ESF-YOLO: an accurate and universal object detector based on neural networks.

As an excellent single-stage object detector based on neural networks, YOLOv5 has found extensive applications in the industrial domain; however, it still exhibits certain design limitations. To address these issues, this paper proposes Efficient Scale Fusion YOLO (ESF-YOLO). Firstly, the Multi-Sampling Conv Module (MSCM) is designed, which enhances the backbone network's learning capability for low-level features through multi-scale receptive fields and cross-scale feature fusion. Secondly, to tackle occlusion issues, a new Block-wise Channel Attention Module (BCAM) is designed, assigning greater weights to channels corresponding to critical information. Next, a lightweight Decoupled Head (LD-Head) is devised. Additionally, the loss function is redesigned to address asynchrony between labels and confidences, alleviating the imbalance between positive and negative samples during the neural network training. Finally, an adaptive scale factor for Intersection over Union (IoU) calculation is innovatively proposed, adjusting bounding box sizes adaptively to accommodate targets of different sizes in the dataset. Experimental results on the SODA10M and CBIA8K datasets demonstrate that ESF-YOLO increases Average Precision at 0.50 IoU (AP50) by 3.93 and 2.24%, Average Precision at 0.75 IoU (AP75) by 4.77 and 4.85%, and mean Average Precision (mAP) by 4 and 5.39%, respectively, validating the model's broad applicability.

estimation of ecosystem water consumption and the suitable scale of cultivated land in the Karamay region and Muzat River basin based on the optimized SEBAL energy balance model and water resource constraint model.

Food security is closely related to the development of human society, and the root of food production lies in cultivated land, with water conservancy as its lifeline. This study estimates the ecological water consumption of located in the arid region of Northwest China (the Karamay region and Muzat River basin) from 1990 to 2020 based on the optimized Land Surface Energy Balance Algorithm. The verification accuracy of SEBAL energy balance model is greatly improved after optimization. It was showed an increasing trend in the Karamay region and Muzat River basin, increasing at the rates of 2.84 mm/year and 2.86 mm/year respectively. The suitability of cultivated land was evaluated by combining four periods of 30 m spatial resolution land use/land cover data from 1990, 2000, 2010, and 2020, as well as the Hydrological Statistical Yearbook and Xinjiang Statistical Yearbook. Through the analysis of spatial optimization for cultivated land, it can be inferred that the primary limiting factors affecting cultivated land suitability in the Karamay region are irrigation guarantee rate (54.03%) and soil salinity (11.98%). Muzat River region are irrigation guarantee rate (32.19%) and soil salinity (18.62%). By comparing the scenarios of setting ecological priority and cultivated land priority, it is concluded that under the conditions of water resource constraints and 50%, 75% and 90% design irrigation assurance rates, Karamay still has cultivated land expansion potential, which can be used as the main preparatory reclamation area. In addition to the traditional agriculture irrigation area, the Muzat River basin still has development potential under the condition of ecological priority and no more than 75% irrigation design assurance rate. The study on the cultivated land suitability under the condition of water resource constraints can provide new ideas for food security.

A Review: Cytochrome P450 in Alcoholic and Non-Alcoholic Fatty Liver Disease.

Alcoholic fatty liver disease (FALD) and non-alcoholic fatty liver disease (NAFLD) have similar pathological spectra, both of which are associated with a series of symptoms, including steatosis, inflammation, and fibrosis. These clinical manifestations are caused by hepatic lipid synthesis and metabolism dysregulation and affect human health. Despite having been studied extensively, targeted therapies remain elusive. The Cytochrome P450 (CYP450) family is the most important drug-metabolising enzyme in the body, primarily in the liver. It is responsible for the metabolism of endogenous and exogenous compounds, completing biological transformation. This process is relevant to the occurrence and development of AFLD and NAFLD. In this review, the correlation between CYP450 and liver lipid metabolic diseases is summarised, providing new insights for the treatment of AFLD and NAFLD.

Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT.

