Visible Light-Induced 6π-Heterocyclization/Dehydroaromatization for Synthesis of Indoloquinolinone Skeletons.

In this work, we report a protocol for the synthesis of an indoloquinolinone skeleton using visible light-induced energy transfer. This method avoids the premodification of substrates and exhibits high yields. For gram-scale reactions, only 0.01 mol % (100 ppm) of photosensitizer is required for rapid conversion. Mechanistic studies revealed that this reaction differs from conventional 6π photocyclization reactions; undergoing a process involving 6π cyclization due to energy transfer and dehydrogenation due to product self-catalysis has been experienced.

Leader-Following Sampled-Data Consensus of Multiagent Systems With Successive Packet Losses and Stochastic Sampling.

In practical applications, sampled-data systems are often affected by unforeseen physical constraints that cause the sampling interval to deviate from the expected value and fluctuate according to a certain probability distribution. This probability distribution can be determined in advance through statistical analysis. Taking into account this stochastic sampling interval, this article focuses on addressing the leader-following sampled-data consensus problem for linear multiagent systems (MASs) with successive packet losses. First, the relationship of the equivalent sampling interval between two successive update instants is established, taking into account the double randomness introduced by both SPLs and stochastic sampling. Then, the equivalent discrete-time MAS is obtained, and the overall leader-following consensus problem is formulated as a stochastic stability problem of the equivalent system by incorporating the sampled-data consensus protocol and properties of the Laplacian matrix. Based on the equivalent the discrete-time system, a consensus criterion is derived under a directed graph by using the Lyapunov theory and leveraging a vectorization technique. By the introduction of a matrix reconstruction approach, the mathematical expectation of a product of three matrices, including the system matrix and its transpose, can be determined. Then, the consensus protocol gain is designed. Finally, an example is provided to validate our theoretical results.

Inhibition of Adipocyte-Derived FABP4 Reduces Adipocyte Inflammation, Improves Angiogenesis, and Facilitates Wound Healing in Metabolic Dysfunctions.

Dermal white adipose tissue may participate in the wound-healing process. Obesity-mediated chronic low-grade inflammation impairs wound healing by suppressing vascularity. Given that FABP4 is upregulated in the skin tissue of animals with obesity, this study aimed to investigate the effects of FABP4 inhibition on wound healing in mice with high-fat diet-induced metabolic dysfunction in vivo. The interaction between adipocyte-derived FABP4 and vascular endothelial cell function was also investigated. In mice with high-fat diet-induced metabolic dysfunction, FABP4 inhibition increased angiogenesis and facilitated wound healing with reduced wound inflammation. FABP4 inhibition not only attenuated systemic inflammation, decreased body weight, and reduced insulin resistance but also improved the sizes of adipocytes and hypoxic conditions in dermal white adipose tissue. In vitro hypoxia was used to induce adipocyte inflammation, and the supernatants from hypoxia-stimulated adipocytes impaired the function and angiogenetic capability of human dermal microvascular endothelial cells (HDMECs). Both of them were improved by FABP4 inhibition. Altogether, FABP4 inhibition reduced systemic and adipocyte inflammation, improved vascular endothelial cell function, and facilitated wound healing in metabolic dysfunctions. Given the complex involvement of wound healing, future studies may be required to validate FABP4 as a potential therapeutic target for wound repair in metabolic dysfunctions.

Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

Chemical modulation of Akt signaling enhances spinal cord regeneration in zebrafish.

Central nervous system lesions often cause permanent motility defects in mammals since the injured neurons cannot regenerate. In contrast, lower vertebrates like zebrafish can regenerate lost neurons and restore motor function. This study investigates the efficacy of SC79, a pan-Akt activator, and A674563, a selective Akt1 inhibitor, as potential therapeutic agents for promoting spinal cord recovery post-injury. Spinal cord injury was induced in zebrafish larvae, and the effects of SC79 and A674563 on neuronal and glial regeneration were examined. SC79 promoted neuronal regeneration without affecting glial bridging, while A674563 induced glial bridging but reduced neuronal regeneration. The combination of SC79 and A674563 induced both glial bridging and neuronal regeneration. Optomotor response tests revealed improved motor function recovery with the combined treatment compared to individual treatments. Additionally, these chemical treatments altered the expression of 12 Akt downstream transcriptional target genes, affirming that the combination treatment preferentially regulates spinal cord regeneration through its action on Akt signaling. These findings highlight the complex interplay of Akt signaling pathways in spinal cord regeneration and suggest potential therapeutic strategies for enhancing functional recovery in spinal cord injury patients.

