Flavonoid derivative DMXAA attenuates cisplatin-induced acute kidney injury independent of STING signaling.

Cisplatin-induced nephrotoxicity is the main adverse effect of cisplatin-based chemotherapy and highly limits its clinical use. DMXAA, a flavonoid derivative, is a promising vascular disrupting agent and known as an agonist of STING. Although cGAS-STING activation has been demonstrated to mediate cisplatin-induced acute kidney injury (AKI), the role of DMXAA in this condition is unclear. Here, we defined an unexpected and critical role of DMXAA in improving renal function, ameliorating renal tubular injury and cell apoptosis, and suppressing inflammation in cisplatin-induced AKI. Moreover, we confirmed that DMXAA combated AKI in a STING-independent manner, as evidenced by its protective effect in STING global knockout mice subjected to cisplatin. Furthermore, we compared the role of DMXAA with another STING agonist SR717 in cisplatin-treated mice and found that DMXAA but not SR717 protected animals against AKI. To better evaluate the role of DMXAA, we performed transcriptome analyses and observed that both inflammatory and metabolic pathways were altered by DMXAA treatment. Due to the established role of metabolic disorders in AKI, which contributes to kidney injury and recovery, we also performed metabolomics using kidney tissues from cisplatin-induced AKI mice with or without DMXAA treatment. Strikingly, our results revealed that DMXAA improved the metabolic disorders in kidneys of AKI mice, especially regulated the tryptophan metabolism. Collectively, therapeutic administration of DMXAA ameliorates cisplatin-induced AKI independent of STING, suggesting a promising potential for preventing nephrotoxicity induced by cisplatin-based chemotherapy.

Gate-Level Circuit Partitioning Algorithm Based on Clustering and an Improved Genetic Algorithm.

Gate-level circuit partitioning is an important development trend for improving the efficiency of simulation in EDA software. In this paper, a gate-level circuit partitioning algorithm, based on clustering and an improved genetic algorithm, is proposed for the gate-level simulation task. First, a clustering algorithm based on betweenness centrality is proposed to quickly identify clusters in the original circuit and achieve the circuit coarse. Next, a constraint-based genetic algorithm is proposed which provides absolute and probabilistic genetic strategies for clustered circuits and other circuits, respectively. This new genetic strategy guarantees the integrity of clusters and is effective for realizing the fine partitioning of gate-level circuits. The experimental results using 12 ISCAS '89 and ISCAS '85 benchmark circuits show that the proposed algorithm is 5% better than Metis, 80% better than KL, and 61% better than traditional genetic algorithms for finding the minimum number of connections between subsets.

Effects of inflammation on voriconazole levels: A systematic review.

AIMS: This study aimed to review the studies evaluating the effect of the inflammatory state on voriconazole (VRZ) levels. METHODS: The study included randomized clinical trials, cohort studies, and case-control studies that focused on the influence of the inflammatory state on VRZ levels. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, relevant articles published until 2021 were searched in several databases, including PubMed, Embase, Web of Science and the Cochrane Library. RESULTS: Twenty studies were included in this review, of which 15 described adult populations, three described paediatric populations, and two included both adult and paediatric populations. Seventeen studies used C-reactive protein (CRP) as an indicator of inflammation, six described a dose-response relationship for the effect of inflammation represented by CRP on VRZ concentrations, and four examined the effect of CRP on the metabolic rate of VRZ. CONCLUSIONS: Our findings showed that the level of inflammation can significantly affect VRZ levels. However, the effect of inflammation on VRZ concentrations in children is controversial and must be analysed along with age. Clinicians dosing VRZ should take into account the patient's inflammatory state. The impact of inflammation on genotype-based dosing decisions requires further study to explain the high pharmacokinetic variability of VRZ.

YOLOv5s-FP: A Novel Method for In-Field Pear Detection Using a Transformer Encoder and Multi-Scale Collaboration Perception.

