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China is betting on ‘optical’ computer chips — will they power AI?
High-throughput methods leveraging robotics and computer vision for the development of therapeutic phage cocktails
Abstract We present the high-throughput automated screening techniques that are being used to develop bacteriophage-based therapeutic products currently under investigation in human clinical trials to combat urinary tract infections 1 . By integrating modern liquid handling robotics, standardized phenotypic assays, and computer vision-based enumeration, we established a platform capable of reproducibly screening large collections of phages against clinically derived bacterial strain panels. This approach enabled systematic assessment of phage-bacteria interactions at scale, facilitating the identification and optimization of phage cocktails with broad in vitro activity. Although bacteriophage therapy has long been investigated as a strategy for treating bacterial infections, few frameworks exist for developing phage combinations in a reproducible and scalable manner. The methods outlined here address this gap and aim to support the broader development of therapeutic assets available to combat antibiotic resistance.
Clinico-pathologic characteristics of sub-epithelial tumor (SET) like gastric cancers
Correction: Measuring novelty in science with word embedding
Daily briefing: Why we enjoy things more when they’re hard to get
Nested spatiotemporal theta–gamma waves organize hierarchical processing across the mouse visual cortex
Climate warming is shifting northern aquatic ecotones
Correction: Predicting unsafe behaviour from the objective assessment of fatigue manifestation among scaffolders: Evidence from a Quasi-experimental simulation study
Can academia handle my religious faith?
Tenascin C+ myofibroblasts exacerbate vascular neointimal hyperplasia by propagation of nerve-macrophage interactions in mice
Optimal cluster-based energy efficient routing scheme for QoS aware IoT-enabled wireless body area network
High-dimensional phenotyping reveals novel macrophage-like and hybrid subsets within murine splenic conventional dendritic cells
Conventional dendritic cells (cDCs) are pivotal antigen-presenting cells (APCs) with critical roles in immune regulation, yet their subset classification remains ambiguous due to phenotypic overlap with macrophages and monocytes, particularly in the spleen. This study employed multi-parametric flow cytometry and clodronate liposome (CL) depletion to systematically re-evaluate splenic CD11c high MHCII high cDCs in C57BL/6 mice. We identified three novel subsets: (1) a tissue-resident T-cell zone macrophage (TZM)-like population (F4/80 inter-low CX3CR1 + MERTK + ) constituting 0.59% of cDC2s with >10-fold CL-depletion resistance ( p < 0.0001); (2) a resident F4/80 high APC subset (CCR2 ⁻ Ly6C⁻) accounting for 2.7% of cDC2s with CL-sensitivity; (3) unconventional CD4⁺CD8α⁺ hybrids present in 2.57% of cDC2 and some cDC1s. These findings demonstrate unprecedented cDC plasticity driven by microenvironmental signals, revising conventional classification frameworks and proposing new targets for DC-based immunotherapies in autoimmunity and cancer. Our phenotypic mapping provides a foundational framework for future functional investigations into these novel subsets.
How DeepMind’s genome AI could help solve rare-disease mysteries
Mechanically heterogeneous hydrogel with cell-programmed network restructuring promotes tissue regeneration by mechano-epigenetic modulation
DEENet: an edge-enhanced CNN–Transformer dual-encoder model for steel surface defect detection
Abstract Steel is an indispensable material in modern industry, and its surface quality directly affects the performance and service life of products. To address problems of insufficient feature extraction capability, weak detection of small defects, and blurred target contours that lead to degraded edge information in steel surface defect detection, this paper proposes a novel edge-enhanced dual-branch steel surface defect recognition model, DEENet. First, a dual-encoder module based on CNN and Transformer is designed to extract image features and enhance the feature extraction capacity of the backbone network. Second, a Dual Channel Fusion module is introduced to perform cross-enhancement between the local features captured by the CNN and the global semantic features modeled by the Transformer, achieving feature complementarity and improving the detection accuracy for small defects. Finally, an edge enhancement module, C2f_EEM, is designed to highlight gradient differences between defective and normal regions through differential operations, thereby strengthening contour information and improving the model’s sensitivity to defect edges. Experimental results on the NEU-DET dataset show that, compared with other algorithms, DEENet achieves a superior mean Average Precision (mAP) of 81.4%, enabling more accurate detection of steel surface defects and providing valuable reference for defect inspection in real-world production scenarios.
