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Complex tissue damage accompanying with bacterial infection challenges healthcare systems globally. Conventional tissue engineering scaffolds normally generate secondary implantation trauma, mismatched regeneration and infection risks. Herein, we developed an easily implanted scaffold with multistep shape memory and photothermal-chemodynamic properties to exactly match repair requirements of each part from the tissue defect by adjusting its morphology as needed meanwhile inhibiting bacterial infection on demand. Specifically, a thermal-induced shape memory scaffold was prepared using hydroxyethyl methacrylate and polyethylene glycol diacrylate, which was further combined with the photothermal agent iron tannate (FeTA) to produce NIR light-induced shape memory property. By varying ingredients ratios in each segment, this scaffold could perform a stepwise recovery under different NIR periods. This process facilitated implantation after shape fixing to avoid trauma caused by conventional methods and gradually filled irregular defects under NIR to perform suitable tissue regeneration. Moreover, FeTA also catalyzed Fenton reaction at bacterial infections with abundant H2O2, which produced excess ROS for chemodynamic antibacterial therapy. As expected, bacteriostatic rate was further enhanced by additional photothermal therapy under NIR. The in vitro and vivo results showed that our scaffold was able to perform high efficacy in both antibiosis, inflammation reduction and wound healing acceleration, indicating a promising candidate for the regeneration of complex tissue damage with bacterial infection.

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Response-adaptive randomization (RAR) procedures have been extensively studied in the literature, but most of the procedures rely on updating the randomization after each response, which is impractical in many clinical trials. In this article, we propose a new family of RAR procedures that dynamically update based on the responses of a group of individuals, either when available or at fixed time intervals (weekly or biweekly). We show that the proposed design retains the essential theoretical properties of Hu and Zhang's doubly adaptive biased coin designs (DBCD), and performs well in scenarios involving delayed and randomly missing responses. Numerical studies have been conducted to demonstrate that the new proposed group doubly adaptive biased coin design has similar properties to the Hu and Zhang's DBCDs in different situations. We also apply the new design to a real clinical trial, highlighting its advantages and practicality. Our findings open the door to studying the properties of other group response adaptive designs, such as urn models, and facilitate the application of response-adaptive randomized clinical trials in practice.

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In this work, we present an innovative and atom-efficient synthesis of trimethine cyanines (Cy3) using formaldehyde (FA) as a single-carbon reagent. The widespread application of Cy3 dyes in bioimaging and genomics/proteomics is often limited by synthetic routes plagued with low atom economy and substantial side-product formation. Through systematic investigation, we have developed a practical and efficient synthetic pathway for both symmetric and asymmetric Cy3 derivatives, significantly minimizing resource utilization. Notably, this approach yields water as the byproduct, in alignment with sustainable chemistry principles. Moreover, the efficient one-pot synthesis facilitates the detection of intracellular FA levels, utilizing the fluorescence signal of Cy3 in live cells. It is also possible to detect the endogenous FA in the intestinal tissues. We observed a significant decrease of FA in the small intestine of the inflammatory bowel disease (IBD) mice compared to the healthy mice. This methodological advancement not only enhances the scope of fluorescent dye synthesis but also contributes to the sustainable practices within chemical manufacturing, offering a significant leap forward in the development of environmentally friendly synthetic strategies.

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Cardiac magnetic resonance imaging (CMR) is an important clinical tool that obtains high-quality images for assessment of cardiac morphology, function, and tissue characteristics. However, the technique may be prone to artifacts that may limit the diagnostic interpretation of images. This article reviews common artifacts which may appear in CMR exams by describing their appearance, the challenges they mitigate true pathology, and offering possible solutions to reduce their impact. Additionally, this article acts as an update to previous CMR artifacts reports by including discussion about new CMR innovations.

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Low-dimensional lead halide materials have proved to be intrinsically stable semiconductor materials. However, the development of one-dimensional (1D) perovskites or perovskitoids with both robust water stability and high optoelectronic performance still faces significant challenges. Here, we report a new class of 1D (TzBIPY)Pb2X6 (X = Cl, Br, I) perovskitoids featuring a π-conjugated diamine cation (TzBIPY = 2,5-di(pyridin-4-yl)thiazolo[5,4-d]thiazole). The TzBIPY2+ cation with delocalized electrons directly contributes to the electronic structure and hence reduces the band gap. Especially, the Br-based material exhibits enhanced carrier separation and transport capacity, benefiting from the improved electronic conjugation together with a type II intramolecular heterojunction between conjugated organic cations and Pb-X octahedra. The (TzBIPY)Pb2Br6 photodetector exhibits an impressive photocurrent on/off ratio of 8.1 × 105, which is much superior to the previous three-dimensional (3D) perovskite benchmark. Additionally, the π-conjugated cations serve as dense protective shields for vulnerable Pb-X inorganic lattice against being attacked by water, thus demonstrating exceptional stability even immersed in water for over 3000 h.

