Search Results (25,108)

Aspergillus fumigatus is a leading cause of invasive fungal disease in humans and is classified as a critical priority threat by the World Health Organization. Triazole antifungals remain the cornerstone of therapy, yet their effectiveness is steadily being eroded by the continuous rise in drug resistance. Most resistance mechanisms trace back to mutations in Cyp51A, spawning well-defined genotypes such as TR₃₄/L98H and TR₄₆/Y121F/T289A. However, the Cyp51A genotype–phenotype landscape in A. fumigatus is far from straightforward. Isolates that share an identical TR genotype can display strikingly divergent susceptibility profiles, and mutational hotspots in Cyp51A, such as G54, M220 and G448, are linked to varying resistances, challenging assumptions about predictable resistance behavior. Complicating matters further, an expanding array of resistance mechanisms, independent of Cyp51A, is now being uncovered. This review summarizes the current state of knowledge on azole resistance in A. fumigatus, dissecting the intricate genotype–phenotype relationships, spotlighting emerging non-Cyp51A pathways and outlining future strategies to enhance the detection and clinical management of antifungal resistance. Full article

Background: The systematic identification of transcriptional repressors remains challenging, as current inference frameworks are predominantly optimized for accessible chromatin, leaving regulatory signals embedded within repressive domains undercharacterized. Methods: Here, we present NuRepress, a computational framework that predicts candidate transcriptional repressors by integrating repressive chromatin architecture, functional signatures, and transcriptional outcomes. NuRepress first identifies well-phased nucleosome arrays within repressive chromatin. These arrays are treated as discrete structural units that capture characteristic local chromatin organization associated with regulatory activity. Since distinct Tn5 cut signal patterns often imply divergent regulatory functions, the framework stratifies these arrays into potential functional subtypes. By synthesizing the quantified repressive efficacy of each subtype with spatial motif enrichment and observed transcriptional dynamics, NuRepress systematically prioritizes and ranks candidate repressors. Results: Our analysis indicated that well-phased nucleosome arrays exhibited accessibility-defined organizational patterns with distinct repressive efficacies, and that these patterns were also observed across species, suggesting that the structural principles captured by NuRepress might extend beyond one specific biological system. Positional motif analysis revealed that distinct TFs exhibited different spatial preferences relative to well-phased nucleosome arrays, suggesting scale-specific preferences for their interactions with these organized chromatin structures. When applied to pancreatic cancer progression, NuRepress identified changes in nucleosome organization associated with stage-specific transcriptional remodeling, highlighting candidate repressors of key oncogenic drivers. Conclusions: NuRepress establishes a structure-aware strategy for repressor inference that extends regulatory genomics beyond accessibility-centered paradigms. By linking well-phased nucleosome organization to transcriptional outcomes, it provides a principled framework for dissecting transcriptional repression across diverse biological settings. Full article

Microchannel plate gated framing camera is commonly used in inertial confinement fusion diagnostics. However, it is a vacuum electronic device with bulkiness and non-single-line-of-sight imaging. To reduce the size of the camera and achieve a single line of sight image, a CMOS image sensor composed of a pixel unit and a readout circuit is presented to form the framing camera. The CMOS image sensor has a 32 × 32 × 4 pixel array with ultrashort shutter-time and four-frame imaging. The pixel array and analog to digital converter (ADC) readout circuit are designed using a standard 0.18 μm CMOS process. The pixel array includes 5T structured pixel units, a voltage-controlled delay, a clock tree and the row decoding scan circuits. A temporal resolution of 65 ps for the pixel circuit is achieved. The ADC readout circuit is composed of a counter, a comparator, a ramp generator and a register, which operates at a sampling frequency of 24.41 kS/s. An effective number of bits of 11.3, a spurious free dynamic range (SFDR) of 73.4 dB, and a signal-to-noise ratio (SNR) of 70.0 dB for the ADC are achieved. The CMOS image sensor will provide a novel and important imaging method for the field of ultrafast science. Full article

FHIR was designed for transactional interoperability but is less well suited to querying and exploratory analysis because its resource-centric structure distributes meaning across deeply nested resources. To address this limitation, we transformed MIMIC-IV Demo FHIR data into an OWL-compliant knowledge graph by flattening nested elements, normalizing repeating arrays, resolving inter-resource references, and promoting frequently queried attributes to direct properties. We also aligned diagnosis and procedure codes to ICD-9-CM and ICD-10-CM terminologies and developed a schema-grounded NL2SPARQL interface for natural-language querying. Structural validation was performed with SHACL and OWL reasoning. Across a curated evaluation set, NL2SPARQL achieved a mean accuracy exceeding 95% relative to expert-authored queries. These results suggest that ontologizing FHIR can improve analytic accessibility while preserving clinically meaningful assertions. Full article

