UPK3A+ umbrella cell damage mediated by TLR3-NR2F6 triggers programmed destruction of urothelium in Hunner-type interstitial cystitis/painful bladder syndrome.

Urothelial damage and barrier dysfunction emerge as the foremost mechanisms in Hunner-type interstitial cystitis/bladder pain syndrome (HIC). Although treatments aimed at urothelial regeneration and repair have been employed, their therapeutic effectiveness remains limited due to the inadequate understanding of specific cell types involved in damage and the lack of specific molecular targets within these mechanisms. Therefore, we harnessed single-cell RNA sequencing to elucidate the heterogeneity and developmental trajectory of urothelial cells within HIC bladders. Through reclustering, we identified eight distinct clusters of urothelial cells. There was a significant reduction in UPK3A+ umbrella cells and a simultaneous increase in progenitor-like pluripotent cells (PPCs) within the HIC bladder. Pseudotime analysis of the urothelial cells in the HIC bladder revealed that cells faced challenges in differentiating into UPK3A+ umbrella cells, while PPCs exhibited substantial proliferation to compensate for the loss of UPK3A+ umbrella cells. The urothelium in HIC remains unrepaired, despite the substantial proliferation of PPCs. Thus, we propose that inhibiting the pivotal signaling pathways responsible for the injury to UPK3A+ umbrella cells is paramount for restoring the urothelial barrier and alleviating lower urinary tract symptoms in HIC patients. Subsequently, we identified key molecular pathways (TLR3 and NR2F6) associated with the injury of UPK3A+ umbrella cells in HIC urothelium. Finally, we conducted in vitro and in vivo experiments to confirm the potential of the TLR3-NR2F6 axis as a promising therapeutic target for HIC. These findings hold the potential to inhibit urothelial injury, providing promising clues for early diagnosis and functional bladder self-repair strategies for HIC patients. © 2024 The Pathological Society of Great Britain and Ireland.

FoxO induces pupal diapause by decreasing TGFß signaling.

Diapause is a form of dormancy used widely by insects to survive adverse seasons. Previous studies have demonstrated that forkhead box O (FoxO) is activated during pupal diapause initiation in the moth Helicoverpa armigera. However, it is unclear how FoxO induces diapause. Here, we show that knockout of FoxO causes H. armigera diapause-destined pupae to channel into nondiapause, indicating that FoxO is a master regulator that induces insect diapause. FoxO activates the ubiquitin-proteasome system (UPS) by promoting ubiquitin c (Ubc) expression via directly binding to the Ubc promoter. Activated UPS decreases transforming growth factor beta (TGFß) receptor signaling via ubiquitination to block developmental signaling to induce diapause. This study significantly advances the understanding of insect diapause by uncovering the detailed molecular mechanism of FoxO.

Activation of protein arginine methyltransferase 1 and subsequent extension of moth lifespan is effected by the ROS/JNK/CREB signaling axis.

Previous studies have demonstrated that high physiological levels of reactive oxygen species induce pupal diapause and extend lifespan in the moth Helicoverpa armigera. This has been shown to occur via protein arginine methyltransferase 1 (PRMT1) blockade of Akt-mediated phosphorylation of the transcription factor FoxO, after which activated FoxO promotes the initiation of diapause. However, it is unclear how PRMT1 is activated upstream of FoxO activity. Here, we show that high reactive oxygen species levels in the brains of H. armigera diapause-destined pupae activate the expression of c-Jun N-terminal kinase, which subsequently activates the transcription factor cAMP-response element binding protein. We show that cAMP-response element binding protein then directly binds to the PRMT1 promoter and upregulates its expression to prevent Akt-mediated FoxO phosphorylation and downstream FoxO nuclear localization. This novel finding that c-Jun N-terminal kinase promotes FoxO nuclear localization in a PRMT1-dependent manner to regulate pupal diapause reveals a complex regulatory mechanism in extending the healthspan of H. armigera.

OFDR shape sensor based on a femtosecond-laser-inscribed weak fiber Bragg grating array in a multicore fiber.