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BACKGROUND: Endoscopic submucosal dissection (ESD) is a well-established treatment for gastrointestinal tumors and enables en bloc resection. Adequate counter traction with good visualization is important for safe and effective dissection. OBJECTIVE: Based on magnetic anchor-guided endoscopic submucosal dissection (MAG-ESD), we would like to explore the feasibility of magnetic hydrogel as an internal magnetic anchor that can be injected into the submucosa through an endoscopic needle to assist colonic endoscopic submucosal dissection. METHODS: This prospective trial was conducted on 20 porcine colons ex vivo. We injected magnetic hydrogel into submucosa of the porcine colons ex vivo for MAG-ESD to evaluate the traction effect and operation satisfaction. RESULTS: Magnetic hydrogel assisted ESD was successfully performed on 20 porcine colons ex vivo. Adequate counter traction with good visualization was successfully obtained during the procedure of dissection. CONCLUSION: Magnetic hydrogel assisted MAG-ESD is feasible and effective.

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Fast and efficient exciton utilization is a crucial solution and highly desirable for achieving high-performance blue organic light-emitting diodes (OLEDs). However, the rate and efficiency of exciton utilization in traditional OLEDs, which employ fully closed-shell materials as emitters, are inevitably limited by spin statistical limitations and transition prohibition. Herein, a new sensitization strategy, namely doublet-sensitized fluorescence (DSF), is proposed to realize high-performance deep-blue electroluminescence. In the DSF-OLED, a doublet-emitting cerium(III) complex, Ce-2, is utilized as sensitizer for multi-resonance thermally activated delayed fluorescence emitter ν-DABNA. Experimental results reveal that holes and electrons predominantly recombine on Ce-2 to form doublet excitons, which subsequently transfer energy to the singlet state of ν-DABNA via exceptionally fast (over 108 s-1) and efficient (≈100%) Förster resonance energy transfer for deep-blue emission. Due to the circumvention of spin-flip in the DSF mechanism, near-unit exciton utilization efficiency and remarkably short exciton residence time of 1.36 µs are achieved in the proof-of-concept deep-blue DSF-OLED, which achieves a Commission Internationale de l'Eclairage coordinate of (0.13, 0.14), a high external quantum efficiency of 30.0%, and small efficiency roll-off of 14.7% at a luminance of 1000 cd m-2. The DSF device exhibits significantly improved operational stability compared with unsensitized reference device.

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Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.

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BACKGROUND: Clindamycin (CLIN), an antibiotic sold in the form of capsules, injectable solution, gel, and lotion, is easily soluble in water and ethanol. However, it lacks eco-efficient methods for evaluating pharmaceutical products. OBJECTIVE AND METHOD: The objective of this review is to provide an overview of the analytical methods present both in the literature and in official compendia for evaluating pharmaceutical matrices based on CLIN in the context of Green Analytical Chemistry (GAC). RESULTS: Firstly, microbiological methods for evaluating the potency of CLIN final products were not found, which already shows the need to develop new methods. Among the methods found, which are all physicalchemical, the most used method is HPLC (71%) followed by UV-Vis (14%). Among the targets of the methods, capsules and raw materials were the most studied (33% each). Among the choices of analytical conditions for the methods, acetonitrile is the preferred solvent (27.7%), even though CLIN is easily soluble in ethanol. CONCLUSION: Thus, the gap in eco-friendly and sustainable analytical methods is a reality and an opportunity for analytical development centers to provide means for evaluating the quality of CLIN-based products.