Ensuring physical layer security for low-altitude economic networking (LAENet) is critical due to the broadcast nature of wireless channels. In dense urban environments, multi-antenna LAENet systems are often impaired by the keyhole effect, which induces rank deficiency and poses significant security challenges. This paper investigates the secrecy performance of a multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system in LAENet with keyhole channels. Depending on the availability of channel state information (CSI) at the transmitter, three wiretap scenarios are considered: (i) broadcasting, (ii) passive eavesdropping, and (iii) spoofing. For each scenario, the optimal precoder is designed to maximize the secrecy transmission rate. Based on these designs, we derive closed-form expressions for the secrecy outage probability (SOP) and average secrecy rate (ASR). To provide insights into the effect of keyholes on secrecy diversity order and array gain under this severe rank-deficiency structure, we also obtain asymptotic expressions for SOP and ASR in the high signal-to-noise ratio (SNR) regime using the Mellin transform. Numerical results validate the analytical expressions and illustrate the influence of key parameters on secrecy performance. These findings provide meaningful guidance for the secure design of future LAENet deployments. Full article

Previous studies have shown that the external quantum efficiency (EQE) of conventional red Micro-Light emitting diodes(Micro-LEDs) decreases markedly with reducing chip size. This degradation is generally attributed to enhanced non-radiative recombination at sidewall defects, which leads to increased carrier loss in size-scaled LEDs. In this work, AlGaInP quaternary semiconductor epitaxial wafers incorporating multiple quantum wells (MQWs) with different well-layer strain states were grown by metal–organic chemical vapor deposition (MOCVD). Through wafer bonding, photolithography, etching, and metal evaporation, these epitaxial structures were fabricated into Micro-LED arrays with single-pixel pitches of 10, 20, 50, and 100 μm. The experimental results reveal that, with increasing indium (In) composition in the GaInP well layers—corresponding to a gradual increase in lattice mismatch (Δa/a) from 0% to 1%—smaller-sized Micro-LED arrays exhibit superior EQE performance. For devices with a pixel pitch of 10 μm, the EQE of Micro-LED arrays with a 1% lattice mismatch in the well layer is approximately three times higher than that of lattice-matched (0%) counterparts. In contrast, for devices with a pixel pitch of 100 μm, the EQE of lattice-matched (0%) Micro-LED arrays is about 1.3 times higher than that of devices with a 1% lattice mismatch. These results indicate that, to achieve maximum EQE in Micro-LEDs, the strain state of the MQW-layer material must be carefully considered as a priority factor. Optimal device performance requires appropriate matching between LED size and the well-layer growth strain. Full article

Vector–matrix multiplication (VMM), implemented through multiply–accumulate (MAC) operations, represents the dominant computational primitive in many artificial intelligence (AI) workloads. When executed on conventional von Neumann architectures, VMM operations suffer from important energy consumption and latency due to the separation between memory and processing units. To overcome these limitations, crossbar arrays built from Resistive Random Access Memory (RRAM) cells have been proposed for accelerating VMM computations. In this work, we investigate the key optimization trade-offs associated with implementing RRAM-based neural networks for classification applications. A simple two-layer neural network is first defined and trained in software to generate the weight matrices and bias parameters. Next, three hardware implementation scenarios are evaluated depending on whether negative floating-point numbers are used: Positive Weights Only (PWO), Positive and Negative Weights Only (PNWO), and Positive and Negative Weights with Biases (PNWB). The different implementations are analyzed at the hardware level by examining classification accuracy, energy efficiency, latency, and area overhead. The study further incorporates important RRAM limitations, including restricted conductance range and device variability. Hardware results show that the PWO scenario offers the lowest energy consumption (189 fJ/MAC) and area overhead but results in the lowest accuracy. PNWO and PNWB significantly improve accuracy (+177% and +180%) but increase energy consumption (+63% and +87%) and area (×2 and ×2.1). Under variability effects, PWO achieves better accuracy (94.65%), followed by PNWO (93.11%) and PNWB (92.11%). Full article