Stereoselective Construction of the ABCDE Pentacyclic Motif of Phainanoids via Norrish-Yang Photocyclization Reaction.

A Norrish-Yang photocyclization reaction has been applied to regio- and stereoselective construction of the ABCDE pentacyclic motif of natural product phainanoids. The observed substrate conformation control implicates this powerful reaction could be applied to the construction of structurally diverse natural product scaffolds.

Early prediction of platelet recovery with immature platelet fraction in patients receiving hematopoietic stem cell transplantation.

Hematopoietic stem cell transplantation (HSCT) is pivotal in treating hematologic disorders, yet it poses the risk of post-transplantation pancytopenia. Prophylactic platelet transfusions are often administered to mitigate this risk. Utilizing practical markers, such as immature platelet fraction (IPF), to predict hematopoietic recovery in advance could reduce unnecessary prophylactic transfusions. Our prospective study, involving 53 HSCT patients at Taipei Veterans General Hospital between September 2022 and May 2023, utilized the Sysmex XN analyzer to assess peripheral blood cell parameters. We investigated whether IPF could predict platelet recovery early, determined the optimal cut-off value, and compared platelet usage. Neutrophil and platelet engraftment occurred 10 (median; range: 10-12) and 15 (median; range: 15-18) days post-HSCT. Notably, 71.7% of patients exhibited an IPF increase exceeding 2% before platelet recovery. The optimal cut-off IPF on day 10 for predicting platelet recovery within five days was 2.15% (specificity 0.89, sensitivity 0.65). On average, patients received 3.89 units of post-transplantation platelet transfusion. Our results indicate that IPF serves as a predictive marker for platelet engraftment, peaking before the increase in platelet count. This insight aids clinicians in assessing the need for prophylactic platelet transfusions. Integrating reference IPF values alongside platelet counts enhances the accuracy of evaluating a patient's hematopoietic recovery status. Anticipating the timing of platelet recovery optimizes blood product usage and mitigates transfusion reaction risks.

Screening of identification algorithm for rodent-induced bare patches based on the drone imagery.

Rodent-infested bald spots are crucial indicators of rodent infestation in grasslands. Leveraging Unmanned Aerial Vehicle (UAV) remote sensing technology for discerning detrimental bald spots among plateau pikas has significant implications for assessing associated ecological hazards. Based on UAV-visible light imagery, we classified and recognized the characteristics of plateau pika habitats with five supervised classification algorithms, i.e., minimum distance classification (MinD), maximum likelihood classification (ML), support vector machine classification (SVM), Mahalanobis distance classification (MD), and neural network classification (NN) . The accuracy of the five methods was evaluated using a confusion matrix. Results showed that NN and SVM exhibited superior performance than other methods in identifying and classifying features indicative of plateau pika habitats. The mapping accuracy of NN for grassland and bald spots was 98.1% and 98.5%, respectively, with corresponding user accuracy was 98.8% and 97.7%. The overall model accuracy was 98.3%, with a Kappa coefficient of 0.97, reflecting minimal misclassification and omission errors. Through practical verification, NN exhibited good stability. In conclusion, the neural network method was suitable for identifying rodent-damaged bald spots within alpine meadows.

Characterization of tumor heterogeneity through segmentation-free representation learning.

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

Dynamic Surface Intelligent Robust Control of Nonlinear Systems With Fixed-Time Sliding-Mode Observer.

The high tracking control precision and fast finite-time convergence for nonlinear systems is a significant challenge due to complex nonlinearity and unknown disturbances. To address this challenge, a dynamic surface intelligent robust control strategy with fixed-time sliding-mode observer (DSIRC-SMO) is proposed to improve the tracking control performance in a finite time. First, adaptive fuzzy neural network based on a predictor (P-AFNN) is designed to imitate the complex nonlinearity. In particular, the weight adaptive law is formulated by utilizing the prediction error information, which improves the accuracy of approximating the nonlinear system. Second, the fixed-time sliding-mode observer (SMO) is integrated into the dynamic surface control technique to deal with unknown disturbances and modeling errors in a fixed time. This integration allows for timely updates the dynamic surface using observation information, thereby enhancing the system's anti-interference capability. Then, the fixed-time convergence of SMO is proven. Third, the proposed DSIRC-SMO can be effectively implemented and the finite-time convergence of DSIRC-SMO is proven in detail based on the fixed-time convergence of SMO. Finally, numerical simulation and actual wastewater treatment processes simulation are conducted to validate the effectiveness of DSIRC-SMO.