Precise pear detection and recognition is an essential step toward modernizing orchard management. However, due to the ubiquitous occlusion in orchards and various locations of image acquisition, the pears in the acquired images may be quite small and occluded, causing high false detection and object loss rate. In this paper, a multi-scale collaborative perception network YOLOv5s-FP (Fusion and Perception) was proposed for pear detection, which coupled local and global features. Specifically, a pear dataset with a high proportion of small and occluded pears was proposed, comprising 3680 images acquired with cameras mounted on a ground tripod and a UAV platform. The cross-stage partial (CSP) module was optimized to extract global features through a transformer encoder, which was then fused with local features by an attentional feature fusion mechanism. Subsequently, a modified path aggregation network oriented to collaboration perception of multi-scale features was proposed by incorporating a transformer encoder, the optimized CSP, and new skip connections. The quantitative results of utilizing the YOLOv5s-FP for pear detection were compared with other typical object detection networks of the YOLO series, recording the highest average precision of 96.12% with less detection time and computational cost. In qualitative experiments, the proposed network achieved superior visual performance with stronger robustness to the changes in occlusion and illumination conditions, particularly providing the ability to detect pears with different sizes in highly dense, overlapping environments and non-normal illumination areas. Therefore, the proposed YOLOv5s-FP network was practicable for detecting in-field pears in a real-time and accurate way, which could be an advantageous component of the technology for monitoring pear growth status and implementing automated harvesting in unmanned orchards.

Improving the ameliorative effects of berberine and curcumin combination via dextran-coated bilosomes on non-alcohol fatty liver disease in mice.

BACKGROUND: The combination of berberine (BER) and curcumin (CUR) has been verified with ameliorative effects on non-alcohol fatty liver disease (NAFLD). However, discrepant bioavailability and biodistribution of BER and CUR remained an obstacle to achieve synergistic effects. Multilayer nanovesicles have great potential for the protection and oral delivery of drug combinations. Therein lies bile salts inserted liposomes, named as bilosomes, that possesses long residence time in the gastrointestinal tract (GIT) and permeability across the small intestine. Diethylaminoethyl dextran (DEAE-DEX) is generally used as an outside layer on the nanovesicles to increase the mucinous stability and promote oral absorption. Herein, we developed a DEAE-DEX-coated bilosome with BER and CUR encapsulated (DEAE-DEX@LSDBC) for the treatment of NAFLD. RESULTS: DEAE-DEX@LSDBC with 150 nm size exhibited enhanced permeation across mucus and Caco-2 monolayer. In vivo pharmacokinetics study demonstrated that DEAE-DEX@LSDBC profoundly prolonged the circulation time and improved the oral absorption of both BER and CUR. Intriguingly, synchronized biodistribution of BER and CUR and highest biodistribution at liver was achieved by DEAE-DEX@LSDBC, which contributed to the optimal ameliorative effects on NAFLD. It was further verified to be mainly mediated by anti-oxidation and anti-inflammation related pathways CONCLUSION: DEAE-DEX coated bilosome displayed promoted oral absorption, prolonged circulation and synchronized biodistribution of BER and CUR, leading to improved ameliorative effects on NAFLD in mice, which provided a promising strategy for oral administration of drug combinations.

Analysis of brain functional response to cutaneous prickling stimulation by single fiber.

BACKGROUND: It is now well understood that, as an uncomfortable sensation evoked by special fabric, prickle derives from the mechanical stimulation of protruding hairiness from fabric surface against the human skin, in which some nociceptors are easy to be triggered by stiff fiber ends. However, up to now, the neural mechanism of the brain for perceiving fabric-evoked prickle is still unclear. MATERIALS AND METHODS: In this work, A type of single-fiber stimulus made from nylon filament was used to repetitively prick the skin of volar forearm at a specific frequency, and the technology of functional magnetic resonance imaging (fMRI) was adopted to detect the brain response synchronously. RESULTS: The results show that repetitive prickling stimulation from the single fiber applied to the volar forearm aroused distributed activation in several brain regions, such as primary somatosensory cortex, secondary somatosensory cortex, motor cortex, bilateral occipital lobe, insular cortex, and ipsilateral limbic lobe. Although the brain activation distribution is similar to pain, the single fiber-evoked prickle sensation possesses unique activation characteristics in several brain regions. CONCLUSION: It is suggested that the sensation evoked by cutaneous prickling stimulation from single fiber belongs to a kind of multidimensional experience involving somatosensory, motor, emotional, cognitive, etc Our study constitutes an important step toward identifying the brain mechanism of fabric-evoked prickle.