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N,N-dimethylformamide (DMF) is an organic solvent with stable chemical properties and high boiling point. Based on its good solubility, DMF is widely used in synthetic textile, leather, electronics, pharmaceutical and pesticide industries. However, the DMF pollutes the environment and does harm to human liver function, kidney function, and nerve function. Herein, an efficient DMF-degrading strain, DM175A1-1, was isolated and identified as Paracoccus sulfuroxidans. This strain can use DMF as the sole source of carbon and nitrogen. Whole-genome sequencing of strain DM175A1-1 revealed that it has a 3.99-Mbp chromosome a 120-kbp plasmid1 and a 40-kbp plasmid2. The chromosome specifically harbors the dmfA1 and dmfA2 essential for the initial steps of DMF degradation. And it also carries the some part of genes facilitating subsequent methylotrophic metabolism and glutathione-dependent pathway. Through further DMF tolerance degradation experiments, DM175A1-1 can tolerate DMF concentrations up to 10,000 mg/L, whereas the majority of Paracoccus strains could only show degradation activity below 1,000 mg/L. And the efficiency of organic nitrogen conversion to NH3-N in DMF can reach 99.0% when the hydraulic retention time (HRT) is controlled at 5 days. Meanwhile, it showed a significant degradation effect at a pharmaceutical enterprise in Zhejiang Province with high concentration of DMF wastewater. This study provides a new strain Paracoccus sulfuroxidans DM175A1-1 which shows a significant influence on DMF degradation, and reveals the characterization on its DMF degradation. It lays the foundation for the application of biological method in the efficient degradation of DMF in industrial wastewater.

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The potential of Euglena gracilis for carbon sequestration offers significant opportunities in the capture and utilization of carbon dioxide (CO2). In this study, a mutant LE-ZW of E. gracilis, capable of efficient growth and carbon sequestration, was obtained through ultraviolet mutagenesis combined with high carbon acclimation. Subsequently, the potential of LE-ZW for carbon assimilation was systematically analyzed. The results demonstrated that the cell density of the LE-ZW was 1.33 times that of the wild type and its carbon sequestration efficiency was 6.67 times that of the wild type when cultured at an optimal CO2 concentration of 5% until day 10. At this time, most key enzyme genes associated with the photosystem membrane protein complex, photosynthetic electron transport chain, antenna protein, and carbon fixation were up-regulated in mutant LE-ZW. Furthermore, after 10 days of culture under 10% CO2, the cell density and carbon sequestration efficiency of LE-ZW reached 1.10 times and 1.54 times of that under 5% CO2, respectively. Transcriptome analysis revealed significant up-regulation of key enzyme genes associated with carbon fixation, central carbon metabolism, and photosynthesis in LE-ZW under a 10% CO2 concentration. Physiological indices such as the amount of oxygen evolution, the values of Fv/Fm, the expression levels of photosynthetic protein genes and the enzyme activity of key enzymes related to photosynthetic carbon assimilation were corroborated by transcriptome data, elucidating that the mutant LE-ZW exhibited augmented photosynthetic carbon sequestration capacity and metabolic activity, thereby demonstrating robust adaptability to a high-carbon environment. This research contributes to a deeper understanding of the carbon assimilation mechanism in photosynthetic protists under elevated CO2 concentrations.

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In online video understanding, which has a wide range of real-world applications, inference speed is crucial. Many approaches involve frame-level visual feature extraction, which often represents the biggest bottleneck. We propose RetinaViT, an efficient method for extracting frame-level visual features in an online video stream, aiming to fundamentally enhance the efficiency of online video understanding tasks. RetinaViT is composed of efficiently approximated Transformer blocks that only take changed tokens (event tokens) as queries and reuse the already processed tokens from the previous timestep for the others. Furthermore, we restrict keys and values to the spatial neighborhoods of event tokens to further improve efficiency. RetinaViT involves tuning multiple parameters, which we determine through a multi-step process. During model training, we randomly vary these parameters and then perform black-box optimization to maximize accuracy and efficiency on the pre-trained model. We conducted extensive experiments on various online video recognition tasks, including action recognition, pose estimation, and object segmentation, validating the effectiveness of each component in RetinaViT and demonstrating improvements in the speed/accuracy trade-off compared to baselines. In particular, for action recognition, RetinaViT built on ViT-B16 reduces inference time by approximately 61.9% on the CPU and 50.8% on the GPU, while achieving slight accuracy improvements rather than degradation.

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Background: Electrocardiogram (EKG) is a commonly used diagnostic tool for the evaluation of the electrical activity of the heart. The purpose of this study was to assess the knowledge and interpretation proficiency of EKG among healthcare professionals (HCPs) in Pakistan. Methods: This prospective cross-sectional study was conducted among HCPs working in different healthcare settings. A structured questionnaire was used to assess the participants' theoretical knowledge and ability to interpret EKG findings. The data were analyzed using descriptive statistics and χ2 tests. The study indicates that EKG knowledge and interpretation proficiency among HCPs in Pakistan is unsatisfactory. The inadequacy of training periods of EKG training sessions and insufficient participation of HCPs in offered training opportunities put forward the need for the formation and introduction of better structured and efficient EKG training programmes. Results: A total of 511 HCPs participated in the study, 28% of whom reportedly had received formal training for EKG interpretation. About 80% of the participants correctly identified theoretical questions pertaining to EKG, while 58% of the participants were able to accurately interpret EKG findings, and most HCPs (69.9%) read fewer than ten EKGs per week. Conclusion: This study demonstrates a lack of expertise and a poor understanding of EKG in HCPs of Pakistan. The low level of EKG knowledge and interpretation proficiency among HCPs may lead to diagnostic errors and poor patient outcomes. Therefore, efforts should be made to improve EKG education and training among HCPs in Pakistan.

