Given the large global population of stroke survivors and limited rehabilitation resources, efficient treatments are urgently needed to help patients regain independence and reintegrate into society. In this review, we discuss how artificial intelligence and neurotechnology can be used to accelerate neurorehabilitation after stroke. First, we introduce neurorehabilitation mechanisms that provide the basis for neurotechnology development. Next, we describe how neurophysiological and neuroimaging biomarkers can be used for multimodal assessment and prognostic prediction. We then provide examples of brain-computer interface (BCI)-driven rehabilitation robots and BCI-triggered transcranial and peripheral neuromodulation for closed-loop rehabilitation training.
Functional diversity estimates increasingly inform ecological research, yet how methodological choices such as trait number and coding affect common metrics remains poorly quantified. Here, we systematically evaluate the effects of trait number and coding strategy on functional diversity metrics using benthic macroinvertebrate traits. Across 14 functional diversity indices representing functional richness, evenness, dispersion, and redundancy, we showed that metric responses to trait number are highly facet-dependent. Functional richness and evenness indices were particularly sensitive to trait number, whereas dispersion and redundancy metrics were comparatively stable. Estimation uncertainty was minimized at intermediate trait number, indicating a potential balance between functional space resolution and statistical robustness. Despite broad consistency between binary and fuzzy coding approaches for several metrics, redundancy metrics showed substantial divergence between coding schemes. These results demonstrate that hidden methodological decisions can substantially influence functional diversity indices. Our study provides a quantitative framework for evaluating metric robustness and reveals that widely used indices differ fundamentally in their response to dimensionality—a mathematical behaviour that must be understood before ecological interpretation. We recommend reporting trait number alongside functional diversity values and exercising caution when comparing communities assessed with different trait sets, particularly for richness and evenness metrics.
Bioplastics are biomaterial-derived plastics and are superior to petrochemical-based plastics in terms of resource renewability, planetary sustainability, and environmental biodegradability. Extensive research has been carried out over the last decades to identify and characterize desirable biomaterials for bioplastic manufacturing, and among those explored, microalgal biomass has received special attention due to its numerous advantages over other bioresources, including high areal productivity, the potential to use non-arable land, and the ability to reduce waste. Nonetheless, the cultivation and biorefinery processes for microalgae still need innovative development to make microalgal bioplastics economically viable. The primary focus of this review is to examine the established and emerging technologies for manufacturing bioplastics from microalgal biomass, starting from the exploration of bioresource availability and outlining technical routes of production. In particular, both upstream and downstream processes of microalgal cultivation pertinent to bioplastic production are reviewed in detail, analyzed in depth, and evaluated from the perspective of economic viability. The technical challenges and research opportunities, as well as prospects of current approaches and future methodologies for microalgal production of bioplastics, are also discussed, mostly based upon our research experiences in microalgal bioengineering, and it is our opinion that, despite these existing challenges, microalgal biomass could still be one of the most promising feedstocks for sustainable manufacturing of bioplastics.
Motion tracking plays a crucial role in the quantitative assessment and clinical rehabilitation of motor symptoms. While optical tracking and inertial sensing are mainstream, they are frequently limited by line-of-sight occlusions or data drift. Electromagnetic tracking (EMT) technology offers a powerful complementary solution due to its unique capabilities in full-pose tracking and occlusion-free measurements. To facilitate the integration of this technology into medical settings, this paper presents a structured overview of EMT approaches within rehabilitation applications. We systematically review the field from foundational physics and hardware architectures to advanced algorithmic frameworks. Particular emphasis is placed on recent breakthroughs in interference compensation and data-driven methods that enhance tracking robustness. Furthermore, we categorize representative clinical applications by scenario and target population, ultimately outlining key research trends and open opportunities to guide future development in this expanding domain.
