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Rescue RM/CS-AKI by blocking strategy with one-dose anti-myoglobin RabMAb
Solvent-dripping modulated 3D/2D heterostructures for high-performance perovskite solar cells
Abstract The controlled growth of two-dimensional (2D) perovskite atop three-dimensional (3D) perovskite films reduces interfacial recombination and impedes ion migration, thus improving the performance and stability of perovskite solar cells (PSCs). Unfortunately, the random orientation of the spontaneously formed 2D phase atop the pre-deposited 3D perovskite film can deteriorate charge extraction owing to energetic disorder, limiting the maximum attainable efficiency and long-term stability of the PSCs. Here, we introduce a meta-amidinopyridine ligand and the solvent post-dripping step to generate a highly ordered 2D perovskite phase on the surface of a 3D perovskite film. The reconstructed 2D/3D perovskite interface exhibits reduced energetic disorder and yields cells with improved performance compared with control 2D/3D samples. PSCs fabricated with the meta-amidinopyridine-induced phase-pure 2D perovskite passivation show a maximum power conversion efficiency of 26.05% (a certified value of 25.44%). Under damp heat and outdoor tests, the encapsulated PSCs maintain 82% and 75% of their initial PCE after 1000 h and 840 h, respectively, demonstrating improved practical durability.
Impacts of irrigation expansion on moist-heat stress based on IRRMIP results
Intratumor heterogeneity of HPV integration in HPV-associated head and neck cancer
Genome-wide profiling of tRNA modifications by Induro-tRNAseq reveals coordinated changes
Longitudinal omics data and preclinical treatment suggest the proteasome inhibitor carfilzomib as therapy for ibrutinib-resistant CLL
Abstract Chronic lymphocytic leukemia is a malignant lymphoproliferative disorder for which primary or acquired drug resistance represents a major challenge. To investigate the underlying molecular mechanisms, we generate a mouse model of ibrutinib resistance, in which, after initial treatment response, relapse under therapy occurrs with an aggressive outgrowth of malignant cells, resembling observations in patients. A comparative analysis of exome, transcriptome and proteome of sorted leukemic murine cells during treatment and after relapse suggests alterations in the proteasome activity as a driver of ibrutinib resistance. Preclinical treatment with the irreversible proteasome inhibitor carfilzomib administered upon ibrutinib resistance prolongs survival of mice. Longitudinal proteomic analysis of ibrutinib-resistant patients identifies deregulation in protein post-translational modifications. Additionally, cells from ibrutinib-resistant patients effectively respond to several proteasome inhibitors in co-culture assays. Altogether, our results from orthogonal omics approaches identify proteasome inhibition as potentially attractive treatment for chronic lymphocytic leukemia patients resistant or refractory to ibrutinib.
Temperature seasonality regulates organic carbon burial in lake
Single soliton microcomb combined with optical phased array for parallel FMCW LiDAR
Mechano-gated iontronic piezomemristor for temporal-tactile neuromorphic plasticity
Abstract In bioneuronal systems, the synergistic interaction between mechanosensitive piezo channels and neuronal synapses can convert and transmit pressure signals into complex temporal plastic pulses with excitatory and inhibitory features. However, existing artificial tactile neuromorphic systems struggle to replicate the elaborate temporal plasticity observed between excitatory and inhibitory features in biological systems, which is critical for the biomimetic processing and memorizing of tactile information. Here we demonstrate a mechano-gated iontronic piezomemristor with programmable temporal-tactile plasticity. This system utilizes a bicontinuous phase-transition heterogel as a stiffness-governed iontronic mechanogate to achieve bidirectional piezoresistive signals, resulting in wide-span dynamic tactile sensing. By micro-integrating the mechanogate with an oscillatory iontronic memristor, it exhibits stiffness-induced bipolarized excitatory and inhibitory neuromorphics, thereby enabling the activation of temporal-tactile memory and learning functions (e.g., Bienenstock–Cooper–Munro and Hebbian learning rules). Owing to dynamic covalent bond network and iontronic features, reconfigurable tactile plasticity can be achieved. Importantly, bridging to bioneuronal interfaces, these systems possess the capacity to construct a biohybrid perception-actuation circuit. We anticipate that such temporal plastic piezomemristor devices for abiotic-biotic interfaces can serve as promising hardware systems for interfacing dynamic tactile behaviors into diverse neuromodulations.
Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes
Abstract Enhancing the ion conduction in solid electrolytes is critically important for the development of high-performance all-solid-state lithium-ion batteries (LIBs). Lithium thiophosphates are among the most promising solid electrolytes, as they exhibit superionic conductivity at room temperature. However, the lack of comprehensive understanding of their ion conduction mechanism, especially the effect of structural disorder on ionic conductivity, is a long-standing problem that limits further innovations in all-solid-state LIBs. Here, we address this challenge by establishing and employing a deep learning potential to simulate Li3PS4 electrolyte systems with varying levels of disorder. The results show that disorder-driven diffusion dynamics significantly enhances the room-temperature conductivity. We further establish bridges between dynamical characteristics, local structural features, and atomic rearrangements by applying a machine learning-based structure fingerprint termed “softness”. This metric allows the classification of the disorder-induced “soft” hopping lithium ions. Our findings offer insights into ion conduction mechanisms in complex disordered structures, thereby contributing to the development of superior solid-state electrolytes for LIBs.
