Clamped-end effect on static detection signals of DNA-microcantilever

Boundary constraint induced inhomogeneous effects are important for mechanical responses of nano/micro-devices. For microcantilever sensors, the clamped-end constraint induced inhomogeneous effect of static deformation, so called the clamped-end effect, has great influence on the detection signals. This paper is devoted to developing an alternative mechanical model to characterize the clamped-end effect on the static detection signals of the DNA-microcantilever. Different from the previous concentrated load models, the DNA adsorption is taken as an equivalent uniformly distributed tangential load on the substrate upper surface, which exactly satisfies the zero force boundary condition at the free-end. Thereout, a variable coefficient differential governing equation describing the non-uniform deformation of the DNA-microcantilever induced by the clamped-end constraint is established by using the principle of minimum potential energy. By reducing the order of the governing equation, the analytical solutions of the curvature distribution and static bending deflection are obtained. By comparing with the previous approximate surface stress models, the clamped-end effect on the static deflection signals is discussed, and the importance of the neutral axis shift effect is also illustrated for the asymmetric laminated microcantilever.

Application of the Spectral-Shift Effect in the Small Lead-Based Reactor SLBR-50

In the design of a nuclear reactor, improving fuel utilization and extending burnup are two of the most important goals. A concept design of spectral-shift control rods is presented to extend cycle length and fuel utilization. First, a small lead-based reactor, SLBR-50, is preliminarily designed, and the design rationality is proved. Next, the concept design of spectral-shift control rods is presented and analyzed. Finally, numerical results of the small reactor design show that the burnup depth is extended by 73.3% and the fuel utilization rate for 235U and 238U is improved by 66.6 and 68.4%. All results are calculated using a Monte-Carlo code RMC. These results show advantages of the concept design for the spectral-shift control rod.

Language Shift Effect on Memory Generalization of Chinese-English Bilinguals

Language shift occurs when people learn information in one language but recall it in another language. This mismatch between encoding and retrieval language is found to impair memory accuracy when memory is tested immediately after learning. However, does the observed language shift effect still exist after a certain period of delay? Would it influence other aspects of memory, especially memory generalization? To address these two questions, we performed a memory experiment among unbalanced Chinese-English bilinguals. In the experiment, participants were required to read two stories (one in English, one in Chinese) and to retell the stories in Chinese from their memories afterward. Delay interval was manipulated in the experiment where participants either took memory recall tests immediately after story-reading or after 24 hours' delay. To analyze memory generalization, we coded the generalized words participants used to retell the stories. The results suggest that language shift (encoding in English and retrieving in Chinese) leads to a more generalized description in a memory recall task. However, the observed language shift effect disappears after 24 hours' delay. It can be concluded that language shift impacts bilingual learners' memory generalization in immediate recall tests, but such effect disappears after 24 hours' delay, which indicates the key role of delay interval in modulating language shift effect.

Various parameters of the multiaxial variable amplitude loading and their effect on fatigue life and fatigue life computation

The paper discusses various partial solutions used for estimating fatigue life under variable amplitude multiaxial loading in the high-cycle fatigue domain. The concurring effects are treated, and their proposed solutions are commented upon. The major focus is on the categories of the phase shift effect and of cycle counting, and on the scope and quality of data, which support discussed theories. Results of own new experimental data set on specimens from S355 steel are provided. Fatigue life estimates for McDiarmid and Findley multiaxial methods and for two different methods of load path decomposition to cycles are shown to highlight some of the points open for discussion. It is concluded that the available experimental data are not sufficient to substantiate a clear decision to follow a definite algorithm.

Early Deformation of Deep Brain Stimulation Electrodes Following Surgical Implantation: Intracranial, Brain, and Electrode Mechanics

IntroductionAlthough deep brain stimulation is nowadays performed worldwide, the biomechanical aspects of electrode implantation received little attention, mainly as physicians focused on the medical aspects, such as the optimal indication of the surgical procedure, the positive and adverse effects, and the long-term follow-up. We aimed to describe electrode deformations and brain shift immediately after implantation, as it may highlight our comprehension of intracranial and intracerebral mechanics.Materials and MethodsSixty electrodes of 30 patients suffering from severe symptoms of Parkinson’s disease and essential tremor were studied. They consisted of 30 non-directional electrodes and 30 directional electrodes, implanted 42 times in the subthalamus and 18 times in the ventrolateral thalamus. We computed the x (transversal), y (anteroposterior), z (depth), torsion, and curvature deformations, along the electrodes from the entrance point in the braincase. The electrodes were modelized from the immediate postoperative CT scan using automatic voxel thresholding segmentation, manual subtraction of artifacts, and automatic skeletonization. The deformation parameters were computed from the curve of electrodes using a third-order polynomial regression. We studied these deformations according to the type of electrodes, the clinical parameters, the surgical-related accuracy, the brain shift, the hemisphere and three tissue layers, the gyration layer, the white matter stem layer, and the deep brain layer (type I error set at 5%).ResultsWe found that the implanted first hemisphere coupled to the brain shift and the stiffness of the type of electrode impacted on the electrode deformations. The deformations were also different according to the tissue layers, to the electrode type, and to the first-hemisphere-brain-shift effect.ConclusionOur findings provide information on the intracranial and brain biomechanics and should help further developments on intracerebral electrode design and surgical issues.

Influence of Selected Multiaxial Stress Paths on Fatigue Estimation under Variable Amplitude Loading

The paper discusses solutions used for estimating fatigue life under variable amplitude multiaxial loading in the high-cycle fatigue domain. Various concurring effects are treated, and their proposed solutions are commented upon. The focus is on the categories of the phase shift effect and of cycle counting. It is concluded that the available experimental data are not sufficient to substantiate a clear decision to follow a definite algorithm. An example of own new experimental data is provided, and the fatigue life estimation run to highlight some more points open for discussion.

Shifting attention between modalities: Revisiting the modality-shift effect in autism

Selective attention to a sensory modality has been observed experimentally in studies of the modality-shift effect – a relative performance benefit for targets preceded by a target in the same modality, compared to a different modality. Differences in selective attention are commonly observed in autism and we investigated whether exogenous (automatic) shift costs between modalities are increased. Autistic adults and neurotypical controls made speeded discrimination responses to simple visual, tactile and auditory targets. Shift costs were observed for each target modality in participant response times and were largest for auditory targets, reflective of fast responses on auditory repeat trials. Critically, shift costs were similar between the groups. However, integrating speed and accuracy data using drift-diffusion modelling revealed that shift costs in drift rates (reflecting the quality of information extracted from the stimulus) were reduced for autistic participants compared with neurotypicals. It may be that, unlike neurotypicals, there is little difference between attention within and between sensory modalities for autistic people. This finding also highlights the benefit of combining reaction time and accuracy data using decision models to better characterise selective attention in autism.

Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.