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Acute kidney injury (AKI) exhibits high morbidity and mortality. Kidney injury molecule-1 (KIM1) is dramatically upregulated in renal tubules upon injury, and acts as a biomarker for various renal diseases. However, the exact role and underlying mechanism of KIM1 in the progression of AKI remain elusive. Herein, we report that renal tubular specific knockout of Kim1 attenuates cisplatin- or ischemia/reperfusion-induced AKI in male mice. Mechanistically, transcription factor Yin Yang 1 (YY1), which is downregulated upon AKI, binds to the promoter of KIM1 and represses its expression. Injury-induced KIM1 binds to the ECD domain of death receptor 5 (DR5), which activates DR5 and the following caspase cascade by promoting its multimerization, thus induces renal cell apoptosis and exacerbates AKI. Blocking the KIM1-DR5 interaction with rationally designed peptides exhibit reno-protective effects against AKI. Here, we reveal a YY1-KIM1-DR5 axis in the progression of AKI, which warrants future exploration as therapeutic targets. KIM1 is dramatically upregulated in acute kidney injury (AKI) and but how KIM1 affects AKI remains unknown. Here, the authors report that renal specific Kim1 knockout relieves AKI, unveil a YY1-KIM1-DR5 axis in the progression of AKI, and suggest potential therapeutic strategies against AKI.

Magnetoencephalography based on optically pumped magnetometers can passively detect the ultra-weak brain magnetic field signals, which has significant clinical application prospects for the diagnosis and treatment of cerebral disorders. This paper proposes a brain magnetic signal measurement method on the basis of the active–passive coupling magnetic shielding strategy and helmet-mounted detection array, which has lower cost and comparable performance over the existing ones. We first utilized the spatially-grid constrained coils and biplanar coils with proportion–integration–differentiation controller with tracking differentiator to ensure a near-zero and stable magnetic field environment with large uniform region. Subsequently, we implemented the brain magnetic signal measurement with the subject randomly moving fingers through tapping a keyboard and with the condition of opening and closing the eyes. Effectively induced brain magnetic signals were detected at the motor functional area and occipital lobe area in the two experiments, respectively. The proposed method will contribute to the development of functional brain imaging. •The active–passive coupling magnetic shielding system is designed to create a large uniform region near-zero magnetic field environment.•A method for the design and simplification of the spatially-grid constrained coils is proposed.•The induced brain magnetic signals at the motor functional area and occipital lobe area were successfully detected.•The proposed method will contribute to the development of lightweight wearable MEG system and functional brain imaging.

Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are neurodegenerative disorders characterized by the accumulation of α-synuclein aggregates. α-synuclein forms droplets via liquid-liquid phase separation (LLPS), followed by liquid-solid phase separation (LSPS) to form amyloids, how this process is physiologically-regulated remains unclear. β-synuclein colocalizes with α-synuclein in presynaptic terminals. Here, we report that β-synuclein partitions into α-synuclein condensates promotes the LLPS, and slows down LSPS of α-synuclein, while disease-associated β-synuclein mutations lose these capacities. Exogenous β-synuclein improves the movement defects and prolongs the lifespan of an α-synuclein-expressing NL5901 Caenorhabditis elegans strain, while disease-associated β-synuclein mutants aggravate the symptoms. Decapeptides targeted at the α-/β-synuclein interaction sites are rationally designed, which suppress the LSPS of α-synuclein, rescue the movement defects, and prolong the lifespan of C. elegans NL5901. Together, we unveil a Yin-Yang balance between α- and β-synuclein underlying the normal and disease states of PD and DLB with therapeutical potentials. The authors report a Yin-Yang balance between α-Synuclein and β-Synuclein via regulating phase separation in physiological states and Parkinson’s disease. AI-designed peptides mitigate the symptoms and prolong the lifespan of C. elegans PD models.

Due to the configuration of coreless stators and two synchronously rotated rotors, hollow-cup machines (HCMs) enjoy the merits of negligible cogging torque and core loss. Consequently, HCMs have been successfully employed as high-speed electric machines in the aerospace field, which requires high precision and low thermal dissipation. However, the permanent magnet (PM) thickness and air-gap length of conventional HCM are uniform, resulting in various harmonics in the air-gap flux density as well as back-EMF. These harmonics inevitably produce an electromagnetic torque ripple, which has not met the increasing demand for ultraprecision in recent years. Since the inner rotor of HCMs only consists of an iron core, this paper proposes a novel sinusoidal-shaped inner rotor, which can change the harmonics of air-gap permeance, to adjust the harmonics of air-gap flux density and back-EMF. HCMs with the proposed inner rotors have a significant 87% reduction in torque ripple compared to conventional HCMs. Meanwhile, compared to conventional methods, HCMs with the proposed inner rotor exhibit comparable torque ripple and higher average torque.

For full-maglev vertical superconducting gravity instruments, displacement control in the non-sensitive axis is a key technique to suppress cross-coupling noise in a dynamic environment. Motion decoupling of the test mass is crucial for the control design. In practice, when levitated, the test mass is always in tilt, and unknown parameters will be introduced to the scale factors of displacement detection, which makes motion decoupling work extremely difficult. This paper proposes a method for decoupling the translation and rotation of the test mass in the non-sensitive axis for full-maglev vertical superconducting gravity instruments. In the method, superconducting circuits at low temperature and adjustable gain amplifiers at room temperature are combined to measure the difference between the scale factors caused by the tilt of the test mass. With the measured difference of the scale factors, the translation and rotation are decoupled according to the theoretical model. This method was verified with a test of a home-made full-maglev vertical superconducting accelerometer in which the translation and rotation were decoupled.