Background. Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages.Objective. To develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early, overcoming the challenges associated with low contrast intensities and complex anatomical structures present in NCCT images.Approach.We design a multiscale and multiperception (MSMP) feature learning network with ResNet50 coupled with a feature pyramid network as the backbone for strengthening feature expressions. We added multiscale atrous convolutions to expand different receptive fields, contextual attention to perceive contextual information, and channel and spatial attention to focus on important channels and spatial regions, respectively. The MSMP network then acts as a feature extractor for proposing an NCCT-based pancreatic lesion detection model with image patches covering the pancreas as its input; Faster R-CNN is employed as the detection method for accurately detecting pancreatic lesions.Main results. By using the new MSMP network as a feature extractor, our model outperforms the conventional object detection algorithms in terms of the recall (75.40% and 90.95%), precision (40.84% and 68.21%), F1 score (52.98% and 77.96%), F2 score (64.48% and 85.26%) and Ap50 metrics (53.53% and 70.14%) at the image and patient levels, respectively.Significance.The good performance of our new model implies that MSMP can mine NCCT imaging features for detecting pancreatic lesions from complex backgrounds well. The proposed detection model is expected to be further developed as an intelligent method for the early detection of pancreatic cancer.

Surface-Phosphided Metal Oxide Microspheres as Catalytic Host of Sulfur to Enhance the Performance of Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries are one of the most promising high-energy density secondary batteries due to their high theoretical energy density of 2600 Wh kg-1. However, the sluggish kinetics and severe "shuttle effect" of polysulfides are the well-known barriers that hinder their practical applications. A carefully designed catalytic host of sulfur may be an effective strategy that not only accelerates the conversion of polysulfides but also limit their dissolution to mitigate the "shuttle effect." Herein, in situ surface-phosphided Ni0.96Co0.03Mn0.01O (p-NCMO) oxide microspheres are prepared via gas-phase phosphidation as a catalytic host of sulfur. The as-prepared unique heterostructured microspheres, with enriched surface-coated metal phosphide, exhibit superior synergistic effect of catalytic conversion and absorption of the otherwise soluble intermediate polysulfides. Correspondingly, the sulfur cathode exhibits excellent electrochemical performance, including a high initial discharge capacity (1162 mAh gs-1 at 0.1C), long cycling stability (491 mAh gs-1 after 1000 cycles at 1C), and excellent rate performance (565 mAh gs-1 at 5C). Importantly, the newly prepared sulfur cathode shows a high areal capacity of 4.0 mAh cm-2 and long cycle stability under harsh conditions (high sulfur loading of 5.3 mg cm-2 and lean electrolyte/sulfur ratio of 5.8 µL mg-1). This work proposes an effective strategy to develop the catalytic hosts of sulfur for achieving high-performance Li-S batteries via surface phosphidation.

LncRNA MRAK052509 competitively adsorbs miR-204-3p to regulate silica dust-induced EMT process.

Silicosis is a systemic disease caused by long-term inhalation of free SiO2 and retention in the lungs. At present, it is still the most important occupational health hazard disease in the world. Existing studies have shown that non-coding RNA can also participate in complex fibrosis regulatory networks. However, its role in regulating silicotic fibrosis is still unclear. In this study, we constructed a NR8383/RLE-6TN co-culture system to simulate the pathogenesis of silicosis in vitro. Design of miR-204-3p mimics and inhibitors to overexpress or downregulate miR-204-3p in RLE-6TN cells. Design of short hairpin RNA (sh-RNA) to downregulate MRAK052509 in RLE-6TN cells. The regulatory mechanism of miR-204-3p and LncRNA MRAK052509 on EMT process was studied by Quantitative real-time PCR, Western blotting, Immunofluorescence and Cell scratch test. The results revealed that miR-204-3p affects the occurrence of silica dust-induced cellular EMT process mainly through regulating TGF-ßRΙ, a key molecule of TGF-ß signaling pathway. In contrast, Lnc MRAK052509 promotes the EMT process in epithelial cells by competitively adsorbing miR-204-3p and reducing its inhibitory effect on the target gene TGF-ßRΙ, which may influence the development of silicosis fibrosis. This study perfects the targeted regulation relationship between LncRNA MRAK052509, miR-204-3p and TGF-ßRΙ, and may provide a new strategy for the study of the pathogenesis and treatment of silicosis.