Sensitive dependence of pairing symmetry on Ni-eg crystal field splitting in the nickelate superconductor La3Ni2O7
LDPC-cat codes for low-overhead quantum computing in 2D
Abstract The main obstacle to large scale quantum computing are the errors present in every physical qubit realization. Correcting these errors requires a large number of additional qubits. Two main avenues to reduce this overhead are (i) low-density parity check (LDPC) codes requiring very few additional qubits to correct errors (ii) cat qubits where bit-flip errors are exponentially suppressed by design. In this work, we combine both approaches to obtain an extremely low overhead architecture. Assuming a physical phase-flip error probability ϵ ≈ 0.1% per qubit and operation, one hundred logical qubits can be implemented on a 758 cat qubit chip, with a total logical error probability per cycle and per logical qubit ϵ L ≤ 10−8. Our architecture also features two major advantages. First, the hardware implementation of the code can be realised with short-range qubit interactions in 2D and low-weight stabilizers, under constraints similar to those of the popular surface code architecture. Second, we demonstrate how to implement a fault-tolerant universal set of logical gates with an additional layer of routing cat qubits stacked on top of the LDPC layer, while maintaining the local connectivity. Furthermore, our architecture benefits from a high capacity of parallelization for these logical gates.
Composition and liquid-to-solid maturation of protein aggregates contribute to bacterial dormancy development and recovery
High-temperature structural disorders stabilize hydrous aluminosilicates in the mantle transition zone
Crucial role of subsurface ocean variability in tropical cyclone genesis
Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm
Label-free proteomic analysis of Duchenne and Becker muscular dystrophy showed decreased sarcomere proteins and increased ubiquitination-related proteins
Abstract Muscular dystrophies (MD) are a group of hereditary diseases marked by progressive muscle loss, leading to weakness and degeneration of skeletal muscles. These conditions often result from structural defects in the Dystrophin–Glycoprotein Complex (DGC), as seen in Duchenne Muscular Dystrophy (DMD) and Becker Muscular Dystrophy (BMD). Since MDs currently have no cure, research has focused on identifying potential therapeutic targets to improve patients’ quality of life. In this study, skeletal muscle tissue samples from DMD and BMD patients, as well as non-dystrophic controls, were analyzed using label-free mass spectrometry (MS/MS) to characterize the proteomic profile of these conditions and identify biomarkers for differential diagnosis. In-silico analysis revealed that dystrophic muscle tissues are linked to biological processes related to cellular energy metabolism, including oxidation of organic compounds, energy production, and cellular respiration. Enrichment of functions associated with cell structure and RNA binding was also observed, including cytoskeletal protein binding and RNA binding. The human phenotypes most related to the proteomic signature were abnormal circulating metabolites, muscle physiology, and weakness. Quantitative analysis identified significant changes in proteins associated with sarcomere organization and protein ubiquitination, such as myomesin, myozenin, and E3 ubiquitin-protein ligase rififylin, suggesting these as potential therapeutic targets.
DLCDroid an android apps analysis framework to analyse the dynamically loaded code
Abstract To combat dynamically loaded code in anti-emulated environments, DLCDroid is an Android app analysis framework. DL-CDroid uses the reflection API to effectively identify information leaks due to dynamically loaded code within malicious apps, incorporating static and dynamic analysis techniques. The Dynamically Loaded Code (DLC) technique employs Java features to allow Android apps to dynamically expand their functionality at runtime. Unfortunately, malicious app developers often exploit DLC techniques to transform seemingly benign apps into malware once installed on real devices. Even the most sophisticated static analysis tools struggle to detect data breaches caused by DLC. Our analysis demonstrates that conventional tools areill-equipped to handle DLC. DLCDroid leverages dynamic code interposition techniques for API hooking to expose concealed malicious behavior without requiring modifications to the Android framework. DLCDroid can unveil suspicious behavior that remains hidden when relying solely on static analysis. We evaluate DLCDroid’s performance using a dataset comprising real-world benign and malware apps from reputed repositories like VirusShare and the Google Play Store. Compared to state-of-the-art approaches, the results indicate a significant improvement in detecting sensitive information leaks, more than 95.6% caused by reflection API. Furthermore, we enhance DLCDroid’s functionality by integrating it with an event-based trigger solution, making the framework more scalable and fully automated in its analysis process.
Remaining useful life prediction of lithium-ion batteries using a novel particle flow filter framework with grey model
Abstract Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries. First, the least squares method is employed to obtain the initial values for double exponential empirical model parameters. Subsequently, the grey model is used to predict the current cycle capacity of LIBs as an observation value for the particle flow filter, solving the inaccurate estimation problem of the state of particle flow filter observation values, and the particle flow filter method is employed to update model parameters. Finally, a test dataset is divided into early, middle, and late stages to predict the RUL of LIBs and obtain the probability distributions. On the CALCE and NASA PCoE LIB dataset, GM-PFF reduces RMSE by 1% compared to PFF, exhibiting a higher prediction accuracy and effectively addressing the particle degradation problem.