Growth increase and gonadal dysfunction of the lined seahorse triggered by zinc exposure.

Seahorses are characterized by unique characteristics such as a male pregnancy reproductive strategy and grasping preferences, which make these species vulnerable to various environmental risks. Zinc (Zn) is one of the most frequently occurring toxic elements in coastal waters; however, little is known about the effect of Zn exposure on seahorses. In the present study, line seahorses (Hippocampus erectus) were exposed to waterborne Zn (0.2 and 1.0 mg/L) and the impact on growth and gonadal development was investigated. Zn exposure induced growth improvement, but also led to gonadal dysfunction in the lined seahorse. Female seahorses exhibited high testosterone levels, immature follicles, and weight increase after Zn exposure, which is the typical characteristics of a polycystic ovary syndrome (PCOS)-like phenotype. Transcriptomic data suggested that the Zn-induced growth promotion resulted from the dysregulated expression of fat accumulation genes. Further investigation of gene expression profiles in the brain, ovaries, and testes indicated that Zn affected the expression of genes involved in growth, immunity, tissue remodeling, and gonadal development by regulating serum steroid hormone levels and androgen receptor expression. This study not only clarifies the complex impact of Zn on seahorses using physiological, histological, and molecular evidence but can also provide new insights into the mechanism underlying PCOS in reproductive-aged women. Moreover, this work demonstrates the risk of the common practice of Zn supplementation in the aquaculture industry as the consequent growth yield may not represent a healthy condition.

Clinicopathologic and endoscopic features of sessile serrated lesions and conventional adenomas: a large inpatient population-based study in China.

Objectives: Sessile serrated lesions (SSLs) are precursors of sporadic colorectal cancer (CRC) and have distinct characteristics compared with conventional adenomas (CAs). Several lifestyle and environmental factors may play critical roles in the development of advanced lesions. Our aim is to describe the features of SSLs and CAs and further explore risk factors for advanced lesions. Methods: This is an observational study that collected demographic, endoscopic, and histological data from the China-Japan Friendship Hospital among the inpatient population with pathologically reported as SSL or CA between 2015 and 2022. We analyzed the clinicopathology and endoscopic differences between SSL alone, CA alone, and synchronous SSL+CA groups, and identified risk factors using multiple regression analysis. Results: A total of 9236 polyps from 6598 patients were included in the cohort. Patients with SSL+CA were more likely to be older (p=0.008), while individuals with SSL alone had a higher proportion of early-onset polyps (p<0.001), and SSLs were more common in advanced polyps than CAs (p<0.001). A greater proportion of advanced polyps in the SSL and CA groups were diagnosed as Yamada III, Yamada IV, and laterally spreading tumor (p=0.002, p<0.001, respectively), and multiple SSLs and CAs were more represented in nonadvanced polyps than in advanced polyps. In multiple regression analysis, older patients were more likely to develop advanced SSLs (aOR 1.05, 95% CI 1.02-1.09, p=0.005). Conclusion: SSLs and CAs have diverse demographic, endoscopic, and histological characteristics, and their advanced lesions share different risk factors, which advances the understanding of the etiology and progression of SSLs.

Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer.

BACKGROUND: The accuracy of artificial intelligence (AI) and experts in diagnosing early esophageal cancer (EC) and its infiltration depth was summarized and analyzed, thus identifying the advantages of AI over traditional manual diagnosis, with a view to more accurately assisting doctors in evaluating the patients' conditions and improving their cure and survival rates. METHODS: The PubMed, EMBASE, Cochrane, Google, and CNKI databases were searched for relevant literature related to AI diagnosis of early EC and its invasion depth published before August 2023. Summary analysis of pooled sensitivity, specificity, summary receiver operating characteristics (SROC) and area under the curve (AUC) of AI in diagnosing early EC were performed, and Review Manager and Stata were adopted for data analysis. RESULTS: A total of 19 studies were enrolled with a low to moderate total risk of bias. The pooled sensitivity of AI for diagnosing early EC was markedly higher than that of novices and comparable to that of endoscopists. Moreover, AI predicted early EC with markedly higher AUCs than novices and experts (0.93 vs. 0.74 vs. 0.89). In addition, pooled sensitivity and specificity in the diagnosis of invasion depth in early EC were higher than that of experts, with AUCs of 0.97 and 0.92, respectively. CONCLUSION: AI-assistance can diagnose early EC and its infiltration depth more accurately, which can help in its early intervention and the customization of personalized treatment plans. Therefore, AI systems have great potential in the early diagnosis of EC.