To address the demand for wide-swath, high-resolution short-wave infrared (SWIR) imaging on resource-constrained spaceborne platforms, this study presents the design and on-orbit validation of a compact dual-channel push-broom (line-scanning) imaging system. The system adopts a transmissive optical architecture and a centralized, compact electronic control unit (ECU) configuration. By interleaving and mosaicking sixteen InGaAs linear array detectors, the system achieves an imaging swath of approximately 187 km and a nominal ground sampling distance of about 24 m, while maintaining a total instrument mass of 10.62 kg and a power consumption of approximately 12 W, thereby demonstrating a high level of integration and efficient resource utilization. To address focal plane consistency issues arising from multi-detector mosaicking, a closed-loop leveling method was developed using the modulation transfer function (MTF) as the primary performance metric. Through defocus estimation and quantitative correction of protrusions on a SiC substrate, convergence toward a unified confocal focal plane among multiple detectors was achieved. On-orbit image quality assessment indicates that the full width at half maximum (FWHM) of the line spread function (LSF) for both channels is approximately 1.38 pixels, with favorable signal-to-noise ratio (SNR) performance. These results validate the effectiveness of the proposed focal plane leveling strategy as well as the opto-mechanical-thermal design of the system. The proposed approach provides a practical pathway for the engineering implementation and consistency control of multi-detector mosaicked SWIR payloads under stringent resource constraints. Full article

Reliable in situ temperature monitoring is essential for the safe operation of liquid-nitrogen-cooled superconducting cables, yet conventional electrical sensors are often difficult to scale to multi-point deployment in cryogenic, high-current environments. This work presents a fiber Bragg grating (FBG) sensing solution for in situ temperature monitoring of superconducting cables in liquid nitrogen. An FBG array packaged with a polyimide-coated fiber inside a 3 mm stainless-steel tube is deployed along the cable centerline to provide multi-point temperature measurements of the cable core. The system is validated under liquid-nitrogen immersion with a 2000 A current turn-on/turn-off test, with a 1 Hz update rate and a steady-state temperature fluctuation within ±0.1 °C. Experimental results show a continuous temperature decrease during liquid-nitrogen cooling, followed by a cryogenic plateau, during which a spatially consistent 0.6–0.7 °C current-induced temperature rise is observed across multiple sensing points in the present 2000 A turn-on/turn-off test, followed by recovery after current shutoff. Small-amplitude fluctuations during the plateau are attributed to packaging-dependent thermal coupling between the centerline-deployed sensor and the cable core. These results indicate that the proposed FBG-based approach enables reliable cryogenic thermometry for superconducting cables in liquid nitrogen and provides a practical tool for in situ operational condition assessment. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article

To address the coupled challenges of renewable power volatility, high operating cost, and electrolyzer degradation in grid-connected wind–PV hydrogen production systems, this paper proposes a hierarchical day-ahead scheduling strategy under time-of-use (TOU) electricity prices. The upper layer performs price-responsive economic dispatch to coordinate renewable utilization, battery operation, grid transactions, and aggregate hydrogen-production power with the objective of minimizing lifecycle operating cost. The lower layer introduces a health-aware non-uniform rotation mechanism to allocate the aggregate power command among electrolyzer units, thereby reducing fluctuation exposure and balancing lifetime consumption across the array. Practical constraints, including multi-state electrolyzer operation, unit-commitment logic, battery state-of-charge dynamics, hydrogen storage limits, and system power balance, are explicitly considered. A case study of a wind–PV hydrogen production project in Northern China shows that the proposed strategy shifts electricity purchases to valley-price periods and promotes electricity export during peak-price periods. Compared with the benchmark strategy, hydrogen production during low wind–PV generation periods increases from 342,000 to 381,000 Nm³, the share of fluctuating operating time decreases from 62.5% to 12.5%, and the average daily start–stop frequency declines from 8.0 to 4.8. Consequently, the degradation penalty is reduced by about 40%, and lifecycle operating cost decreases by 27.3%. Full article

Achieving a high frame rate and high dynamic range (HDR) under complex illumination remains a significant challenge for airborne push-broom visible-near-infrared (VNIR) hyperspectral cameras. Problematic scenarios typically include high-contrast scenes, such as ocean whitecaps alongside deep water or concurrently sunlit and shadowed urban surfaces. To address this, a real-time HDR acquisition system based on a dual-gain complementary metal–oxide–semiconductor (CMOS) image sensor is proposed. Specifically, a four-pixel HDR fusion method is developed, utilizing an optical calibration setup to accurately determine the fusion parameters and configure the spectral region of interest (ROI) for reduced data volume. The complete workflow, encompassing spectral–spatial four-pixel binning and piecewise dual-gain fusion, is implemented on a field-programmable gate array (FPGA) using a dual-port RAM-based buffering strategy and a low-latency five-stage pipeline. Experimental results demonstrate a minimal processing latency of 0.0183 ms and a maximum frame rate of 290 frames/s. By extending the output bit depth from 11 to 15 bits, the system achieves a digital dynamic range of the final output of 2.03 × 10⁴:1, representing a 9.58-fold improvement over the original low-gain data. The fused HDR data maintain high linearity and good spectral fidelity, with spectral angle mapper (SAM) values at the 10⁻³ level. Featuring a compact and low-power design, this system provides a practical engineering solution for efficient airborne VNIR hyperspectral acquisition. Full article