Has sentiment returned to the pre-pandemic level? A sentiment analysis using U.S. college subreddit data from 2019 to 2022.

BACKGROUND: As the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people's emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level. METHOD: We collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment. RESULTS: Compared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values. CONCLUSIONS: Our study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

Neuroprotective effects of dendrobium endophytes metabolites in SH-SY5Y cells via the Nrf2/Keap1 pathway.

Recent studies have revealed that endophytes in plants can produce metabolites with activity that is comparable to or identical to the host. Dendrobine has attracted much attention in the field of neurodegenerative diseases by exhibiting anti-oxidative stress and neuroprotective effects. This study aimed to investigate the protective effects and mechanisms of metabolites of dendrobium endophytes Pseudomonas protegens CM-YJ44 and Priestia megaterium D-HT207 against H2O2-induced oxidative stress injury in SH-SY5Y cells. Results showed that there were 50 neuroprotective compounds in CM-YJ44 and 72 neuroprotective compounds in D-HT207. Those both increased significantly cell viability, decreased contents of ROS in H2O2-induced SH-SY5Y cells. It was confirmed that metabolites of CM-YJ44 and D-HT207 inhibited the H2O2-induced oxidative stress injury in SH-SY5Y cells, which mechanism is related to inhibition of ROS production, alteration of MMP, and inhibition of apoptosis and inflammatory factors expression via the Nrf2/Keap1 pathway.

Intelligent maneuver strategy for hypersonic vehicles in three-player pursuit-evasion games via deep reinforcement learning.

Aiming at the rapid development of anti-hypersonic collaborative interception technology, this paper designs an intelligent maneuver strategy of hypersonic vehicles (HV) based on deep reinforcement learning (DRL) to evade the collaborative interception by two interceptors. Under the meticulously designed collaborative interception strategy, the uncertainty and difficulty of evasion are significantly increased and the opportunity for maneuvers is further compressed. This paper, accordingly, selects the twin delayed deep deterministic gradient (TD3) strategy acting on the continuous action space and makes targeted improvements combining deep neural networks to grasp the maneuver strategy and achieve successful evasion. Focusing on the time-coordinated interception strategy of two interceptors, the three-player pursuit and evasion (PE) problem is modeled as the Markov decision process, and the double training strategy is proposed to juggle both interceptors. In reward functions of the training process, the energy saving factor is set to achieve the trade-off between miss distance and energy consumption. In addition, the regression neural network is introduced into the deep neural network of TD3 to enhance intelligent maneuver strategies' generalization. Finally, numerical simulations are conducted to verify that the improved TD3 algorithm can effectively evade the collaborative interception of two interceptors under tough situations, and the improvements of the algorithm in terms of convergence speed, generalization, and energy-saving effect are verified.

Application of fluorocarbon nanoparticles of 131I-fulvestrant as a targeted radiation drug for endocrine therapy on human breast cancer.

BACKGROUND: Breast cancer is the most prevalent malignant tumor among women, with hormone receptor-positive cases constituting 70%. Fulvestrant, an antagonist for these receptors, is utilized for advanced metastatic hormone receptor-positive breast cancer. Yet, its inhibitory effect on tumor cells is not strong, and it lacks direct cytotoxicity. Consequently, there's a significant challenge in preventing recurrence and metastasis once cancer cells develop resistance to fulvestrant. METHOD: To address these challenges, we engineered tumor-targeting nanoparticles termed 131I-fulvestrant-ALA-PFP-FA-NPs. This involved labeling fulvestrant with 131I to create 131I-fulvestrant. Subsequently, we incorporated the 131I-fulvestrant and 5-aminolevulinic acid (ALA) into fluorocarbon nanoparticles with folate as the targeting agent. This design facilitates a tri-modal therapeutic approach-endocrine therapy, radiotherapy, and PDT for estrogen receptor-positive breast cancer. RESULTS: Our in vivo and in vitro tests showed that the drug-laden nanoparticles effectively zeroed in on tumors. This targeting efficiency was corroborated using SPECT-CT imaging, confocal microscopy, and small animal fluorescence imaging. The 131I-fulvestrant-ALA-PFP-FA-NPs maintained stability and showcased potent antitumor capabilities due to the synergism of endocrine therapy, radiotherapy, and CR-PDT. Throughout the treatment duration, we detected no notable irregularities in hematological, biochemical, or histological evaluations. CONCLUSION: We've pioneered a nanoparticle system loaded with radioactive isotope 131I, endocrine therapeutic agents, and a photosensitizer precursor. This system offers a combined modality of radiotherapy, endocrine treatment, and PDT for breast cancer.