Application of machine learning to analyze ozone sensitivity to influencing factors: A case study in Nanjing, China.

Ground-level ozone (O3) has been an emerging concern in China. Due to its complicated formation mechanisms, understanding the effects of influencing factors is critical for making effective efforts on the pollution control. This study aims to present and demonstrate the practicality of a data-driven technique that applies a machine learning (ML) model coupled with the SHapley Additive exPlanations (SHAP) approach in O3 simulation and sensitivity analysis. Based on hourly measured concentrations of O3 and its major precursors, as well as meteorological factors in a northern area of Nanjing, China, a Light Gradient Boosting Machine (LightGBM) model was established to simulate O3 concentrations in different seasons, and the SHAP approach was applied to conduct in-depth analysis on the impacts of influencing factors on O3 formation. The results indicated a reliable performance of the ML model in simulating O3 concentrations, with the coefficient of determination (R2) between the measured and simulated larger than 0.80, and the impacts of influencing factors were reasonably evaluated by the SHAP approach on both seasonal and diurnal time scales. It was found that although volatile organic compounds (VOCs) and nitrogen oxides (NOx), as well as temperature and relative humidity, were generally the main influencing factors, their sensitivities to O3 formation varied significantly in different seasons and with time of the day. This study suggests that the data-driven ML model is a practicable technique and may act as an alternative way to perform mechanism analysis to some extent, and has immense potential to be applied in both problem research and decision-making for air pollution control.

Multi-timescale variation characteristics of PM2.5 in different regions of China during 2014-2022.

Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.

Bioorthogonal Aptamer-ATTEC Conjugates for Degradation of Alpha-Synuclein via Autophagy-Lysosomal Pathway.

Autophagosome-tethering compound (ATTEC) technology has recently been emerging as a novel approach for degrading proteins of interest (POIs). However, it still faces great challenges in how to design target-specific ATTEC molecules. Aptamers are single-stranded DNA or RNA oligonucleotides that can recognize their target proteins with high specificity and affinity. Here, ATTEC is combined with aptamers for POIs degradation. As a proof of concept, pathological protein α-synuclein (α-syn) is chosen as the target and an efficient α-syn degrader is generated. Aptamer as a targeting warhead of α-syn is conjugated with LC3B-binding compound 5,7-dihydroxy-4-phenylcoumarin (DP) via bioorthogonal click reaction. It is demonstrated that the aptamer conjugated with DP is capable of clearing α-syn through LC3 and autophagic degradation. These results indicate that aptamer-based ATTECs are a versatile approach to degrade POIs by taking advantage of the well-defined different aptamers for targeting diverse proteins, which provides a new way for the design of ATTECs to degradation of targeted proteins.

[Pollution Characteristics and Source Apportionment of Volatile Organic Compounds in Lishui Area of Nanjing].

To understand the changes in the components of volatile organic compounds(VOCs), the contribution proportion of each component to ozone, and the VOCs sources, we monitored the VOCs for a year in Lishui. The results showed that theρ(TVOC) was 223.46 µg·m-3, ρ(alkanes) was 49.45 µg·m-3(22.3%), ρ(OVOCs) was 50.63 µg·m-3(22.66%), ρ(halogenated hydrocarbons) was 64.73 µg·m-3(28.95%), ρ(aromatic hydrocarbons) was 35.46 µg·m-3(15.87%), ρ(alkenes) was 18.26 µg·m-3(8.19%), and ρ(others) was 4.9 µg·m-3(2.2%). ρ(TVOC) was higher in summer(263.75 µg·m-3) and lower in winter(187.2 µg·m-3), with 246.11 µg·m-3 in spring and 204.77 µg·m-3 in autumn. The daily concentration of VOCs showed two peaks, one from 9:00 to 10:00 and another from 14:00 to 15:00, and the high concentration was mainly found in the urban main road area with dense human activities. The ozone formation potential(OFP) was 278.92 µg·m-3, and those of olefin and aromatic hydrocarbon were 114.47 µg·m-3(41.1%) and 113.49 µg·m-3(40.8%), respectively, contributing over 80%, which was an important precursor of ozone. On the other hand, the ratio of characteristic compounds to toluene/benzene(T/B) was 4.13, which indicated that it was greatly affected by the solvent usage. In the end, the results of positive matrix factorization(PMF) source apportionment showed that VOCs mainly came from solvent usage, industrial production, and traffic emissions. The VOCs pollution had a great influence on ozone, so it was necessary to strengthen the treatment of industrial production, solvent usages, and traffic emissions.

