This paper presents a longitudinal narrative ethnography tracing the embodied experiences of Hippo, a young woman navigating clinical depression and a shifting relationship with physical activity over 6 years. Set against prevailing discourses that promote exercise-as-cure for mental illness, the paper does not seek to test efficacy, but to explore what movement does, emotionally, socially, and existentially, for someone living with chronic distress. Rather than offering generalisable claims or neat resolutions, this study privileges temporality, subjectivity, and storied complexity. Hippo’s journey unfolds not as a linear transformation, but through slow, recursive encounters with walking, swimming, running, and moments of stillness. Her initial curiosity about movement was sparked not by medical advice but by reading a running narrative. Over time, she experiments with exercise as a form of survival, not salvation. The story was co-constructed through diaries, unstructured interviews, conversational dialogue, fieldnotes, and collaborative storytelling. Grounded in storytelling and phenomenological sensibility, the paper argues that physical activity can foster a mode of ‘being-with’ depression rather than simply offering a route out. Hippo’s story highlights traits of slowness, coexistence, ambivalence, and embodied re-sensation, challenging dominant health narratives that assume movement leads to measurable recovery. In advocating for narrative as a method and as knowledge, this work contributes to growing calls for research that centres the lived, affective, and complex realities of those whose stories do not fit mainstream frameworks. It invites readers to understand movement not just as medicine, but as a mode of being with difficulty and of becoming.
Achieving sound field reproduction (SFR) with high sound quality and accurate spatial localization in automotive cabins is particularly challenging due to complex acoustics and constrained loudspeaker layouts. This paper proposes a learning-based method to address this challenge, integrating a spatial domain physics-informed constraint based on plane-wave decomposition (PWD) with a multi-position control strategy. Results from both objective evaluations and in-situ subjective listening tests consistently validated the superiority of the proposed approach over several baseline methods. Moreover, we show that the correlation of spatial power maps (SPMs) derived from PWD provides a reliable objective metric that closely reflects perceived spatial localization in the cabin environment.
Home-based care has become a predominant mode of elderly care in China amidst rapid population aging. This paper draws on a ‘narratives-as-practices’ approach (De Fina & Georgakopoulou, 2012; De Fina, 2021) to analyze storytelling in naturally occurring, face-to-face interactions in four Chinese families. We examine the types of narratives that are told, how they are constructed, and the interactional functions they serve in home-based eldercare. Two primary modes of storytelling were identified: 1) collaborative storytelling that involves interactions between older persons and their caregivers; and 2) unilateral storytelling where narratives are constructed by older persons alone, with limited participation or engagement from the listeners. Three narrative types were further distinguished, namely, personal, collective, and vicarious narratives. All three types of narratives were observed in the collaborative mode of storytelling, whereas the unilateral mode of storytelling exclusively features vicarious narratives. We show that these narratives were produced to perform a range of interactional goals in home-based eldercare, including fostering caregivers’ understanding, advocating generational values, conveying expectations, and sustaining social bonds. By revealing the narrative patterns in home-based eldercare communication and how they reflect and constitute the ‘power-solidarity’ dynamics in the family, this study sheds light on the complexities of intergenerational communication in the emerging context of home-based elderly care in China.
The effects and mechanisms of carbon (C)- and nitrogen (N)-deficient nutrient conditions (prevalent in natural environment) on bacterial mobile performance in porous media are unclear. This study systematically investigated the transport/retention performance of Gram-negative Escherichia coli and Gram-positive Bacillus subtilis experiencing different nutrient conditions (i.e. nutrient-sufficient, C-deficient, or N-deficient conditions) in column, parallel plate flow chamber (PPFC) and microfluidic chamber systems. We found that compared to those in nutrient-sufficient condition, bacteria (regardless of their type) exposure to C-deficient nutrient condition exhibited 7–14% reduced mobility in porous media, whereas those experienced N-deficient condition had 7–20% enhanced transport in both simulated electrolyte solutions and real groundwater samples. The underlying mechanisms driving to different mobile performance of bacteria exposure to different nutrient conditions were correlated with the composition of proteins (one major component of extracellular polymeric substances (EPS)). Compared to nutrient-sufficient condition, C-deficient condition increased EPS hydrophobicity via enhancing hydrophobic amino acids contents and altering secondary structure within proteins thus decreased bacterial transport, while N-deficient condition decreased EPS hydrophobicity through decreasing the abundance of hydrophobic amino acids within proteins and increased cell mobility. The results showed that via changing cell surface hydrophobicity, exposure bacteria to different nutrient conditions could induce different mobile performance of bacteria.
