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Gender inequality in academic promotion trajectories in Japan
Engineering technology of implantable galvanic cells for silencing the bone-neural axis in gouty arthritis therapy
Research on a fast self-healing control method for tie-line faults in regional distribution networks based on source–load–storage interaction
Giant magnetic moment increase by ultrafast laser light
Abstract It is now well established that a few femtosecond laser pulse will induce an ultrafast loss of moment in a magnetic material. Here we show that the opposite effect can also occur: an ultrafast increase in moment. Employing both tight-binding and state-of-the-art time dependent density functional theory we find that laser light tuned to the majority spin conduction band in the 2d magnets CrI 3 and CrSBr generates an ultrafast giant moment increase, of up to 33% in the case of CrI 3 (2 μ B ). Underpinning this is spin-orbit induced valence band spin texture that, in combination with a strong field light pulse, facilitates an optical spin flip transition involving both intra- and inter-band excitation. Our findings, that establish a general mechanism by which ultrafast light pulses may enhance as well as decrease the magnetic moment, point towards rich possibilities for light control over magnetic matter at femtosecond times.
Characterizing the behavioural correlates of error awareness with the novel speeded inference task
Abstract Influential theories on error processing assume that when we make errors, adaptive processes are triggered to improve our behaviour. These processes appear to be more effective after participants have detected an error. Therefore, the assessment of error awareness, allowing a differential analysis of detected and undetected errors, and behavioural adjustments have gained increasing attention in the field of cognitive control over the past decades. A common methodological challenge in studies investigating error detection is that the number of undetected errors is usually relatively low. Here, we introduce a new experimental task that pursued the primary goal to generate a high error rate including many detected and undetected errors which is stable over the time on task. Study 1 (n = 21 adults) clearly showed the task was successful in producing those indented error rates. Exploratory analyses of study 1 further suggested that post-error adjustments (measured as post-error slowing and post-error accuracy) are unrelated to error awareness in the current study. Based on findings of study 1, study 2 (n = 20 adults) used the experimental task to test whether error awareness could specifically be manipulated within one experimental session by changing a single task feature. As expected, the modulation affected error detection, but did not affect the total error rate. Potential applications of the Speeded Inference Task (short SIT) are discussed. With this newly developed paradigm, we wish to lay the foundation for future research to better understand (neural) processes associated with error awareness.
High encoding-sensitivity vision sensor with complementary nonlinear neuromorphic computing
Comparative effectiveness of interventions incorporating physical activity in individuals with comorbid low back pain and depressive symptoms: a systematic review with network meta-analysis
Abstract This systematic review with network meta-analysis aimed to identify the most effective intervention incorporating a substantial physical activity component in individuals with comorbid depressive symptoms and nonspecific chronic low back pain (LBP). A search of six databases (PubMed/Medline, PsycINFO, Web of Science Core Collection, EMBASE, CINAHL, CENTRAL) was conducted up to July 30, 2025. Randomised controlled trials on participants > 15 years with comorbid clinically relevant depressive symptoms (e.g., Beck Depression Inventory (BDI)-II > 13) and chronic (> 12 weeks) LBP were included. Interventions involved ≥ 33% active movement and lasted ≥ 2 weeks. Outcome-wise risk of bias (RoB) for the self-reported outcomes was assessed using the Cochrane RoB 2 tool. Primary outcomes were (i) changes in depressive symptoms (mandatory) and (ii) pain intensity, disability and quality of life, with each study required to include at least one of three additional outcomes. We calculated frequentist-based network meta-analysis. Of 2138 studies, five studies ( N = 834, 50.1% female, mean age: 52.8 years) with eight treatments were included. Overall RoB ranged from low to some concerns. Yoga with education was most effective at mid-term follow-up (intervention duration closest to 12 weeks, k = 9), showing improvements in pain (SMD=−1.05 [95%CI=−1.38 to −0.72]) and clinically relevant improvements in depressive symptoms (SMD=−1.48 [95%CI=−1.81 to −1.14]), all vs. usual care. Antidepressant therapy with pain self-management (e.g., physical activity, relaxation, breathing) moderately affected depressive symptoms (SMD=−0.56 [95%CI=−0.60 to −0.53]) and pain (SMD=−0.53 [95%CI=−0.56 to −0.49]). At long-term follow-up, antidepressant therapy with pain self-management, and online- and mobile-based therapy showed superiority over usual care. The overall certainty of evidence across outcomes was low to very low. With very low certainty evidence, yoga combined with education yielded the most favourable effects across outcomes. However, these findings should be interpreted with caution and require adequately powered future trials. While other approaches showed small to moderate effects, their clinical relevance remains uncertain. PROSPERO 2024 ID : CRD42024523604.