A Real-Time 3D Measurement System for the Blast Furnace Burden Surface Using High-Temperature Industrial Endoscope.

Capturing the three-dimensional (3D) shape of the burden surface of a blast furnace (BF) in real-time with high accuracy is crucial for improving gas flow distribution, optimizing coke operation, and stabilizing BF operation. However, it is difficult to perform 3D shape measurement of the burden surface in real-time during the ironmaking process because of the high-temperature, high-dust, and lightless enclosed environment inside the BF. To solve this problem, a real-time 3D measurement system is developed in this study by combining an industrial endoscope with a virtual multi-head camera array 3D reconstruction method. First, images of the original burden surface are captured using a purpose-built industrial endoscope. Second, a novel micro-pixel luminance polarization method is proposed and applied to compensate for the heavy noise in the backlit images due to high dust levels and poor light in the enclosed environment. Third, to extract depth information, a multifeature-based depth key frame classifier is designed to filter out images with high levels of clarity and displacement. Finally, a 3D shape burden surface reconstruction method based on a virtual multi-head camera array is proposed for capturing the real-time 3D shape of the burden surface in an operational BF. The results of an industrial experiment illustrate that the proposed method can measure the 3D shape of the entire burden surface and provide reliable burden surface shape information for BF control.

Jitter detection based on parallax observations and attitude data for Chinese Heavenly Palace-1 satellite.

Parallax observations from staggered charge-coupled devices (CCDs) have been applied to satellite jitter detection. Nevertheless, the jitter during the initial period of an imaging process cannot be detected. This paper presents an approach that combines parallax observations with the attitude data from attitude-measuring sensors in order to detect the global jitter, including the initial jitter. Low-frequency components, which can be reconstructed from attitude data, account for most jitter energy, and determine the jitter curve's overall shape. We introduce attitude data into parallax observations to constrain the initial jitter and find its optimum estimate. Meanwhile, an offset is extracted from parallax observation images by using a comprehensive matching method. A mathematical model is developed to demonstrate how to calculate the global jitter with the initial jitter and offset. Numerical simulation results indicate that, for pixel-level offset error, the root-mean-square error (RMSE) of the proposed method is 1.4 pixels, while the measurement error near integer multiples of characteristic frequency is amplified significantly. Experiments performed on Chinese Heavenly Palace-1 satellite show that the jitter at 0.12Hz with an amplitude about 6 pixels exists in the cross-track direction, while the down-track jitter results fail to show obvious periodicity.

Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks.

Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

Temperature Measurement Method for Blast Furnace Molten Iron Based on Infrared Thermography and Temperature Reduction Model.

The temperature measurement of blast furnace (BF) molten iron is a mandatory requirement in the ironmaking process, and the molten iron temperature is significant in estimating the molten iron quality and control blast furnace condition. However, it is not easy to realize real-time measurement of molten iron temperature because of the harsh environment in the blast furnace casthouse and the high-temperature characteristics of molten iron. To achieve continuous detection of the molten iron temperature of the blast furnace, this paper proposes a temperature measurement method based on infrared thermography and a temperature reduction model. Firstly, an infrared thermal imager is applied to capture the infrared thermal image of the molten iron flow after the skimmer. Then, based on the temperature distribution of the molten iron flow region, a temperature mapping model is established to measure the molten iron temperature after the skimmer. Finally, a temperature reduction model is developed to describe the relationship between the molten iron temperature at the taphole and skimmer, and the molten iron temperature at the taphole is calculated according to the temperature reduction model and the molten iron temperature after the skimmer. Industrial experiment results illustrate that the proposed method can achieve simultaneous measurement of molten iron temperature at the skimmer and taphole and provide reliable temperature data for regulating the blast furnace.