With seven temperate Earth-sized planets revolving around an ultracool red dwarf, the nearby TRAPPIST-1 system offers a unique opportunity to verify models of exoplanet composition, differentiation, and interior structure. In particular, the low bulk densities of the TRAPPIST-1 planets, compared to terrestrial planets in our Solar system, require either substantial amount of volatiles to be present or a core-free scenario where the metallic core is fully oxidized. Here, we test the validity of the core-free scenario given thermodynamic constraints. In particular, we update a metal–silicate partitioning model within the equilibrium differentiation framework. We show that during core–mantle differentiation, oxygen becomes more siderophile (iron-loving) with increasing pressure, implying larger planet radii. For the seven TRAPPIST-1 planets, however, we find that they are not sufficiently massive to oxidize all the iron in the core, if they differentiate from an Earth-like composition. Oxygen partitioning in rocky worlds thermodynamically precludes coreless planets up to 4 M⊕. The observed density deficit in the TRAPPIST-1 planets, and more generally in M dwarf systems if confirmed by future observations, may be explained by system-dependent element budgets during planet formation, which are intrinsically linked to their stellar metallicity.
The rapid development of multimodal epidermal sensing requires scalable, energy-efficient data processing architectures capable of processing large volumes of raw data. Conventional systems suffer from high energy consumption and transmission latency due to the physical separation of sensors and processors. Here, we present an ultrathin flexible edge computing circuit based on carbon nanotube thin-film transistors (CNT-TFTs) and machine learning (ML)-assisted design. By incorporating substrate engineering, ML-derived device modeling, and industry-compatible design methodologies, we establish a complete toolchain from device to system. The ML model achieves 91.2% prediction accuracy, enabling simulation-guided optimization of logic gates. A CNT-based standard cell library enables the construction of flexible circuits with 361 transistors and 160 logic gates. Monolithic integration with an 8-channel tilt sensor achieves 62.5% data compression while maintaining functionality after undergoing 360° deformation. This work establishes an ML-assisted CNT circuit design framework for fully integrated flexible edge computing, enabling scalable wearable applications.
Stable isotopes are widely used to reconstruct Earth’s history. Accurate applications require mechanistic knowledge of isotope fractionation during crystal formation via nucleation followed by growth, but isotope fractionation during nucleation has not been directly measured. By experimentally isolating crystal nucleation on organic films from growth in bulk solution, this study provides a measurement of isotope fractionation associated with nucleation, and finds that nucleation produces near-equilibrium isotope signatures, distinct from kinetic fractionation during growth. These findings extend isotope fractionation theory beyond growth-dominated low supersaturation regimes and provide a mechanistic basis for interpreting paradoxes in isotope fractionation which could not be explained by an ion-by-ion growth model. These results also have broad implications for isotope fractionation during biomineralization and interface-promoted nucleation.
