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Overexpression of bacterial γ‐glutamylcysteine synthetase in the cytosol of Populus tremula × P. alba produces higher glutathione (GSH) concentrations in leaves, thereby indicating the potential for cadmium (Cd) phytoremediation. However, the net Cd²⁺influx in association with H⁺/Ca²⁺, Cd tolerance, and the underlying molecular and physiological mechanisms are uncharacterized in these poplars. We assessed net Cd²⁺influx, Cd tolerance and the transcriptional regulation of several genes involved in Cd²⁺transport and detoxification in wild‐type and transgenic poplars. Poplars exhibited highest net Cd²⁺influxes into roots at pH 5.5 and 0.1 mM Ca²⁺. Transgenics had higher Cd²⁺uptake rates and elevated transcript levels of several genes involved in Cd²⁺transport and detoxification compared with wild‐type poplars. Transgenics exhibited greater Cd accumulation in the aerial parts than wild‐type plants in response to Cd²⁺exposure. Moreover, transgenic poplars had lower concentrations of O₂˙⁻and H₂O₂; higher concentrations of total thiols, GSH and oxidized GSH in roots and/or leaves; and stimulated foliar GSH reductase activity compared with wild‐type plants. These results indicate that transgenics are more tolerant of 100 μM Cd²⁺than wild‐type plants, probably due to the GSH‐mediated induction of the transcription of genes involved in Cd²⁺transport and detoxification.

Providing sufficient driving force for charge separation and transfer (CST) is a critical issue in photoelectrochemical (PEC) energy conversion. Normally, the driving force is derived mainly from band bending at the photoelectrode/electrolyte interface but negligible in the bulk. To boost the bulky driving force, we report a rational strategy to create effective electric field via controllable lattice distortion in the bulk of a semiconductor film. This concept is verified by the lithiation of a classic TiO 2 (Li-TiO 2 ) photoelectrode, which leads to significant distortion of the TiO 6 unit cells in the bulk with well-aligned dipole moment. A remarkable internal built-in electric field of ~2.1 × 10 2  V m −1 throughout the Li-TiO 2 film is created to provide strong driving force for bulky CST. The photoelectrode demonstrates an over 750% improvement of photocurrent density and 100 mV negative shift of onset potential upon the lithiation compared to that of pristine TiO 2 film. The driving force for charge transfer in photoelectrochemical systems is typically derived from band bending at a surface-electrolyte interface. In this work, battery-type lithiation of TiO 2 generates a built-in electric field in the bulk material, giving a 750% enhancement in photocurrent density.

Ammonia, a vital component in the synthesis of fertilizers, plastics, and explosives, is traditionally produced via the energy‐intensive and environmentally detrimental Haber–Bosch process. Given its considerable energy consumption and significant greenhouse gas emissions, there is a growing shift toward electrocatalytic ammonia synthesis as an eco‐friendly alternative. However, developing efficient electrocatalysts capable of achieving high selectivity, Faraday efficiency, and yield under ambient conditions remains a significant challenge. This review delves into the decades‐long research into electrocatalytic ammonia synthesis, highlighting the evolution of fundamental principles, theoretical descriptors, and reaction mechanisms. An in‐depth analysis of the nitrogen reduction reaction (NRR) and nitrate reduction reaction (NitRR) is provided, with a focus on their electrocatalysts. Additionally, the theories behind electrocatalyst design for ammonia synthesis are examined, including the Gibbs free energy approach, Sabatier principle, d‐band center theory, and orbital spin states. The review culminates in a comprehensive overview of the current challenges and prospective future directions in electrocatalyst development for NRR and NitRR, paving the way for more sustainable methods of ammonia production. This review delves into the decades‐long research into electrocatalytic ammonia synthesis, highlighting the evolution of fundamental principles, theoretical descriptors, and reaction mechanisms and paving the way for more sustainable methods of ammonia production.