Observation on Microenvironment Changes of Dynamic Catalysts in Acidic CO2 Reduction.

Electrochemical CO2 reduction reaction (CO2RR) in acid can solve alkalinity issues while highly corrosive and reductive acidic electrolytes usually cause catalyst degradation. Inhibiting catalyst degradation is crucial for the stability of acidic CO2RR. Here, we reveal the microenvironment changes of dynamic Bi-based catalysts and develop a pulse chronoamperometry (CA) strategy to improve the stability of acidic CO2RR. In situ fluorescence mappings show that the local pH changes from neutral to acid, and the in situ Raman spectra reveal the dynamic evolution of interfacial water structures in the microenvironment. We propose that the surface charge properties of dynamic catalysts affect the competitive adsorption of K+ and protons, thereby causing the differences in local pH and CO2RR intermediate adsorption. We also develop a pulse CA strategy to reactivate catalysts, and the stability of acidic CO2RR is improved by 2 orders of magnitude for 100 h operation, which is higher than most reports on the stability of acidic CO2RR. This work gives insights on how microenvironment changes affecting the stability of acidic CO2RR, and provides guidance for designing stable catalysts in acidic electrolytes.

Quantification of derivatized phenylalanine and tyrosine in dried blood spots using liquid chromatography with tandem spectrometry for newborn screening of phenylketonuria.

Phenylketonuria (PKU) is an autosomal genetic disorder caused by a deficiency of the phenylalanine hydroxylase (PAH) enzyme. The lack of PAH results in the inability of phenylalanine (PHE) to transform into tyrosine (TYR). Consequently, this leads to the accumulation of PHE in the blood samples of newborns causing metabolic diseases such as irreversible neurological problems. An analysis was required for determining the values of PHE and TYR in blood samples from newborn babies. In this study, therefore, we developed a derivatized method to monitor PHE and TYR in plasma samples using liquid phase chromatography linked with quadrupole mass spectrometry. Accessible formaldehyde isotopes and cyanoborohydride were used to react with PHE and TYR amino groups to generate h2-formaldehyde-modified PHE and TYR (as standards) and d2-formaldehyde-modified PHE and TYR (as internal standards). We used tandem mass spectrometry for multiple reaction monitoring. We demonstrated a derivatized method suitable for the PKU screening of newborns. The recoveries for PHE and TYR were 85% and 90%, respectively. Furthermore, we compared the values of PHE and TYR in different human plasma sample storage methods, including direct plasma and dried blood spots, and the results showed no significant difference.

Integrating Photoactive Ligands into Crystalline Ultrathin 2D Metal-Organic Framework Nanosheets for Efficient Photoinduced Energy Transfer.

3D metal-organic frameworks (MOFs) have gained attention as heterogeneous photocatalysts due to their porosity and unique host-guest interactions. Despite their potential, MOFs face challenges, such as inefficient mass transport and limited light penetration in photoinduced energy transfer processes. Recent advancements in organic photocatalysis have uncovered a variety of photoactive cores, while their heterogenization remains an underexplored area with great potential to build MOFs. This gap is bridged by incorporating photoactive cores into 2D MOF nanosheets, a process that merges the realms of small-molecule photochemistry and MOF chemistry. This approach results in recyclable heterogeneous photocatalysts that exhibit an improved mass transfer efficiency. This research demonstrates a bottom-up synthetic method for embedding photoactive cores into 2D MOF nanosheets, successfully producing variants such as PCN-641-NS, PCN-643-NS, and PCN-644-NS. The synthetic conditions were systematically studied to optimize the crystallinity and morphology of these 2D MOF nanosheets. Enhanced host-guest interactions in these 2D structures were confirmed through various techniques, particularly solid-state NMR studies. Additionally, the efficiency of photoinduced energy transfer in these nanosheets was evidenced through photoborylation reactions and the generation of reactive oxygen species (ROS).