Test-time adaptation (TTA) aims to adapt the model trained on source domain to unseen target domain using a few unlabeled images during inference, which holds great value for the deployment of models in the clinical practice. In this setting, the model can only access online unlabeled test samples and pre-trained model on the source domain. Because unlabeled test samples may arrive sequentially, the model needs to adjust online for the cross-domain distribution shift from different medical institutions, the scale of which would change concurrently and continually over time. However, unstable optimization and abnormal distribution will lead to error accumulation and catastrophic forgetting. Considering the role of brain extracellular space in balancing neural homeostasis and signal transmission, we recognize that the existing TTA methods lack a dedicated component to ensure the stability and accuracy of the model. In this paper, we propose a robust TTA approach for cross-domain segmentation as MemTTA. Specifically, firstly, we introduce transductive batch normalization to ensure stability, which calculates the mean and the variance from the source domain and current test batch. Secondly, we propose a memorized spatial pixel-level clustering strategy to represent each category with multiple and anisotropic prototypes for feature alignment, which can be associated with the parametric classifier. During test time, we adapt the segmentation model to each test batch with self-supervision augmentation consistency learning to improve the inference performance. MemTTA needs only one epoch training on each test batch, and then is comparable to standard models as the traditional inference pipeline. The proposed method is extensively evaluated on neuron, brain metastases, cardiac, and abdominal organ image segmentation. The experimental results demonstrate that our proposed MemTTA can effectively mitigate test-time domain shift and catastrophic forgetting, and is superior to existing state-of-the-art approaches.
Shortly after the failed PKI uprisings of 1926/27, Tan Malaka and his associates established the Partai Republik Indonesia (PARI). Although he acted as the party chairman and chief strategist, his involvement in the party operation was minimal as he lived in exile. Nevertheless, he loomed large in the eyes of both his followers and enemies. Not only was Tan Malaka a legendary guru for Indonesian revolutionaries, but also an enormous threat to colonial authorities across East and Southeast Asia. This chapter explores Tan Malaka's exile in China between 1927 and 1936 and how such experiences reflect his shifting relationship with Indonesia's ongoing struggles for independence, the international communist movement, and the surveillance and policing practices of multiple colonial states.
Microbial functions and metabolism are intrinsic drivers of pollutant removal in mixotrophic denitrification systems. Four pyrite-based mixotrophic denitrifying biofilters were constructed and monitored for 304 days. Variations in pollutant characteristics indicated that the hot zones of heterotrophic denitrification, autotrophic denitrification, and sulfate reduction were located in the bottom, middle-lower, and upper parts of biofilters, respectively. These hot zones corresponded to preferential enrichment of heterotrophic denitrifying, S-based mixotrophic denitrifying, and sulfate-reducing bacteria, respectively, highlighting microbial spatial stratification. Differential functional gene analysis for S reduction revealed that only a dissimilated sulfate reduction process could consistently provide biogenic S0 as a new electron donor via the flavocytochrome c sulfide dehydrogenase (Fcc) enzyme and extracellular polymeric substance protection systems, enhancing the denitrification process. X-ray photoelectron spectroscopy confirmed the accumulation of biogenic S0 . Untargeted metabolomic analysis suggested that vitamin B12 and tryptophan might be the key metabolites for realizing synergistic promotion of autotrophic and heterotrophic denitrification. The microbe-metabolite network indicated that dominant bacteria (e.g., Thiothrix and unclassified\_f\_Rhodocyclaceae) were specialists with less cross-feeding metabolism, while rare species (e.g., Thiobacillus and Desulfobacter) were generalists with complex cross-feeding metabolism in the constructed mixotrophic denitrification systems. The electron transfer pattern indicated that most of the electrons released from S, C, and Fe oxidation were utilized in denitrification processes as the dominant nitrogen removal pathway, including S2- /S0-based autotrophic, fermentation acetic acid production-heterotrophic, and Fe(II)-based autotrophic denitrification. Some electrons were utilized for coupling dissimilatory nitrate reduction to ammonia (DNRA) and anammox processes as an auxiliary pathway for systemic nitrogen removal. The findings of this study advance our understanding of the deeper intrinsic drivers of nitrogen removal by pyrite-based mixotrophic denitrifying biofilters, facilitating their optimization. (c) 2025 