase. PP2A activation was dependent on PP1 subunits Reg1 and Shp1 while PP1 activation was dependent on PP2A subunit Rts1. Rts1 interacted with both Pph21 and Glc7 under different conditions and these interactions were Reg1 dependent. Reg1-Glc7 interaction is responsible for PP1 involvement in the main glucose repression pathway and we show that deletion of Shp1 also causes strong derepression of the invertase gene SUC2. Deletion of the PP2A subunits Pph21 and Pph22, Rrd1 and Rrd2, specifically enhanced the derepression level of SUC2, indicating that PP2A counteracts SUC2 derepression. Interestingly, the effect of the regulatory subunit Rts1 was consistent with its role as a subunit of both PP2A and PP1, affecting derepression and repression of SUC2, respectively. We also show that abolished phosphatase activation, except by reg1Δ, does not completely block Snf1 dephosphorylation after addition of glucose. Finally, we show that glucose activation of the cAMP-PKA (protein kinase A) pathway is required for glucose activation of both PP2A and PP1. Our results provide novel insight into the complex regulatory role of these two major protein phosphatases in glucose regulation