Clinical and DCE-CT signs in predicting microvascular invasion in cHCC-ICC.

BACKGROUND: To predict the microvascular invasion (MVI) in patients with cHCC-ICC. METHODS: A retrospective analysis was conducted on 119 patients who underwent CT enhancement scanning (from September 2006 to August 2022). They were divided into MVI-positive and MVI-negative groups. RESULTS: The proportion of patients with CEA elevation was higher in the MVI-positive group than in the MVI-negative group, with a statistically significant difference (P = 0.02). The MVI-positive group had a higher rate of peritumoral enhancement in the arterial phase (P = 0.01) whereas the MVI-negative group had more oval and lobulated masses (P = 0.04). According to the multivariate analysis, the increase in CEA (OR = 10.15, 95% CI: 1.11, 92.48, p = 0.04), hepatic capsular withdrawal (OR = 4.55, 95% CI: 1.44, 14.34, p = 0.01) and peritumoral enhancement (OR = 6.34, 95% CI: 2.18, 18.40, p < 0.01) are independent risk factors for predicting MVI. When these three imaging signs are combined, the specificity of MVI prediction was 70.59% (series connection), and the sensitivity was 100% (parallel connection). CONCLUSIONS: Our multivariate analysis found that CEA elevation, liver capsule depression, and arterial phase peritumoral enhancement were independent risk factors for predicting MVI in cHCC-ICC.

Leukocyte immunoglobulin-like receptor B2 overexpression as a promising therapeutic target and noninvasive screening biomarker for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) has become the second most deadly malignancy in the world, and the exploration of screening markers and precise therapeutic targets is urgent. Our previous research identified leukocyte immunoglobulin-like receptor B2 (LILRB2) protein as a characteristic protein of CRC, but the association between LILRB2 expression and clinicopathological features, the internal mechanism related to CRC progression, and screening diagnostic efficacy are not clear. Therefore, we hypothesized that LILRB2 is significantly highly expressed in CRC tissues, correlated with advanced stage and a poor prognosis, and could be used as a therapeutic target and potential screening biomarker for CRC. AIM: To explore whether LILRB2 can be used as a potential therapeutic target and noninvasive screening biomarker for CRC. METHODS: Patients who underwent radical surgery for CRC at China-Japan Friendship Hospital between February 2021 and October 2022 were included. Cancer and paracancerous tissues were collected to verify LILRB2 expression, and the association between LILRB2 expression and clinicopathological features was analysed. Serum was collected from CRC patients, adenoma patients and healthy controls during the same period to assess the diagnostic value of LILRB2 as a noninvasive screening biomarker, and its diagnostic value was further compared with that of the traditional markers carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9). RESULTS: A total of 58 CRC patients were included, and LILRB2 protein was significantly overexpressed in cancer tissues compared with paracancerous tissues (P < 0.001). Angiopoietin-like protein 2 (ANGPTL2) protein, as the ligand of LILRB2, was synergistically overexpressed in CRC tissues (P < 0.001), and overexpression of LILRB2 and ANGPTL2 protein was significantly correlated with poor to moderate differentiation, vascular involvement, lymph node metastasis, distant metastasis, advanced tumor-node-metastasis stage and a poor prognosis (P < 0.05), which suggested that LILRB2 and ANGPTL2 are closely associated with CRC progression. In addition, serum LILRB2 concentrations increased stepwise in healthy individuals, adenoma patients and CRC patients with statistically significant differences. The sensitivity of serum LILRB2 for the diagnosis of CRC was 89.74%, the specificity was 88.89%, the area under the curve was 0.95, and the diagnostic efficacy was better than that of conventional CEA and CA19-9. CONCLUSION: LILRB2 protein can be used as a potential novel therapeutic target and noninvasive screening biomarker for CRC, which is beneficial for early screening and precise treatment.

Targeting mitochondrial degradation by chimeric autophagy-tethering compounds.