An optical frequency domain reflectometry (OFDR) shape sensor was demonstrated based on a femtosecond-laser-inscribed weak fiber Bragg grating (WFBG) array in a multicore fiber (MCF). A WFBG array consisting of 60 identical WFBGs was successfully inscribed in each core along a 60 cm long MCF using the femtosecond-laser point-by-point technology, where the length and space of each WFBG were 2 and 8 mm, respectively. The strain distribution of each core in two-dimensional (2D) and three-dimensional (3D) shape sensing was successfully demodulated using the traditional cross correlation algorithm, attributed to the accurate localization of each WFBG. The minimum reconstruction error per unit length of the 2D and 3D shape sensors has been improved to 1.08% and 1.07%, respectively, using the apparent curvature vector method based on the Bishop frame.

Length-extended 3D shape sensor using wavelength/space-division multiplexing grating arrays in a multicore fiber.

Limited by the multiplexing number of fiber Bragg grating (FBG), further improvement in the length of 3D shape sensing based on FBG technology is challenging. In this Letter, a wavelength-division and space-division multiplexing multicore fiber grating method is proposed, which extends the sensing length. Employing the femtosecond-laser point-by-point technology, we inscribed WDM grating arrays in six outer cores of a seven-core fiber, respectively. Three cores were utilized as a segment for shape sensing, and two such segments were offset by a specific length and combined to form a shape sensor. Utilizing an FBG interrogator, the proposed shape sensor achieved 2D and 3D shape sensing at a length of 967 mm and effectively mitigated the effects of temperature variations. In experiments, maximum shape reconstruction errors per unit lengths are 1.89%, 2.72%, and 1.47% for 2D shape, 3D shape, and an arbitrary shape under variable temperature conditions, respectively. The proposed method holds promise for further extending the shape sensing length by utilizing multicore fibers or fiber clusters containing more cores.

Mucinous histology is a negative predictor of neoadjuvant chemoradiotherapy for locally advanced rectal adenocarcinoma.

BACKGROUND: Neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME) is the standard treatment for locally advanced rectal cancer (LARC). Mucinous adenocarcinoma (MAC) is a potential poor prognosis subgroup of rectal cancer. However, the predictive value of MAC in NCRT treatment of LARC is controversial. METHODS: A comprehensive literature search of PubMed, Embase, and the Cochrane Library was performed. All studies examining the effect of MAC on CRT response in LARC were included. Outcomes of MAC were compared with non-specific adenocarcinoma (AC) by using random-effects methods. Data were presented as odds ratios (ORs) with 95% confidence intervals (CIs). The main outcomes were the rates of pathological complete response (pCR), tumor and nodal down-staging, positive resection margin rate, local recurrence, and overall mortality. RESULTS: Fifteen studies containing comparative data on outcomes in a total of 9,238 patients receiving NCRT for LARC were eligible for inclusion. MAC had a reduced rate of pCR (OR, 0.38; 95% CI, 0.18-0.78) and tumor down-staging (OR, 0.31; 95% CI, 0.22-0.44) following NCRT compared with AC. MAC did not significantly affect nodal down-staging (OR, 0.42; 95% CI, 0.16-1.12) after NCRT. CONCLUSION: MAC of LARC was found to be a negative predictor of response to NCRT with lower rates of pCR and tumor down-staging for LARC. The nodal down-staging of MAC was relatively lower than that of AC, although the differences were not statistically significant.

The influence of the molecular chain length of PVA on the toughening mechanism of calcium silicate hydrates.

In recent years, polymers have been demonstrated to effectively toughen cementitious materials. However, the mechanism of interaction between the polymers and C-S-H at the nanoscale remains unclear, and the quantitative impact of the polymer chain length on toughening effectiveness is lacking in research. This study employs molecular dynamics techniques to examine the impact of the polyvinyl alcohol (PVA) chain length on the tensile performance and toughening mechanism of C-S-H. The toughening effect in both the X and Z directions exhibits an initial enhancement followed by a decline with increasing chain length. The optimal degrees of polymerization are determined to be 8 and 12 in the X and Z directions, respectively, resulting in an improvement of fracture energy by 146.7% and 29.5%, respectively. During the stretching process along the X and Z axes, the chain length of PVA molecules significantly influences the variation in the number of Ca⋯O bonds in the system, leading to different stress responses. Additionally, PVA molecules form C-O-Si bonds with the silicate layers of C-S-H, bridging the adjacent layers in a left-right or up-down manner. The toughening effect of PVA on C-S-H depends on the behavior of PVA molecules with different chain lengths, and there exists an optimal range of chain length for PVA, enabling it to enhance structural uniformity and adjust its own conformation to absorb strain energy. When the length of PVA molecular chains is too short, it can easily cause stress concentration in the system and its connection with silicates is not significant. Conversely, when the length of PVA molecular chains is too long, the large molecular structure restricts its extension in the defects of C-S-H, and as the stretching progresses, PVA molecules break and form numerous small segments, thereby losing the advantage of the chain length. This study provides a theoretical basis for the ability of polymers to toughen cementitious materials.

