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© 2020 Elsevier Ltd Scaling up of microbial fuel cells is a challenge for practical applications in wastewater treatment. In addition, the flow control is an important aspect for the electrochemical reactions occurring at the electrodes are influenced by fluid motions. By using dimensionless parameter analysis fluid regimes can be investigated in different scales of reactors. In this study, four important dimensionless flow parameters such as Reynolds number, Péclet number, Schmidt number, and Sherwood number were used for systematic analysis of hydrodynamic effects and power performance of recirculation mode microbial fuel cells together with computational fluid dynamics method. Results showed that the higher value of Reynolds number enhanced the convective flow of anolyte due to the dominant inertial forces in the flow field. Therefore, Reynolds number of 1.6 × 101 were obtained high mass transfer coefficient of 4.76 × 10−7 m s-1 and thin diffusion layer thickness of 2.52 × 10-3 m. Maximum power density and limited current density of 2422.8 mW m-2 and 4736.4 mA m-2 were obtained respectively which were higher than Reynolds number of 0 by 1.61 and 1.69 times. These findings shall be useful for effective recirculation flow mode MFCs power production and have a great possibility for large scale applications.

© The Author(s) 2019. This randomized controlled trial explored the effects of a Ving Tsun (VT) Chinese martial art training program on reactive standing balance performance, postural muscle reflex contraction latency, leg muscle performance, balance confidence and falls in community-dwelling older adults. We randomly assigned 33 healthy older adults to either a VT group (mean age = 67.5 years) or a control group (mean age = 72.1 years). The VT group received two 1-hour VT training sessions per week for three months (24 sessions). Primary outcome measures collected before and after the intervention period were electromyographic muscle activation onset latencies of the hamstring and gastrocnemius and the center of pressure path, length and movement velocity in standing (reactive balance performance). Secondary outcome measures included isometric peak force and time to isometric peak force of the knee extensors and flexors, the Activities-Specific Balance Confidence Scale score, and fall history. Results revealed that the mean gastrocnemius muscle activation onset latency was significantly longer (22.53 ms) in the VT group after the intervention. The peak force of the knee flexors significantly increased (by 1.58 kg) in the control group over time but not in the VT group. The time to reach peak force in the knee flexors was significantly longer (by 0.51 s) in the control group (but not the VT group) at posttest compared with the pretest value. No other significant group, time, or group-by-time interaction effects were noted. We discussed possible reasons for the failure of three months of martial art training to benefit fall risks among these older adults.

© The Author(s) 2019. This randomized controlled trial explored the effects of a Ving Tsun (VT) Chinese martial art training program on reactive standing balance performance, postural muscle reflex contraction latency, leg muscle performance, balance confidence and falls in community-dwelling older adults. We randomly assigned 33 healthy older adults to either a VT group (mean age = 67.5 years) or a control group (mean age = 72.1 years). The VT group received two 1-hour VT training sessions per week for three months (24 sessions). Primary outcome measures collected before and after the intervention period were electromyographic muscle activation onset latencies of the hamstring and gastrocnemius and the center of pressure path, length and movement velocity in standing (reactive balance performance). Secondary outcome measures included isometric peak force and time to isometric peak force of the knee extensors and flexors, the Activities-Specific Balance Confidence Scale score, and fall history. Results revealed that the mean gastrocnemius muscle activation onset latency was significantly longer (22.53 ms) in the VT group after the intervention. The peak force of the knee flexors significantly increased (by 1.58 kg) in the control group over time but not in the VT group. The time to reach peak force in the knee flexors was significantly longer (by 0.51 s) in the control group (but not the VT group) at posttest compared with the pretest value. No other significant group, time, or group-by-time interaction effects were noted. We discussed possible reasons for the failure of three months of martial art training to benefit fall risks among these older adults.