Tuning the stiffness balance is crucial to full-band common-mode rejection for a superconducting gravity gradiometer (SGG). A reliable method to do so has been proposed and experimentally tested. In the tuning scheme, the frequency response functions of the displacement of individual test mass upon common-mode accelerations were measured and thus determined a characteristic frequency for each test mass. A reduced difference in characteristic frequencies between the two test masses was utilized as the criterion for an effective tuning. Since the measurement of the characteristic frequencies does not depend on the scale factors of displacement detection, stiffness tuning can be done independently. We have tested this new method on a single-component SGG and obtained a reduction of two orders of magnitude in stiffness mismatch.

UTX (also known as KDM6A) is a histone H3K27 demethylase that acts as an important tumor regulator. UTX has been reported to participate in genome‐wide histone modifications and gene expression in tumorigenesis and its mutations are identified in human cancers. Here, UTX is demonstrated to localize both in the cytoplasm and nucleus, notably, cytoplasmic UTX forms puncta and co‐localizes in stress granules (SGs) upon different stresses in vitro. Mechanistically, the TPR domain of UTX plays a critical role in regulating SG disassembly by interacting with G3BP1, the central hub of SG, to disrupt the scaffold network of SG under endoplasmic reticulum stress. Importantly, a clinical UTX mutation, D336G in TPR domain, increases cytoplasmic location of UTX, and stabilizes SG. While UTXD336G promotes, WT UTX or UTXTPR inhibits, cell growth and tumorigenesis by regulating SGs both in vitro and in nude mice, and such regulation is G3BP1 dependent. Together, the results suggest a novel cytoplasmic function of UTX as a negative regulator of SG homeostasis, which is involved in stress and disease states such as tumorigenesis. These findings indicate that cytoplasmic UTX forms puncta and co‐localizes in stress granules (SGs) upon various stresses. UTX TPR‐domain‐dependently and demethylase‐activity‐independently destabilize SGs by binding G3BP1, the SG hub protein, to disrupt SG network, thus affects tumorigenesis. D336G, a clinical UTX mutation in TPR domain, promotes tumorigenesis by stabilizing SGs in vitro and in nude mice.

In order to address the issue of enhancing the utilization of traction energy consumption in high-speed railway trains while ensuring safety and punctuality, a methodology is proposed that is founded on the Double Deep Q-Network (DDQN) algorithm. The methodology is initiated with the construction of a high-speed railway train traction control model, followed by the conversion of the entire train operation process into a Markov decision-making process. The train operation simulation environment, state action value function and reward function are then designed according to the fundamental composition of a reinforcement learning algorithm. The subsequent section details the optimization method of high-speed railway train traction control based on DDQN algorithm, trained by combining actual line and train operation data. Finally, a section of a bureau between two stations is selected for example analysis. The results demonstrate that the proposed method achieves a traction energy saving of 11.74kW eh in comparison to the conventional DQN algorithm, while ensuring on-time and stop location accuracy. Furthermore, the study investigates the impact of two hyperparameters, the learning rate a and the selection probability decay value Ag, on the training effect of the model are analyzed, and the results show that with the order of magnitude of the learning rate a of 0.001 and the selection probability decay value Az of 1/2500, the model has the highest average training reward value and the best training effect.

In this work, CoAl-layered double hydroxide (CoAl-LDH) was synthesized by a facile one-step process and utilized as an adsorbent for the removal of thiocyanate (SCN−) from environmental water. The characterization results revealed that CoAl-LDH presents a homogeneous nanosized plate with intercalation of NO3− in the interlayer space. The main factors affecting the removal efficiencies were investigated, and results revealed that CoAl-LDH possessed high removal efficiencies for SCN− and was suitable for a wide range of pH and ambient temperature conditions. Furthermore, the results of the mechanism analysis revealed that the mechanism of adsorption of SCN− by CoAl-LDH mainly includes interlayer ion exchange, electrostatic interactions, and surface ligand exchange. Model fitting of the kinetic data showed that SCN− sorption on CoAl-LDH followed the pseudo-second-order model and the removal rate of SCN− could reach 91.4% with 10 min contact time. Freundlich adsorption isotherm model could describe the adsorption process most accurately, and the maximum adsorption values of SCN− were 187 mg/g at 25℃ and pH 6.0. Meanwhile, the spent CoAl-LDH could be regenerated in Fe(NO3)3 solution and was reused up to four cycles. The overall results demonstrate that CoAl-LDH had a great application potential in the removal of SCN− from aqueous solution.

Large Language models (LLMs) have become a research hotspot. To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique. However, as the inference length increases, growing KV caches might lead to out-of-memory issues. Many existing methods address this issue through KV cache compression, primarily by preserving key tokens throughout all layers to reduce information loss. Most of them allocate a uniform budget size for each layer to retain. However, we observe that the minimum budget sizes needed to retain essential information vary across layers and models based on the perspectives of attention and hidden state output. Building on this observation, this paper proposes a simple yet effective KV cache compression method that leverages layer uncertainty to allocate budget size for each layer. Experimental results show that the proposed method can reduce memory usage of the KV caches to only \\(\\sim\\)20\\% when compared to Full KV inference while achieving nearly lossless performance.