Seasonal, diurnal and nocturnal variations of carbonyl compounds in the semi-urban environment of Orléans, France.

Atmospheric carbonyls were measured at a semi-urban site in Orléans, France, from October 2010 to July 2011. Formaldehyde, acetaldehyde and acetone were found to be the most abundant carbonyls, with average concentrations of 3.1, 1.0, 2.0 ppb, respectively in summer, 2.3, 0.7, 2.2 ppb, respectively in autumn, 2.2, 1.0, 2.1 ppb, respectively in spring, and 1.5, 0.7, 1.1 ppb, respectively in winter. Photo-oxidation of volatile organic compounds (VOCs) was found to make a remarkable contribution to atmospheric carbonyls in the semi-urban site based on the distinct seasonal and diurnal variations of the carbonyls, as well as the significantly positive correlations between the carbonyls and ozone. The significantly negative correlations between NOx and O3 as well as the carbonyls and the positive correlations between wind speed and O3 as well as the carbonyls implied that the carbonyls and O3 at the semi-urban site were probably formed during air mass transport from neighboring cities.

RSSI-based smooth localization for indoor environment.

Radio frequency (RF) technique, for its better penetrability over traditional techniques such as infrared or ultrasound, is widely used for indoor localization and tracking. In this paper, three novel measurements, point decision accuracy, path matching error and wrong jumping ratio, are firstly defined to express the localization efficiency. Then, a novel RSSI-based smooth localization (RSL) algorithm is designed, implemented, and evaluated on the WiFi networks. The tree-based mechanism determines the current position and track of the entity by assigning the weights and accumulative weights for all collected RSSI information of reference points so as to make the localization smooth. The evaluation results indicate that the proposed algorithm brings better localization smoothness of reducing 10% path matching error and 30% wrong jumping ratio over the RADAR system.

Reinforcement Learning for Blast Furnace Ironmaking Operation With Safety and Partial Observation Considerations.

Making proper decision online in complex environment during the blast furnace (BF) operation is a key factor in achieving long-term success and profitability in the steel manufacturing industry. Regulatory lags, ore source uncertainty, and continuous decision requirement make it a challenging task. Recently, reinforcement learning (RL) has demonstrated state-of-the-art performance in various sequential decision-making problems. However, the strict safety requirements make it impossible to explore optimal decisions through online trial and error. Therefore, this article proposes a novel offline RL approach designed to ensure safety, maximize return, and address issues of partially observed states. Specifically, it utilizes an off-policy actor-critic framework to infer the optimal decision from expert operation trajectories. The "actor" in this framework is jointly trained by the supervision and evaluation signals to make decision with low risk and high return. Furthermore, we investigate a recurrent version of the actor and critic networks to better capture the complete observations, which solves the partially observed Markov decision process (POMDP) arising from sensor limitations. Verification within the BF smelting process demonstrates the improvements of the proposed algorithm in performance, i.e., safety and return.

Asymmetric ionic bond shielding encountering with carboxylate capturing metal ions for enhancing the flame retardant durability of regenerated cellulose fibers.

Enhancing the flame retardancy and durability of cellulose fibers, particularly environmentally friendly regenerated cellulose fibers types like Lyocell fibers, is essential for advancing their broader application. This study introduced a novel approach to address this challenge. Cationic-modified Lyocell fibers (Lyocell@CAT) were prepared by introducing quaternary ammonium structures into the molecular chain of Lyocell fibers. Simultaneously, a flame retardant, APA, containing -COO-NH4+ and -P=O(O-NH4+)2 groups was synthesized. APA was then covalently bonded to Lyocell@CAT to prepare Lyocell@CAT@APA. Even after undergoing 30 laundering cycles (LCs), Lyocell@CAT@APA maintained a LOI value of 37.2 %, exhibiting outstanding flame retardant durability. The quaternary ammonium structure within Lyocell@CAT@APA formed asymmetric ionic bonds with the phosphate and carboxylate groups in APA, effectively shielding the binding of Na+ ions with phosphate groups during laundering, thereby enhancing the durability. Additionally, the consumption of Na+ ions by carboxylate groups further prevented their binding to phosphate groups, which contributed to enhance the durability properties. Flame retardant mechanism analysis revealed that both gas and condensed phase synergistically endowed excellent flame retardancy to Lyocell fibers. Overall, this innovative strategy presented a promising prospect for developing bio-safe, durable, and flame retardant cellulose textiles.