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Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality and optimizes crop yield but also provides a sustainable alternative to herbicide use. However, existing DL-based algorithms on weed detection are mainly developed based on supervised learning approaches, typically demanding large-scale datasets with manual-labeled annotations, which can be time-consuming and labor-intensive. As such, label-efficient learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision and have demonstrated promising performance. These methods aim to utilize a small number of labeled data samples along with a great number of unlabeled samples to develop high-performing models comparable to the supervised learning counterpart trained on a large amount of labeled data samples. In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS (Fully Convolutional One-Stage Object Detection) and Faster-RCNN (Faster Region-based Convolutional Networks). Specifically, we evaluate a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for the unlabeled data. To enhance generalization, an ensemble student network is employed to facilitate the training process. Experimental results show that the proposed approach is able to achieve approximately 76% and 96% detection accuracy as the supervised methods with only 10% of labeled data in CottonWeedDet3 and CottonWeedDet12, respectively. We offer access to the source code (https://github.com/JiajiaLi04/SemiWeeds), contributing a valuable resource for ongoing semi-supervised learning research in weed detection and beyond.

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Cyclopamine, a compound found in wild Veratrum has shown promising potential as a lead anti-cancer drug by effectively blocking cancer signaling pathways. However, its complex chemical structure poses challenges for artificial synthesis, thus limiting its supply and downstream drug production. This study comprehensively utilizes induction, system optimization, and transgenic technologies to establish an efficient suspension culture system for the high-yield production of cyclopamine and its precursor, veratramine. Experimental results demonstrate that methyl jasmonate (MeJA) effectively promotes the content of veratramine and cyclopamine in Veratrum californicum var. callus tissue, while yeast extract (YE) addition significantly increases cell biomass. The total content of veratramine and cyclopamine reached 0.0638 mg after synergistic treatment of suspension system with these two elicitors. And the content of the two substances was further increased to 0.0827 mg after the optimization by response surface methodology. Subsequently, a genetic transformation system for V. californicum callus was established and a crucial enzyme gene VnOSC1, involved in the steroidal alkaloid biosynthesis pathway, was screened and identified for genetic transformation. Combined suspension culture and synergistic induction system, the total content of the two substances in transgenic suspension system was further increased to 0.1228 mg, representing a 276.69% improvement compared to the initial culture system. This study proposes a complete and effective genetic transformation and cultivation scheme for V. californicum tissue cells, achieving milligram-level production of the anticancer agent cyclopamine and its direct precursor veratramine for the first time. It provides a theoretical basis for the industrial-scale production of these substances.

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How to solve the contradiction between the efficiency and adsorption rate of porous materials in adsorbing pollutants has always been one of the focus issues. In this study, the small landscape cypress trees structure like biomimetic of a hierarchical and dual morphology 3D porous HA-based aerogel was designed and synthesized to use humic acid (HA), pectin (PE) and chitosan (CTS) as raw materials, which it was formed by the disorderly overlapping of lamella composed of fiber networks in 3D space. Due to its special microstructure, it can be used like separation membrane, which allowing for rapid adsorption of pollutants in the water while the water flow passes through quick. In general, this work provides a new concept for owning fast adsorption rate and efficient adsorption of porous materials of preparation to use green method.

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Parameter efficient transfer learning (PETL) methods provide an efficient alternative for fine-tuning. However, typical PETL methods inject the same structures to all Pre-trained Language Model (PLM) layers and only use the final hidden states for downstream tasks, regardless of the knowledge diversity across PLM layers. Additionally, the backpropagation path of existing PETL methods still passes through the frozen PLM during training, which is computational and memory inefficient. In this paper, we propose FLAT, a generic PETL method that explicitly and individually combines knowledge across all PLM layers based on the tokens to perform a better transferring. FLAT considers the backbone PLM as a feature extractor and combines the features in a side-network, hence the backpropagation does not involve the PLM, which results in much less memory requirement than previous methods. The results on the GLUE benchmark show that FLAT outperforms other tuning techniques in the low-resource scenarios and achieves on-par performance in the high-resource scenarios with only 0.53% trainable parameters per task and 3.2× less GPU memory usagewith BERTbase. Besides, further ablation study is conducted to reveal that the proposed fusion layer effectively combines knowledge from PLM and helps the classifier to exploit the PLM knowledge to downstream tasks. We will release our code for better reproducibility.