The ability to regulate mitophagy in a living system with small molecules remains a great challenge. We hypothesize that adding fragments specific to the key autophagosome protein LC3 to mitochondria will mimic receptor-mediated mitophagy, thus engaging the autophagy-lysosome pathway to induce mitochondrial degradation. Herein, we develop a general biochemical approach to modulate mitophagy, dubbed mito-ATTECs, which employ chimera molecules composed of LC3-binding moieties linked to mitochondria-targeting ligands. Mito-ATTECs trigger mitophagy via targeting mitochondria to autophagosomes through direct interaction between mito-ATTECs and LC3 on mitochondrial membranes. Subsequently, autophagosomes containing mitochondria rapidly fuse with lysosomes to facilitate the degradation of mitochondria. Therefore, mito-ATTECs circumvent the detrimental effects related to disruption of mitochondrial membrane integrity by inducers routinely used to manipulate mitophagy, and provide a versatile biochemical approach to investigate the physiological roles of mitophagy. Furthermore, we found that sustained mitophagy lead to mitochondrial depletion and autophagic cell death in several malignant cell lines (lethal mitophagy). Among them, apoptosis-resistant malignant melanoma cell lines are particularly sensitive to lethal mitophagy. The therapeutic efficacy of mito-ATTECs has been further evaluated by using subcutaneous and pulmonary metastatic melanoma models. Together, the mitochondrial depletion achieved by mito-ATTECs may demonstrate the general concept of inducing cancer cell lethality through excessive mitochondrial clearance, establishing a promising therapeutic paradigm for apoptosis-resistant tumors.

Osteoporosis prediction in lumbar spine X-ray images using the multi-scale weighted fusion contextual transformer network.

Osteoporosis is a bone-related disease characterized by decreased bone density and mass, leading to brittle fractures. Osteoporosis assessment from radiographs using a deep learning algorithm has proven a low-cost alternative to the golden standard DXA. Due to the considerable noise and low contrast, automated diagnosis of osteoporosis in X-ray images still poses a significant challenge for traditional diagnostic methods. In this paper, an end-to-end transformer-style network was proposed, termed FCoTNet, to overcome the shortcoming of insufficient fusion of texture information and local features in the traditional CoTNet. To extract complementary geometric representations at each scale of the transformer module, we integrated parallel multi-scale feature extraction architectures in each unit layer of FCoTNet to utilize convolution to aggregate features from different receptive fields. Moreover, in order to extract small-scale texture features which were more critical to the diagnosis of osteoporosis in radiographs, larger fusion weights were assigned to the feature maps with small-size receptive fields. Afterward, the multi-scale global modeling was conducted by self-attention mechanism. The proposed model was first investigated on a private lumbar spine X-ray dataset with the 5-fold cross-validation strategy, obtaining an average accuracy of 78.29 ± 0.93 %, an average sensitivity of 69.72 ± 2.35 %, and an average specificity of 88.92 ± 0.67 % for the multi-classification of normal, osteopenia, and osteoporosis categories. We then conducted a controlled trial with five orthopedic clinicians to evaluate the clinical value of the model. The average clinician's accuracy improved from 61.50 ± 10.79 % unaided to 80.00 ± 5.92 % aided (18.50 % improvement), sensitivity improved from 64.38 ± 8.07 % unaided to 83.31 ± 5.43 % aided (18.93 % improvement), and specificity improved from 80.11 ± 4.72 % unaided to 89.94 ± 3.82 % aided (9.83 % improvement). Meanwhile, the prediction consistency among clinicians significantly improved with the assistance of FCoTNet. Furthermore, the proposed model showed good robustness on an external test dataset. These investigations indicate that the proposed deep learning model achieves state-of-the-art performance for osteoporosis prediction, which substantially improves osteoporosis screening and reduced osteoporosis fractures.

Genomic Structure and Molecular Characterization of Toll-like Receptors in Black Scraper Thamnaconus Modestus and Their Expression Response to Two Types of Pathogens.

The black scraper (Thamnaconus modestus) is an important commercial species in China. However, with the rapid expansion of aquaculture, the culture of this species faces substantial economic losses due to infectious diseases. Toll-like receptors (TLRs) recognize a wide range of pathogen-associated molecular patterns (PAMPs) and play a crucial role in disease resistance by initiating innate immune responses in the host. The genome of the black scraper comprises eight TLR members, which can be classified into five subfamilies based on evolutionary analysis. Moreover, the TmTLRs were identified on 6 out of the 20 chromosomes in the black scraper. The functional similarity within the same subfamilies is evident by conserved motifs and gene structures. The qRT-PCR experiments revealed diverse TmTLR expression patterns in the liver, intestine, spleen, head kidney, heart, and brain of black scrapers, with high expression levels observed in immune organs, suggesting that TmTLRs may participate in the regulation of immune mechanisms and other physiological functions in the black scraper. At least six TmTLRs showed significantly upregulated expression in response to poly (I: C) or lipopolysaccharide (LPS) stresses, thus indicating their potential roles in regulating abiotic stress responses. In conclusion, our findings not only provide a foundation for future research on the TLR gene family in the black scraper but also offer guidance for disease prevention and vaccine development.