Magnetic covalent organic frameworks for extraction and determination of endocrine-disrupting chemicals in beverage and water samples.

BACKGROUND: Phenolic endocrine-disrupting chemicals (EDCs) are widespread and easily ingested through the food chain. They pose a serious threat to human health. Magnetic solid-phase extraction (MSPE) is an effective sample pre-treatment technology to determine traces of phenolic EDCs. RESULTS: Magnetic covalent organic framework (COF) (Fe3 O4 @COF) nanospheres were prepared and characterized. The efficient and selective extraction of phenolic EDCs relies on a large specific surface and the inherent porosity of COFs and hydrogen bonding, π-π, and hydrophobic interactions between COF shells and phenolic EDCs. Under optimal conditions, the proposed magnetic solid-phase extraction-high-performance liquid chromatography-ultra violet (MSPE-HPLC-UV) based on the metallic covalent organic framework method for phenolic EDCs shows good linearities (0.002-6 µg mL-1 ), with R2 of 0.995 or higher, and low limits of detection (6-1.200 ng mL-1 ). CONCLUSION: Magnetic covalent organic frameworks (Fe3 O4 @COFs) with good MSPE performance for phenolic EDCs were synthesized by the solvothermal method. The magnetic covalent organic framework-based MSPE-HPLC-UV method was applied successfully to determine phenolic EDCs in beverage and water samples with satisfactory recoveries (90.200%-123%) and relative standard deviations (2.100%-12.100%). © 2023 Society of Chemical Industry.

Self-Initiated Nano-Micelles Mediated Covalent Modification of mRNA for Labeling and Treatment of Tumors.

As a promising gene therapy strategy, controllable small molecule-mRNA covalent modification in tumor cells could be initiated by singlet oxygen (1O2) to complete the modification process. However, in vivo generation of 1O2 is usually dependent on excitation of external light, and the limited light penetration of tissues greatly interferes the development of deep tumor photo therapy. Here, we constructed a tumor-targeting nano-micelle for the spontaneous intracellular generation of 1O2 without the need for external light, and inducing a high level of covalent modification of mRNA in tumor cells. Luminol and Ce6 were chemically bonded to produce 1O2 by chemiluminescence resonance energy transfer (CRET) triggered by high levels of hydrogen peroxide (H2O2) in the tumor microenvironment (TME). The sufficient 1O2 oxidized the loaded furan to highly reactive dicarbonyl moiety, which underwent cycloaddition reaction with adenine (A), cytosine (C) or guanine (G) on the mRNA for interfering with the tumor cell protein expression, thereby inhibiting tumor progression. In vitro and in vivo experiments demonstrated that this self-initiated gene therapy nano-micelle could induce covalent modification of mRNA by 1O2 without external light, and the process could be monitored in real time by fluorescence imaging, which provided an effective strategy for RNA-based tumor gene therapy.

Inhibition of hyaluronic acid degradation pathway suppresses glioma progression by inducing apoptosis and cell cycle arrest.