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The electrochemical conversion of CO2 into valuable chemicals using renewable electricity shows significant promise for achieving carbon neutrality and providing alternative energy storage solutions. However, its practical application still faces significant challenges, including high energy consumption, poor selectivity, and limited stability. Here, we propose a hybrid acid/alkali electrolyzer that couples the acidic CO2 reduction reaction (CO2RR) at the cathode with alkaline methanol oxidation reaction (MOR) at the anode. This dual electro-synthesis cell is implemented by developing Bi nanosheets as cathode catalysts and oxide-decorated Cu2Se nanoflowers as anode catalysts, enabling high-efficiency electron utilization for formate production with over 180% coulombic efficiency and more than 90% selectivity for both CO2RR and MOR conversion. The hybrid acid/alkali CO2RR-MOR cell also demonstrates long-term stability exceeding 100 hours of continuous operation, delivers a formate partial current density of 130 mA cm-2 at a voltage of only 2.1 V, and significantly reduces electricity consumption compared to the traditional CO2 electrolysis system. This study illuminates an innovative electron-efficiency and energy-saving techniques for CO2 electrolysis, as well as the development of highly efficient electrocatalysts.

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Event of the disastrous scenarios are actively discussed on microblogging platforms like Twitter which can lead to chaotic situations. In the era of machine learning and deep learning, these chaotic situations can be effectively controlled by developing efficient methods and models that can assist in classifying real and fake tweets. In this research article, an efficient method named BERT Embedding based CNN model with RMSProp Optimizer is proposed to effectively classify the tweets related disastrous scenario. Tweet classification is carried out via some of the popular the machine learning algorithms such as logistic regression and decision tree classifiers. Noting the low accuracy of machine learning models, Convolutional Neural Network (CNN) based deep learning model is selected as the primary classification method. CNNs performance is improved via optimization of the parameters with gradient based optimizers. To further elevate accuracy and to capture contextual semantics from the text data, BERT embeddings are included in the proposed model. The performance of proposed method - BERT Embedding based CNN model with RMSProp Optimizer achieved an F1 score of 0.80 and an Accuracy of 0.83. The methodology presented in this research article is comprised of the following key contributions:•Identification of suitable text classification model that can effectively capture complex patterns when dealing with large vocabularies or nuanced language structures in disaster management scenarios.•The method explores the gradient based optimization techniques such as Adam Optimizer, Stochastic Gradient Descent (SGD) Optimizer, AdaGrad, and RMSprop Optimizer to identify the most appropriate optimizer that meets the characteristics of the dataset and the CNN model architecture.•"BERT Embedding based CNN model with RMSProp Optimizer" - a method to classify the disaster tweets and capture semantic representations by leveraging BERT embeddings with appropriate feature selection is presented and models are validated with appropriate comparative analysis.

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In karst desertification (KD) regions, surface water (SW) easily enters underground through pore fissures and sinkholes despite the presence of abundant precipitation. Such regions have a typical distribution of "soil above and water below", and, thus, the unique "karst drought" occurs. Hence, an urgent and primary problem in combating KD is to reach highly efficient utilization of water resources in these regions. We selected three karst research areas with different levels of karst desertification and different geomorphic types. By monitoring the storage and transformation of five types of water in the agroforestry system-precipitation, SW, groundwater (GW), soil water (SoW), plant water (PW), the following results were obtained: (1) In KD regions, a positive correlation was found among available precipitation, rainfall, and land evapotranspiration (LE), and LE was approximately equivalent to soil evaporation. (2) To varying degrees, agroforestry brings ecological benefits, including reducing surface runoff, increasing soil infiltration, lowering the transpiration rate, and reducing soil evaporation, thus achieving efficient use of water resources. (3) From 100 % rainfall, the transformation rates of SW, GW, PW, and SoW reached 0.14-12.71 %, 9.43-30.20 %, 9.79-49.97 %, and 40.72-82.58 %, respectively, and SoW showed a larger reserve than the other three types. (4) Drought stress contributes to the improvement of water use efficiency (WUE). Affected by drought stress, WUE was found to be the highest in a medium-intensity karst desertification environment. The transformation mechanisms of the five types of water observed in the agroforestry system provide a reference for efficient utilization of water resources in KD regions as well as theoretical support for addressing karst drought. They are also essential in helping to advance the ecological derivative industry, boosting the economy in karst mountainous areas, and controlling karst desertification.