A novel bifunctional task-specific ionic liquid(TSIL),i.e.[trialkylmethylammonium][sec-nonylphenoxy acetate]([A336] [CA-100]) was impregnated on intermediate polarized XAD-7 resin,and the prepared solvent impreganated...A novel bifunctional task-specific ionic liquid(TSIL),i.e.[trialkylmethylammonium][sec-nonylphenoxy acetate]([A336] [CA-100]) was impregnated on intermediate polarized XAD-7 resin,and the prepared solvent impreganated resin(SIR) was studied for rare earth(RE) separation.Adsorption ability of the SIR was indicated to be obviously higher than that prepared by [A336][NO3] because of the functional anion of [A336][CA-100].Adsorption kinetics,adsorption isotherm,separation and desorption of the SIR were also stu...展开更多
Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplemen...Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.展开更多
In this paper, a comparative analysis of walking patterns during different cognitive states is conducted, followed by the classification of our database into Fallers and Non-fallers;by Fallers we describe subjects wit...In this paper, a comparative analysis of walking patterns during different cognitive states is conducted, followed by the classification of our database into Fallers and Non-fallers;by Fallers we describe subjects with repeated falling history. Vertical Ground Reaction Forces (VGRF) acquired from underneath the heel and toes of both feet are processed and analyzed for that endeavor. The subjects underwent three levels of tasks: 1) Single task: Walking at self-selected-speed (MS), 2) Dual task: Walking while performing a verbal fluency task (MF) and 3) Complex Dual task: Walking while counting backwards (MD).The ultimate objective of our research is fall prediction among the elderly by characterizing the variation of time-domain feature of Gait signals. For that, walking VGRF is analyzed and tested for the existence of indicators of the effect of dual task on subject falling susceptibility, whether parametric or pattern-wise analysis. As a result to our work, dual task in Fallers VGRF signals were recognized at 74% while at those non-fallers were recognized at 85%. Most importantly, subjects with history of fall have shown more potential to change the way they walk while performing mathematical cognitive task.展开更多
Artificial intelligence(AI),particularly deep learning,has demonstrated remarkable performance in medical imaging across a variety of modalities,including X-ray,computed tomography(CT),magnetic resonance imaging(MRI),...Artificial intelligence(AI),particularly deep learning,has demonstrated remarkable performance in medical imaging across a variety of modalities,including X-ray,computed tomography(CT),magnetic resonance imaging(MRI),ultrasound,positron emission tomography(PET),and pathological imaging.However,most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities.Compared to these task-specific models,emerging foundation models represent a significant milestone in AI development.These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning.Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice.This article reviews the clinical applications of both task-specific and foundation models,highlighting their differences,complementarities,and clinical relevance.We also examine their future research directions and potential challenges.Unlike the replacement relationship seen between deep learning and traditional machine learning,task-specific and foundation models are complementary,despite inherent differences.While foundation models primarily focus on segmentation and classification,task-specific models are integrated into nearly all medical image analyses.However,with further advancements,foundation models could be applied to other clinical scenarios.In conclusion,all indications suggest that task-specific and foundation models,especially the latter,have the potential to drive breakthroughs in medical imaging,from image processing to clinical workflows.展开更多
Stroke causes long-term disability, and rehabilitative training is commonly used to improve the consecutive functional recovery. Following brain damage, surviving neurons undergo morphological alterations to reconstru...Stroke causes long-term disability, and rehabilitative training is commonly used to improve the consecutive functional recovery. Following brain damage, surviving neurons undergo morphological alterations to reconstruct the remaining neural network. In the motor system, such neural network remodeling is observed as a motor map reorganization. Because of its significant correlation with functional recovery, motor map reorganization has been regarded as a key phenomenon for functional recovery after stroke. Although the mechanism underlying motor map reorganization remains unclear, increasing evidence has shown a critical role for axonal remodeling in the corticospinal tract. In this study, we review previous studies investigating axonal remodeling in the corticospinal tract after stroke and discuss which mechanisms may underlie the stimulatory effect of rehabilitative training. Axonal remodeling in the corticospinal tract can be classified into three types based on the location and the original targets of corticospinal neurons, and it seems that all the surviving corticospinal neurons in both ipsilesional and contralesional hemisphere can participate in axonal remodeling and motor map reorganization. Through axonal remodeling, corticospinal neurons alter their output selectivity from a single to multiple areas to compensate for the lost function. The remodeling of the corticospinal axon is influenced by the extent of tissue destruction and promoted by various therapeutic interventions, including rehabilitative training. Although the precise molecular mechanism underlying rehabilitation-promoted axonal remodeling remains elusive, previous data suggest that rehabilitative training promotes axonal remodeling by upregulating growth-promoting and downregulating growth-inhibiting signals.展开更多
Several task-specific ionic liquids（TSILs） with weak alkalinity have been designed based on tetraalkyl-ammonium cation and L-alanine anion([N<sub>2222</sub>][L-Ala]) for the CO<sub>2</sub> ab...Several task-specific ionic liquids（TSILs） with weak alkalinity have been designed based on tetraalkyl-ammonium cation and L-alanine anion([N<sub>2222</sub>][L-Ala]) for the CO<sub>2</sub> absorption.[N<sub>2222</sub>][L-Ala]has been chosen as a green and efficient activator for methyldiethanolamine（MDEA）.The densities,viscosities and absorption properties of the equimolar[N<sub>2222</sub>][L-Ala]-MDEA blended absorbents were investigated.Low viscosity and density values support the idea that blended absorbents are preferred in the industrial applications.[N<sub>2222</sub>][L-Ala]-MDEA behave similarly to the aqueous counterparts but offer more advantages,such as large absorption capacities,fast absorption rate and relatively low damage to the environment.展开更多
基金supported by ‘Hundreds Talents Program’from Chinese Academy of Sciences, National Natural Science Foundation of China (50574080, 20901073)National Key Technology R&D Program of China (2006BAC02A10)Distinguished Young Scholar Foundation of Jilin Province (20060114)
文摘A novel bifunctional task-specific ionic liquid(TSIL),i.e.[trialkylmethylammonium][sec-nonylphenoxy acetate]([A336] [CA-100]) was impregnated on intermediate polarized XAD-7 resin,and the prepared solvent impreganated resin(SIR) was studied for rare earth(RE) separation.Adsorption ability of the SIR was indicated to be obviously higher than that prepared by [A336][NO3] because of the functional anion of [A336][CA-100].Adsorption kinetics,adsorption isotherm,separation and desorption of the SIR were also stu...