BACKGROUND: Abnormal hyaluronic acid (HA) metabolism is a major factor in tumor progression, and the metabolic regulation of HA mainly includes HA biosynthesis and catabolism. In glioma, abnormal HA biosynthesis is intimately involved in glioma malignant biological properties and the formation of immunosuppressive microenvironment; however, the role of abnormal HA catabolism in glioma remains unclear. METHODS: HA catabolism is dependent on hyaluronidase. In TCGA and GEPIA databases, we found that among the 6 human hyaluronidases (HYAL1, HYAL2, HYAL3, HYAL4, HYALP1, SPAM1), only HYAL2 expression was highest in glioma. Next, TCGA and CGGA database were further used to explore the correlation of HYAL2 expression with glioma prognosis. Then, the mRNA expression and protein level of HYAL2 was determined by qRT-PCR, Western blot and Immunohistochemical staining in glioma cells and glioma tissues, respectively. The MTT, EdU and Colony formation assay were used to measure the effect of HYAL2 knockdown on glioma. The GSEA enrichment analysis was performed to explore the potential pathway regulated by HYAL2 in glioma, in addition, the HYAL2-regulated signaling pathways were detected by flow cytometry and Western blot. Finally, small molecule compounds targeting HYAL2 in glioma were screened by Cmap analysis. RESULTS: In the present study, we confirmed that Hyaluronidase 2 (HYAL2) is abnormally overexpressed in glioma. Moreover, we found that HYAL2 overexpression is associated with multiple glioma clinical traits and acts as a key indicator for glioma prognosis. Targeting HYAL2 could inhibit glioma progression by inducing glioma cell apoptosis and cell cycle arrest. CONCLUSION: Collectively, these observations suggest that HYAL2 overexpression could promote glioma progression. Thus, treatments that disrupt HA catabolism by altering HYAL2 expression may serve as effective strategies for glioma treatment.

The effects of chemotherapy, primary tumor location and histological subtype on the survival of stage III colon cancer patients.

OBJECTIVE: Colon cancer (CC) is one of the most common cancers worldwide and has a poor prognosis. Surgery followed by adjuvant chemotherapy is the standard treatment strategy for stage III CC patients. Primary tumor location (PTL) is an important factor for the long-term survival of CC. However, the difference in the prognosis between the histological subtypes of mucinous adenocarcinoma (MAC) and nonspecific adenocarcinoma (AC) in stage III CC patients is unclear. The correlation of chemotherapy, PTL and histological subtype with the overall survival (OS) of stage III CC patients has not yet been explored. METHODS: Patients diagnosed with stage III CC from 2010 to 2016 in the Surveillance, Epidemiology, and End Results (SEER) database were retrieved. The clinicopathological features and OS were analyzed according to the chemotherapy, PTL and histological subtype. RESULTS: A total of 28,765 eligible stage III CC patients were enrolled in this study. The results showed that chemotherapy, left-sided CC (LCC) and AC were favorable prognostic factors for OS. Right-sided CC (RCC) had worse OS than LCC regardless of chemotherapy. MAC had worse OS than AC in the patients with chemotherapy, but the survival benefits disappeared in the patients without chemotherapy. Additionally, in LCC, MAC had worse OS than AC regardless of chemotherapy. However, in RCC, MAC had worse OS than AC in patients with chemotherapy but had similar OS to AC in patients without chemotherapy. In the AC group, RCC had worse OS than LCC regardless of chemotherapy. In the MAC group, RCC had comparable OS to LCC regardless of chemotherapy. Four subgroups, i.e., RCC/MAC, RCC/AC, LCC/MAC and LCC/AC, all showed benefits from chemotherapy. Among them, LCC/AC had the best OS, and RCC/MAC had the worst OS compared with the other three subgroups. CONCLUSION: The prognosis of MAC is worse than that of AC in stage III CC. LCC/AC has the best OS, while RCC/MAC has the worst OS but still benefits from chemotherapy. The impact of chemotherapy on survival is greater than that of histological subtype, but the impact of histological subtype on survival is similar to that of PTL.

A toughening mechanism of the strain rate-optimal chain length on polymer-modified calcium silicate hydrates (CSH).

Polymers are known to effectively improve the toughness of inorganic matrices; however, the mechanism at the molecular level is still unclear. In this study, we used molecular dynamics simulations to unravel the effects and mechanisms of different molecular chain lengths of polyacrylic acid (PAA) on toughening calcium silicate hydrate (CSH), which is the basic building block of cement-based materials. Our simulation results indicate that an optimal molecular chain length of polymers contributes to the largest toughening effect on the matrix, leading to up to 60.98% increase in fracture energy. During the uniaxial tensile tests along the x-axis and z-axis direction, the configuration evolution of the PAA molecule determines the toughening effect. As the polymer unfolds and its size matches the defects of CSH, the stress distribution of the system becomes more homogeneous, which favors an increase in toughness. Furthermore, based on our simulation results and a mathematical model, we propose a theory of "strain rate/optimal chain length". This theory suggests that the optimal toughening effect can be achieved when the molecular chain length of the organic component is 1.3-1.5 times the largest defect size of the inorganic matrix. This work provides molecular-scale insights into the toughening mechanisms of an organic/inorganic system and may have practical implications for improving the toughness of cement-based materials.