Ten-Day Vonoprazan-Amoxicillin Dual Therapy vs Standard 14-Day Bismuth-Based Quadruple Therapy for First-Line Helicobacter pylori Eradication: A Multicenter Randomized Clinical Trial.

INTRODUCTION: Whether 10-day short-course vonoprazan-amoxicillin dual therapy (VA-dual) is noninferior to the standard 14-day bismuth-based quadruple therapy (B-quadruple) against Helicobacter pylori eradication has not been determined. This trial aimed to compare the eradication rate, adverse events, and compliance of 10-day VA-dual regimen with standard 14-day B-quadruple regimen as first-line H. pylori treatment. METHODS: This prospective randomized clinical trial was performed at 3 institutions in eastern China. A total of 314 treatment-naive, H. pylori -infected patients were randomly assigned in a 1:1 ratio to either 10-day VA-dual group or 14-day B-quadruple group. Eradication success was determined by 13 C-urea breath test at least 4 weeks after treatment. Eradication rates, adverse events, and compliance were compared between groups. RESULTS: Eradication rates of VA-dual and B-quadruple groups were 86.0% and 89.2% ( P = 0.389), respectively, by intention-to-treat (ITT) analysis; 88.2% and 91.5% ( P = 0.338), respectively, by modified ITT analysis; and 90.8% and 91.3% ( P = 0.884), respectively, by per-protocol (PP) analysis. The efficacy of the VA-dual remained noninferior to B-quadruple therapy in all ITT, modified ITT, and PP analyses. The incidence of adverse events in the VA-dual group was significantly lower compared with that in the B-quadruple group ( P < 0.001). Poor compliance contributed to eradication failure in the VA-dual group ( P < 0.001), while not in the B-quadruple group ( P = 0.110). DISCUSSION: The 10-day VA-dual therapy provided satisfactory eradication rates of >90% (PP analysis) and lower rates of adverse events compared with standard 14-day B-quadruple therapy as first-line H. pylori therapy. TRAIL REGISTRATION NUMBER: ChiCTR2300070100.

In Situ Mapping of Activity Distribution of V(II)/V(III) and Onset Potential Distribution of Hydrogen Evolution Side Reaction in Vanadium Flow Batteries.

Vanadium flow batteries (VFBs) face a challenge with the low reaction rates of the V(II)/V(III) redox couple, which limits the performance of VFBs. Additionally, the negative electrode in VFBs is often accompanied by the persistent hydrogen evolution reaction (HER), which is difficult to eliminate. Therefore, understanding the spatial distribution of activity on the negative electrode and the HER side reaction on the electrode surface is of critical importance. This study proposes a weak measurement imaging method to characterize the spatial distribution of surface activity and HER onset potential on the negative electrode in VFBs). This method enables the visualization and in situ detection of key parameters such as the absolute values of |ipa |, |ipc |, |∆E|, |ipc /ipa |, and the HER onset potential. By comparing three different types of graphite felts with varying activity levels, it validates the feasibility of this method. Furthermore, electrochemical stability tests are conducted to study the electrodes repeatability, uniformity, and durability. This method holds promise in guiding the design of electrodes with enhanced activity, good reversibility, minimized HER side reactions, and uniform distribution.

Temporal change of serum xanthine oxidase levels and its relation to clinical outcome of severe traumatic brain injury: a prospective cohort study.