Rapid earthquake magnitude classification via P-wave strains from borehole strainmeters and Distributed Acoustic Sensing
Abstract Distributed Acoustic Sensing (DAS) offers a promising approach for earthquake early warning (EEW) in settings where seismic networks are costly to maintain. By repurposing fiber optic cables as dense strainmeter arrays, DAS enables real-time earthquake detection wherever those fibers are accessible. However, poor azimuthal coverage and challenges in estimating magnitude from strain measurements remain key hurdles in applying DAS to earthquake monitoring. Here, we develop a machine learning method to distinguish moderate-to-large (defined here as M ≥ 5.4) earthquakes from smaller ones within the first 4 sec of a strain waveform after a P-wave arrival without determining the earthquake location. Using ensemble decision tree models trained on borehole strainmeter data (3.5 ≤ M ≤ 7.1) and tested on onshore DAS waveforms (including the 2024 M7 Offshore Cape Mendocino earthquake), we find that low-frequency (0.2–0.5 Hz) continuous wavelet transform coefficients are the strongest predictors of magnitude, in addition to strain amplitude. Both DAS and borehole strainmeters effectively capture long-period strain signals, making these results valuable for EEW systems. Our method shows high precision compared to the real-time EEW system, ShakeAlert®, supporting the position that DAS is a viable technology for earthquake monitoring and magnitude classification. Fiber optic cables plus machine learning could aid earthquake early warning by estimating earthquake size within the first few seconds of shaking.
Integrated femtosecond-laser workflow for decapsulation, delayering, and cross-sectioning of advanced package-on-package devices
Abstract Advanced packaging devices integrate multiple heterogeneous materials and components (e.g., mold compounds, redistribution/interposer structures, stacked memory, and logic dies) into compact assemblies whose failures can propagate into high-impact system-level outages. High-resolution, layer-by-layer 3D imaging is therefore essential for (i) failure analysis (FA) and root-cause determination, (ii) verification and validation (V&V) via as-designed versus as-built comparison, and (iii) security-focused inspection aimed at identifying unintended or malicious structural modifications. However, conventional approaches—mechanical polishing, chemical etching, and focused ion beam (FIB) delayering—are often slow, limited in precision, inconsistent over large areas, or difficult to scale for systematic workflows. In this work, we have shown that laser delayering can promise an effective alternative that is simultaneously fast and precise. Here, we demonstrate an integrated femtosecond-laser workflow for advanced package-on-package deprocessing that combines decapsulation, DRAM/component removal, sequential redistribution-layer exposure and removal, and targeted logic-die cross-sectioning on a single laser-processing platform. Process outcomes were validated using laser-scanning confocal microscopy for high-fidelity surface topography and scanning electron microscopy (SEM) for microstructural assessment. The results demonstrate a practical, rapid, and reproducible pathway for advanced-package system deconstruction and design reconstruction workflows supporting FA, V&V, and hardware assurance.
Pervasive convergent evolution of sperm conjugation across the Arthropoda tree of life
Decentralised rapid diagnostic tests enable cholera diagnosis by non-laboratory health workers during outbreaks in Cameroon
Diffusional aging at water/oil interfaces laden with charged nanoparticles studied by single-molecule tracking
privateST: a feasible framework for privacy-preserving spatial transcriptomics prediction from histopathology images
Abstract Predicting spatial transcriptomics from histology images offers cost-effective insights but faces privacy barriers preventing cross-institutional data sharing. To address this, we present privateST, a homomorphic-encryption-optimized framework that substantiates the feasibility of secure spatial transcriptomics prediction. To accommodate the computational constraints of homomorphic encryption, we downsampled the input images using bilinear interpolation. To improve prediction accuracy for the 100 target genes, we incorporated predictions for an auxiliary set of 150 highly expressed genes. This approach enables the model to learn from a more diverse genomic context, thereby refining the feature representations for the high-priority target genes. To adapt the architecture for homomorphic encryption, we replaced Max-Pooling and ReLU with Average-Pooling and polynomial approximation, respectively. These modifications eliminate non-linear comparison operations, significantly reducing the multiplicative depth. We also implemented multiplexed packing to optimize the efficiency of encrypted data processing. Remarkably, our results demonstrate that despite the reduced input resolution, this proposed approach achieves accuracy comparable to the original ResNet-18 configurations without downsampling.
Decreased degree of adipocyte differentiation in visceral adipose tissue contributes to metabolic dysfunction-associated steatotic liver disease
Abstract The mechanisms connecting the human fat depots, subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), to metabolic dysfunction-associated steatotic liver disease (MASLD) remain elusive. We hypothesize that in individuals with obesity, a decreased degree of adipocyte differentiation may contribute to ectopic fat accumulation in the liver, seen in MASLD. Here we show, using single nucleus RNA-sequencing from adipose tissue biopsies, that the predicted degree of VAT adipocyte differentiation is decreased in individuals with MASLD, with an attenuated impact observed in SAT adipocytes. Next, we discover that regional variants of the VAT adipocyte differentiation gene set explain a substantial proportion (17%) of MASLD heritability. These genes largely overlap (>50%) with adipocyte genes differentially expressed by MASLD, regulated by variants in cis . Finally, we show that these genes are linked to smaller adipocyte size. Together, our findings reveal that a decreased predicted degree of VAT adipocyte differentiation contributes to MASLD.