Learning lightweight tea detector with reconstructed feature and dual distillation.

Currently, image recognition based on deep neural networks has become the mainstream direction of research; therefore, significant progress has been made in its application in the field of tea detection. Many deep models exhibit high recognition rates in tea leaves detection. However, deploying these models directly on tea-picking equipment in natural environments is impractical; the extremely high parameters and computational complexity of these models make it challenging to perform real-time tea leaves detection. Meanwhile, lightweight models struggle to achieve competitive detection accuracy; therefore, this paper addresses the issue of computational resource constraints in remote mountain areas and proposes Reconstructed Feature and Dual Distillation (RFDD) to enhance the detection capability of lightweight models for tea leaves. In our method, the Reconstructed Feature selectively masks the feature of the student model based on the spatial attention map of the teacher model; it utilizes a generation block to force the student model to generate the teacher's full feature. The Dual Distillation comprises Decoupled Distillation and Global Distillation. Decoupled Distillation divides the reconstructed feature into foreground and background features based on the Ground-Truth. This compels the student model to allocate different attention to foreground and background, focusing on their critical pixels and channels. However, Decoupled Distillation leads to the loss of relation knowledge between foreground and background pixels. Therefore, we further perform Global Distillation to extract this lost knowledge. Since RFDD only requires loss calculation on feature map, it can be easily applied to various detectors. We conducted experiments on detectors with different frameworks, using a tea dataset collected at the Huangshan Houkui Tea Plantation. The experimental results indicate that, under the guidance of RFDD, the student detectors have achieved performance improvements to varying degrees. For instance, a one-stage detector like RetinaNet (ResNet-50) experienced a 3.14% increase in Average Precision (AP) after RFDD guidance. Similarly, a two-stage model like Faster RCNN (ResNet-50) obtained a 3.53% improvement in AP. This offers promising prospects for lightweight models to efficiently perform real-time tea leaves detection tasks.

Comparison of cell delivery and cell membrane camouflaged PLGA nanoparticles in the delivery of shikonin for colorectal cancer treatment.

Inspired by the "natural camouflage" strategy, cell-based biomimetic drug delivery systems (BDDS) have shown great potential in cancer therapy. Red blood cell (RBC) delivery vehicles and red blood cell membrane (RBCm)-camouflaged vehicles were commonly used strategies for drug delivery. We prepared shikonin-encapsulated PLGA nanoparticles (PLGA/SK) with different surface charges to obtain both RBC delivery and RBCm-camouflaged PLGA NPs. The physicochemical properties, in vivo circulation and antitumor effects of these biomimetic preparations were studied. Since the positive PLGA NPs may affect the morphology and function of RBCs, the biomimetic preparations prepared by the negative PLGA NPs showed better in vitro stability. However, positive PLGA NP-based biomimetic preparations exhibited longer circulation time and higher tumor region accumulation, leading to stronger anti-tumor effects. Meanwhile, the RBC delivery PLGA(+) NPs possessed better in vitro cytotoxicity, longer circulation time and higher tumor accumulation than RBCm-camouflaged PLGA(+) NPs. Collectively, RBC delivery vehicles possessed more potential than RBCm-camouflaged vehicles on drug delivery for tumor treatment, especially with positive NPs-loaded.

Immune infiltration related CENPI associates with the malignant features and drug resistance of lung adenocarcinoma.