Background: Afatinib has demonstrated clinical benefit in patients with non-small-cell lung cancer progressing after treatment with erlotinib/gefitinib. This phase III trial prospectively assessed whether continued irreversible ErbB-family blockade with afatinib plus paclitaxel has superior outcomes versus switching to chemotherapy alone in patients acquiring resistance to erlotinib/gefitinib and afatinib monotherapy. Patients and methods: Patients with relapsed/refractory disease following ≥ 1 line of chemotherapy, and whose tumors had progressed following initial disease control (≥ 12 weeks) with erlotinib/gefitinib and thereafter afatinib (50 mg/day), were randomized 2:1 to receive afatinib plus paclitaxel (40 mg/day; 80 mg/m2/week) or investigator's choice of single-agent chemotherapy. The primary end point was progression-free survival (PFS). Other end points included objective response rate (ORR), overall survival (OS), safety and patient-reported outcomes. Results: Two hundred and two patients with progressive disease following clinical benefit from afatinib were randomized to afatinib plus paclitaxel (n ≥ 134) or single-agent chemotherapy (n ≥ 68). PFS (median 5.6 versus 2.8 months, hazard ratio 0.60, P ≥ 0.003) and ORR (32.1% versus 13.2%, P ≥ 0.005) significantly improved with afatinib plus paclitaxel. There was no difference in OS. Global health status/quality of life was maintained with afatinib plus paclitaxel over the entire treatment period. The median treatment duration was 133 and 51 days with afatinib plus paclitaxel and single-agent chemotherapy, respectively; 48.5% of patients receiving afatinib plus paclitaxel and 30.0% of patients receiving single-agent chemotherapy experienced drug-related grade 3/4 adverse events. Treatment-related adverse events were consistent with those previously reported with each agent. Conclusion: Afatinib plus paclitaxel improved PFS and ORR compared with single-agent chemotherapy in patients who acquired resistance t

Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed necessary. In this paper, we present the idea of performing level set evolution in a point-based environment where the sampling location and density of the domains are adaptively determined by level set geometry and image information, thus rid of the needs for computational grids and the associated refinements. We have implemented the general geometric deformable models using this representation and computational strategy, including the incorporation of region-based prior information in both domain sampling and curve evolution processes, and have evaluated the performance of the method on synthetic data with ground truth and performed surface segmentation of brain structures from three-dimensional magnetic resonance images.

While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that influence from local neighbors of a front point is explicitly incorporated, and it is thus capable of robustly dealing with the boundary leakage problem. The fundamental power of this strategy rests with the local integration of evolution forces for each front point within its local influence domain, which is adaptively determined by the local level set geometry and image/prior information. Due to the combined effects of internal and external constraints on a point and the interactions with those of its neighbors, our method allows stable boundary detection when the edge information is noisy and possibly discontinuous (e.g. gaps in the boundaries) while maintaining the abilities to handle topological changes, thanks to the level set implementation. The algorithm has been implemented using the meshfree particle domain representation, and experimental results on synthetic and real images demonstrate its superior performance.

Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed necessary. In this paper, we present the idea of performing level set evolution in a point-based environment where the sampling location and density of the domains are adaptively determined by level set geometry and image information, thus rid of the needs for computational grids and the associated refinements. We have implemented the general geometric deformable models using this representation and computational strategy, including the incorporation of region-based prior information in both domain sampling and curve evolution processes, and have evaluated the performance of the method on synthetic data with ground truth and performed surface segmentation of brain structures from three-dimensional magnetic resonance images.

While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that influence from local neighbors of a front point is explicitly incorporated, and it is thus capable of robustly dealing with the boundary leakage problem. The fundamental power of this strategy rests with the local integration of evolution forces for each front point within its local influence domain, which is adaptively determined by the local level set geometry and image/prior information. Due to the combined effects of internal and external constraints on a point and the interactions with those of its neighbors, our method allows stable boundary detection when the edge information is noisy and possibly discontinuous (e.g. gaps in the boundaries) while maintaining the abilities to handle topological changes, thanks to the level set implementation. The algorithm has been implemented using the meshfree particle domain representation, and experimental results on synthetic and real images demonstrate its superior performance.

Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed necessary. In this paper, we present the idea of performing level set evolution in a point-based environment where the sampling location and density of the domains are adaptively determined by level set geometry and image information, thus rid of the needs for computational grids and the associated refinements. We have implemented the general geometric deformable models using this representation and computational strategy, including the incorporation of region-based prior information in both domain sampling and curve evolution processes, and have evaluated the performance of the method on synthetic data with ground truth and performed surface segmentation of brain structures from three-dimensional magnetic resonance images.

While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that influence from local neighbors of a front point is explicitly incorporated, and it is thus capable of robustly dealing with the boundary leakage problem. The fundamental power of this strategy rests with the local integration of evolution forces for each front point within its local influence domain, which is adaptively determined by the local level set geometry and image/prior information. Due to the combined effects of internal and external constraints on a point and the interactions with those of its neighbors, our method allows stable boundary detection when the edge information is noisy and possibly discontinuous (e.g. gaps in the boundaries) while maintaining the abilities to handle topological changes, thanks to the level set implementation. The algorithm has been implemented using the meshfree particle domain representation, and experimental results on synthetic and real images demonstrate its superior performance.