Microphysiological systems (MPSs) have emerged as promising platforms for drug discovery and in vitro pharmacological testing. MPSs aid to reproduce physiologically relevant microenvironments, in which controlled perfusion can play important role. In this study, an on-chip AC electrothermal (ACET) pump was developed for pulsatile perfusion in microfluidic cell culture systems. The proposed pump generates fluid motion through the interaction between an applied electric field and temperature-dependent gradients in the electrical properties of the fluid. Pulsatile perfusion was produced by periodic application of an AC voltage to the electrode array, and the pulsation cycle could be controlled electrically. The maximum flow velocity increased with the applied AC voltage, demonstrating tunable flow generation by the ACET pump. To evaluate the applicability of the developed system to cell culture, human mesenchymal stem cells (hMSCs) were cultured under pulsatile perfusion conditions for five days. The results showed that osteogenic differentiation under pulsatile perfusion was higher than that under static culture conditions. These findings demonstrate the potential of the proposed on-chip ACET pump as a simple and effective platform for generating physiologically relevant pulsatile perfusion in microphysiological systems. Full article

Synthetic dyes frequently persist through conventional wastewater treatment, motivating the use of advanced oxidation processes capable of breaking down these stable molecules. Metal-doped carbon dots (CDs) offer a tuneable platform for catalytic dye degradation in water, although their performance varies strongly with operating conditions. The aim of this work was to determine how temperature, H₂O₂ dosage, and pH influence the catalytic behaviour of Fe-, Cu-, Zn-, and Mg-doped CDs during the degradation of methylene blue (MB) and rhodamine B (RB), optimised using a Taguchi L27 orthogonal array design. Temperature and oxidant loading were the dominant factors: higher temperatures accelerated reactions through Arrhenius-type kinetics, while increasing H₂O₂ availability improved removal until excessive levels began to suppress •OH generation. Across all condition sets, apparent rate constants spanned 7.0 × 10⁻⁴–2.65 × 10⁻² min⁻¹, with t₅₀ values of 26–217 min and t₉₀ extending from ~86 min to >700 min; final decolourisation ranged from ~17% to nearly 100%. pH played a secondary role, mainly affecting dye speciation and surface adsorption. Dopant identity shifted the optimum operating region for each catalyst: Fe- and Cu-CDs achieved complete or near-complete removal of both dyes at pH 7 and 50 °C with relatively low H₂O₂ dosage (0.5–1.0 mL); Zn-CDs reached equivalent performance at pH 7 and 25 °C but required higher oxidant loading (1.5 mL of H₂O₂), reflecting their photo-induced rather than thermally driven activation mechanism; Mg-CDs performed comparably under the same conditions as Fe- and Cu-CDs. The resulting condition–catalyst map highlights the operating regimes that maximise efficiency while minimising chemical input, providing a practical framework for selecting carbon-dot-based catalysts for water treatment applications. Full article

As the number of elderly individuals living alone increases, the risk of fall-related accidents correspondingly rises, underscoring the need for rapid fall detection systems. Because falls are difficult to predict in terms of location, detection systems must be deployed in a distributed manner, which in turn requires compact and low-power implementations. Unlike camera sensors, radar sensors do not raise privacy concerns and are not limited by line-of-sight constraints. Moreover, compared with wearable sensors, radar enables continuous monitoring without user intervention. However, prior radar-based approaches incur high computational complexity, leading to increased power consumption and larger hardware area, thereby necessitating efficient hardware design. This paper proposes a lightweight fall detection system based on continuous-wave (CW) radar and a binarized convolutional neural network (BCNN). Radar signals are preprocessed using short-time Fourier transform (STFT) to generate binary spectrograms, which are then fed into a BCNN-based classification network. The proposed system performs binary classification of five fall activities and seven non-fall activities with an accuracy of 96.1%. The preprocessing module and classification network were implemented as hardware accelerators and integrated with a microprocessor in a system-on-chip (SoC) architecture on a field-programmable gate array (FPGA). Compared with the software implementation, the proposed hardware achieved speedups of 387.5× and 86.7× for the preprocessing and classification modules, respectively. Furthermore, the overall system processing time was 2.58 ms, corresponding to an 89.5× speedup over the software baseline. Full article