Xanthine oxidase (XO) may be involved in the induction of oxidative stress and inflammation. We measured serum XO levels at multiple days to determine whether it is associated with the severity and prognosis of severe traumatic brain injury (sTBI). In this prospective cohort study, we quantified serum XO levels in 112 sTBI patients and 112 controls. Serum XO levels of patients were measured at admission and at days 1, 3, 5, 7, and 10 after sTBI. Extended Glasgow outcome scale scores of 1-4 at post-trauma 180 days were defined as a poor prognosis. Multivariate analysis was employed to determine the relationship between poor prognosis and serum XO levels at multiple days. Serum XO levels were significantly increased at admission among patients, afterwards elevated gradually, peaked at day 3, and then diminished gradually until day 10, and were substantially higher during 10 days in patients than in controls. Serum XO levels at 6 different days were all correlated with admission Rotterdam computed tomography (CT) scores and Glasgow coma scale (GCS) scores. Serum XO levels at 6 different days were all substantially higher in patients with poor prognosis than in those with good prognosis. Serum XO levels at days 7 and 10, but not at days 1, 3, and 5, had significantly lower area under receiver operating characteristic (AUC) than those at admission. Serum XO levels at admission and at days 1 and 3, but not at day 5, were independently associated with 180-day poor prognosis. Prognostic prediction model containing GCS scores, Rotterdam CT scores, and serum XO levels at admission (or at days 1 and 3) showed substantially higher AUC than GCS scores and Rotterdam CT scores alone. The models were visually described using nomograms, which were comparatively stable under calibration curve and were relatively of clinical benefit under decision curve. Elevated serum XO levels during early period of sTBI are more closely associated with trauma severity and clinical adverse outcomes, assuming that serum XO may serve as a potential prognostic biomarker in sTBI.

A prospective cohort study on serum A20 as a prognostic biomarker of aneurysmal subarachnoid hemorrhage.

BACKGROUND: A20 may be a neuroprotective factor. Herein, we aimed to investigate whether serum A20 levels were associated with disease severity, delayed cerebral ischemia (DCI), and outcome after aneurysmal subarachnoid hemorrhage (aSAH). METHODS: In this prospective cohort study containing 112 aSAH patients and 112 controls, serum A20 levels were quantified. At 90 d poststroke, Modified Rankin Scale (MRS) scores ≥3 were defined as a poor outcome. All correlations and associations were assessed using multivariate analysis. RESULTS: Compared with controls, there was a significant elevation of serum A20 levels in patients (median 123.7 pg/mL vs. 25.8 pg/mL; P<0.001). Serum A20 levels were independently correlated with Hunt-Hess scores (ß 9.854; 95% confidence interval [95% CI] 2.481-17.227, P=0.009) and modified Fisher scores (ß 10.349, 95% CI 1.273-19.424, P=0.026). Independent associations were found between serum A20 levels and poor outcome (odds ratio [OR] 1.015, 95% CI 1.000-1.031, P=0.047) and DCI (OR 1.018, 95% CI 1.001-1.035, P=0.042). Areas under the curve for predicting poor outcome and DCI were 0.771 (95% CI 0.682-0.845) and 0.777 (95% CI 0.688-0.850), respectively. Serum A20 levels ≥128.15 pg/mL predicted poor outcome, with a sensitivity of 73.9% and specificity of 74.2%, and A20 levels ≥160.55 pg/mL distinguished the risk of DCI with 65.5% sensitivity and 89.2% specificity. Its ability to predict poor outcome and DCI was similar to those of Hunt-Hess scores and modified Fisher scores (both P>0.05). CONCLUSION: Enhanced serum A20 levels are significantly associated with stroke severity and poor clinical outcome after aSAH, implying that serum A20 may be a potential prognostic biomarker for aSAH.

Endoscopic image classification algorithm based on Poolformer.

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model's capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper's approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.

A dinoflagellate bloom caused by multiple species of Kareniaceae in the coastal waters of Fujian in June 2022 and its adverse impacts on Brachionus plicatilis and Artemia salina.

Recently, dinoflagellate blooms have frequently occurred in the coastal waters of Fujian, East China Sea. In June 2022, a fish-killing bloom of Kareniaceae species occurred in this region. In this study, four species of Kareniaceae, namely, Karenia longicanalis, K. papilionacea, Karlodinium veneficum, and Karl. digitatum were identified from this bloom event based on the results of single-cell PCR and clone libraries, and intraspecies genetic diversity was found in the Karl. veneficum population. The results of acute toxicity assays of the bloom water to two zooplankton species (Brachionus plicatilis and Artemia salina) demonstrated this bloom event strongly inhibited their swimming capacities and survival. The results of this study suggested that the bloom events caused by multiple species of Kareniaceae in the Fujian coastal waters had adverse impacts on the local fishery resources and zooplankton community.