Gene loss and co-option of toll-like receptors facilitate paternal immunological adaptation in the brood pouch of pregnant male seahorses.

Male pregnancy in syngnathids (seahorses, pipefishes, and sea dragons) is an evolutionary innovation in the animal kingdom. Paternal immune resistance to the fetus is a critical challenge, particularly in seahorses with fully enclosed brood pouches and sophisticated placentas. In this study, comparative genomic analysis revealed that all syngnathid species lost three vertebrate-conserved Toll-like receptors (TLR1, TLR2, and TLR9), of which all play essential roles in immune protection and immune tolerance in the uterus and placenta. Quantitative real-time PCR (qRT-PCR) analysis showed that the TLR paralog genes including TLR18, TLR25, and TLR21 were highly expressed in the placenta inside the seahorse brood pouch and changed dynamically during the breeding cycle, suggesting the potentially important role of the TLRs during male pregnancy. Furthermore, the immune challenge test in vitro showed a remarkable expression response from all three TLR genes to specific pathogenic antigens, confirming their immune function in seahorse brood pouches. Notably, the altered antigen recognition spectrum of these genes appeared to functionally compensate in part for the lost TLRs, in contrast to that observed in other species. Therefore, we suggest that gene loss and co-option of TLRs may be a typical evolutionary strategy for facilitating paternal immunological adaptation during male pregnancy.

Engineering 2D MXene and LDH into 3D Hollow Framework for Boosting Photothermal Energy Storage and Microwave Absorption.

2D MXene is highly preferred for photothermal energy conversion and microwave absorption. However, the aggregation issue, insufficient dielectric loss capacity, and lack of magnetic loss capacity for MXene severely hinder its practical applications. Herein, the authors propose multi-dimensional nanostructure engineering to electrostatically assemble 2D MXene and layered double hydroxides (LDH) derived from ZIF-67 polyhedron into a 3D hollow framework (LDH@MXene), and subsequently calcined to construct a Co nanoparticle-modified 3D hollow C-LDH@MXene framework to encapsulate a paraffin wax (PW) phase change material (PCM). The 3D hollow C-LDH@MXene framework not only prevents 2D MXene from aggregation but also contributes a high thermal energy storage density (131.04 J g-1 ). Benefiting from a 3D conductive network facilitating the rapid transport of photons and phonons from the interface to the interior and the synergistic localized surface plasmon resonance (LSPR) effect of MXene and Co magnetic nanoparticles, the C-LDH@MXene-PW composite PCM yielded a high photothermal storage efficiency of 96.52%. Besides, C-LDH@MXene-PW composite PCMs also exhibited efficient microwave absorption with a minimum reflection loss of -20.87 dB at 13.30 GHz with a matching thickness of only 2 mm. This distinctive design provides constructive references for the development of integrated composite materials for energy storage and microwave absorption.

A bimetallic nanoplatform for STING activation and CRISPR/Cas mediated depletion of the methionine transporter in cancer cells restores anti-tumor immune responses.

Lack of sufficient cytotoxic T lymphocytes (CD8+ T cells) infiltration and dysfunctional state of CD8+ T cells are considered enormous obstacles to antitumor immunity. Herein, we construct a synergistic nanoplatform to promote CD8+ T cell infiltration in tumors while restoring T cell function by regulating methionine metabolism and activating the STING innate immune pathway. The CRISPR/Cas9 system down-regulates the methionine transporter SLC43A2 and restricts the methionine uptake by tumor cells, thereby relieving the methionine competition pressure of T cells; simultaneously, the released nutrition metal ions activate the cGAS/STING pathway. In this work, the described nanoplatform can enhance the effect of immunotherapy in preclinical cancer models in female mice, enhancing STING pathway mediated immunity and facilitating the development of amino acid metabolic intervention-based cancer therapy.

Bioorthogonal Disruption of Pyroptosis Checkpoint for High-Efficiency Pyroptosis Cancer Therapy.