基金Project(51318010402)supported by General Armament Department Pre-Research Program of China
文摘Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.
文摘In this paper, a comparative analysis of walking patterns during different cognitive states is conducted, followed by the classification of our database into Fallers and Non-fallers;by Fallers we describe subjects with repeated falling history. Vertical Ground Reaction Forces (VGRF) acquired from underneath the heel and toes of both feet are processed and analyzed for that endeavor. The subjects underwent three levels of tasks: 1) Single task: Walking at self-selected-speed (MS), 2) Dual task: Walking while performing a verbal fluency task (MF) and 3) Complex Dual task: Walking while counting backwards (MD).The ultimate objective of our research is fall prediction among the elderly by characterizing the variation of time-domain feature of Gait signals. For that, walking VGRF is analyzed and tested for the existence of indicators of the effect of dual task on subject falling susceptibility, whether parametric or pattern-wise analysis. As a result to our work, dual task in Fallers VGRF signals were recognized at 74% while at those non-fallers were recognized at 85%. Most importantly, subjects with history of fall have shown more potential to change the way they walk while performing mathematical cognitive task.
基金supported by grants from Beijing Hospitals Authority’s Ascent Plan(No.DFL20220303)Beijing Municipal Science&Technology Commission(No.Z221100003522008).
文摘Artificial intelligence(AI),particularly deep learning,has demonstrated remarkable performance in medical imaging across a variety of modalities,including X-ray,computed tomography(CT),magnetic resonance imaging(MRI),ultrasound,positron emission tomography(PET),and pathological imaging.However,most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities.Compared to these task-specific models,emerging foundation models represent a significant milestone in AI development.These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning.Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice.This article reviews the clinical applications of both task-specific and foundation models,highlighting their differences,complementarities,and clinical relevance.We also examine their future research directions and potential challenges.Unlike the replacement relationship seen between deep learning and traditional machine learning,task-specific and foundation models are complementary,despite inherent differences.While foundation models primarily focus on segmentation and classification,task-specific models are integrated into nearly all medical image analyses.However,with further advancements,foundation models could be applied to other clinical scenarios.In conclusion,all indications suggest that task-specific and foundation models,especially the latter,have the potential to drive breakthroughs in medical imaging,from image processing to clinical workflows.
基金supported by the JSPSKAKENHI Grant-in-Aid for Scientific Research(B),Grant Numbers24700572 and 30614276
文摘Stroke causes long-term disability, and rehabilitative training is commonly used to improve the consecutive functional recovery. Following brain damage, surviving neurons undergo morphological alterations to reconstruct the remaining neural network. In the motor system, such neural network remodeling is observed as a motor map reorganization. Because of its significant correlation with functional recovery, motor map reorganization has been regarded as a key phenomenon for functional recovery after stroke. Although the mechanism underlying motor map reorganization remains unclear, increasing evidence has shown a critical role for axonal remodeling in the corticospinal tract. In this study, we review previous studies investigating axonal remodeling in the corticospinal tract after stroke and discuss which mechanisms may underlie the stimulatory effect of rehabilitative training. Axonal remodeling in the corticospinal tract can be classified into three types based on the location and the original targets of corticospinal neurons, and it seems that all the surviving corticospinal neurons in both ipsilesional and contralesional hemisphere can participate in axonal remodeling and motor map reorganization. Through axonal remodeling, corticospinal neurons alter their output selectivity from a single to multiple areas to compensate for the lost function. The remodeling of the corticospinal axon is influenced by the extent of tissue destruction and promoted by various therapeutic interventions, including rehabilitative training. Although the precise molecular mechanism underlying rehabilitation-promoted axonal remodeling remains elusive, previous data suggest that rehabilitative training promotes axonal remodeling by upregulating growth-promoting and downregulating growth-inhibiting signals.
基金supported by the National Natural Science Foundation (21076101)Technological Support Project of Jiangsu Province(SBE 201000600)
文摘Several task-specific ionic liquids（TSILs） with weak alkalinity have been designed based on tetraalkyl-ammonium cation and L-alanine anion([N<sub>2222</sub>][L-Ala]) for the CO<sub>2</sub> absorption.[N<sub>2222</sub>][L-Ala]has been chosen as a green and efficient activator for methyldiethanolamine（MDEA）.The densities,viscosities and absorption properties of the equimolar[N<sub>2222</sub>][L-Ala]-MDEA blended absorbents were investigated.Low viscosity and density values support the idea that blended absorbents are preferred in the industrial applications.[N<sub>2222</sub>][L-Ala]-MDEA behave similarly to the aqueous counterparts but offer more advantages,such as large absorption capacities,fast absorption rate and relatively low damage to the environment.