The association of dietary intake of riboflavin and thiamine with kidney stone: a cross-sectional survey of NHANES 2007-2018.

BACKGROUND: Kidney stone disease (KSD) is a common condition that affects 10% population in the United States (US). The relationship between thiamine and riboflavin intake and KSD has not been well-studied. We aimed to investigate the prevalence of KSD and the association between dietary thiamine and riboflavin intake with KSD in the US population. METHODS: This large-scale, cross-sectional study included subjects from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. KSD and dietary intake were collected from questionnaires and 24-hour recall interviews. Logistic regression and sensitivity analyses were performed to investigate the association. RESULTS: This study included 26,786 adult participants with a mean age of 50.12 ± 17.61 years old. The prevalence of KSD was 9.62%. After adjusting for all potential covariates, we found that higher riboflavin intake was negatively related to KSD compared with dietary intake of riboflavin < 2 mg/day in the fully-adjusted model (OR = 0.541, 95% CI = 0.368 to 0.795, P = 0.002). After stratifying by gender and age, we found that the impact of riboflavin on KSD still existed in all age subgroups (P < 0.05) but only in males (P = 0.001). No such associations were found between dietary intake of thiamine and KSD in any of the subgroups. CONCLUSIONS: Our study suggested that a high intake of riboflavin is independently inversely associated with kidney stones, especially in male population. No association was found between dietary intake of thiamine and KSD. Further studies are needed to confirm our results and explore the causal relationships.

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology.

In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV's heading direction, considering its impact on the AUV's speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV's navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

Development and validation of a prognostic nomogram for patients with stage II colon mucinous adenocarcinoma.

PURPOSE: Mucinous histology is generally considered as a risk factor of prognosis in stage II colon cancer, but there is no appropriate model for prognostic evaluation and treatment decision in patients with stage II colon mucinous adenocarcinoma (C-MAC) Thus, it is urgent to develop a comprehensive, individualized evaluation tool to reflect the heterogeneity of stage II C-MAC. METHODS: Patients with stage II C-MAC who underwent surgical treatment in the Surveillance, Epidemiology, and End Results Program were enrolled and randomly divided into training cohort (70%) and internal validation cohort (30%). Prognostic predictors which were determined by univariate and multivariate analysis in the training cohort were included in the nomogram. The calibration curves, decision curve analysis, X-tile analysis, and Kaplan-Meier curve of the nomogram were validated in the internal validation cohort. RESULTS: Three thousand seven hundred sixty-two patients of stage II C-MAC were enrolled. The age, pathological T (pT) stage, tumor number, serum carcinoembryonic antigen (CEA), and perineural invasion (PNI) were independent predictors of overall survival (OS), which were used to establish a nomogram. Calibration curves of the nomogram indicated good consistency between nomogram prediction and actual survival for 1-, 3- and 5-year OS. Besides, patients with stage II C-MAC could be divided into high-, middle-, and low-risk subgroups by the nomogram. Further subgroup analysis indicated that patients in the high-risk group could have a survival benefit from chemotherapy after surgical treatment. CONCLUSIONS: We established the first nomogram to accurately predict the survival of stage II C-MAC patients who underwent surgical treatment. In addition, the nomogram identified low-, middle-, and high-risk subgroups of patients and found chemotherapy might improve survival in the high-risk subgroup of stage II C-MAC patients.

Notch is an alternative splicing gene in brown planthopper, Nilaparvata lugens.

Much research has assumed that Notch codes one protein. Then the protein will be cleaved into two parts and regenerates a heterodimers receptor to construct Notch signal pathways to regulate development in the past three decades. Here, we show that Notch in brown planthopper is a complex alternatively spliced gene has at least three transcriptional start sites, four exon skips, and 21 transcriptional endpoints that uses these to form variants and codes a series of proteins. When used dsRNAs to suppression different regions of the full-length variant NlNF resulted in a similar phenotype. Insects were molting after treatment, sensation circles on antennas near to root decayed, bristles on wings shortened, thickened or disappeared, accompanied by thickening veins and blades of fore-wing apex regions thickened. These results suggested that Notch influenced developmental of sensation circles, bristles, veins, and blades in nymph late periods. This study has deepened our understanding of Notch.