Pyroptosis is an inflammatory form of programmed cell death that holds great promise in cancer therapy. However, autophagy as the crucial pyroptosis checkpoint and the self-protective mechanism of cancer cells significantly weakens the therapeutic efficiency. Here, a bioorthogonal pyroptosis nanoregulator is constructed to induce pyroptosis and disrupt the checkpoint, enabling high-efficiency pyroptosis cancer therapy. The nanoregulator allows the in situ synthesis and accumulation of the photosensitizer PpIX in the mitochondria of cancer cells to directly produce mitochondrial ROS, thus triggering pyroptosis. Meanwhile, the in situ generated autophagy inhibitor via palladium-catalyzed bioorthogonal chemistry can disrupt the pyroptosis checkpoint to boost the pyroptosis efficacy. With the biomimetic cancer cell membrane coating, this platform for modulating pyroptosis presents specificity to cancer cells and poses no harm to normal tissue, resulting in a highly efficient and safe antitumor treatment. To our knowledge, this is the first report on a disrupting intrinsic protective mechanism of cancer cells for tumor pyroptosis therapy. This work highlights that autophagy as a checkpoint plays a key regulative role in pyroptosis therapy, which would motivate the future design of therapeutic regimens.

Robust vessel segmentation in laser speckle contrast images based on semi-weakly supervised learning.

Objective.The goal of this study is to develop a robust semi-weakly supervised learning strategy for vessel segmentation in laser speckle contrast imaging (LSCI), addressing the challenges associated with the low signal-to-noise ratio, small vessel size, and irregular vascular aberration in diseased regions, while improving the performance and robustness of the segmentation method.Approach.For the training dataset, the healthy vascular images denoted as normal-vessel samples were manually labeled, while the diseased LSCI images involving tumor or embolism were denoted as abnormal-vessel samples and annotated as pseudo labels by the traditional semantic segmentation methods. In the training phase, the pseudo labels were constantly updated to improve the segmentation accuracy based on DeepLabv3+. Objective evaluation was conducted on the normal-vessel test set, while subjective evaluation was performed on the abnormal-vessel test set.Main results.The proposed method achieved an IOU of 0.8671, a Dice of 0.9288, and a mean relative percentage difference (mRPD) with supervised learning of 0.5% in the objective evaluation. In the subjective evaluation, our method significantly outperformed other methods in main vessel segmentation, tiny vessel segmentation, and blood vessel connection. Additionally, our method exhibited robustness when abnormal-vessel style noise was added to normal-vessel samples using a style translation network.Significance.The proposed semi-weakly supervised learning strategy demonstrates high efficiency and excellent robustness for vascular segmentation in LSCI, providing a potential tool for assessing the morphological and structural features of vessels in clinical applications.

Size-resolved light-absorbing organic carbon and organic molecular markers in Nanjing, east China: Seasonal variations and sources.

Owing to the potential influence of light-absorbing organic carbon (OC), also termed "brown carbon" (BrC), on the planetary radiation budget, many studies have focused on its absorption in single-sized ranges of particulate matter (PM). However, the size distribution and organic tracer-based source apportionment of BrC absorption have not been extensively investigated. In this study, size-resolved PM samples were collected using multi-stage impactors from eastern Nanjing during each season in 2017. The light absorption of methanol-extractable OC at 365 nm (Abs365, Mm-1) was determined using spectrophotometry, and a series of organic molecular markers (OMMs) was measured using a gas chromatography-mass spectrometer. Fine PM with an aerodynamic diameter <2.1 µm (PM2.1) dominated Abs365 (79.8 ± 10.4%) of the total size ranges with maxima and minima in winter and summer, respectively. The distributions of Abs365 shifted to larger PM sizes from winter to spring and summer due to lower primary emissions and increased BrC chromophores in dust. Except for low-volatility (po,*L < 10-10 atm) polycyclic aromatic hydrocarbons (PAHs), the non-polar OMMs, including n-alkanes, PAHs, oxygenated PAHs, and steranes, showed a bimodal distribution pattern. Secondary products of biogenic precursors and biomass burning tracers presented a unimodal distribution peaking at 0.4-0.7 µm, while sugar alcohols and saccharides were enriched in coarse PM. Their seasonal variations in average concentrations reflected intense photochemical reactions in summer, more biomass burning emissions in winter, and stronger microbial activity in spring and summer. Positive matrix factorization was used for the source apportionment of Abs365 in fine and coarse PM samples. Biomass burning contributed an average of 53.9% to the Abs365 of PM2.1 extracts. The Abs365 of coarse PM extracts was associated with various dust-related sources where the aging processes of aerosol organics could occur.