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Cerebral cavernous malformations (CCMs) are a cause of stroke and seizure for which no effective medical therapies yet exist. CCMs arise from the loss of an adaptor complex that negatively regulates MEKK3-KLF2/4 signalling in brain endothelial cells, but upstream activators of this disease pathway have yet to be identified. Here we identify endothelial Toll-like receptor 4 (TLR4) and the gut microbiome as critical stimulants of CCM formation. Activation of TLR4 by Gram-negative bacteria or lipopolysaccharide accelerates CCM formation, and genetic or pharmacologic blockade of TLR4 signalling prevents CCM formation in mice. Polymorphisms that increase expression of the TLR4 gene or the gene encoding its co-receptor CD14 are associated with higher CCM lesion burden in humans. Germ-free mice are protected from CCM formation, and a single course of antibiotics permanently alters CCM susceptibility in mice. These studies identify unexpected roles for the microbiome and innate immune signalling in the pathogenesis of a cerebrovascular disease, as well as strategies for its treatment.

In this work, N-doped and oxygen vacancy-rich NiCo2O4/NiO/Co3O4 composites are synthesized by the direct calcination method. Noticeably, by changing the additive concentrations of urea dissolved in DMF (N–N dimethylformamide), the electromagnetic wave (EMW) absorption abilities of NiCo2O4/NiO/Co3O4 composite effectively. A maximum reflection loss (RLmax) value at 12.94 GHz for a 2.8 mm thick sheet is −29.76 dB, while its effective absorption bandwidth (RL < −10 dB) reaches 4.21 GHz. In-depth research of possible mechanisms of EMW absorption enhancement. Owing to its simple preparation method and superb EMW absorption properties, the NiCo2O4/NiO/Co3O4 composites have a chance to be suitable candidates for high-property EMW absorbers. [ABSTRACT FROM AUTHOR]

Fiber gel dye‐sensitized solar cells (FGDSCs) are recognized as an effective solution for high‐performance, durable and safe wearable power supply. However, poor and unstable interfaces between fiber electrodes and gel electrolytes are bottleneck problems that have restricted the photovoltaic performances of FGDSCs and their stabilities during deformations. Here, an FGDSC with stable interlaced interfaces is designed by incorporating polymerized gel electrolyte into the aligned channels in fiber electrodes and the gaps between photoanode and counter electrode in situ. The interlaced structure of gel electrolyte in FGDSC improves the interfacial stability and provided stable channels for rapid ion diffusions, offering efficient charge transports at interfaces. The resulting FGDSC thus produced a high power conversion efficiency of 7.95%, and it is further maintained by over 90% after bending for 5000 cycles. These FGDSCs can be integrated with fiber batteries as a self‐charging power system, demonstrating an effective power solution for wearables. [ABSTRACT FROM AUTHOR]

Ternary I-III-VI quantum dots, unlike traditional binary counterparts, offer advantages like low toxicity, cadmium-free composition, tunable electronic properties, and near-infrared light emission, making them increasingly popular for their cost-effectiveness and versatile applications in light emission. In this study, monodispersive, water-soluble, highly emissive CuInS2@ZnS quantum dots were synthesized using cysteine and threonine as ligands by a simple one-pot solution method. By adjusting, the ratio of cysteine to threonine to 1:5 and the heating time during the synthesis process to 40 min, CuInS2@ZnS quantum dots achieved the highest fluorescence intensity. Then, CuInS2@ZnS quantum dots were applied for ciprofloxacin (CIP) detection in food sample, showing a linear range of 2–18 μg/ml and a lowest detection limit of 0.28 μg/ml. The fluorescent probe was successfully established by quenching process as CIP concentration increasing, due to photoinduced electron transfer. Experimental results demonstrated the detection method has high sensitivity and good selectivity. We, then utilized smartphones to visually distinguish CIP concentrations based on the colors emitted by the CuInS2@ZnS quantum dots under UV light. The method was finally effectively to detect residual CIP in porcine kidneys, with recoveries of 92%–98%. This approach has promising potential for enhancing food safety testing. [ABSTRACT FROM AUTHOR]

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems. [ABSTRACT FROM AUTHOR]

In view of problems in existing rolling bearing fault diagnosis methods for coal mine rotating machinery, such as incomplete signal feature extraction, low fault diagnosis accuracy and low efficiency, a rolling bearing fault diagnosis method based on wavelet packet decomposition and particle swarm optimization BP neural network was proposed. The method includes signal feature extraction and fault type recognition. In the signal feature extraction part, the collected vibration signals of rolling bearing are decomposed by wavelet packet to obtain energy of each sub-frequency band and total energy of the signal. After normalization processing, feature vector representing state of rolling bearing is obtained. In the fault type recognition part, initial weight and threshold of BP neural network are optimized by particle swarm optimization to accelerate convergence speed of the network and avoid falling into local minimum. The experimental results show that the method improves fault diagnosis efficiency and accuracy of rolling bearing.

Researchers have typically studied the brain by monitoring characteristic signals, such as electrophysiology and neurotransmitters by implanted electronics. Here, real‐time monitoring of dynamic deformations of the brain tissue in vivo, is demonstrated, as a new characteristic parameter that is reflective of brain states. As a proof of concept, a thin capacitive deformation sensor is fabricated and implanted between the skull and cortex, and the sensor is shown to effectively monitor dynamic deformations of the cortical surface in the rat brain as induced by respiration and heartbeat under different degrees of anesthesia. This brain monitoring approach based on deformation signals opens up a new direction for understanding the brain. [ABSTRACT FROM AUTHOR]

In view of problems that existing coal mine machinery online monitoring and diagnosis technology did not realize online extraction of fault characteristics and automatic identification of fault types, an online fault diagnosis and early warning system for coal mine rotating machinery based on LabVIEW was designed. The system analyzes vibration signal by means of spectrum analysis, power spectrum analysis, envelope spectrum analysis and cepstrum analysis, and obtains characteristic parameters of each component in running process of the rotating machine, and compares it with characteristic parameters in fault type database to realize fault diagnosis. Two kinds of fault diagnosis modes are designed including fine diagnosis and rough diagnosis, the two modes are associated by interlocking. If structural parameters of the main components of rotating machine are known, the fine diagnosis mode can be selected,otherwise, the rough diagnosis mode can be selected. Performance of the system is verified by simulating rotor unbalance fault of rotating machine, and the results show that the system can accurately identify faults and issue prompts with simple operation and high reliability.