Centromere protein I (CENPI) is an important member of centromeric proteins family, which is crucial to chromosome alignment and segregation. Nevertheless, the interrelation between CENPI expression and tumor progression is in the shadows. In this reserch, we carried out a panoramic bioinformatic analysis about CENPI with TCGA, Timer 2.0, Oncomine, GEPIA, Cbioportal, LinkedOmics and CancerSEA databases. Besides, our bioinformatic results have been further confirmed through in vitro experiments, including Real-Time quantitative PCR (RT-qPCR), immunofluorescence (IF), immunohistochemistry (IHC), western blotting (WB), cell proliferation assays, EdU, cell cycle and apoptosis test. Our results suggested that CENPI was increased in most of the cancers, and may serve as a potential biomarker. What's more, the knock down of CENPI inhibited the expression of CDK2 in lung adenocarcinoma (LUAD), and resulted in the arrest of G0/G1 phase and apoptosis. Besides, CENPI was related to immune cells infiltration and drug sensitivity in pan-cancer, and can act as a potential treatment target to cure cancer patients.

Research on high sensitivity piezoresistive sensor based on structural design.

With the popularity of smart terminals, wearable electronic devices have shown great market prospects, especially high-sensitivity pressure sensors, which can monitor micro-stimuli and high-precision dynamic external stimuli, and will have an important impact on future functional development. Compressible flexible sensors have attracted wide attention due to their simple sensing mechanism and the advantages of light weight and convenience. Sensors with high sensitivity are very sensitive to pressure and can detect resistance/current changes under pressure, which has been widely studied. On this basis, this review focuses on analyzing the performance impact of device structure design strategies on high sensitivity pressure sensors. The design of structures can be divided into interface microstructures and three-dimensional framework structures. The preparation methods of various structures are introduced in detail, and the current research status and future development challenges are summarized.

Dynamical behaviors, circuit design, and synchronization of a novel symmetric chaotic system with coexisting attractors.

In this paper, we introduce a novel three-dimension chaotic system with strange characteristic by applying construction of a 3D chaotic circuit method. Multiple equilibria and abundant coexisting attractors exist in this system. A mathematical model is developed and detailed stability analyses for equilibrium points are executed with obtaining significant results of the period-doubling bifurcation patterns confirmed by phase plane plots and Lyapunov exponent spectra. By varying the initial value and unique controlled parameter, the double-scroll chaotic attractor is broken up into a pair of symmetric singular attractors. Then, the local basins of attraction are investigated concerning the initial condition. Next, the circuit synthesis results generated by Multisim simulation tool validate the self-excitation characteristics of this system. Finally, the feedback control technique is used to study difference synchronization of this system. Main conclusions prove the validity and reliability of difference synchronization.

Global trends in research of achilles tendon injury/rupture: A bibliometric analysis, 2000-2021.

Background: The Achilles tendon is the strongest and most susceptible tendon in humans. Achilles tendon injuries and ruptures have gradually attracted research attention. However, a bibliometric analysis of global research in this field is lacking. This study involved a bibliometric analysis of the developmental trends and research hotspots in Achilles tendon injuries/ruptures from 2000 to 2021. Methods: Articles published between 2001 and 2021 were retrieved from an extended database of the Science Citation Index using Web of Science. VOSviewer and CiteSpace were used to analyze the relationships between publications, countries, institutions, journals, authors, references, and keywords. Results: This study included 3,505 studies of 73 countries, 3,274 institutions, and 12,298 authors and explored the cooperation between them and the relationships between citations. Over the past 22 years, the number of publications has significantly increased. Foot Ankle International has published the most papers on Achilles tendon injuries/ruptures, and British Journal of Sports Medicine is the most famous journal. Re-rupture, exosomes, acute Achilles tendon rupture, and tendon adhesions gradually become the research focus over the past few years. Conclusion: Achilles tendon injury and rupture are important research topics. A vast number of newly published papers on this topic have demonstrated that clinicians and researchers are interested in their study. Over time, these recent studies will be widely cited; therefore, this bibliometric analysis should be constantly updated.