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Vanadium-titanium magnetite (VTM) mines are often associated with metal contamination, organic carbon depletion, and ecological degradation, yet how indigenous microbiomes respond to vertical geochemical heterogeneity remains poorly understood. Here, we investigated a ∼260 m vertical gradient in the Zhulan Iron Mine, China, by integrating elemental and mineralogical analyses with 16S rRNA gene amplicon and metagenomic profiling. Pronounced vertical stratification was observed. The lower-elevation section was enriched in vanadium (V) and Fe(II)-bearing minerals and harbored microbial communities with lower diversity but a more highly connected co-occurrence network. Representative lower-section taxa, including Thiobacillus, Sulfuricaulis, and Chloroflexota members, were linked to functional potentials for V-associated redox transformation, Fe/S metabolism, extracellular electron transfer, and autotrophic carbon fixation. These patterns suggest that Fe(II)-bearing minerals, particularly magnetite, may serve as potential inorganic electron sources supporting mineral-based lithotrophy and carbon fixation under metal-rich and organic-carbon-limited conditions. In contrast, the upper-elevation section exhibited lower V stress, higher microbial diversity, sparse vegetation, and enrichment of heterotrophic and plant-associated taxa, such as Sphingomonas, Flavisolibacter, and Actinomycetota, indicating a shift toward plant-supported microbial functions during early ecological recovery. Overall, this study links vertical geochemical stratification to microbial functional differentiation and highlights indigenous mineral-microbe interactions as a potential basis for targeted, low-input restoration strategies in metal-contaminated mining ecosystems.
Social media has emerged as an important channel for mental health education, allowing health educators to communicate mental health knowledge to the public via short-form videos. Drawing on critical multimodal discourse analysis, this paper examines the use of multimodal metaphors in animated depression educational videos on the Chinese social media platform Bilibili.com and considers the broader social implications of these metaphors for understanding and managing depression. The findings show that multimodal metaphors are employed to represent the nature of depression, its symptoms, potential causes, and treatments. Conventional metaphors (e.g., Experiencing Depression is Being Engulfed by a Blanket of Darkness) transform mental distress into a visible and embodied presence, thereby validating the sufferers’ illness experiences; creative metaphors (e.g., Depression is the Grim Reaper and Depression is a Black Dog) reframe depression as external agents, while also attributing a certain level of agency to individuals and combating social stigma. Moreover, by personifying brain chemicals and simplifying neurological mechanisms, multimodal biomedical metaphors make complex knowledge more understandable to the audience. While these metaphors play an important role in removing barriers to public understanding, by prioritizing a biomedical account, they also risk dehumanizing and overmedicalizing individuals who are affected by depression. Therefore, we argue that a more balanced approach that incorporates both biomedical and psychosocial aspects of depression is needed for a more holistic and socially oriented mental health education.
Nanoconfined water—ubiquitous across both engineered nanoporous adsorbents and subsurface geological formations—plays a pivotal yet underexplored role in carbon capture and storage (CCS). This review systematically examines the physicochemical properties and functional implications of water confined within nanoporous environments, emphasizing its dualistic impact on both CO2 capture and geological CO2 storage. We first summarize recent advances from computational simulations and experimental characterizations, highlighting the altered thermodynamic and structural features, dynamic behavior, dielectric properties, and chemical reactivity of nanoconfined water. We then integrate insights from surface chemistry, materials science, and geoscience to elucidate how nanoconfined water influences CCS processes through competitive adsorption, pore accessibility, wettability, solubility, and mineralization kinetics, spanning systems from nanoporous adsorbents such as zeolites, metal–organic frameworks (MOFs), and activated carbon (AC) to unconventional formations including shale and tight sandstone. These findings also suggest opportunities for practical applications, such as guiding the design of hydrophobic MOFs for improved CO2 capture and supporting strategies to preserve caprock integrity in subsurface storage. Finally, we identify key challenges in bridging molecular-level understanding with material- and reservoir-scale performance, emphasizing the need for multiscale experimental techniques, realistic molecular modeling, and cross-disciplinary strategies to fully harness the functional potential of nanoconfined water in CCS.