The autonomy paradox in AI-generated content adoption: Creative-specific alternative to TAM model in China’s micro-short drama industry
In China’s booming micro-short drama industry, Artificial Intelligence Generated Content (AIGC) presents creators with an ‘autonomy paradox’: improving efficiency while sparking fears of lost control, amplified by collectivist culture that heightens tensions between AI-driven productivity and loss of autonomy. Based on a mixed-methods study of 607 micro-short drama creators, this research proposes and tests the Creative Industries Technology Acceptance Model (CITAM), which builds upon TAM foundations while adapting constructs for creative contexts to reveal key dynamics in adoption intentions. Building upon TAM theoretical foundations while introducing innovation compatibility (IC) and creative autonomy retention (CAR), CITAM is grounded in Diffusion of Innovations and Self-Determination Theory to address both rational and psychological adoption factors in creative contexts. Using a mixed methods approach with SEM in 607 surveys and 10 in-depth interviews, the results reveal that CAR positively influences AIGC adoption through IC as a mediator, while CAR negatively moderates the positive influence of IC on adoption intentions, highlighting a modest but significant psychological tension. Qualitative insights on ‘Creativity Amplification’ complement this, showing that creators perceive AIGC as an idea enhancer, not a replacement for the essential ‘human spark.’ CITAM provides a customized extension of TAM for creative industries, offering practical guidance. The findings can help developers design tools that preserve the agency of the creator and inform policy makers about balancing AIGC innovation with creator rights. These discoveries offer an initial framework for the adoption of ethical AI in the global creative economy, calling for cross-cultural validation to improve generalizability in AI-driven creative ecosystems.
Mosaic partial epidermal reprogramming remodels neighbors and niches to refine skin homeostasis and repair
Abstract Adult stem cells and their niches communicate intricately for tissue maintenance and regeneration. However, effectively coordinating these complex interactions is challenging. Here, we demonstrate that transient dedifferentiation of a fraction of epithelial stem cell progenies orchestrates beneficial changes within the entire skin’s cellular networks to favor repair. We achieved this by inducing a mosaic and reversible expression of reprogramming factors ( Oct-4 , Sox2 , Klf4 , and c-Myc ) in the mouse epidermis. This in vivo partial epidermal reprogramming not only affected the partially reprogrammed cells, but also their microenvironment, including neighboring epithelial cells and T cells, conferring widespread healing characteristics even in the absence of injury. When a wound was introduced, these collective changes accelerated re-epithelialization in both wild-type and a hyperglycemic mouse disease model. Furthermore, the effects extended to dermal healing, leading to reduced scarring and altered angiogenesis. In conclusion, our work reveals that mosaic partial reprogramming of the epidermis influences various cell types within the skin during homeostasis and repair, leading to enhanced cutaneous wound healing.
Prognostic value of the third thoracic vertebra skeletal muscle measurements in patients with digestive system malignancies: a comparative study with the third lumbar vertebra indices
Abstract Skeletal muscle mass assessment using computed tomography (CT) is crucial for evaluating nutritional status and prognosis in cancer patients. While the third lumbar vertebra (L3) level is widely accepted for this purpose, not all patients undergo abdominal CT scans. This study aimed to explore the potential of the third thoracic vertebra (T3) level as an alternative measurement site. This retrospective study included 257 patients with digestive system malignancies. Skeletal muscle area (SMA) and skeletal muscle index (SMI) were measured at both T3 and L3 levels using CT scans. Correlation analyses, linear regression models, and cox regression analyses were performed to evaluate the relationship between T3 and L3 measurements and their prognostic value. Strong correlations were observed between T3 and L3 measurements ( r = 0.833 for SMA, r = 0.747 for SMI). A multivariate linear regression model effectively predicted L3 SMA from T3 SMA (adjusted R ² = 0.829). Cox regression analyses revealed that lower T3 SMA and SMI were independently associated with increased mortality risk. Patients in the lowest quartile of T3 SMA had significantly higher mortality risk compared to those in the highest quartile (HR = 5.82, 95% CI: 1.86–18.16, P = 0.002), after adjusting for confounders. Similar results were observed for T3 SMI and L3 measurements. T3 skeletal muscle measurements strongly correlate with L3 measurements and serve as independent prognostic factors in patients with digestive system malignancies. T3 measurements offer a viable alternative for assessing skeletal muscle mass and predicting prognosis when L3 measurements are unavailable.
Biochemical metabolic enhancement acting as a dominant driver in intra-leaf CO2 diffusional response to soil nitrogen supplying in Soybean
Biochemical metabolism and anatomical structure within leaf tissues have been proposed as the two principal mechanisms underpinning the rapid responsiveness of mesophyll conductance ( g m ) to environmental perturbations; nevertheless, empirical evidence distinguishing which of these factors acts as the dominant driver remains scarce. The response of intra-leaf CO 2 diffusion conductance including g m and stomatal conductance ( g sc ) to soil nitrogen (N) change in soybean was systematically quantified in leaf biochemical and structural characteristics. Our data revealed that (i) soil N made a positive effect on intra-leaf CO 2 diffusion and carbon assimilation that g m and A n (net photosynthetic rate) exhibited a significant positive response to increasing N supplying from 7.5 to 15.0 g urea m -2 while g sc showed no significant N-dependence. (ii) The enhanced intra-leaf CO 2 diffusion capacity induced by N application was principally attributable to the increase in g m .(iii) The enhancement of biochemical metabolism rather than the modifications in the leaf anatomical structure constituted the predominant mechanism by which N supplementation facilitated CO 2 diffusion and carbon assimilation in soybean. (iv)Furthermore, the improvement in water use efficiency (WUE) appeared to be more closely linked to aquaporin-mediated water relations, as supported by subsequent correlation analyses.These findings will advance our understanding of the key drivers that shape g m responsiveness to abrupt environmental variations.