Bearings are critical components whose failures in industrial machinery can lead to catastrophic breakdowns and costly downtime; yet, accurate early-stage diagnosis remains challenging due to the non-stationary, nonlinear nature of vibration signals and noise interference. This study proposes a multidataset-integrated bearing fault diagnosis methodology incorporating variational mode decomposition (VMD), refined composite multiscale weighted permutation entropy (RCMWPE) feature extraction, and whale optimization algorithm (WOA)-optimized support vector machine (SVM). Addressing the non-stationary and nonlinear characteristics of bearing vibration signals, raw signals are first decomposed via VMD to effectively separate intrinsic mode functions (IMFs) carrying distinct frequency components. Subsequently, RCMWPE features are extracted from each IMF component to construct high-dimensional feature vectors. To address visualization challenges and mitigate feature redundancy, the t-distributed stochastic neighbor embedding (t-SNE) algorithm is employed for dimensionality reduction. Finally, WOA optimizes critical SVM parameters to establish an efficient fault classification model. The methodology is validated on two public bearing datasets: PRONOSTIA and CWRU. For four-class fault diagnosis on the PRONOSTIA dataset, the model achieves 96.5% accuracy. Extended to ten-class diagnosis on the CWRU dataset, accuracy reaches 99.67%. Experimental results demonstrate that the proposed method exhibits exceptional fault identification capability, robustness, and generalization performance across diverse datasets and complex fault modes. This approach offers an effective technical pathway for early bearing fault warning and maintenance decision making.

Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In ovarian cancer, the few established serum biomarkers are highly specific, yet insufficiently sensitive to detect early-stage disease and to impact the mortality rates of patients with this cancer. Here we show that a ‘disease fingerprint’ acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best clinical screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography). We used 269 serum samples to train and validate several machine-learning classifiers for the discrimination of patients with ovarian cancer from those with other diseases and from healthy individuals. The predictive values of the best classifier could not be attained via known protein biomarkers, suggesting that the array of nanotube sensors responds to unidentified serum biomarkers. Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.

As the cyclable sodium ions' primary suppliers, O3‐type layer‐structured manganese‐based oxides are recognized as one of the most competitive cathode candidates for sodium‐ion batteries. Suffering from complex structural transformations and transition metal migration during the sodium intercalation/deintercalation process, particularly at high voltage, the energy density and lifespan cannot satisfy the increasing demand. The orbital and electronic structure of the octahedral center metal element plays an important role in maintaining the octahedral structural integrity and improving the Na+ diffusivity by the introduced heterogeneous [Me–O] (Me: transition metals) chemical bonding. Herein, inspired by the 4f and 5d orbital bonding possibility from the abundant configuration of extranuclear electrons and large ion radius, O3‐type Na[La0.01Ni0.3Mn0.54Cu0.1Ti0.05]O2 was synthesized with a nearly single crystal structure. Based on the experimental and computational results, the introduced heterogeneous [La–O] chemical bond with larger bond strength can not only ensure the stability of the lattice oxygen framework and the reversibility of oxygen redox but also optimize the oxygen local electronic structure resulting from La 5d and O 2p orbital mixing due to O 2p → La 5d charge transfer. It delivers an optimal electrochemical performance with a high energy density and cycling lifespan. The large‐sized nearly single‐crystallized particles have a high ion diffusion without hindering by the crystal gap. Meanwhile, the uneven sodium ions' insertion and extraction inside the particles with oxygen release will be alleviated with varying degrees of success. It can efficiently increase the cycling lifespan by alleviating electrolyte side reactions.

Intervertebral disc degeneration (IDD), an important cause of chronic low back pain (LBP), is considered the pathological basis for various spinal degenerative diseases. A series of factors, including inflammatory response, oxidative stress, autophagy, abnormal mechanical stress, nutritional deficiency, and genetics, lead to reduced extracellular matrix (ECM) synthesis by intervertebral disc (IVD) cells and accelerate IDD progression. Mammalian target of rapamycin (mTOR) is an evolutionarily conserved serine/threonine kinase that plays a vital role in diverse degenerative diseases. Recent studies have shown that mTOR signalling is involved in the regulation of autophagy, oxidative stress, inflammatory responses, ECM homeostasis, cellular senescence, and apoptosis in IVD cells. Accordingly, we reviewed the mechanism of mTOR signalling in the pathogenesis of IDD to provide innovative ideas for future research and IDD treatment. Abnormal autophagy, oxidative stress, inflammatory response, ECM degeneration, senescence and apoptosis are involoved in the occurrence of intervertebral disc degeneration (IDD). Mammalian target of rapamycin (mTOR) is an evolutionarily conserved serine/threonine kinase that plays a vital role in IDD progression. Recent studies have shown that mTOR signalling is involved in the regulation of autophagy, oxidative stress, inflammatory responses, ECM homeostasis, cellular senescence, and apoptosis in IVD cells. We reviewed the mechanism of mTOR signalling in the pathogenesis of IDD to provide innovative ideas for future research and IDD treatment.

