The impact of detergents on the tissue decellularization process: a ToF-SIMS study

Biologic scaffolds are derived from mammalian tissues, which must be decellularized to remove cellular antigens that would otherwise incite an adverse immune response. Although widely used clinically, the optimum balance between cell removal and the disruption of matrix architecture and surface ligand landscape remains a considerable challenge. Here we describe the use of time of flight secondary ion mass spectroscopy (ToF-SIMS) to provide sensitive, molecular specific, localized analysis of detergent decellularized biologic scaffolds. We detected residual detergent fragments, specifically from Triton X-100, sodium deoxycholate and sodium dodecyl sulphate (SDS) in decellularized scaffolds; increased SDS concentrations from 0.1% to 1.0% increased both the intensity of SDS fragments and adverse cell outcomes. We also identified cellular remnants, by detecting phosphate and phosphocholine ions in PAA and CHAPS decellularized scaffolds. The present study demonstrates ToF-SIMS is not only a powerful tool for characterization of biologic scaffold surface molecular functionality, but also enables sensitive assessment of decellularization efficacy

Motor primitives in space and time via targeted gain modulation in cortical networks

Motor cortex (M1) exhibits a rich repertoire of activities to support the generation of complex movements. Recent network models capture many qualitative aspects of M1 dynamics, but they can generate only a few distinct movements (all of the same duration). We demonstrate that simple modulation of neuronal input–output gains in recurrent neuronal network models with fixed connectivity can dramatically reorganize neuronal activity and consequently downstream muscle outputs. We show that a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, novel movements can be assembled from previously-learned primitives and we can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.Our work was supported by grants from the Wellcome Trust (TPV and JPS WT100000, 246 GH 202111/Z/16/Z) and the Engineering and Physical Sciences Research Council (JPS)

Tenacity, fracture mechanics and unknown coater web breaks

A web break model, based on fracture mechanics, was used to investigate unknown coater web breaks. Runnability was defined as Lb , the length between breaks. The model includes the size distribution of defects (holes and light spots, for example), web strength and web tension. Defects, such as boles, were most detrimental to Lb.Tenacity and tensile were used interchangeably, due to their high correlation on these grades. Tenacity is a simple, precise, valid fracture toughness test that is easy to use. Strength correlated strongly with web breaks. A 10% increase in tensile related to a 26% increase in Lb

Integrated Systems Design for Customer Focused Health Care Performance Measurement: A Strategic Service Unit Approach

The health care industry can expect an expanding need to measure and report the quality of performance and related outcomes. This article presents a flexible application operationalizing the strategies of total quality management and continual and rapid improvement in the area of assessing patient satisfaction. Mountain States Health Alliance established seven strategic criteria for the Outcomes Assessment Strategy and Information System (OASIS) design based on its own strategic initiatives and quality improvement principals. These initiatives are supported by the software application referred to as ContAct. Substantial process improvements have resulted. As pressures from stakeholders continue to mount, it will become increasingly important that patient satisfaction information be used to improve processes. The system presented provides one piece of an overall approach that will result in a rise to world-class status for the health care industry

Internal Supply Chain Performance Measurement: A Health Care Continuous Improvement Implementation

Purpose - The purpose of this article is to present one example of how the strategies of total quality management (TQM) and continuous improvement are being used by US health care providers to meet the challenges of the future. Design/methodology/approach - This article presents an application utilizing the strategies of TQM and continual and rapid improvement in the area of assessing internal customer satisfaction in the health care arena. Satisfaction information concerning internal processes is critically important to the health care provider, and this article presents the development and application of an instrument designed to provide timely and relevant internal customer satisfaction information to individual health care providers. This provides information on problem identification and improvement opportunities for a world-class continuous improvement program. Findings - The article finds that customer satisfaction is increasingly being recognized as an appropriate measure for determining how well a particular organization is accomplishing its mission and, while customer satisfaction surveys provide valuable information and may be used to improve the entire operation, they provide limited insight into the details of the inner workings of each cost center. Each of the measures discussed in this article is potentially equally insightful and may provide more directly usable information when applied to internal customers. Originality/value - This article provides useful information on providing customer satisfaction in the health care arena

Dimensional reduction for reward-based learning

Reward-based learning in neural systems is challenging because a large number of parameters that affect network function must be optimized solely on the basis of a reward signal that indicates improved performance. Searching the parameter space for an optimal solution is particularly difficult if the network is large. We show that Hebbian forms of synaptic plasticity applied to synapses between a supervisor circuit and the network it is controlling can effectively reduce the dimension of the space of parameters being searched to support efficient reinforcement-based learning in large networks. The critical element is that the connections between the supervisor units and the network must be reciprocal. Once the appropriate connections have been set up by Hebbian plasticity, a reinforcement-based learning procedure leads to rapid learning in a function approximation task. Hebbian plasticity within the network being supervised ultimately allows the network to perform the Learning often takes place solely through the reinforcement of improved performance. Reinforcement-based learning is challenging because no information is provided to indicat