Clinical validation of the use of prototype software for automatic cartilage segmentation to quantify knee cartilage in volunteers.

BACKGROUND: The cartilage segmentation algorithms make it possible to accurately evaluate the morphology and degeneration of cartilage. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence the accuracy of segmentation. It is valuable to evaluate and compare the accuracy and clinical value of volume and mean T2* values generated directly from automatic knee cartilage segmentation with those from manually corrected results using prototype software. METHOD: Thirty-two volunteers were recruited, all of whom underwent right knee magnetic resonance imaging examinations. Morphological images were obtained using a three-dimensional (3D) high-resolution Double-Echo in Steady-State (DESS) sequence, and biochemical images were obtained using a two-dimensional T2* mapping sequence. Cartilage score criteria ranged from 0 to 2 and were obtained using the Whole-Organ Magnetic Resonance Imaging Score (WORMS). The femoral, patellar, and tibial cartilages were automatically segmented and divided into subregions using the post-processing prototype software. Afterwards, all the subregions were carefully checked and manual corrections were done where needed. The dice coefficient correlations for each subregion by the automatic segmentation were calculated. RESULTS: Cartilage volume after applying the manual correction was significantly lower than automatic segmentation (P < 0.05). The percentages of the cartilage volume change for each subregion after manual correction were all smaller than 5%. In all the subregions, the mean T2* relaxation time within manual corrected subregions was significantly lower than in regions after automatic segmentation (P < 0.05). The average time for the automatic segmentation of the whole knee was around 6 min, while the average time for manual correction of the whole knee was around 27 min. CONCLUSIONS: Automatic segmentation of cartilage volume has a high dice coefficient correlation and it can provide accurate quantitative information about cartilage efficiently without individual bias. Advances in knowledge: Magnetic resonance imaging is the most promising method to detect structural changes in cartilage tissue. Unfortunately, due to the structure and morphology of the cartilages obtaining accurate segmentations can be problematic. There are some factors (location of cartilage subregions, hydrarthrosis and cartilage degeneration) that may influence segmentation accuracy. We therefore assessed the factors that influence segmentations error.

Core-shell-structured magnetic covalent organic frameworks for effective extraction of parabens prior to their determination by HPLC.

Covalent organic framework (COF)-decorated magnetic nanoparticles (Fe3O4@DhaTab) with core-shell structure have been synthesized by one-pot method. The prepared Fe3O4@DhaTab was well characterized, and parameters of magnetic solid-phase extraction (MSPE) for parabens were also investigated in detail. Under optimized conditions, the adsorbent dosage was only 3 mg and extraction time was 10 min. The developed Fe3O4@DhaTab-based MSPE-HPLC analysis method offered good linearity (0.01-20 µg mL-1) with R2 (0.999) and low limits of detection (3.3-6.5 µg L-1) using UV detector at 254 nm. The proposed method was applied to determine four parabens in environmental water samples with recoveries in the range 64.0-105% and relative standard deviations of 0.16-7.8%. The adsorption mechanism was explored and indicated that porous DhaTab shell provided π-π, hydrophobic, and hydrogen bonding interactions in the MSPE process. The results revealed the potential of magnetic-functionalized COFs in determination of environmental contaminants.

A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning.

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea.

Deep Reinforcement Learning for the Detection of Abnormal Data in Smart Meters.

The rapidly growing power data in smart grids have created difficulties in security management. The processing of large-scale power data with the use of artificial intelligence methods has become a hotspot research topic. Considering the early warning detection problem of smart meters, this paper proposes an abnormal data detection network based on Deep Reinforcement Learning, which includes a main network and a target network composed of deep learning networks. This work uses the greedy policy algorithm to find the action of the maximum value of Q based on the Q-learning method to obtain the optimal calculation policy. It also uses the reward value and discount factor to optimize the target value. In particular, this study uses the fuzzy c-means method to predict the future state information value, which improves the computational accuracy of the Deep Reinforcement Learning model. The experimental results show that compared with the traditional smart meter data anomaly detection method, the proposed model improves the accuracy of meter data anomaly detection.