This study addresses the problem of simultaneous variable selection and model estimation in multivariate failure time data, a common challenge in clinical trials with multiple correlated time-to-event endpoints. We propose a unified framework that identifies predictors shared across outcomes, applicable to both low- and high-dimensional settings. For linear marginal hazard models, we develop a penalized pseudo-partial likelihood approach with a group LASSO-type penalty applied to the ℓ2 norms of coefficients corresponding to the same covariates across marginal hazard functions. To capture potential nonlinear effects, we further extend the approach to a sparse-input neural network model with structured group penalties on input-layer weights. Both methods are optimized using a composite gradient descent algorithm combining standard gradient steps with proximal updates. Simulation studies demonstrate that the proposed methods yield superior variable selection and predictive performance compared to traditional and outcome-specific approaches, while remaining robust to violations of the common predictor assumption. In an application to advanced prostate cancer data, the framework identifies both established clinical factors and potentially novel prognostic single-nucleotide polymorphisms for overall and progression-free survival. This work provides a flexible and robust tool for analyzing complex multivariate survival data, with potential utility in prognostic modeling and personalized medicine.

MicroRNAs have emerged as important post-transcriptional regulators of gene expression and are involved in diverse diseases and cellular process. Decreased expression of miR-181a has been observed in the patients with coronary artery disease, but its function and mechanism in atherogenesis is not clear. This study was designed to determine the roles of miR-181a-5p, as well as its passenger strand, miR-181a-3p, in vascular inflammation and atherogenesis. We found that the levels of both miR-181a-5p and miR-181a-3p are decreased in the aorta plaque and plasma of apoE −/− mice in response to hyperlipidemia and in the plasma of patients with coronary artery disease. Rescue of miR-181a-5p and miR-181a-3p significantly retards atherosclerotic plaque formation in apoE −/− mice. MiR-181a-5p and miR-181a-3p have no effect on lipid metabolism but decrease proinflammatory gene expression and the infiltration of macrophage, leukocyte and T cell into the lesions. In addition, gain-of-function and loss-of-function experiments show that miR-181a-5p and miR-181a-3p inhibit adhesion molecule expression in HUVECs and monocytes-endothelial cell interaction. MiR-181a-5p and miR-181a-3p cooperatively receded endothelium inflammation compared with single miRNA strand. Mechanistically, miR-181a-5p and miR-181a-3p prevent endothelial cell activation through blockade of NF-κB signaling pathway by targeting TAB2 and NEMO, respectively. In conclusion, these findings suggest that miR-181a-5p and miR-181a-3p are both antiatherogenic miRNAs. MiR-181a-5p and miR-181a-3p mimetics retard atherosclerosis progression through blocking NF-κB activation and vascular inflammation by targeting TAB2 and NEMO, respectively. Therefore, restoration of miR-181a-5p and miR-181a-3p may represent a novel therapeutic approach to manage atherosclerosis.

Accurate and efficient cloud detection is critical for maintaining the usability of optical remote sensing imagery, particularly in large-scale Earth observation systems. In this study, we propose FEMNet, a lightweight dual-branch network that combines state space modeling with convolutional encoding for multi-class cloud segmentation. The Mamba-based encoder captures long-range semantic dependencies with linear complexity, while a parallel CNN path preserves spatial detail. To address the semantic inconsistency across feature hierarchies and limited context perception in decoding, we introduce the following two targeted modules: a cross-stage semantic enhancement (CSSE) block that adaptively aligns low- and high-level features, and a multi-scale context aggregation (MSCA) block that integrates contextual cues at multiple resolutions. Extensive experiments on five benchmark datasets demonstrate that FEMNet achieves state-of-the-art performance across both binary and multi-class settings, while requiring only 4.4M parameters and 1.3G multiply–accumulate operations. These results highlight FEMNet’s suitability for resource-efficient deployment in real-world remote sensing applications.